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  Subjects -> TRANSPORTATION (Total: 172 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (22 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (6 journals)
    - SHIPS AND SHIPPING (34 journals)
    - TRANSPORTATION (96 journals)

TRANSPORTATION (96 journals)

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Partially Free   (Followers: 90)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 4)
Archives of Transport     Open Access   (Followers: 17)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 12)
Cities in the 21st Century     Open Access   (Followers: 15)
Economics of Transportation     Partially Free   (Followers: 13)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 11)
European Transport Research Review     Open Access   (Followers: 21)
Geosystem Engineering     Hybrid Journal   (Followers: 1)
IATSS Research     Open Access  
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 9)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 8)
IFAC-PapersOnLine     Open Access  
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 8)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 9)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 3)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 9)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 10)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 10)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 11)
International Journal of Services Technology and Management     Hybrid Journal  
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 12)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 15)
International Journal of Transportation Science and Technology     Open Access   (Followers: 9)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 12)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 6)
Journal of Navigation     Hybrid Journal   (Followers: 227)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 11)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 2)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 8)
Journal of Transport and Land Use     Open Access   (Followers: 22)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 12)
Journal of Transport Geography     Hybrid Journal   (Followers: 24)
Journal of Transport History     Hybrid Journal   (Followers: 15)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 8)
Journal of Transportation Security     Hybrid Journal   (Followers: 3)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 16)
Journal of Transportation Technologies     Open Access   (Followers: 14)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 9)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics     Open Access   (Followers: 1)
Logistics & Sustainable Transport     Open Access   (Followers: 2)
Logistique & Management     Full-text available via subscription  
Mobility in History     Full-text available via subscription   (Followers: 2)
Modern Transportation     Open Access   (Followers: 10)
Nonlinear Dynamics     Hybrid Journal   (Followers: 16)
Open Journal of Safety Science and Technology     Open Access   (Followers: 9)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 15)
Public Transport     Hybrid Journal   (Followers: 18)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 5)
Revista Transporte y Territorio     Open Access   (Followers: 1)
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 2)
Sport, Education and Society     Hybrid Journal   (Followers: 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: 2)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 5)
Transport     Hybrid Journal   (Followers: 14)
Transport and Telecommunication Journal     Open Access   (Followers: 4)
Transport in Porous Media     Hybrid Journal   (Followers: 1)
Transport Problems     Open Access   (Followers: 1)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 9)
Transportation     Hybrid Journal   (Followers: 27)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportation Journal     Full-text available via subscription   (Followers: 13)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 4)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 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: 5)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 34)
Transportation Science     Full-text available via subscription   (Followers: 21)
TRANSPORTES     Open Access   (Followers: 5)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 5)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 2)
Transportrecht     Unknown  
Travel Behaviour and Society     Full-text available via subscription   (Followers: 7)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
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]
  • 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)
  • Identification of communities in urban mobility networks using multi-layer
           graphs of network traffic
    • Authors: Mehmet Yildirimoglu; Jiwon Kim
      Pages: 254 - 267
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Mehmet Yildirimoglu, Jiwon Kim
      This paper proposes a novel approach to identify the pockets of activity or the community structure in a city network using multi-layer graphs that represent the movement of disparate entities (i.e. private cars, buses and passengers) in the network. First, we process the trip data corresponding to each entity through a Voronoi segmentation procedure which provides a natural null model to compare multiple layers in a real world network. Second, given nodes that represent Voronoi cells and link weights that define the strength of connection between them, we apply a community detection algorithm and partition the network into smaller areas independently at each layer. The partitioning algorithm returns geographically well connected regions in all layers and reveal significant characteristics underlying the spatial structure of our city. Third, we test an algorithm that reveals the unified community structure of multi-layer networks, which are combinations of single-layer networks coupled through links between each node in one network layer to itself in other layers. This approach allows us to directly compare the resulting communities in multiple layers where connection types are categorically different.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.015
      Issue No: Vol. 89 (2018)
  • Airline-driven ground delay programs: A benefits assessment
    • Authors: Chiwei Yan; Vikrant Vaze; Cynthia Barnhart
      Pages: 268 - 288
      Abstract: Publication date: April 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 89
      Author(s): Chiwei Yan, Vikrant Vaze, Cynthia Barnhart
      Three decades of research studies in ground delay program (GDP) decision-making, and air traffic flow management in general, have produced several analytical models and decision support tools to design GDPs with minimum delay costs. Most of these models are centralized, i.e., the central authority almost completely decides the GDP design by optimizing certain centralized objectives. In this paper, we assess the benefits of an airline-driven decentralized approach for designing GDPs. The motivation for an airline-driven approach is the ability to incorporate the inherent differences between airlines when prioritizing, and responding to, different GDP designs. Such differences arise from the airlines’ diverse business objectives and operational characteristics. We develop an integrated platform for simulating flight operations during GDPs, an airline recovery module for mimicking the recovery actions of each individual airline under a GDP, and an algorithm for fast solution of the recovery problems to optimality. While some of the individual analytical components of our framework, model and algorithm share certain similarities with those used by previous researchers, to the best of our knowledge, this paper presents the first comprehensive platform for simulating and optimizing airline operations under a GDP and is the most important technological contribution of this paper. Using this framework, we conduct detailed computational experiments based on actual schedule data at three of the busiest airports in the United States. We choose the recently developed Majority Judgment voting and grading method as our airline-driven decentralized approach for GDP design because of the superior theoretical and practical benefits afforded by this approach as shown by multiple recent studies. The results of our evaluation suggest that adopting this airline-driven approach in designing the GDPs consistently and significantly reduces airport-wide delay costs compared to the state-of-the-research centralized approaches. Moreover, the cost reduction benefits of the resultant airline-driven GDP designs are equitably distributed across different airlines.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2018.02.013
      Issue No: Vol. 89 (2018)
  • Reinforcement learning approach for coordinated passenger inflow control
           of urban rail transit in peak hours
    • Authors: Zhibin Jiang; Wei Fan; Wei Liu; Bingqin Zhu; Jinjing Gu
      Pages: 1 - 16
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Zhibin Jiang, Wei Fan, Wei Liu, Bingqin Zhu, Jinjing Gu
      In peak hours, when the limited transportation capacity of urban rail transit is not adequate enough to meet the travel demands, the density of the passengers waiting at the platform can exceed the critical density of the platform. Coordinated passenger inflow control strategy is required to adjust/meter the inflow volume and relieve some of the demand pressure at crowded metro stations so as to ensure both operational efficiency and safety at such stations for all passengers. However, such strategy is usually developed by the operation staff at each station based on their practical working experience. As such, the best strategy/decision cannot always be made and sometimes can even be highly undesirable due to their inability to account for the dynamic performance of all metro stations in the entire rail transit network. In this paper, a new reinforcement learning-based method is developed to optimize the inflow volume during a certain period of time at each station with the aim of minimizing the safety risks imposed on passengers at the metro stations. Basic principles and fundamental components of the reinforcement learning, as well as the reinforcement learning-based problem-specific algorithm are presented. The simulation experiment carried out on a real-world metro line in Shanghai is constructed to test the performance of the approach. Simulation results show that the reinforcement learning-based inflow volume control strategy is highly effective in minimizing the safety risks by reducing the frequency of passengers being stranded. Additionally, the strategy also helps to relieve the passenger congestion at certain stations.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.008
      Issue No: Vol. 88 (2018)
  • A frequency based transit assignment model that considers online
    • Authors: Nurit Oliker; Shlomo Bekhor
      Pages: 17 - 30
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Nurit Oliker, Shlomo Bekhor
      This paper develops a frequency based transit assignment model considering that online information of predicted arrival times is available to passengers. The methodology is developed for two information levels: (1) full, where the arrival times are available for all intermediate stops in the candidate paths, and (2) partial, where the arrival times are available at the boarding stop only. Passengers are assumed to consider the estimated arrival times together with the expected travel time when choosing their path. The assignment procedure includes the finding of attractive paths, setting of route choice decision rules for different cases of predicted arrival times, and the probability calculation for these different cases. The developed model is illustrated by an application for the Winnipeg network. In comparison to the well-known optimal strategies method, the suggested model produced significantly different assignment results and a notable reduction in the total travel time. The results illustrate the potential impact of online information on assignment results, and emphasize the need for its consideration in planning models.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.004
      Issue No: Vol. 88 (2018)
  • Adaptive coordinated traffic control for stochastic demand
    • Authors: Lubing Li; Wei Huang; Hong K. Lo
      Pages: 31 - 51
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Lubing Li, Wei Huang, Hong K. Lo
      Traffic arrivals at intersections are inherently uncertain. This paper develops an adaptive coordinated traffic control approach in the presence of traffic demand uncertainty via the notion of Phase Clearance Reliability (PCR). We propose a method to adjust signal offset adaptively in order to deal with stochastic demands. Based on the cumulative queuing regime, this study first extends the delay models, which are usually formulated for isolated intersections, to the case of coordinated intersections by explicitly incorporating the effects of residual queue and signal offset. Two types of delay formulae are considered regarding two different arrival patterns on the intersection approaches, i.e. coordinated approach and non-coordinated approach delay. We then formulate a two-stage stochastic program to optimize (minimize) the expected total delay for the coordinated control system. The base timing plan is derived at the first stage, while the recourse decisions of adaptive signal offsets are made at the second stage to compensate for the overflow effects. Furthermore, a PCR-based gradient solution algorithm is developed to solve the two-stage stochastic program. The case study on a test network confirms the effectiveness of the proposed PCR-based control method in terms of minimizing average total delay. The optimal control performance stems partly from the short cycle lengths, which are attributed to the fact that part of the random arrivals are addressed by adjusting the signal offsets adaptively. This effective use of signal offset provides a new perspective for designing coordinated signal control plans.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.007
      Issue No: Vol. 88 (2018)
  • A method of hold baggage security screening system throughput analysis
           with an application for a medium-sized airport
    • Authors: Jacek Skorupski; Piotr Uchroński; Adrian Łach
      Pages: 52 - 73
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Jacek Skorupski, Piotr Uchroński, Adrian Łach
      The hold baggage security screening (HBSS) is one of the essential steps in a pre-flight operation process. With the continuous increase in the overall air traffic volume, the development of security screening (and control) technologies, and the changes in applicable regulations of law, the structure and equipment of HBSS systems require frequent upgrades. In order to make good, effective decisions about the upgrades, airport management requires tools for quantitative determination of their results. The aim of this work is to analyse the HBSS system throughput. The analysis can serve as an aid in airport management in on-the-go solving of operating issues and making decisions about HBSS upgrades. A mathematical model was established for the analysis in the form of a coloured timed Petri net, and implemented in a computer-aided solution. It was a microscale simulation model, in which every piece of hold baggage is localised in time and with a resolution of a single belt conveyor. The computer-simulated experiments completed with the model helped (i) determine the actual throughput of the HBSS system operated at the Katowice International Airport, (ii) determine the effects of disturbances on the HBSS system operation, (iii) evaluate the impact of the time windows available to SSO (security screening operators), the SSO's work organisation and the efficiency of automatic security screening on the HBSS system throughput, and (iv) determine the throughput for specific alternative variants of the HBSS organisation, including doubled automatic security screening. These results allow a conclusion, that a four-level HBSS system, which includes automatic security screening, two SSO screening levels based on X-ray imaging, and manual control is an HBSS solution adequate for a regional medium-sized airport. It was also found that, given the growth of airport facility complexity and area, an increase of HBSS throughput is viable rather not by improving the capacity of specific HBSS components, but by deploying them in parallel processing lines. The highest throughput growth potential lies in parallel deployment of automatic security screening lines.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.009
      Issue No: Vol. 88 (2018)
  • Special issue on Integrated optimization models and algorithms in rail
           planning and control
    • Authors: Lingyun Meng; Francesco Corman; Xuesong Zhou; Tao Tang
      Pages: 87 - 90
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Lingyun Meng, Francesco Corman, Xuesong Zhou, Tao Tang

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.003
      Issue No: Vol. 88 (2018)
  • Battery electric propulsion: An option for heavy-duty vehicles'
           Results from a Swiss case-study
    • Authors: Emir Çabukoglu; Gil Georges; Lukas Küng; Giacomo Pareschi; Konstantinos Boulouchos
      Pages: 107 - 123
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Emir Çabukoglu, Gil Georges, Lukas Küng, Giacomo Pareschi, Konstantinos Boulouchos
      Road freight is the most energy-intensive freight mode (per tkm) and runs almost exclusively on fossil fuels. Electrification could change that, but can batteries really power actual heavy-duty operations' This study introduces a data-driven, bottom-up approach to explore the technical limits of electrification using real data from the entire Swiss truck fleet. Full electrification increased the total Swiss electricity demand by about 5 % (3 TW h per year) over its current level and avoid about 1 megaton of CO 2 per year (accounting for emissions of generation). Realizing this potential required (1) an allowance to exceed current maximum permissible weight regulations, (2) a high-capacity grid access for charging at the home-base (at least 50 kW ) and (3) a supporting intra-day energy infrastructure (we explored battery swapping). Boosting the gravimetric energy density of the battery cells was generally beneficial, but only effective if the aforementioned conditions were met. Thus, right now, battery electric trucks are no drop-in replacements for their Diesel counterparts. To allow their wide-spread usage, the road-freight sector would have to transform well beyond the vehicle. The required changes are substantial, but not unthinkable. Therefore, we think electric trucks deserve further exploration, in particular regarding their costs, life-cycle impact, technological variants and comparison to competing technologies.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.013
      Issue No: Vol. 88 (2018)
  • Missing data imputation for traffic flow speed using spatio-temporal
    • Authors: Bumjoon Bae; Hyun Kim; Hyeonsup Lim; Yuandong Liu; Lee D. Han; Phillip B. Freeze
      Pages: 124 - 139
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Bumjoon Bae, Hyun Kim, Hyeonsup Lim, Yuandong Liu, Lee D. Han, Phillip B. Freeze
      Modern transportation systems rely increasingly on the availability and accuracy of traffic detector data to monitor traffic operational conditions and assess system performance. Missing data, which occurs almost inevitably for a number of reasons, can lead to suboptimal operations and ineffective decisions if not remedied in a timely and systematic fashion through data imputation. A review of literature suggests that most traffic data imputation studies considered the temporal continuity of the data but often overlooked the spatial correlations that exist. Few of the studies explored the randomness of the patterns of the missing data. Therefore, this paper proposes two cokriging methods that exploit the existence of spatio-temporal dependency in traffic data and employ multiple data sources, each with independently missing data, to impute high-resolution traffic speed data under different data missing pattern scenarios. The two proposed cokriging methods, both using multiple independent data sources, were benchmarked against classic simple and ordinary kriging methods, which use only the primary data source. An array of testing scenarios were designed to test these methods under different missing rates (10–40% data loss) and different missing patterns (random in time and location, random only in location, and non-random blocks of missing data). The results suggest that using multiple data sources with the spatio-temporal simple cokriging method effectively improves the imputation accuracy if the missing data were clustered, or in blocks. On the other hand, if the missing data were randomly scattered in time and location, the classic ordinary or simple kriging method using only the primary data source can be more effective. Our study, which employs empirical traffic speed data from radar detectors and vehicle probes, demonstrates that the overall predictions of the kriging-based imputation approach are accurate and reliable for all combinations of missing patterns and missing rates investigated.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.015
      Issue No: Vol. 88 (2018)
  • A human-like game theory-based controller for automatic lane changing
    • Authors: Hongtao Yu; H. Eric Tseng; Reza Langari
      Pages: 140 - 158
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Hongtao Yu, H. Eric Tseng, Reza Langari
      Lane changing is a critical task for autonomous driving, especially in heavy traffic. Numerous automatic lane-changing algorithms have been proposed. However, surrounding vehicles are usually treated as moving obstacles without considering the interaction between vehicles/drivers. This paper presents a game theory-based lane-changing model, which mimics human behavior by interacting with surrounding drivers using the turn signal and lateral moves. The aggressiveness of the surrounding vehicles/drivers is estimated based on their reactions. With this model, the controller is capable of extracting information and learning from the interaction in real time. As such, the optimal timing and acceleration for changing lanes with respect to a variety of aggressiveness in target lane vehicle behavior are found accordingly. The game theory-based controller was tested in Simulink and dSPACE. Scenarios were designed so that a vehicle controlled by a game theory-based controller could interact with vehicles controlled by both robot and human drivers. Test results show that the game theory-based controller is capable of changing lanes in a human-like manner and outperforms fixed rule-based controllers.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.016
      Issue No: Vol. 88 (2018)
  • Carrot and stick: A game-theoretic approach to motivate cooperative
           driving through social interaction
    • Authors: Markus Zimmermann; David Schopf; Niklas Lütteken; Zhengzhenni Liu; Konrad Storost; Martin Baumann; Riender Happee; Klaus J. Bengler
      Pages: 159 - 175
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Markus Zimmermann, David Schopf, Niklas Lütteken, Zhengzhenni Liu, Konrad Storost, Martin Baumann, Riender Happee, Klaus J. Bengler
      This driving-simulator study aimed to motivate cooperative lane-change maneuvers in automated freeway driving under human supervision. Two interaction concepts were designed based on game theory. These concepts supported drivers’ cooperation by applying both rewards and sanctions as the proverbial carrot and stick. The social-status interaction rewards gap creation by revealing a driver’s prior cooperative behavior to other road users. The trade-off interaction introduces a system in which points compensate time loss and gain. Both concepts were evaluated from the left- and right-lane perspective, framing 39 participants to “be fast.” Drivers in the right lane asked those in the left lane to open a gap to overtake, mediated through a vehicle-to-vehicle connection and an augmented-reality user interface. Only 67% of the merging requests were accepted by left-lane drivers due to time pressure in the baseline condition. The social-status interaction enhanced acceptance to 86% on average and even to 97% for requests made by drivers marked as cooperative. The trade-off interaction enhanced acceptance to 87% as drivers gained a virtual benefit for losing one second. The subjective evaluation was positive for all conditions, and the social concepts were rated significantly higher on items associated with social relationships. Both social interaction concepts motivate cooperation and shape drivers’ behavior even under time pressure. Social mechanisms power maneuver-based local cooperation between traffic participants. It is expected that involving drivers in cooperative maneuvers has a beneficial effect on traffic performance, which microscopic traffic flow modeling should validate next. Gamified interaction and interface elements involve drivers of automated vehicles into strategic decisions and could help to mitigate automation effects. Since they don’t “drive” any more, cooperative interaction concepts now make them “play driving” and formulate pleasing strategies.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.017
      Issue No: Vol. 88 (2018)
  • Reliable frequency determination: Incorporating information on service
           uncertainty when setting dispatching headways
    • Authors: K. Gkiotsalitis; O. Cats
      Pages: 187 - 207
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): K. Gkiotsalitis, O. Cats
      Frequency setting requires the determination of the dispatching headways of all bus lines in a city network and constitutes the main activity in the tactical planning of public transport operations. Determining the dispatching headways of bus services in a city network is a multi-criteria problem that typically involves balancing between passenger demand coverage and operational costs. In this study, the problem of setting the optimal dispatching headways is formulated with the explicit consideration of operational variability issues for mitigating the adverse effects of passenger demand and travel time variations inherent to bus operations. The proposed model for setting the dispatching headways of bus lines considers the demand, headway and travel time variations along every section of each bus route for different times of the day, as well as operational costs, vehicle capacity and fleet size constraints. We first formulate the problem while accounting for the consequences of variability in service operations. The resulting optimization problem is then solved by employing a Branch and Bound approach together with Sequential Quadratic Programming in order to find the optimal dispatching headway for each bus line. Experimental results demonstrate (a) the improvement potential of the base case dispatching headways when considering the service reliability; (b) the sensitivity of the determined dispatching headways to changes in different criteria, such as passenger demand and/or bus running costs, and (c) the convergence accuracy of the proposed solution method when compared to heuristic approaches.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.026
      Issue No: Vol. 88 (2018)
  • A time-varying parameters vector auto-regression model to disentangle the
           time varying effects between drivers’ responses and tolling on high
           occupancy toll facilities
    • Authors: Xiaolei Ma; Shuo Sun; Xiaoyue Cathy Liu; Chuan Ding; Zhuo Chen; Yunpeng Wang
      Pages: 208 - 226
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Xiaolei Ma, Shuo Sun, Xiaoyue Cathy Liu, Chuan Ding, Zhuo Chen, Yunpeng Wang
      High Occupancy Toll (HOT) lane systems are considered one of most effective countermeasures to mitigate freeway congestion. Existing studies have largely focused on developing optimal tolling strategies to maximize the benefits of congestion pricing. Limited effort has been made to model the dynamic feedback mechanism of drivers’ responses to tolling. A thorough understanding of how the interactive relationship between demands (in both HOT lane and general purpose lanes) and toll rates evolves over time is necessary. The underlying mechanism can be used directly for guiding future HOT facilities investment decisions. This study builds upon the traditional vector autoregressive model and enables its parameters to be time-varying. Such a relaxation, namely, time-varying parameter vector autoregressive model (TVP-VAR), is used to answer the following two questions: (1) Is there a time varying effect between general purpose lane volume, HOT lane volume and dynamic toll rate' (2) If there is, how to quantify such time-varying interdependencies' Based on the empirical data from loop detectors and toll logs on Washington State Route 167 (SR167), we identified the existence of time-varying effects between drivers’ responses and toll rates, and quantified the evolving interactions amongst HOT demand, general purpose demand and tolling via time-varying impulse responses. In addition, we found that drivers’ perceptions on HOT lanes across distinct geographical locations are significantly different.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.025
      Issue No: Vol. 88 (2018)
  • Statistical inference of probabilistic origin-destination demand using
           day-to-day traffic data
    • Authors: Wei Ma; Zhen (Sean) Qian
      Pages: 227 - 256
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Wei Ma, Zhen (Sean) Qian
      Recent transportation network studies on uncertainty and reliability call for modeling the probabilistic O-D demand and probabilistic network flow. Making the best use of day-to-day traffic data collected over many years, this paper develops a novel theoretical framework for estimating the mean and variance/covariance matrix of O-D demand considering the day-to-day variation induced by travelers’ independent route choices. It also estimates the probability distributions of link/path flow and their travel cost where the variance stems from three sources, O-D demand, route choice and unknown errors. The framework estimates O-D demand mean and variance/covariance matrix iteratively, also known as iterative generalized least squares (IGLS) in statistics. Lasso regularization is employed to obtain sparse covariance matrix for better interpretation and computational efficiency. Though the probabilistic O-D estimation (ODE) works with a much larger solution space than the deterministic ODE, we show that its estimator for O-D demand mean is no worse than the best possible estimator by an error that reduces with the increase in sample size. The probabilistic ODE is examined on two small networks and two real-world large-scale networks. The solution converges quickly under the IGLS framework. In all those experiments, the results of the probabilistic ODE are compelling, satisfactory and computationally plausible. Lasso regularization on the covariance matrix estimation leans to underestimate most of variance/covariance entries. A proper Lasso penalty ensures a good trade-off between bias and variance of the estimation.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2017.12.015
      Issue No: Vol. 88 (2018)
  • Analysing the impact of travel information for minimising the regret of
           route choice
    • Authors: Gabriel de O. Ramos; Ana L.C. Bazzan; Bruno C. da Silva
      Pages: 257 - 271
      Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Gabriel de O. Ramos, Ana L.C. Bazzan, Bruno C. da Silva
      In the route choice problem, self-interested drivers aim at choosing routes that minimise travel costs between their origins and destinations. We model this problem as a multiagent reinforcement learning scenario. Here, since agents must adapt to each others’ decisions, the minimisation goal is seen as a moving target. Regret is a well-known performance measure in such settings, and considers how much worse an agent performs compared to the best fixed action in hindsight. In general, regret cannot be computed (and used) by agents because its calculation requires observing the costs of all available routes (including non-taken ones). In contrast to previous works, here we show how agents can compute regret by building upon their experience and via information provided by a mobile (Waze-like) navigation app. Specifically, we compute the regret of each action as a linear combination of local (experience-based) and global (app-based) information. We refer to such a measure as the action regret, which can be used by the agents as reinforcement signal. Under these conditions, agents are able to minimise their external regret even when the cost of routes is not known in advance. Based on experimental evaluation in several abstract road networks, we show that the system converges to approximate User Equilibria.

      PubDate: 2018-02-26T18:49:00Z
      DOI: 10.1016/j.trc.2017.11.011
      Issue No: Vol. 88 (2018)
  • Developing an algorithm to assess the rear-end collision risk under fog
           conditions using real-time data
    • Authors: Yina Wu; Mohamed Abdel-Aty; Qing Cai; Jaeyoung Lee; Juneyoung Park
      Pages: 11 - 25
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Yina Wu, Mohamed Abdel-Aty, Qing Cai, Jaeyoung Lee, Juneyoung Park
      This study aims to propose a new algorithm to evaluate the rear-end collision risk under fog conditions considering reduced visibility. The proposed algorithm compares the safe stopping distance of the leading and following vehicles. According to the relationship between clearance distance between the two consecutive vehicles and visibility distance, the car-following maneuver is divided into different situations and the algorithms to calculate the safe stopping distances are suggested correspondingly. The visibility distance is collected by a new visibility detection system with adaptive learning modules and the clearance distance is obtained from a vehicle-based detector. By comparing the safe stopping distances of the following and leading vehicles, the potential rear-end collision can be identified. Subsequently, statistical tests are conducted to analyze rear-end collision risk and compare the different impact of reduced visibility on the collision risk for different vehicle types and lanes. Furthermore, random parameters logistic and negative binomial models are estimated by using individual vehicle data and aggregated traffic flow data, respectively, in order to explore the relationship between the potential rear-end crash and the reduced visibility together with other traffic parameters. The results suggest that the proposed algorithm works well in evaluating rear-end collision risk under fog conditions. It is found that reduced visibility has significant impact on the rear-end collision risk and the impact vary by the different vehicle types and by lane. Further, it is concluded that the driving maneuver of the leading and following vehicles can affect the rear-end collision risk. It is expected that the proposed algorithm can be implemented in a Traffic Management context to improve road safety under fog conditions. Specifically, it is suggested to implement the proposed algorithms in real-time and integrate it with ITS technologies such as Variable Speed Limit (VSL) and Dynamic Message Signs (DMS) to enhance traffic safety when the visibility declines. This car following algorithm could also be extended to adapt for the advent of Connected Vehicles in Fog conditions.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.012
      Issue No: Vol. 87 (2018)
  • Peer pressure enables actuation of mobility lifestyles
    • Authors: Sid Feygin; Alexei Pozdnoukhov
      Pages: 26 - 45
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Sid Feygin, Alexei Pozdnoukhov
      This paper explores the utility of peer pressure as an actionable mechanism to induce socially responsible and environmentally-conscious mobility habits. We adopt a two-stage game theoretic model of peer pressure to investigate feedback between social, geographic, and temporal dimensions of agent choices in a hyper-realistic micro-simulation of travel. The results show that peer pressure helps in achieving desirable equilibrium properties while reducing congestion and emissions due to sustained mode shift. With a way to initiate the required social norming and a proper concern for privacy and ethics, these cost-effective mechanisms may soon begin to find use in improving community welfare.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.008
      Issue No: Vol. 87 (2018)
  • Leveraging rapid simulation and analysis of large urban road systems on
    • Authors: Wojciech Turek; Leszek Siwik; Aleksander Byrski
      Pages: 46 - 57
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Wojciech Turek, Leszek Siwik, Aleksander Byrski
      The scalable implementation of a microscopic simulation, presented in our previous work, opens new areas of applications for traffic simulation, namely short term traffic forecasting. It can be used for real-time prediction of local, exceptional situations. Moreover it can be used as a hypothesis verification tool for evaluating different strategies of traffic control. In order to realize such simulations in a very short time (what is crucial here) a sufficient computing power is necessary what can be achieved using dedicated HPC hardware. In this paper we present methods for rapid analysis of simulation results for large urban road systems, where a local, exceptional situation can remain unnoticed. We propose new metrics for detecting and locating such situations. The detected situation can be handled automatically with the method based on the multi-variant planning approach.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.014
      Issue No: Vol. 87 (2018)
  • On data processing required to derive mobility patterns from
           passively-generated mobile phone data
    • Authors: Feilong Wang; Cynthia Chen
      Pages: 58 - 74
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Feilong Wang, Cynthia Chen
      Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile phone data: Call Details Record (CDR) data and sightings data, and propose a data processing framework and the associated algorithms to address two key issues associated with the sightings data: locational uncertainty and oscillation. We show the effectiveness of our proposed methods in addressing these two issues compared to the state of art algorithms in the field. We also demonstrate that without proper processing applied to the data, the statistical regularity of human mobility patterns—a key, significant trait identified for human mobility—is over-estimated. We hope this study will stimulate more studies in examining the properties of such data and developing methods to address them. Though not as glamorous as those directly deriving insights on mobility patterns (such as statistical regularity), understanding properties of such data and developing methods to address them is a fundamental research topic on which important insights are derived on mobility patterns.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.003
      Issue No: Vol. 87 (2018)
  • Mitigating the impact of selfish routing: An optimal-ratio control scheme
           (ORCS) inspired by autonomous driving
    • Authors: Kenan Zhang; Yu (Marco) Nie
      Pages: 75 - 90
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Kenan Zhang, Yu (Marco) Nie
      Fully controllable autonomous vehicles offer unprecedented opportunities to address the inefficiency associated with selfish routing, a fundamental issue in transportation network modeling. This study proposes a route control scheme that aims to strike a balance between gains in the system efficiency and the control intensity, defined as the demand flow under control for each origin-destination (OD) pair. The proposed model has a bi-level structure and is formulated as a mathematical program with equilibrium constraints (MPEC). A specialized algorithm based on sensitivity analysis and the alternative direction method of multiplier (ADMM) is developed to find a local optimum for the MPEC. Results of numerical experiments show that (1) in all tested cases, controlling a minority of vehicles (less than 10% in some case) could bring the system very close to the system optimum; (2) some O-D pairs enjoy a higher control priority than the others, mostly due to the underlying network topology rather than the demand magnitude; (3) the proposed algorithm is computationally efficient; (4) starting from different initial solutions, the algorithm produces very similar local optimal solutions.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.011
      Issue No: Vol. 87 (2018)
  • Analyzing battery electric vehicle feasibility from taxi travel patterns:
           The case study of New York City, USA
    • Authors: Liang Hu; Jing Dong; Zhenhong Lin; Jie Yang
      Pages: 91 - 104
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Liang Hu, Jing Dong, Zhenhong Lin, Jie Yang
      Electric taxis have the potential to improve urban air quality and save driver’s energy expenditure. Although battery electric vehicles (BEVs) have drawbacks such as the limited range and charging inconvenience, technological progress has been presenting promising potential for electric taxis. Many cities around the world including New York City, USA are taking initiatives to replace gasoline taxis with plug-in electric vehicles. This paper extracts ten variables from the trip data of the New York City yellow taxis to represent their spatial-temporal travel patterns in terms of driver-shift, travel demand and dwell, and examines the implications of these driving patterns on the BEV taxi feasibility. The BEV feasibility of a taxi is quantified as the percentage of occupied trips that can be completed by BEVs of a given driving range during a year. It is found that the currently deployed 280 public charging stations in New York City are far from sufficient to support a large BEV taxi fleet. However, adding merely 372 new charging stations at various locations where taxis frequently dwell can potentially make BEVs with 200- and 300-mile ranges feasible for more than half of the taxi fleet. The results also show that taxis with certain characteristics are more suitable for switching to BEV-200 or BEV-300, such as fewer daily shifts, fewer drivers assigned to the taxi, shorter daily driving distance, fewer daily dwells but longer dwelling time, and higher likelihood to dwell at the borough of Manhattan.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.017
      Issue No: Vol. 87 (2018)
  • Using structural topic modeling to identify latent topics and trends in
           aviation incident reports
    • Authors: Kenneth D. Kuhn
      Pages: 105 - 122
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Kenneth D. Kuhn
      The Aviation Safety Reporting System includes over a million confidential reports describing aviation safety incidents. Natural language processing techniques allow for relatively rapid and largely automated analysis of large collections of text data. Interpretation of the results and further investigations by subject matter experts can produce meaningful results. This explains the many commercial and academic applications of natural language processing to aviation safety reports. Relatively few published articles have, however, employed topic modeling, an approach that can identify latent structure within a corpus of documents. Topic modeling is more flexible and relies less on subject matter experts than alternative document categorization and clustering methods. It can, for example, uncover any number of topics hidden in a set of incident reports that have been, or would be, assigned to the same category when using labels and methods applied in earlier research. This article describes the application of structural topic modeling to Aviation Safety Reporting System data. The application identifies known issues. The method also reveals previously unreported connections. Sample results reported here highlight fuel pump, tank, and landing gear issues and the relative insignificance of smoke and fire issues for private aircraft. The results also reveal the prominence of the Quiet Bridge Visual and Tip Toe Visual approach paths at San Francisco International Airport in safety incident reports. These results would, ideally, be verified by subject matter experts before being used to set priorities when planning future safety studies.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.018
      Issue No: Vol. 87 (2018)
  • Public transport trip purpose inference using smart card fare data
    • Authors: Azalden Alsger; Ahmad Tavassoli; Mahmoud Mesbah; Luis Ferreira; Mark Hickman
      Pages: 123 - 137
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Azalden Alsger, Ahmad Tavassoli, Mahmoud Mesbah, Luis Ferreira, Mark Hickman
      Although smart card fare data has recently become more prevalent as a rich, comprehensive and continuous source of information, there is still some missing information which inhibits its capability in the research field. One key missing piece of information is the passengers’ trip purpose. This paper investigates the potential of the smart card data to infer passengers’ trip purpose, thereby reducing the use of the expensive and time-consuming Household Travel Surveys (HTS). On this basis, an improved model has been proposed, calibrated and validated for trip purpose inference by integrating different data sources, namely: HTS, a land use database, the South East Queensland Strategic Transport Model (SEQSTM), the General Transit Feed Specification (GTFS) data, O-D survey data, and most importantly the unique smart card fare data from Brisbane, Queensland. As smart card fare data does not record passengers’ trip purpose, the calibration and validation procedures are performed on HTS data. Based on the validation results, the proposed methodology shows a strong capability to predict trip purpose at a high level of accuracy. The results show an overall 67% correct inference after applying spatial attributes, but the correct inference increases to 78% after applying temporal attributes. Different trip purposes show different sensitivities to the applied spatial and temporal attributes. Work and home trips have the highest correct inference results, at 92% and 96%, respectively. Furthermore, the results of correct inference for shopping and education trips improved after applying the temporal attributes.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.016
      Issue No: Vol. 87 (2018)
  • Region-based prescriptive route guidance for travelers of multiple classes
    • Authors: Antonis F. Lentzakis; Simon I. Ware; Rong Su; Changyun Wen
      Pages: 138 - 158
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Antonis F. Lentzakis, Simon I. Ware, Rong Su, Changyun Wen
      The performance and complicated interactions of different classes of travelers on regional urban networks are presented and analyzed. A new multi-class extension of a regional dynamic traffic model, the Network Transmission Model is proposed. The classes in question correspond to travelers using autonomous vehicles, conventional vehicles, equipped with Route Guidance and Information Systems, and unequipped vehicles. Each class is represented by a different routing method. Incremental Route Planning, an innovative predictive simulation-based routing method, Proxy Regret Matching, a non-predictive strategic learning-based method and Multinomial Logit-based Routing for 1st, 2nd and 3rd class respectively. All routing methods include a Public Transit Diversion mechanism and are assumed to provide prescriptive route guidance, with pre-trip information dissemination for every departing vehicle. We consider the possibility of non-compliance for conventional vehicles equipped with Route Guidance and Information Systems. We also consider 2 possible scenarios for autonomous vehicles that affect their travel time prediction accuracy. We simulate regional traffic dynamics for simultaneous application of all aforementioned routing methods, employing a market penetration scheme for each class of travelers. We analyze results regarding the overall network performance for various combinations of traveler class market penetration rates and non-compliance rates. We come to the conclusion that autonomous vehicles will not only provide benefits for 1st class travelers, but for all traveler classes on the network.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.006
      Issue No: Vol. 87 (2018)
  • A modeling framework for the dynamic management of free-floating
           bike-sharing systems
    • Authors: Leonardo Caggiani; Rosalia Camporeale; Michele Ottomanelli; Wai Yuen Szeto
      Pages: 159 - 182
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Leonardo Caggiani, Rosalia Camporeale, Michele Ottomanelli, Wai Yuen Szeto
      Given the growing importance of bike-sharing systems nowadays, in this paper we suggest an alternative approach to mitigate the most crucial problem related to them: the imbalance of bicycles between zones owing to one-way trips. In particular, we focus on the emerging free-floating systems, where bikes can be delivered or picked-up almost everywhere in the network and not just at dedicated docking stations. We propose a new comprehensive dynamic bike redistribution methodology that starts from the prediction of the number and position of bikes over a system operating area and ends with a relocation Decision Support System. The relocation process is activated at constant gap times in order to carry out dynamic bike redistribution, mainly aimed at achieving a high degree of user satisfaction and keeping the vehicle repositioning costs as low as possible. An application to a test case study, together with a detailed sensitivity analysis, shows the effectiveness of the suggested novel methodology for the real-time management of the free-floating bike-sharing systems.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.001
      Issue No: Vol. 87 (2018)
  • Relative economic competitiveness of light-duty battery electric and fuel
           cell electric vehicles
    • Authors: Geoff Morrison; John Stevens; Fred Joseck
      Pages: 183 - 196
      Abstract: Publication date: February 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 87
      Author(s): Geoff Morrison, John Stevens, Fred Joseck
      This paper estimates battery electric (BEV) and hydrogen fuel cell electric vehicle (FCEV) costs from today through 2040 to explore the potential market size of each vehicle type. Two main tasks are performed. First, the total cost of ownership (TCO) – including vehicle purchase, fuel, maintenance, resale, and refueling inconvenience – is estimated for 77 light-duty vehicle (LDV) segments, defined by driving range and size class. Second, data on individual travel behavior is used to estimate the fraction of vehicle owners within each of the 77 segments. In 2020, BEVs are estimated to be the cheaper vehicle option in 79–97 percent of the LDV fleet and have a weighted average cost advantage of $0.41 per mile below FCEVs across all vehicle segments and drivers. However, costs of the two powertrains quickly converge between 2025 and 2030. By 2040, FCEVs are estimated to be less expensive than BEVs per mile in approximately 71–88 percent of the LDV fleet and have notable cost advantages within larger vehicle size classes and for drivers with longer daily driving ranges. This analysis demonstrates a competitive market space for both FCEVs and BEVs to meet the different needs of LDV consumers.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2018.01.005
      Issue No: Vol. 87 (2018)
  • Perspectives of the use of smartphones in travel behaviour studies:
           Findings from a literature review and a pilot study
    • Abstract: Publication date: March 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 88
      Author(s): Jędrzej Gadziński
      Human travel behaviour has recently been one of the most popular topics in transport studies. Therefore, the ability to obtain valuable sets of data has become one of the key challenges for researchers. Traditional mobility surveys have many important limitations. In this situation, the potential of the use of smartphones and dedicated applications in the identification of individual travel behaviour seem very promising. We set ourselves a goal to indicate strengths and weaknesses of data obtained with this method and assess the perspectives of its use for the needs of public policies. For these purposes we prepared a low-cost mobile application and conducted a pilot study among students in Poznań (Poland). In effect, trajectories of more than 100 people with almost 3 billion of location data were collected. Based on a literature review and our results we discuss the main problems, limitations and challenges of the broader use of the data obtained with smartphones. In the conclusion, we argue that there is a huge and increasing potential connected with mobile phones, but still some important barriers exist including sampling problems, limitations in big data analyses and technological issues. Therefore, a broader use of smartphones in travel behaviour surveys seems to be rather a distant perspective.

      PubDate: 2018-02-05T14:41:59Z
  • A capacity maximization scheme for intersection management with automated
    • Authors: Weili Sun; Jianfeng Zheng; Henry X. Liu
      Abstract: Publication date: Available online 1 February 2018
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Weili Sun, Jianfeng Zheng, Henry X. Liu
      With the advent of connected and automated vehicle technology, in this paper, we propose an innovative intersection operation scheme named as MCross: Maximum Capacity inteRsection Operation Scheme with Signals. This new scheme maximizes intersection capacity by utilizing all lanes of a road simultaneously. Lane assignment and green durations are dynamically optimized by solving a multi-objective mixed-integer non-linear programming problem. The demand conditions under which full capacity can be achieved in MCross are derived analytically. Numerical examples show that MCross can almost double the intersection capacity (increase by as high as 99.51% in comparison to that in conventional signal operation scheme).

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.12.006
  • Fair dynamic resource allocation in transit-based evacuation planning
    • Authors: Soheila Aalami; Lina Kattan
      Abstract: Publication date: Available online 3 January 2018
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Soheila Aalami, Lina Kattan
      Resource allocation in transit-based emergency evacuation is studied in this paper. The goal is to find a method for allocation of resources to communities in an evacuation process which is (1) fair, (2) reasonably efficient, and (3) able to dynamically adapt to the changes to the emergency situation. Four variations of the resource allocation problem, namely maximum rate, minimum clearance time, maximum social welfare, and proportional fair resource allocation, are modeled and compared. It is shown that the optimal answer to each problem can be found efficiently. Additionally, a distributed and dynamic algorithm based on the Lagrangian dual approach, called PFD2A, is developed to find the proportional fair allocation of resources and update the evacuation process in real time whenever new information becomes available. Numerical results for a sample scenario are presented.

      PubDate: 2018-02-05T14:41:59Z
      DOI: 10.1016/j.trc.2017.10.018
  • An empirical study on travel patterns of internet based ride-sharing
    • Authors: Yongqi Dong; Shuofeng Wang; Li Li; Zuo Zhang
      Pages: 1 - 22
      Abstract: Publication date: January 2018
      Source:Transportation Research Part C: Emerging Technologies, Volume 86
      Author(s): Yongqi Dong, Shuofeng Wang, Li Li, Zuo Zhang
      The rapid growth of internet based ride-sharing brings great changes to residents' travel and city traffic. However, few studies had employed empirical data to examine the unique travel patterns of internet based ride-sharing trips. In this paper, we compare taxi trip records and internet based ride-sharing trip records provided by DiDi company. Results reveal many interesting findings that had never been reported before. From the viewpoint of service patterns, ride-sharing mainly increases supplies in hot areas and peak hours. By applying a non-negative matrix factorization method, we find that ride-sharing principally serves as an approach for commuting. So, as an effective supplement to traditional taxi service, it regulates spatial and temporal supply-demand imbalance, especially during morning and evening rush periods. From the viewpoint of individual behavior patterns, we use a clustering method to identify two kinds of internet based ride-sharing drivers. The first kind of drivers usually provides ride-sharing along daily home-work commuting. Trips served by these drivers have relatively constant origin-designation (OD) pairs. The second kind of drivers does not serve regularly and roams around the city even in working hours. Therefore, there are no constant OD pairs in their ride-sharing trips. Counterintuitively, we find that home-work commuting drivers account for only a small part of total drivers and they only serve a small number of commuting trips. In addition, internet based ride-sharing is not just traditional hitchhiking worked through mobile internet. We find that internet based ride-sharing drivers intend to make long distance trips, and they intend to detour further to pick up or drop off passengers than traditional hitchhike drivers since they are paid. All these findings are helpful for policy makers at all levels to make informed decisions about deployment of internet based ride-sharing service. This paper also verifies that big data analytics is particularly useful and powerful in the analysis of ride-sharing and taxi service patterns.

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

      PubDate: 2017-11-10T06:40:31Z
      DOI: 10.1016/j.trc.2017.11.001
      Issue No: Vol. 86 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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