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  Subjects -> TRANSPORTATION (Total: 176 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (22 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (8 journals)
    - SHIPS AND SHIPPING (34 journals)
    - TRANSPORTATION (98 journals)

TRANSPORTATION (98 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: 18)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 12)
Cities in the 21st Century     Open Access   (Followers: 13)
Economics of Transportation     Partially Free   (Followers: 12)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 12)
European Transport Research Review     Open Access   (Followers: 19)
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: 9)
IFAC-PapersOnLine     Open Access  
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 7)
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: 7)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 10)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 9)
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: 13)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 6)
Journal of Navigation     Hybrid Journal   (Followers: 212)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 11)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 2)
Journal of the Transportation Research Forum     Open Access   (Followers: 7)
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: 21)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 11)
Journal of Transport Geography     Hybrid Journal   (Followers: 23)
Journal of Transport History     Hybrid Journal   (Followers: 11)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 6)
Journal of Transportation Security     Hybrid Journal   (Followers: 1)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 12)
Journal of Transportation Technologies     Open Access   (Followers: 14)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 8)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics     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: 18)
Open Journal of Safety Science and Technology     Open Access   (Followers: 9)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 1)
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: 15)
Promet : Traffic &Transportation     Open Access  
Public Transport     Hybrid Journal   (Followers: 17)
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: 10)
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: 1)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 5)
Transport     Open Access   (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: 8)
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: 5)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 33)
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: 4)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 32)
Transportation Science     Full-text available via subscription   (Followers: 19)
TRANSPORTES     Open Access   (Followers: 5)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 5)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 1)
Transportrecht     Unknown  
Travel Behaviour and Society     Full-text available via subscription   (Followers: 8)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 1)
Urban, Planning and Transport Research     Open Access   (Followers: 26)
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  [3177 journals]
  • A tabu search algorithm to solve the integrated planning of container on
           an inter-terminal network connected with a hinterland rail network
    • Authors: Qu Hu; Francesco Corman; Bart Wiegmans; Gabriel Lodewijks
      Pages: 15 - 36
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Qu Hu, Francesco Corman, Bart Wiegmans, Gabriel Lodewijks
      Transport demand for containers has been increasing for decades, which places pressure on road transport. As a result, rail transport is stimulated to provide better intermodal freight transport services. This paper investigates mathematical models for the planning of container movements in a port area, integrating the inter-terminal transport of containers (ITT, within the port area) with the rail freight formation and transport process (towards the hinterland). An integer linear programming model is used to formulate the container transport across operations at container terminals, the network interconnecting them, railway yards and the railway networks towards the hinterland. A tabu search algorithm is proposed to solve the problem. The practical applicability of the algorithm is tested in a realistic infrastructure case and different demand scenarios. Our results show the degree by which internal (ITT) and external (hinterland) transport processes interact, and the potential for improvement of overall operations when the integrated optimization proposed is used. Instead, if the planning of containers in the ITT system is optimized as a stand-alone problem, the railway terminals may suffer from longer delay times or additional train cancellations. When planning the transport of 4060 TEU containers within one day, the benefits of the ITT planning without considering railway operations account for 17% ITT cost reduction but 93% railway operational cost growth, while the benefits of integrating ITT and railway account for a reduction of 20% in ITT cost and 44% in railway operational costs.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.019
      Issue No: Vol. 91 (2018)
  • A fine discrete field cellular automaton for pedestrian dynamics
           integrating pedestrian heterogeneity, anisotropy, and time-dependent
    • Authors: Zhijian Fu; Qihao Jia; Junmin Chen; Jian Ma; Ke Han; Lin Luo
      Pages: 37 - 61
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Zhijian Fu, Qihao Jia, Junmin Chen, Jian Ma, Ke Han, Lin Luo
      This paper proposes a discrete field cellular automaton (CA) model that integrates pedestrian heterogeneity, anisotropy, and time-dependent characteristics. The pedestrian movement direction, moving/staying, and steering are governed by the transfer equations. Compared with existing studies on fine-discretized CA models, the proposed model is advantageous in terms of flexibility, higher spatial accuracy, wider speed range, relatively low computational cost, and elaborated conflict resolution with synchronous update scheme. Three different application scenarios are created by adjusting the definite conditions of the model: (1) The first one is a unidirectional pedestrian movement in a channel, where a complete jam in the high-density region is observed from the proposed model, which is missing from existing floor field CA models. (2) The second one is evacuation from a room, where the evacuation time is independent of the discretization factor, which is different from previous work. (3) The third one is an ascending evacuation through a 21-storey stair system, where pedestrians move with constant speed or with fatigue. The evacuation time in the latter case is nearly twice of that in the former.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.022
      Issue No: Vol. 91 (2018)
  • Traffic dynamics in a bi-modal transportation network with information
           provision and adaptive transit services
    • Authors: Xinwei Li; Wei Liu; Hai Yang
      Pages: 77 - 98
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Xinwei Li, Wei Liu, Hai Yang
      This paper has two major components. The first one is the day-to-day evolution of travelers’ mode and route choices in a bi-modal transportation system where traffic information (predicted travel cost) is available to travelers. The second one is a public transit operator adjusting or adapting its service over time (from period to period) based on observed system conditions. Particularly, we consider that on each day both travelers’ past travel experiences and the predicted travel cost (based on information provision) can affect travelers’ perceptions of different modes and routes, and thus affect their mode choice and/or route choice accordingly. This evolution process from day to day is formulated by a discrete dynamical model. The properties of such a dynamical model are then analyzed, including the existence, uniqueness and stability of the fixed point. Most importantly, we show that the predicted travel cost based on information provision may help stabilize the dynamical system even if it is not fully accurate. Given the day-to-day traffic evolution, we then model an adaptive transit operator who can adjust frequency and fare for public transit from period to period (each period contains a certain number of days). The adaptive frequency and fare in one period are determined from the realized transit demands and transit profits of the previous periods, which is to achieve a (locally) maximum transit profit. The day-to-day and period-to-period models and their properties are also illustrated by numerical experiments.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.026
      Issue No: Vol. 91 (2018)
  • The fleet size and mix dial-a-ride problem with reconfigurable vehicle
    • Authors: Oscar Tellez; Samuel Vercraene; Fabien Lehuédé; Olivier Péton; Thibaud Monteiro
      Pages: 99 - 123
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Oscar Tellez, Samuel Vercraene, Fabien Lehuédé, Olivier Péton, Thibaud Monteiro
      This paper introduces a fleet size and mix dial-a-ride problem with multiple passenger types and a heterogeneous fleet of reconfigurable vehicles. In this new variant of the dial-a-ride problem, en-route modifications of the vehicle’s inner configuration are allowed. The main consequence is that the vehicle capacity is defined by a set of configurations and the choice of vehicle configuration is associated with binary decision variables. The problem is modeled as a mixed-integer program derived from the model of the heterogeneous dial-a-ride problem. Vehicle reconfiguration is a lever to efficiently reduce transportation costs, but the number of passengers and vehicle fleet setting make this problem intractable for exact solution methods. A large neighborhood search metaheuristic combined with a set covering component with a reactive mechanism to automatically adjust its parameters is therefore proposed. The resulting framework is evaluated against benchmarks from the literature, used for similar routing problems. It is also applied to a real case, in the context of the transportation of disabled children from their home to medical centers in the city of Lyon, France.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.020
      Issue No: Vol. 91 (2018)
  • Measurement of congestion and intrinsic risk in pedestrian crowds
    • Authors: Claudio Feliciani; Katsuhiro Nishinari
      Pages: 124 - 155
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Claudio Feliciani, Katsuhiro Nishinari
      In this study, we present a method to quantify the amount of congestion and the intrinsic risk in pedestrian crowds. Levels of congestion are estimated based on the velocity vector field obtained from the analysis of video recordings of moving crowds. By using data collected during supervised experiments, we show that the so-called “congestion level” allows to define a threshold for congestion under safe conditions and to measure the smoothness of pedestrian flows. The proposed approach has been compared with alternative quantities such as density, flow or the “crowd pressure” showing a more universal and consistent description of crowd motion. Later, the “crowd danger” of different pedestrian streams has been computed confirming that multidirectional motion is more dangerous than unidirectional one for equal levels of density. From a more practical perspective, the congestion level allowed to get a complete picture of the region in front of bottlenecks and to identify the formation of organized structures also under constant density and flow conditions. In addition, since only velocities are used in the computational process of the congestion level, it is more suitable for applications involving computer vision and emerging technologies, since density is usually difficult to obtain in very crowded situations. The congestion level and the crowd danger may help in the design of pedestrian facilities by simplifying interpretation of results from simulation and efficiently identify hotspots or design flaws. Finally, crowd control may benefit from the methods presented by potentially allowing a clear identification of dangerous locations during mass events.
      Graphical abstract image

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.027
      Issue No: Vol. 91 (2018)
  • Exploring relationships between driving events identified by in-vehicle
           data recorders, infrastructure characteristics and road crashes
    • Authors: Victoria Gitelman; Shlomo Bekhor; Etti Doveh; Fany Pesahov; Roby Carmel; Smadar Morik
      Pages: 156 - 175
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Victoria Gitelman, Shlomo Bekhor, Etti Doveh, Fany Pesahov, Roby Carmel, Smadar Morik
      There is an increasing interest in technology-based solutions that can assist drivers in reducing their risk of involvement in road crashes. Previous studies showed that driving events produced by in-vehicle data recorders (IVDR) are applicable for identification of unsafe driving patterns, while combined examinations of driving events and road infrastructure characteristics are rare. This study explored the relationship between the IVDR-driving events, road characteristics and crashes, to examine a potential of the events for predicting crashes and identification of high-risk locations on the road network. The study database included 3500 segments of the interurban roads in Israel, for which the automatically produced IVDR events were matched with road infrastructure characteristics and crashes. Negative-binomial regression models were adjusted for the relationships between road characteristics and driving events, and subsequently, between events and crashes, given the exposure. Significant impacts were found, yet various event types showed different relations to the infrastructure characteristics and different effects on crashes, on various road types. Better road conditions were associated with a decrease in “braking” events and an increase in the “speed alert” events, where road layout constraints and junction proximity were associated with an opposite effect on events. “Braking” and total events showed better potential for predicting crashes on single-carriageway roads, with a positive link to crashes, where for other road types the “speed alert” events were stronger related to crashes, but with a negative link. The heterogeneity of findings indicates a need in further research of the above relationship, with a particular focus on definitions of driving events produced by the IVDR or other technologies.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.04.003
      Issue No: Vol. 91 (2018)
  • Vehicle classification from low-frequency GPS data with recurrent neural
    • Authors: Matteo Simoncini; Leonardo Taccari; Francesco Sambo; Luca Bravi; Samuele Salti; Alessandro Lori
      Pages: 176 - 191
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Matteo Simoncini, Leonardo Taccari, Francesco Sambo, Luca Bravi, Samuele Salti, Alessandro Lori
      The categorization of the type of vehicles on a road network is typically achieved using external sensors, like weight sensors, or from images captured by surveillance cameras. In this paper, we leverage the nowadays widespread adoption of Global Positioning System (GPS) trackers and investigate the use of sequences of GPS points to recognize the type of vehicle producing them (namely, small-duty, medium-duty and heavy-duty vehicles). The few works which already exploited GPS data for vehicle classification rely on hand-crafted features and traditional machine learning algorithms like Support Vector Machines. In this work, we study how performance can be improved by deploying deep learning methods, which are recently achieving state of the art results in the classification of signals from various domains. In particular, we propose an approach based on Long Short-Term Memory (LSTM) recurrent neural networks that are able to learn effective hierarchical and stateful representations for temporal sequences. We provide several insights on what the network learns when trained with GPS data and contextual information, and report experiments on a very large dataset of GPS tracks, where we show how the proposed model significantly improves upon state-of-the-art results.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.024
      Issue No: Vol. 91 (2018)
  • Solving the station-based one-way carsharing network planning problem with
           relocations and non-linear demand
    • Authors: Kai Huang; Goncalo Homem de Almeida Correia; Kun An
      Pages: 1 - 17
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Kai Huang, Goncalo Homem de Almeida Correia, Kun An
      One-way station-based carsharing systems allow users to return a rented car to any designated station, which could be different from the origin station. Existing research has been mainly focused on the vehicle relocation problem to deal with the travel demand fluctuation over time and demand imbalance in space. However, the strategic planning of the stations’ location and their capacity for one-way carsharing systems has not been well studied yet, especially when considering vehicle relocations simultaneously. This paper presents a Mixed-integer Non-linear Programming (MINLP) model to solve the carsharing station location and capacity problem with vehicle relocations. This entails considering several important components which are for the first time integrated in the same model. Firstly, relocation operations and corresponding relocation costs are taken into consideration to address the imbalance between trip requests and vehicle availability. Secondly, the flexible travel demand at various time steps is taken as the input to the model avoiding deterministic requests. Thirdly, a logit model is constructed to represent the non-linear demand rate by using the ratio of carsharing utility and private car utility. To solve the MINLP model, a customized gradient algorithm is proposed. The application to the SIP network in Suzhou, China, demonstrates that the algorithm can solve a real world large scale problem in reasonable time. The results identify the pricing and parking space rental costs as the key factors influencing the profitability of carsharing operators. Also, the carsharing station location and fleet size impact the vehicle relocation and carsharing patronage.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.020
      Issue No: Vol. 90 (2018)
  • On the imputation of missing data for road traffic forecasting: New
           insights and novel techniques
    • Authors: Ibai Laña; Ignacio (Iñaki) Olabarrieta; Manuel Vélez; Javier Del Ser
      Pages: 18 - 33
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Ibai Laña, Ignacio (Iñaki) Olabarrieta, Manuel Vélez, Javier Del Ser
      Vehicle flow forecasting is of crucial importance for the management of road traffic in complex urban networks, as well as a useful input for route planning algorithms. In general traffic predictive models rely on data gathered by different types of sensors placed on roads, which occasionally produce faulty readings due to several causes, such as malfunctioning hardware or transmission errors. Filling in those gaps is relevant for constructing accurate forecasting models, a task which is engaged by diverse strategies, from a simple null value imputation to complex spatio-temporal context imputation models. This work elaborates on two machine learning approaches to update missing data with no gap length restrictions: a spatial context sensing model based on the information provided by surrounding sensors, and an automated clustering analysis tool that seeks optimal pattern clusters in order to impute values. Their performance is assessed and compared to other common techniques and different missing data generation models over real data captured from the city of Madrid (Spain). The newly presented methods are found to be fairly superior when portions of missing data are large or very abundant, as occurs in most practical cases.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.021
      Issue No: Vol. 90 (2018)
  • Data-driven optimization of railway maintenance for track geometry
    • Authors: Siddhartha Sharma; Yu Cui; Qing He; Reza Mohammadi; Zhiguo Li
      Pages: 34 - 58
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Siddhartha Sharma, Yu Cui, Qing He, Reza Mohammadi, Zhiguo Li
      Railway big data technologies are transforming the existing track inspection and maintenance policy deployed for railroads in North America. This paper develops a data-driven condition-based policy for the inspection and maintenance of track geometry. Both preventive maintenance and spot corrective maintenance are taken into account in the investigation of a 33-month inspection dataset that contains a variety of geometry measurements for every foot of track. First, this study separates the data based on the time interval of the inspection run, calculates the aggregate track quality index (TQI) for each track section, and predicts the track spot geo-defect occurrence probability using random forests. Then, a Markov chain is built to model aggregated track deterioration, and the spot geo-defects are modeled by a Bernoulli process. Finally, a Markov decision process (MDP) is developed for track maintenance decision making, and it is optimized by using a value iteration algorithm. Compared with the existing maintenance policy using Markov chain Monte Carlo (MCMC) simulation, the maintenance policy developed in this paper results in an approximately 10% savings in the total maintenance costs for every 1 mile of track.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.019
      Issue No: Vol. 90 (2018)
  • Aircraft initial mass estimation using Bayesian inference method
    • Authors: Junzi Sun; Joost Ellerbroek; Jacco M. Hoekstra
      Pages: 59 - 73
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Junzi Sun, Joost Ellerbroek, Jacco M. Hoekstra
      Aircraft mass is a crucial piece of information for studies on aircraft performance, trajectory prediction, and many other topics of aircraft traffic management. However, It is a common challenge for researchers, as well as air traffic control, to access this proprietary information. Previously, several studies have proposed methods to estimate aircraft weight based on specific parts of the flight. Due to inaccurate input data or biased assumptions, this often leads to less confident or inaccurate estimations. In this paper, combined with a fuel-flow model, different aircraft initial masses are computed independently using the total energy model and reference model at first. It then adopts a Bayesian approach that uses a prior probability of aircraft mass based on empirical knowledge and computed aircraft initial masses to produce the maximum a posteriori estimation. Variation in results caused by dependent factors such as prior, thrust and wind are also studied. The method is validated using 50 test flights of a Cessna Citation II aircraft, for which measurements of the true mass were available. The validation results show a mean absolute error of 4.3% of the actual aircraft mass.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.022
      Issue No: Vol. 90 (2018)
  • Review of optimal sensor location models for travel time estimation
    • Authors: M. Gentili; Pitu B. Mirchandani
      Pages: 74 - 96
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): M. Gentili, Pitu B. Mirchandani
      The problem of optimally locating fixed sensors on a traffic network infrastructure has been object of growing interest in the past few years. Sensor location decisions models differ from each other according to the type of sensors that are to be located and the objective that one would like to optimize. This paper surveys the existing contributions in the literature related to the problem of locating fixed sensors on the network to estimate travel times. The review consists of two parts: the first part reviews the methodological approaches for the optimal location of counting sensors on a freeway for travel time estimation; the second part focuses on the results related to the optimal location of Automatic Vehicle Identification (AVI) readers on the links of a network to get travel time information.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.01.021
      Issue No: Vol. 90 (2018)
  • Large-scale transit market segmentation with spatial-behavioural features
    • Authors: Le Minh Kieu; Yuming Ou; Chen Cai
      Pages: 97 - 113
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Le Minh Kieu, Yuming Ou, Chen Cai
      Transit market segmentation enables transit providers to comprehend the commonalities and heterogeneities among different groups of passengers, so that they can cater for individual transit riders’ mobility needs. The problem has recently been attracting a great interest with the proliferation of automated data collection systems such as Smart Card Automated Fare Collection (AFC), which allow researchers to observe individual travel behaviours over a long time period. However, there is a need for an integrated market segmentation method that incorporating both spatial and behavioural features of individual transit passengers. This algorithm also needs to be efficient for large-scale implementation. This paper proposes a new algorithm named Spatial Affinity Propagation (SAP) based on the classical Affinity Propagation algorithm (AP) to enable large-scale spatial transit market segmentation with spatial-behavioural features. SAP segments transit passengers using spatial geodetic coordinates, where passengers from the same segment are located within immediate walking distance; and using behavioural features mined from AFC data. The comparison with AP and popular algorithms in literature shows that SAP provides nearly as good clustering performance as AP while being 52% more efficient in computation time. This efficient framework would enable transit operators to leverage the availability of AFC data to understand the commonalities and heterogeneities among different groups of passengers.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.003
      Issue No: Vol. 90 (2018)
  • Dynamic traffic assignment of cooperative adaptive cruise control
    • Authors: Christopher L. Melson; Michael W. Levin; Britton E. Hammit; Stephen D. Boyles
      Pages: 114 - 133
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Christopher L. Melson, Michael W. Levin, Britton E. Hammit, Stephen D. Boyles
      Advances in connected and automated vehicle technologies have resulted in new vehicle applications, such as cooperative adaptive cruise control (CACC). Microsimulation models have shown significant increases in capacity and stability due to CACC, but most previous work has relied on microsimulation. To study the effects of CACC on larger networks and with user equilibrium route choice, we incorporate CACC into the link transmission model (LTM) for dynamic network loading. First, we derive the flow-density relationship from the MIXIC car-following model of CACC (at 100% CACC market penetration). The flow-density relationship has an unusual shape; part of the congested regime has an infinite congested wave speed. However, we verify that the flow predictions match observations from MIXIC modeled in VISSIM. Then, we use the flow-density relationship from MIXIC in LTM. Although the independence of separate links restricts the maximum congested wave speed, for common freeway link lengths the congested wave speed is sufficiently high to fit the observed flows from MIXIC. Results on a freeway and regional networks (with CACC-exclusive lanes) indicate that CACC could reduce freeway congestion, but naïve deployment of CACC-exclusive lanes could cause an increase in total system travel time.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.002
      Issue No: Vol. 90 (2018)
  • Locating emergency vehicles with an approximate queuing model and a
           meta-heuristic solution approach
    • Authors: M. Altan Akdoğan; Z. Pelin Bayındır; Cem Iyigun
      Pages: 134 - 155
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): M. Altan Akdoğan, Z. Pelin Bayındır, Cem Iyigun
      In this paper, the location of emergency service (ES) vehicles is studied on fully connected networks. Queuing theory is utilized to obtain the performance metrics of the system. An approximate queuing model the (AQM) is proposed. For the AQM, different service rate formulations are constructed. These formulations are tested with a simulation study for different approximation levels. A mathematical model is proposed to minimize the mean response time of ES systems based on AQM. In the model, multiple vehicles are allowed at a single location. The objective function of the model has no closed form expression. A genetic algorithm is constructed to solve the model. With the help of the genetic algorithm, the effect of assigning multiple vehicles on the mean response time is reported.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.01.014
      Issue No: Vol. 90 (2018)
  • A hybrid deep learning based traffic flow prediction method and its
    • Authors: Yuankai Wu; Huachun Tan; Lingqiao Qin; Bin Ran; Zhuxi Jiang
      Pages: 166 - 180
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Yuankai Wu, Huachun Tan, Lingqiao Qin, Bin Ran, Zhuxi Jiang
      Deep neural networks (DNNs) have recently demonstrated the capability to predict traffic flow with big data. While existing DNN models can provide better performance than shallow models, it is still an open issue of making full use of spatial-temporal characteristics of the traffic flow to improve their performance. In addition, our understanding of them on traffic data remains limited. This paper proposes a DNN based traffic flow prediction model (DNN-BTF) to improve the prediction accuracy. The DNN-BTF model makes full use of weekly/daily periodicity and spatial-temporal characteristics of traffic flow. Inspired by recent work in machine learning, an attention based model was introduced that automatically learns to determine the importance of past traffic flow. The convolutional neural network was also used to mine the spatial features and the recurrent neural network to mine the temporal features of traffic flow. We also showed through visualization how DNN-BTF model understands traffic flow data and presents a challenge to conventional thinking about neural networks in the transportation field that neural networks is purely a “black-box” model. Data from open-access database PeMS was used to validate the proposed DNN-BTF model on a long-term horizon prediction task. Experimental results demonstrated that our method outperforms the state-of-the-art approaches.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.001
      Issue No: Vol. 90 (2018)
  • SIV-DSS: Smart In-Vehicle Decision Support System for driving at
           signalized intersections with V2I communication
    • Authors: Xiao-Feng Xie; Zun-Jing Wang
      Pages: 181 - 197
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Xiao-Feng Xie, Zun-Jing Wang
      In this paper, we present a Smart In-Vehicle Decision Support System (SIV-DSS) to help making better stop/go decisions in the indecision zone as a vehicle is approaching a signalized intersection. Supported by the Vehicle-to-Infrastructure (V2I) communications, the system integrates and utilizes the information from both vehicle and intersection. The effective decision support models of SIV-DSS are realized with the probabilistic sequential decision making process with the capability of combining a variety of advantages gained from a set of decision rules, where each decision rule is responsible to specific situations for making right decisions even without complete information. The decision rules are either extracted from the existing parametric models of the indecision zone problem, or designed as novel ones based on physical models utilizing the integrated information containing the key inputs from vehicle motion, vehicle-driver characteristics, intersection geometry and topology, signal phase and timings, and the definitions of red-light running (RLR). In SIV-DSS, the generality is reached through physical models utilizing a large number of accurate physical parameters, and the heterogeneity is treated by including a few behavioral parameters in driver characteristics. The performance of SIV-DSS is evaluated with systematic simulation experiments. The results show that the system can not only ensure traffic safety by greatly reducing the RLR probability, but also improve mobility by significantly reducing unnecessary stops at the intersection. Finally, we briefly discuss some relevant aspects and implications for SIV-DSS in practical implementations.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.008
      Issue No: Vol. 90 (2018)
  • A range-restricted recharging station coverage model for drone delivery
           service planning
    • Authors: Insu Hong; Michael Kuby; Alan T. Murray
      Pages: 198 - 212
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Insu Hong, Michael Kuby, Alan T. Murray
      Unmanned Aerial Vehicles (UAVs) are attracting significant interest for delivery service of small packages in urban areas. The limited flight range of electric drones powered by batteries or fuel cells requires refueling or recharging stations for extending coverage to a wider area. To develop such service, optimization methods are needed for designing a network of station locations and delivery routes. Unlike ground-transportation modes, however, UAVs do not follow a fixed network but rather can fly directly through continuous space. But, paths must avoid barriers and other obstacles. In this paper, we propose a new location model to support spatially configuring a system of recharging stations for commercial drone delivery service, drawing on literature from planar-space routing, range-restricted flow-refueling location, and maximal coverage location. We present a mixed-integer programming formulation and an efficient heuristic algorithm, along with results for a large case study of Phoenix, AZ to demonstrate the effectiveness and efficiency of the model.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.017
      Issue No: Vol. 90 (2018)
  • Coordinated platooning with multiple speeds
    • Authors: Fengqiao Luo; Jeffrey Larson; Todd Munson
      Pages: 213 - 225
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Fengqiao Luo, Jeffrey Larson, Todd Munson
      In a platoon, vehicles travel one after another with small intervehicle distances; trailing vehicles in a platoon save fuel because they experience less aerodynamic drag. This work presents a coordinated platooning model with multiple speed options that integrates scheduling, routing, speed selection, and platoon formation/dissolution in a mixed-integer linear program that minimizes the total fuel consumed by a set of vehicles while traveling between their respective origins and destinations. The performance of this model is numerically tested on a grid network and the Chicago-area highway network. We find that the fuel-savings factor of a multivehicle system significantly depends on the time each vehicle is allowed to stay in the network; this time affects vehicles’ available speed choices, possible routes, and the amount of time for coordinating platoon formation. For problem instances with a large number of vehicles, we propose and test a heuristic decomposed approach that applies a clustering algorithm to partition the set of vehicles and then routes each group separately. When the set of vehicles is large and the available computational time is small, the decomposed approach finds significantly better solutions than does the full model.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.02.011
      Issue No: Vol. 90 (2018)
  • Recent applications of big data analytics in railway transportation
           systems: A survey
    • Authors: Faeze Ghofrani; Qing He; Rob M.P. Goverde; Xiang Liu
      Pages: 226 - 246
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Faeze Ghofrani, Qing He, Rob M.P. Goverde, Xiang Liu
      Big data analytics (BDA) has increasingly attracted a strong attention of analysts, researchers and practitioners in railway transportation and engineering. This urges the necessity for a review of recent research development in this field. This survey aims to provide a comprehensive review of the recent applications of big data in the context of railway engineering and transportation by a novel taxonomy framework, proposed by Mayring (2003). The survey covers three areas of railway transportation where BDA has been applied, namely operations, maintenance and safety. Also, the level of big data analytics, types of big data models and a variety of big data techniques have been reviewed and summarized. The results of this study identify the existing research gaps and thereby directions of future research in BDA in railway transportation systems.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.010
      Issue No: Vol. 90 (2018)
  • Understanding travellers’ preferences for different types of trip
           destination based on mobile internet usage data
    • Authors: Yihong Wang; Gonçalo Homem de Almeida Correia; Bart van Arem; H.J.P. (Harry) Timmermans
      Pages: 247 - 259
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Yihong Wang, Gonçalo Homem de Almeida Correia, Bart van Arem, H.J.P. (Harry) Timmermans
      New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.009
      Issue No: Vol. 90 (2018)
  • A tensor-based Bayesian probabilistic model for citywide personalized
           travel time estimation
    • Authors: Kun Tang; Shuyan Chen; Zhiyuan Liu; Aemal J. Khattak
      Pages: 260 - 280
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Kun Tang, Shuyan Chen, Zhiyuan Liu, Aemal J. Khattak
      Urban travel time information is of great importance for many levels of traffic management and operation. This paper develops a tensor-based Bayesian probabilistic model for citywide and personalized travel time estimation, using the large-scale and sparse GPS trajectories generated by taxicabs. Combined with the knowledge learned from historical trajectories, travel times of different drivers on all road segments in some time slots are modeled with a 3-order tensor. This tensor-based modeling approach incorporates both the spatial correlation between different road segments and the person-specific variation between different drivers, as well as the coarse-grain temporal correlation between recent and historical traffic conditions and the fine-grain temporal correlation between different time slots. To account for the variability caused by the intrinsic uncertainties in urban road network, each travel time entry in the built tensor is treated as a variable following a log-normal distribution. With the help of the fully Bayesian treatment, the model achieves automatic hyper-parameter tuning and model complexity controlling, and therefore the problem of over-fitting is prevented even when the used data is large-scale and sparse. The proposed model is applied to a real case study on the citywide road network of Beijing, China, using the large-scale and sparse GPS trajectories collected from over 32,670 taxicabs for a period of two months. Empirical results of extensive experiments demonstrate that the proposed model provides an effective and robust approach for urban travel time estimation and outperforms the considered competing methods.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.004
      Issue No: Vol. 90 (2018)
  • A combined use of microscopic traffic simulation and extreme value methods
           for traffic safety evaluation
    • Authors: Chen Wang; Chengcheng Xu; Jingxin Xia; Zhendong Qian; Linjun Lu
      Pages: 281 - 291
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Chen Wang, Chengcheng Xu, Jingxin Xia, Zhendong Qian, Linjun Lu
      This paper proposes a combined usage of microscopic traffic simulation and Extreme Value Theory (EVT) for safety evaluation. Ten urban intersections in Fengxian District in Shanghai were selected in the study and three calibration strategies were applied to develop simulation models for each intersection: a base strategy with fundamental data input, a semi-calibration strategy adjusting driver behavior parameters based on Measures of Effectiveness (MOE), and a full-calibration strategy altering driver behavior parameters by both MOE and Measures of Safety (MOS). SSAM was used to extract simulated conflict data from vehicle trajectory files from VISSIM and video-based data collection was introduced to assist trained observers to collect field conflict data. EVT-based methods were then employed to model both simulated/field conflict data and derive the Estimated Annual Crash Frequency (EACF), used as Surrogate Safety Measures (SSM). PET was used for EVT measurement for three conflict types: crossing, rear-end, and lane change. EACFs based on three simulation calibration strategies were compared with field-based EACF, conventional SSM based on Traffic Conflict Techniques (TCT), and actual crash frequency, in terms of direct correlation, rank correlation, and prediction accuracy. The results showed that, MOS should be considered during simulation model calibration and EACF based on the full-calibration strategy appeared to be a better choice for simulation-based safety evaluation, compared to other candidate safety measures. In general, the combined usage of microscopic traffic simulation and EVT is a promising tool for safety evaluation.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.011
      Issue No: Vol. 90 (2018)
  • Passenger arrival and waiting time distributions dependent on train
    • Authors: Jesper Bláfoss Ingvardson; Otto Anker Nielsen; Sebastián Raveau; Bo Friis Nielsen
      Pages: 292 - 306
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Jesper Bláfoss Ingvardson, Otto Anker Nielsen, Sebastián Raveau, Bo Friis Nielsen
      Waiting time at public transport stops is perceived by passengers to be more onerous than in-vehicle time, hence it strongly influences the attractiveness and use of public transport. Transport models traditionally assume that average waiting times are half the service headway by assuming random passenger arrivals. However, research agree that two distinct passenger behaviour types exist: one group arrives randomly, whereas another group actively tries to minimise their waiting time by arriving in a timely manner at the scheduled departure time. This study proposes a general framework for estimating passenger waiting times which incorporates the arrival patterns of these two groups explicitly, namely by using a mixture distribution consisting of a uniform and a beta distribution. The framework is empirically validated using a large-scale automatic fare collection system from the Greater Copenhagen Area covering metro, suburban, and regional rail stations thereby giving a range of service headways from 2 to 60 min. It was shown that the proposed mixture distribution is superior to other distributions proposed in the literature. This can improve waiting time estimations in public transport models. The results show that even at 5-min headways 43% of passengers arrive in a timely manner to stations when timetables are available. The results bear important policy implications in terms of providing actual timetables, even at high service frequencies, in order for passengers to be able to minimise their waiting times.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.006
      Issue No: Vol. 90 (2018)
  • Roads in transition: Integrated modeling of a
           manufacturer-traveler-infrastructure system in a mixed autonomous/human
           driving environment
    • Authors: Mohamadhossein Noruzoliaee; Bo Zou; Yang Liu
      Pages: 307 - 333
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Mohamadhossein Noruzoliaee, Bo Zou, Yang Liu
      This paper develops an integrated model to characterize the market penetration of autonomous vehicles (AVs) in urban transportation networks. The model explicitly accounts for the interplay among the AV manufacturer, travelers with heterogeneous values of travel time (VOTT), and road infrastructure capacity. By making in-vehicle time use more leisurely or productive, AVs reduce travelers’ VOTT. In addition, AVs can move closer together than human-driven vehicles because of shorter safe reaction time, which leads to increased road capacity. On the other hand, the use of AV technologies means added manufacturing cost and higher price. Thus, traveler adoption of AVs will trade VOTT savings with additional out-of-pocket cost. The model is structured as a leader (AV manufacturer)-follower (traveler) game. Given the cost of producing AVs, the AV manufacturer sets AV price to maximize profit while anticipating AV market penetration. Given an AV price, the vehicle and routing choice of heterogeneous travelers are modeled by combining a multinomial logit model with multi-modal multi-class user equilibrium (UE). The overall problem is formulated as a mathematical program with complementarity constraints (MPCC), which is challenging to solve. We propose a solution approach based on piecewise linearization of the MPCC as a mixed-integer linear program (MILP) and solving the MILP to global optimality. Non-uniform distribution of breakpoints that delimit piecewise intervals and feasibility-based domain reduction are further employed to reduce the approximation error brought by linearization. The model is implemented in a simplified Singapore network with extensive sensitivity analyses and the Sioux Falls network. Computational results demonstrate the effectiveness and efficiency of the solution approach and yield valuable insights about transportation system performance in a mixed autonomous/human driving environment.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.014
      Issue No: Vol. 90 (2018)
  • Implementation and application of a stochastic aircraft boarding model
    • Authors: Michael Schultz
      Pages: 334 - 349
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Michael Schultz
      The aircraft turnaround processes is mainly controlled by the ground handling, airport or airline staff, except the aircraft boarding, which is driven by the passengers’ experience and willingness or ability to follow the proposed boarding procedures. The paper uses a prior developed, calibrated, stochastic aircraft boarding model, which is applied to different boarding strategies (chronological order of passenger arrival, hand luggage handling), group constellations and innovative infrastructural changes (Flying Carpet, Side-Slip Seat, Foldable Passenger Seat). In this context, passenger boarding is assumed to be a stochastic, agent-based, forward-directed, one-dimensional and discrete process. The stochastic model covers individual passenger behavior as well as operational constraints and deviations. A comprehensive assessment using one model allows for efficient comparison of current research approaches and innovative operational solutions for efficient passenger boarding.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.016
      Issue No: Vol. 90 (2018)
  • Estimation of population origin–interchange–destination flows on
           multimodal transit networks
    • Authors: Jason B. Gordon; Haris N. Koutsopoulos; Nigel H.M. Wilson
      Pages: 350 - 365
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Jason B. Gordon, Haris N. Koutsopoulos, Nigel H.M. Wilson
      Previous research has combined automated fare-collection (AFC) and automated vehicle-location (AVL) data to infer the times and locations of passenger origins, interchanges (transfers), and destinations on multimodal transit networks. The resultant origin–interchange–destination flows (and the origin–destination (OD) matrices that comprise those flows), however, represent only a sample of total ridership, as they contain only those journeys made using the AFC payment method that have been successfully recorded or inferred. This paper presents a method for scaling passenger-journey flows (i.e., linked-trip flows) using additional information from passenger counts at each station gate and bus farebox, thereby estimating the flows of non-AFC passengers and of AFC passengers whose journeys were not successfully inferred. The proposed method is applied to a hypothetical test network and to AFC and AVL data from London’s multimodal public transit network. Because London requires AFC transactions upon both entry and exit for rail trips, a rail-only OD matrix is extracted from the estimated multimodal linked-trip flows, and is compared to a rail OD matrix generated using the iterative proportional fitting method.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.007
      Issue No: Vol. 90 (2018)
  • Robust supply vessel routing and scheduling
    • Authors: Yauheni Kisialiou; Irina Gribkovskaia; Gilbert Laporte
      Pages: 366 - 378
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Yauheni Kisialiou, Irina Gribkovskaia, Gilbert Laporte
      We solve the problem of tactical supply vessel planning arising in the upstream offshore petroleum logistics. Supply vessels deliver all the necessary materials and equipment to offshore installations from an onshore supply base according to a delivery schedule. The planning of supply vessels should be done so that their number is minimized and at the same time provide a reliable flow of supplies from the base. The execution of a weekly sailing plan is affected by weather conditions, especially in winter time. Harsh weather conditions increase the number of vessels required to perform the operations as well as the service times at the installations, and thus disrupt the schedule, leading to additional costs and reduced service level. We present a methodology for robust supply vessel planning enabling a trade-off analysis to be made between the schedules’ service level and vessels’ cost. The methodology involves the generation of multiple vessel schedules with different level of robustness using an adaptive large neighbourhood search metaheuristic and a subsequent discrete event simulation procedure for the assessment of the service level. To control the level of robustness we developed a concept of slacks and incorporated it into the metaheuristic algorithm.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.012
      Issue No: Vol. 90 (2018)
  • Utilizing naturalistic driving data for in-depth analysis of driver
           lane-keeping behavior in rain: Non-parametric MARS and parametric logistic
           regression modeling approaches
    • Authors: Ali Ghasemzadeh; Mohamed M. Ahmed
      Pages: 379 - 392
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Ali Ghasemzadeh, Mohamed M. Ahmed
      It is known that adverse weather conditions can affect driver performance due to reduction in visibility and slippery surface conditions. Lane keeping is one of the main factors that might be affected by weather conditions. Most of the previous studies on lane keeping have investigated driver lane-keeping performance from driver inattention perspective. In addition, the majority of previous lane-keeping studies have been conducted in controlled environments such as driving simulators. Therefore, there is a lack of studies that investigate driver lane-keeping ability considering adverse weather conditions in naturalistic settings. In this study, the relationship between weather conditions and driver lane-keeping performance was investigated using the SHRP2 naturalistic driving data for 141 drivers between 19 and 89 years of age. Moreover, a threshold was introduced to differentiate lane keeping and lane changing in naturalistic driving data. Two lane-keeping models were developed using the logistic regression and multivariate adaptive regression splines (MARS) to better understand factors affecting driver lane-keeping ability considering adverse weather conditions. The results revealed that heavy rain can significantly increase the standard deviation of lane position (SDLP), which is a very widely used method for analyzing lane-keeping ability. It was also found that traffic conditions, driver age and experience, and posted speed limits have significant effects on driver lane-keeping ability. An interesting finding of this study is that drivers have a better lane-keeping ability in roadways with higher posted speed limits. The results from this study might provide better insights into understanding the complex effect of adverse weather conditions on driver behavior.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.018
      Issue No: Vol. 90 (2018)
  • Short-term planning of liquefied natural gas deliveries
    • Authors: Mohamed Kais Msakni; Mohamed Haouari
      Pages: 393 - 410
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Mohamed Kais Msakni, Mohamed Haouari
      The ability of a supplier of liquefied natural gas (LNG) to deliver cargoes at desired times, while effectively managing a fleet of cryogenic vessels can significantly impact its profits. We investigate in this paper an LNG short-term delivery planning problem by considering mandatory cargoes as well as optional cargoes to select, along with the scheduling of a heterogeneous vessel fleet with controllable cruising speeds. Several technical constraints are accommodated including time windows, berth availability, bunkering restrictions, inventory, liquefaction terminal storage capacity, maximum waiting time, and planned maintenance restrictions. The objective is to maximize the net profit. We propose a mixed-integer programming formulation that includes a polynomial number of variables and constraints and accommodates all of the problem features. Also, we describe an optimization-based variable neighborhood search procedure that embeds the proposed compact formulation. To assess the quality of the generated solutions, we propose a second valid formulation with an exponential number of decision variables and we solve its linear programming relaxation using column generation. We provide the results of extensive computational results that were carried out on a set of large-scale set of realistic instances, with up to 62 vessels and 160 cargoes, provided by a major LNG producer. These results provide evidence that the proposed improvement procedure yields high-quality solutions.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.013
      Issue No: Vol. 90 (2018)
  • An analytical model to conduct a person-based evaluation of transit
           preferential treatments on signalized arterials
    • Authors: Yashar Z. Farid; Eleni Christofa; John Collura
      Pages: 411 - 432
      Abstract: Publication date: May 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 90
      Author(s): Yashar Z. Farid, Eleni Christofa, John Collura
      The complexities of urban transportation networks where multiple modes with different characteristics and needs travel in combination with constraints on space and funding make the sustainable management of these systems a challenge. In order to improve transit service, space (e.g., dedicated bus lanes) and time (e.g., transit signal priority strategies) Transit Preferential Treatments (TPT) are deployed to improve transit operations. The objective of this paper is to develop an analytical model that allows for a person-based evaluation of alternative TPTs when considered individually and in combination. In particular, the analytical model is developed to assess person delay and person discharge flow at any intersection that is part of a signalized arterial, where auto arrivals are in platoons. The performance of TPTs is evaluated using both the analytical model and through microsimulation tests on two intersections of San Pablo Avenue in Berkeley, CA. Space TPTs such as dedicated bus lanes and queue jumper lanes are beneficial in reducing bus person delay when provided in addition to the existing lanes; however, the effectiveness of time TPTs such as green extension depends on the level of auto demand in combination with signal settings. Changes in person discharge flow are not significant for any of the treatments tested with the exception of the bus lane substitution with and without green extension, which led to a significant decrease in person discharge flow. Increased bus frequency increases the effectiveness of transit signal priority in reducing total and bus person delay. The analytical model results produce ranking outcomes that are comparable with the microsimulation ones and therefore, the model may be used for a quantitative evaluation of TPTs without the need for data intensive and time consuming calibration efforts required for microsimulation models. The developed model can be used to guide infrastructure and investment decisions on where such TPTs should be implemented and under what conditions space TPTs should be combined with time TPTs to improve person mobility.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2017.12.010
      Issue No: Vol. 90 (2018)
  • Effects of demurrage and detention regimes on dry-port-based inland
           container transport
    • Authors: Stefano Fazi; Kees Jan Roodbergen
      Pages: 1 - 18
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Stefano Fazi, Kees Jan Roodbergen
      Increase of congestion at container deep seaports and shortage of capacity has led inland transport systems worldwide to rely more and more on inland terminals, and on the use of high capacity modes of transport to generate economies of scale and reduce negative effects of trucking. In this setting, planning the transport of maritime containers between a deep seaport and a final inland destination must also consider due dates and soft time windows, the latter known as Demurrage and Detention (D&D). In this paper, we formalize the concept of D&D, model the multimodal planning problem, and assess the impact of different D&D regimes on the emerging inland transport systems. By means of an experimental framework, we compare different D&D policies and provide managerial insights. The experiments highlight the effects of existing D&D regimes on transport efficiency and provide guidelines for their choice in practice. D&D are shown to have a twofold effect: first to limit consolidation opportunities and force the use of trucks as buffer, and second to push containers to dwell unnecessarily at the seaports.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.012
      Issue No: Vol. 89 (2018)
  • Individual mobility prediction using transit smart card data
    • Authors: Zhan Zhao; Haris N. Koutsopoulos; Jinhua Zhao
      Pages: 19 - 34
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Zhan Zhao, Haris N. Koutsopoulos, Jinhua Zhao
      For intelligent urban transportation systems, the ability to predict individual mobility is crucial for personalized traveler information, targeted demand management, and dynamic system operations. Whereas existing methods focus on predicting the next location of users, little is known regarding the prediction of the next trip. The paper develops a methodology for predicting daily individual mobility represented as a chain of trips (including the null set, no travel), each defined as a combination of the trip start time t, origin o, and destination d. To predict individual mobility, we first predict whether the user will travel (trip making prediction), and then, if so, predict the attributes of the next trip ( t , o , d ) (trip attribute prediction). Each of the two problems can be further decomposed into two subproblems based on the triggering event. For trip attribute prediction, we propose a new model, based on the Bayesian n-gram model used in language modeling, to estimate the probability distribution of the next trip conditional on the previous one. The proposed methodology is tested using the pseudonymized transit smart card records from more than 10,000 users in London, U.K. over two years. Based on regularized logistic regression, our trip making prediction models achieve median accuracy levels of over 80%. The prediction accuracy for trip attributes varies by the attribute considered—around 40% for t, 70–80% for o and 60–70% for d. Relatively, the first trip of the day is more difficult to predict. Significant variations are found across individuals in terms of the model performance, implying diverse travel behavior patterns.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.022
      Issue No: Vol. 89 (2018)
  • Jointly optimizing ship sailing speed and bunker purchase in liner
           shipping with distribution-free stochastic bunker prices
    • Authors: Yadong Wang; Qiang Meng; Haibo Kuang
      Pages: 35 - 52
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Yadong Wang, Qiang Meng, Haibo Kuang
      This paper jointly designs the optimal ship sailing speeds on shipping voyages and the optimal amount of bunker fuel to purchase at each port of a shipping network operated by a container liner shipping company. Bunker prices at these ports are assumed to be correlated random variables. Considering the difficulties in calibrating these prices into specific joint probability distribution in practice, this study merely requires some fundamental descriptive statistics information of these bunker prices, including lower and upper bounds, means and covariances, which can be tangibly estimated from historical data. To solve this problem, a mixed integer programming model is first formulated for deterministic bunker prices to minimize the sum of ship operating cost and bunker consumption cost. This model is subsequently extended to incorporate stochastic bunker prices by developing a series of approximation techniques using the bunker price descriptive statistics information. A numerical example based on real-case price data of a liner shipping network from an international shipping company shows that the proposed model is able to simultaneously control the average bunker purchase cost as well as the risk resulting from the extremely high bunker prices.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.020
      Issue No: Vol. 89 (2018)
  • Ontologies for transportation research: A survey
    • Authors: Megan Katsumi; Mark Fox
      Pages: 53 - 82
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Megan Katsumi, Mark Fox
      Transportation research relies heavily on a variety of data. From sensors to surveys, data supports day-to-day operations as well as long-term planning and decision-making. The challenges that arise due to the volume and variety of data that are found in transportation research can be effectively addressed by ontologies. This opportunity has already been recognized – there are a number of existing transportation ontologies, however the relationship between them is unclear. The goal of this work is to provide an overview of the opportunities for ontologies in transportation research and operation, and to present a survey of existing transportation ontologies to serve two purposes: (1) to provide a resource for the transportation research community to aid in understanding (and potentially selecting between) existing transportation ontologies; and (2) to identify future work for the development of transportation ontologies, by identifying areas that may be lacking.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.023
      Issue No: Vol. 89 (2018)
  • A distribution-fitting-free approach to calculating travel time
           reliability ratio
    • Authors: Zhaoqi Zang; Xiangdong Xu; Chao Yang; Anthony Chen
      Pages: 83 - 95
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Zhaoqi Zang, Xiangdong Xu, Chao Yang, Anthony Chen
      Empirical studies have revealed that travel time variability (TTV) can significantly affect travelers’ behaviors and planners’ cost-benefit assessment of transportation projects. It is therefore important to systematically quantify the value of TTV (VTTV) and its impact. Recently, Fosgerau’s valuation method makes this quantification possible by converting the value of travel time (VTT) and the VTTV into monetary unit. Travel time reliability ratio (TTRR), defined as a ratio of the VTTV to the VTT, is a key parameter in Fosgerau’s valuation method. Calculating TTRR involves an integral of the inverse cumulative distribution function (CDF) of the standardized travel time distribution (STTD), i.e., the mean lateness factor. Using a well-fitted STTD is a straightforward way to calculate TTRR. However, it will encounter the following challenges: (1) determination of a well-fitted STTD; (2) non-existence of an algebraic expression for the CDF and its inverse CDF; and (3) lack of a closed-form expression to efficiently calculate TTRR. To circumvent the above issues, this paper proposes a distribution-fitting-free analytical approach based on the Cornish-Fisher expansion as an alternative way to calculate TTRR without the need to fit the whole CDF. The validity domain is rigorously derived for guaranteeing the accuracy of the proposed method. Realistic travel time datasets that cover 17 links are used to systematically explore the feature and accuracy of the proposed method in estimating TTRR. The comparative results demonstrate that the proposed method can efficiently and effectively estimate TTRR. When travel time datasets satisfy the validity domain, the proposed method outperforms the distribution fitting method in estimating TTRR.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.01.027
      Issue No: Vol. 89 (2018)
  • Multi-day activity-travel pattern sampling based on single-day data
    • Authors: Anpeng Zhang; Jee Eun Kang; Kay Axhausen; Changhyun Kwon
      Pages: 96 - 112
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Anpeng Zhang, Jee Eun Kang, Kay Axhausen, Changhyun Kwon
      Although it is important to consider multi-day activities in transportation planning, multi-day activity-travel data are expensive to acquire and therefore rarely available. In this study, we propose to generate multi-day activity-travel data through sampling from readily available single-day household travel survey data. A key observation we make is that the distribution of interpersonal variability in single-day travel activity datasets is similar to the distribution of intrapersonal variability in multi-day. Thus, interpersonal variability observed in cross-sectional single-day data of a group of people can be used to generate the day-to-day intrapersonal variability. The proposed sampling method is based on activity-travel pattern type clustering, travel distance and variability distribution to extract such information from single-day data. Validation and stability tests of the proposed sampling methods are presented.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.01.024
      Issue No: Vol. 89 (2018)
  • Macroscopic multiple-station short-turning model in case of complete
           railway blockages
    • Authors: Nadjla Ghaemi; Oded Cats; Rob M.P. Goverde
      Pages: 113 - 132
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Nadjla Ghaemi, Oded Cats, Rob M.P. Goverde
      In case of railway disruptions, traffic controllers are responsible for dealing with disrupted traffic and reduce the negative impact for the rest of the network. In case of a complete blockage when no train can use an entire track, a common practice is to short-turn trains. Trains approaching the blockage cannot proceed according to their original plans and have to short-turn at a station close to the disruption on both sides. This paper presents a Mixed Integer Linear Program that computes the optimal station and times for short-turning the affected train services during the three phases of a disruption. The proposed solution approach takes into account the interaction of the traffic between both sides of the blockage before and after the disruption. The model is applied to a busy corridor of the Dutch railway network. The computation time meets the real-time solution requirement. The case study gives insight into the importance of the disruption period in computing the optimal solution. It is concluded that different optimal short-turning solutions may exist depending on the start time of the disruption and the disruption length. For periodic timetables, the optimal short-turning choices repeat due to the periodicity of the timetable. In addition, it is observed that a minor extension of the disruption length may result in less delay propagation at the cost of more cancellations.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.006
      Issue No: Vol. 89 (2018)
  • Assessing the impact of tactical airport surface operations on airline
           schedule block time setting
    • Authors: Lei Kang; Mark Hansen
      Pages: 133 - 147
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Lei Kang, Mark Hansen
      With the growth of air traffic, airport surfaces are congested and air traffic operations are disrupted by the formation of bottlenecks on the surface. Hence, improving the efficiency and predictability of airport surface operations is not only a key goal of NASA’s initiatives in Integrated Arrival/Departure/Surface (IADS) operations, but also has been recognized as a critical aspect of the FAA NextGEN implementation plan. While a number of tactical initiatives have been shown to be effective in improving airport surface operations from a service provider’s perspective, their impacts on airlines’ scheduled block time (SBT) setting, which has been found to have direct impact on airlines’ on-time performance and operating cost, have received little attention. In this paper, we assess this impact using an econometric model of airline SBT combined with a before/after analysis of the implementation of surface congestion management (SCM) at John F. Kennedy International Airport (JFK) in 2010. Since airlines do not consider gate delay in setting SBT, we find that reduction in taxi-out time variability resulting from SCM leads to more predictable taxi-out times and thus decreases in SBT. The JFK SCM implementation is used as a case study to validate model prediction performance. The observed SBT decrease between 2009 and 2011 at JFK is 4.8 min and our model predicts a 4.2 min decrease. In addition, Charlotte Douglas International Airport (CLT) is used as an example to demonstrate how different surface operations improvements scenarios can be evaluated in terms of SBT reduction.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.01.018
      Issue No: Vol. 89 (2018)
  • Modeling and analysis of mixed flow of cars and powered two wheelers
    • Authors: Sosina Gashaw; Paola Goatin; Jérôme Härri
      Pages: 148 - 167
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Sosina Gashaw, Paola Goatin, Jérôme Härri
      In modern cities, a rapid increase of motorcycles and other types of Powered Two-Wheelers (PTWs) is observed as an answer to long commuting in traffic jams and complex urban navigation. Such increasing penetration rate of PTWs creates mixed traffic flow conditions with unique characteristics that are not well understood at present. Our objective is to develop an analytical traffic flow model that reflects the mutual impacts of PTWs and Cars. Unlike cars, PTWs filter between cars, have unique dynamics, and do not respect lane discipline, therefore requiring a different modeling approach than traditional “Passenger Car Equivalent” or “Follow the Leader”. Instead, this work follows an approach that models the flow of PTWs similarly to a fluid in a porous medium, where PTWs “percolate” between cars depending on the gap between them. Our contributions are as follows: (I) a characterization of the distribution of the spacing between vehicles by the densities of PTWs and cars; (II) a definition of the equilibrium speed of each class as a function of the densities of PTWs and cars; (III) a mathematical analysis of the model’s properties (IV) an impact analysis of the gradual penetration of PTWs on cars and on heterogeneous traffic flow characteristics. The proposed model could contribute as an enabler for ‘PTW-aware’ future Cooperative Intelligent Transport Systems technologies and traffic regulations.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.004
      Issue No: Vol. 89 (2018)
  • Generating lane-based intersection maps from crowdsourcing big trace data
    • Authors: Xue Yang; Luliang Tang; Le Niu; Xia Zhang; Qingquan Li
      Pages: 168 - 187
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Xue Yang, Luliang Tang, Le Niu, Xia Zhang, Qingquan Li
      Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.007
      Issue No: Vol. 89 (2018)
  • Infrastructure-cooperative algorithm for effective intersection collision
    • Authors: Yuchuan Fu; Changle Li; Tom H. Luan; Yao Zhang; Guoqiang Mao
      Pages: 188 - 204
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Yuchuan Fu, Changle Li, Tom H. Luan, Yao Zhang, Guoqiang Mao
      To guarantee the road safety by avoiding collisions at the intersections is one of the major tasks of intelligent transportation systems (ITSs), which contributes to the minimal fatalities and property loss in crashes. This paper proposes an effective algorithm for infrastructure-cooperative intersection accident pre-warning system with the aid of vehicular communications. The proposed algorithm realizes accurate and efficient collision avoidances through five steps, i.e., defining variable, reasoning the vehicles evolution state, verifying safe driving behavior, assessing risk, and making decision. The critical factors are theoretically analyzed, and a vehicle state evolution model based on the Dynamic Bayesian Networks (DBNs) is established. The efficient risk assessment method based on identifying the dangerous driving behavior at intersection and different collision avoidance strategies are proposed according to the actual situation. Finally, extensive simulations are carried out to verify the performance of the proposal, and simulation results show that the proposed algorithm can effectively detect risk and accurately migrate the collision.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.003
      Issue No: Vol. 89 (2018)
  • Dissipation of stop-and-go waves via control of autonomous vehicles: Field
    • Authors: Raphael E. Stern; Shumo Cui; Maria Laura Delle Monache; Rahul Bhadani; Matt Bunting; Miles Churchill; Nathaniel Hamilton; R’mani Haulcy; Hannah Pohlmann; Fangyu Wu; Benedetto Piccoli; Benjamin Seibold; Jonathan Sprinkle; Daniel B. Work
      Pages: 205 - 221
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Raphael E. Stern, Shumo Cui, Maria Laura Delle Monache, Rahul Bhadani, Matt Bunting, Miles Churchill, Nathaniel Hamilton, R’mani Haulcy, Hannah Pohlmann, Fangyu Wu, Benedetto Piccoli, Benjamin Seibold, Jonathan Sprinkle, Daniel B. Work
      Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.005
      Issue No: Vol. 89 (2018)
  • Shared autonomous electric vehicle (SAEV) operations across the Austin,
           Texas network with charging infrastructure decisions
    • Authors: Benjamin Loeb; Kara M. Kockelman; Jun Liu
      Pages: 222 - 233
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Benjamin Loeb, Kara M. Kockelman, Jun Liu
      Shared autonomous vehicles, or SAVs, have attracted significant public and private interest because of their opportunity to simplify vehicle access, avoid parking costs, reduce fleet size, and, ultimately, save many travelers time and money. One way to extend these benefits is through an electric vehicle (EV) fleet. EVs are especially suited for this heavy usage due to their lower energy costs and reduced maintenance needs. As the price of EV batteries continues to fall, charging facilities become more convenient, and renewable energy sources grow in market share, EVs will become more economically and environmentally competitive with conventionally fueled vehicles. EVs are limited by their distance range and charge times, so these are important factors when considering operations of a large, electric SAV (SAEV) fleet. This study simulated performance characteristics of SAEV fleets serving travelers across the Austin, Texas 6-county region. The simulation works in sync with the agent-based simulator MATSim, with SAEV modeling as a new mode. Charging stations are placed, as needed, to serve all trips requested (under 75 km or 47 miles in length) over 30 days of initial model runs. Simulation of distinctive fleet sizes requiring different charge times and exhibiting different ranges, suggests that the number of station locations depends almost wholly on vehicle range. Reducing charge times does lower fleet response times (to trip requests), but increasing fleet size improves response times the most. Increasing range above 175 km (109 miles) does not appear to improve response times for this region and trips originating in the urban core are served the quickest. Unoccupied travel accounted for 19.6% of SAEV mileage on average, with driving to charging stations accounting for 31.5% of this empty-vehicle mileage. This study found that there appears to be a limit on how much response time can be improved through decreasing charge times or increasing vehicle range.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.01.019
      Issue No: Vol. 89 (2018)
  • An autonomous system for maintenance scheduling data-rich complex
           infrastructure: Fusing the railways’ condition, planning and cost
    • Authors: Isidro Durazo-Cardenas; Andrew Starr; Christopher J. Turner; Ashutosh Tiwari; Leigh Kirkwood; Maurizio Bevilacqua; Antonios Tsourdos; Essam Shehab; Paul Baguley; Yuchun Xu; Christos Emmanouilidis
      Pages: 234 - 253
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Isidro Durazo-Cardenas, Andrew Starr, Christopher J. Turner, Ashutosh Tiwari, Leigh Kirkwood, Maurizio Bevilacqua, Antonios Tsourdos, Essam Shehab, Paul Baguley, Yuchun Xu, Christos Emmanouilidis
      National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.010
      Issue No: Vol. 89 (2018)
  • Integrated scheduling of m-truck, m-drone, and m-depot constrained by
           time-window, drop-pickup, and m-visit using constraint programming
    • Authors: Andy Ham
      Abstract: Publication date: June 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 91
      Author(s): Andy M. Ham
      The idea of deploying unmanned aerial vehicles, also known as drones, for final-mile delivery in logistics operations has vitalized this new research stream. One conceivable scenario of using a drone in conjunction with a traditional delivery truck to distribute parcels is discussed in earlier literature and termed the parallel drone scheduling traveling salesman problem (PDSTSP). This study extends the problem by considering two different types of drone tasks: drop and pickup. After a drone completes a drop, the drone can either fly back to depot to deliver the next parcels or fly directly to another customer for pickup. Integrated scheduling of multiple depots hosting a fleet of trucks and a fleet of drones is further studied to achieve an operational excellence. A vehicle that travels near the boundary of the coverage area might be more effective to serve customers that belong to the neighboring depot. This problem is uniquely modeled as an unrelated parallel machine scheduling with sequence dependent setup, precedence-relationship, and reentrant, which gives us a framework to effectively consider those operational challenges. A constraint programming approach is proposed and tested with problem instances of m-truck, m-drone, m-depot, and hundred-customer distributed across an 8-mile square region.

      PubDate: 2018-04-15T06:21:25Z
  • Smartphones as an integrated platform for monitoring driver behaviour: The
           role of sensor fusion and connectivity
    • Authors: Stratis Kanarachos; Stavros-Richard G. Christopoulos; Alexander Chroneos
      Abstract: Publication date: Available online 5 April 2018
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Stratis Kanarachos, Stavros-Richard G. Christopoulos, Alexander Chroneos
      Nowadays, more than half of the world’s web traffic comes from mobile phones, and by 2020 approximately 70 percent of the world’s population will be using smartphones. The unprecedented market penetration of smartphones combined with the connectivity and embedded sensing capability of smartphones is an enabler for the large-scale deployment of Intelligent Transportation Systems (ITS). On the downside, smartphones have inherent limitations such as relatively limited energy capacity, processing power, and accuracy. These shortcomings may potentially limit their role as an integrated platform for monitoring driver behaviour in the context of ITS. This study examines this hypothesis by reviewing recent scientific contributions. The Cybernetics theoretical framework was employed to allow a systematic comparison. First, only a few studies consider the smartphone as an integrated platform. Second, a lack of consistency between the approaches and metrics used in the literature is noted. Last but not least, areas such as fusion of heterogeneous information sources, Deep Learning and sparse crowd-sensing are identified as relatively unexplored, and future research in these directions is suggested.

      PubDate: 2018-04-15T06:21:25Z
      DOI: 10.1016/j.trc.2018.03.023
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