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  Subjects -> TRANSPORTATION (Total: 164 journals)
    - AIR TRANSPORT (7 journals)
    - AUTOMOBILES (21 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (6 journals)
    - SHIPS AND SHIPPING (30 journals)
    - TRANSPORTATION (95 journals)

TRANSPORTATION (95 journals)

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Partially Free   (Followers: 76)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2)
Archives of Transport     Open Access   (Followers: 16)
Botswana Journal of Technology     Full-text available via subscription  
Case Studies on Transport Policy     Hybrid Journal   (Followers: 8)
Cities in the 21st Century     Open Access   (Followers: 13)
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: 9)
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: 8)
IET Electrical Systems in Transportation     Hybrid Journal   (Followers: 9)
IET Intelligent Transport Systems     Hybrid Journal   (Followers: 7)
IFAC-PapersOnLine     Open Access  
International Innovation - Transport     Open Access   (Followers: 8)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 7)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 7)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 1)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 8)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 9)
International Journal of Micro-Nano Scale Transport     Full-text available via subscription   (Followers: 2)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 9)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 10)
International Journal of Services Technology and Management     Hybrid Journal  
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 10)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 14)
International Journal of Transportation Science and Technology     Full-text available via subscription   (Followers: 9)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
International Journal of Vehicular Technology     Open Access   (Followers: 4)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 11)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 5)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 6)
Journal of Navigation     Hybrid Journal   (Followers: 174)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 11)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 1)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 6)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 10)
Journal of Transport Geography     Hybrid Journal   (Followers: 22)
Journal of Transport History     Full-text available via subscription   (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: 14)
Journal of Transportation Technologies     Open Access   (Followers: 14)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 6)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics & Sustainable Transport     Open Access   (Followers: 2)
Logistique & Management     Full-text available via subscription  
Modern Transportation     Open Access   (Followers: 10)
Nonlinear Dynamics     Hybrid Journal   (Followers: 5)
Open Journal of Safety Science and Technology     Open Access   (Followers: 8)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 9)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 12)
Public Transport     Hybrid Journal   (Followers: 18)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 4)
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: 12)
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: 13)
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: 12)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 3)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 31)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 29)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 20)
Transportation Research Procedia     Open Access   (Followers: 4)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 34)
Transportation Science     Full-text available via subscription   (Followers: 20)
TRANSPORTES     Open Access   (Followers: 5)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 4)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 2)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 5)
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: 3)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 5)
Транспортні системи та технології перевезень     Open Access  
Journal Cover Transportation Research Part B: Methodological
  [SJR: 3.905]   [H-I: 87]   [29 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0191-2615
   Published by Elsevier Homepage  [3040 journals]
  • Optimal perimeter control synthesis for two urban regions with aggregate
           boundary queue dynamics
    • Authors: Jack Haddad
      Pages: 1 - 25
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Jack Haddad
      Perimeter control policies for urban regions with Macroscopic Fundamental Diagram (MFD) modeling have been presented in previous works. The control policies might meter the number of transferring vehicles from one region to another, resulting in queueing vehicles at regional boundaries. Concentrated vehicles at boundaries might affect the existence of well-defined MFDs. Most previous works neglect the effect of the boundary concentrated vehicles on the traffic flow dynamics, and do not explicitly consider their effect on the perimeter control policy. This paper introduces a new MFD-based model for two-region networks with aggregate boundary queue dynamics. The dynamic flow characteristics for the two urban regions are modeled by the MFD functions, while aggregate boundary queue dynamics for both regions are modeled by input-output balance differential equations. Maximum lengths are imposed on the aggregate boundary queues, that aim at maintaining the existence of well-defined MFDs and their dynamics. Based on the developed model, the optimal control policy to maximize the total network throughput is found. Analytical solutions for the optimal perimeter control problem, with constrained perimeter control inputs and constrained lengths of aggregate boundary queues, are derived. The optimal synthesis for principal cases are found and verified by numerical tests. The numerical results demonstrate the effect of aggregate boundary queues on the optimal perimeter control policy.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.10.016
      Issue No: Vol. 96 (2016)
       
  • Enhancing model-based feedback perimeter control with data-driven online
           adaptive optimization
    • Authors: Anastasios Kouvelas; Mohammadreza Saeedmanesh; Nikolas Geroliminis
      Pages: 26 - 45
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Anastasios Kouvelas, Mohammadreza Saeedmanesh, Nikolas Geroliminis
      Most feedback perimeter control approaches that are based on the Macroscopic Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow from the external boundary of the network. Although such a measure is beneficial for the network performance, it creates virtual queues that do not interact with the rest of the traffic and assumes small unrestricted flow (i.e. almost zero disturbance). In reality, these queues can have a negative impact to traffic conditions upstream of the protected network that is not modelled. In this work an adaptive optimization scheme for perimeter control of heterogeneous transportation networks is developed and the aforementioned boundary control limitation is dropped. A nonlinear model is introduced that describes the evolution of the multi-region system over time, assuming the existence of well-defined MFDs. Multiple linear approximations of the model (for different set-points) are used for designing optimal multivariable integral feedback regulators. Since the resulting regulators are derived from approximations of the nonlinear dynamics, they are further enhanced in real-time with online learning/adaptive optimization, according to performance measurements. An iterative data-driven technique is integrated with the model-based design and its objective is to optimize the gain matrices and set-points of the multivariable perimeter controller based on real-time observations. The efficiency of the derived multi-boundary control scheme is tested in microsimulation for a large urban network with more than 1500 roads that is partitioned in multiple regions. The proposed control scheme is demonstrated to achieve a better distribution of congestion (by creating “artificial” inter-regional queues), thus preventing the network degradation and improving total delay and outflow.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.10.011
      Issue No: Vol. 96 (2016)
       
  • Multiperiod-based timetable optimization for metro transit networks
    • Authors: Xin Guo; Huijun Sun; Jianjun Wu; Jiangang Jin; Jin Zhou; Ziyou Gao
      Pages: 46 - 67
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Xin Guo, Huijun Sun, Jianjun Wu, Jiangang Jin, Jin Zhou, Ziyou Gao
      This paper tackles the train timetable optimization problem for metro transit networks (MTN) in order to enhance the performance of transfer synchronization between different rail lines. Train timetables of connecting lines are adjusted in such a way that train arrivals at transfer stations can be well synchronized. This study particularly focuses on the timetable optimization problem in the transitional period (from peak to off-peak hours or vice versa) during which train headway changes and passenger travel demand varies significantly. A mixed integer nonlinear programming model is proposed to generate an optimal train timetable and maximize the transfer synchronization events. Secondly, an efficient hybrid optimization algorithm based on the Particle Swarm Optimization and Simulated Annealing (PSO-SA) is designed to obtain near-optimal solutions in an efficient way. Meanwhile, in order to demonstrate the effectiveness of the proposed method, the results of numerical example solved by PSO-SA are compared with a branch-and-bound method and other heuristicalgorithms. Finally, a real-world case study based on the Beijing metro network and travel demand is conducted to validate the proposed timetabling model. Computational results demonstrate the effectiveness of adjusting train timetables and the applicability of the developed approach to real-world metro networks.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.11.005
      Issue No: Vol. 96 (2016)
       
  • Optimizing on-time arrival probability and percentile travel time for
           elementary path finding in time-dependent transportation networks: Linear
           mixed integer programming reformulations
    • Authors: Lixing Yang; Xuesong Zhou
      Pages: 68 - 91
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Lixing Yang, Xuesong Zhou
      Aiming to provide a generic modeling framework for finding reliable paths in dynamic and stochastic transportation networks, this paper addresses a class of two-stage routing models through reformulation of two commonly used travel time reliability measures, namely on-time arrival probability and percentile travel time, which are much more complex to model in comparison to expected utility criteria. A sample-based representation is adopted to allow time-dependent link travel time data to be spatially and temporally correlated. A number of novel reformulation methods are introduced to establish equivalent linear integer programming models that can be easily solved. A Lagrangian decomposition approach is further developed to dualize the non-anticipatory coupling constraints across different samples and then decompose the relaxed model into a series of computationally efficient time-dependent least cost path sub-problems. Numerical experiments are implemented to demonstrate the solution quality and computational performance of the proposed approaches.

      PubDate: 2016-12-05T07:58:22Z
      DOI: 10.1016/j.trb.2016.11.012
      Issue No: Vol. 96 (2016)
       
  • Analyzing the performance of distributed conflict resolution among
           autonomous vehicles
    • Authors: Ítalo Romani de Oliveira
      Pages: 92 - 112
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Ítalo Romani de Oliveira
      This paper presents a study on how cooperation versus non-cooperation, and centralization versus distribution impact the performance of a traffic game of autonomous vehicles. A model using a particle-based, Lagrange representation, is developed, instead of an Eulerian, flow-based one, usual in routing problems of the game-theoretical approach. This choice allows representation of phenomena such as fuel exhaustion, vehicle collision, and wave propagation. The elements necessary to represent interactions in a multi-agent transportation system are defined, including a distributed, priority-based resource allocation protocol, where resources are nodes and links in a spatial network and individual routing strategies are performed. A fuel consumption dynamics is developed in order to account for energy cost and vehicles having limited range. The analysis shows that only the scenarios with cooperative resource allocation can achieve optimal values of either collective cost or equity coefficient, corresponding respectively to the centralized and to the distributed cases.

      PubDate: 2016-12-05T07:58:22Z
      DOI: 10.1016/j.trb.2016.11.011
      Issue No: Vol. 96 (2016)
       
  • Modeling technical and service efficiency
    • Authors: Efthymios Tsionas; A. George Assaf; David Gillen; Anna S. Mattila
      Pages: 113 - 125
      Abstract: Publication date: February 2017
      Source:Transportation Research Part B: Methodological, Volume 96
      Author(s): Efthymios Tsionas, A. George Assaf, David Gillen, Anna S. Mattila
      Previous research on service failures, often measured by customer complaints, has not examined how organizations can measure or monitor their service efficiency. In this article, we introduce a new model that is suitable for measuring both service efficiency and technical efficiency when both bad outputs (i.e. service complaints) and good outputs (i.e. passenger trips and flights) are present. We develop our model with an output distance function, using Bayesian methods of inference organized around Markov chain Monte Carlo (MCMC). We illustrate our model with an application in the U.S. airline industry, an industry sector beset with service failures affecting both revenues and costs. We present the service inefficiency results of various US airlines and discuss the determinants of bad outputs in this industry. We also test whether our results are in line with market expectations by comparing the service efficiency estimates against the “American Customer Satisfaction Index” data.

      PubDate: 2016-12-05T07:58:22Z
      DOI: 10.1016/j.trb.2016.11.010
      Issue No: Vol. 96 (2016)
       
  • Cruise service planning considering berth availability and decreasing
           marginal profit
    • Authors: Kai Wang; Shuaian Wang; Lu Zhen; Xiaobo Qu
      Pages: 1 - 18
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Kai Wang, Shuaian Wang, Lu Zhen, Xiaobo Qu
      This paper addresses a decision problem on planning cruise services for a cruise ship so as to maximize the total profit during a planning horizon. The service is a sequence of ports (harbor cities) that the cruise ship visits. In this decision problem, the constraint about the availability of berths at each port is taken into account. In reality, if a cruise service is executed by the ship repeatedly for several times, the profit earned by the cruise service in each time decreases gradually. This effect of decreasing marginal profit is also considered in this study. We propose a nonlinear integer programming model to cater to the concavity of the function for the profit of operating a cruise service repeatedly. To solve the nonlinear model, two linearization methods are developed, one of which takes advantage of the concavity for a tailored linearization. Some properties of the problem are also investigated and proved by using the dynamic programming (DP) and two commonly used heuristics. In particular, we prove that if there is only one candidate cruise service, a greedy algorithm can derive the optimal solution. Numerical experiments are conducted to validate the effectiveness of the proposed models and the efficiency of the proposed linearization methods. In case some parameters needed by the model are estimated inexactly, the proposed decision model demonstrates its robustness and can still obtain a near-optimal plan, which is verified by experiments based on extensive real cases.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.020
      Issue No: Vol. 95 (2016)
       
  • Strategic fleet planning for city logistics
    • Authors: Anna Franceschetti; Dorothée Honhon; Gilbert Laporte; Tom Van Woensel; Jan C. Fransoo
      Pages: 19 - 40
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Anna Franceschetti, Dorothée Honhon, Gilbert Laporte, Tom Van Woensel, Jan C. Fransoo
      We study the strategic problem of a logistics service provider managing a (possibly heterogeneous) fleet of vehicles to serve a city in the presence of access restrictions. We model the problem as an area partitioning problem in which a rectangular service area has to be divided into sectors, each served by a single vehicle. The length of the routes, which depends on the dimension of the sectors and on customer density in the area, is calculated using a continuous approximation. The aim is to partition the area and to determine the type of vehicles to use in order to minimize the sum of ownership or leasing, transportation and labor costs. We formulate the problem as a mixed integer linear problem and as a dynamic program. We develop efficient algorithms to obtain an optimal solution and present some structural properties regarding the optimal partition of the service area and the set of vehicle types to use. We also derive some interesting insights, namely we show that in some cases traffic restrictions may actually increase the number of vehicles on the streets, and we study the benefits of operating a heterogeneous fleet of vehicles.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.005
      Issue No: Vol. 95 (2016)
       
  • A method to directly derive taste heterogeneity of travellers’ route
           choice in public transport from observed routes
    • Authors: Sung-Pil Hong; Kyung min Kim; Geunyeong Byeon; Yun-Hong Min
      Pages: 41 - 52
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Sung-Pil Hong, Kyung min Kim, Geunyeong Byeon, Yun-Hong Min
      The heterogeneity of passengers’ route choice has been explained by randomizing the parameters, also known as taste parameters, that determine the way the attributes are relatively weighed in the disutility he/she perceives from a route. Growing availability of massive route choice data from, e.g. GPS or Smart Card system has made expected a model that derives the distribution of taste parameters from RP-data rather than relies on a prescribed distribution. This study availed itself of the intensive set of route choice data from Smart Card system as well as inverse optimization to calibrate the joint pdf of taste parameters to best signify the user-optimality of observed routes. Tested on 5 daily sets of real route choice, which amounts to 50,000 trips from the metro of Seoul metropolitan area, the proposed model notably enhanced the predictability compared to the previous models adopting a mixed-logit-based SUE or a non-parametric estimation method.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.012
      Issue No: Vol. 95 (2016)
       
  • A link-based mean-excess traffic equilibrium model under uncertainty
    • Authors: Xiangdong Xu; Anthony Chen; Lin Cheng; Chao Yang
      Pages: 53 - 75
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Xiangdong Xu, Anthony Chen, Lin Cheng, Chao Yang
      Traffic equilibrium models under uncertainty characterize travelers’ route choice behaviors under travel time variability. In this paper, we develop a link-based mean-excess traffic equilibrium (L-METE) model by integrating the sub-additivity property and complete travel time variability characterization of mean-excess travel time (METT), and the computationally tractable additive route cost structure of the conventional user equilibrium (UE) problem. Compared to the majority of relevant models formulated in the route domain, the link-based modeling has two desirable features on modeling flexibility and algorithmic development. First, it avoids the normal route travel time distribution assumption (uniformly imposed for all routes) that inherits from the Central Limit Theorem in most route-based models, permitting the use of any suitable link travel time distributions from empirical studies. Second, the additive route cost structure makes the L-METE model solvable by readily adapting existing UE algorithms without the need of storing/enumerating routes while avoiding the computationally demanding nonadditive shortest path problem and route flow allocations in route-based models, which is a significant benefit for large-scale network applications under uncertainty.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.018
      Issue No: Vol. 95 (2016)
       
  • Discrete intermodal freight transportation network design with route
           choice behavior of intermodal operators
    • Authors: Xinchang Wang; Qiang Meng
      Pages: 76 - 104
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Xinchang Wang, Qiang Meng
      We consider a discrete intermodal network design problem for freight transportation, in which the network planner needs to determine whether or not to build up or expand a link to minimize the total operating cost of carriers and hub operators under a general route choice model of intermodal operators. We formulate the problem as a mixed-integer nonlinear and non-convex program that involves congestion effects, piecewise linear cost functions, and a fixed-point constraint. We develop a series of relaxed and equivalent models to reduce the hardness of the problem and provide theoretical results to show the equivalences. We present two solution methods to solve the problem with one returning heuristic solutions and the other generating a globally optimal solution. We offer two numerical experiments to test the two solution algorithms and also shed light on their performance comparisons.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.11.001
      Issue No: Vol. 95 (2016)
       
  • Crowding cost estimation with large scale smart card and vehicle location
           data
    • Authors: Daniel Hörcher; Daniel J. Graham; Richard J. Anderson
      Pages: 105 - 125
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Daniel Hörcher, Daniel J. Graham, Richard J. Anderson
      Crowding discomfort is an external cost of public transport trips imposed on fellow passengers that has to be measured in order to derive optimal supply-side decisions. This paper presents a comprehensive method to estimate the user cost of crowding in terms of the equivalent travel time loss, in a revealed preference route choice framework. Using automated demand and train location data we control for fluctuations in crowding conditions on the entire length of a metro journey, including variations in the density of standing passengers and the probability of finding a seat. The estimated standing penalty is 26.5% of the uncrowded value of in-vehicle travel time. An additional passenger per square metre on average adds 11.9% to the travel time multiplier. These results are in line with earlier revealed preference values, and suggest that stated choice methods may overestimate the user cost of crowding. As a side-product, and an important input of the route choice analysis, we derive a novel passenger-to-train assignment method to recover the daily crowding and standing probability pattern in the metro network.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.015
      Issue No: Vol. 95 (2016)
       
  • A spatial generalized ordered-response model with skew normal kernel error
           terms with an application to bicycling frequency
    • Authors: Chandra R. Bhat; Sebastian Astroza; Amin S. Hamdi
      Pages: 126 - 148
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Chandra R. Bhat, Sebastian Astroza, Amin S. Hamdi
      This paper proposes a new spatial generalized ordered response model with skew-normal kernel error terms and an associated estimation method. It contributes to the spatial analysis field by allowing a flexible and parametric skew-normal distribution for the kernel error term in traditional specifications of the spatial model. The resulting model is estimated using Bhat's (2011) maximum approximate composite marginal likelihood (MACML) inference approach. The model is applied to an analysis of bicycling frequency, using data from the 2014 Puget Sound household travel survey undertaken in the Puget Sound region in the State of Washington in the United States. Our results underscore the important effects of demographic variables, as well as the miles of bicycle lanes in an individual's immediate residential neighborhood, on bicycling propensity. An interesting finding is that women and young individuals (18–34 years of age) in particular “warm up” to bicycling as more investment is made in bicycling infrastructure, thus leading not only to a larger pool of bicyclists due to bicycling infrastructure enhancements, but also a more diverse and inclusive one. The results highlight the importance of introducing social dependence effects and non-normal kernel error terms from a policy standpoint. Specifically, our results suggest that ignoring these effects, as has been done by all earlier bicycling studies, can underestimate the impacts of bicycling infrastructure improvements and public campaigns on bicycle use frequency, potentially leading to under-investments in bicycling infrastructure projects.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.014
      Issue No: Vol. 95 (2016)
       
  • A dynamic network loading model for anisotropic and congested pedestrian
           flows
    • Authors: Flurin S. Hänseler; William H.K. Lam; Michel Bierlaire; Gael Lederrey; Marija Nikolić
      Pages: 149 - 168
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Flurin S. Hänseler, William H.K. Lam, Michel Bierlaire, Gael Lederrey, Marija Nikolić
      A macroscopic loading model for multi-directional, time-varying and congested pedestrian flows is proposed. Walkable space is represented by a network of streams that are each associated with an area in which they interact. To describe this interaction, a stream-based pedestrian fundamental diagram is used that relates density and walking speed in multi-directional flow. The proposed model is applied to two different case studies. The explicit modeling of anisotropy in walking speed is shown to significantly improve the ability of the model to reproduce empirically observed walking time distributions. Moreover, the obtained model parametrization is in excellent agreement with the literature.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.017
      Issue No: Vol. 95 (2016)
       
  • Time-dependent vehicle routing problem with path flexibility
    • Authors: Yixiao Huang; Lei Zhao; Tom Van Woensel; Jean-Philippe Gross
      Pages: 169 - 195
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Yixiao Huang, Lei Zhao, Tom Van Woensel, Jean-Philippe Gross
      Conventionally, vehicle routing problems are defined on a network in which the customer locations and arcs are given. Typically, these arcs somehow represent the distances or expected travel time derived from the underlying road network. When executed, the quality of the solutions obtained from the vehicle routing problem depends largely on the quality of the road network representation. This paper explicitly considers path selection in the road network as an integrated decision in the time-dependent vehicle routing problem, denoted as path flexibility (PF). This means that any arc between two customer nodes has multiple corresponding paths in the road network (geographical graph). Hence, the decisions to make are involving not only the routing decision but also the path selection decision depending upon the departure time at the customers and the congestion levels in the relevant road network. The corresponding routing problem is a time-dependent vehicle routing problem with path flexibility (TDVRP–PF). We formulate the TDVRP–PF models under deterministic and stochastic traffic conditions. We derive important insights, relationships, and solution structures. Based on a representative testbed of instances (inspired on the road network of Beijing), significant savings are obtained in terms of cost and fuel consumption, by explicitly considering path flexibility. Having both path flexibility and time-dependent travel time seems to be a good representation of a wide range of stochasticity and dynamics in the travel time, and path flexibility serves as a natural recourse under stochastic conditions. Exploiting this observation, we employ a Route-Path approximation method generating near-optimal solutions for the TDVRP–PF under stochastic traffic conditions.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.013
      Issue No: Vol. 95 (2016)
       
  • A statistical method for estimating predictable differences between daily
           traffic flow profiles
    • Authors: F. Crawford; D.P. Watling; R.D. Connors
      Pages: 196 - 213
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): F. Crawford, D.P. Watling, R.D. Connors
      It is well known that traffic flows in road networks may vary not only within the day but also between days. Existing models including day-to-day variability usually represent all variability as unpredictable fluctuations. In reality, however, some of the differences in flows on a road may be predictable for transport planners with access to historical data. For example, flow profiles may be systematically different on Mondays compared to Fridays due to predictable differences in underlying activity patterns. By identifying days of the week or times of year where flows are predictably different, models can be developed or model inputs can be amended (in the case of day-to-day dynamical models) to test the robustness of proposed policies or to inform the development of policies which vary according to these predictably different day types. Such policies could include time-of-day varying congestion charges that themselves vary by day of the week or season, or targeting public transport provision so that timetables are more responsive to the day of the week and seasonal needs of travellers. A statistical approach is presented for identifying systematic variations in daily traffic flow profiles based on known explanatory factors such as the day of the week and the season. In order to examine day-to-day variability whilst also considering within-day dynamics, the distribution of flows throughout a day are analysed using Functional Linear Models. F-type tests for functional data are then used to compare alternative model specifications for the predictable variability. The output of the method is an average flow profile for each predictably different day type, which could include day of the week or time of year. An application to real-life traffic flow data for a two-year period is provided. The shape of the daily profile was found to be significantly different for each day of the week, including differences in the timing and width of peak flows and also the relationship between peak and inter-peak flows. Seasonal differences in flow profiles were also identified for each day of the week.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.11.004
      Issue No: Vol. 95 (2016)
       
  • An integrated optimization-simulation framework for vehicle and personnel
           relocations of electric carsharing systems with reservations
    • Authors: Burak Boyacı; Konstantinos G. Zografos; Nikolas Geroliminis
      Pages: 214 - 237
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Burak Boyacı, Konstantinos G. Zografos, Nikolas Geroliminis
      One-way electric vehicle carsharing systems are receiving increasing attention due to their mobility, environmental, and societal benefits. One of the major issues faced by the operators of these systems is the optimization of the relocation operations of personnel and vehicles. These relocation operations are essential in order to ensure that vehicles are available for use at the right place at the right time. Vehicle availability is a key indicator expressing the level of service offered to customers. However, the relocation operations, that ensure this availability, constitute a major cost component for the provision of these services. Therefore, clearly there is a trade-off between the cost of vehicle and personnel relocation and the level of service offered. In this paper we are developing, solving, and applying, in a real world context, an integrated multi-objective mixed integer linear programming (MMILP) optimization and discrete event simulation framework to optimize operational decisions for vehicle and personnel relocation in a carsharing system with reservations. We are using a clustering procedure to cope with the dimensionality of the operational problem without compromising on the quality of the obtained results. The optimization framework involves three mathematical models: (i) station clustering, (ii) operations optimization and (iii) personnel flow. The output of the optimization is used by the simulation in order to test the feasibility of the optimization outcome in terms of vehicle recharging requirements. The optimization model is solved iteratively considering the new constraints restricting the vehicles that require further charging to stay in the station until the results of the simulation are feasible in terms of electric vehicles’ battery charging levels. The application of the proposed framework using data from a real world system operating in Nice, France sheds light to trade-offs existing between the level of service offered, resource utilization, and certainty of fulfilling a trip reservation.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.007
      Issue No: Vol. 95 (2016)
       
  • Stated and revealed exit choices of pedestrian crowd evacuees
    • Authors: Milad Haghani; Majid Sarvi
      Pages: 238 - 259
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Milad Haghani, Majid Sarvi
      Introduction Understanding fundamental behavioural features regulating the escape wayfinding decisions of pedestrian humans in built environments has major implications for the safety planning and the risk-analysis of crowded public facilities. In contrast to the vast interest invested in modelling the momentary responses of evacuees to their immediate surroundings (i.e. the collision-avoidance models), their global navigation behaviour is poorly understood albeit believed to be a major determinant of the accuracy of the crowd simulation models. The primary challenge arises from the scarcity of reliable data suitable for modelling purposes causing the experimental knowledge in the field lagging substantially behind the corresponding model developments. Observations derived from fully natural emergency contexts (in the form of modelling material) are rare and collecting data in realistic experimental settings poses its own major challenges. Only few experimental modelling attempts have been reported thus far in relation with this problem primarily using the stated-choice (SC) methods. Modelling based on revealed choices (RC), however, has remained absent in this context leaving the findings of the SC experiments mostly unverified. It is still unclear whether we can reliably learn from the wayfinding choices made in virtually visualised environments without the decision-makers interacting with real individuals and the physical elements of the environment as they do in the real-world settings. Furthermore, the extent to which the findings of these experiments are specific to the particular characteristics of the environment visualised in the experiments is also unclear. Methods To bridge this gap, here we report on discrete-choice estimates derived from observations of both types. Three datasets of stated exit choices (4958 observations) were collected through face-to-face interviews with pedestrians in three public places with the experiments referring to the particular geometry of the place in which the participants were interviewed. Also, 3015 disaggregate real (or more precisely, “realistic”) exit choices were extracted through individual-level video analysis of the footage of a series of novel evacuation trials that simulate pedestrians’ emergency escape. The participants competed and interacted with real individuals in an actual crowd and made actual wayfinding decisions to make the quickest possible escape. Our particular interest was investigating the sample-to-sample variations and the context-dependence of the inferred estimates. Results We observed fairly similar parameter estimate patterns emerged from all four datasets, and identified many behavioural aspects upon which all models consistently agreed regardless of their context of origin. Moreover, despite the significant differences between the parameter scales of the four models, the SC-generated models made predictions that were reasonably similar to those of the RC model as well as to those of the model derived from the combined data. Applications Our findings provide promising evidence as to the potential applicability of the SC methods in particular as well as other forms of virtual-reality decision experiments in general as a practical, flexible and ethical approach for the continuation of research and advancing the state of knowledge in this field.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.019
      Issue No: Vol. 95 (2016)
       
  • Optimization for gate re-assignment
    • Authors: Dong Zhang; Diego Klabjan
      Pages: 260 - 284
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Dong Zhang, Diego Klabjan
      Disruptions such as adverse weather, flight delays and flight cancellations are a frequent occurrence in airport operations. A sophisticated gate assignment plan can be easily disrupted and serious consequences might be caused. Therefore, an efficient gate re-assignment methodology is of great importance for the airline industry. In this paper, we propose an efficient gate re-assignment methodology to deal with the disruptions, in which the objective function is to minimize the weighted sum of the total flight delays, the number of gate re-assignment operations and the number of missed passenger connections. Two multi-commodity network flow models are built for the pure gate re-assignment problem and the gate re-assignment problem with connecting passengers. Recognizing the inherent NP hard nature of the gate re-assignment problem, two heuristic algorithms are proposed to solve the models efficiently. The proposed models and algorithms are tested based on real-world data of a large U.S. carrier and computational results reveal that the proposed methodologies can provide high quality solutions within a short computational time.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.11.006
      Issue No: Vol. 95 (2016)
       
  • Optimization of the issuance of evacuation orders under evolving hurricane
           conditions
    • Authors: Wenqi Yi; Linda Nozick; Rachel Davidson; Brian Blanton; Brian Colle
      Pages: 285 - 304
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Wenqi Yi, Linda Nozick, Rachel Davidson, Brian Blanton, Brian Colle
      This paper develops a bi-level programming model to optimize the issuance of evacuation orders with explicit consideration of (i) the highly uncertain evolution of the storm, and (ii) the complexity of the behavioral reaction to evolving storm conditions. A solution procedure based on progressive hedging is developed. A realistic case study for the eastern portion of the state of North Carolina is presented. Through the case study we demonstrate (1) the value of developing an evacuation order policy based on the evolution of the storm in contrast to a static policy; (2) the richness in the insights that can be provided by linking the behavioral models for evacuation decision-making with dynamic traffic assignment-based network flow models in a hurricane context; and (3) the computational promise of a progressive hedging-based solution procedure to solve large instances of the model.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.10.008
      Issue No: Vol. 95 (2016)
       
  • On the analytical approximation of joint aggregate queue-length
           distributions for traffic networks: A stationary finite capacity Markovian
           network approach
    • Authors: Carolina Osorio; Carter Wang
      Pages: 305 - 339
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Carolina Osorio, Carter Wang
      This paper is motivated by recent results in the design of signal plans for Manhattan that highlight the importance of providing signal control algorithms with an analytical description of between-link dependencies. This is particularly important for congested networks prone to the occurrence of spillbacks. This paper formulates a probabilistic network model that proposes an aggregate description of the queue-length, and then approximates the joint aggregate queue-length distribution of subnetworks. The goal is to model between-queue dependencies beyond first-order moments, yet to do so in a tractable manner such that these techniques can be used for optimization purposes. This paper models an urban road network as a finite space capacity Markovian queueing network. Exact evaluation of the stationary joint queue-length distribution of such a network with arbitrary size and topology can be obtained numerically. Nonetheless, the main challenge to such an approach remains the dimensionality of the network state space, which is exponential in the number of queues. This paper proposes to address the dimensionality issue by: 1) describing the state of the network aggregately, and 2) decomposing the network into overlapping subnetworks. We propose an analytical approximation of the stationary aggregate joint queue-length distribution of a subnetwork. The model consists of a system of nonlinear equations with a dimension that is linear, instead of exponential, in the number of queues and that is independent of the space capacity of the individual queues. The method is derived for tandem Markovian finite capacity queueing networks. The proposed model is computationally tractable and scalable, it can be efficiently used for the higher-order distributional analysis of large-scale networks. The model is validated versus simulation estimates and versus other decomposition methods. We then use it to address an urban traffic control problem. We show the added value of accounting for higher-order spatial between-queue dependency information in the control of congested urban networks.

      PubDate: 2016-11-21T08:54:35Z
      DOI: 10.1016/j.trb.2016.07.013
      Issue No: Vol. 95 (2016)
       
  • A hybrid large neighborhood search for the static multi-vehicle
           bike-repositioning problem
    • Authors: Sin C. Ho; W.Y. Szeto
      Pages: 340 - 363
      Abstract: Publication date: January 2017
      Source:Transportation Research Part B: Methodological, Volume 95
      Author(s): Sin C. Ho, W.Y. Szeto
      This paper addresses the multi-vehicle bike-repositioning problem, a pick-up and delivery vehicle routing problem that arises in connection with bike-sharing systems. Bike-sharing is a green transportation mode that makes it possible for people to use shared bikes for travel. Bikes are retrieved and parked at any of the stations within the bike-sharing network. One major challenge is that the demand for and supply of bikes are not always matched. Hence, vehicles are used to pick up bikes from surplus stations and transport them to deficit stations to satisfy a particular service level. This operation is called a bike-repositioning problem. In this paper, we propose a hybrid large neighborhood search for solving the problem. Several removal and insertion operators are proposed to diversify and intensify the search. A simple tabu search is further applied to the most promising solutions. The heuristic is evaluated on three sets of instances with up to 518 stations and five vehicles. The results of computational experiments indicate that the heuristic outperforms both CPLEX and the math heuristic proposed by Forma et al. (2015) [Transportation Research Part B 71: 230–247]. The average improvement of our heuristic over the math heuristic is 1.06%, and it requires only a small fraction of the computation time.

      PubDate: 2016-11-28T01:31:20Z
      DOI: 10.1016/j.trb.2016.11.003
      Issue No: Vol. 95 (2016)
       
  • Proactive route guidance to avoid congestion
    • Authors: E. Angelelli; I. Arsik; V. Morandi; M. Savelsbergh; M.G. Speranza
      Pages: 1 - 21
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): E. Angelelli, I. Arsik, V. Morandi, M. Savelsbergh, M.G. Speranza
      We propose a proactive route guidance approach that integrates a system perspective: minimizing congestion, and a user perspective: minimizing travel inconvenience. The approach assigns paths to users so as to minimize congestion while not increasing their travel inconvenience too much. A maximum level of travel inconvenience is ensured and a certain level of fairness is maintained by limiting the set of considered paths for each Origin-Destination pair to those whose relative difference with respect to the shortest (least-duration) path, called travel inconvenience, is below a given threshold. The approach hierarchically minimizes the maximum arc utilization and the weighted average experienced travel inconvenience. Minimizing the maximum arc utilization in the network, i.e., the ratio of the number of vehicles entering an arc per time unit and the maximum number of vehicles per time unit at which vehicles can enter the arc and experience no slowdown due to congestion effects, is a system-oriented objective, while minimizing the weighted average experienced travel inconvenience, i.e., the average travel inconvenience over all eligible paths weighted by the number of vehicles per time unit that traverse the path, is a user-oriented objective. By design, to ensure computational efficiency, the approach only solves linear programming models. In a computational study using benchmark instances reflecting a road infrastructure encountered in many cities, we analyze, for different levels of maximum travel inconvenience and, the minimum maximum arc utilization and the weighted average experienced travel inconvenience. We find that accepting relatively small levels of maximum travel inconvenience can result in a significant reduction, or avoiding, of congestion.

      PubDate: 2016-09-17T12:17:20Z
      DOI: 10.1016/j.trb.2016.08.015
      Issue No: Vol. 94 (2016)
       
  • Envy-minimizing pareto efficient intersection control with brokered
           utility exchanges under user heterogeneity
    • Authors: Roger Lloret-Batlle; R. Jayakrishnan
      Pages: 22 - 42
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Roger Lloret-Batlle, R. Jayakrishnan
      We propose PEXIC (Priced EXchanges in Intersection Control), a new concept and algorithm for traffic signal control that incorporates user heterogeneity on value of delay savings. The algorithm assigns phases with associated delays, taking into account the vehicle travelers’ values for experienced delay. Applying principles of envy-freeness, we develop a pricing scheme that addresses fairness by minimizing user envy via compensatory monetary transfers among users. PEXIC is Pareto efficient and budget balanced, and thus financially self-sustainable without external subsidy. The optimization is solved sequentially on a rolling horizon basis: first the phasing, and next the pricing. PEXIC achieves significant cost reductions for a large range of volumes and users’ value heterogeneity levels. Inclusion of user heterogeneity also proved to be fairer than standard delay minimization that disregards individual vehicles’ values for delay savings. Furthermore, we show that arbitrage is not possible, thus there are no incentives to drive just to collect those payments. The method used has polynomial complexity and it is suitable for real-world implementation.

      PubDate: 2016-09-17T12:17:20Z
      DOI: 10.1016/j.trb.2016.08.014
      Issue No: Vol. 94 (2016)
       
  • Autonomous cars and dynamic bottleneck congestion: The effects on
           capacity, value of time and preference heterogeneity
    • Authors: Vincent A.C. van den Berg; Erik T. Verhoef
      Pages: 43 - 60
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Vincent A.C. van den Berg, Erik T. Verhoef
      ‘Autonomous cars’ are cars that can drive themselves without human control. Autonomous cars can safely drive closer together than cars driven by humans, thereby possibly increasing road capacity. By allowing drivers to perform other activities in the vehicle, they may reduce the value of travel time losses (VOT). We investigate the effects of autonomous cars using a dynamic equilibrium model of congestion that captures three main elements: the resulting increase in capacity, the decrease in the VOT for those who acquire one and the implications of the resulting changes in the heterogeneity of VOTs. We do so for three market organizations: private monopoly, perfect competition and public supply. Even though an increased share of autonomous cars raises average capacity, it may hurt existing autonomous car users as those who switch to an autonomous car will impose increased congestion externalities due to their altered departure time behaviour. Depending on which effect dominates, switching to an autonomous vehicle may impose a net negative or positive externality. Often public supply leads to 100% autonomous cars, but it may be optimal to have a mix of car types, especially when there is a net negative externality. With a positive (negative) externality, perfect competition leads to an undersupply (oversupply) of autonomous cars, and a public supplier needs to subsidise (tax) autonomous cars to maximise welfare. A monopolist supplier ignores the capacity effect and adds a mark-up to its price.

      PubDate: 2016-09-22T07:43:35Z
      DOI: 10.1016/j.trb.2016.08.018
      Issue No: Vol. 94 (2016)
       
  • A disjunctive convex programming approach to the pollution-routing problem
    • Authors: Ricardo Fukasawa; Qie He; Yongjia Song
      Pages: 61 - 79
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Ricardo Fukasawa, Qie He, Yongjia Song
      The pollution-routing problem (PRP) aims to determine a set of routes and speed over each leg of the routes simultaneously to minimize the total operational and environmental costs. A common approach to solve the PRP exactly is through speed discretization, i.e., assuming that speed over each arc is chosen from a prescribed set of values. In this paper, we keep speed as a continuous decision variable within an interval and propose new formulations for the PRP. In particular, we build two mixed-integer convex optimization models for the PRP, by employing tools from disjunctive convex programming. These are the first arc-based formulations for the PRP with continuous speed. We also derive several families of valid inequalities to further strengthen both models. We test the proposed formulations on benchmark instances. Some instances are solved to optimality for the first time.

      PubDate: 2016-09-28T07:54:13Z
      DOI: 10.1016/j.trb.2016.09.006
      Issue No: Vol. 94 (2016)
       
  • A new random utility model with flexible correlation pattern and
           closed-form covariance expression: The CoRUM
    • Authors: Andrea Papola
      Pages: 80 - 96
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Andrea Papola
      This paper proposes a new random utility model characterised by a cumulative distribution function (cdf) obtained as a finite mixture of different cdfs. This entails that choice probabilities, covariances and elasticities of this model are also a finite mixture of choice probabilities, covariances and elasticities of the mixing models. As a consequence, by mixing nested logit cdfs, a model is generated with closed-form expressions for choice probabilities, covariances and elasticities and with, potentially, a very flexible correlation pattern. Importantly, the closed-form covariance expression opens up interesting application possibilities in some special choice contexts, like route choice, where prior expectations in terms of the covariance matrix can be formulated.

      PubDate: 2016-09-28T07:54:13Z
      DOI: 10.1016/j.trb.2016.09.008
      Issue No: Vol. 94 (2016)
       
  • A branch-and-price approach for solving the train unit scheduling problem
    • Authors: Zhiyuan Lin; Raymond S.K. Kwan
      Pages: 97 - 120
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Zhiyuan Lin, Raymond S.K. Kwan
      We propose a branch-and-price approach for solving the integer multicommodity flow model for the network-level train unit scheduling problem (TUSP). Given a train operator’s fixed timetable and a fleet of train units of different types, the TUSP aims at determining an assignment plan such that each train trip in the timetable is appropriately covered by a single or coupled train units. The TUSP is challenging due to its complex nature. Our branch-and-price approach includes a branching system with multiple branching rules for satisfying real-world requirements that are difficult to realize by linear constraints, such as unit type coupling compatibility relations and locations banned for coupling/decoupling. The approach also benefits from an adaptive node selection method, a column inheritance strategy and a feature of estimated upper bounds with node reservation functions. The branch-and-price solver designed for TUSP is capable of handling instances of up to about 500 train trips. Computational experiments were conducted based on real-world problem instances from First ScotRail. The results are satisfied by rail practitioners and are generally competitive or better than the manual ones.

      PubDate: 2016-09-28T07:54:13Z
      DOI: 10.1016/j.trb.2016.09.007
      Issue No: Vol. 94 (2016)
       
  • Designing a supply chain resilient to major disruptions and supply/demand
           interruptions
    • Authors: Armin Jabbarzadeh; Behnam Fahimnia; Jiuh-Biing Sheu; Hani Shahmoradi Moghadam
      Pages: 121 - 149
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Armin Jabbarzadeh, Behnam Fahimnia, Jiuh-Biing Sheu, Hani Shahmoradi Moghadam
      Global supply chains are more than ever under threat of major disruptions caused by devastating natural and man-made disasters as well as recurrent interruptions caused by variations in supply and demand. This paper presents a hybrid robust-stochastic optimization model and a Lagrangian relaxation solution method for designing a supply chain resilient to (1) supply/demand interruptions and (2) facility disruptions whose risk of occurrence and magnitude of impact can be mitigated through fortification investments. We study a realistic problem where a disruption can cause either a complete facility shutdown or a reduced supply capacity. The probability of disruption occurrence is expressed as a function of facility fortification investment for hedging against potential disruptions in the presence of certain budgetary constraints. Computational experiments and thorough sensitivity analyses are completed using some of the existing widely-used datasets. The performance of the proposed model is also examined using a Monte Carlo simulation method. To explore the practical application of the proposed model and methodology, a real world case example is discussed which addresses mitigating the risk of facility fires in an actual oil production company. Our analysis and investigation focuses on exploring the extent to which supply chain design decisions are influenced by factors such as facility fortification strategies, a decision maker's conservatism degree, demand fluctuations, supply capacity variations, and budgetary constraints.

      PubDate: 2016-10-04T13:37:22Z
      DOI: 10.1016/j.trb.2016.09.004
      Issue No: Vol. 94 (2016)
       
  • Parametric search for the bi-attribute concave shortest path problem
    • Authors: Yuli Zhang; Zuo-Jun Max Shen; Shiji Song
      Pages: 150 - 168
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Yuli Zhang, Zuo-Jun Max Shen, Shiji Song
      A bi-attribute concave shortest path (BC-SP) problem seeks to find an optimal path in a bi-attribute network that minimizes a linear combination of two path costs, one of which is evaluated by a nondecreasing concave function. Due to the nonadditivity of its objective function, Bellman’s principle of optimality does not hold. This paper proposes a parametric search method to solve the BC-SP problem, which only needs to solve a series of shortest path problems, i.e., the parameterized subproblems (PSPs). Several techniques are developed to reduce both the number of PSPs and the computation time for these PSPs. Specifically, we first identify two properties of the BC-SP problem to guide the parametric search using the gradient and concavity of its objective function. Based on the properties, a monotonic descent search (MDS) and an intersection point search (IPS) are proposed. Second, we design a speedup label correcting (LC) algorithm, which uses optimal solutions of previously solved PSPs to reduce the number of labeling operations for subsequent PSPs. The MDS, IPS and speedup LC techniques are embedded into a branch-and-bound based interval search to guarantee optimality. The performance of the proposed method is tested on the mean-standard deviation shortest path problem and the route choice problem with a quadratic disutility function. Experiments on both real transportation networks and grid networks show that the proposed method reduces the computation time of existing algorithms by one to two orders of magnitude.

      PubDate: 2016-10-04T13:37:22Z
      DOI: 10.1016/j.trb.2016.09.009
      Issue No: Vol. 94 (2016)
       
  • The two-echelon time-constrained vehicle routing problem in
           linehaul-delivery systems
    • Authors: Hongqi Li; Lu Zhang; Tan Lv; Xinyu Chang
      Pages: 169 - 188
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Hongqi Li, Lu Zhang, Tan Lv, Xinyu Chang
      Most of the studies address issues relating to the delivery from satellites to customers, which is throughout the end part of the linehaul-delivery system. Differing from the long-term strategic problems including the two-echelon vehicle routing problem (2E-VRP), the two-echelon location routing problem (2E-LRP) and the truck and trailer routing problem (TTRP) which make location decisions in depots or satellites, the paper introduces a short-term tactical problem named the two-echelon time-constrained vehicle routing problem in linehaul-delivery systems (2E-TVRP) that does not involve location decisions. The linehaul level and the delivery level are linked through city distribution centers (CDCs) located on the outskirts of cities. The 2E-TVRP has inter-CDC linehaul on the first level and urban delivery from CDCs to satellites on the second level. Vehicle routes on different levels are interacted by time constraints. A mixed integer nonlinear programming model for the 2E-TVRP is put forward, and a mixed integer linear programming model is used as the benchmark model. The Clarke and Wright savings heuristic algorithm (CW) improved by a local search phase is adopted. The 2E-TVRP formulations and the heuristic algorithm are tested by using 140 randomly-generated instances with up to 10 CDCs and 500 satellites. The computational results indicate that the heuristic can effectively solve various instances of the 2E-TVRP.

      PubDate: 2016-10-12T02:54:40Z
      DOI: 10.1016/j.trb.2016.09.012
      Issue No: Vol. 94 (2016)
       
  • Finding the k reliable shortest paths under travel time uncertainty
    • Authors: Bi Yu Chen; Qingquan Li; William H.K. Lam
      Pages: 189 - 203
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Bi Yu Chen, Qingquan Li, William H.K. Lam
      This paper investigates the problem of finding the K reliable shortest paths (KRSP) in stochastic networks under travel time uncertainty. The KRSP problem extends the classical K loopless shortest paths problem to the stochastic networks by explicitly considering travel time reliability. In this study, a deviation path approach is established for finding K α-reliable paths in stochastic networks. A deviation path algorithm is proposed to exactly solve the KRSP problem in large-scale networks. The A* technique is introduced to further improve the KRSP finding performance. A case study using real traffic information is performed to validate the proposed algorithm. The results indicate that the proposed algorithm can determine KRSP under various travel time reliability values within reasonable computational times. The introduced A* technique can significantly improve KRSP finding performance.

      PubDate: 2016-10-12T02:54:40Z
      DOI: 10.1016/j.trb.2016.09.013
      Issue No: Vol. 94 (2016)
       
  • The impact of source terms in the variational representation of traffic
           flow
    • Authors: Jorge A. Laval; Guillaume Costeseque; Bargavarama Chilukuri
      Pages: 204 - 216
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Jorge A. Laval, Guillaume Costeseque, Bargavarama Chilukuri
      This paper revisits the variational theory of traffic flow, now under the presence of continuum lateral inflows and outflows to the freeway. It is found that a VT solution apply only in Eulerian coordinates when source terms are exogenous, but not when they are a function of traffic conditions, e.g. as per a merge model. In discrete time, however, these dependencies become exogenous, which allowed us to propose improved numerical solution methods. In space-Lagrangian and time-Lagrangian coordinates, VT solutions may not apply even if source terms are exogenous.

      PubDate: 2016-10-12T02:54:40Z
      DOI: 10.1016/j.trb.2016.09.011
      Issue No: Vol. 94 (2016)
       
  • Eco-system optimal time-dependent flow assignment in a congested network
    • Authors: Chung-Cheng Lu; Jiangtao Liu; Yunchao Qu; Srinivas Peeta; Nagui M. Rouphail; Xuesong Zhou
      Pages: 217 - 239
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Chung-Cheng Lu, Jiangtao Liu, Yunchao Qu, Srinivas Peeta, Nagui M. Rouphail, Xuesong Zhou
      This research addresses the eco-system optimal dynamic traffic assignment (ESODTA) problem which aims to find system optimal eco-routing or green routing flows that minimize total vehicular emission in a congested network. We propose a generic agent-based ESODTA model and a simplified queueing model (SQM) that is able to clearly distinguish vehicles’ speed in free-flow and congested conditions for multi-scale emission analysis, and facilitates analyzing the relationship between link emission and delay. Based on the SQM, an expanded space-time network is constructed to formulate the ESODTA with constant bottleneck discharge capacities. The resulting integer linear model of the ESODTA is solved by a Lagrangian relaxation-based algorithm. For the simulation-based ESODTA, we present the column-generation-based heuristic, which requires link and path marginal emissions in the embedded time-dependent least-cost path algorithm and the gradient-projection-based descent direction method. We derive a formula of marginal emission which encompasses the marginal travel time as a special case, and develop an algorithm for evaluating path marginal emissions in a congested network. Numerical experiments are conducted to demonstrate that the proposed algorithm is able to effectively obtain coordinated route flows that minimize the system-wide vehicular emission for large-scale networks.

      PubDate: 2016-10-12T02:54:40Z
      DOI: 10.1016/j.trb.2016.09.015
      Issue No: Vol. 94 (2016)
       
  • On accommodating spatial interactions in a Generalized Heterogeneous Data
           Model (GHDM) of mixed types of dependent variables
    • Authors: Chandra R. Bhat; Abdul R. Pinjari; Subodh K. Dubey; Amin S. Hamdi
      Pages: 240 - 263
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Chandra R. Bhat, Abdul R. Pinjari, Subodh K. Dubey, Amin S. Hamdi
      We develop an econometric framework for incorporating spatial dependence in integrated model systems of latent variables and multidimensional mixed data outcomes. The framework combines Bhat's Generalized Heterogeneous Data Model (GHDM) with a spatial (social) formulation to parsimoniously introduce spatial (social) dependencies through latent constructs. The applicability of the spatial GHDM framework is demonstrated through an empirical analysis of spatial dependencies in a multidimensional mixed data bundle comprising a variety of household choices – household commute distance, residential location (density) choice, vehicle ownership, parents’ commute mode choice, and children's school mode choice – along with other measurement variables for two latent constructs – parent's safety concerns about children walking/biking to school and active lifestyle propensity. The GHDM framework identifies an intricate web of causal relationships and endogeneity among the endogenous variables. Furthermore, the spatial (social) version of the GHDM model reveals a high level of spatial (social) dependency in the latent active lifestyle propensity of different households and moderate level of spatial dependency in parents’ safety concerns. Ignoring spatial (social) dependencies in the empirical model results in inferior data fit, potential bias and statistical insignificance of the parameters corresponding to nominal variables, and underestimation of policy impacts.

      PubDate: 2016-10-12T02:54:40Z
      DOI: 10.1016/j.trb.2016.09.002
      Issue No: Vol. 94 (2016)
       
  • Preferences for travel time under risk and ambiguity: Implications in path
           selection and network equilibrium
    • Authors: Jin Qi; Melvyn Sim; Defeng Sun; Xiaoming Yuan
      Pages: 264 - 284
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Jin Qi, Melvyn Sim, Defeng Sun, Xiaoming Yuan
      In this paper, we study the preferences for uncertain travel times in which probability distributions may not be fully characterized. In evaluating an uncertain travel time, we explicitly distinguish between risk, where the probability distribution is precisely known, and ambiguity, where it is not. In particular, we propose a new criterion called ambiguity-aware CARA travel time (ACT) for evaluating uncertain travel times under various attitudes of risk and ambiguity, which is a preference based on blending the Hurwicz criterion and Constant Absolute Risk Aversion (CARA). More importantly, we show that when the uncertain link travel times are independently distributed, finding the path that minimizes travel time under the ACT criterion is essentially a shortest path problem. We also study the implications on Network Equilibrium (NE) model where travelers on the traffic network are characterized by their knowledge of the network uncertainty as well as their risk and ambiguity attitudes under the ACT. We derive and analyze the existence and uniqueness of solutions under NE. Finally, we obtain the Price of Anarchy that characterizes the inefficiency of this new equilibrium. The computational study suggests that as uncertainty increases, the influence of selfishness on inefficiency diminishes.

      PubDate: 2016-10-16T15:47:55Z
      DOI: 10.1016/j.trb.2016.09.014
      Issue No: Vol. 94 (2016)
       
  • Carrier collaboration with endogenous fleets and load factors when
           networks are complementary
    • Authors: Achim I. Czerny; Vincent A.C. van den Berg; Erik T. Verhoef
      Pages: 285 - 297
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Achim I. Czerny, Vincent A.C. van den Berg, Erik T. Verhoef
      This paper analyzes the effect of carrier collaboration on fleet capacity, fleet structures in terms of the number and the size of vehicles, and load factors. The model features complementary networks, scheduling, price elastic demands, and demand uncertainty. For the case of a given number of vehicles, the analysis shows that carrier collaboration increases vehicle sizes (thus, fleet capacity) if marginal seat costs are low while fleet capacity remains unchanged if marginal seat costs are high. If both vehicle sizes and vehicle numbers can be varied, then collaboration will always increase vehicle numbers and fleet capacity, while the effects on vehicle sizes and, thus, also load factors, are ambiguous and therewith hard to predict. Numerical simulations indicate that collaboration increases expected load factors also when the number of vehicles is endogenous.

      PubDate: 2016-10-16T15:47:55Z
      DOI: 10.1016/j.trb.2016.09.005
      Issue No: Vol. 94 (2016)
       
  • Optimal public transport networks for a general urban structure
    • Authors: Andrés Fielbaum; Sergio Jara-Diaz; Antonio Gschwender
      Pages: 298 - 313
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Andrés Fielbaum, Sergio Jara-Diaz, Antonio Gschwender
      Using a quite general parametric description of an urban setting in terms of its network, centers structure and demand pattern, we find the optimal spatial arrangement of transit lines out of four basic strategic competing options: direct, exclusive, hub-and-spoke and feeder-trunk. We identify clearly the relation between the characteristics of both the urban setting (mostly monocentric, polycentric or dispersed) and the users (transfer penalty, patronage) with the line structure that shows the best response.

      PubDate: 2016-10-16T15:47:55Z
      DOI: 10.1016/j.trb.2016.10.003
      Issue No: Vol. 94 (2016)
       
  • Constrained optimization and distributed computation based car following
           control of a connected and autonomous vehicle platoon
    • Authors: Siyuan Gong; Jinglai Shen; Lili Du
      Pages: 314 - 334
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Siyuan Gong, Jinglai Shen, Lili Du
      Motivated by the advancement in connected and autonomous vehicle technologies, this paper develops a novel car-following control scheme for a platoon of connected and autonomous vehicles on a straight highway. The platoon is modeled as an interconnected multi-agent dynamical system subject to physical and safety constraints, and it uses the global information structure such that each vehicle shares information with all the other vehicles. A constrained optimization based control scheme is proposed to ensure an entire platoon’s transient traffic smoothness and asymptotic dynamic performance. By exploiting the solution properties of the underlying optimization problem and using primal-dual formulation, this paper develops dual based distributed algorithms to compute optimal solutions with proven convergence. Furthermore, the asymptotic stability of the unconstrained linear closed-loop system is established. These stability analysis results provide a principle to select penalty weights in the underlying optimization problem to achieve the desired closed-loop performance for both the transient and the asymptotic dynamics. Extensive numerical simulations are conducted to validate the efficiency of the proposed algorithms.

      PubDate: 2016-10-16T15:47:55Z
      DOI: 10.1016/j.trb.2016.09.016
      Issue No: Vol. 94 (2016)
       
  • Day-to-day traffic dynamics considering social interaction: From
           individual route choice behavior to a network flow model
    • Authors: Fangfang Wei; Ning Jia; Shoufeng Ma
      Pages: 335 - 354
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Fangfang Wei, Ning Jia, Shoufeng Ma
      Social interaction is increasingly recognized as an important factor that influences travelers’ behaviors. It remains challenging to incorporate its effect into travel choice behaviors, although there has been some research into this area. Considering random interaction among travelers, we model travelers’ day-to-day route choice under the uncertain traffic condition. We further explore the evolution of network flow based on the individual-level route choice model, though that travelers are heterogeneous in decision-making under the random-interaction scheme. We analyze and prove the existence of equilibrium and the stability of equilibrium. We also analyzed and described the specific properties of the network flow evolution and travelers’ behaviors. Two interesting phenomena are found in this study. First, the number of travelers that an individual interacts with can affect his route choice strategy. However, the interaction count exerts no influence on the evolution of network flow at the aggregate-level. Second, when the network flow reaches equilibrium, the route choice strategy at the individual-level is not necessarily invariable. Finally, two networks are used as numerical examples to show model properties and to demonstrate the two study phenomena. This study improves the understanding of travelers’ route choice dynamics and informs how the network flow evolves under the influence of social interaction.

      PubDate: 2016-10-16T15:47:55Z
      DOI: 10.1016/j.trb.2016.10.002
      Issue No: Vol. 94 (2016)
       
  • A multi-period dial-a-ride problem with driver consistency
    • Authors: Kris Braekers; Attila A. Kovacs
      Pages: 355 - 377
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Kris Braekers, Attila A. Kovacs
      Dial-a-ride services provide disabled and elderly people with a personalized mode of transportation to preserve their mobility. Typically, several users with different pickup and dropoff locations are transported on a vehicle simultaneously. The focus in dial-a-ride problems (DARPs) is mainly on minimizing routing cost. Service quality has been taken into account in the models by imposing time windows and limiting the maximum ride time of each user. We extend the classical DARP by an additional feature of service quality referred to as driver consistency. Customers of dial-a-ride services are often sensitive to changes in their daily routine. This aspect includes the person who is providing the transportation service, i.e., the driver of the vehicle. Our problem, called the driver consistent dial-a-ride problem (DC-DARP), considers driver consistency by bounding the maximum number of different drivers that transport a user over a multi-period planning horizon. We propose different formulations of the problem and examine their efficiency when applied in a Branch-and-Cut fashion. Additionally, we develop a large neighborhood search algorithm that generates near-optimal solutions in a short amount of time. Over 1000 instances are generated with close reference to real world scenarios. Extensive computational experiments are conducted in order to assess the quality of the solution approaches and to provide insights into the new problem. Results reveal that the cost of offering driver consistency varies greatly in magnitude. Depending on the instance, the cost of assigning one driver to each user can be up to 27.98% higher compared to a low-cost solution. However, routing cost increases by not more than 5.80% if users are transported by at least two drivers.

      PubDate: 2016-10-31T03:31:49Z
      DOI: 10.1016/j.trb.2016.09.010
      Issue No: Vol. 94 (2016)
       
  • Robust models for transportation service network design
    • Authors: ManWo Ng; Hong K. Lo
      Pages: 378 - 386
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): ManWo Ng, Hong K. Lo
      In this paper robust models are presented for the transportation service network design problem, using the ferry service network design problem as an example application. The base assumption is that only the mean and an upper bound on the passenger demand are known. In one robust model, this information is supplemented by a lower bound on the demand, whereas in a second robust model, the assumption is made that the variance of the demand is known, in addition to the mean and upper bound. The relationship between the two models is investigated and characterized analytically. A case study using the ferry service in Hong Kong is provided to illustrate the models.

      PubDate: 2016-10-31T03:31:49Z
      DOI: 10.1016/j.trb.2016.10.001
      Issue No: Vol. 94 (2016)
       
  • A new look at the rate of change of energy consumption with respect to
           journey time on an optimal train journey
    • Authors: Phil Howlett
      Pages: 387 - 408
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Phil Howlett
      We present a new derivation of a key formula for the rate of change of energy consumption with respect to journey time on an optimal train journey. We use a standard mathematical model (Albrecht et al., 2015b; Howlett, 2000; Howlett et al., 2009; Khmelnitsky, 2000; Liu and Golovitcher, 2003) to define the problem and show by explicit calculation of switching points that the formula also applies for all basic control subsequences within the optimal strategy on appropriately chosen fixed track segments. The rate of change was initially derived as a known strictly decreasing function of the optimal driving speed in a text edited by  Isayev (1987, Section 14.2, pp 259–260) using an empirical resistance function. An elegant derivation by Liu and Golovitcher (2003, Section 3) with a general resistance function required an underlying assumption that the optimal strategy is unique and that the associated optimal driving speed is a strictly decreasing and continuous function of journey time. An earlier proof of uniqueness (Khmelnitsky, 2000) showed that the optimal driving speed decreases when journey time increases. A subsequent constructive proof (Albrecht et al., 2013a, 2015c) used a local energy minimization principle to find optimal switching points and show explicitly that the optimal driving speed is a strictly decreasing and continuous function of journey time. Our new derivation of the key formula also uses the local energy minimization principle and depends on the following observations. If no speed limits are imposed the optimal strategy consists of a finite sequence of phases with only five permissible control modes. By considering all basic control subsequences and subdividing the track into suitably chosen fixed segments we show that the key formula is valid on each individual segment. The formula is extended to the entire journey by summation. The veracity of the formula is demonstrated with an elementary but realistic example.

      PubDate: 2016-10-31T03:31:49Z
      DOI: 10.1016/j.trb.2016.10.004
      Issue No: Vol. 94 (2016)
       
  • Simultaneous passenger train routing and timetabling using an efficient
           train-based Lagrangian relaxation decomposition
    • Authors: Wenliang Zhou; Hualiang Teng
      Pages: 409 - 439
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Wenliang Zhou, Hualiang Teng
      This paper focuses on the simultaneous passenger train routing and timetabling problem on the rail network consisting of both unidirectional and bidirectional tracks using an efficient train-based Lagrangian relaxation decomposition. We first build an integer linear programming model with many 0–1 binary and non-negative integer decision variables, after then reformulate it as a train path-choice model for providing an easier train-based Lagrangian relaxation decomposition mechanism based on the construction of space-time discretized network extending from node-cell-based rail network. Moreover, through reformulating safety usage interval restrictions with a smaller number of constraints in this reformulated model, the train-based decomposition needs fewer Lagrangian multipliers to relax these constraints. On the basis of this decomposition, a solving framework including a heuristic algorithm is proposed to simultaneously optimize both the dual and feasible solutions. A set of numerical experiments demonstrate the proposed Lagrangian relaxation decomposition approach has better performances in terms of minimizing both train travel time and computational times.

      PubDate: 2016-10-31T03:31:49Z
      DOI: 10.1016/j.trb.2016.10.010
      Issue No: Vol. 94 (2016)
       
  • Discrete choice with spatial correlation: A spatial autoregressive binary
           probit model with endogenous weight matrix (SARBP-EWM)
    • Authors: Yiwei Zhou; Xiaokun Wang; José Holguín-Veras
      Pages: 440 - 455
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Yiwei Zhou, Xiaokun Wang, José Holguín-Veras
      Discrete choice modeling is widely applied in transportation studies. However, the need to consider correlation between observations creates a challenge. In spatial econometrics, a spatial lag term with a pre-defined weight matrix is often used to capture such a correlation. In most previous studies, the weight matrix is assumed to be exogenous. However, this assumption is invalid in many cases, leading to biased and inconsistent parameter estimates. Although some attempts have been made to address the endogenous weight matrix issue, none has focused on discrete choice modeling. This paper fills an existing gap by developing a Spatial Autoregressive Binary Probit Model with Endogenous Weight Matrix (SARBP-EWM). The SARBP-EWM model explicitly considers the endogeneity by using two equations whose error terms are correlated. Markov Chain Monte Carlo (MCMC) method is used to estimate the model. Model validation with simulated data shows that the model parameters can converge to their true values and the endogenous weight matrix can be reliably recovered. The model is then applied to a simplified firm relocation choice problem, assuming that similar size firms influence one another. The model quantifies the peer effect, and takes into consideration other independent variables including industry type and population density. The estimation results suggest that peer influence among firms indeed affect their relocation choices. The application results offer important insights into business location choice and can inform future policy making. The sample size for applying the model is currently limited to hundreds of observations. This paper contributes to the existing literature on discrete choice modeling and spatial econometrics. It provides a new tool to discover spatial correlations that are hidden in a wide range of transportation issues, such as land development, location choice, and various travel behavior. Those hidden spatial correlations are otherwise difficult to identify and estimation results may be biased. Establishing a new model that explicitly considers endogenous weight matrix and applying the model to a real life transportation issue represent a significant contribution to the body of literature.

      PubDate: 2016-10-31T03:31:49Z
      DOI: 10.1016/j.trb.2016.10.009
      Issue No: Vol. 94 (2016)
       
  • High-speed rail and air transport competition and cooperation: A vertical
           differentiation approach
    • Authors: Wenyi Xia; Anming Zhang
      Pages: 456 - 481
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Wenyi Xia, Anming Zhang
      This paper considers vertical differentiation between air transport and high-speed rail (HSR) with different ranges of travel distance to analyze the air-HSR competition effects on fares, traffic volumes and welfare, as well as the conditions under which air-HSR cooperation is welfare-enhancing. The analysis is conducted in a hub-and-spoke network with a network carrier, an HSR operator, and a spoke airline, taking into account potential hub airport capacity constraint. We find that air-HSR competition in the connecting market may result in the network airline charging an excessively high price in the HSR-inaccessible market. This effect is present even when the HSR-inaccessible route is a duopoly-airline market. On the other hand, air-HSR cooperation increases fares in the connecting market, and an improvement in rail speed or air-HSR connecting time reduces airfare on the routes where HSR and the airline compete. When the airline cannot serve all the markets due to limited hub airport capacity, it would withdraw from the market in which it has less competitive advantage over HSR. Finally, air-HSR cooperation is more likely to be welfare-improving when the hub airport is capacity constrained, and when either air transport or HSR exhibits strong economies of traffic density.

      PubDate: 2016-11-14T06:47:10Z
      DOI: 10.1016/j.trb.2016.10.006
      Issue No: Vol. 94 (2016)
       
  • The key principles of optimal train control—Part 1: Formulation of the
           model, strategies of optimal type, evolutionary lines, location of optimal
           switching points
    • Authors: Amie Albrecht; Phil Howlett; Peter Pudney; Xuan Vu; Peng Zhou
      Pages: 482 - 508
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Amie Albrecht, Phil Howlett, Peter Pudney, Xuan Vu, Peng Zhou
      We discuss the problem of finding an energy-efficient driving strategy for a train journey on an undulating track with steep grades subject to a maximum prescribed journey time. We review the state-of-the-art and establish the key principles of optimal train control for a general model with continuous control. The model with discrete control is not considered. We assume only that the tractive and braking control forces are bounded by non-increasing speed-dependent magnitude constraints and that the rate of energy dissipation from frictional resistance is given by a non-negative strictly convex function of speed. Partial cost recovery from regenerative braking is allowed. The cost of the strategy is the mechanical energy required to drive the train. Minimising the mechanical energy is an effective way of reducing the fuel or electrical energy used by the traction system. The paper is presented in two parts. In Part 1 we discuss formulation of the model, determine the characteristic optimal control modes, study allowable control transitions, establish the existence of optimal switching points and consider optimal strategies with speed limits. We find algebraic formulae for the adjoint variables in terms of speed on track with piecewise-constant gradient and draw phase plots of the associated optimal evolutionary lines for the state and adjoint variables. In Part 2 we will establish important integral forms of the necessary conditions for optimal switching, find general bounds on the positions of the optimal switching points, justify the local energy minimization principle and show how these ideas are used to calculate optimal switching points. We will prove that an optimal strategy always exists and use a perturbation analysis to show the strategy is unique. Finally we will discuss computational techniques in realistic examples with steep gradients and describe typical optimal strategies for a complete journey.

      PubDate: 2016-11-28T01:31:20Z
      DOI: 10.1016/j.trb.2015.07.023
      Issue No: Vol. 94 (2016)
       
  • The key principles of optimal train control—Part 2: Existence of an
           optimal strategy, the local energy minimization principle, uniqueness,
           computational techniques
    • Authors: Amie Albrecht; Phil Howlett; Peter Pudney; Xuan Vu; Peng Zhou
      Pages: 509 - 538
      Abstract: Publication date: December 2016
      Source:Transportation Research Part B: Methodological, Volume 94
      Author(s): Amie Albrecht, Phil Howlett, Peter Pudney, Xuan Vu, Peng Zhou
      We discuss the problem of finding an energy-efficient driving strategy for a train journey on an undulating track with steep grades subject to a maximum prescribed journey time. In Part 1 of this paper we reviewed the state-of-the-art and established the key principles of optimal train control for a general model with continuous control. We assumed only that the tractive and braking control forces were bounded by non-increasing speed-dependent magnitude constraints and that the rate of energy dissipation from frictional resistance was given by a non-negative strictly convex function of speed. Partial cost recovery from regenerative braking was allowed. Our aim was to minimize the mechanical energy required to drive the train. We examined the characteristic optimal control modes, studied allowable control transitions and established the existence of optimal switching points. We found algebraic formulae for the adjoint variables in terms of speed on track with piecewise-constant gradient and drew phase plots of the associated optimal evolutionary lines for the state and adjoint variables. In Part 2 we will establish integral forms of the necessary conditions for optimal switching, find general bounds on the positions of the optimal switching points, justify an extended local energy minimization principle and show how these ideas can be used to calculate the optimal strategy. We prove that an optimal strategy always exists and use a perturbation analysis to show that the optimal strategy is unique. Finally we discuss computation of optimal switching points in two realistic examples with steep grades and describe the optimal control strategies and corresponding speed profiles for a complete journey with several different allowed journey times. In practice the strategies described here have been shown to reduce the costs of energy used by as much as 20%.

      PubDate: 2016-11-28T01:31:20Z
      DOI: 10.1016/j.trb.2015.07.024
      Issue No: Vol. 94 (2016)
       
 
 
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