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  Subjects -> TRANSPORTATION (Total: 170 journals)
    - AIR TRANSPORT (8 journals)
    - AUTOMOBILES (22 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (6 journals)
    - SHIPS AND SHIPPING (34 journals)
    - TRANSPORTATION (95 journals)

TRANSPORTATION (95 journals)

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Partially Free   (Followers: 86)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 4)
Archives of Transport     Open Access   (Followers: 17)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 11)
Cities in the 21st Century     Open Access   (Followers: 14)
Economics of Transportation     Partially Free   (Followers: 13)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 11)
European Transport Research Review     Open Access   (Followers: 21)
Geosystem Engineering     Hybrid Journal   (Followers: 1)
IATSS Research     Open Access  
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 9)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 8)
IFAC-PapersOnLine     Open Access  
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 8)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 9)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 3)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 9)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 9)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 10)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 11)
International Journal of Services Technology and Management     Hybrid Journal  
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 12)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 15)
International Journal of Transportation Science and Technology     Open Access   (Followers: 9)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 12)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 7)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 6)
Journal of Navigation     Hybrid Journal   (Followers: 213)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 12)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 1)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 8)
Journal of Transport and Land Use     Open Access   (Followers: 22)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 12)
Journal of Transport Geography     Hybrid Journal   (Followers: 21)
Journal of Transport History     Hybrid Journal   (Followers: 15)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 8)
Journal of Transportation Security     Hybrid Journal   (Followers: 3)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 16)
Journal of Transportation Technologies     Open Access   (Followers: 14)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 9)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics & Sustainable Transport     Open Access   (Followers: 2)
Logistique & Management     Full-text available via subscription  
Mobility in History     Full-text available via subscription   (Followers: 2)
Modern Transportation     Open Access   (Followers: 10)
Nonlinear Dynamics     Hybrid Journal   (Followers: 16)
Open Journal of Safety Science and Technology     Open Access   (Followers: 7)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 13)
Public Transport     Hybrid Journal   (Followers: 18)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 5)
Revista Transporte y Territorio     Open Access   (Followers: 1)
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 2)
Sport, Education and Society     Hybrid Journal   (Followers: 11)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 1)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 2)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 5)
Transport     Hybrid Journal   (Followers: 14)
Transport and Telecommunication Journal     Open Access   (Followers: 4)
Transport in Porous Media     Hybrid Journal   (Followers: 1)
Transport Problems     Open Access   (Followers: 1)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 9)
Transportation     Hybrid Journal   (Followers: 27)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportation Journal     Full-text available via subscription   (Followers: 13)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 4)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 32)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 30)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 22)
Transportation Research Procedia     Open Access   (Followers: 5)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 35)
Transportation Science     Full-text available via subscription   (Followers: 21)
TRANSPORTES     Open Access   (Followers: 5)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 5)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 2)
Transportrecht     Unknown  
Travel Behaviour and Society     Full-text available via subscription   (Followers: 6)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
Urban, Planning and Transport Research     Open Access   (Followers: 25)
Vehicular Communications     Full-text available via subscription   (Followers: 4)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 6)
Транспортні системи та технології перевезень     Open Access  
Journal Cover Transportation Research Part C: Emerging Technologies
  [SJR: 2.062]   [H-I: 72]   [22 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [3089 journals]
  • An empirical study on travel patterns of internet based ride-sharing
    • Authors: Yongqi Dong; Shuofeng Wang; Li Li; Zuo Zhang
      Pages: 1 - 22
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Yongqi Dong, Shuofeng Wang, Li Li, Zuo Zhang
      The rapid growth of internet based ride-sharing brings great changes to residents' travel and city traffic. However, few studies had employed empirical data to examine the unique travel patterns of internet based ride-sharing trips. In this paper, we compare taxi trip records and internet based ride-sharing trip records provided by DiDi company. Results reveal many interesting findings that had never been reported before. From the viewpoint of service patterns, ride-sharing mainly increases supplies in hot areas and peak hours. By applying a non-negative matrix factorization method, we find that ride-sharing principally serves as an approach for commuting. So, as an effective supplement to traditional taxi service, it regulates spatial and temporal supply-demand imbalance, especially during morning and evening rush periods. From the viewpoint of individual behavior patterns, we use a clustering method to identify two kinds of internet based ride-sharing drivers. The first kind of drivers usually provides ride-sharing along daily home-work commuting. Trips served by these drivers have relatively constant origin-designation (OD) pairs. The second kind of drivers does not serve regularly and roams around the city even in working hours. Therefore, there are no constant OD pairs in their ride-sharing trips. Counterintuitively, we find that home-work commuting drivers account for only a small part of total drivers and they only serve a small number of commuting trips. In addition, internet based ride-sharing is not just traditional hitchhiking worked through mobile internet. We find that internet based ride-sharing drivers intend to make long distance trips, and they intend to detour further to pick up or drop off passengers than traditional hitchhike drivers since they are paid. All these findings are helpful for policy makers at all levels to make informed decisions about deployment of internet based ride-sharing service. This paper also verifies that big data analytics is particularly useful and powerful in the analysis of ride-sharing and taxi service patterns.

      PubDate: 2017-11-10T06:40:31Z
      DOI: 10.1016/j.trc.2017.10.022
      Issue No: Vol. 86 (2017)
  • An online estimation of driving style using data-dependent pointer model
    • Authors: Evgenia Suzdaleva; Ivan Nagy
      Pages: 23 - 36
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Evgenia Suzdaleva, Ivan Nagy
      The paper focuses on a task of stochastic modeling the driving style and its online estimation while driving. The driving style is modeled by means of a mixture model with normal and categorical components as well as a data-dependent pointer. The mixture parameters and the actual driving style are estimated with the help of a recursive algorithm under the Bayesian methodology. The main contributions of the presented approach are: (i) the online estimation of the driving style while driving, taking into account data up to the current time instant; (ii) the joint model for continuous and discrete data measured on a vehicle; (iii) the data-dependent model of the driving style conditioned by the values of fuel consumption; (iv) the use of the model both for detection of clusters according to the driving style and prediction of the fuel consumption along with other variables; and (v) the universal modeling with the help of mixtures, which allows us to use different combinations of components and pointer models as well as to specify the initialization approach suitable for the considered problem. Results of the driving style detection in real measurements and comparison with the theoretical counterparts are demonstrated.

      PubDate: 2017-11-10T06:40:31Z
      DOI: 10.1016/j.trc.2017.11.001
      Issue No: Vol. 86 (2017)
  • Data-driven optimal charging decision making for connected and automated
           electric vehicles: A personal usage scenario
    • Authors: Zonggen Yi; Matthew Shirk
      Pages: 37 - 58
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Zonggen Yi, Matthew Shirk
      This study introduces an optimal charging decision making framework for connected and automated electric vehicles under a personal usage scenario. This framework aims to provide charging strategies, i.e. the choice of charging station and the amount of charged energy, by considering constraints from personal daily itineraries and existing charging infrastructure. A data-driven method is introduced to establish a stochastic energy consumption prediction model with consideration of realistic uncertainties. This is performed by analyzing a large scale electric vehicle data set. A real-time updating method is designed to construct this prediction model from new consecutive data points in an adaptive way for real-world applications. Based on this energy cost prediction framework from real electric vehicle data, multistage optimal charging decision making models are introduced, including a deterministic model for average outcome decision making and a robust model for safest charging strategies. A dynamic programming algorithm is proposed to find the optimal charging strategies. Detailed simulations and case studies demonstrate the performance of the proposed algorithms to find optimal charging strategies. They also show the potential capability of connected and automated electric vehicles to reduce the range anxiety and charging infrastructure dependency.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.10.014
      Issue No: Vol. 86 (2017)
  • Spatial-temporal traffic speed patterns discovery and incomplete data
           recovery via SVD-combined tensor decomposition
    • Authors: Xinyu Chen; Zhaocheng He; Jiawei Wang
      Pages: 59 - 77
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Xinyu Chen, Zhaocheng He, Jiawei Wang
      Missing data is an inevitable and ubiquitous problem in data-driven intelligent transportation systems. While there are several studies on the missing traffic data recovery in the last decade, it is still an open issue of making full use of spatial-temporal traffic patterns to improve recovery performance. In this paper, due to the multi-dimensional nature of traffic speed data, we treat missing data recovery as the problem of tensor completion, a three-procedure framework based on Tucker decomposition is proposed to accomplish the recovery task by discovering spatial-temporal patterns and underlying structure from incomplete data. Specifically, in the missing data initialization, intrinsic multi-mode biases based traffic pattern is extracted to perform a robust recovery. Thereby, the truncated singular value decomposition (SVD) is introduced to capture main latent features along each dimension. Finally, applying these latent features, the missing data is eventually estimated by the SVD-combined tensor decomposition (STD). Empirically, relying on the large-scale traffic speed data collected from 214 road segments within two months at 10-min interval, our experiment covers two missing scenarios – element-like random missing and fiber-like random missing. The impacts of different initialization strategies for tensor decomposition are evaluated. From numerical analysis, a sensitivity-driven rank selection can not only choose an appropriate core tensor size but also determine how much features we actually need. By comparison with two baseline tensor decomposition models, our method is shown to successfully recover missing data with the highest accuracy as the missing rate ranges from 20% to 80% under two missing scenarios. Moreover, the results have also indicated that an optimal initialization for tensor decomposition could suggest a better performance.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.10.023
      Issue No: Vol. 86 (2017)
  • Effects of real-time warning systems on driving under fog conditions using
           an empirically supported speed choice modeling framework
    • Authors: Yina Wu; Mohamed Abdel-Aty; Juneyoung Park; Ryan M. Selby
      Pages: 97 - 110
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Yina Wu, Mohamed Abdel-Aty, Juneyoung Park, Ryan M. Selby
      Fog warning systems can convey warning messages to drivers and help to reduce crashes that may occur due to the sudden occurrence of low visibility conditions. This study aims to assess the effectiveness of real-time fog warning systems by quantifying and characterizing drivers’ speed adjustments under different roadway types, traffic conditions, and fog levels. In order to explore how a driver perceives the fog warning systems (i.e., beacon and dynamic message signs (DMS)) when approaching a fog area, this paper divides the roads into three zones (i.e., clear zone, transition zone, fog zone) according to visibility levels and suggests a hierarchical assessment concept to explore the driver’s speed adjustment maneuvers. For the three different zones, different indexes are computed corresponding to drivers’ speed adjustments. Two linear regression models with random effects and one hurdle beta regression model are estimated for the indexes. In addition, the three models were modified by allowing the parameters to vary across the participants to account for the unobserved heterogeneity. To validate the proposed analysis framework, an empirical driving simulator study was conducted based on two real-world roads in a fog prone area in Florida. The results revealed that the proposed modeling framework is able to reflect drivers’ speed adjustment in risk perception and acceleration/deceleration maneuvering when receiving real-time warning massages. The results suggested that installing a beacon could be beneficial to speed reduction before entering the fog area. Meanwhile, DMS may affect drivers’ brake reaction at the beginning section of reduced visibility. However, no effects of warning systems for drivers’ final speed choice in the fog can be observed. It is suggested that proper warning systems should be considered for different conditions since they have different effects. It is expected that more efficient technology can be developed to enhance traffic safety under fog conditions with a better understanding of the drivers’ speed adjustments revealed in this study.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.10.025
      Issue No: Vol. 86 (2017)
  • Longitudinal train dynamics model for a rail transit simulation system
    • Authors: Jinghui Wang; Hesham A. Rakha
      Pages: 111 - 123
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Jinghui Wang, Hesham A. Rakha
      The paper develops a longitudinal train dynamics model in support of microscopic railway transportation simulation. The model can be calibrated without any mechanical data making it ideal for implementation in transportation simulators. The calibration and validation work is based on data collected from the Portland light rail train fleet. The calibration procedure is mathematically formulated as a constrained non-linear optimization problem. The validity of the model is assessed by comparing instantaneous model predictions against field observations, and also evaluated in the domains of acceleration/deceleration versus speed and acceleration/deceleration versus distance. A test is conducted to investigate the adequacy of the model in simulation implementation. The results demonstrate that the proposed model can adequately capture instantaneous train dynamics, and provides good performance in the simulation test. The model provides a simple theoretical foundation for microscopic simulators and will significantly support the planning, management and control of railway transportation systems.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.10.011
      Issue No: Vol. 86 (2017)
  • Solving the train formation plan network problem of the single-block train
           and two-block train using a hybrid algorithm of genetic algorithm and tabu
    • Authors: Jie Xiao; Boliang Lin; Jiaxi Wang
      Pages: 124 - 146
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Jie Xiao, Boliang Lin, Jiaxi Wang
      This paper presents a formulation and solution of the railway freight Train Formation Plan (TFP) network problem in China using both the single-block trains and the two-block trains. Firstly, the single-block TFP model is established under given shipment demands, classification capacity and track quantity at the yards. Then the benefits which can be achieved by replacing single-block trains with two-block trains are systematically analyzed and summarized. The comprehensive optimization model of the train formulation plan using both the single-block trains and two-block trains is established aiming at the minimization of the total car-hour consumption at all yards. A hybrid algorithm of genetic algorithm and tabu search is developed to solve the single-block TFP model and then a greedy algorithm is proposed to replace single-block trains with two-block trains. Finally, the model and the solution approach are tested in an actual 19-yard railway sub-network in China.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.10.006
      Issue No: Vol. 86 (2017)
  • To travel or not to travel: ‘Weather’ is the question. Modelling the
           effect of local weather conditions on bus ridership
    • Authors: Sui Tao; Jonathan Corcoran; Francisco Rowe; Mark Hickman
      Pages: 147 - 167
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Sui Tao, Jonathan Corcoran, Francisco Rowe, Mark Hickman
      While the influence of weather on public transport performance and ridership has been the topic for some research, the real-time response of transit usage to variations in weather conditions is yet to be fully understood. This paper redresses this gap by modelling the effect that local weather conditions exert on hourly bus ridership in sub-tropical Brisbane, Australia. Drawing on a transit smart card data set and detailed weather measurements, a suite of time-series regression models are computed to capture the concurrent and lagged effects that weather conditions exert on bus ridership. Our findings highlight that changes in particularly temperature and rainfall were found to induce significant hour-to-hour changes in bus ridership, with such effects varying markedly across both a 24 h period and the transit network. These results are important for public transport service operations in their capacity to inform timely responses to real-time changes in passengers’ travel demand induced by the onset of particular weather conditions.

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.11.005
      Issue No: Vol. 86 (2017)
  • Technologies and control for sustainable transportation
    • Authors: Adam J. Pel; Niels Agatz; Cathy Macharis; Lucas P. Veelenturf
      Pages: 168 - 170
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Adam J. Pel, Niels Agatz, Cathy Macharis, Lucas P. Veelenturf

      PubDate: 2017-11-17T03:28:21Z
      DOI: 10.1016/j.trc.2017.11.006
      Issue No: Vol. 86 (2017)
  • Jointly analyzing freeway traffic incident clearance and response time
           using a copula-based approach
    • Authors: Yajie Zou; Xin Ye; Kristian Henrickson; Jinjun Tang; Yinhai Wang
      Pages: 171 - 182
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Yajie Zou, Xin Ye, Kristian Henrickson, Jinjun Tang, Yinhai Wang
      Understanding factors that influence the duration time of incidents are important for traffic incident management agencies to design effective mitigation strategies. Previous studies have proposed different approaches for examining the impact of influential factors on incident clearance and response time. However, very few studies have considered incident clearance and response time as two dependent variables and used a joint modeling framework. The objectives of this paper are to investigate the dependence between incident clearance and response time and to examine the applicability of the copula approach to the joint analysis of these two variables. To demonstrate advantages of the proposed copula modelling framework, incident clearance and response time data collected on freeway road sections in Seattle, Washington State are examined. Parameter estimation and prediction results from the proposed copula models are presented and compared with the conventional accelerated failure time model. The modeling results suggest that the proposed copula model can better describe the estimated conditional survival probability of incident clearance time, and can provide marginally more accurate prediction results. The proposed model can also provide different inferences about effects of factors on incident clearance and response time data. Overall, the findings in this paper provide a framework for jointly modeling incident clearance and response time by considering their dependence.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.004
      Issue No: Vol. 86 (2017)
  • An agent-based model of the emergence of cooperation and a fair and stable
           system optimum using ATIS on a simple road network
    • Authors: Ido Klein; Nadav Levy; Eran Ben-Elia
      Pages: 183 - 201
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Ido Klein, Nadav Levy, Eran Ben-Elia
      Traffic congestion threats the growth and vitality of cities. Policy measures like punishments or rewards often fail to create a long term remedy. The rise of Information and Communication Technologies (ICT) enable provision of travel information through advanced traveler information systems (ATIS). Current ATIS based on shortest path routing might expedite traffic to converge towards the suboptimal User Equilibrium (UE) state. We consider that ATIS can persuade drivers to cooperate, pushing the road network in the long run towards the System Optimum (SO) instead. We develop an agent based model that simulates day-to-day evolution of road traffic on a simple binary road network, where the behavior of agents is reinforced by their previous experiences. Scenarios are generated based on various network designs, information recommendation allocations and incentive mechanisms and tested regarding efficiency, stability and equity criteria. Results show that agents learn to cooperate without incentives, but this is highly sensitive to the type of recommendation allocation and network-specific design. Punishment or rewards are useful incentives, especially when cooperation between agents requires them to change behavior against their natural tendencies. The resulting system optimal states are to most parts efficient, stable and not least equitable. The implications for future ATIS design and operations are further discussed.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.007
      Issue No: Vol. 86 (2017)
  • Real-time crash prediction in an urban expressway using disaggregated data
    • Authors: Franco Basso; Leonardo J. Basso; Francisco Bravo; Raul Pezoa
      Pages: 202 - 219
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Franco Basso, Leonardo J. Basso, Francisco Bravo, Raul Pezoa
      We develop accident prediction models for a stretch of the urban expressway Autopista Central in Santiago, Chile, using disaggregate data captured by free-flow toll gates with Automatic Vehicle Identification (AVI) which, besides their low failure rate, have the advantage of providing disaggregated data per type of vehicle. The process includes a random forest procedure to identify the strongest precursors of accidents, and the calibration/estimation of two classification models, namely, Support Vector Machine and Logistic regression. We find that, for this stretch of the highway, vehicle composition does not play a first-order role. Our best model accurately predicts 67.89% of the accidents with a low false positive rate of 20.94%. These results are among the best in the literature even though, and as opposed to previous efforts, (i) we do not use only one partition of the data set for calibration and validation but conduct 300 repetitions of randomly selected partitions; (ii) our models are validated on the original unbalanced data set (where accidents are quite rare events), rather than on artificially balanced data.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.014
      Issue No: Vol. 86 (2017)
  • Tailored Wakeby-type distribution for random bus headway adherence ratio
    • Authors: Man Zhang; Qiang Meng; Liujiang Kang; Wenquan Li
      Pages: 220 - 244
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Man Zhang, Qiang Meng, Liujiang Kang, Wenquan Li
      This paper addresses an interesting and practical bus headway adherence issue for a given public bus route with a number of bus stops. It first defines the random headway adherence ratio (HAR) at a particular bus stop of a specific bus route as the ratio of difference between actual bus headway and scheduled headway with respect to the scheduled headway. This study proceeds to customize a four-step procedure to estimate a probability distribution that can describe the random HAR at each bus stop of the bus route by using the automatic vehicle location (AVL) data. Our real case studies with 44,025 HAR data show that the 19 existing probability distributions including Lognormal, Gamma, Beta and Wakeby are unable to well fit these HAR data. This study thus proposes a tailored Wakeby-type distribution with five parameters. After deriving two fundamental propositions for the tailored Wakeby-type distribution, a tangible L-moment based method to estimate those parameters involved the tailored Wakeby distribution is presented. The tailored Wakeby-type distributions can meet our expectation via our real case studies. Finally, applications of the tailored Wakeby-type distribution derived for the random HAR are conducted.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.013
      Issue No: Vol. 86 (2017)
  • Pricing and penalty/compensation strategies of a taxi-hailing platform
    • Authors: Fang He; Xiaolei Wang; Xi Lin; Xindi Tang
      Pages: 263 - 279
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Fang He, Xiaolei Wang, Xi Lin, Xindi Tang
      Smartphone-based taxi-hailing applications (apps) bring about significant changes to the taxi market in recent years. As platforms that connect customers and taxi drivers, taxi-hailing apps charge different rates for completed orders and penalize reservation-cancellation behaviors with different fines. In this paper, an equilibrium framework is proposed to depict the operations of a regulated taxi market on a general network with both street-hailing and e-hailing modes for taxi services, considering the reservation-cancellation behaviors of e-hailing customers. Based on the proposed equilibrium model, an optimal design problem of taxi-hailing platform’s pricing and penalty/compensation strategies is formulated and solved by the penalty successive linear programming algorithm. To demonstrate the practicability of the proposed solution algorithms and the optimal pricing and penalty/compensation schemes, large-scale numerical examples are presented based on a realistic taxi network of Beijing.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.003
      Issue No: Vol. 86 (2017)
  • A high resolution agent-based model to support walk-bicycle infrastructure
           investment decisions: A case study with New York City
    • Authors: H.M. Abdul Aziz; Byung H. Park; April Morton; Robert N. Stewart; M. Hilliard; M. Maness
      Pages: 280 - 299
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): H.M. Abdul Aziz, Byung H. Park, April Morton, Robert N. Stewart, M. Hilliard, M. Maness
      Active transportation modes–walk and bicycle–are central for low carbon transport, healthy living, and complete streets initiative. Building a community with amenable walk and bicycle facilities asks for smart planning and investments. It is critical to investigate the impact of infrastructure building or expansion on the overall walk and bicycle mode usage prior to making investment choices utilizing public tax money. This research developed a high performance agent-based model to support investment decisions that allows to assess the impact of changes in walk-bike infrastructures at a fine spatial resolution (e.g., block group level). We built the agent-based model (ABM) in Repast-HPC platform and calibrated the model using Simultaneous Perturbation Stochastic Simulation (SPSA) technique. The ABM utilizes data from a synthetic population simulator that generates agents with corresponding socio-demographic characteristics, and integrates facility attributes regarding walking and bicycling such as sidewalk width and total length bike lane into the mode choice decision making process. Moreover, the ABM accounts for the effect of social interactions among agents who share identical home and work geographic locations. Finally, GIS-based maps are developed at block group resolution that allows examining the effect of walk-bike infrastructure related investments. The results from New York City case study indicate that infrastructure investments such as widening sidewalk and increasing bike lane network can positively influence the active transportation mode choices. Also, the impact varies with geographic locations–different boroughs of New York City will have different impacts. Our ABM simulation results also indicate that social promotions foucsing on active transportation can positively reinforce the impacts of infrastructure changes.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.008
      Issue No: Vol. 86 (2017)
  • Towards a quantitative method to analyze the long-term innovation
           diffusion of automated vehicles technology using system dynamics
    • Authors: Jurgen Nieuwenhuijsen; Gonçalo Homem de Almeida Correia; Dimitris Milakis; Bart van Arem; Els van Daalen
      Pages: 300 - 327
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Jurgen Nieuwenhuijsen, Gonçalo Homem de Almeida Correia, Dimitris Milakis, Bart van Arem, Els van Daalen
      This paper presents a novel simulation model that shows the dynamic and complex nature of the innovation system of vehicle automation in a quantitative way. The model simulates the innovation diffusion of automated vehicles (AVs) on the long-term. It looks at the system of AVs from a functional perspective and therefore categorizes this technology into six different levels. Each level is represented by its own fleet size, its own technology maturity and its own average purchase price and utility. These components form the core of the model. The feedback loops between the components form a dynamic behavior that influences the diffusion of AVs. The model was applied to the Netherlands both for a base and an optimistic scenario (strong political support and technology development) named “AV in-bloom”. In these experiments, we found that the system is highly uncertain with market penetration varying greatly with the scenarios and policies adopted. Having an ‘AV in bloom’ eco-system for AVs is connected with a great acceleration of the market take-up of high levels of automation. As a policy instrument, a focus on more knowledge transfer and the creation of an external fund (e.g. private investment funds or European research funds) has shown to be most effective to realize a positive innovation diffusion for AVs. Providing subsidies may be less effective as these give a short-term impulse to a higher market penetration, but will not be able to create a higher market surplus for vehicle automation.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.016
      Issue No: Vol. 86 (2017)
  • Integration of adaptive signal control and freeway off-ramp priority
           control for commuting corridors
    • Authors: Xianfeng Yang; Yao Cheng; Gang-Len Chang
      Pages: 328 - 345
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Xianfeng Yang, Yao Cheng, Gang-Len Chang
      Congestion at the downstream of an off-ramp often propagates the traffic queue to the freeway mainline, and thus reduces the freeway capacity at the interchange area. To prevent such potential queue spillover and improve traffic control efficiency over the entire corridor, this study develops an integrated control system which includes three primary functions: off-ramp queue estimation, arterial adaptive signal operations, and freeway off-ramp priority control. Using detected flow data, the system firstly estimates the queue length on the target off-ramp. If no potential queue spillover is predicted, the adaptive signal control function will then adjust the intersection signal timings and provide dynamic signal progression to critical path-flows. Otherwise, the off-ramp priority control function will be activated to clear the queuing vehicles at the off-ramp. To evaluate the effectiveness of the proposed system, this study has conducted numerical studies on a freeway interchange using a well-calibrated simulation platform. The experimental results reveal that the overall network performance can indeed be improved under the proposed control system, compared with other operational strategies. Further analyses of freeway time-dependent travel time distribution also evidence the effectiveness of the proposed system in preventing off-ramp queue spillover.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.019
      Issue No: Vol. 86 (2017)
  • Impact of combined alignments on lane departure: A simulator study for
           mountainous freeways
    • Authors: Yixin Chen; Mohammed Quddus; Xuesong Wang
      Pages: 346 - 359
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Yixin Chen, Mohammed Quddus, Xuesong Wang
      Lane departures are responsible for many side-swipe, rear-end and single-vehicle run-off-road crashes. There is a dearth of research, however, on how lane departures are impacted by roadway alignments. The objective of this paper is to examine which geometric design characteristics, including road alignment at the current segment and the adjacent segments, have significant influence on lane departure. Lane departure data from a total 30 drivers were collected from a driving simulator study of a four-lane (two lanes in each direction) divided mountainous freeway. Lane departures were classified into lane keeping, lane departure to the left and lane departure to the right for all-alignments (Dataset I), and lane keeping, lane departure to the inside and lane departure to the outside for curves-only (Dataset II). A mixed multinomial logit model for each dataset was employed to examine the contributory factors. This approach allows for the possibility that the estimated model parameters can vary randomly to account for unobserved effects potentially relating to heterogeneous driver behaviors. Fixed parameters that had a significant increase on lane departure were horizontal curvature at the current segment, and the difference (max-min) in horizontal curvature within the 300-m adjacent upstream alignment. Downward slope and upward slope with fixed parameters significantly decreased lane departure. Estimated parameters related to the direction of the curve, driving lane (bordering median or hard shoulder) and driving speed had found to have randomly distributed over the drivers. This indicates that driver behavior is not consistent in the effect of these three variables on lane departure. These results can assist engineers in designing safer mountainous freeways.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.010
      Issue No: Vol. 86 (2017)
  • Inferring transportation modes from GPS trajectories using a convolutional
           neural network
    • Authors: Sina Dabiri; Kevin Heaslip
      Pages: 360 - 371
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Sina Dabiri, Kevin Heaslip
      Identifying the distribution of users’ transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters’ mobility mode(s) is to leverage their GPS trajectories. A majority of studies have proposed mode inference models based on hand-crafted features and traditional machine learning algorithms. However, manual features engender some major drawbacks including vulnerability to traffic and environmental conditions as well as possessing human’s bias in creating efficient features. One way to overcome these issues is by utilizing Convolutional Neural Network (CNN) schemes that are capable of automatically driving high-level features from the raw input. Accordingly, in this paper, we take advantage of CNN architectures so as to predict travel modes based on only raw GPS trajectories, where the modes are labeled as walk, bike, bus, driving, and train. Our key contribution is designing the layout of the CNN’s input layer in such a way that not only is adaptable with the CNN schemes but represents fundamental motion characteristics of a moving object including speed, acceleration, jerk, and bearing rate. Furthermore, we ameliorate the quality of GPS logs through several data preprocessing steps. Using the clean input layer, a variety of CNN configurations are evaluated to achieve the best CNN architecture. The highest accuracy of 84.8% has been achieved through the ensemble of the best CNN configuration. In this research, we contrast our methodology with traditional machine learning algorithms as well as the seminal and most related studies to demonstrate the superiority of our framework.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.11.021
      Issue No: Vol. 86 (2017)
  • Observing individual dynamic choices of activity chains from
           location-based crowdsourced data
    • Authors: Shuaidong Zhao; Kuilin Zhang
      Pages: 1 - 22
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Shuaidong Zhao, Kuilin Zhang
      The existing efforts on studying human mobility and activity using location-based crowdsourced data mainly focus on obtaining the activity chain pattern in a region at an aggregate level. To observe individual dynamic choices of activity chains, this paper presents a data-driven approach to estimating individual-specific activity chain set and corresponding choice probabilities for a given person over a 24-h period using crowdsourced data from location-based service apps. We detect an individual-specific stochastic activity set using a contextual-parcel data analysis. Based on the time geography theory, we refine a space-time bicone concept to construct an activity-travel space-time-state network from the stochastic activity set. These space-time bicone constraints define a set of potential activity choices to reduce the search space of activity location and duration choices. We construct an activity state transition graph from the space-time-state network and calculate a Markov matrix for activity choice probabilities. Furthermore, we calculate the probabilities of activity chain choices using the Markov matrix. We also visualize individual-specific activity chain set in a space-time-state network to show the dynamic choices of individual daily mobility and activity. We demonstrate the proposed approach through conducting numerical analyses using crowdsourced data from location-based service apps - Foursquare and Twitter to construct individual-specific activity choice sets and corresponding choice probabilities.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.09.005
      Issue No: Vol. 85 (2017)
  • A dynamic behavioural traffic assignment model with strategic agents
    • Authors: Johan Barthélemy; Timoteo Carletti
      Pages: 23 - 46
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Johan Barthélemy, Timoteo Carletti
      Foresee traffic conditions and demand is a major issue nowadays that is very often approached using simulation tools. The aim of this work is to propose an innovative strategy to tackle such problem, relying on the presentation and analysis of a behavioural dynamic traffic assignment. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time already spent in his journey. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents’ strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large and not previously explored networks. Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against existing approaches and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results in terms of accuracy and computational efficiency. This indicates that the proposed approach has the potential to provide better tools to investigate and forecast drivers’ choice behaviours. Eventually these tools can improve the delivery and efficiency of traffic information to the drivers.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.09.004
      Issue No: Vol. 85 (2017)
  • Investigating heterogeneity in social influence by social distance in
           car-sharing decisions under uncertainty: A regret-minimizing hybrid choice
           model framework based on sequential stated adaptation experiments
    • Authors: Jinhee Kim; Soora Rasouli; Harry J.P. Timmermans
      Pages: 47 - 63
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Jinhee Kim, Soora Rasouli, Harry J.P. Timmermans
      The present study is designed to investigate social influence in car-sharing decisions under uncertainty. Social influence indicates that individuals’ decisions are influenced by the choices made by members of their social networks. An individual may experience different degrees of influence depending on social distance, i.e. the strength of the social relationship between individuals. Such heterogeneity in social influence has been largely ignored in the previous travel behavior research. The data used in this study stems from an egocentric social network survey, which measures the strength of the social relationships of each respondent. In addition, a sequential stated adaptation experiment was developed to capture more explicitly the effect of social network choices on the individual decision-making process. Social distance is regarded as a random latent variable. The estimated social distance and social network choices are incorporated into a social influence variable, which is treated as an explanatory variable in the car-sharing decision model. To simultaneously estimate latent social distance and the effects of social influence on the car-sharing decision, we expand the hybrid choice framework to incorporate the latent social distance model into discrete choice analysis. The estimation results show substantial social influence in car-sharing decisions. The magnitude of social influence varies according to the type of relationship, similarity of socio-demographics and the number of social interactions.

      PubDate: 2017-09-20T20:16:54Z
      DOI: 10.1016/j.trc.2017.09.001
      Issue No: Vol. 85 (2017)
  • Dynamic traffic routing in a network with adaptive signal control
    • Authors: Huajun Chai; H.M. Zhang; Dipak Ghosal; Chen-Nee Chuah
      Pages: 64 - 85
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Huajun Chai, H.M. Zhang, Dipak Ghosal, Chen-Nee Chuah
      In real traffic networks, travellers’ route choice is affected by traffic control strategies. In this research, we capture the interaction between travellers’ route choice and traffic signal control in a coherent framework. For travellers’ route choice, a VANET (Vehicular Ad hoc NETwork) is considered, where travellers have access to the real-time traffic information through V2V/V2I (Vehicle to Vehicle/Vehicle to Infrastructure) infrastructures and make route choice decisions at each intersection using hyper-path trees. We test our algorithm and control strategy by simulation in OmNet++ (A network communication simulator) and SUMO (Simulation of Urban MObility) under several scenarios. The simulation results show that with the proposed dynamic routing, the overall travel cost significantly decreases. It is also shown that the proposed adaptive signal control reduces the average delay effectively, as well as reduces the fluctuation of the average speed within the whole network.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.08.017
      Issue No: Vol. 85 (2017)
  • Closed-form multiclass cell transmission model enhanced with overtaking,
           lane-changing, and first-in first-out properties
    • Authors: Kamonthep Tiaprasert; Yunlong Zhang; Chaodit Aswakul; Jian Jiao; Xin Ye
      Pages: 86 - 110
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Kamonthep Tiaprasert, Yunlong Zhang, Chaodit Aswakul, Jian Jiao, Xin Ye
      A novel multiclass macroscopic model is proposed in this article. In order to enhance first-in, first-out property (FIFO) and transmission function in the multiclass traffic modeling, a new multiclass cell transmission model with FIFO property (herein called FM-CTM) is extended from its prior multiclass cell transmission model (M-CTM). Also, to enhance its analytical compactness and resultant computational convenience, FM-CTM is formulated in this paper asa set of closed-form matrix equations. The objective is to improve the accuracy of traffic state estimation by enforcing FIFO property when a fast vehicle cannot overtake a slow vehicle due to a limitation of a single-lane road. Moreover, the proposed model takes into account a different priority for vehicles of each class to move forward through congested road conditions, and that makes the flow calculation independent from their free-flow speeds. Some hypothetical and real-world freeway networks with a constant or varying number of lanes are selected to verify FM-CTM by comparing with M-CTM and the conventional CTM. Observed densities of VISSIM and real-world dataset of I-80 are selected to compare with the simulated densities from the three CTMs. The numerical results show that FM-CTM outperforms the other two models by 15% of accuracy measures in most cases. Therefore, the proposed model is expected to be well applicable to the road network with a mixed traffic and varying number of lanes.

      PubDate: 2017-09-20T20:16:54Z
      DOI: 10.1016/j.trc.2017.09.008
      Issue No: Vol. 85 (2017)
  • A driver advisory system with dynamic losses for passenger electric
           multiple units
    • Authors: Nima Ghaviha; Markus Bohlin; Christer Holmberg; Erik Dahlquist; Robert Skoglund; Daniel Jonasson
      Pages: 111 - 130
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Nima Ghaviha, Markus Bohlin, Christer Holmberg, Erik Dahlquist, Robert Skoglund, Daniel Jonasson
      Driver advisory systems, instructing the driver how to control the train in an energy efficient manner, is one the main tools for minimizing energy consumption in the railway sector. There are many driver advisory systems already available in the market, together with significant literature on the mathematical formulation of the problem. However, much less is published on the development of such mathematical formulations, their implementation in real systems, and on the empirical data from their deployment. Moreover, nearly all the designed driver advisory systems are designed as an additional hardware to be added in drivers’ cabin. This paper discusses the design of a mathematical formulation and optimization approach for such a system, together with its implementation into an Android-based prototype, the results from on-board practical experiments, and experiences from the implementation. The system is based on a more realistic train model where energy calculations take into account dynamic losses in different components of the propulsion system, contrary to previous approaches. The experimental evaluation shows a significant increase in accuracy, as compared to a previous approach. Tests on a double-track section of the Mälaren line in Sweden demonstrates a significant potential for energy saving.

      PubDate: 2017-09-27T16:39:41Z
      DOI: 10.1016/j.trc.2017.09.010
      Issue No: Vol. 85 (2017)
  • Dynamic change of aircraft seat condition for fast boarding
    • Authors: Michael Schultz
      Pages: 131 - 147
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Michael Schultz
      Aircraft boarding is a process mainly impacted by the boarding sequence, individual passenger behavior and the amount of hand luggage. Whereas these aspects are widely addressed in scientific research and considered in operational improvements, the influence of infrastructural changes is only focused upon in the context of future aircraft design. The paper provides a comprehensive analysis of the innovative approach of a Side-Slip Seat, which allows passengers to pass each other during boarding. The seat holds the potential to reduce the boarding time by approx. 20%, even considering operational constraints, such as passenger conformance to the proposed boarding strategy. A validated stochastic boarding model is extended to analyze the impact of the Side-Slip Seat. The implementation of such fundamental change inside the aircraft cabin demands for adapted boarding strategies, in order to cover all the benefits that accompany this new dynamic seating approach. To reasonably identify efficient strategies, an evolutionary algorithm is used to systematically optimize boarding sequences. As a result, the evolutionary algorithm depicts that operationally relevant boarding strategies implementing the Side-Slip Seat should differentiate between the left and the right side of the aisle, instead of the current operationally preferred boarding from the back to the front.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.09.014
      Issue No: Vol. 85 (2017)
  • A methodology to evaluate driving efficiency for professional drivers
           based on a maturity model
    • Authors: Laura Pozueco; Xabiel G. Pañeda; Alejandro G. Tuero; Gabriel Díaz; Roberto García; David Melendi; Alejandro G. Pañeda; José A. Sánchez
      Pages: 148 - 167
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Laura Pozueco, Xabiel G. Pañeda, Alejandro G. Tuero, Gabriel Díaz, Roberto García, David Melendi, Alejandro G. Pañeda, José A. Sánchez
      Over the last decade, transport companies have tried to reduce fuel consumption using efficient driving programs. In them, motorists have to apply different specific techniques while driving. Thus, to succeed in this learning process there are two key elements: the knowledge of efficient driving techniques and the drivers’ motivation. The latter is a human factor which companies usually bring about by using reward systems. In this case, having a fair evaluation mechanism is the keystone to determine goal fulfilment. This paper presents a complete methodology to evaluate driving efficiency of drivers in professional fleets. The evaluation methodology is based on a continuous process which determines the maturity of the motorist in different aspects, such as the efficiency during the start of the vehicle movement, during motion or in stop events. In addition, the evaluation methodology includes an early-classification method to establish the initial efficiency level of the individual drivers which permits an adaptation of the learning process from the beginning. A dashboard has also been developed to support the evaluation methodology. 880 professional drivers have been evaluated with this methodology. Results show that the evaluation methodology identifies drivers’ weaknesses, to be improved in successive iterations of the learning process.
      Graphical abstract image

      PubDate: 2017-10-04T17:16:10Z
      DOI: 10.1016/j.trc.2017.09.017
      Issue No: Vol. 85 (2017)
  • Modeling the dynamic effect of information on drivers’ choice behavior
           in the context of an Advanced Traveler Information System
    • Authors: Mauro Dell'Orco; Mario Marinelli
      Pages: 168 - 183
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Mauro Dell'Orco, Mario Marinelli
      In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers’ dynamic choice behavior under an Advanced Traveler Information System (ATIS). The proposed model uses the Possibility Theory to model Uncertainty embedded in human perception of information. We have introduced a time-dependent Possibility Distribution of Information to model the users’ changing perception of travel time also based on current network conditions. Drivers’ choice models are often developed and calibrated by using Stated Preference (SP) surveys, amongst others. In this work, we present an experiment to set up an SP-tool based on a driving simulator developed at the Polytechnic University of Bari. The results obtained by the proposed model are analyzed and compared with the driver dynamic behavior observed in the experiment.

      PubDate: 2017-10-04T17:16:10Z
      DOI: 10.1016/j.trc.2017.09.019
      Issue No: Vol. 85 (2017)
  • Improving recovery-to-optimality robustness through efficiency-balanced
           design of timetable structure
    • Authors: Chao Lu; Jinjin Tang; Leishan Zhou; Yixiang Yue; Zhitong Huang
      Pages: 184 - 210
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Chao Lu, Jinjin Tang, Leishan Zhou, Yixiang Yue, Zhitong Huang
      To improve the service quality of the railway system (e.g., punctuality and travel times) and to enhance the robust timetabling methods further, this paper proposes an integrated two-stage approach to consider the recovery-to-optimality robustness into the optimized timetable design without predefined structure information (defined as flexible structure) such as initial departure times, overtaking stations, train order and buffer time. The first-stage timetabling model performs an iterative adjustment of all departure and arrival times to generate an optimal timetable with balanced efficiency and recovery-to-optimality robustness. The second-stage dispatching model evaluates the recovery-to-optimality robustness by simulating how each timetable generated from the first-stage could recover under a set of restricted scenarios of disturbances using the proposed dispatching algorithm. The concept of recovery-to-optimality is examined carefully for each timetable by selecting a set of optimally refined dispatching schedules with minimum recovery cost under each scenario of disturbance. The robustness evaluation process enables an updating of the timetable by using the generated dispatching schedules. Case studies were conducted in a railway corridor as a special case of a simple railway network to verify the effectiveness of the proposed approach. The results show that the proposed approach can effectively attain a good trade-off between the timetable efficiency and obtainable robustness for practical applications.

      PubDate: 2017-10-04T17:16:10Z
      DOI: 10.1016/j.trc.2017.09.015
      Issue No: Vol. 85 (2017)
  • A bi-level model for single-line rail timetable design with consideration
           of demand and capacity
    • Authors: Yuting Zhu; Baohua Mao; Yun Bai; Shaokuan Chen
      Pages: 211 - 233
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Yuting Zhu, Baohua Mao, Yun Bai, Shaokuan Chen
      This paper proposes abi-level model to solve the timetable design problem for an urban rail line. The upper level model aims at determining the headways between trains to minimize total passenger cost, which includes not only the usual perceived travel time cost, but also penalties during travel. With the headways given by the upper level model, passengers’ arrival times at their origin stops are determined by the lower level model, in which the cost-minimizing behavior of each passenger is taken into account. To make the model more realistic, explicit capacity constraints of individual trains are considered. With these constraints, passengers cannot board a full train, but wait in queues for the next coming train. A two-stage genetic algorithm incorporating the method of successive averages is introduced to solve the bi-level model. Two hypothetical examples and a real world case are employed to evaluate the effectiveness of the proposed bi-level model and algorithm. Results show that the bi-level model performs well in reducing total passenger cost, especially in reducing waiting time cost and penalties. And the section loading-rates of trains in the optimized timetable are more balanced than the even-headway timetable. The sensitivity analyses show that passenger’s desired arrival time interval at destination and crowding penalty factor have a high influence on the optimal solution. And with the dispersing of passengers' desired arrival time intervals or the increase of crowding penalty factor, the section loading-rates of trains become more balanced.

      PubDate: 2017-10-04T17:16:10Z
      DOI: 10.1016/j.trc.2017.09.002
      Issue No: Vol. 85 (2017)
  • Agent-based simulation framework for mixed traffic of cars, pedestrians
           and trams
    • Authors: Hideki Fujii; Hideaki Uchida; Shinobu Yoshimura
      Pages: 234 - 248
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Hideki Fujii, Hideaki Uchida, Shinobu Yoshimura
      In this paper, we report on the construction of a new framework for simulating mixed traffic consisting of cars, trams, and pedestrians that can be used to support discussions about road management, signal control, and public transit. Specifically, a layered road structure that was designed for car traffic simulations was extended to interact with an existing one-dimensional (1D) car-following model and a two-dimensional (2D) discrete choice model for pedestrians. The car model, pedestrian model, and interaction rules implemented in the proposed framework were verified through simulations involving simple road environments. The resulting simulated values were in near agreement with the empirical data. We then used the proposed framework to assess the impact of a tramway extension plan for a real city. The simulation results showed that the impact of the proposed tramway on existing car traffic would not be serious, and by extension, implied that the proposed framework could help stakeholders decide on expansion scenarios that are satisfactory to both tram users and private car owners.

      PubDate: 2017-10-04T17:16:10Z
      DOI: 10.1016/j.trc.2017.09.018
      Issue No: Vol. 85 (2017)
  • Open PFLOW: Creation and evaluation of an open dataset for typical people
           mass movement in urban areas
    • Authors: Takehiro Kashiyama; Yanbo Pang; Yoshihide Sekimoto
      Pages: 249 - 267
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Takehiro Kashiyama, Yanbo Pang, Yoshihide Sekimoto
      Understanding people flow at a citywide level is critical for urban planning and commercial development. Thanks to the ubiquity of human location tracking devices, many studies on people mass movement with mobility logs have been conducted. However, high cost and severe privacy policy constraints still complicate utilization of these data in practice. There is no dataset that anyone can freely access, use, modify, and share for any purpose. To tackle this problem, we propose a novel dataset creation approach (called Open PFLOW) that continuously reports the spatiotemporal positions of all individual’s in urban areas based on open data. With fully consideration of the privacy protection, each entity in our dataset does not match the actual movement of any real person, so that the dataset can be totally open to public as part of data infrastructure. Because the result is shown at a disaggregate level, users can freely modify, process, and visualize the dataset for any purpose. We evaluate the accuracy of the dataset by comparing it with commercial datasets and traffic census indicates that it has a high correlation with mesh population and link-based traffic volume.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.09.016
      Issue No: Vol. 85 (2017)
  • Integrated sequencing and merging aircraft to parallel runways with
           automated conflict resolution and advanced avionics capabilities
    • Authors: Man Liang; Daniel Delahaye; Pierre Maréchal
      Pages: 268 - 291
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Man Liang, Daniel Delahaye, Pierre Maréchal
      Congestion in Terminal Maneuvering Area (TMA) in hub airports is the main problem in Chinese air transportation. In this paper we propose a new system to integrated sequence and merge aircraft to parallel runways at Beijing Capital International Airport (BCIA). This system is based on the advanced avionics capabilities. Our methodology integrates a Multi-Level Point Merge (ML-PM) system, an economical descent approaches procedure, and a tailored heuristic algorithm to find a good, systematic, operationally-acceptable solution. First, Receding Horizontal Control (RHC) technique is applied to divide the entire 24h of traffic into several sub-problems. Then in each sub-problem, it is optimized on given objectives (conflict, deviation from Estimated Time of Arrival (ETA) on the runway and makespan of the arrival flow). Four decision variables are designed to control the trajectory: the entry time, the entry speed, the turning time on the sequencing leg, and the landing runway allocation. Based on these variables, the real time trajectories are generated by the simulation module. Simulated Annealing (SA) algorithm is used to search the best solution for aircraft to execute. Finally, the conflict-free, least-delay, and user-preferred trajectories from the entry point of TMA to the landing runway are defined. Numerical results show that our optimization system has very stable de-conflict performance to handle continuously dense arrivals in transition airspace. It can also provide the decision support to assist flow controllers to handle the asymmetric arrival flows on different runways with less fuel consumption, and to assist tactical controllers to easily re-sequence aircraft with more relaxed position shifting. Moreover, our system can provide the fuel consumption prediction, and runway assignment information to assist airport and airlines managers for optimal decision making. Theoretically, it realizes an automated, cooperative and green control of routine arrival flows. Although the methodology defined here is applied to the airport BCIA, it could also be applied to other airports in the world.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.09.012
      Issue No: Vol. 85 (2017)
  • Forecasting journey time distribution with consideration to abnormal
           traffic conditions
    • Authors: R.X. Zhong; J.C. Luo; H.X. Cai; A. Sumalee; F.F. Yuan; Andy H.F. Chow
      Pages: 292 - 311
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): R.X. Zhong, J.C. Luo, H.X. Cai, A. Sumalee, F.F. Yuan, Andy H.F. Chow
      Travel time is an important index for managers to evaluate the performance of transportation systems and an intuitive measure for travelers to choose routes and departure times. An important part of the literature focuses on predicting instantaneous travel time under recurrent traffic conditions to disseminate traffic information. However, accurate travel time prediction is important for assessing the effects of abnormal traffic conditions and helping travelers make reliable travel decisions under such conditions. This study proposes an online travel time prediction model with emphasis on capturing the effects of anomalies. The model divides a path into short links. A Functional Principal Component Analysis (FPCA) framework is adopted to forecast link travel times based on historical data and real-time measurements. Furthermore, a probabilistic nested delay operator is used to calculate path travel time distributions. To ensure that the algorithm is fast enough for online applications, parallel computation architecture is introduced to overcome the computational burden of the FPCA. Finally, a rolling horizon structure is applied to online travel time prediction. Empirical results for Guangzhou Airport Expressway indicate that the proposed method can capture an abrupt change in traffic state and provide a promising and reliable travel time prediction at both the link and path levels. In the case where the original FPCA is modified for parallelization, accuracy and computational effort are evaluated and compared with those of the sequential algorithm. The proposed algorithm is found to require only a piece rather than a large set of traffic incident records.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.08.021
      Issue No: Vol. 85 (2017)
  • Percolation phenomenon in connected vehicle network through a multi-agent
           approach: Mobility benefits and market penetration
    • Authors: Alireza Mostafizi; Shangjia Dong; Haizhong Wang
      Pages: 312 - 333
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Alireza Mostafizi, Shangjia Dong, Haizhong Wang
      This paper presents an integrated multi-agent approach, coupled with percolation theory and network science, to measure the mobility impacts (i.e., mean travel time of the system) of connected vehicle (CVtio) network at varying levels of market penetration rate. We capture the characteristics of a CV network, i.e., node degree distribution, vehicular clustering, and giant component size to verify the existence of percolation phenomenon, and further connect the emergence of mobility benefits to the percolation phase transition in the CV network. We show the percolation phase transition properties to appear in a dynamic CV network with time-correlated link and node dynamics. An analytical framework was developed to evaluate the CV network attributes with varying market penetrations (MP) and connection ranges (CR) to identify percolation phenomenon in a mixed CV and Non-CV environment. In addition, a multi-agent CV simulation platform was created to further measure (1) how varying MPs and CRs affect the network-wide mobility measured by the mean travel time of the network; and (2) when percolation transition occurs in CV network to capture the critical MP and CR. Percolation phenomenon in CV network was further validated with the analytical assessments. The results show that (1) percolation phase transition phenomenon is a function of both market penetration and communication range; (2) percolation phase transitions in both mobility and CV network are highly correlated; (3) the application can reduce the average travel time of the system by up to 20% with reasonable market penetration and communication range; (4) critical market penetration is sensitive to communication range, and vice versa; (5) at least 70% of the CVs on the network are required to show in the same cluster for mobility benefits to appear; and (6) for high levels of MP or CR, a low probability of connectivity (PC) does not dramatically change the mean travel time. These results provide solid supports to create evidence-driven frameworks to guide future CV deployment and CV network analysis.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.09.013
      Issue No: Vol. 85 (2017)
  • Controlled time of arrival windows for already initiated energy-neutral
           continuous descent operations
    • Authors: Ramon Dalmau; Xavier Prats
      Pages: 334 - 347
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Ramon Dalmau, Xavier Prats
      Continuous descent operations with controlled times of arrival at one or several metering fixes could enable environmentally friendly procedures without compromising terminal airspace capacity. This paper focuses on controlled time of arrival updates once the descent has been already initiated, assessing the feasible time window (and associated fuel consumption) of continuous descent operations requiring neither thrust nor speed-brake usage along the whole descent (i.e. only elevator control is used to achieve different metering times). Based on previous works, an optimal control problem is formulated and numerically solved. The earliest and latest times of arrival at the initial approach fix have been computed for the descent of an Airbus A320 under different scenarios, considering the potential altitudes and distances to go when receiving the controlled time of arrival update. The effects of the aircraft mass, initial speed, longitudinal wind and position of the initial approach fix on the time window have been also investigated. Results show that time windows about three minutes could be achieved for certain conditions, and that there is a trade-off between robustness facing controlled time of arrival updates during the descent and fuel consumption. Interestingly, minimum fuel trajectories almost correspond to those of minimum time.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.09.024
      Issue No: Vol. 85 (2017)
  • Traffic data imputation via tensor completion based on soft thresholding
           of Tucker core
    • Authors: J.H. de M. Goulart; A.Y. Kibangou; G. Favier
      Pages: 348 - 362
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): J.H. de M. Goulart, A.Y. Kibangou, G. Favier
      Technological limitations and practical difficulties cause inevitable losses of traffic data in the typical processing chain of an intelligent transportation system. This has motivated the development of imputation algorithms for mitigating the consequences of such losses. As the involved datasets are usually multidimensional and bear strong spatio-temporal correlations, we propose for traffic data imputation a tensor completion algorithm which promotes parsimony of an estimated orthogonal Tucker model by iteratively softly thresholding its core. The motivation of this strategy is discussed on the basis of characteristics typically possessed by real-world datasets. An evaluation of the proposed method using speed data from the Grenoble south ring (France) shows that our algorithm outperforms other imputation methods, including tensor completion algorithms, and delivers good results even when the loss is severely systematic, being mostly concentrated in long time windows (of up to three hours) spread along the considered time horizon.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.09.011
      Issue No: Vol. 85 (2017)
  • Electrification of the two-car household: PHEV or BEV'
    • Authors: Lars-Henrik Björnsson; Sten Karlsson
      Pages: 363 - 376
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Lars-Henrik Björnsson, Sten Karlsson
      In previous works, we have shown two-car households to be better suited than one-car households for leveraging the potential benefits of the battery electric vehicle (BEV), both when the BEV simply replaces the second car and when it is used optimally in combination with a conventional car to overcome the BEV’s range limitation and increase its utilization. Based on a set of GPS-measured car movement data from 64 two-car households in Sweden, we here assess the potential electric driving of a plug-in hybrid electric vehicle (PHEV) in a two-car household and compare the resulting economic viability and potential fuel substitution to that of a BEV. Using estimates of near-term mass production costs, our results suggest that, for Swedish two-car households, the PHEV in general should have a higher total cost of ownership than the BEV, provided the use of the BEV is optimized. However, the PHEV will increasingly be favored if, for example, drivers cannot or do not want to optimize usage. In addition, the PHEV and the BEV are not perfect substitutes. The PHEV may be favored if drivers require that the vehicle be able to satisfy all driving needs (i.e., if drivers don’t accept the range and charge-time restrictions of the BEV) or if drivers requires an even larger battery in the BEV to counter range anxiety. We find that, given a particular usage strategy, the electric drive fraction (EDF) of the vehicle fleet is less dependent on whether PHEVs or BEVs are used to replace one of the conventional cars in two-car households. Instead, the EDF depends more on the usage strategy, i.e., on whether the PHEV/BEV is used to replace the conventional car with the higher annual mileage (“the first car”), the less used car (“the second car”), or is used flexibly to substitute for either in order to optimize use. For example, from a fuel replacement perspective it is often better to replace the first car with a PHEV than to replace the second with a BEV.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.09.021
      Issue No: Vol. 85 (2017)
  • Modeling the information flow propagation wave under vehicle-to-vehicle
    • Authors: Yong Hoon Kim; Srinivas Peeta; Xiaozheng He
      Pages: 377 - 395
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Yong Hoon Kim, Srinivas Peeta, Xiaozheng He
      Vehicle-to-vehicle (V2V) communications under the connected vehicle context have the potential to provide new paradigms to enhance the safety, mobility and environmental sustainability of surface transportation. Understanding the information propagation characteristics in space and time is a key enabler for V2V-based traffic systems. Most existing analytical models assume instantaneous propagation of information flow through multi-hop communications. Such an assumption ignores the spatiotemporal relationships between the traffic flow dynamics and V2V communication constraints. This study proposes a macroscopic two-layer model to characterize the information flow propagation wave (IFPW). The traffic flow propagation is formulated in the lower layer as a system of partial differential equations based on the Lighthill-Whitham-Richards model. Due to their conceptual similarities, the upper layer adapts and modifies a spatial Susceptible-Infected epidemic model to describe information dissemination between V2V-equipped vehicles using integro-differential equations. A closed-form solution is derived for the IFPW speed under homogeneous conditions. The IFPW speed is numerically determined for heterogeneous conditions. Numerical experiments illustrate the impact of traffic density and market penetration of V2V-equipped vehicles on the IFPW speed. The proposed model can capture the spatiotemporal relationships between the traffic and V2V communication layers, and aid in the design of novel information propagation strategies to manage traffic conditions under V2V-based traffic systems.

      PubDate: 2017-10-12T04:16:35Z
      DOI: 10.1016/j.trc.2017.09.023
      Issue No: Vol. 85 (2017)
  • Potentials of using social media to infer the longitudinal travel
           behavior: A sequential model-based clustering method
    • Authors: Zhenhua Zhang; Qing He; Shanjiang Zhu
      Pages: 396 - 414
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Zhenhua Zhang, Qing He, Shanjiang Zhu
      This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.

      PubDate: 2017-10-18T15:03:05Z
      DOI: 10.1016/j.trc.2017.10.005
      Issue No: Vol. 85 (2017)
  • A comparative study of aviation safety briefing media: card, video, and
           video with interactive controls
    • Authors: Luca Chittaro
      Pages: 415 - 428
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Luca Chittaro
      Passengers’ safety knowledge is a key factor in determining the chance of surviving any life- or injury-threatening situation that could occur in civil aviation. Aviation regulations require airlines to provide safety briefings to inform passengers of safety procedures on board. The safety briefing card and the safety briefing video are the two media that airlines routinely employ on board to this purpose. Unfortunately, research on aviation safety briefing media has cast serious doubts about their efficacy, urging researchers to better understand what makes safety briefing media effective as well as improving their effectiveness. This paper contributes to such goals in two different ways. First, it proposes the introduction of interactive technology into aviation safety briefings for improving their effectiveness. Second, it illustrates a controlled study that compares the effectiveness of three safety briefing media: the two briefing media that airlines currently employ on-board (safety briefing card and safety briefing video) and a safety briefing video extended with basic interactive controls. The results obtained by the study highlight a superior effectiveness of the two video media over the card media for aviation safety briefings. Moreover, the video with interactive controls produced improvements over the card in a larger number of effectiveness measures than the traditional video. The paper includes a discussion of factors that can explain the better results obtained with the video conditions, and in particular the video with interactive controls, and of possible additional extensions to increase the interactivity of aviation safety briefings.

      PubDate: 2017-10-18T15:03:05Z
      DOI: 10.1016/j.trc.2017.10.007
      Issue No: Vol. 85 (2017)
  • Customized bus service design for jointly optimizing passenger-to-vehicle
           assignment and vehicle routing
    • Authors: Lu (Carol) Tong; Leishan Zhou; Jiangtao Liu; Xuesong Zhou
      Pages: 451 - 475
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Lu (Carol) Tong, Leishan Zhou, Jiangtao Liu, Xuesong Zhou
      Emerging transportation network services, such as customized buses, hold the promise of expanding overall traveler accessibility in congested metropolitan areas. A number of internet-based customized bus services have been planned and deployed for major origin-destination (OD) pairs to/from inner cities with limited physical road infrastructure. In this research, we aim to develop a joint optimization model for addressing a number of practical challenges for providing flexible public transportation services. First, how to maintain minimum loading rate requirements and increase the number of customers per bus for the bus operators to reach long-term profitability. Second, how to optimize detailed bus routing and timetabling plans to satisfy a wide range of specific user constraints, such as passengers’ pickup and delivery locations with preferred time windows, through flexible decision for matching passengers to bus routes. From a space-time network modeling perspective, this paper develops a multi-commodity network flow-based optimization model to formulate a customized bus service network design problem so as to optimize the utilization of the vehicle capacity while satisfying individual demand requests defined through space-time windows. We further develop a solution algorithm based on the Lagrangian decomposition for the primal problem and a space-time prism based method to reduce the solution search space. Case studies using both the illustrative and real-world large-scale transportation networks are conducted to demonstrate the effectiveness of the proposed algorithm and its sensitivity under different practical operating conditions.

      PubDate: 2017-10-26T00:30:27Z
      DOI: 10.1016/j.trc.2017.09.022
      Issue No: Vol. 85 (2017)
  • Probe data-driven travel time forecasting for urban expressways by
           matching similar spatiotemporal traffic patterns
    • Authors: Zhihao Zhang; Yunpeng Wang; Peng Chen; Zhengbing He; Guizhen Yu
      Pages: 476 - 493
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Zhihao Zhang, Yunpeng Wang, Peng Chen, Zhengbing He, Guizhen Yu
      Travel time is an effective measure of roadway traffic conditions. The provision of accurate travel time information enables travelers to make smart decisions about departure time, route choice and congestion avoidance. Based on a vast amount of probe vehicle data, this study proposes a simple but efficient pattern-matching method for travel time forecasting. Unlike previous approaches that directly employ travel time as the input variable, the proposed approach resorts to matching large-scale spatiotemporal traffic patterns for multi-step travel time forecasting. Specifically, the Gray-Level Co-occurrence Matrix (GLCM) is first employed to extract spatiotemporal traffic features. The Normalized Squared Differences (NSD) between the GLCMs of current and historical datasets serve as a basis for distance measurements of similar traffic patterns. Then, a screening process with a time constraint window is implemented for the selection of the best-matched candidates. Finally, future travel times are forecasted as a negative exponential weighted combination of each candidate’s experienced travel time for a given departure. The proposed approach is tested on Ring 2, which is a 32km urban expressway in Beijing, China. The intermediate procedures of the methodology are visualized by providing an in-depth quantitative analysis on the speed pattern matching and examples of matched speed contour plots. The prediction results confirm the desirable performance of the proposed approach and its robustness and effectiveness in various traffic conditions.

      PubDate: 2017-10-26T00:30:27Z
      DOI: 10.1016/j.trc.2017.10.010
      Issue No: Vol. 85 (2017)
  • Optimal design of electric vehicle public charging system in an urban
           network for Greenhouse Gas Emission and cost minimization
    • Authors: Jinwoo Lee; Samer Madanat
      Pages: 494 - 508
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Jinwoo Lee, Samer Madanat
      In this paper, we address the optimization problem of allocation of Electric Vehicle (EV) public fast charging stations over an urban grid network. The objective is to minimize Greenhouse Gas Emissions (GHG) under multiple constraints including a limited agency budget, accessibility of charging stations in every possible charging request and charging demands during peak hours. Additionally, we address bi-criteria problems to consider user costs as the second objective. A convex parsimonious model that depends on relatively few assumptions and input parameters is proposed and it is shown to be useful for obtaining conceptual insights for high-level planning. In a parametric study using a hypothetical urban network model generated based on realistic parameters, we show that GHG emissions decrease with agency budget, and that the reductions vary depending on multiple factors related to EV market and EV technologies. The optimal solutions found from the bi-criteria problems are shown to be close to the solution minimizing GHG emissions only, meaning that the emission minimizing policy can also minimize user costs.

      PubDate: 2017-10-26T00:30:27Z
      DOI: 10.1016/j.trc.2017.10.008
      Issue No: Vol. 85 (2017)
  • A discounted recursive logit model for dynamic gridlock network analysis
    • Authors: Yuki Oyama; Eiji Hato
      Pages: 509 - 527
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Yuki Oyama, Eiji Hato
      Emerging sensing technologies such as probe vehicles equipped with Global Positioning System (GPS) devices on board provide us real-time vehicle trajectories. They are helpful for the understanding of the cases that are significant but difficult to observe because of the infrequency, such as gridlock networks. On the premise of this type of emerging technology, this paper propose a sequential route choice model that describes route choice behavior, both in ordinary networks, where drivers acquire spatial knowledge of networks through their experiences, and in extraordinary networks, which are situations that drivers rarely experience, and applicable to real-time traffic simulations. In extraordinary networks, drivers do not have any experience or appropriate information. In such a context, drivers have little spatial knowledge of networks and choose routes based on dynamic decision making, which is sequential and somewhat forward-looking. In order to model these decision-making dynamics, we propose a discounted recursive logit model, which is a sequential route choice model with the discount factor of expected future utility. Through illustrative examples, we show that the discount factor reflects drivers’ decision-making dynamics, and myopic decisions can confound the network congestion level. We also estimate the parameters of the proposed model using a probe taxis’ trajectory data collected on March 4, 2011 and on March 11, 2011, when the Great East Japan Earthquake occurred in the Tokyo Metropolitan area. The results show that the discount factor has a lower value in gridlock networks than in ordinary networks.
      Graphical abstract image

      PubDate: 2017-10-26T00:30:27Z
      DOI: 10.1016/j.trc.2017.10.001
      Issue No: Vol. 85 (2017)
  • Conflict-point formulation of intersection control for autonomous vehicles
    • Authors: Michael W. Levin; David Rey
      Pages: 528 - 547
      Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Michael W. Levin, David Rey
      Reservation-based intersection controls, in which vehicles reserve space-time paths through the intersection, have the potential to make greater use of intersection capacity than traffic signals. However, the efficiency of previous microsimulations of reservations has been severely limited by a protocol that requires vehicles to request reservations and the intersection manager to accept or reject them. We propose a new protocol, AIM ∗ , in which the intersection manager assigns reservations to vehicles, to greatly increase the optimization possibilities. Then, we present a mixed integer linear program for optimally choosing vehicle reservations under AIM ∗ . The formulation is similar to conflict resolution models for aviation, and ensures separation at all points that vehicles might intersect. We therefore present a rolling-horizon algorithm to extend the method to larger numbers of vehicles. Results show that the optimal reservation assignments from AIM ∗ significantly reduce delays over previous protocols. Furthermore, the rolling horizon solutions have similar delays to a fixed horizon, thereby providing an efficient method of implementing AIM ∗ .

      PubDate: 2017-10-26T00:30:27Z
      DOI: 10.1016/j.trc.2017.09.025
      Issue No: Vol. 85 (2017)
  • Accurate and cost-effective traffic information acquisition using adaptive
           sampling: Centralized and V2V schemes
    • Authors: Shiau Hong Lim; Yeow Khiang Chia; Laura Wynter
      Abstract: Publication date: Available online 6 December 2017
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Shiau Hong Lim, Yeow Khiang Chia, Laura Wynter
      The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC). As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses. Since cellular communications are costly, in the basic centralized scheme, the vehicle is not aware of the road conditions on the network. We extend the scheme to handle non-cellular communications via roadside units and vehicle-to-vehicle (V2V) communication. Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements. Our analytical results are further supported via simulations based on actual road networks for both the centralized and V2V settings.

      PubDate: 2017-12-11T13:56:47Z
      DOI: 10.1016/j.trc.2017.10.017
  • Dynamic model for pedestrian crossing in congested traffic based on
           probabilistic navigation function
    • Authors: Shlomi Hacohen; Nir Shvalb Shraga Shoval
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Shlomi Hacohen, Nir Shvalb, Shraga Shoval
      More than 1.2 million people die in road crashes each year, and more than 20 million are severely injured, making it the 9th leading cause of death in the world (2.2% of all deaths globally). Pedestrian deaths comprise more than 35% of road accident deaths, mostly as a result of pedestrian-vehicle crashes. This paper proposes a new model for formulating the dynamics of the interaction between drivers and pedestrians at congested conflict spots where drivers and/or pedestrians do not closely follow the traffic laws and regulations. In this type of spots, characterized by heavy traffic, pedestrians and vehicles interact in close proximity, often requiring sharp and aggressive maneuvers to avoid crashes. The model is based on the Probabilistic Navigation Function (PNF), originally developed for robotics motion planning, that constructs a trajectory according to the probabilistic collision risks. According to this model, pedestrians construct a virtual risk map that assigns the entire crossing area with probabilities for a collision with vehicles, and then select their actions based on their perceived probability for collision. Many accidents can be interpreted in terms of the proposed model, either as a result of incorrect perception of risks, or, despite proper estimation of risks, by a wrong choice of collision maneuvers. The development of the model follows a theoretical and experimental investigation of pedestrian/vehicle interactions at crosswalks. The model is implemented in an agent-based simulation system for pedestrian/driver interaction, and is validated using video clips taken at several congested road spots. It can be used for analyzing the effect of changes in location architecture and traffic regulations for each spot. The model can also serve as a standard tool in simulations for assessing accident risks in urban environments. Finally, it can be utilized in control systems of autonomous vehicles and in drivers’ on-board alert systems.
      Graphical abstract image

      PubDate: 2017-11-17T03:28:21Z
  • Variable speed limit design based on mode dependent Cell Transmission
    • Abstract: Publication date: December 2017
      Source:Transportation Research Part C: Emerging Technologies, Volume 85
      Author(s): Alfréd Csikós, Balázs Kulcsár
      In this paper a mode dependent variable speed limit (VSL) control strategy is developed for motorway networks. The suggested rolling horizon and coordinated algorithm uses switching mode Cell Transmission Model (CTM) and purports to maximize network throughput. In this line, first, a VSL signal scheduled piecewise affine switching mode CTM is derived based on the polyhedral description of Godunov fluxes. Second, a two-stage, coordinated, rolling horizon VSL sequence generation procedure is proposed. The set of possible VSL signs is selected by applying input constraints in order to eliminate spatial and temporal VSL oscillations. Then, the set of modes is further reduced according to the stable and adjacent reachable modes of the switching mode CTM. Over the remaining set of input signals, network capacity is maximized with the help of solving a mixed integer optimization problem under the form of reference density tracking objective. The method is implemented in simulation environment to demonstrate its computational efficiency and viability to attenuate shockwaves.

      PubDate: 2017-10-18T15:03:05Z
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