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TRANSPORTATION (99 journals)

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Journal Cover   Transportation Research Part C: Emerging Technologies
  [SJR: 1.943]   [H-I: 55]   [16 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0968-090X
   Published by Elsevier Homepage  [2811 journals]
  • The concept and impact analysis of a flexible mobility on demand system
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Bilge Atasoy , Takuro Ikeda , Xiang Song , Moshe E. Ben-Akiva
      This paper introduces an innovative transportation concept called Flexible Mobility on Demand (FMOD), which provides personalized services to passengers. FMOD is a demand responsive system in which a list of travel options is provided in real-time to each passenger request. The system provides passengers with flexibility to choose from a menu that is optimized in an assortment optimization framework. For operators, there is flexibility in terms of vehicle allocation to different service types: taxi, shared-taxi and mini-bus. The allocation of the available fleet to these three services is carried out dynamically so that vehicles can change roles during the day. The FMOD system is built based on a choice model and consumer surplus is taken into account in order to improve passenger satisfaction. Furthermore, profits of the operators are expected to increase since the system adapts to changing demand patterns. In this paper, we introduce the concept of FMOD and present preliminary simulation results. It is shown that the dynamic allocation of the vehicles to different services provides significant benefits over static allocation. Furthermore, it is observed that the trade-off between consumer surplus and operator’s profit is critical. The optimization model is adapted in order to take into account this trade-off by controlling the level of passenger satisfaction. It is shown that with such control mechanisms FMOD provides improved results in terms of both profit and consumer surplus.


      PubDate: 2015-05-21T09:27:42Z
       
  • Model and a solution algorithm for the dynamic resource allocation problem
           for large-scale transportation network evacuation
    • Abstract: Publication date: Available online 21 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Xiaozheng He , Hong Zheng , Srinivas Peeta
      Allocating movable resources dynamically enables evacuation management agencies to improve evacuation system performance in both the spatial and temporal dimensions. This study proposes a mixed integer linear program (MILP) model to address the dynamic resource allocation problem for transportation evacuation planning on large-scale networks. The proposed model is built on the earliest arrival flow formulation that significantly reduces problem size. A set of binary variables, specifically, the beginning and the ending time of resource allocation at a location, enable a strong formulation with tight constraints. A solution algorithm is developed to solve for an optimal solution on large-scale network applications by adopting Benders decomposition. In this algorithm, the MILP model is decomposed into two sub-problems. The first sub-problem, called the restricted master problem, identifies a feasible dynamic resource allocation plan. The second sub-problem, called the auxiliary problem, models dynamic traffic assignment in the evacuation network given a resource allocation plan. A numerical study is performed on the Dallas–Fort Worth network. The results show that the Benders decomposition algorithm can solve an optimal solution efficiently on a large-scale network.


      PubDate: 2015-05-21T09:27:42Z
       
  • A novel three-step procedure to forecast the inspection volume
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): J.J. Ruiz-Aguilar , I.J. Turias , M.J. Jiménez-Come
      The inspection process of freight traffic at Border Inspection Posts (BIPs) generates significant time delays and congestion within the transport system. The use of forecasting methods to anticipate these situations could be a good solution. Traditional methodologies for time series prediction usually consist on: applying single techniques, combining these techniques with some others such as clustering techniques or hybridizing single prediction techniques. A novel methodology based on a three-step procedure is proposed in this paper in order to better predict the number of inspections at BIPs, integrating a clustering technique and a hybrid prediction model. Specifically, the seasonal auto-regressive integrated moving averages (SARIMA) is used first to predict the data. Then, self-organizing maps (SOM) decomposes the time series into smaller regions with similar statistical properties. Finally, Artificial Neural Networks (ANNs) are applied in each homogeneous regions to forecast the inspections volume, testing different hybrid approaches based on the inputs of the model. The experimental results show that the performance of inspection prediction can be enhanced by using the novel three-stage procedure, providing relevant information for resource planning and turning into a powerful decision-making tool, not only at the inspection process of seaports or airports, but also in the field of time series prediction.
      Graphical abstract image

      PubDate: 2015-05-21T09:27:42Z
       
  • How comfortable are your cycling tracks? A new method for objective
           bicycle vibration measurement
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Michal Bíl , Richard Andrášik , Jan Kubeček
      Cycling comfort consists of several factors. Their relevant values are important in the process of bicycle facility planning. Poor surface pavement quality manifests itself in terms of vibrations of a bicycle. This strongly influences the perception of a cycle track, general cycling comfort and the route choice as well. We introduce dynamic comfort index (DCI) which is capable of objectively describing the vibration properties of surface pavement on a track. The DCI is derived from data gathered when riding a bicycle equipped with a GPS device and an accelerometer. The most common types of devices were selected to make the DCI widely applicable. We tested DCI values on various bicycles and surface pavements. DCI values on individual cycling tracks were compared with the subjective feelings of 43 cyclists via questionnaires. A strong correlation (−0.94) was obtained between the objectively measured DCI values and the subjectively assessed evaluations. This makes the DCI approach transferable to any other environment. This method has been applied to an entire road network within the historical center of the city of Olomouc (Czech Republic). It can further be used by bicycle track administrators to monitor surface quality, by planners to obtain relevant surface pavement values, and by individual cyclists for optimal route choice.


      PubDate: 2015-05-21T09:27:42Z
       
  • An android based ecodriving assistance system to improve safety and
           efficiency of internal combustion engine passenger cars
    • Abstract: Publication date: Available online 18 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Olivier Orfila , Guillaume Saint Pierre , Mickaël Messias
      Recent improvements of communication technologies leads to several innovations in road vehicles energy consumption. As an example, several ecodriving applications already appeared on all smartphone application markets. Using embedded smartphone signals, such applications provide real time feedback to drivers according to their performances. However most of these applications does not take into account upcoming events such as curves, slopes or crossings to advise the driver on the best actions to undertake to lower energy consumption. Furthermore, they do not analyze data coming from vehicle sensors. In this paper, we present an android application, developed within the FP7 European project ecoDriver, which provides several innovative properties: advice according to upcoming events, a real time evaluation of the driving behavior, the analysis of past actions, an interface with OBD2 connector and some more. This paper further develops the complete architecture and links between each innovative function. Future works will concentrate on integrating image processing in this application in order to detect the possible presence of a front vehicle.


      PubDate: 2015-05-21T09:27:42Z
       
  • AM-Band: An Asymmetrical Multi-Band model for arterial traffic signal
           coordination
    • Abstract: Publication date: Available online 19 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Chao Zhang , Yuanchang Xie , Nathan H. Gartner , Chronis Stamatiadis , Tugba Arsava
      MAXBAND and MULTIBAND are two well-recognized methods for arterial signal control. In this paper, an Asymmetrical Multi-BAND (AM-BAND) model is developed by relaxing the symmetrical progression band requirement in MULTIBAND. Such a relaxation allows the AM-BAND model to better utilize the available green times in each direction and to provide additional opportunities for vehicular progression. Similar to MULTIBAND, the proposed AM-BAND model is formulated as a mixed-integer linear program. However, in AM-BAND the green bands in each directional section of the arterials do not have to be symmetrical with respect to the progression line. The performance of the AM-BAND model is evaluated for two arterial network data using the AIMSUN microscopic traffic simulation tool. The optimal signal coordination plans are computed by the IBM CPLEX Optimization Studio and compared with signal timing plans generated by AM-BAND, MULTIBAND, and MAXBAND. Simulation results indicate that the traffic signal coordination plans generated by AM-BAND can provide significant benefits compared to those generated by MAXBAND and MULTIBAND.


      PubDate: 2015-05-21T09:27:42Z
       
  • A two-stage approach to modeling vacant taxi movements
    • Abstract: Publication date: Available online 19 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): R.C.P. Wong , W.Y. Szeto , S.C. Wong
      In this paper, a two-stage modeling approach is proposed to predict vacant taxi movements in searching for customers. The taxi movement problem is formulated into a two-stage model that consists of two sub-models, namely the first and second stage sub-models. The first stage sub-model estimates the zone choice of vacant taxi drivers for customer-search and the second stage sub-model determines the circulation time and distance of vacant taxi drivers in each zone by capturing their local customer-search decisions in a cell-based network within the zone chosen in the first stage sub-model. These two sub-models are designed to influence each other, and hence an iterative solution procedure is introduced to solve for a convergent solution. The modeling concept, advantages, and applications are illustrated by the global positioning system data of 460 Hong Kong urban taxis. The results demonstrate that the proposed model formulation offers a great improvement in terms of root mean square error as compared with the existing taxi customer-search models, and show the model capabilities of predicting the changes in vacant taxi trip distributions with respect to the variations in the fleet size and fare. Potential taxi policies are investigated and discussed according to the findings to provide insights in managing the Hong Kong taxi market.


      PubDate: 2015-05-21T09:27:42Z
       
  • The fleet renewal problem with regional emission limitations: Case study
           from Roll-on/Roll-off shipping
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Øyvind S. Patricksson , Kjetil Fagerholt , Jørgen G. Rakke
      In this paper, the maritime fleet renewal problem (MFRP) is extended to include regional limitations in the form of emission control areas. The motivation for including this aspect is that strengthening of emission regulations in such areas is expected to be challenging for deep sea shipping in the years to come. In the proposed model, various means to cope with these stricter emission regulations are evaluated for new vessels, and the possibility of upgrading existing vessels with new emission reduction technology is introduced. We consider future fuel prices to be important for the problem, and have chosen to treat them as uncertain, and thus, a stochastic programming model is chosen. A fleet renewal problem faced by the liner shipping operator Wallenius Wilhelmsen Logistics, concerning whether to use low sulphur fuel or have an exhaust gas scrubber system installed to comply with sulphur regulation in emission control areas from 2015, is used as a case study. Furthermore, tests show that the savings from including the aspect of emission control areas in the MFRP are substantial.


      PubDate: 2015-05-21T09:27:42Z
       
  • Assessing the viability of enabling a round-trip carsharing system to
           accept one-way trips: Application to Logan Airport in Boston
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Diana Jorge , Cynthia Barnhart , Gonçalo Homem de Almeida Correia
      Although one-way carsharing is suitable for more trip purposes than round-trip carsharing, many companies in the world operate only in the round-trip market. In this paper, we develop a method that optimizes the design of a one-way carsharing service between selected origin–destination pairs of an existing round-trip carsharing system. The goal is to supplement the established round-trip services with new one-way services and increase profitability. We develop an integer programming model to select the set of new one-way services and apply it to the case study of Boston, USA, considering only trips with one endpoint at a station in the round-trip Zipcar service network and the other endpoint at Logan Airport. The airport was chosen as a necessary endpoint for a one-way service because it is a very significant trip generator for which the round-trip carsharing is not suitable. Results show that these supplemental one-way services could be profitable. Enabling relocation operations between the existing round-trip stations and the Airport greatly improves the demand effectively satisfied, leads to an acceptable airport station size (in terms of the number of parking spots required), and is profitable; however, these benefits come with the need to manage relocation operations.


      PubDate: 2015-05-21T09:27:42Z
       
  • Optimal competitive freight network design as hierarchical variational
           inequalities programming problems
    • Abstract: Publication date: Available online 13 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Loukas Dimitriou
      Freight networks are a case of systems that multiple participants are composing interrelations along the complete supply chain. Their interrelations correspond to alternative behavior, namely, cooperation, non-cooperation and competition, while they are large-scale spatially distributed systems combining multiple means of transportation and the infrastructure and equipment typically utilized for servicing demand, results to a complex system integration. In this paper, the case of the optimal design of freight networks is investigated, aiming to highlight the particularities emerging in this case of transportation facilities strategic and/or operational planning and the multiple game-theoretic and equilibrium problems that are structured in cascade and in hierarchies. The application that is investigated here focuses in the design of a significant ‘player’ of the freight supply chain, namely container terminals, while the proposed framework will aim on analyzing investment strategies built on integrated demand–supply models and the optimal network design format. The approach will build on the multilevel Mathematical Programming with Equilibrium Constraints (MPECs) formulation, but is further extended to cope with the properties introduced by the ‘designers’ (infrastructure authorities), shippers and carriers competition in all levels of MPECs. Since container terminals are typically competing each other, the nomenclature used here for formulating appropriate MPECs problems are based on hierarchies of Variational Inequalities (VI) problems, able to capture the alternative relationships emerging in realistic freight supply chains. The proposed formulations of the competitive network design case is addressed by a novel approach of co-evolutionary agents, which can be regarded as new in equilibrium estimation. Finally, the results are compared with alternative network design cases, namely the centralized cooperative and exchanging design. Under this analysis it is able to highlight the differences among alternative design cases, but moreover an estimation of the ‘price of anarchy’ in transportation systems design is offered, an element of both theoretical as well as practical relevance.


      PubDate: 2015-05-17T09:24:12Z
       
  • A robust optimization approach for dynamic traffic signal control with
           emission considerations
    • Abstract: Publication date: Available online 4 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      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: 2015-05-07T22:56:08Z
       
  • How driver behaviour and parking alignment affects inductive charging
           systems for electric vehicles
    • Abstract: Publication date: Available online 4 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Stewart A. Birrell , Daniel Wilson , Chek Pin Yang , Gunwant Dhadyalla , Paul Jennings
      Inductive charging, a form of wireless charging, uses an electromagnetic field to transfer energy between two objects. This emerging technology offers an alternative solution to users having to physically plug in their electric vehicle (EV) to charge. Whilst manufacturers claim inductive charging technology is market ready, the efficiency of transfer of electrical energy is highly reliant on the accurate alignment of the coils involved. Therefore understanding the issue of parking misalignment and driver behaviour is an important human factors question, and the focus of this paper. Two studies were conducted, one a retrospective analysis of 100 pre-parked vehicles, the second a dynamic study where 10 participants parked an EV aiming to align with a charging pad with no bay markings as guidance. Results from both studies suggest that drivers are more accurate at parking laterally than in the longitudinal direction, with a mean lateral distance from the centre of the bay being 12.12 and 9.57cm (retrospective and dynamic studies respectively) compared to longitudinally 23.73 and 73.48cm. With current inductive charging systems having typical tolerances of approximately ±10cm from their centre point, this study has shown that only 5% of vehicles in both studies would be aligned sufficiently accurately to allow efficient transfer of electrical energy through induction.
      Graphical abstract image

      PubDate: 2015-05-07T22:56:08Z
       
  • Flow-based accessibility measurement: The Place Rank approach
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Guihua Wang , Yuanguang Zhong , Chung-Piaw Teo , Qizhang Liu
      In this paper, we empirically test the viability of a flow-based approach as an alternative to transport accessibility measurement. To track where commuters travel from and to (but not commute times), we use transactional smartcard data from residents in Singapore to construct the (daily) spatial network of trips generated. We use the Place Rank method to demonstrate the viability of the flow-based approach to study accessibility. We compute the Place Rank of each of 44 planning areas in Singapore. Interestingly, even though the spatial network is constructed using only origin–destination information, we find that the travel time of the trips out of each planning area generally decreases as the area’s Place Rank increases. The same is also the case for in-vehicle time, number of transfers in the network and transfer time. This shows that a flow-based approach can be used to measure the notion of accessibility, which is traditionally assessed using travel time information in the system. We also compare Place Rank with other indicators, namely, bus stop density, eigenvector centrality, clustering coefficient and typographical coefficient to evaluate an area’s accessibility. The results show that these indicators are not as effective as the Place Rank method.


      PubDate: 2015-05-07T22:56:08Z
       
  • The path most traveled: Travel demand estimation using big data resources
    • Abstract: Publication date: Available online 7 May 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Jameson L. Toole , Serdar Colak , Bradley Sturt , Lauren P. Alexander , Alexandre Evsukoff , Marta C. González
      Rapid urbanization is placing increasing stress on already burdened transportation infrastructure. Ubiquitous mobile computing and the massive data it generates presents new opportunities to measure the demand for this infrastructure, diagnose problems, and plan for the future. However, before these benefits can be realized, methods and models must be updated to integrate these new data sources into existing urban and transportation planning frameworks for estimating travel demand and infrastructure usage. While recent work has made great progress extracting valid and useful measurements from new data resources, few present end-to-end solutions that transform and integrate raw, massive data into estimates of travel demand and infrastructure performance. Here we present a flexible, modular, and computationally efficient software system to fill this gap. Our system estimates multiple aspects of travel demand using call detail records (CDRs) from mobile phones in conjunction with open- and crowdsourced geospatial data, census records, and surveys. We bring together numerous existing and new algorithms to generate representative origin–destination matrices, route trips through road networks constructed using open and crowd-sourced data repositories, and perform analytics on the system’s output. We also present an online, interactive visualization platform to communicate these results to researchers, policy makers, and the public. We demonstrate the flexibility of this system by performing analyses on multiple cities around the globe. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into customary transportation demand modeling.


      PubDate: 2015-05-07T22:56:08Z
       
  • Efficiency of a highway use reservation system for morning commute
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Wei Liu , Hai Yang , Yafeng Yin
      This paper examines the design and efficiency of a highway use reservation system where commuters need reservations to access a highway facility at specific times. We show that, by accommodating reservation requests to the level that the highway capacity allows, traffic congestion can be relieved. Generally, a more differentiated design of the reservation system yields a higher reduction of travel cost and thus achieves a higher efficiency. The efficiency bound of the system is established. We also show that braking or tactical waiting behaviors of drivers would cause a loss of efficiency, which thus need be proactively accommodated. Given that user heterogeneity cause further loss of efficiency, we explore how two specific types of user heterogeneity affect the system efficiency. Auction-based reservation is then proposed to mitigate the efficiency loss.


      PubDate: 2015-05-04T08:16:09Z
       
  • Application of Lagrangian relaxation approach to α-reliable path
           finding in stochastic networks with correlated link travel times
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Weiliang Zeng , Tomio Miwa , Yukiko Wakita , Takayuki Morikawa
      This study investigates the important problem of determining a reliable path in a stochastic network with correlated link travel times. First, the distribution of path travel time is quantified by using trip records from GPS probe vehicles. Second, the spatial correlation of link travel time is explicitly considered by using a correlation coefficient matrix, which is incorporated into the α-reliable path problem by Cholesky decomposition. Third, the Lagrangian relaxation based framework is used to handle the α-reliable path problem, by which the intractable problem with a non-linear and non-additive structure can be decomposed into several easy-to-solve problems. Finally, the path-finding performance of this approach is tested on a real-world network. The results show that 15 iterations of calculation can yield a small relative gap between upper and lower bounds of the optimal solution and the average running time is about 5s for most OD settings. The applicability of α-reliable path finding is validated by a case study.
      Graphical abstract image

      PubDate: 2015-05-04T08:16:09Z
       
  • The design of an in-vehicle assistance system to support eco-driving
    • Abstract: Publication date: Available online 24 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Daryl L. Hibberd , Hamish Jamson , Samantha L. Jamson
      This driving simulator study was the second of two studies investigating the most effective and acceptable in-vehicle system for the provision of guidance on fuel efficient accelerator usage. Three eco-driving interfaces were selected for test (a second-order display visual display with auditory alerts and two haptic accelerator pedal systems) following a pilot study of 12 different interfaces. These systems were tested in a range of eco-driving scenarios involving acceleration, deceleration and speed maintenance, and assessed through their effects on fuel economy, vehicle control, distraction, and driver subjective feedback. The results suggest that a haptic accelerator pedal system is most effective for preventing over-acceleration, whilst minimal differences were observed between systems in terms of the effect of the assistance provided to prevent under-acceleration. The visual–auditory interface lowered the time spent looking towards the road, indicating a potential negative impact on driver safety from using this modality to provide continuous green driving support. Subjective results were consistent with the objective findings, with haptic pedal systems creating lower perceived workload than a visual–auditory interface. Driver acceptability ratings suggested a slight favouring of a haptic-force system for its usefulness, whereas the more subtle haptic-stiffness system was judged more acceptable to use. These findings offer suggestions for the design of a user-friendly, eco-driving device that can help drivers improve their fuel economy, specifically through the provision of real-time guidance on the manipulation of the accelerator pedal position.


      PubDate: 2015-04-29T11:00:38Z
       
  • A bi-level model of dynamic traffic signal control with continuum
           approximation
    • Abstract: Publication date: Available online 24 April 2015
      Source:Transportation Research Part C: Emerging Technologies
      Author(s): Ke Han , Yuqi Sun , Hongcheng Liu , Terry L. Friesz , Tao Yao
      This paper proposes a bi-level model for traffic network signal control, which is formulated as a dynamic Stackelberg game and solved as a mathematical program with equilibrium constraints (MPEC). The lower-level problem is a dynamic user equilibrium (DUE) with embedded dynamic network loading (DNL) sub-problem based on the LWR model (Lighthill and Whitham, 1955; Richards, 1956). The upper-level decision variables are (time-varying) signal green splits with the objective of minimizing network-wide travel cost. Unlike most existing literature which mainly use an on-and-off (binary) representation of the signal controls, we employ a continuum signal model recently proposed and analyzed in Han et al. (2014), which aims at describing and predicting the aggregate behavior that exists at signalized intersections without relying on distinct signal phases. Advantages of this continuum signal model include fewer integer variables, less restrictive constraints on the time steps, and higher decision resolution. It simplifies the modeling representation of large-scale urban traffic networks with the benefit of improved computational efficiency in simulation or optimization. We present, for the LWR-based DNL model that explicitly captures vehicle spillback, an in-depth study on the implementation of the continuum signal model, as its approximation accuracy depends on a number of factors and may deteriorate greatly under certain conditions. The proposed MPEC is solved on two test networks with three metaheuristic methods. Parallel computing is employed to significantly accelerate the solution procedure.


      PubDate: 2015-04-29T11:00:38Z
       
  • Air cargo operations: Literature review and comparison with practices
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Bo Feng , Yanzhi Li , Zuo-Jun Max Shen
      This study reviews the literature on air cargo operations and compares theoretical studies with the practical problems of airlines, freight forwarders, and terminal service providers. In particular, we review studies in which mathematical models were used to identify the essential characteristics of air cargo operations, such as the intrinsic differences from passenger operations, and to explore the service processes in air cargo operations. The typical models used in previous studies are summarized. We then highlight the insightful findings from an industrial interview and present the gaps between previous research and practical realities. We finally discuss the new research opportunities of air cargo operations according to the gaps.


      PubDate: 2015-04-29T11:00:38Z
       
  • Coordinated cruise control for high-speed train movements based on a
           multi-agent model
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Shukai Li , Lixing Yang , Ziyou Gao
      This paper investigates the coordinated cruise control strategy for multiple high-speed trains’ movement. The motion of an ordered set of high-speed trains running on a railway line is modeled by a multi-agent system, in which each train communicates with its neighboring trains to adjust its speed. By using the potential fields and LaSalles invariance principle, we design a new coordinated cruise control strategy for each train based on the neighboring trains’ information, under which each train can track the desired speed, and the headway distances between any two neighboring trains are stabilized in a safety range. Numerical examples are given to illustrate the effectiveness of the proposed methods.


      PubDate: 2015-04-29T11:00:38Z
       
  • Automated classification based on video data at intersections with heavy
           pedestrian and bicycle traffic: Methodology and application
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Sohail Zangenehpour , Luis F. Miranda-Moreno , Nicolas Saunier
      Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.


      PubDate: 2015-04-24T18:38:57Z
       
  • Highway voting system: Embracing a possible paradigm shift in traffic data
           acquisition
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Wei-Hua Lin , Hong K. Lo
      The integration of internet and mobile phones has opened the door to a new wave of utilizing private vehicles as probes not only for performance evaluation but for traffic control as well, gradually replacing the role of traffic surveillance systems as the dominant source of traffic data. To prepare for such a paradigm shift, one needs to overcome some key institutional barriers, in particular, the privacy issue. A Highway Voting System (HVS) is proposed to address this issue in which drivers provide link- and/or path-based vehicle data to the traffic management system in the form of “votes” in order to receive favorable service from traffic control. The proposed HVS offers a platform that links data from individual vehicles directly with traffic control. In the system, traffic control responds to voting vehicles in a way similar to the current system responding to prioritized vehicles and providing the requested services accordingly. We show in the paper that the proposed “voting” system can effectively resolve the privacy issue which often hampers traffic engineers from getting detailed data from drivers. Strategies to entice drivers into “voting” so as to increase the market penetration level under all traffic conditions are discussed. Though the focus of the paper is on addressing the institutional issues associated with data acquisition from individual vehicles, other research topics associated with the proposed system are identified. Two examples are given to demonstrate the impact of the proposed system on algorithm development and traffic control.


      PubDate: 2015-04-24T18:38:57Z
       
  • Location privacy preferences: A survey-based analysis of consumer
           awareness, trade-off and decision-making
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Caitlin D. Cottrill , Piyushimita “Vonu” Thakuriah
      With the advent and rapid dissemination of location-sensing information technology, the issue of location information privacy is receiving growing attention. Perhaps of greatest concern is ensuring that potential users of mobile Information and Communications Technologies (e.g., Location-Based Services and Intelligent Transportation Systems) are comfortable with the levels of privacy protection afforded them, as well as with the benefits they will receive in return for providing private location information. This paper explores the concepts of privacy risks, benefits, willingness to trade, and compensation in relationship to mobile and locational technologies using a stated preference survey to ascertain areas of interest in determining the trade-offs that consumers will be willing to make in return for mobility enhancements. Analysis of the survey leads to findings that while respondents believe that sharing data in the mobile environment may pose privacy risks, they do not generally take steps necessary to address these risks; that privacy preferences are impacted by a range of factors, including both personal and contextual considerations (such as factors arising from their specific situation at the time of information seeking); and that willingness to trade private location data is dependent upon a number of factors related to context, personal characteristics, expected benefits and degree of trust in the collecting organization.


      PubDate: 2015-04-24T18:38:57Z
       
  • Orchestration of driving simulator scenarios based on dynamic actor
           preparation and automated action planning
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Zhitao Xiong , Johan Olstam
      In driving simulation, a scenario includes definitions of the road environment, the traffic situation, simulated vehicles’ interactions with the participant’s vehicle and measurements that need to be collected. The scenarios need to be designed in such a way that the research questions to be studied can be answered, which commonly imply exposing the participant for a couple of predefined specific situations that has to be both realistic and repeatable. This article presents an integrated algorithm based on Dynamic Actor Preparation and Automated Action Planning to control autonomous simulated vehicles in the simulation in order to generate predefined situations. This algorithm is thus able to plan driving actions for autonomous vehicles based on specific tasks with relevant contextual information as well as handling longitudinal transportation of simulated vehicles based on the contextual information in an automated manner. The conducted experiment shows that the algorithm is able to guarantee repeatability under autonomous traffic flow. The presented algorithm can benefit not only the driving simulation community, but also relevant areas, such as autonomous vehicle and in-vehicle device design by providing them with an algorithm for target pursue and driving task accomplishment, which can be used to design a human-vehicle cooperation system in the coming era of autonomous driving.


      PubDate: 2015-04-24T18:38:57Z
       
  • Effect of speed limits in degradable transport networks
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Chen-Yang Yan , Rui Jiang , Zi-You Gao , Hu Shao
      This paper studies how link-specific speed limits influence the performance of degradable transport networks, in which the capacity of each link is a degradable random variable. The distribution and cumulative distribution of link travel time have been presented with the effect of speed limits taken into account. The mean and variance of link and route travel time are formulated. Three link states have been classified, and their physical meanings have been discussed. The relationship between critical capacity, travel time and speed limit has been elaborated. We have proposed a Speed Limit- and Reliability-based User Equilibrium (SLRUE), adopting travel time budget as the principle of travelers’ route choice. A heuristic method employing the method of successive averages is developed to solve the SLRUE in degradable networks. Through numerical studies, we find that for some networks both the mean and standard deviation of the total travel time could be reduced simultaneously by imposing some speed limits. The speed limit design problem has been studied, and it is found that imposing speed limits cannot always reduce the total travel time budget of a network.


      PubDate: 2015-04-24T18:38:57Z
       
  • Vehicle detection grammars with partial occlusion handling for traffic
           surveillance
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Bin Tian , Ming Tang , Fei-Yue Wang
      Traffic surveillance is an important topic in intelligent transportation systems (ITS). Robust vehicle detection is one challenging problem for complex traffic surveillance. In this paper, we propose an efficient vehicle detection method by designing vehicle detection grammars and handling partial occlusion. The grammar model is implemented by novel detection grammars, including structure, deformation and pairwise SVM grammars. First, the vehicle is divided into its constitute parts, called semantic parts, which can represent the vehicle effectively. To increase the robustness of part detection, the semantic parts are represented by their detection score maps. The semantic parts are further divided into sub-parts automatically. The two-layer division of the vehicle is modeled into a grammar model. Then, the grammar model is trained by a designed training procedure to get ideal grammar parameters, including appearance models and grammar productions. After that, vehicle detection is executed by a designed detection procedure with respect to the grammar model. Finally, the issue of vehicle occlusion is handled by designing and training specific grammars. The strategy adopted by our method is first to divide the vehicle into the semantic parts and sub-parts, then to train the grammar productions for semantic parts and sub-parts by introducing novel pairwise SVM grammars and finally to detect the vehicle by applying the trained grammars. Experiments in practical urban scenarios are carried out for complex traffic surveillance. It can be shown that our method adapts to partial occlusion and various challenging cases.


      PubDate: 2015-04-24T18:38:57Z
       
  • An integrated solution for lane level irregular driving detection on
           highways
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Rui Sun , Washington Yotto Ochieng , Shaojun Feng
      Global Navigation Satellite Systems (GNSS) has been widely used in the provision of Intelligent Transportation System (ITS) services. Current meter level system availability can fulfill the road level applications, such as route guide, fleet management and traffic control. However, meter level of system performance is not sufficient for the advanced safety applications. These lane level safety applications requires centimeter/decimeter positioning accuracy, with high integrity, continuity and availability include lane control, collision avoidance and intelligent speed assistance, etc. Detecting lane level irregular driving behavior is the basic requirement for these safety related ITS applications. The two major issues involved in the lane level irregular driving identification are accessing to high accuracy positioning and vehicle dynamic parameters and extraction of erratic driving behaviour from this and other related information. This paper proposes an integrated solution for the lane level irregular driving detection. Access to high accuracy positioning is enabled by GNSS and Inertial Navigation System (INS) integration using filtering with precise vehicle motion models and lane information. The detection of different types of irregular driving behaviour is based on the application of a Fuzzy Inference System (FIS). The evaluation of the designed integrated systems in the field test shows that 0.5m accuracy positioning source is required for lane level irregular driving detection algorithm and the designed system can detect irregular driving styles.


      PubDate: 2015-04-24T18:38:57Z
       
  • Electric vehicles in multi-vehicle households
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Michael A. Tamor , Miloš Milačić
      The suitability of an electric vehicle of a given range to serve in place of a given conventional vehicle is not limited by the daily travel over distances within that that range, but rather by the occasional inconvenience of finding alternative transport for longer trips. While the frequency of this inconvenience can be computed from usage data, the willingness of individual users to accept that replacement depends on details of available transportation alternatives and their willingness to use them. The latter can be difficult to assess. Fortunately, 65% of US households have access to the most convenient alternative possible: a second car. In this paper we describe an analysis of prospective EV acceptance and travel electrification in two-car households in the Puget Sound region. We find that EVs with 60miles of useful range could be acceptable (i.e. incur inconvenience no more than three days each year) to nearly 90% of two-car households and electrify nearly 55% of travel in those households (32% of all travel). This compares to 120miles range required to achieve the same fraction of electrified travel via one-for-one replacement of individual vehicles. Even though only one third of personal vehicles in the US may be replaced in this paradigm, the ‘EV as a second-car’ concept is attractive in that a significant fraction of travel can be electrified by vehicles with modest electric range and virtually no dependence on public charging infrastructure.


      PubDate: 2015-04-24T18:38:57Z
       
  • Closing the loop in real-time railway control: Framework design and
           impacts on operations
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): F. Corman , E. Quaglietta
      Railway traffic is heavily affected by disturbances and/or disruptions, which are often cause of delays and low performance of train services. The impact and the propagation of such delays can be mitigated by relying on automatic tools for rescheduling traffic in real-time. These tools predict future track conflict based on current train information and provide suitable control measures (e.g. reordering, retiming and/or rerouting) by using advanced mathematical models. A growing literature is available on these tools, but their effects on real operations are blurry and not yet well known, due to the very scarce implementation of such systems in practice. In this paper we widen the knowledge on how automatic real-time rescheduling tools can influence train performance when interfaced with railway operations. To this purpose we build up a novel traffic control framework that couples the state-of-the art automatic rescheduling tool ROMA, with the realistic railway traffic simulation environment EGTRAIN, used as a surrogate of the real field. At regular times ROMA is fed with current traffic information measured from the field (i.e. EGTRAIN) in order to predict possible conflicts and compute (sub) optimal control measures that minimize the max consecutive delay on the network. We test the impact of the traffic control framework based on different types of interaction (i.e. open loop, multiple open loop, closed loop) between the rescheduling tool and the simulation environment as well as different combinations of parameter values (such as the rescheduling interval and prediction horizon). The influence of different traffic prediction models (assuming e.g. aggressive versus conservative driving behaviour) is also investigated together with the effects on traffic due to control delays of the dispatcher in implementing the control measures computed by the rescheduling tool. Results obtained for the Dutch railway corridor Utrecht–Den Bosch show that a closed loop interaction outperforms both the multiple open loop and the open loop approaches, especially with large control delays and limited information on train entrance delays and dwell times. A slow rescheduling frequency and a large prediction horizon improve the quality of the control measure. A limited control delay and a conservative prediction of train speed help filtering out uncertain traffic dynamics thereby increasing the effectiveness of the implemented measures.


      PubDate: 2015-04-24T18:38:57Z
       
  • Editorial Board/Copyright Information
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54




      PubDate: 2015-04-24T18:38:57Z
       
  • Overview of missing physical commodity trade data and its imputation using
           data augmentation
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): J. Farhan
      The physical aspects of commodity trade are becoming increasingly important on a global scale for transportation planning, demand management for transportation facilities and services, energy use, and environmental concerns. Such aspects (for example, weight and volume) of commodities are vital for logistics industry to allow for medium-to-long term planning at the strategic level and identify commodity flow trends. However, incomplete physical commodity trade databases impede proper analysis of trade flow between various countries. The missing physical values could be due to many reasons such as, (1) non-compliance of reporter countries with the prescribed regulations by World Customs Organization (WCO) (2) confidentiality issues, (3) delays in processing of data, or (4) erroneous reporting. The traditional missing data imputation methods, such as the substitution by mean, substitution by linear interpolation/extrapolation using adjacent points, the substitution by regression, and the substitution by stochastic regression, have been proposed in the context of estimating physical aspects of commodity trade data. However, a major demerit of these single imputation methods is their failure to incorporate uncertainty associated with missing data. The use of computationally complex stochastic methods to improve the accuracy of imputed data has recently become possible with the advancement of computer technology. Therefore, this study proposes a sophisticated data augmentation algorithm in order to impute missing physical commodity trade data. The key advantage of the proposed approach lies in the fact that instead of using a point estimate as the imputed value, it simulates a distribution of missing data through multiple imputations to reflect uncertainty and to maintain variability in the data. This approach also provides the flexibility to include fundamental distributional property of the variables, such as physical quantity, monetary value, price elasticity of demand, price variation, and product differentiation, and their correlations to generate reasonable average estimates of statistical inferences. An overview and limitations of most commonly used data imputation approaches is presented, followed by the theoretical basis and imputation procedure of the proposed approach. Lastly, a case study is presented to demonstrate the merits of the proposed approach in comparison to traditional imputation methods.


      PubDate: 2015-04-24T18:38:57Z
       
  • Analyzing passenger train arrival delays with support vector regression
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Nikola Marković , Sanjin Milinković , Konstantin S. Tikhonov , Paul Schonfeld
      We propose machine learning models that capture the relation between passenger train arrival delays and various characteristics of a railway system. Such models can be used at the tactical level to evaluate effects of various changes in a railway system on train delays. We present the first application of support vector regression in the analysis of train delays and compare its performance with the artificial neural networks which have been commonly used for such problems. Statistical comparison of the two models indicates that the support vector regression outperforms the artificial neural networks. Data for this analysis are collected from Serbian Railways and include expert opinions about the influence of infrastructure along different routes on train arrival delays.


      PubDate: 2015-04-24T18:38:57Z
       
  • Categorizing bicycling environments using GPS-based public bicycle speed
           data
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Shinhye Joo , Cheol Oh , Eunbi Jeong , Gunwoo Lee
      A promising alternative transportation mode to address growing transportation and environmental issues is bicycle transportation, which is human-powered and emission-free. To increase the use of bicycles, it is fundamental to provide bicycle-friendly environments. The scientific assessment of a bicyclist’s perception of roadway environment, safety and comfort is of great interest. This study developed a methodology for categorizing bicycling environments defined by the bicyclist’s perceived level of safety and comfort. Second-by-second bicycle speed data were collected using global positioning systems (GPS) on public bicycles. A set of features representing the level of bicycling environments was extracted from the GPS-based bicycle speed and acceleration data. These data were used as inputs for the proposed categorization algorithm. A support vector machine (SVM), which is a well-known heuristic classifier, was adopted in this study. A promising rate of 81.6% for correct classification demonstrated the technical feasibility of the proposed algorithm. In addition, a framework for bicycle traffic monitoring based on data and outcomes derived from this study was discussed, which is a novel feature for traffic surveillance and monitoring.


      PubDate: 2015-04-24T18:38:57Z
       
  • Marginal cost congestion pricing based on the network fundamental diagram
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): M.D. Simoni , A.J. Pel , R.A. Waraich , S.P. Hoogendoorn
      Congestion pricing schemes have been traditionally derived based on analytical representations of travel demand and traffic flows, such as in bottleneck models. A major limitation of these models, especially when applied to urban networks, is the inconsistency with traffic dynamics and related phenomena such as hysteresis and the capacity drop. In this study we propose a new method to derive time-varying tolling schemes using the concept of the Network Fundamental Diagram (NFD). The adopted method is based on marginal cost pricing, while it also enables to account realistically for the dynamics of large and heterogeneous traffic networks. We derive two alternative cordon tolls using network-aggregated traffic flow conditions: a step toll that neglects the spatial distribution of traffic by simply associating the marginal costs of any decrease in production within the NFD to the surplus of traffic; and a step toll that explicitly accounts for how network performance is also influenced by the spatial variance in a 3D-NFD. This pricing framework is implemented in the agent-based simulation model MATSim and applied to a case study of the city of Zurich. The tolling schemes are compared with a uniform toll, and they highlight how the inhomogeneous distribution of traffic may compromise the effectiveness of cordon tolls.


      PubDate: 2015-04-24T18:38:57Z
       
  • Real-time estimation of lane-based queue lengths at isolated signalized
           junctions
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Seunghyeon Lee , S.C. Wong , Y.C. Li
      In this study, we develop a real-time estimation approach for lane-based queue lengths. Our aim is to determine the numbers of queued vehicles in each lane, based on detector information at isolated signalized junctions. The challenges involved in this task are to identify whether there is a residual queue at the start time of each cycle and to determine the proportions of lane-to-lane traffic volumes in each lane. Discriminant models are developed based on time occupancy rates and impulse memories, as calculated by the detector and signal information from a set of upstream and downstream detectors. To determine the proportions of total traffic volume in each lane, the downstream arrivals for each cycle are estimated by using the Kalman filter, which is based on upstream arrivals and downstream discharges collected during the previous cycle. Both the computer simulations and the case study of real-world traffic show that the proposed method is robust and accurate for the estimation of lane-based queue lengths in real time under a wide range of traffic conditions. Calibrated discriminant models play a significant role in determining whether there are residual queued vehicles in each lane at the start time of each cycle. In addition, downstream arrivals estimated by the Kalman filter enhance the accuracy of the estimates by minimizing any error terms caused by lane-changing behavior.


      PubDate: 2015-04-24T18:38:57Z
       
  • Does it pay to reveal safety information? The effect of safety
           information on flight choice
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Aliza Fleischer , Anat Tchetchik , Tomer Toledo
      The case of flights safety lends itself as a natural case study for choice under of information asymmetry that involves dread risk and emotional factors. Specifically it allows one to experiment how the releasing of information will affect consumer choice. Previous studies, which followed the deregulation of commercial aviation, raised concerns about the corresponding potential for a marked deterioration in airline safety. Measures to prevent that decline were subsequently proposed. Specifically, it was argued that the public sector should establish and release flight safety indicators in addition to accidents’ statistics, which are currently available. It was argued that such safety indicators will also enable airlines to diversify their safety offerings. Underlying this argument are the assumptions that consumers’ flight safety preferences vary and that, provided with safety information, consumers will use it when making decisions. The present work, however, refutes the first assumption and sheds light on the second. It further investigates whether and how consumers react to and interpret safety information when choosing a flight, while accounting explicitly for a psychological trait. Employing an advanced experimental design and econometric approach, we find that: 1. When formal flight safety ratings are supplied, individuals abandoned their priors and rely on the information provided. 2. When it comes to “bad death” probabilities, people are not sensitive to the different shades of safety, and instead, they simply discern flights as either safe or unsafe. 3. Under a certain conditions disclosed information can alleviated fear and change the decision making of airline passengers.


      PubDate: 2015-04-24T18:38:57Z
       
  • Vehicular Ad-Hoc Networks sampling protocols for traffic monitoring and
           incident detection in Intelligent Transportation Systems
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Andrea Baiocchi , Francesca Cuomo , Mario De Felice , Gaetano Fusco
      Vehicular Ad-Hoc Networks (VANETs) are an emerging technology soon to be brought to everyday life. Many Intelligent Transport Systems (ITS) services that are nowadays performed with expensive infrastructure, like reliable traffic monitoring and car accident detection, can be enhanced and even entirely provided through this technology. In this paper, we propose and assess how to use VANETs for collecting vehicular traffic measurements. We provide two VANET sampling protocols, named SAME and TOME, and we design and implement an application for one of them, to perform real time incident detection. The proposed framework is validated through simulations of both vehicular micro-mobility and communications on the 68km highway that surrounds Rome, Italy. Vehicular traffic is generated based on a large real GPS traces set measured on the same highway, involving about ten thousand vehicles over many days. We show that the sampling monitoring protocol, SAME, collects data in few seconds with relative errors less than 10%, whereas the exhaustive protocol TOME allows almost fully accurate estimates within few tens of seconds. We also investigate the effect of a limited deployment of the VANET technology on board of vehicles. Both traffic monitoring and incident detection are shown to still be feasible with just 50% of equipped vehicles.


      PubDate: 2015-04-24T18:38:57Z
       
  • Corrigendum to “Methodology for safety improvement programming using
           constrained network-level optimization” [Transportation Research
           Part C: Emerging Technologies 50 (2015) 106–116]
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jackeline Murillo-Hoyos , Nathee Athigakunagorn , Samuel Labi



      PubDate: 2015-04-24T18:38:57Z
       
  • Long short-term memory neural network for traffic speed prediction using
           remote microwave sensor data
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Xiaolei Ma , Zhimin Tao , Yinhai Wang , Haiyang Yu , Yunpeng Wang
      Neural networks have been extensively applied to short-term traffic prediction in the past years. This study proposes a novel architecture of neural networks, Long Short-Term Neural Network (LSTM NN), to capture nonlinear traffic dynamic in an effective manner. The LSTM NN can overcome the issue of back-propagated error decay through memory blocks, and thus exhibits the superior capability for time series prediction with long temporal dependency. In addition, the LSTM NN can automatically determine the optimal time lags. To validate the effectiveness of LSTM NN, travel speed data from traffic microwave detectors in Beijing are used for model training and testing. A comparison with different topologies of dynamic neural networks as well as other prevailing parametric and nonparametric algorithms suggests that LSTM NN can achieve the best prediction performance in terms of both accuracy and stability.


      PubDate: 2015-04-24T18:38:57Z
       
  • A dynamic Bayesian network model for real-time crash prediction using
           traffic speed conditions data
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jie Sun , Jian Sun
      Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.


      PubDate: 2015-04-24T18:38:57Z
       
  • A fuzzy reasoning system for evaluating the efficiency of cabin baggage
           screening at airports
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Jacek Skorupski , Piotr Uchroński
      The growing threat of unlawful interference and terrorist acts has led to widespread implementation of screening systems for checking people and baggage at airports. Introducing limits regarding objects permitted to be transported and screening procedures themselves have decreased the comfort of travelling and reduced the capacity of terminals. It is therefore important to examine the efficiency of screening, whether carried out under regular circumstances or in a situation where threat level is high. The purpose of this study is to develop an effective method and calculation tool making it possible to quickly and exactly determine the effectiveness of cabin baggage screening, depending on the equipment available, the choice of screening staff, and the organisational solutions applied. What is more, the human factor is of great significance as far as cabin baggage screening is concerned. It introduces a certain amount of subjectivity, imprecision, and incompleteness of description. Due to this, fuzzy reasoning solutions have been employed. The results indicate that it is possible for the efficiency of cabin baggage screening to vary significantly at various screening checkpoints (SC), even within one airport. It is also demonstrated that it is possible to actually manage the level of screening efficiency, also in a situation where the risk of an attack is greater than usual. One should avoid taking global decisions and, instead, focus on assessing screening at particular SCs and take steps on the basis of the results of such an assessment. Results obtained with the use of a computer tool under the name of COBAFAS demonstrate that it is then possible to improve the efficiency of screening without hindering the capacity of the airport at the same time.


      PubDate: 2015-04-24T18:38:57Z
       
  • An optimization model of energy and transportation systems: Assessing the
           high-speed rail impacts in the United States
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Venkat Krishnan , Eirini Kastrouni , V. Dimitra Pyrialakou , Konstantina Gkritza , James D. McCalley
      This paper presents a long-term investment planning model that co-optimizes infrastructure investments and operations across transportation and electric infrastructure systems for meeting the energy and transportation needs in the United States. The developed passenger transportation model is integrated within the modeling framework of a National Long-term Energy and Transportation Planning (NETPLAN) software, and the model is applied to investigate the impact of high-speed rail (HSR) investments on interstate passenger transportation portfolio, fuel and electricity consumption, and 40-year cost and carbon dioxide (CO2) emissions. The results show that there are feasible scenarios under which significant HSR penetration can be achieved, leading to reasonable decrease in national long-term CO2 emissions and costs. At higher HSR penetration of approximately 30% relative to no HSR in the portfolio promises a 40-year cost savings of up to $0.63T, gasoline and jet fuel consumption reduction of up to 34% for interstate passenger trips, CO2 emissions reduction by about 0.8 billion short tons, and increased resilience against petroleum price shocks. Additionally, sensitivity studies with respect to light-duty vehicle mode share reveal that in order to realize such long-term cost and emission benefits, a change in the passenger mode choice is essential to ensure higher ridership for HSR.


      PubDate: 2015-04-24T18:38:57Z
       
  • A distributed VNS algorithm for optimizing dial-a-ride problems in
           large-scale scenarios
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Santiago Muelas , Antonio LaTorre , José-María Peña
      These days, transportation and logistic problems in large cities are demanding smarter transportation services that provide flexibility and adaptability. A possible solution to this arising problem is to compute the best routes for each new scenario. In this problem, known in the literature as the dial-a-ride problem, a number of passengers are transported between pickup and delivery locations trying to minimize the routing costs while respecting a set of prespecified constraints. This problem has been solved in the literature with several approaches from small to medium sized problems. However, few efforts have dealt with large scale problems very common in massive scenarios (big cities or highly-populated regions). In this study, a new distributed algorithm based on the partition of the requests space and the combination of the routes is presented and tested on a set of 24 different scenarios of a large-scale problem (up to 16,000 requests or 32,000 locations) in the city of San Francisco. The results show that, not only the distributed algorithm is able to solve large problem instances that the corresponding sequential algorithm is unable to solve in a reasonable time, but also to have an average improvement of 9% in the smaller problems. The results have been validated by means of statistical procedures proving that the distributed algorithm can be an effective way to solve high dimensional dial-a-ride problems.


      PubDate: 2015-04-24T18:38:57Z
       
  • The flying sidekick traveling salesman problem: Optimization of
           drone-assisted parcel delivery
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Chase C. Murray , Amanda G. Chu
      Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.


      PubDate: 2015-04-24T18:38:57Z
       
  • Co-design of traffic network topology and control measures
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Zhe Cong , Bart De Schutter , Robert Babuška
      The two main directions to improve traffic flows in networks involve changing the network topology and introducing new traffic control measures. In this paper, we consider a co-design approach to apply these two methods jointly to improve the interaction between different methods and to get a better overall performance. We aim at finding the optimal network topology and the optimal parameters of traffic control laws at the same time by solving a co-optimization problem. However, such an optimization problem is usually highly non-linear and non-convex, and it possibly involves a mixed-integer form. Therefore, we discuss four different solution frameworks that can be used for solving the co-optimization problem, according to different requirements on the computational complexity and speed. A simulation-based study is implemented on the Singapore freeway network to illustrate the co-design approach and to compare the four different solution frameworks.


      PubDate: 2015-04-24T18:38:57Z
       
  • Competing risk mixture model and text analysis for sequential incident
           duration prediction
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Ruimin Li , Francisco C. Pereira , Moshe E. Ben-Akiva
      Predicting the duration of traffic incidents sequentially during the incident clearance period is helpful in deploying efficient measures and minimizing traffic congestion related to such incidents. This study proposes a competing risk mixture hazard-based model to analyze the effect of various factors on traffic incident duration and predict the duration sequentially. First, topic modeling, a text analysis technique, is used to process the textual features of the traffic incident to extract time-dependent topics. Given four specific clearance methods and the uncertainty of these methods when used during traffic incidents, the proposed mixture model uses the multinomial logistic model and parametric hazard-based model to assess the influence of covariates on the probability of clearance methods and on the duration of the incident. Subsequently, the performance of estimated mixture model in sequentially predicting the incident duration is compared with that of the non-mixture model. The prediction results show that the presented mixture model outperforms the non-mixture model.


      PubDate: 2015-04-24T18:38:57Z
       
  • A modified reinforcement learning algorithm for solving coordinated
           signalized networks
    • Abstract: Publication date: May 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 54
      Author(s): Cenk Ozan , Ozgur Baskan , Soner Haldenbilen , Halim Ceylan
      This study proposes Reinforcement Learning (RL) based algorithm for finding optimum signal timings in Coordinated Signalized Networks (CSN) for fixed set of link flows. For this purpose, MOdified REinforcement Learning algorithm with TRANSYT-7F (MORELTRANS) model is proposed by way of combining RL algorithm and TRANSYT-7F. The modified RL differs from other RL algorithms since it takes advantage of the best solution obtained from the previous learning episode by generating a sub-environment at each learning episode as the same size of original environment. On the other hand, TRANSYT-7F traffic model is used in order to determine network performance index, namely disutility index. Numerical application is conducted on medium sized coordinated signalized road network. Results indicated that the MORELTRANS produced slightly better results than the GA in signal timing optimization in terms of objective function value while it outperformed than the HC. In order to show the capability of the proposed model for heavy demand condition, two cases in which link flows are increased by 20% and 50% with respect to the base case are considered. It is found that the MORELTRANS is able to reach good solutions for signal timing optimization even if demand became increased.


      PubDate: 2015-04-24T18:38:57Z
       
  • Empirical analysis of free-floating carsharing usage: The Munich and
           Berlin case
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Stefan Schmöller , Simone Weikl , Johannes Müller , Klaus Bogenberger
      Carsharing has become an important addition to existing mobility services over the last years. Today, several different systems are operating in many big cities. For an efficient and economic operation of any carsharing system, the identification of customer demand is essential. This demand is investigated within the presented research by analyzing booking data of a German free-floating carsharing system. The objectives of this paper are to describe carsharing usage and to identify factors that have an influence on the demand for carsharing. Therefore, the booking data are analyzed for temporal aspects, showing recurring patterns of varying lengths. The spatial distribution of bookings is investigated using a geographic information system and indicates a relationship between city structure and areas with high demand for carsharing. The temporal and spatial facets are then combined by applying a cluster analysis to identify groups of days with similar spatial booking patterns and show asymmetries in the spatiotemporal distribution of vehicle supply and demand. Influences on demand can be either short-term or long-term. The paper shows that changes in the weather conditions are a short-term influence as users of free-floating carsharing react to those. Furthermore, the application of a linear regression analysis reveals that socio-demographic data are suitable for making long-term demand predictions since booking numbers show quite a strong correlation with socio-demography, even in a simple model.


      PubDate: 2015-04-24T18:38:57Z
       
  • A continuous-flow-intersection-lite design and traffic control for
           oversaturated bottleneck intersections
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): Weili Sun , Xinkai Wu , Yunpeng Wang , Guizhen Yu
      Oversaturation has become a severe problem for urban intersections, especially the bottleneck intersections that cause queue spillover and network gridlock. Further improvement of oversaturated arterial traffic using traditional mitigation strategies, which aim to improve intersection capacity by merely adjusting signal control parameters, becomes challenging since exiting strategies may (or already) have reached their “theoretical” limits of optimum. Under such circumstance, several novel unconventional intersection designs, including the well-recognized continuous flow intersection (CFI) design, are originated to improve the capacity at bottleneck intersections. However, the requirement of installing extra sub-intersections in a CFI design would increase vehicular stops and, more critically, is unacceptable in tight urban areas with closed spaced intersections. To address these issues, this research proposes a simplified continuous flow intersection (called CFI-Lite) design that is ideal for arterials with short links. It benefits from the CFI concept to enable simultaneous move of left-turn and through traffic at bottleneck intersections, but does not need installation of sub-intersections. Instead, the upstream intersection is utilized to allocate left-turn traffic to the displaced left-turn lane. It is found that the CFI-Lite design performs superiorly to the conventional design and regular CFI design in terms of bottleneck capacity. Pareto capacity improvement for every traffic stream in an arterial system can be achieved under effortless conditions. Case study using data collected at Foothill Blvd in Los Angeles, CA, shows that the new design is beneficial in more than 90% of the 408 studied cycles. The testing also shows that the average improvements of green bandwidths for the synchronized phases are significant.


      PubDate: 2015-04-24T18:38:57Z
       
  • A heuristic model of bounded route choice in urban areas
    • Abstract: Publication date: July 2015
      Source:Transportation Research Part C: Emerging Technologies, Volume 56
      Author(s): E.J. Manley , S.W. Orr , T. Cheng
      There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.


      PubDate: 2015-04-19T12:07:04Z
       
 
 
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