for Journals by Title or ISSN
for Articles by Keywords
help
  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)

The end of the list has been reached or no journals were found for your choice.
Journal Cover Transportation Research Part C: Emerging Technologies
  [SJR: 2.062]   [H-I: 72]   [21 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [3039 journals]
  • Estimating potential increases in travel with autonomous vehicles for the
           non-driving, elderly and people with travel-restrictive medical conditions
           
    • Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Corey D. Harper, Chris T. Hendrickson, Sonia Mangones, Constantine Samaras
      Automated vehicles represent a technology that promises to increase mobility for many groups, including the senior population (those over age 65) but also for non-drivers and people with medical conditions. This paper estimates bounds on the potential increases in travel in a fully automated vehicle environment due to an increase in mobility from the non-driving and senior populations and people with travel-restrictive medical conditions. In addition, these bounding estimates indicate which of these demographics could have the greatest increases in annual vehicle miles traveled (VMT) and highlight those age groups and genders within these populations that could contribute the most to the VMT increases. The data source is the 2009 National Household Transportation Survey (NHTS), which provides information on travel characteristics of the U.S. population. The changes to light-duty VMT are estimated by creating and examining three possible travel demand wedges. In demand wedge one, non-drivers are assumed to travel as much as the drivers within each age group and gender. Demand wedge two assumes that the driving elderly (those over age 65) without medical conditions will travel as much as a younger population within each gender. Demand wedge three makes the assumption that working age adult drivers (19–64) with medical conditions will travel as much as working age adults without medical conditions within each gender, while the driving elderly with medical any travel-restrictive conditions will travel as much as a younger demographic within each gender in a fully automated vehicle environment. The combination of the results from all three demand wedges represents an upper bound of 295billion miles or a 14% increase in annual light-duty VMT for the US population 19 and older. Since traveling has other costs besides driving effort, these estimates serve to bound the potential increase from these populations to inform the scope of the challenges, rather than forecast specific VMT scenarios.


      PubDate: 2016-09-24T11:26:00Z
       
  • A model and optimization-based heuristic for the operational aircraft
           maintenance routing problem
    • Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Nayla Ahmad Al-Thani, Mohamed Ben Ahmed, Mohamed Haouari
      This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.


      PubDate: 2016-09-24T11:26:00Z
       
  • Emergence of cooperation in congested road networks using ICT and future
           and emerging technologies: A game-based review
    • Abstract: Publication date: November 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 72
      Author(s): Ido Klein, Eran Ben-Elia
      Information and communications technologies (ICT) and future and emerging technologies (FET) are expected to revolutionize transportation in the next generation. Travelers’ behavioral adaptation is a key to their success. We discuss the notion of managing traffic congestion by enhancing cooperation in road networks enabled with ICT and FET. Cooperation is an emergent social state related to the dynamics and complexity of road traffic and reinforced learning. Game theory and research in behavioral economics show that cooperation can be leveraged to efficiently solve social dilemmas similar to traffic congestion. We review the applicability of behavioral economics and game theory concepts to route, mode and departure time choice problems. Beyond advancing theory, research on cooperation in the context of transportation is still in its infancy. We discuss state-of-the-art methodologies and their weaknesses and review the unexplored opportunities inherent in game-based methodologies. A behavioral-technological research agenda for FET is also discussed.


      PubDate: 2016-09-24T11:26:00Z
       
  • Bus control strategies in corridors with signalized intersections
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Miquel Estrada, Josep Mensión, Josep M. Aymamí, Laura Torres
      This paper proposes a new dynamic bus control strategy aimed at reducing the negative effects of time-headway variations on route performance, based on real-time bus tracking data at stops. In routes with high demand, any delay of a single vehicle ends up causing an unstable motion of buses and producing the bus bunching phenomena. This strategy controls the cruising speed of buses and considers the extension of the green phase of traffic lights at intersections, when a bus is significantly delayed. The performance of this strategy will be compared to the current static operation technique based on the provision of slack times at holding points. An operational model is presented in order to estimate the effects of each controlling strategy, taking into account the vehicle capacity constraint. Control strategies are assessed in terms of passenger total travel time, operating cost as well as on the coefficient of headway variation. The effects of controlling strategies are tested in an idealized bus route under different operational settings and in the bus route of highest demand in Barcelona by simulation. The results show that the proposed dynamic controlling strategy reduces total system cost (user and agency) by 15–40% as well as the coefficient of headway variation 53–78% regarding the uncontrolled case, providing a bus performance similar to the expected when time disturbance is not presented.


      PubDate: 2016-09-12T00:17:46Z
       
  • Generic model for resource allocation in transportation. Application to
           urban parking management
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Mahdi Zargayouna, Flavien Balbo, Khadim Ndiaye
      In this paper, we define the online localized resource allocation problem, especially relevant for modeling transportation applications. The problem modeling takes into account simultaneously the geographical location of consumers and resources together with their online nondeterministic appearance. We use urban parking management as an illustration of this problem. In fact, urban parking management is an online localized resource allocation problem, where the question is how to find an efficient allocation of parking spots to drivers, while they all have dynamic geographical positions and appear nondeterministically. We define this problem and propose a multiagent system to solve it. The objective of the system is to decrease, for private vehicles drivers, the parking spots search time. The drivers are organized in communities and share information about spots availability. We have defined two cooperative models and compared them: a fully cooperative model, where agents share all the available information, and a “coopetitive” model, where drivers do not share information about the spot that they have chosen. Results show the superiority of the first model.


      PubDate: 2016-09-12T00:17:46Z
       
  • Efficient multiple model particle filtering for joint traffic state
           estimation and incident detection
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Ren Wang, Shimao Fan, Daniel B. Work
      This article proposes an efficient multiple model particle filter (EMMPF) to solve the problems of traffic state estimation and incident detection, which requires significantly less computation time compared to existing multiple model nonlinear filters. To incorporate the on ramps and off ramps on the highway, junction solvers for a traffic flow model with incident dynamics are developed. The effectiveness of the proposed EMMPF is assessed using a benchmark hybrid state estimation problem, and using synthetic traffic data generated by a micro-simulation software. Then, the traffic estimation framework is implemented using field data collected on Interstate 880 in California. The results show the EMMPF is capable of estimating the traffic state and detecting incidents and requires an order of magnitude less computation time compared to existing algorithms, especially when the hybrid system has a large number of rare models.


      PubDate: 2016-09-12T00:17:46Z
       
  • Optimal vehicle trajectory planning in the context of cooperative merging
           on highways
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Ioannis A. Ntousakis, Ioannis K. Nikolos, Markos Papageorgiou
      One of the main triggers of traffic congestion on highways is vehicle merging at on-ramps. The development of automated procedures for cooperative vehicle merging is aimed to ensure safety and alleviate congestion problems. In this work, a longitudinal trajectory planning methodology is presented, developed to assist the merging of vehicles on highways; it achieves safe and traffic-efficient merging, while minimizing the engine effort and passenger discomfort through the minimization of acceleration and its first and second derivatives during the merging maneuver. The problem is formulated as a finite-horizon optimal control problem and is solved analytically. This enables the solution to be stored on-board, saving computational time and rendering the methodology suitable for practical applications. The tunable weights, used for taking into account the different optimization criteria, may serve as parameters to match the individual driver’s preferences. The proposed methodology is first developed for a pair of cooperating vehicles, a merging one and its putative leader. Moreover, an alternative solution procedure via a time-variant Linear-Quadratic Regulator approach is also presented. A Model Predictive Control (MPC) scheme is utilized to compensate possible disturbances in the trajectories of the cooperating vehicles, whereby the analytical optimal solution is applied repeatedly in real time, using updated measurements, until the merging procedure is actually finalized. Subsequently, the methodology is generalized for a set of vehicles inside the merging area. Various numerical simulations illustrate the validity and applicability of the method.


      PubDate: 2016-09-06T07:36:30Z
       
  • Detecting trip purposes from smartphone-based travel surveys with
           artificial neural networks and particle swarm optimization
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Guangnian Xiao, Zhicai Juan, Chunqin Zhang
      Travel surveys based on global positioning system (GPS) data have exponentially increased over the past decades. Trip characteristics, including trip ends, travel modes, and trip purposes need to be detected from GPS data. Compared with other trip characteristics, studies on trip purpose detection are limited. These studies struggle with three types of limitations, namely, data validation, classification approach-related issues, and result comparison under multiple scenarios. Therefore, we attempt to obtain full understanding and improve these three aspects when detecting trip purposes in the current study. First, a smartphone-based travel survey is employed to collect GPS data, and a surveyor-intervened prompted recall survey is used to validate trip characteristics automatically detected from the GPS data. Second, artificial neural networks combined with particle swarm optimization are used to detect trip purposes from the GPS data. Third, four scenarios are constructed by employing two methods for land-use type coding, i.e., polygon-based information and point of interest, and two methods for selecting training dataset, i.e., equal proportion selection and equal number selection. The accuracy of trip purpose detection is then compared under these scenarios. The highest accuracies of 97.22% for the training dataset and 96.53% for the test dataset are achieved under the scenario of polygon-based information and equal proportion selection by comparing the detected and validated trip purposes. Promising results indicate that a smartphone-based travel survey can complement conventional travel surveys.


      PubDate: 2016-09-06T07:36:30Z
       
  • Validation of an agent based model using a participatory simulation gaming
           approach: The case of city logistics
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Nilesh Anand, David Meijer, J.H.R. van Duin, Lóránt Tavasszy, Sebastiaan Meijer
      Agent-based modeling is used for simulating the actions and interactions of autonomous entities aiming to assessing their effects on the system as a whole. At an abstract level, an agent-based model (ABM) is a representation of the many simple agents and interactions among them. The decision making of the agents is based on the rules given to them. In an ABM, the model output is the result of internal decision-making and may differ with alteration in the decision path. On the contrary, with the set of rules embedded in agents, their behavior is modeled to take a ‘certain action’ in a ‘certain situation’. It suggests that the internal decision making behavior of agents is truly responsible for the model output and thus it cannot be ignored while validating ABMs. This research article focuses on the validating agents’ behavior by evaluating decision-making processes of agents. For this purpose, we propose a validation framework based on a participatory simulation game. Using this framework we engage a human player (i.e. a domain stakeholder) to allow us to collect information about choices and validate the behavior of an individual agent. A proof-of-concept game is developed for a city logistics ABM to test the framework.


      PubDate: 2016-09-06T07:36:30Z
       
  • A study of pedestrian group behaviors in crowd evacuation based on an
           extended floor field cellular automaton model
    • Abstract: Publication date: Available online 3 September 2016
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Lili Lu, Ching-Yao Chan, Jian Wang, Wei Wang
      In the study of pedestrian movements, a consideration of group behaviors is important because of their potential impacts on pedestrian flow dynamics. In this paper, we investigate the group behaviors during emergency evacuation, which is a critical case for emergency crowd management but has not been fully explored and understood. It has been well recognized that in evacuation situations, some people within a crowd, especially those who are with families and friends, often move in small groups and act in particular patterns distinct from individuals. As a result, the crowd is a mixture of individuals and groups rather than a pure collection of individuals. To capture and evaluate the influence of group behaviors on crowd evacuation, we propose an extended floor field cellular automaton (CA) model that takes into account such phenomena. Our model is formulated by leveraging the leader-follower behavior rule that is evident in pedestrian group behaviors. To calibrate and validate the proposed model, a few field experiments of crowd evacuation were conducted in a university building. Through a representative case study, it is demonstrated that the proposed extended floor field CA model can replicate the well-known phenomena in crowd evacuation such as collective arch-like clogging at the exit as well as other commonly observed group behaviors in evacuation. Moreover, it is found that the total crowd evacuation time significantly increases with the presence of pedestrian groups in the crowd. The results also show that such negative effects of group behaviors in crowd evacuation intensify when the density of the crowd is higher. Subsequently, sensitivity analyses are performed to further explore how pedestrian group behaviors are influenced by model parameters that reflect the pedestrian flow dynamics in evacuation scenarios. With its capability of realistically replicating the field pedestrian evacuation, the proposed model can serve as a valuable tool for predicting crowd evacuation time and designing guidelines for pedestrian evacuation in emergency situations, in particular when group behaviors are salient.


      PubDate: 2016-09-06T07:36:30Z
       
  • Selected papers from the eighth Triennial Symposium on Transportation
           Analysis (TRISTAN VIII)
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Cristián E. Cortés, Pablo A. Rey, Doris Sáez



      PubDate: 2016-08-31T07:35:35Z
       
  • A real-life Multi Depot Multi Period Vehicle Routing Problem with a
           Heterogeneous Fleet: Formulation and Adaptive Large Neighborhood Search
           based Matheuristic
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Simona Mancini
      In this paper, a new rich Vehicle Routing Problem that could arise in a real life context is introduced and formalized: the Multi Depot Multi Period Vehicle Routing Problem with a Heterogeneous Fleet. The goal of the problem is to minimize the total delivery cost. A heterogeneous fleet composed of vehicles with different capacity, characteristics (i.e. refrigerated vehicles) and hourly costs is considered. A limit on the maximum route duration is imposed. Unlike what happens in classical multi-depot VRP, not every customer may/will be served by all the vehicles or from all the depots. The planning horizon, as in most real life applications, consists of multiple periods, and the period in which each route is performed is a variable of the problem. The set of periods, within the time horizon, in which the delivery may be carried out is known for each customer. A Mixed Integer Programming (MIP) formulation for MDMPVRPHF is presented in this paper, and an Adaptive Large Neighborhood Search (ALNS) based Matheuristic approach is proposed, in which different destroy operators are defined. Computational results, pertaining to realistic instances, which show the effectiveness of the proposed method, are provided.


      PubDate: 2016-08-31T07:35:35Z
       
  • A multi-space sampling heuristic for the green vehicle routing problem
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Alejandro Montoya, Christelle Guéret, Jorge E. Mendoza, Juan G. Villegas
      The green vehicle routing problem (Green VRP) is an extension of the VRP in which routes are performed using alternative fuel vehicles (AFVs). AFVs have limited tank capacity, so routes may visit alternative fuel stations (AFSs) en-route. We propose a simple yet effective two-phase heuristic to tackle the Green VRP. In the first phase our heuristic builds a pool of routes via a set of randomized route-first cluster-second heuristics with an optimal AFSs insertion procedure. In the second phase our approach assembles a Green VRP solution by solving a set partitioning formulation over the columns (routes) stored in the pool. To test our approach, we performed experiments on a set of 52 instances from the literature. The results show that our heuristic is competitive with state-of-the-art methods. Our heuristic unveiled 8 new best-known solutions, matched another 40, and delivered solutions with an average gap of 0.14% for the 4 remaining instances.


      PubDate: 2016-08-31T07:35:35Z
       
  • Reprint of: Modelling the impact of traffic incidents on travel time
           reliability
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Ahmad Tavassoli Hojati, Luis Ferreira, Simon Washington, Phil Charles, Ameneh Shobeirinejad
      Traffic incidents are recognised as one of the key sources of non-recurrent congestion that often leads to reduction in travel time reliability (TTR), a key metric of roadway performance. A method is proposed here to quantify the impacts of traffic incidents on TTR on freeways. The method uses historical data to establish recurrent speed profiles and identifies non-recurrent congestion based on their negative impacts on speeds. The locations and times of incidents are used to identify incidents among non-recurrent congestion events. Buffer time is employed to measure TTR. Extra buffer time is defined as the extra delay caused by traffic incidents. This reliability measure indicates how much extra travel time is required by travellers to arrive at their destination on time with 95% certainty in the case of an incident, over and above the travel time that would have been required under recurrent conditions. An extra buffer time index (EBTI) is defined as the ratio of extra buffer time to recurrent travel time, with zero being the best case (no delay). A Tobit model is used to identify and quantify factors that affect EBTI using a selected freeway segment in the Southeast Queensland, Australia network. Both fixed and random parameter Tobit specifications are tested. The estimation results reveal that models with random parameters offer a superior statistical fit for all types of incidents, suggesting the presence of unobserved heterogeneity across segments. What factors influence EBTI depends on the type of incident. In addition, changes in TTR as a result of traffic incidents are related to the characteristics of the incidents (multiple vehicles involved, incident duration, major incidents, etc.) and traffic characteristics.


      PubDate: 2016-08-31T07:35:35Z
       
  • A copula-based approach to accommodate the dependence among microscopic
           traffic variables
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Yajie Zou, Yunlong Zhang
      Developing microscopic traffic simulation models requires the knowledge of probability distributions of microscopic traffic variables. Although previous studies have proposed extensive mathematical distributions for describing traffic variables (e.g., speed, headway, vehicle length, etc.), these studies usually consider microscopic traffic observations to be independent variables and distributions for these variables are investigated separately. As a result, some traditional approaches consider microscopic traffic variables as independent inputs to the traffic simulation process and these methods may ignore the possible dependence among different traffic variables. The objectives of this paper are to investigate the dependence structure among microscopic traffic variables and to examine the applicability of the copula approach to the joint modeling of these variables. Copulas are functions that relate multivariate distribution functions of random variables to their one-dimensional marginal distribution functions. The concept of copulas has been well recognized in the statistics field and recently has been introduced in transportation studies. The proposed copula approach is applied to the 24-h traffic data collected on IH-35 in Austin, Texas. The preliminary data analysis indicates that there exists dependence among microscopic traffic variables. Moreover, the modeling and simulation results suggest that copula models can adequately accommodate and accurately reproduce the dependence structure revealed by the traffic observations. Overall, the findings in this paper provide a framework for generating multiple microscopic traffic variables simultaneously by considering their dependence.


      PubDate: 2016-08-31T07:35:35Z
       
  • Real-time prediction of secondary incident occurrences using vehicle probe
           data
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Hyoshin Park, Ali Haghani
      Effective incident management system requires quantifying non-recurring congestion and detecting a secondary incident under the negative influence of a primary incident. Previously suggested thresholds and measurement parameters provide no universal definition of a secondary incident, regardless of discussions on the topic. To solve this dilemma, we propose a Bayesian structure equation model to recognize congestion patterns for road segments using INRIX Data. An adjustment of the boxplot is applied to capture segments at the tail of the queue and at the head of the queue where secondary incidents might occur. The resulting contour plot provides temporal–spatial area under congestion to identify secondary incidents. The likelihood of classified secondary incidents are sequentially predicted from the point of incident response to the road clearance. The prediction performance of the principled Bayesian learning approach to neural networks outperforms the logistic model. The quality of predictions improve as new information (e.g. notification-arrival of response units, speed) becomes available. A pedagogical rule extraction approach will improve the ability to understand secondary incidents by extracting comprehensible rules from the neural networks. The symbolic description represents a series of decisions to assist emergency operators in their decision-making capabilities.


      PubDate: 2016-08-31T07:35:35Z
       
  • Model-based predictive control for bicycling in urban intersections
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): C. Portilla, F. Valencia, J. Espinosa, A. Núñez, B. De Schutter
      In this paper, a model predictive control approach for improving the efficiency of bicycling as part of intermodal transportation systems is proposed. Considering a dedicated bicycle lanes infrastructure, the focus in this paper is to optimize the dynamic interaction between bicycles and vehicles at the multimodal urban traffic intersections. In the proposed approach, a dynamic model for the flows, queues, and number of both vehicles and bicycles is explicitly incorporated in the controller. For obtaining a good trade-off between the total time spent by the cyclists and by the drivers, a Pareto analysis is proposed to adjust the objective function of the MPC controller. Simulation results for a two-intersections urban traffic network are presented and the controller is analyzed considering different methods of including in the MPC controller the inflow demands of both vehicles and bicycles.


      PubDate: 2016-08-31T07:35:35Z
       
  • Analysis of dilemma zone for pedestrians at high-speed uncontrolled
           midblock crossing
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Digvijay S. Pawar, Vinit Kumar, Navdeep Singh, Gopal R. Patil
      Pedestrians are vulnerable to accidents and are at risk whenever they share road space with vehicular traffic. The risk to pedestrians is especially high in countries like India where a large number of pedestrians are seen on roads with vehicular traffic not yielding to their needs. Therefore, it is not surprising that many of the victims of road accidents in India, particularly in urban areas, are pedestrians. One of the primary reasons for accidents at pedestrian crossing (when drivers do not yield to pedestrians) lies in the inability of pedestrians to judge the safe gap while crossing a road. Often pedestrians are in a state of confusion/dilemma while making a decision on whether to accept or reject a gap. Under these circumstances, decision making for pedestrians during road crossing is a tough task. This paper aims at analyzing and quantifying the dilemma zone for crossing pedestrians at high-speed uncontrolled midblock crossings. A field study was undertaken in which crossing pedestrians and through-moving vehicles were recorded by placing video cameras at selected midblock road sections, having marked pedestrian crossing. Total of 1107 pedestrian lags/gaps were extracted and studied with consideration of both temporal and spatial lags/gaps for analysis. Dilemma zone was determined by using different methods such as the gap cumulative distribution method, the binary logit method, the support vector machine and the probabilistic method. For the selected midblock sections, dilemma zone started at 49m and ended at 62m upstream from the marked pedestrian crossing. The upper and lower boundaries of dilemma zone can be further used to develop a pedestrian assistance system at midblock crossing for the safe movement of pedestrian.


      PubDate: 2016-08-31T07:35:35Z
       
  • Advances in Traffic Management and Planning
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Jean-Patrick Lebacque, Megan M. Khoshyaran



      PubDate: 2016-08-31T07:35:35Z
       
  • A robust optimization approach for dynamic traffic signal control with
           emission considerations
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Ke Han, Hongcheng Liu, Vikash V. Gayah, Terry L. Friesz, Tao Yao
      We consider an analytical signal control problem on a signalized network whose traffic flow dynamic is described by the Lighthill–Whitham–Richards (LWR) model (Lighthill and Whitham, 1955; Richards, 1956). This problem explicitly addresses traffic-derived emissions as constraints or objectives. We seek to tackle this problem using a mixed integer mathematical programming approach. Such class of problems, which we call LWR-Emission (LWR-E), has been analyzed before to certain extent. Since mixed integer programs are practically efficient to solve in many cases (Bertsimas et al., 2011b), the mere fact of having integer variables is not the most significant challenge to solving LWR-E problems; rather, it is the presence of the potentially nonlinear and nonconvex emission-related constraints/objectives that render the program computationally expensive. To address this computational challenge, we proposed a novel reformulation of the LWR-E problem as a mixed integer linear program (MILP). This approach relies on the existence of a statistically valid macroscopic relationship between the aggregate emission rate and the vehicle occupancy on the same link. This relationship is approximated with certain functional forms and the associated uncertainties are handled explicitly using robust optimization (RO) techniques. The RO allows emissions-related constraints and/or objectives to be reformulated as linear forms under mild conditions. To further reduce the computational cost, we employ a link-based LWR model to describe traffic dynamics with the benefit of fewer (integer) variables and less potential traffic holding. The proposed MILP explicitly captures vehicle spillback, avoids traffic holding, and simultaneously minimizes travel delay and addresses emission-related concerns.


      PubDate: 2016-08-31T07:35:35Z
       
  • A new rail optimisation model by integration of traffic management and
           train automation
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Xiaolu Rao, Markus Montigel, Ulrich Weidmann
      This paper reviews and classifies the traffic optimisation schemes of current mainline railway into two groups. One is to improve the efficiency of traffic management by providing resolutions for traffic conflicts, while the other is to improve trains’ driving behaviour by providing driver assistance or introducing train automation. Based on a comparison of these two groups, this paper proposes to combine the functions of traffic management and train automation into an integrated optimisation model. This model includes the following contributions. First, in the function of traffic management, this paper explores the flexibility in generating different train running trajectories to prevent potential traffic conflicts. The trajectory can improve traffic flow by avoiding unplanned train stops. This is regarded as a supplementary conflict resolution to train reordering or rerouting or retiming. Second, this paper defines a series of train control commands to determine different intensities of the train’s tractive force and braking force. These commands are seen as the key to train automation. Moreover, a decision-making procedure is introduced to select the most attractive train running trajectory or train control command according to different optimisation objectives. Lastly, this paper proves the importance of bidirectional communication between traffic management and train automation based on a case study.


      PubDate: 2016-08-31T07:35:35Z
       
  • Real-time estimation of secondary crash likelihood on freeways using
           high-resolution loop detector data
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Chengcheng Xu, Pan Liu, Bo Yang, Wei Wang
      This study aimed to develop a secondary crash risk prediction model on freeways using real-time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five years in California, United States. The secondary crashes were identified by a method based on speed contour plot. The random effect logit model was used to link the probability of secondary crashes with the real-time traffic flow conditions, primary crash characteristics, environmental conditions, and geometric characteristics. The results showed that real-time traffic variables significantly affect the likelihood of secondary crashes. These traffic variables include the traffic volume, average speed, standard deviation of detector occupancy, and volume difference between adjacent lanes. In addition, the primary crash characteristics, environmental conditions and geometric characteristics also significantly affect the risks of secondary crashes. The model evaluation results showed that the predictive performance of the developed model was deemed satisfactory. The inclusion of traffic flow variables and random effect increases prediction accuracy by 16.6% and 7.7%, respectively. These results have the potential to be used in advanced traffic management systems to develop proactive traffic control strategies to prevent the occurrences of secondary crashes on freeways.


      PubDate: 2016-08-31T07:35:35Z
       
  • Drivers’ rear end collision avoidance behaviors under different
           levels of situational urgency
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Xuesong Wang, Meixin Zhu, Ming Chen, Paul Tremont
      Rear-end collisions have been estimated to account for 20–30% of all crashes, and about 10% of all fatal crashes. A thorough investigation of drivers’ collision avoidance behaviors when exposed to rear end collision risks is needed to help guide the development of effective countermeasures. Urgency or criticality of the situation affects drivers’ collision behavior, but has not been systematically investigated. A high fidelity driving simulator was used to examine the effects of differing levels of situational urgency on drivers’ collision avoidance behaviors. Drivers’ braking and steering decisions, perception response times, throttle release response times, throttle to brake transition times, brake delays, maximum brake pedal pressures and peak decelerations were recorded under lead vehicle decelerations of 0.3g, 0.5g, and 0.75g and under headways of 1.5s and 2.5s. Results showed (1) as situational urgency increased, drivers released the accelerator and braked to maximum more quickly; (2) the transition time between initial throttle release and brake initiation was not affected by situational urgency; (3) at low situational urgency, multi-stage braking behavior led to longer delays from brake initiation to full braking. These findings show that effects of situational urgency on drivers’ response times, braking delays, and braking intensity should be considered when developing forward collision warnings systems.


      PubDate: 2016-08-31T07:35:35Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70




      PubDate: 2016-08-31T07:35:35Z
       
  • Crowdsourcing mobility insights – Reflection of attitude based segments
           on high resolution mobility behaviour data
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Ivana Semanjski, Sidharta Gautama
      Recently, the use of market segmentation techniques to promote sustainable transport has significantly increased. Populations are segmented into meaningful groups that share similar attitudes and preferences. This segmentation provides valuable information about how policy options, such as pricing measures or advertising campaigns, should be designed and promoted in order to successfully target different user groups. In this paper, we aim to bridge between psychological, social marketing and ICT research in the field of transportation. We explore how attitude based segments are reflected in high resolution mobility behaviour data, crowdsourced via mobile phones. We use support vector machines to map eight attitudinal segments, as defined under the European project SEGMENT, to the n dimensional space defined by crowdsourced data. The success rate of the proposed approach is 98.9%. This demonstrates the applicability of the method as a way to automatically map attitudinal segments to a wider population based on observed mobility data instead of using explicit attitudinal surveys. In addition, the proposed approach can facilitate the delivery of personalised target messages to individuals (e.g. via smartphones) or at target locations where users, belonging to specific segment, are located at specific time windows since the data includes the time-space indications.
      Graphical abstract image

      PubDate: 2016-08-31T07:35:35Z
       
  • An adaptive large neighborhood search heuristic for fleet deployment
           problems with voyage separation requirements
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): Rikard Bakkehaug, Jørgen G. Rakke, Kjetil Fagerholt, Gilbert Laporte
      In this paper we propose an adaptive large neighborhood search heuristic for a ship routing and scheduling problem with voyage separation requirements. The voyage separation requirement is modeled as the minimum time elapsed between two consecutive sailings on a trade, and yields solutions with voyages fairly evenly spread over the planning horizon. Voyage separation requires the schedule of each ship to be synchronized with those of the others, since the start of service of a voyage could affect the feasible start time of another voyage. The proposed heuristic is compared with an exact algorithm on several instances, and yields good quality solutions within relatively short computation times.


      PubDate: 2016-08-31T07:35:35Z
       
  • Ordered median hub location problems with capacity constraints
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): J. Puerto, A.B. Ramos, A.M. Rodríguez-Chía, M.C. Sánchez-Gil
      The Single Allocation Ordered Median Hub Location problem is a recent hub model introduced in Puerto et al. (2011) that provides a unifying analysis of a wide class of hub location models. In this paper, we deal with the capacitated version of this problem, presenting two formulations as well as some preprocessing phases for fixing variables. In addition, a strengthening of one of these formulations is also studied through the use of some families of valid inequalities. A battery of test problems with data taken from the AP library are solved where it is shown that the running times have been significantly reduced with the improvements presented in the paper.


      PubDate: 2016-08-31T07:35:35Z
       
  • Capacity, information and minority games in public transport
    • Abstract: Publication date: September 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 70
      Author(s): P.C. Bouman, Leo Kroon, Peter Vervest, Gábor Maróti
      Many public transport operators are faced with high peak demands. This leads to crowded vehicles and discomfort for the passengers. The increasing availability of information technologies creates new opportunities for passengers to avoid crowding and for operators to inform passengers and reallocate capacities quicker than before. We define and implement a simple model based on minority games, a class of games that deals with crowding dynamics, adapted to a public transport setting. We propose a model which includes multiple resources and heterogeneous passenger preferences. We have conducted two simulation studies, investigating the dynamics of crowding within a simplified public transport setting. In our first experiment we investigate the effect of the availability of information on crowding. In our second experiment we study dynamic optimization of rolling stock capacities. We find that both the information disclosed and the capacity optimization mechanism have an impact on the number of passengers utilizing resources and their satisfaction. As such, these models enable the development and analysis of new operator policies to deal with crowded situations.
      Graphical abstract image

      PubDate: 2016-08-31T07:35:35Z
       
  • Second order macroscopic traffic flow model validation using automatic
           differentiation with resilient backpropagation and particle swarm
           optimisation algorithms
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Adam Poole, Apostolos Kotsialos
      The problem of validating the Modéle d’Écoulement de Trafic sur Autoroute NETworks (METANET) model of a motorway section is considered. Model calibration is formulated as a least squares error minimisation problem with explicit penalisation of fundamental diagram parameter variation. The Automatic Differentiation by Overloading in C++ (ADOL-C) library is incorporated into the METANET source code and is coupled with the Resilient Back Propagation (RPROP) heuristic for solving the minimisation problem. The result is a very efficient system which is able to be calibrate METANET by determining the density and speed equation parameters as well as the fundamental diagrams used. Information obtained from the system’s Jacobian provides extra insight into the dynamics showing how sensitivities propagate into the network. A 22km site near Sheffield, UK, using data from three different days is considered. In addition to the ADOL-C/RPROP system, three particle swarm optimisation algorithms are used for solving the calibration problem. In all cases, the optimal parameter sets found are verified on data not used during calibration. Although, all three sets of data display a similar congestion pattern, the verification process showed that only one of them is capable of leading to parameter sets that capture the underlying dynamics of the traffic flow process.


      PubDate: 2016-08-26T14:56:06Z
       
  • Heuristic search for the coupled runway sequencing and taxiway routing
           problem
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Una Benlic, Alexander E.I. Brownlee, Edmund K. Burke
      This paper presents the first local search heuristic for the coupled runway sequencing (arrival & departure) and taxiway routing problems, based on the receding horizon (RH) scheme that takes into account the dynamic nature of the problem. As test case, we use Manchester Airport, the third busiest airport in the UK. From the ground movement perspective, the airport layout requires that departing aircraft taxi across the arrivals runway. This makes it impossible to separate arrival from departure sequencing in practice. Operationally, interactions between aircraft on the taxiways could prevent aircraft from taking off from, or landing on, runways during the slots assigned to them by an algorithm optimizing runway use alone. We thus consider the interactions between arrival and departure aircraft on the airport surface. Compared to sequentially optimized solutions, the results obtained with our approach indicate a significant decrease in the taxiway routing delay, with generally no loss in performance in terms of the sequencing delay for a regular day of operations. Another benefit of such a simultaneous optimization approach is the possibility of holding aircraft at the stands for longer, without the engines running. This significantly reduces the fuel burn, as well as bottlenecks and traffic congestion during peak hours that are often the cause of flight delays due to the limited amount of airport surface space available. Given that the maximum computing time per horizon is around 95s, real-time operation might be practical with increased computing power.


      PubDate: 2016-08-26T14:56:06Z
       
  • A simple reservation and allocation model of shared parking lots
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Chaoyi Shao, Hai Yang, Yi Zhang, Jintao Ke
      With increasing auto demands, efficient parking management is by no means less important than road traffic congestion control. This is due to shortages of parking spaces within the limited land areas of the city centers in many metropolises. The parking problem becomes an integrated part of traffic planning and management. On the other hand, it is a fact that many private parking spots are available during daytime in nearby residential compound because those residents drive their cars out to work. These temporarily vacant parking lots can be efficiently utilized to meet the parking demand of other drivers who are working at nearby locations or drivers who come for shopping or other activities. This paper proposes a framework and a simple model for embracing shared use of residential parking spaces between residents and public users. The proposed shared use is a winning strategy because it maximizes the use of private resources to benefit the community as a whole. It also creates a new business model enabled by the fast-growing mobile apps in our daily lives.


      PubDate: 2016-08-26T14:56:06Z
       
  • Adaptive scheduling for real-time and temporal information services in
           vehicular networks
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Penglin Dai, Kai Liu, Liang Feng, Qingfeng Zhuge, Victor C.S. Lee, Sang H. Son
      Vehicular networks represent a research area of significant importance in improving the safety, efficiency and sustainability of transportation systems. One of the key research problems in vehicular networks is real-time data dissemination, which is crucial to the satisfactory performance of many emergent applications providing real-time information services in vehicular networks. Specifically, the two issues need to be addressed in this problem are maintenance of temporal data freshness and timely dissemination of data. Most existing works only considered periodical data update via backbone wired networks in maintaining temporal data freshness. However, many applications rely on passing vehicles to upload their collected information via wireless network, which imposes new challenges as the uplink data update will have to compete with the downlink data dissemination for the limited wireless bandwidth. With such observations, we propose a temporal information service system, in which vehicles are able to collect up-to-date temporal information and upload them to the roadside units (RSU) along their trajectories. Meanwhile, RSU can disseminate its available data items to vehicles based on their specific requests. Particularly, in this paper, we first quantitatively analyze the freshness of temporal data and propose a mathematical model to evaluate the usefulness of the temporal data. Next, we give the formulation of the proposed real-time and temporal information service (RTIS) problem, and prove the NP-hardness of this problem by constructing a polynomial-time reduction from 0–1 knapsack problem. Subsequently, we establish a probabilistic model to theoretically analyze the tradeoff between timely temporal data update and requested data dissemination sharing a common communication resource, which provides a deeper insight of the proposed RTIS. Further, a heuristic algorithm, namely adaptive update request scheduling (AURS), is designed to enhance the efficacy of RTIS by synthesizing the broadcast effect, the real-time service requirement and the service quality in making scheduling decisions. The computational complexity and scalability analysis of AURS is also discussed. Last but not least, a simulation model is implemented and a comprehensive performance evaluation has been carried out to demonstrate the superiority of ARUS against several state-of-the-art approaches in a variety of application scenarios.


      PubDate: 2016-08-26T14:56:06Z
       
  • Spatial-temporal traffic flow pattern identification and anomaly detection
           with dictionary-based compression theory in a large-scale urban network
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Zhenhua Zhang, Qing He, Hanghang Tong, Jizhan Gou, Xiaoling Li
      Traffic flow pattern identification, as well as anomaly detection, is an important component for traffic operations and control. To reveal the characteristics of regional traffic flow patterns in large road networks, this paper employs dictionary-based compression theory to identify the features of both spatial and temporal patterns by analyzing the multi-dimensional traffic-related data. An anomaly index is derived to quantify the network traffic in both spatial and temporal perspectives. Both pattern identifications are conducted in three different geographic levels: detector, intersection, and sub-region. From different geographic levels, this study finds several important features of traffic flow patterns, including the geographic distribution of traffic flow patterns, pattern shifts at different times-of-day, pattern fluctuations over different days, etc. Both spatial and temporal traffic flow patterns defined in this study can jointly characterize pattern changes and provide a good performance measure of traffic operations and management. The proposed method is further implemented in a case study for the impact of a newly constructed subway line. The before-and-after study identifies the major changes of surrounding road traffic near the subway stations. It is found that new metro stations attract more commute traffic in weekdays as well as entertaining traffic during weekends.


      PubDate: 2016-08-21T12:10:39Z
       
  • Traffic signal control with partial grade separation for oversaturated
           conditions
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Qing He, Ramya Kamineni, Zhenhua Zhang
      Increasing individual vehicular traffic is a major concern all around the world. This leads to more and more oversaturated intersections. Traffic signal control under oversaturated condition is a long-lasting challenge. To address this challenge thoroughly, this paper introduces grade separation at signalized intersections. A lane-based optimization model is developed for the integrated design of grade-separated lanes (e.g. tunnels), lane markings (e.g. left turns, through traffic, right turns, etc.) and signal timing settings. We take into account two types of lane configurations. One is conventional surface lanes controlled by signals, and the other is grade-separated lanes. This problem is formulated as a Mixed Integer Linear Program (MILP), and this can be solved using the regular branch-and-bound methods. The integer decision variables help in finding if the movement is on grade-separated or surface lanes, and also the successor functions to govern the order of signal display. The continuous variables include the assigned lane flow, common flow multiplier, cycle length, and start and duration of green for traffic movements and lanes. The optimized signal time settings and lane configurations are then represented in Vissim simulation. Numerical examples, along with a benefit-cost analysis show the good savings of the proposed optimization model for oversaturated traffic conditions. The benefit-cost ratio for installing 4 grade-separated lanes (as a tunnel) at a heavily oversaturated intersection (intersection capacity utilization rate equal to 1.57) exceeds 5.4.
      Graphical abstract image

      PubDate: 2016-08-16T12:04:51Z
       
  • Economic analysis of ride-sourcing markets
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Liteng Zha, Yafeng Yin, Hai Yang
      Ride-sourcing refers to an emerging urban mobility service that private car owners drive their own vehicles to provide for-hire rides. This paper analyzes the ride-sourcing market using an aggregate model where the matchings between customers and drivers are captured by an exogenous matching function. It is found that without any regulatory intervention a monopoly ride-sourcing platform will maximize the joint profit with its drivers. On the other hand, the first-best solution is not sustainable when the matching function exhibits increasing returns to scale and the cost function of the platform is subject to economies of scale. Regardless of the examined market scenarios, the average waiting time of customers is proportional to the average searching time of drivers. We establish conditions for regulators to solely regulate the commission charged by the platform to guarantee the second-best. We further investigate the competition of ride-sourcing platforms and find that competition does not necessarily lower the price level or improve social welfare. In the latter case, regulators may rather encourage the merger of the platforms and regulate them directly as a monopolist.


      PubDate: 2016-08-16T12:04:51Z
       
  • Capturing the conditions that introduce systematic variation in
           bike-sharing travel behavior using data mining techniques
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Maria Bordagaray, Luigi dell’Olio, Achille Fonzone, Ángel Ibeas
      The potential of smart-card transactions within bike-sharing systems (BSS) is still to be explored. This research proposes an original offline data mining procedure that takes advantage of the quality of these data to analyze the bike usage casuistry within a sharing scheme. A difference is made between usage and travel behavior: the usage is described by the actual trip-chaining gathered with every smart-card transaction and is directly influenced by the limitations of the BSS as a public renting service, while the travel behavior relates to the spatio-temporal distribution, the travel time and trip purpose. The proposed approach is based on the hypothesis that there are systematic usage types which can be described through a set of conditions that permit to classify the rentals and reduce the heterogeneity in travel patterns. Hence, the proposed algorithm is a powerful tool to characterize the actual demand for bike-sharing systems. Furthermore, the results show that its potential goes well beyond that since service deficiencies rapidly arise and their impacts can be measured in terms of demand. Consequently, this research contributes to the state of knowledge on cycling behavior within public systems and it is also a key instrument beneficial to both decision makers and operators assisting the demand analysis, the service redesign and its optimization.


      PubDate: 2016-08-11T03:43:28Z
       
  • Signal coordination models for long arterials and grid networks
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Lihui Zhang, Ziqi Song, Xiaojun Tang, Dianhai Wang
      This paper proposes two models to tackle traffic signal coordination problems for long arterials and grid networks. Both models, denoted as MaxBandLA and MaxBandGN, are built based on Little’s bandwidth maximization model, and the resulting formulations are both small-sized mixed-integer linear programs. Model MaxBandLA can optimize arterial partition plan and signal coordination plans of all the subsystems simultaneously. Model MaxBandGN directly optimizes the offsets for all the signals in a grid network, and as such, no ’cycle constraints’ need to be constructed. Numerical tests are presented to show that both models have the potential to produce coordination plans that are comparable to signal plans optimized by Synchro.


      PubDate: 2016-08-11T03:43:28Z
       
  • Understanding user acceptance factors of electric vehicle smart charging
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Christian Will, Alexander Schuller
      Smart charging has been the focus of considerable research efforts but so far there is little notion of users’ acceptance of the concept. This work considers potentially influential factors for the acceptance of smart charging from the literature and tests their viability employing a structural equation model, following the partial least squares approach. For a sample of 237 early electric vehicle adopters from Germany our results show that contributing to grid stability and the integration of renewable energy sources are key motivational factors for acceptance of smart charging. In addition, the individual need for flexibility should not be impaired through charging control. Further well known influential factors like economic incentives do not seem to have a significant impact in the sample group under scrutiny. These and further findings should be taken into account by aggregators when designing attractive business models that incentivize the participation of early adopters and ease market rollout.


      PubDate: 2016-08-06T01:51:32Z
       
  • Modeling, calibrating, and validating car following and lane changing
           behavior
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Zuduo Zheng, Majid Sarvi



      PubDate: 2016-08-06T01:51:32Z
       
  • Data fusion algorithm for macroscopic fundamental diagram estimation
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Lukas Ambühl, Monica Menendez
      A promising framework that describes traffic conditions in urban networks is the macroscopic fundamental diagram (MFD), relating average flow and average density in a relatively homogeneous urban network. It has been shown that the MFD can be used, for example, for traffic access control. However, an implementation requires an accurate estimation of the MFD with the available data sources. Most scientific literature has considered the estimation of MFDs based on either loop detector data (LDD) or floating car data (FCD). In this paper, however, we propose a methodology for estimating the MFD based on both data sources simultaneously. To that end, we have defined a fusion algorithm that separates the urban network into two sub-networks, one with loop detectors and one without. The LDD and the FCD are then fused taking into account the accuracy and network coverage of each data type. Simulations of an abstract grid network and the network of the city of Zurich show that the fusion algorithm always reduces the estimation error significantly with respect to an estimation where only one data source is used. This holds true, even when we account for the fact that the probe penetration rate of FCD needs to be estimated with loop detectors, hence it might also include some errors depending on the number of loop detectors, especially when probe vehicles are not homogeneously distributed within the network.


      PubDate: 2016-08-06T01:51:32Z
       
  • A probabilistic model of pedestrian crossing behavior at signalized
           intersections for connected vehicles
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Yoriyoshi Hashimoto, Yanlei Gu, Li-Ta Hsu, Miho Iryo-Asano, Shunsuke Kamijo
      Active safety systems which assess highly dynamic traffic situations including pedestrians are required with growing demands in autonomous driving and Connected Vehicles. In this paper, we focus on one of the most hazardous traffic situations: the possible collision between a pedestrian and a turning vehicle at signalized intersections. This paper presents a probabilistic model of pedestrian behavior to signalized crosswalks. In order to model the behavior of pedestrian, we take not only pedestrian physical states but also contextual information into account. We propose a model based on the Dynamic Bayesian Network which integrates relationships among the intersection context information and the pedestrian behavior in the same way as a human. The particle filter is used to estimate the pedestrian states, including position, crossing decision and motion type. Experimental evaluation using real traffic data shows that this model is able to recognize the pedestrian crossing decision in a few seconds from the traffic signal and pedestrian position information. This information is assumed to be obtained with the development of Connected Vehicle.


      PubDate: 2016-08-02T00:51:22Z
       
  • Influence of connected and autonomous vehicles on traffic flow stability
           and throughput
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Alireza Talebpour, Hani S. Mahmassani
      The introduction of connected and autonomous vehicles will bring changes to the highway driving environment. Connected vehicle technology provides real-time information about the surrounding traffic condition and the traffic management center’s decisions. Such information is expected to improve drivers’ efficiency, response, and comfort while enhancing safety and mobility. Connected vehicle technology can also further increase efficiency and reliability of autonomous vehicles, though these vehicles could be operated solely with their on-board sensors, without communication. While several studies have examined the possible effects of connected and autonomous vehicles on the driving environment, most of the modeling approaches in the literature do not distinguish between connectivity and automation, leaving many questions unanswered regarding the implications of different contemplated deployment scenarios. There is need for a comprehensive acceleration framework that distinguishes between these two technologies while modeling the new connected environment. This study presents a framework that utilizes different models with technology-appropriate assumptions to simulate different vehicle types with distinct communication capabilities. The stability analysis of the resulting traffic stream behavior using this framework is presented for different market penetration rates of connected and autonomous vehicles. The analysis reveals that connected and autonomous vehicles can improve string stability. Moreover, automation is found to be more effective in preventing shockwave formation and propagation under the model’s assumptions. In addition to stability, the effects of these technologies on throughput are explored, suggesting substantial potential throughput increases under certain penetration scenarios.


      PubDate: 2016-08-02T00:51:22Z
       
  • Multi-step prediction of experienced travel times using agent-based
           modeling
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Hao Chen, Hesham A. Rakha
      This paper develops an agent-based modeling approach to predict multi-step ahead experienced travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in a decision-making system. Each expert predicts the travel time for each time interval according to experiences from a historical dataset. A set of agent interactions is developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents associated with negligible weights with new agents. Consequently, the aggregation of each agent’s recommendation (predicted travel time with associated weight) provides a macroscopic level of output, namely the predicted travel time distribution. Probe vehicle data from a 95-mile freeway stretch along I-64 and I-264 are used to test different predictors. The results show that the agent-based modeling approach produces the least prediction error compared to other state-of-the-practice and state-of-the-art methods (instantaneous travel time, historical average and k-nearest neighbor), and maintains less than a 9% prediction error for trip departures up to 60min into the future for a two-hour trip. Moreover, the confidence boundaries of the predicted travel times demonstrate that the proposed approach also provides high accuracy in predicting travel time confidence intervals. Finally, the proposed approach does not require offline training thus making it easily transferable to other locations and the fast algorithm computation allows the proposed approach to be implemented in real-time applications in Traffic Management Centers.


      PubDate: 2016-07-27T13:10:39Z
       
  • A discrete dynamical system of formulating traffic assignment: Revisiting
           Smith’s model
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Ren-Yong Guo, Hai-Jun Huang
      Through relaxing the behavior assumption adopted in Smith’s model (Smith, 1984), we propose a discrete dynamical system to formulate the day-to-day evolution process of traffic flows from a non-equilibrium state to an equilibrium state. Depending on certain preconditions, the equilibrium state can be equivalent to a Wardrop user equilibrium (UE), Logit-based stochastic user equilibrium (SUE), or boundedly rational user equilibrium (BRUE). These equivalence properties indicate that, to make day-to-day flows evolve to equilibrium flows, it is not necessary for travelers to choose their routes based on actual travel costs of the previous day. Day-to-day flows can still evolve to equilibrium flows provided that travelers choose their routes based on estimated travel costs which satisfy these preconditions. We also show that, under a more general assumption than the monotonicity of route cost function, the trajectory of the dynamical system converges to a set of equilibrium flows by reasonably setting these parameters in the dynamical system. Finally, numerical examples are presented to demonstrate the application and properties of the dynamical system. The study is helpful for understanding various processes of forming traffic jam and designing an algorithm for calculating equilibrium flows.


      PubDate: 2016-07-27T13:10:39Z
       
  • Non-recurrent congestion analysis using data-driven spatiotemporal
           approach for information construction
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Zhuo Chen, Xiaoyue Cathy Liu, Guohui Zhang
      A systematic approach to quantify Incident-Induced Delay (IID) is proposed in this study. The paper complements existing literature by developing a data-driven method to dynamically determine the spatiotemporal extent of individual incidents. The information construction process can be further used to uncover a variety of features that are associated with any specific incidents for optimal freeway management. Additionally, this study contributes two particular highlights: secondary incident identification and K-Nearest Neighbor (KNN) pattern matching. Secondary incident identification, as a pre-processing for IID estimation, disentangles the convoluted influences of subsequent incidents. The proposed method uses KNN pattern matching, an essentially heuristic search process to separate the delay solely induced by incidents from the recurrent congestion. The proposed algorithm on IID quantification was implemented on Interstate 15 in the state of Utah using data obtained from 2013. Results and implications are presented. Hot spot analysis is conducted that can be potentially used for incident mitigation and to inform investment decisions. The proposed methodology is easily transferable to any traffic operation system that has access to sensor data at a corridor level.


      PubDate: 2016-07-19T13:06:25Z
       
  • Integrated traffic-transit stochastic equilibrium model with park-and-ride
           facilities
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Cristobal Pineda, Cristián E. Cortés, Pedro Jara-Moroni, Eduardo Moreno
      We propose an Integrated Stochastic Equilibrium model that considers both private automobile traffic and transit networks to incorporate the interactions between these two modes in terms of travel time and generalized costs. In addition, in the general version of the model, travelers are allowed to switch from personal vehicles to mass transit at specific locations in a park-and-ride scheme. The assignment for traffic equilibrium is based on the Markovian Traffic Equilibrium model of Baillon and Cominetti (2008), whereas the equilibrium of the transit network is represented by the Stochastic Transit Equilibrium model of Cortés et al. (2013). Stochastic travel decisions are made at the node level, thereby avoiding the enumeration of routes or strategies and incorporating various perception and uncertainty issues. We propose a Method-of-Successive-Averages algorithm to calculate an Integrated Stochastic Equilibrium and conduct numerical experiments to highlight the effect of stochasticity on equilibrium flows and travel times. Our experiments show that higher stochasticity implies greater dispersion of equilibrium flows and longer expected travel times. Results on a real network with mode combination and park and ride facilities provide insights regarding the use of park and ride in terms of number and location, potential modal share of the combined mode option under different circumstances, and travel time impact due to the implementation of such park and ride facilities in a real setting.


      PubDate: 2016-07-19T13:06:25Z
       
  • The influence of attitude towards individuals’ choice for a remotely
           piloted commercial flight: A latent class logit approach
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Brett R.C. Molesworth, Tay T.R. Koo
      The Remotely Piloted Commercial Passenger Aircraft Attitude Scale (RPCPAAS) was created to measure positive and negative attitudes towards a new and plausible form of air travel. This information was then used, in combination with a latent class logit model built on data generated from a stated choice experiment to gain insight into the choice behaviour between conventionally piloted aircraft (CPA) with a pilot on-board and remotely piloted aircraft (RPA) with a pilot on the ground. The results revealed that individuals, on-average, if presented a choice between a CPA and a RPA of equivalent attributes, would elect for the CPA option. However, there is variability in the passengers’ sensitivity to various flight attributes, and these sensitivities were influenced by individuals’ attitude towards the new technology (i.e., RPA). From an operational perspective, and assuming that one day passengers of commercial airlines are offered the choice between CPA and RPA, the strategies employed by airlines to encourage the use of the new technology need to be different, based on individuals’ attitude towards RPA.


      PubDate: 2016-07-19T13:06:25Z
       
  • Uni- and bi-directional pedestrian flow in the view-limited condition:
           Experiments and modeling
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Ning Guo, Qing-Yi Hao, Rui Jiang, Mao-Bin Hu, Bin Jia
      In this paper, the impact of vision on the uni- and bi-directional flow has been investigated via experiment and modeling. In the experiments, pedestrians are asked to walk clockwise/anti-clockwise in a ring-shaped corridor under view-limited condition and normal view condition. As expected, the flow rate under the view-limited condition decreases comparing with that under the normal view condition, no matter in uni- or bi-directional flow. In bidirectional flow, pedestrians segregate into two opposite moving streams very quickly under the normal view condition, and clockwise/anti-clockwise walking pedestrians are always in the inner/outer ring due to right-walking preference. In the first set of experiment, spontaneous lane formation has not occurred under the view-limited condition. Pedestrian flow does not evolve into stationary state. Local congestion occurs and dissipates from time to time. However, in the later sets of experiments, spontaneous lane formation has re-occurred. This is because participants learned from the experience and adapted right-walking preference to avoid collision. To model the flow dynamics, an improved force-based model has been proposed. The driving force has been modified. The right-walking preference has been taken into account. The fact that pedestrians cannot judge the moving direction accurately under limited-view condition has been considered. Simulation results are in good agreement with the experimental ones.


      PubDate: 2016-07-19T13:06:25Z
       
  • A lane-level road network model with global continuity
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Tao Zhang, Stefano Arrigoni, Marco Garozzo, Dian-ge Yang, Federico Cheli
      An increasing number of Intelligent Transportation System (ITS) applications require high accurate vehicle positioning, e.g., positioning at the lane-level. This requirement motives the development of modeling the road network at the lane-level. In this paper we propose a novel lane-level road network model. It can be considered an improvement to existing models in its capability of representing the road and intersection details at the lane-level in a uniform and precise way. As a result, the model can guarantee the global continuity for arbitrary map route, which better approximates the real vehicle trajectory. In addition, the map construction algorithms are also developed. Following the proposed methods, the lane parameters can be extracted efficiently under flexible precision requirement, and intersections with varying appearances can be precisely modeled with limited extra data. Experiments were performed to verify the proposed model in representing the lane-level geometrical and topological details of an urban area of Milan. The results also demonstrate the effectiveness of the map construction methods.


      PubDate: 2016-07-19T13:06:25Z
       
  • Optimization of traffic flow at freeway sags by controlling the
           acceleration of vehicles equipped with in-car systems
    • Abstract: Publication date: October 2016
      Source:Transportation Research Part C: Emerging Technologies, Volume 71
      Author(s): Bernat Goñi-Ros, Victor L. Knoop, Toshimichi Takahashi, Ichiro Sakata, Bart van Arem, Serge P. Hoogendoorn
      Sags are bottlenecks in freeway networks. According to previous research, the main cause is that most drivers do not accelerate enough at sags. Consequently, they keep longer headways than expected given their speed, which leads to congestion in high demand conditions. Nowadays, there is growing interest in the development of traffic control measures for sags based on the use of in-car systems. This paper aims to determine the optimal acceleration behavior of vehicles equipped with in-car systems at sags and the related effects on traffic flow, thereby laying the theoretical foundation for developing effective traffic management applications. We formulate an optimal control problem in which a centralized controller regulates the acceleration of some vehicles of a traffic stream moving along a single-lane freeway stretch with a sag. The control objective is to minimize total travel time. The problem is solved for scenarios with different numbers of controlled vehicles and positions in the stream, assuming low penetration rates. The results indicate that the optimal behavior involves performing a deceleration-acceleration-deceleration-acceleration (DADA) maneuver in the sag area. This maneuver induces the first vehicles located behind the controlled vehicle to accelerate fast along the vertical curve. As a result, traffic speed and flow at the end of the sag (bottleneck) increase for a time. The maneuver also triggers a stop-and-go wave that temporarily limits the inflow into the sag, slowing down the formation of congestion at the bottleneck. Moreover, in some cases controlled vehicles perform one or more deceleration-acceleration maneuvers upstream of the sag. This additional strategy is used to manage congestion so that inflow is regulated more effectively. Although we cannot guarantee global optimality, our findings reveal a potentially highly effective and innovative way to reduce congestion at sags, which could possibly be implemented using cooperative adaptive cruise control systems.


      PubDate: 2016-07-12T23:40:56Z
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.91.243.175
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016