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  Subjects -> TRANSPORTATION (Total: 166 journals)
    - AIR TRANSPORT (7 journals)
    - AUTOMOBILES (19 journals)
    - RAILROADS (5 journals)
    - ROADS AND TRAFFIC (6 journals)
    - SHIPS AND SHIPPING (29 journals)
    - TRANSPORTATION (100 journals)

TRANSPORTATION (100 journals)

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Journal Cover Transportation Research Part C: Emerging Technologies
  [SJR: 1.943]   [H-I: 55]   [21 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [2970 journals]
  • A joint optimization model for liner container cargo assignment problem
           using state-augmented shipping network framework
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Hua Wang, Xiaoning Zhang, Shuaian Wang
      This paper proposes a state-augmented shipping (SAS) network framework to integrate various activities in liner container shipping chain, including container loading/unloading, transshipment, dwelling at visited ports, in-transit waiting and in-sea transport process. Based on the SAS network framework, we develop a chance-constrained optimization model for a joint cargo assignment problem. The model attempts to maximize the carrier’s profit by simultaneously determining optimal ship fleet capacity setting, ship route schedules and cargo allocation scheme. With a few disparities from previous studies, we take into account two differentiated container demands: deterministic contracted basis demand received from large manufacturers and uncertain spot demand collected from the spot market. The economies of scale of ship size are incorporated to examine the scaling effect of ship capacity setting in the cargo assignment problem. Meanwhile, the schedule coordination strategy is introduced to measure the in-transit waiting time and resultant storage cost. Through two numerical studies, it is demonstrated that the proposed chance-constrained joint optimization model can characterize the impact of carrier’s risk preference on decisions of the container cargo assignment. Moreover, considering the scaling effect of large ships can alleviate the concern of cargo overload rejection and consequently help carriers make more promising ship deployment schemes.


      PubDate: 2016-05-17T03:59:28Z
       
  • Distributed model predictive control for railway traffic management
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Bart Kersbergen, Ton van den Boom, Bart De Schutter
      Every day small delays occur in almost all railway networks. Such small delays are often called “disturbances” in literature. In order to deal with disturbances dispatchers reschedule and reroute trains, or break connections. We call this the railway management problem. In this paper we describe how the railway management problem can be solved using centralized model predictive control (MPC) and we propose several distributed model predictive control (DMPC) methods to solve the railway management problem for entire (national) railway networks. Furthermore, we propose an optimization method to determine a good partitioning of the network in an arbitrary number of sub-networks that is used for the DMPC methods. The DMPC methods are extensively tested in a case study using a model of the Dutch railway network and the trains of the Nederlandse Spoorwegen. From the case study it is clear that the DMPC methods can solve the railway traffic management problem, with the same reduction in delays, much faster than the centralized MPC method.


      PubDate: 2016-05-17T03:59:28Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67




      PubDate: 2016-05-17T03:59:28Z
       
  • Path-constrained traffic assignment: A trip chain analysis under range
           anxiety
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Tong-Gen Wang, Chi Xie, Jun Xie, Travis Waller
      This paper proposes and analyzes a distance-constrained traffic assignment problem with trip chains embedded in equilibrium network flows. The purpose of studying this problem is to develop an appropriate modeling tool for characterizing traffic flow patterns in emerging transportation networks that serve a massive adoption of plug-in electric vehicles. This need arises from the facts that electric vehicles suffer from the “range anxiety” issue caused by the unavailability or insufficiency of public electricity-charging infrastructures and the far-below-expectation battery capacity. It is suggested that if range anxiety makes any impact on travel behaviors, it more likely occurs on the trip chain level rather than the trip level, where a trip chain here is defined as a series of trips between two possible charging opportunities (Tamor et al., 2013). The focus of this paper is thus given to the development of the modeling and solution methods for the proposed traffic assignment problem. In this modeling paradigm, given that trip chains are the basic modeling unit for individual decision making, any traveler’s combined travel route and activity location choices under the distance limit results in a distance-constrained, node-sequenced shortest path problem. A cascading labeling algorithm is developed for this shortest path problem and embedded into a linear approximation framework for equilibrium network solutions. The numerical result derived from an illustrative example clearly shows the mechanism and magnitude of the distance limit and trip chain settings in reshaping network flows from the simple case characterized merely by user equilibrium.


      PubDate: 2016-05-17T03:59:28Z
       
  • Modeling the impacts of mandatory and discretionary lane-changing
           maneuvers
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): T.L. Pan, William H.K. Lam, A. Sumalee, R.X. Zhong
      In this paper, a novel mesoscopic multilane model is proposed to enable simultaneous simulation of mandatory and discretionary lane-changing behaviors to realistically capture multilane traffic dynamics. The model considers lane specific fundamental diagrams to simulate dynamic heterogeneous lane flow distributions on expressways. Moreover, different priority levels are identified according to different lane-changing motivations and the corresponding levels of urgency. Then, an algorithm is proposed to estimate the dynamic mandatory and discretionary lane-changing demands. Finally, the lane flow propagation is defined by the reaction law of the demand–supply functions, which can be regarded as an extension of the Incremental-Transfer and/or Priority Incremental-Transfer principles. The proposed mesoscopic multilane cell transmission model is calibrated and validated on a complex weaving section of the State Route 241 freeway in Orange County, California, showing both the positive and negative impact of lane changing maneuvers, e.g., balancing effect and capacity drop, respectively. Moreover, the empirical study verifies that the model requires no additional data other than the cell transmission model does. Thus, the proposed model can be deployed as a simple simulation tool for accessing dynamic mesoscopic multilane traffic state from data available to most management centers, and also the potential application in predicting the impact of traffic incident or lane control strategy.


      PubDate: 2016-05-11T10:37:50Z
       
  • A general corridor model for designing plug-in electric vehicle charging
           infrastructure to support intercity travel
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Mehrnaz Ghamami, Ali Zockaie, Yu (Marco) Nie
      This paper proposes to optimally configure plug-in electric vehicle (PEV) charging infrastructure for supporting long-distance intercity travel using a general corridor model that aims to minimize a total system cost inclusive of infrastructure investment, battery cost and user cost. Compared to the previous work, the proposed model not only allows realistic patterns of origin–destination demands, but also considers flow-dependent charging delay induced by congestion at charging stations. With these extensions, the model is better suited to performing a sketchy design of charging infrastructure along highway corridors. The proposed model is formulated as a mixed integer program with nonlinear constraints and solved by a specialized metaheuristic algorithm based on Simulated Annealing. Our numerical experiments show that the metaheuristic produces satisfactory solutions in comparison with benchmark solutions obtained by a mainstream commercial solver, but is more computationally tractable for larger problems. Noteworthy findings from numerical results are: (1) ignoring queuing delay inducted by charging congestion could lead to suboptimal configuration of charging infrastructure, and its effect is expected to be more significant when the market share of PEVs rises; (2) in the absence of the battery cost, it is important to consider the trade-off between the costs of charging delay and the infrastructure; and (3) building long-range PEVs with the current generation of battery technology may not be cost effective from the societal point of view.


      PubDate: 2016-05-11T10:37:50Z
       
  • A novel network approach to study communication activities of air traffic
           controllers
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Yanjun Wang, Jian Bu, Ke Han, Rui Sun, Minghua Hu, Chenping Zhu
      Air traffic controllers play critical roles in the safety, efficiency, and capacity of air traffic management. However, there is inadequate knowledge of the dynamics of the controllers’ activities, especially from a quantitative perspective. This paper presents a novel network approach to uncover hidden patterns of the controllers’ behavior based on the voice communication data. We convert the time series of the controllers’ communication activities, which contain flights’ information, into a time-varying network and a static network, referred to as temporal network and time-aggregated network, respectively. These networks reflect the interaction between the controllers and the flights on a sector level, and allow us to leverage network techniques to yield new and insightful findings regarding regular patterns and unique characteristics of the controllers’ communication activities. The proposed methodology is applied to three real-world datasets, and the resulting networks are closely examined and compared in terms of degree distribution, community structure, and motifs. This network approach introduces a “spatial” element to the conventional analysis of the controllers’ communication events, by identifying connectivity and proximity among flights. It constitutes a major step towards the quantitative description of the controller-flight dynamics, which is not widely seen in the literature.


      PubDate: 2016-05-11T10:37:50Z
       
  • Modeling railway disruption lengths with Copula Bayesian Networks
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Aurelius A. Zilko, Dorota Kurowicka, Rob M.P. Goverde
      Decreasing the uncertainty in the lengths of railway disruptions is a major help to disruption management. To assist the Dutch Operational Control Center Rail (OCCR) during disruptions, we propose the Copula Bayesian Network method to construct a disruption length prediction model. Computational efficiency and fast inference features make the method attractive for the OCCR’s real-time decision making environment. The method considers the factors influencing the length of a disruption and models the dependence between them to produce a prediction. As an illustration, a model for track circuit (TC) disruptions in the Dutch railway network is presented in this paper. Factors influencing the TC disruption length are considered and a disruption length model is constructed. We show that the resulting model’s prediction power is sound and discuss its real-life use and challenges to be tackled in practice.


      PubDate: 2016-04-29T23:25:05Z
       
  • Network sensor health problem
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Zhe Sun, Wen-Long Jin, ManWo Ng
      Many existing studies on the sensor health problem determine an individual sensor’s health status based on the statistical characteristics of collected data by the sensor. In this research, we study the sensor health problem at the network level, which is referred to as the network sensor health problem. First, based on the conservation principle of daily flows in a network, we separate all links into base links and non-base links, such that the flows on the latter can be calculated from those on the former. In reality, the network flow conservation principle can be violated due to the existence of unhealthy sensors. Then we define the least inconsistent base set of links as those that minimize the sum of squares of the differences between observed and calculated flows on non-base links. But such least inconsistent base sets may not be unique in a general road network. Finally we define the health index of an individual sensor as the frequency that it appears in all of the least inconsistent base sets. Intuitively, a lower health index suggests that the corresponding sensor is more likely to be unhealthy. We present the brute force method to find all least inconsistent base sets and calculate the health indices. We also propose a greedy search algorithm to calculate the approximate health indices more efficiently. We solve the network sensor health problem for a real-world example with 16 nodes and 30 links, among which 18 links are monitored with loop detectors. Using daily traffic count data from the Caltrans Performance Measurement System (PeMS) database, we use both the brute-force and greedy search methods to calculate the health indices for all the sensors. We find that all the four sensors flagged as unhealthy (high value) by PeMS have the lowest health indices. This confirms that a sensor with a lower health index is more likely to be unhealthy. Therefore, we can use such health indices to determine the relative reliability of different sensors’ data and prioritize the maintenance of sensors.


      PubDate: 2016-04-29T23:25:05Z
       
  • A global optimization algorithm for trajectory data based car-following
           model calibration
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Li Li, Xiqun (Micheal) Chen, Lei Zhang
      How to calibrate the parameters of car-following models based on observed traffic data is a vital problem in traffic simulation. Usually, the core of calibration is cast into an optimization problem, in which the decision variables are car-following model parameters and the objective function usually characterizes the difference between empirical vehicle movements and their simulated correspondences. Since the objective function is usually nonlinear and non-convex, various greedy or stochastic algorithms had been proposed during the last two decades. However, the performance of these algorithms remains to be further examined. In this paper, we revisit this important problem with a special focus on the geometric feature of the objective function. First, we prove that, from a global perspective, most existing objective functions are Lipschitz continuous. Second, we show that, from a local perspective, many of these objective functions are relatively flat around the global optimal solution. Based on these two features, we propose a new global optimization algorithm that integrates global direct search and local gradient search to find the optimal solution in an efficient manner. We compare this new algorithm with several existing algorithms, including Nelder–Mead (NM) algorithm, sequential quadratic programming (SQP) algorithm, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA) algorithm, on NGSIM trajectory datasets. Results demonstrate that the proposed algorithm has a fast convergence speed and a high probability of finding the global optimal solution. Moreover, it has only two major configuration parameters that can be easily determined in practice.


      PubDate: 2016-04-29T23:25:05Z
       
  • Dynamics of modal choice of heterogeneous travelers with responsive
           transit services
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Xinwei Li, Hai Yang
      In this paper, we investigate travelers’ day-to-day modal choice in a bi-modal transportation system with responsive transit services under various economic objectives. A group of travelers with heterogeneous preferences adjust their modal choice each day based on their perceived travel cost of each mode, aiming to minimize their travel cost. Meanwhile, the transit operator sets frequency each period according to the realized transit demand and previous frequency, trying to achieve different profit targets. For a given profit target, the fixed point of equilibrium may not be unique. We establish the condition for existence of multiple fixed points and examine the stability of the fixed points in each case. Furthermore, in view of a socially desirable mode choice, we also investigate the impacts of total travel demand and bus size on the convergence of the system to various fixed points associated with different targeted mode split. Finally, we use several numerical examples to illustrate the theoretical results and their practical implications for the transit operator to design appropriate transit schemes in a dynamic transportation environment.


      PubDate: 2016-04-29T23:25:05Z
       
  • A unified-adaptive large neighborhood search metaheuristic for periodic
           location-routing problems
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Çağrı Koç
      This paper introduces three variants of the Periodic Location-Routing Problem (PLRP): the Heterogeneous PLRP with Time Windows (HPTW), the Heterogeneous PLRP (HP) and the homogeneous PLRP with Time Windows (PTW). These problems extend the well-known location-routing problem by considering a homogeneous or heterogeneous fleet, multiple periods and time windows. The paper develops a powerful Unified-Adaptive Large Neighborhood Search (U-ALNS) metaheuristic for these problems. The U-ALNS successfully uses existing algorithmic procedures and also offers a number of new advanced efficient procedures capable of handling a multi-period horizon, fleet composition and location decisions. Computational experiments on benchmark instances show that the U-ALNS is highly effective on PLRPs. The U-ALNS outperforms previous methods on a set of standard benchmark instances for the PLRP. We also present new benchmark results for the PLRP, HPTW, HP and PTW.


      PubDate: 2016-04-24T22:12:26Z
       
  • The promises of big data and small data for travel behavior (aka human
           mobility) analysis
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Cynthia Chen, Jingtao Ma, Yusak Susilo, Yu Liu, Menglin Wang
      The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called “travel behavior analysis” or “human mobility analysis”). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.


      PubDate: 2016-04-24T22:12:26Z
       
  • Integrated optimal eco-driving on rolling terrain for hybrid electric
           vehicle with vehicle-infrastructure communication
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Jia Hu, Yunli Shao, Zongxuan Sun, Meng Wang, Joe Bared, Peter Huang
      This research presents an integrated optimal controller to maximize the fuel efficiency of a Hybrid Electric Vehicle (HEV) traveling on rolling terrain. The controller optimizes both the vehicle acceleration and the hybrid powertrain operation. It takes advantage of the emerging Connected Vehicle (CV) technology and utilizes present and future information as optimization input, which includes road topography, and dynamic speed limit. The optimal control problem was solved using Pontryagin’s Minimum Principle (PMP). Efforts were made to reduce the computational burden of the optimization process. The evaluation shows that the benefit of the proposed optimal controller is significant compared to regular HEV cruising at the speed limit on rolling terrain. The benefit ranges from 5.0% to 8.9% on mild slopes and from 15.7% to 16.9% on steep slopes. The variation is caused by the change of hilly road density.


      PubDate: 2016-04-20T05:50:20Z
       
  • Platoon based cooperative driving model with consideration of realistic
           inter-vehicle communication
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Dongyao Jia, Dong Ngoduy
      Recent developments of information and communication technologies (ICT) have enabled vehicles to timely communicate with each other through wireless technologies, which will form future (intelligent) traffic systems (ITS) consisting of so-called connected vehicles. Cooperative driving with the connected vehicles is regarded as a promising driving pattern to significantly improve transportation efficiency and traffic safety. Nevertheless, unreliable vehicular communications also introduce packet loss and transmission delay when vehicular kinetic information or control commands are disseminated among vehicles, which brings more challenges in the system modeling and optimization. Currently, no data has been yet available for the calibration and validation of a model for ITS, and most research has been only conducted for a theoretical point of view. Along this line, this paper focuses on the (theoretical) development of a more general (microscopic) traffic model which enables the cooperative driving behavior via a so-called inter-vehicle communication (IVC). To this end, we design a consensus-based controller for the cooperative driving system (CDS) considering (intelligent) traffic flow that consists of many platoons moving together. More specifically, the IEEE 802.11p, the de facto vehicular networking standard required to support ITS applications, is selected as the IVC protocols of the CDS, in order to investigate how the vehicular communications affect the features of intelligent traffic flow. This study essentially explores the relationship between IVC and cooperative driving, which can be exploited as the reference for the CDS optimization and design.


      PubDate: 2016-04-20T05:50:20Z
       
  • Prediction of vehicle CO2 emission and its application to eco-routing
           navigation
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Weiliang Zeng, Tomio Miwa, Takayuki Morikawa
      Transportation sector accounts for a large proportion of global greenhouse gas and toxic pollutant emissions. Even though alternative fuel vehicles such as all-electric vehicles will be the best solution in the future, mitigating emissions by existing gasoline vehicles is an alternative countermeasure in the near term. The aim of this study is to predict the vehicle CO2 emission per kilometer and determine an eco-friendly path that results in minimum CO2 emissions while satisfying travel time budget. The vehicle CO2 emission model is derived based on the theory of vehicle dynamics. Particularly, the difficult-to-measure variables are substituted by parameters to be estimated. The model parameters can be estimated by using the current probe vehicle systems. An eco-routing approach combining the weighting method and k-shortest path algorithm is developed to find the optimal path along the Pareto frontier. The vehicle CO2 emission model and eco-routing approach are validated in a large-scale transportation network in Toyota city, Japan. The relative importance analysis indicates that the average speed has the largest impact on vehicle CO2 emission. Specifically, the benefit trade-off between CO2 emission reduction and the travel time buffer is discussed by carrying out sensitivity analysis in a network-wide scale. It is found that the average reduction in CO2 emissions achieved by the eco-friendly path reaches a maximum of around 11% when the travel time buffer is set to around 10%.
      Graphical abstract image

      PubDate: 2016-04-14T18:40:00Z
       
  • Extracting accurate location information from a highly inaccurate traffic
           accident dataset: A methodology based on a string matching technique
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Mario Miler, Filip Todić, Marko Ševrović
      The objective of this research was to develop a model for validating traffic accident locations that would be applicable worldwide, regardless of linguistic or cultural differences. In order to achieve this, a Volunteered Geographic Information (VGI) dataset was used, the OpenStreetMap (OSM) project. To test the developed model, a total of 8550 accidents with fatal or non-fatal injuries that occurred in the City of Zagreb from 2010 to 2014 were evaluated. Traffic accident data was collected using the pen-and-paper method while the traffic accident locations were determined using Global Positioning System (GPS) receivers embedded within police vehicles. This form of data entry invariably introduces errors in both geometric and contextual attributes. To fully counteract these errors, the developed model consists of two key concepts: the Jaro–Winkler string matching technique and the Inverse Distance Weighting method. Over 66% of traffic accident locations were validated, which is an increase of 15% when compared to the classical approach. The model outlined in this paper shows a significant improvement in estimating the correct location of traffic accidents. This in turn results in a drastic decrease in resources needed to estimate the quality of accident locations.


      PubDate: 2016-04-14T18:40:00Z
       
  • Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication
           in a heterogeneous wireless network – Performance evaluation
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Kakan Chandra Dey, Anjan Rayamajhi, Mashrur Chowdhury, Parth Bhavsar, James Martin
      Connected Vehicle Technology (CVT) requires wireless data transmission between vehicles (V2V), and vehicle-to-infrastructure (V2I). Evaluating the performance of different network options for V2V and V2I communication that ensure optimal utilization of resources is a prerequisite when designing and developing robust wireless networks for CVT applications. Though dedicated short range communication (DSRC) has been considered as the primary communication option for CVT safety applications, the use of other wireless technologies (e.g., Wi-Fi, LTE, WiMAX) allow longer range communications and throughput requirements that could not be supported by DSRC alone. Further, the use of other wireless technology potentially reduces the need for costly DSRC infrastructure. In this research, the authors evaluated the performance of Het-Net consisting of Wi-Fi, DSRC and LTE technologies for V2V and V2I communications. An application layer handoff method was developed to enable Het-Net communication for two CVT applications: traffic data collection, and forward collision warning. The handoff method ensures the optimal utilization of available communication options (i.e., eliminate the need of using multiple communication options at the same time) and corresponding backhaul communication infrastructure depending on the connected vehicle application requirements. Field studies conducted in this research demonstrated that the use of Het-Net broadened the range and coverage of V2V and V2I communications. The use of the application layer handoff technique to maintain seamless connectivity for CVT applications was also successfully demonstrated and can be adopted in future Het-Net supported connected vehicle applications. A long handoff time was observed when the application switches from LTE to Wi-Fi. The delay is largely due to the time required to activate the 802.11 link and the time required for the vehicle to associate with the RSU (i.e., access point). Modifying the application to implement a soft handoff where a new network is seamlessly connected before breaking from the existing network can greatly reduce (or eliminate) the interruption of network service observed by the application. However, the use of a Het-Net did not compromise the performance of the traffic data collection application as this application does not require very low latency, unlike connected vehicle safety applications. Field tests revealed that the handoff between networks in Het-Net required several seconds (i.e., higher than 200ms required for safety applications). Thus, Het-Net could not be used to support safety applications that require communication latency less than 200ms. However, Het-Net could provide additional/supplementary connectivity for safety applications to warn vehicles upstream to take proactive actions to avoid problem locations. To validate and establish the findings from field tests that included a limited number of connected vehicles, ns-3 simulation experiments with a larger number of connected vehicles were conducted involving a DSRC and LTE Het-Net scenario. The latency and packet delivery error trend obtained from ns-3 simulation were found to be similar to the field experiment results.


      PubDate: 2016-04-14T18:40:00Z
       
  • Using GPS data to analyse the distance travelled to the first accident at
           fault in pay-as-you-drive insurance
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Mercedes Ayuso, Montserrat Guillén, Ana María Pérez Marín
      In this paper we employ survival analysis methods to analyse the impact of driving patterns on distance travelled before a first claim is made by young drivers underwriting a pay-as-you-drive insurance scheme. An empirical application is presented in which we analyse real data collected by a GPS system from a leading Spanish insurer. We show that men have riskier driving patterns than women and, moreover, that there are gender differences in the impact driving patterns have on the risk of being involved in an accident. The implications of these results are discussed in terms of the ‘no-gender’ discrimination regulation.


      PubDate: 2016-04-14T18:40:00Z
       
  • Online calibration for microscopic traffic simulation and dynamic
           multi-step prediction of traffic speed
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Vasileia Papathanasopoulou, Ioulia Markou, Constantinos Antoniou
      Simulating driving behavior in high accuracy allows short-term prediction of traffic parameters, such as speeds and travel times, which are basic components of Advanced Traveler Information Systems (ATIS). Models with static parameters are often unable to respond to varying traffic conditions and simulate effectively the corresponding driving behavior. It has therefore been widely accepted that the model parameters vary in multiple dimensions, including across individual drivers, but also spatially across the network and temporally. While typically on-line, predictive models are macroscopic or mesoscopic, due to computational and data considerations, nowadays microscopic models are becoming increasingly practical for dynamic applications. In this research, we develop a methodology for online calibration of microscopic traffic simulation models for dynamic multi-step prediction of traffic measures, and apply it to car-following models, one of the key models in microscopic traffic simulation models. The methodology is illustrated using real trajectory data available from an experiment conducted in Naples, using a well-established car-following model. The performance of the application with the dynamic model parameters consistently outperforms the corresponding static calibrated model in all cases, and leads to less than 10% error in speed prediction even for ten steps into the future, in all considered data-sets.


      PubDate: 2016-04-14T18:40:00Z
       
  • A cell transmission model for dynamic lane reversal with autonomous
           vehicles
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Michael W. Levin, Stephen D. Boyles
      Autonomous vehicles admit consideration of novel traffic behaviors such as reservation-based intersection controls and dynamic lane reversal. We present a cell transmission model formulation for dynamic lane reversal. For deterministic demand, we formulate the dynamic lane reversal control problem for a single link as an integer program and derive theoretical results. In reality, demand is not known perfectly at arbitrary times in the future. To address stochastic demand, we present a Markov decision process formulation. Due to the large state size, the Markov decision process is intractable. However, based on theoretical results from the integer program, we derive an effective heuristic. We demonstrate significant improvements over a fixed lane configuration both on a single bottleneck link with varying demands, and on the downtown Austin network.


      PubDate: 2016-04-14T18:40:00Z
       
  • Infrastructure planning for fast charging stations in a competitive market
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Zhaomiao Guo, Julio Deride, Yueyue Fan
      Most existing studies on EV charging infrastructure planning take a central planner’s perspective, by assuming that investment decision on charging facilities can be controlled by a single decision entity. In this paper, we establish modeling and computational methods to support business-driven EV charging infrastructure investment planning problem, where the infrastructure system is shaped by collective actions of multiple decision entities who do not necessarily coordinate with each other. A network-based multi-agent optimization modeling framework is developed to simultaneously capture the selfish behaviors of individual investors and travelers and their interactions over a network structure. To overcome computational difficulty imposed by non-convexity of the problem, we rely on recent theoretical development on variational convergence of bivariate functions to design a solution algorithm with analysis on its convergence properties. Numerical experiments are implemented to study the performance of proposed method and draw practical insights.


      PubDate: 2016-04-14T18:40:00Z
       
  • Combining speed and acceleration to define car users’ safe or unsafe
           driving behaviour
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Laura Eboli, Gabriella Mazzulla, Giuseppe Pungillo
      Speed and acceleration describe the motion of a vehicle. Therefore, these parameters are fundamental to define the behaviour of a driver. To this aim, it is useful to analyse instantaneous and geo-referenced kinematic parameters of the vehicle recorded by real tests on the road. Among all the available methods in the scientific literature, a way for characterizing driver behaviour is the g–g diagram, that shows the longitudinal and lateral accelerations on the y and x-axes, normalized with respect to gravity, recorded on a vehicle during a real test on the road. However, we retain that also speed has to be considered for characterizing drivers’ behaviour, being acceleration and speed strictly interrelated. Starting from the g–g diagram, we propose a methodology which describes the relationship between lateral and longitudinal accelerations and speeds, and represents a tool to classify car drivers’ behaviour as safe or unsafe. An app for smartphone allows the geo-referenced kinematic parameters of the vehicle to be detected. The experimental survey supporting the methodology was carried out on a rural two-lane road in Southern Italy.


      PubDate: 2016-04-14T18:40:00Z
       
  • Management of intersections with multi-modal high-resolution data
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Ajith Muralidharan, Samuel Coogan, Christopher Flores, Pravin Varaiya
      A high-resolution (HR) data system for an intersection collects the location (lane), speed, and turn movement of every vehicle as it enters an intersection, together with the signal phase. Some systems also provide video monitoring; others measure pedestrian and bicycle movements; and some have vehicle to infrastructure (V2I) communication capability. The data are available in real time and archived. Real time data are used to implement signal control. Archived data are used to evaluate intersection, corridor, and network performance. The system operates 24 × 7 . Uses of a HR data system for assessing intersection performance and improving mobility and safety are discussed. Mobility applications include evaluation of intersection performance, and the design of better signal control. Safety applications include estimates of dilemma zones, red-light violations, and pedestrian–vehicle conflicts.


      PubDate: 2016-04-09T09:26:26Z
       
  • Development of distress condition index of asphalt pavements using LTPP
           data through structural equation modeling
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Xueqin Chen, Qiao Dong, Hehua Zhu, Baoshan Huang
      Traditional pavement distress index such as the Pavement Condition Index (PCI) developed by U.S. Army Corps of Engineers determines coefficients of distresses based on subjective ratings. This study proposed an asphalt pavement distress condition index based on various types of distress data collected from the Long-Term Pavement Performance (LTPP) database through Structural Equation Modeling (SEM). The SEM method treated the overall distress index as a latent variable while various distresses were treated as endogenous and other influence factors such as age, layer thickness, material type, weather, environment and traffic, were exogenous observed variables. The SEM method modeled the contributions of various distresses as well as the influence of other factors on the overall pavement distress condition. Influences of age, layer thickness, material type, environment and traffic on the latent distress condition were in accordance with previous studies. Compared with previous attempts to model latent pavement condition index utilizing SEM method, more pavement condition measurements and influencing factors were included. Specifically, this study adopted the robust maximum likelihood estimator (MLR) to estimate parameters for non-normally distributed data and derived the explicit expression of latent variables with intercepts. A multiple regression prediction model was built to calculate an overall condition index utilizing those measured distress data. The established pavement distress index prediction model provided a rational estimation of weighting coefficients for each distress type. The prediction model showed that alligator cracking, longitudinal cracking in wheel path, non-wheel path longitudinal cracking, transverse cracking, block cracking, edge cracking, patch and bleeding were significant for the latent pavement distress index.


      PubDate: 2016-04-09T09:26:26Z
       
  • Delivering improved alerts, warnings, and control assistance using basic
           safety messages transmitted between connected vehicles
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Jun Liu, Asad J. Khattak
      When vehicles share their status information with other vehicles or the infrastructure, driving actions can be planned better, hazards can be identified sooner, and safer responses to hazards are possible. The Safety Pilot Model Deployment (SPMD) is underway in Ann Arbor, Michigan; the purpose is to demonstrate connected technologies in a real-world environment. The core data transmitted through Vehicle-to-Vehicle and Vehicle-to-Infrastructure (or V2V and V2I) applications are called Basic Safety Messages (BSMs), which are transmitted typically at a frequency of 10Hz. BSMs describe a vehicle’s position (latitude, longitude, and elevation) and motion (heading, speed, and acceleration). This study proposes a data analytic methodology to extract critical information from raw BSM data available from SPMD. A total of 968,522 records of basic safety messages, gathered from 155 trips made by 49 vehicles, was analyzed. The information extracted from BSM data captured extreme driving events such as hard accelerations and braking. This information can be provided to drivers, giving them instantaneous feedback about dangers in surrounding roadway environments; it can also provide control assistance. While extracting critical information from BSMs, this study offers a fundamental understanding of instantaneous driving decisions. Longitudinal and lateral accelerations included in BSMs were specifically investigated. Varying distributions of instantaneous longitudinal and lateral accelerations are quantified. Based on the distributions, the study created a framework for generating alerts/warnings, and control assistance from extreme events, transmittable through V2V and V2I applications. Models were estimated to untangle the correlates of extreme events. The implications of the findings and applications to connected vehicles are discussed in this paper.


      PubDate: 2016-04-09T09:26:26Z
       
  • Mobility and environment improvement of signalized networks through
           Vehicle-to-Infrastructure (V2I) communications
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Gerard Aguilar Ubiergo, Wen-Long Jin
      Traffic signals, even though crucial for safe operations of busy intersections, are one of the leading causes of travel delays in urban settings, as well as the reason why billions of gallons of fuel are burned, and tons of toxic pollutants released to the atmosphere each year by idling engines. Recent advances in cellular networks and dedicated short-range communications make Vehicle-to-Infrastructure (V2I) communications a reality, as individual cars and traffic signals can now be equipped with communication and computing devices. In this paper, we first presented an integrated simulator with V2I, a car-following model and an emission model to simulate the behavior of vehicles at signalized intersections and calculate travel delays in queues, vehicle emissions, and fuel consumption. We then present a hierarchical green driving strategy based on feedback control to smooth stop-and-go traffic in signalized networks, where signals can disseminate traffic signal information and loop detector data to connected vehicles through V2I communications. In this strategy, the control variable is an individual advisory speed limit for each equipped vehicle, which is calculated from its location, signal settings, and traffic conditions. Finally, we quantify the mobility and environment improvements of the green driving strategy with respect to market penetration rates of equipped vehicles, traffic conditions, communication characteristics, location accuracy, and the car-following model itself, both in isolated and non-isolated intersections. In particular, we demonstrate savings of around 15% in travel delays and around 8% in fuel consumption and greenhouse gas emissions. Different from many existing ecodriving strategies in signalized road networks, where vehicles’ speed profiles are totally controlled, our strategy is hierarchical, since only the speed limit is provided, and vehicles still have to follow their leaders. Such a strategy is crucial for maintaining safety with mixed vehicles.


      PubDate: 2016-04-09T09:26:26Z
       
  • An integrated approach for airline scheduling, aircraft fleeting and
           routing with cruise speed control
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Hüseyin Gürkan, Sinan Gürel, M. Selim Aktürk
      To place an emphasis on profound relations among airline schedule planning problems and to mitigate the effect of unexpected delays, we integrate schedule design, fleet assignment and aircraft routing problems within a daily planning horizon while passengers’ connection service levels are ensured via chance constraints. We propose a nonlinear mixed integer programming model due to the nonlinear fuel consumption and CO2 emission cost terms in the objective function, which is handled by second order conic reformulation. The key contribution of this study is to take into account the cruise time control for the first time in an integrated model of these three stages of airline operations. Changing cruise times of flights in an integrated model enables to construct a schedule to increase utilization of fuel efficient aircraft and even to decrease total number of aircraft needed while satisfying the same service level and maintenance requirements for aircraft fleeting and routing. There is a critical tradeoff between the number of aircraft needed to fulfill the required flights and overall operational expenses. We also propose two heuristic methods to solve larger size problems. Finally, computational results using real data obtained from a major U.S. carrier are presented to demonstrate potential profitability in applying the proposed solution methods.


      PubDate: 2016-04-05T03:25:38Z
       
  • Integration of Weigh-in-Motion (WIM) and inductive signature data for
           truck body classification
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Sarah V. Hernandez, Andre Tok, Stephen G. Ritchie
      Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately in the US, currently available commercial vehicle data contain critical gaps when it comes to linking vehicle and operational characteristics. Leveraging existing traffic sensor infrastructure, we developed a novel, readily implementable approach of integrating two complementary data collection devices, Weigh-in-Motion (WIM) systems and advanced inductive loop detectors (ILD), to produce high resolution truck data. For each vehicle traversing a WIM site, an inductive signature was collected along with WIM measurements such as axle spacing and weight which were then used as inputs to a series of truck body classification models that encompass all truck classes in the most common axle-based Federal Highway Administration (FHWA) classification scheme in the US. Since body configuration can be linked to commodity carried, drive and duty cycle, and other distinct operating characteristics, body class data is undeniably useful for freight planning and air quality monitoring. A multiple classifier systems (MCS) method was adopted to increase the classification accuracy for minority body classes. In all, eight separate body classifications models were developed from an extensive data set of 18,967 truck records distinguishing an unprecedented total of 23 single unit truck and 31 single and semi-trailer body configurations, each with over 80% correct classification rates (CCR). Remarkably, the body class model for five axle semi-tractor trailers – the most diverse truck category – achieved MCS CCRs above 85% for several industry specific classes including refrigerated and non-refrigerated intermodal containers, livestock, and logging trailers.


      PubDate: 2016-03-31T05:09:49Z
       
  • Experiment of boundedly rational route choice behavior and the model under
           satisficing rule
    • Abstract: Publication date: July 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 68
      Author(s): Chuan-Lin Zhao, Hai-Jun Huang
      In this paper, we study the boundedly rational route choice behavior under the Simon’s satisficing rule. A laboratory experiment was carried out to verify the participants’ boundedly rational route choice behavior. By introducing the concept of aspiration level which is specific to each person, we develop a novel model of the problem in a parallel-link network and investigate the properties of the boundedly rational user equilibrium (BRUE) state. Conditions for ensuring the existence and uniqueness of the BRUE solution are derived. A solution method is proposed to find the unique BRUE state. Extensions to general networks are conducted. Numerical examples are presented to demonstrate the theoretical analyses.


      PubDate: 2016-03-31T05:09:49Z
       
  • Wireless charging in California: Range, recharge, and vehicle
           electrification
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Micah Fuller
      This research evaluated the potential for wireless dynamic charging (charging while moving) to address range and recharge issues of modern electric vehicles by considering travel to regional destinations in California. A 200-mile electric vehicle with a real range of 160miles plus 40miles reserve was assumed to be used by consumers in concert with static and dynamic charging as a strict substitute for gasoline vehicle travel. Different combinations of wireless charging power (20–120kW) and vehicle range (100–300miles) were evaluated. One of the results highlighted in the research indicated that travel between popular destinations could be accomplished with a 200-mile EV and a 40kW dynamic wireless charging system at a cost of about $2.5billion. System cost for a 200-mile EV could be reduced to less than $1billion if wireless vehicle charging power levels were increased to 100kW or greater. For vehicles consuming 138kWh of dynamic energy per year on a 40kW dynamic system, the capital cost of $2.5billion plus yearly energy costs could be recouped over a 20-year period at an average cost to each vehicle owner of $512 per year at a volume of 300,000 vehicles or $168 per year at a volume of 1,000,000 vehicles. Cost comparisons of dynamic charging, increased battery capacity, and gasoline refueling were presented. Dynamic charging, coupled with strategic wayside static charging, was shown to be more cost effective to the consumer over a 10-year period than gasoline refueling at $2.50 or $4.00 per gallon. Notably, even at very low battery prices of $100 per kWh, the research showed that dynamic charging can be a more cost effective approach to extending range than increasing battery capacity.


      PubDate: 2016-03-21T16:33:45Z
       
  • From Twitter to detector: Real-time traffic incident detection using
           social media data
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Yiming Gu, Zhen (Sean) Qian, Feng Chen
      The effectiveness of traditional incident detection is often limited by sparse sensor coverage, and reporting incidents to emergency response systems is labor-intensive. We propose to mine tweet texts to extract incident information on both highways and arterials as an efficient and cost-effective alternative to existing data sources. This paper presents a methodology to crawl, process and filter tweets that are accessible by the public for free. Tweets are acquired from Twitter using the REST API in real time. The process of adaptive data acquisition establishes a dictionary of important keywords and their combinations that can imply traffic incidents (TI). A tweet is then mapped into a high dimensional binary vector in a feature space formed by the dictionary, and classified into either TI related or not. All the TI tweets are then geocoded to determine their locations, and further classified into one of the five incident categories. We apply the methodology in two regions, the Pittsburgh and Philadelphia Metropolitan Areas. Overall, mining tweets holds great potentials to complement existing traffic incident data in a very cheap way. A small sample of tweets acquired from the Twitter API cover most of the incidents reported in the existing data set, and additional incidents can be identified through analyzing tweets text. Twitter also provides ample additional information with a reasonable coverage on arterials. A tweet that is related to TI and geocodable accounts for approximately 5% of all the acquired tweets. Of those geocodable TI tweets, 60–70% are posted by influential users (IU), namely public Twitter accounts mostly owned by public agencies and media, while the rest is contributed by individual users. There is more incident information provided by Twitter on weekends than on weekdays. Within the same day, both individuals and IUs tend to report incidents more frequently during the day time than at night, especially during traffic peak hours. Individual tweets are more likely to report incidents near the center of a city, and the volume of information significantly decays outwards from the center.


      PubDate: 2016-03-21T16:33:45Z
       
  • Methods using belief functions to manage imperfect information concerning
           events on the road in VANETs
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Mira Bou Farah, David Mercier, François Delmotte, Éric Lefèvre
      Different models using belief functions are proposed and compared in this article to share and manage imperfect information about events on the road in vehicular networks. In an environment without infrastructure, the goal is to provide to driver the synthesis of the situation on the road from all acquired information. Different strategies are considered: discount or reinforce towards the absence of the event to take into account messages agings, keep the original messages or only the fusion results in vehicles databases, consider the world update, manage the spatiality of traffic jams by taking into account neighborhood. Methods are tested and compared using a Matlab™ simulator. Two strategies are introduced to tackle fog blankets spatiality; they are compared through an example.


      PubDate: 2016-03-12T17:31:31Z
       
  • Customizing driving cycles to support vehicle purchase and use decisions:
           Fuel economy estimation for alternative fuel vehicle users
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Jun Liu, Xin Wang, Asad Khattak
      Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.


      PubDate: 2016-03-12T17:31:31Z
       
  • Modelling acceleration decisions in traffic streams with weak lane
           discipline: A latent leader approach
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Charisma F. Choudhury, Md. Mozahidul Islam
      Acceleration is an important driving manoeuvre that has been modelled for decades as a critical element of the microscopic traffic simulation tools. The state-of-the art acceleration models have however primarily focused on lane based traffic. In lane based traffic, every driver has a single distinct lead vehicle in the front and the acceleration of the driver is typically modelled as a function of the relative speed, position and/or type of the corresponding leader. On the contrary, in a traffic stream with weak lane discipline, the subject driver may have multiple vehicles in the front. The subject driver is therefore subjected to multiple sources of stimulus for acceleration and reacts to the stimulus from the governing leader. However, only the applied accelerations are observed in the trajectory data, and the governing leader is unobserved or latent. The state-of-the-art models therefore cannot be directly applied to traffic streams with weak lane discipline. This prompts the current research where we present a latent leader acceleration model. The model has two components: a random utility based dynamic class membership model (latent leader component) and a class-specific acceleration model (acceleration component). The parameters of the model have been calibrated using detailed trajectory data collected from Dhaka, Bangladesh. Results indicate that the probability of a given front vehicle of being the governing leader can depend on the type of the lead vehicle and the extent of lateral overlap with the subject driver. The estimation results are compared against a simpler acceleration model (where the leader is determined deterministically) and a significant improvement in the goodness-of-fit is observed. The proposed models, when implemented in microscopic traffic simulation tools, are expected to result more realistic representation of traffic streams with weak lane discipline.


      PubDate: 2016-03-12T17:31:31Z
       
  • How to assess the benefits of connected vehicles? A simulation
           framework for the design of cooperative traffic management strategies
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Maxime Guériau, Romain Billot, Nour-Eddin El Faouzi, Julien Monteil, Frédéric Armetta, Salima Hassas
      Advances in Information and Communication Technologies (ICT) allow the transportation community to foresee dramatic improvements for the incoming years in terms of a more efficient, environmental friendly and safe traffic management. In that context, new ITS paradigms like Cooperative Systems (C-ITS) enable an efficient traffic state estimation and traffic control. C-ITS refers to three levels of cooperation between vehicles and infrastructure: (i) equipped vehicles with Advanced Driver Assistance Systems (ADAS) adjusting their motion to surrounding traffic conditions; (ii) information exchange with the infrastructure; (iii) vehicle-to-vehicle communication. Therefore, C-ITS makes it possible to go a step further in providing real time information and tailored control strategies to specific drivers. As a response to an expected increasing penetration rate of these systems, traffic managers and researchers have to come up with new methodologies that override the classic methods of traffic modeling and control. In this paper, we discuss some potentialities of C-ITS for traffic management with the methodological issues following the expansion of such systems. Cooperative traffic models are introduced into an open-source traffic simulator. The resulting simulation framework is robust and able to assess potential benefits of cooperative traffic control strategies in different traffic configurations.


      PubDate: 2016-03-12T17:31:31Z
       
  • Repeated anticipatory network traffic control using iterative optimization
           accounting for model bias correction
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Wei Huang, Francesco Viti, Chris M.J. Tampère
      Anticipatory signal control in traffic networks adapts the signal timings with the aim of controlling the resulting (equilibrium) flows and route choice patterns in the network. This study investigates a method to support control decisions for successful applications in real traffic systems that operate repeatedly, for instance from day to day, month to month, etc. The route choice response to signal control is usually predicted through models; however this leads to suboptimality because of unavoidable prediction errors between model and reality. This paper proposes an iterative optimizing control method to drive the traffic network towards the real optimal performance by observing modeling errors and correcting for them. Theoretical analysis of this Iterative Optimizing Control with Model Bias Correction (IOCMBC) on matching properties between the modeled optimal solution and the real optimum is presented, and the advantages over conventional iterative schemes are demonstrated. A local convergence analysis is also elaborated to investigate conditions required for a convergent scheme. The main innovation is the calculation of the sensitivity (Jacobian) information of the real route choice behavior with respect to signal control variables. To avoid performing additional perturbations, we introduce a measurement-based implementation method for estimating the operational Jacobian that is associated with the reality. Numerical tests confirm the effectiveness of the proposed IOCMBC method in tackling modeling errors, as well as the influence of the optimization step size on the reality-tracking convergence.


      PubDate: 2016-03-12T17:31:31Z
       
  • Calibrating the Wiedemann’s vehicle-following model using mixed
           vehicle-pair interactions
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Umair Durrani, Chris Lee, Hanna Maoh
      Microscopic traffic simulation models require the calibration of car-following (or vehicle-following) models. The parameters of vehicle-following models control individual driver’s spacing, time gap, speed variation and acceleration during different driving conditions. In recent studies, these parameters have been determined for different vehicle classes separately since heavy vehicles generally keep longer spacing and time gap than light vehicles. These parameters have been commonly estimated based on the observed macroscopic traffic flow data such as average volume and speed. However, these data cannot reflect actual vehicle-following behavior of individual vehicles. Also, the effect of the lead vehicle class on the following vehicle’s behavior has been neglected in the parameter estimation. Thus, this study estimates the driving behavior parameters for cars and heavy vehicles in the Wiedemann 99 vehicle-following model. For the estimation, 2169 vehicle trajectories were obtained from a 640-m segment of US-101 in Los Angeles, California during the morning peak hours. Separate parameters were estimated for three vehicle classes (cars, heavy vehicles, and motorcycles) and three vehicle-following cases (car following car, car following heavy vehicle, and heavy vehicle following car). From the comparison of the driving behavior parameters between cars and heavy vehicles, it was found that heavy vehicles keep longer spacing and time gap with the lead vehicle, are less sensitive to the lead car behavior, and apply smaller acceleration when they start from stationary position compared to cars. It was also found that the parameters significantly varied across different vehicle pairs even for the same vehicle class and the same vehicle-following case. The estimated parameters were also validated as the VISSIM simulation with the estimated parameters better reflected the observed cumulative average speed and acceleration distributions than the simulation with the default parameters. The results indicate that differences in the parameters among different vehicle-following cases and the variability of parameters for different vehicle pairs must be considered in the fixed parameters for each vehicle class currently used in the Wiedemann’s model.


      PubDate: 2016-03-07T08:22:18Z
       
  • Weather and road geometry impact on longitudinal driving behavior:
           Exploratory analysis using an empirically supported acceleration modeling
           framework
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Samer H. Hamdar, Lingqiao Qin, Alireza Talebpour
      The objective of this paper is to quantify and characterize driver behavior under different roadway geometries and weather conditions. In order to explore how a driver perceives the rapidly changing driving surrounding (i.e. different weather conditions and road geometry configurations) and executes acceleration maneuvers accordingly, this paper extends a Prospect Theory based acceleration modeling framework. A driving simulator is utilized to conduct 76 driving experiments. Foggy weather, icy and wet roadway surfaces, horizontal and vertical curves, and different lane and shoulder widths are simulated while having participants driving behind a yellow cab at speeds/headways of their choice. After studying the driving trends observed in the different driving experiments, the extended Prospect Theory based acceleration model is calibrated using the produced trajectory data. The extended Prospect Theory based model parameters are able to reflect a change in risk-perception and acceleration maneuvering when receiving different parameterized exogenous information. The results indicate that drivers invest more attention and effort to deal with the roadway challenges compared to the effort to deal with the weather conditions. Moreover, the calibrated model is used to simulate a highway segment and observe the produced fundamental diagram. The preliminary results suggest that the model is capable of capturing driver behavior under different roadway and weather conditions leading to changes in capacity and traffic disruptions.


      PubDate: 2016-03-07T08:22:18Z
       
  • Development of hybrid optimization of train schedules model for N-track
           rail corridors
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Hamed Pouryousef, Pasi Lautala, David Watkins
      From a capacity perspective, efficient utilization of a railway corridor has two main objectives; avoidance of schedule conflicts, and finding a proper balance between capacity utilization and level of service (LOS). There are several timetable tools and commercial rail simulation packages available to assist in reaching these objectives, but few of them offer both automatic train conflict resolution and automatic timetable management features for the different types of corridor configurations. This research presents a new rescheduling model to address some of the current limitations. The multi-objective linear programming (LP) model is called “Hybrid Optimization of Train Schedules” (HOTS), and it works together with commercial rail simulation tools to improve capacity utilization or LOS metrics. The HOTS model uses both conflict resolution and timetable compression techniques and is applicable to single-, double-, and multiple-track corridors (N-track networks), using both directional and bi-directional operations. This paper presents the approach, formulation and data requirements for the HOTS model. Single and multi-track case studies test and demonstrate the model’s train conflict resolution and timetable compression capabilities, and the model’s results are validated by using RailSys simulation package. The HOTS model performs well in each tested scenario, providing comparable results (either improved or similar) to the commercial packages.


      PubDate: 2016-03-07T08:22:18Z
       
  • Multi-objective re-synchronizing of bus timetable: Model, complexity and
           solution
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Yinghui Wu, Hai Yang, Jiafu Tang, Yang Yu
      This work is originally motived by the re-planning of a bus network timetable. The existing timetable with even headways for the network is generated using line by line timetabling approach without considering the interactions between lines. Decision-makers (i.e., schedulers) intend to synchronize vehicle timetable of lines at transfer nodes to facilitate passenger transfers while being concerned with the impacts of re-designed timetable on the regularity of existing timetable and the accustomed trip plans of passengers. Regarding this situation, we investigate a multi-objective re-synchronizing of bus timetable (MSBT) problem, which is characterized by headway-sensitive passenger demand, uneven headways, service regularity, flexible synchronization and involvement of existing bus timetable. A multi-objective optimization model for the MSBT is proposed to make a trade-off between the total number of passengers benefited by smooth transfers and the maximal deviation from the departure times of the existing timetable. By clarifying the mathematical properties and solution space of the model, we prove that the MSBT problem is NP-hard, and its Pareto-optimal front is non-convex. Therefore, we design a non-dominated sorting genetic (NSGA-II) based algorithm to solve this problem. Numerical experiments show that the designed algorithm, compared with enumeration method, can generate high-quality Pareto solutions within reasonable times. We also find that the timetable allowing larger flexibility of headways can obtain more and better Pareto-optimal solutions, which can provide decision-makers more choice.


      PubDate: 2016-03-07T08:22:18Z
       
  • A binary decision model for discretionary lane changing move based on
           fuzzy inference system
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Esmaeil Balal, Ruey Long Cheu, Thompson Sarkodie-Gyan
      This paper presents a Fuzzy Inference System (FIS) which models a driver’s binary decision to or not to execute a discretionary lane changing move on freeways. It answers the following question “Is it time to begin to move into the target lane?” after the driver has decided to change lane and have selected the target lane. The system uses four input variables: the gap between the subject vehicle and the preceding vehicle in the original lane, the gap between the subject vehicle and the preceding vehicle in the target lane, the gap between the subject vehicle and the following vehicle in the target lane, and the distance between the preceding and following vehicles in the target lanes. The input variables were selected based on the outcomes of a drivers survey, and can be measured by sensors instrumented in the subject vehicle. The FIS was trained with Next Generation SIMulation (NGSIM) vehicle trajectory data collected at the I-80 Freeway in Emeryville, California, and then tested with data collected at the U.S. Highway 101 in Los Angeles, California. The results of the test have shown that the system made lane change recommendations of “yes, change lane” with 82.2% accuracy, and “no, do not change lane” with 99.5% accuracy. These accuracies are better than the same performance measures given by the TRANSMODELER’s gap acceptance model for discretionary lane change on freeways, which is also calibrated with NGSIM data. The developed FIS has a potential to be implemented in lane change advisory systems, in autonomous vehicles, as well as microscopic traffic simulation tools.


      PubDate: 2016-03-07T08:22:18Z
       
  • Incorporating institutional and spatial factors in the selection of the
           optimal locations of public electric vehicle charging facilities: A case
           study of Beijing, China
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Sylvia Y. He, Yong-Hong Kuo, Dan Wu
      In this paper, we present a case study on planning the locations of public electric vehicle (EV) charging stations in Beijing, China. Our objectives are to incorporate the local constraints of supply and demand on public EV charging stations into facility location models and to compare the optimal locations from three different location models. On the supply side, we analyse the institutional and spatial constraints in public charging infrastructure construction to select the potential sites. On the demand side, interviews with stakeholders are conducted and the ranking-type Delphi method is used when estimating the EV demand with aggregate data from municipal statistical yearbooks and the national census. With the estimated EV demand, we compare three classic facility location models – the set covering model, the maximal covering location model, and the p-median model – and we aim to provide policy-makers with a comprehensive analysis to better understand the effectiveness of these traditional models for locating EV charging facilities. Our results show that the p-median solutions are more effective than the other two models in the sense that the charging stations are closer to the communities with higher EV demand, and, therefore, the majority of EV users have more convenient access to the charging facilities. From the experiments of comparing only the p-median and the maximal covering location models, our results suggest that (1) the p-median model outperforms the maximal covering location model in terms of satisfying the other’s objective, and (2) when the number of charging stations to be built is large, or when minor change is required, the solutions to both models are more stable as p increases.


      PubDate: 2016-03-07T08:22:18Z
       
  • Quasi-optimal feedback control for an isolated intersection under
           oversaturation
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Weili Sun, Yunpeng Wang, Guizhen Yu, Henry X. Liu
      How to manage signalized intersections under oversaturated conditions is a long-standing problem in traffic science and engineering. However, although research works in this area date back to 1960s, an on-line control strategy with theoretically bounded performance is missing, even for the control of an isolated intersection under oversaturation. This paper makes one step further in this area by proposing a QUEUE-based quasi-optimal feedback control (abbreviated as QUEUE) strategy for an isolated oversaturated intersection. The QUEUE strategy is intuitive, simple, and proved to match the off-line optimum in the case of constant demand. More importantly, the bounds of sub-optimality of the QUEUE strategy can be specified quantitatively in general piece-wise constant demand cases. To better deal with the maximum queue constraints, the oversaturation period is divided into the queuing period and the dissipation period with two different objectives. In the queuing period, the primary objective is to keep the queue length within the maximum value; but for the dissipation period, the primary objective is to eliminate all queues at the earliest time. Interestingly, we found that both control objectives can be realized with the same QUEUE strategy. Numerical examples show that the QUEUE strategy approximates the off-line optimum very well. The average sub-optimality in comparison with the off-line optimum in the challenging conditions with Poisson distributed random demand is below 5%.


      PubDate: 2016-03-07T08:22:18Z
       
  • The Share-a-Ride problem with stochastic travel times and stochastic
           delivery locations
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Baoxiang Li, Dmitry Krushinsky, Tom Van Woensel, Hajo A. Reijers
      We consider two stochastic variants of the Share-a-Ride problem: one with stochastic travel times and one with stochastic delivery locations. Both variants are formulated as a two-stage stochastic programming model with recourse. The objective is to maximize the expected profit of serving a set of passengers and parcels using a set of homogeneous vehicles. Our solution methodology integrates an adaptive large neighborhood search heuristic and three sampling strategies for the scenario generation (fixed sample size sampling, sample average approximation, and sequential sampling procedure). A computational study is carried out to compare the proposed approaches. The results show that the convergence rate depends on the source of stochasticity in the problem: stochastic delivery locations converge faster than stochastic travel times according to the numerical test. The sample average approximation and the sequential sampling procedure show a similar performance. The performance of the fixed sample size sampling is better compared to the other two approaches. The results suggest that the stochastic information is valuable in real-life and can dramatically improve the performance of a taxi sharing system, compared to deterministic solutions.


      PubDate: 2016-03-07T08:22:18Z
       
  • A three-level framework for performance-based railway timetabling
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Rob M.P. Goverde, Nikola Bešinović, Anne Binder, Valentina Cacchiani, Egidio Quaglietta, Roberto Roberti, Paolo Toth
      The performance of railway operations depends highly on the quality of the railway timetable. In particular for dense railway networks it can be challenging to obtain a stable robust conflict-free and energy-efficient timetable with acceptable infrastructure occupation and short journey times. This paper presents a performance-based railway timetabling framework integrating timetable construction and evaluation on three levels: microscopic, macroscopic, and a corridor fine-tuning level, where each performance indicator is optimized or evaluated at the appropriate level. A modular implementation of the three-level framework is presented and demonstrated on a case study on the Dutch railway network illustrating the feasibility of this approach to achieve the highest timetabling design level.


      PubDate: 2016-03-07T08:22:18Z
       
  • Speed variation during peak and off-peak hours on urban arterials in
           Shanghai
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Xuesong Wang, Tianxiang Fan, Weinan Li, Rongjie Yu, Darcy Bullock, Bing Wu, Paul Tremont
      Increased speed variation on urban arterials is associated with reductions in both operational performance and safety. Traffic flow, mean speed, traffic control parameters and geometric design features are known to affect speed variation. An exploratory study of the relationships among these variables could provide a foundation for improving the operational and safety performance of urban arterials, however, such a study has been hampered by problems in measuring speeds. The measurement of speed has traditionally been accomplished using spot speed collection methods such as radar, laser and loop detectors. These methods can cover only limited locations, and consequently are not able to capture speed distributions along an entire network, or even throughout any single road segment. In Shanghai, it is possible to acquire the speed distribution of any roadway segment, over any period of interest, by capturing data from Shanghai’s 50,000+ taxis equipped with Global Positional Systems (GPS). These data, hereafter called Floating Car Data, were used to calculate mean speed and speed variation on 234 road segments from eight urban arterials in downtown Shanghai. Hierarchical models with random variables were developed to account for spatial correlations among segments within each arterial and heterogeneities among arterials. Considering that traffic demand changes throughout the day, AM peak, Noon off-peak, and PM peak hours were studied separately. Results showed that increases in number of lanes and number of access points, the presence of bus stops and increases in mean speed were all associated with increased speed variation, and that increases in traffic volume and traffic signal green times were associated with reduced speed variation. These findings can be used by engineers to minimize speed differences during the road network planning stage and continuing through the traffic management phase.


      PubDate: 2016-03-07T08:22:18Z
       
  • Assessing public opinions of and interest in new vehicle technologies: An
           Austin perspective
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Prateek Bansal, Kara M. Kockelman, Amit Singh
      Technological advances are bringing connected and autonomous vehicles (CAVs) to the ever-evolving transportation system. Anticipating public acceptance and adoption of these technologies is important. A recent internet-based survey polled 347 Austinites to understand their opinions on smart-car technologies and strategies. Results indicate that respondents perceive fewer crashes to be the primary benefit of autonomous vehicles (AVs), with equipment failure being their top concern. Their average willingness to pay (WTP) for adding full (Level 4) automation ($7253) appears to be much higher than that for adding partial (Level 3) automation ($3300) to their current vehicles. Ordered probit and other model specifications estimate the impact of demographics, built-environment variables, and travel characteristics on Austinites’ WTP for adding various automation technologies and connectivity to their current and coming vehicles. It also estimates adoption rates of shared autonomous vehicles (SAVs) under different pricing scenarios ($1, $2, and $3 per mile), choice dependence on friends’ and neighbors’ adoption rates, and home-location decisions after AVs and SAVs become a common mode of transport. Higher-income, technology-savvy males, who live in urban areas, and those who have experienced more crashes have a greater interest in and higher WTP for the new technologies, with less dependence on others’ adoption rates. Such behavioral models are useful to simulate long-term adoption of CAV technologies under different vehicle pricing and demographic scenarios. These results can be used to develop smarter transportation systems for more efficient and sustainable travel.


      PubDate: 2016-02-24T16:24:32Z
       
  • Incorporating observed and unobserved heterogeneity in route choice
           analysis with sampled choice sets
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Dawei Li, Tomio Miwa, Takayuki Morikawa, Pan Liu
      An increasing number of researchers have shown an interest in enhancing discrete choice models by incorporating psychological and behavioral factors. The main objective of this paper is to explore the effect of observed and unobserved heterogeneity on route choice. The mixed logit framework is applied to consider the heterogeneity. In contrast with previous research, the repeat choice problem is dealt with by treating the random coefficients as consisting of three parts: individual specific term, O–D pair specific term and choice situation specific term. The solution of choice set generation in this study is the sampling method based on random walk. The sampling biases are corrected in the choice models. GPS data collected by private vehicles in Toyota city, Japan is used to estimate the choice models proposed in this study. This empirical analysis demonstrates that incorporation of observed characteristics and unobserved O–D pair specific heterogeneity can enhance route choice models significantly. It is also confirmed that drivers’ taste is significantly affected by age, gender, vehicle displacement, O–D distance and familiarity with the O–D.


      PubDate: 2016-02-24T16:24:32Z
       
  • Optimization of airport bus timetable in cultivation period considering
           passenger dynamic airport choice under conditions of uncertainty
    • Abstract: Publication date: June 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 67
      Author(s): Jing Lu, Zhongzhen Yang, Harry Timmermans, Wendi Wang
      An airport bus service, which is newly introduced in a multi-airport region, commonly leads to a gradually increasing market share of airports until a new state of equilibrium is reached. With the goal of speeding up and enlarging the increase in market share, this paper proposes a timetable optimization model by incorporating reactions of airport-loyal passengers to bus service quality. The simulation part of the model, which uses cumulative prospect theory to formulate discrete airport choices, results in predicted passenger demand needed in the optimization part. Then a genetic algorithm for multi-objective optimization problems called NSGA-II is applied to solve the model. To illustrate the model, the “Lukou airport-Wuxi” airport bus in China is taken as an example. The results show that the optimized timetables shorten the cultivation period and impel the market share to grow rapidly.


      PubDate: 2016-02-24T16:24:32Z
       
 
 
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