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Showing papers in "Transportation Research Part C-emerging Technologies in 2011"


Journal ArticleDOI
TL;DR: The most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.
Abstract: Smart card automated fare collection systems are being used more and more by public transit agencies. While their main purpose is to collect revenue, they also produce large quantities of very detailed data on onboard transactions. These data can be very useful to transit planners, from the day-to-day operation of the transit system to the strategic long-term planning of the network. This review covers several aspects of smart card data use in the public transit context. First, the technologies are presented: the hardware and information systems required to operate these tools; and privacy concerns and legal issues related to the dissemination of smart card data, data storage, and encryption are addressed. Then, the various uses of the data at three levels of management are described: strategic (long-term planning), tactical (service adjustments and network development), and operational (ridership statistics and performance indicators). Also reported are smart card commercialization experiments conducted all over the world. Finally, the most promising research avenues for smart card data in this field are presented; for example, comparison of planned and implemented schedules, systematic schedule adjustments, and the survival models applied to ridership.

810 citations


Journal ArticleDOI
TL;DR: Differences and similarities between these two approaches to data analysis are discussed, relevant literature is reviewed and a set of insights are provided for selecting the appropriate approach.
Abstract: In the field of transportation, data analysis is probably the most important and widely used research tool available. In the data analysis universe, there are two ‘schools of thought’; the first uses statistics as the tool of choice, while the second – one of the many methods from – Computational Intelligence. Although the goal of both approaches is the same, the two have kept each other at arm’s length. Researchers frequently fail to communicate and even understand each other’s work. In this paper, we discuss differences and similarities between these two approaches, we review relevant literature and attempt to provide a set of insights for selecting the appropriate approach.

752 citations


Journal ArticleDOI
Wanli Min1, Laura Wynter1
TL;DR: The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance for real-time road traffic prediction to be both fast and scalable to full urban networks.
Abstract: Real-time road traffic prediction is a fundamental capability needed to make use of advanced, smart transportation technologies. Both from the point of view of network operators as well as from the point of view of travelers wishing real-time route guidance, accurate short-term traffic prediction is a necessary first step. While techniques for short-term traffic prediction have existed for some time, emerging smart transportation technologies require the traffic prediction capability to be both fast and scalable to full urban networks. We present a method that has proven to be able to meet this challenge. The method presented provides predictions of speed and volume over 5-min intervals for up to 1 h in advance.

594 citations


Journal ArticleDOI
TL;DR: In this article, a full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs).
Abstract: One full year of high-resolution driving data from 484 instrumented gasoline vehicles in the US is used to analyze daily driving patterns, and from those infer the range requirements of electric vehicles (EVs). We conservatively assume that EV drivers would not change their current gasoline-fueled driving patterns and that they would charge only once daily, typically at home overnight. Next, the market is segmented into those drivers for whom a limited-range vehicle would meet every day’s range need, and those who could meet their daily range need only if they make adaptations on some days. Adaptations, for example, could mean they have to either recharge during the day, borrow a liquid-fueled vehicle, or save some errands for the subsequent day. From this analysis, with the stated assumptions, we infer the potential market share for limited-range vehicles. For example, we find that 9% of the vehicles in the sample never exceeded 100 miles in one day, and 21% never exceeded 150 miles in one day. These drivers presumably could substitute a limited-range vehicle, like electric vehicles now on the market, for their current gasoline vehicle without any adaptation in their driving at all. For drivers who are willing to make adaptations on 2 days a year, the same 100 mile range EV would meet the needs of 17% of drivers, and if they are willing to adapt every other month (six times a year), it would work for 32% of drivers. Thus, it appears that even modest electric vehicles with today’s limited battery range, if marketed correctly to segments with appropriate driving behavior, comprise a large enough market for substantial vehicle sales. An additional analysis examines driving versus parking by time of day. On the average weekday at 5 pm, only 15% of the vehicles in the sample are on the road; at no time during the year are fewer than 75% of vehicles parked. Also, because the return trip home is widely spread in time, even if all cars plug in and begin charging immediately when they arrive home and park, the increased demand on the electric system is less problematic than prior analyses have suggested.

541 citations


Journal ArticleDOI
TL;DR: This paper reviews the methods and technologies for congestion pricing of roads and recommends three main technology categories: roadside-only systems employing digital photography, tag & beacon systems that use short-range microwave technology, and in-vehicle- only systems based on either satellite or cellular network communications.
Abstract: This paper reviews the methods and technologies for congestion pricing of roads. Congestion tolls can be implemented at scales ranging from individual lanes on single links to national road networks. Tolls can be differentiated by time of day, road type and vehicle characteristics, and even set in real time according to current traffic conditions. Conventional toll booths have largely given way to electronic toll collection technologies. The main technology categories are roadside-only systems employing digital photography, tag & beacon systems that use short-range microwave technology, and in-vehicle-only systems based on either satellite or cellular network communications. The best technology choice depends on the application. The rate at which congestion pricing is implemented, and its ultimate scope, will depend on what technology is used and on what other functions and services it can perform.

379 citations


Journal ArticleDOI
TL;DR: This paper intends to design quantitative methods to inspect trajectory data, involving jerk analysis, consistency analysis and spectral analysis, and is applied to the complete set of NGSIM databases.
Abstract: Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases.

349 citations


Journal ArticleDOI
TL;DR: The concept of Queue Rear No-delay Arrival Time is introduced which is related to the non-smoothness of queuing delay patterns and queue length changes and can be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can be constructed.
Abstract: We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.

305 citations


Journal ArticleDOI
TL;DR: The results show that the proposed models to predict bus arrival times at the same bus stop but with different routes are more accurate than the models based on the bus running times of single route.
Abstract: Provision of accurate bus arrival information is vital to passengers for reducing their anxieties and waiting times at bus stop. This paper proposes models to predict bus arrival times at the same bus stop but with different routes. In the proposed models, bus running times of multiple routes are used for predicting the bus arrival time of each of these bus routes. Several methods, which include support vector machine (SVM), artificial neural network (ANN), k nearest neighbours algorithm (k-NN) and linear regression (LR), are adopted for the bus arrival time prediction. Observation surveys are conducted to collect bus running and arrival time data for validation of the proposed models. The results show that the proposed models are more accurate than the models based on the bus running times of single route. Moreover, it is found that the SVM model performs the best among the four proposed models for predicting the bus arrival times at bus stop with multiple routes.

297 citations


Journal ArticleDOI
TL;DR: In this paper, the authors present a multi-start local search heuristic to solve the problem of ship routing and scheduling with speed optimization, where speed on each sailing leg is introduced as a decision variable.
Abstract: Tramp shipping companies are committed to transport a set of contracted cargoes and try to derive additional revenue from carrying optional spot cargoes. Traditionally, models for ship routing and scheduling problems are based on fixed speed and a given fuel consumption rate for each ship. However, in real life a ship’s speed is variable within an interval, and fuel consumption per time unit can be approximated by a cubic function of speed. Here we present the tramp ship routing and scheduling problem with speed optimization, where speed on each sailing leg is introduced as a decision variable. We present a multi-start local search heuristic to solve this problem. To evaluate each move in the local search we have to determine the optimal speed for each sailing leg of a given ship route. To do this we propose two different algorithms. Extensive computational results show that the solution method solves problems of realistic size and that taking speed into consideration in tramp ship routing and scheduling significantly improves the solutions.

289 citations


Journal ArticleDOI
TL;DR: The experiment results suggest that the proposed Bayesian inference-based dynamic linear model to predict online short-term travel time on a freeway stretch is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.
Abstract: This paper presents a Bayesian inference-based dynamic linear model (DLM) to predict online short-term travel time on a freeway stretch. The proposed method considers the predicted freeway travel time as the sum of the median of historical travel times, time-varying random variations in travel time, and a model evolution error, where the median is employed to recognize the primary travel time pattern while the variation captures unexpected supply (i.e. capacity) reduction and demand fluctuations. Bayesian forecasting is a learning process that revises sequentially the state of a priori knowledge of travel time based on newly available information. The prediction result is a posterior travel time distribution that can be employed to generate a single-value (typically but not necessarily the mean) travel time as well as a confidence interval representing the uncertainty of travel time prediction. To better track travel time fluctuations during non-recurrent congestion due to unforeseen events (e.g., incidents, accidents, or bad weather), the DLM is integrated into an adaptive control framework that can automatically learn and adjust the system evolution noise level. The experiment results based on the real loop detector data of an I-66 segment in Northern Virginia suggest that the proposed method is able to provide accurate and reliable travel time prediction under both recurrent and non-recurrent traffic conditions.

228 citations


Journal ArticleDOI
TL;DR: This paper develops a general travel decision-making rule utilizing Cumulative prospect theory, investigates the mechanism of travelers’ behavior, examines the probability of applying CPT as a measure of commute utility, and establishes a general utility measurement system.
Abstract: To make practical use of research into travelers’ behavior in route choice modeling, a link is required to connect objective travel scenarios with the subjective decisions made by travelers. Cumulative prospect theory (CPT) offers an alternative framework of route choice behavior that goes beyond the conventional expected utility theory (EUT) models. This paper develops a general travel decision-making rule utilizing CPT. It investigates the mechanism of travelers’ behavior, examines the probability of applying CPT as a measure of commute utility, and establishes a general utility measurement system, the results of which are found to be more consistent with the experimental data than those of EUT-based route choice models. In addition, an approach to confirm the reference point value is suggested. The main techniques adopted in this study are demonstration analysis, a questionnaire survey, and statistical approaches.

Journal ArticleDOI
TL;DR: In this article, the authors use a large sample of trajectory observations collected by means of a helicopter to identify differences between the car-following behaviors of: (1) passenger cars, (2) passenger car drivers and truck drivers, and (3) driver following a passenger car and following a truck.
Abstract: The aim of this paper is to gain insights into the level of heterogeneity in car-following behavior in real traffic. We use a large sample of trajectory observations collected by means of a helicopter to identify differences between the car-following behaviors of: (1) passenger car drivers, (2) passenger car drivers and truck drivers and (3) passenger car drivers following a passenger car and passenger car drivers following a truck. We thereto calibrate eight car-following models making different assumptions about the way in which drivers follow their leader(s) on the same lane. We show that considerable behavioral differences exist between passenger car drivers. Different passenger car drivers do not only consider different stimuli (like speed difference(s) with the leading car(s) and distance headway(s) to leading car(s)) but also the extents to which these stimuli influence their behavior differ. Truck drivers turn furthermore out to adopt in general a more robust car-following behavior than passenger car drivers. Their speeds show, for example, less variation over time. We also find indications that the desired headways of passenger car drivers are lower when following a truck than when following a passenger car.

Journal ArticleDOI
TL;DR: In this paper, the authors focus on the analysis of CO2 emissions for different levels of congestion and time-definitive customer demands, using travel time data from an extensive archive of freeway sensors, timedependent vehicle routing algorithms, and problems-instances with different types of binding constraints.
Abstract: Increased congestion during peak morning and afternoon periods in urban areas is increasing logistics costs. In addition, environmental, social, and political pressures to limit the impacts associated with CO2 emissions are mounting rapidly. A key challenge for transportation agencies and businesses is to improve the efficiency of urban freight and commercial vehicle movements while ensuring environmental quality, livable communities, and economic growth. However, research and policy efforts to analyze and quantify the impacts of congestion and freight public policies on CO2 emissions are hindered by the complexities of vehicle routing problems with time-dependent travel times and the lack of network-wide congestion data. This research focuses on the analysis of CO2 emissions for different levels of congestion and time-definitive customer demands. Travel time data from an extensive archive of freeway sensors, time-dependent vehicle routing algorithms, and problems-instances with different types of binding constraints are used to analyze the impacts of congestion on commercial vehicle emissions. Results from the case study indicate that the impacts of congestion or speed limits on commercial vehicle emissions are significant but difficult to predict since it is shown that it is possible to construct instances where total route distance or duration increases but emissions decrease. Public agencies should carefully study the implications of policies that regulate depot locations and travel speeds as they may have unintended negative consequences in terms of CO2 emissions.

Journal ArticleDOI
TL;DR: In this paper, a branch and cut-and-price algorithm for the exact solution of a variation of the vehicle routing problem with time windows in which the transportation fleet is made by vehicles with different capacities and fixed costs, based at different depots.
Abstract: We present a branch-and-cut-and-price algorithm for the exact solution of a variation of the vehicle routing problem with time windows in which the transportation fleet is made by vehicles with different capacities and fixed costs, based at different depots. We illustrate different pricing and cutting techniques and we present an experimental evaluation of their combinations. Computational results are reported on the use of the algorithm both for exact optimization and as a heuristic method.

Journal ArticleDOI
TL;DR: Positive effects of the SASPENCE system are shown in terms of fewer alarm situations, shorter alarm lengths, shorter reaction times, increased headway and better interactions with vulnerable road users at intersections, while on the negative side, driver performance worsened slightly.
Abstract: The effects of a driver assistance system for keeping safe speed and safe distance (referred to as SASPENCE) on driver behaviour, reactions and acceptance were evaluated in a test carried out in 2006. Twenty test drivers, recruited by ads, drove two times (once with the system off and once with the system on) in real traffic conditions along a 50 km long test route containing urban and rural roads and motorway sections outside Turin, Italy. Driving data was logged and the test drivers were observed by means of an in-car observation method, in this case by two observers riding along in the car with the driver. Driver opinions were collected through questionnaires. The findings show positive effects of the system in terms of fewer alarm situations, shorter alarm lengths, shorter reaction times, increased headway and better interactions with vulnerable road users at intersections. On the negative side, driver performance worsened slightly, the number of centre line crossings increased, there was worse facilitating behaviour with regard to other drivers and harder braking at traffic lights. No major effect on speed behaviour of the driver, lane choice, lane keeping, lane change, overtaking, red running, use of turning indicator and workload was found.

Journal ArticleDOI
TL;DR: Staff seats, patient seats, stretchers and wheelchair places are introduced into state-of-the-art formulations and branch-and-cut algorithms for the standard dial-a-ride problem and a recent metaheuristic method is adapted to this new problem.
Abstract: Dial-a-ride problems deal with the transportation of people between pickup and delivery locations. Given the fact that people are subject to transportation, constraints related to quality of service are usually present, such as time windows and maximum user ride time limits. In many real world applications, different types of users exist. In the field of patient and disabled people transportation, up to four different transportation modes can be distinguished. In this article we consider staff seats, patient seats, stretchers and wheelchair places. Furthermore, most companies involved in the transportation of the disabled or ill dispose of different types of vehicles. We introduce both aspects into state-of-the-art formulations and branch-and-cut algorithms for the standard dial-a-ride problem. Also a recent metaheuristic method is adapted to this new problem. In addition, a further service quality related issue is analyzed: vehicle waiting time with passengers aboard. Instances with up to 40 requests are solved to optimality. High quality solutions are obtained with the heuristic method.

Journal ArticleDOI
TL;DR: In this paper, the authors developed a safety-related approaching behavior model by considering the variability of following vehicle's speed to the leading vehicle's one and the relative distances among vehicles.
Abstract: In order to understand driver’s safety-related approaching behaviour during car-following process in more depth, it is necessary to achieve the comprehensive analysis of vehicle-to-vehicle dynamic interactions. Based on qualitative description of driving shaping behaviour associated with driving human factors of influencing driver’s car-following behaviour, this paper presents briefly the fundamentals of simulation modelling of driver’s safety approaching behaviour in urban traffic operation. The emphasis on our research is placed on the development of a driver’s safety approaching behavioural model by considering the variability of following vehicle’s speed to the leading vehicle’s one and the relative distances among vehicles. Furthermore, we have carried out simulation and analysis of driver’s deceleration and acceleration behaviour under different driving situations after identified the key safety-related parameters. Finally the developed model has been validated by using detailed vehicle trajectory data that was collected in naturalistic driving environment. The results show that the safety-based approaching behavioural model could be used to analyze driver’s car-following behaviour for driving support and to reveal the essence of traffic flow characteristics at the microscopic level.

Journal ArticleDOI
TL;DR: In this paper, an integrated Lagrangian relaxation and tabu search solution method was developed to tackle the combinatorial complexity arising from this combined treatment of discrete network capacity and connectivity settings, and the results from optimizing a regional evacuation network for a nuclear power plant illustrate the validness and usefulness of the modeling and solution methodology in evacuation planning practice.
Abstract: This paper formulates and solves a lane-based evacuation network optimization problem that integrates lane reversal and crossing elimination strategies. To tackle the combinatorial complexity arising from this combined treatment of discrete network capacity and connectivity settings, an integrated Lagrangian relaxation and tabu search solution method is developed. The method takes advantage of Lagrangian relaxation for problem decomposition and complexity reduction while its algorithmic logic is designed based on the principles of tabu search. Numerical results from optimizing a regional evacuation network for a nuclear power plant illustrate the validness and usefulness of the modeling and solution methodology in evacuation planning practice.

Journal ArticleDOI
TL;DR: An arterial signal optimization model that features its effectiveness on explicitly modeling physical queue evolution on arterial links by lane-group to account for shared-lane traffic interactions and capturing the dynamic interactions of spillback queues among lane groups and between neighboring intersections is presented.
Abstract: This paper presents an arterial signal optimization model that features its effectiveness on: (1) explicitly modeling physical queue evolution on arterial links by lane-group to account for shared-lane traffic interactions; and (2) capturing the dynamic interactions of spillback queues among lane groups and between neighboring intersections due to high demand, geometric constraints, or signal settings. Depending on the detected traffic patterns, one can select the control objective to be either minimizing the total travel time or maximizing the total throughput over the target area. The solution procedures developed with the Genetic Algorithm (GA) have been tested with an example arterial of four intersections under different demand scenarios. Extensive experimental analyses in comparison with results from TRANSYT-7F (version 8) reveal that the proposed model and solution method are quite promising for use in design of arterial signals, especially under congested, high demand traffic conditions.

Journal ArticleDOI
TL;DR: A new method to solve the simultaneous adjustment of a dynamic traffic demand matrix is presented, searching for a reliable solution with acceptable computational times for off-line applications and using as an input traffic counts and speeds, prior O–D matrices and other aggregate demand data.
Abstract: The temporal demand matrix is an essential input to both on-line and off-line applications of dynamic traffic assignment (DTA). This paper presents a new method to solve the simultaneous adjustment of a dynamic traffic demand matrix, searching for a reliable solution with acceptable computational times for off-line applications and using as an input traffic counts and speeds, prior O–D matrices and other aggregate demand data (traffic demand productions by zone). The proposed solving procedure is a modification of the basic Simultaneous Perturbation Stochastic Approximation (SPSA) path search optimization method; it can find a good solution when the starting point (the seed matrix) is assumed to be “near” the optimal one, working with a gradient approximation based on a simultaneous perturbation of each demand variable.

Journal ArticleDOI
TL;DR: In this article, a rolling horizon heuristic (RHH) is proposed to solve the problem by iteratively solving sub-problems with shorter planning horizons, and the RHH finds solutions to the problem within a reasonable amount of time.
Abstract: In this paper a maritime inventory routing problem for one of the world's largest producers of liquefied natural gas (LNG) is presented. The producer is responsible for the LNG inventories at the liquefaction plant, the loading port with a limited number of berths, and the routing and scheduling of a heterogeneous fleet of LNG ships. In addition, the producer has to fulfill a set of long-term contracts to customers all around the world. The goal is to create an annual delivery program (ADP) to fulfill the long-term contracts at minimum cost, while maximizing revenue from selling LNG in the spot market. An ADP is a complete schedule of every ship's sailing plan for the coming year. A mixed integer programming (MIP) formulation of the ADP planning problem is presented, and it is based on a priori generation of all possible scheduled voyages within the planning horizon. Due to the size and complexity of the problem, a rolling horizon heuristic (RHH) is proposed. The RHH solves the problem by iteratively solving sub-problems with shorter planning horizons. The RHH finds solutions to the problem within a reasonable amount of time, and creates very good ADPs according to the problem owner.

Journal ArticleDOI
TL;DR: In this article, the authors examined the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro, and proposed four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs.
Abstract: In this paper we examine the transit network design problem under the assumption of elastic demand, focusing on the problem of designing the frequencies of a regional metro. In this problem, investments in transit services have appreciable effects on modal split. Neglecting demand elasticity can lead to solutions that may not represent the actual objectives of the design. We propose four different objective functions that can be adopted to assume demand as elastic, considering the costs of all transportation systems (car, bus and rail) as well as the external costs, and we define the constraints of the problem. Heuristic and meta-heuristic solution algorithms are also proposed. The models and algorithms are tested on a small network and on a real-scale network.

Journal ArticleDOI
TL;DR: In this paper, the authors proposed a flexible solution methodology for the vehicle routing problem with stochastic demands (VRPSD), which takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exist.
Abstract: After introducing the Vehicle Routing Problem with Stochastic Demands (VRPSD) and some related work, this paper proposes a flexible solution methodology. The logic behind this methodology is to transform the issue of solving a given VRPSD instance into an issue of solving a small set of Capacitated Vehicle Routing Problem (CVRP) instances. Thus, our approach takes advantage of the fact that extremely efficient metaheuristics for the CVRP already exists. The CVRP instances are obtained from the original VRPSD instance by assigning different values to the level of safety stocks that routed vehicles must employ to deal with unexpected demands. The methodology also makes use of Monte Carlo simulation (MCS) to obtain estimates of the reliability of each aprioristic solution – that is, the probability that no vehicle runs out of load before completing its delivering route – as well as for the expected costs associated with corrective routing actions (recourse actions) after a vehicle runs out of load before completing its route. This way, estimates for expected total costs of different routing alternatives are obtained. Finally, an extensive numerical experiment is included in the paper with the purpose of analyzing the efficiency of the described methodology under different uncertainty scenarios.

Journal ArticleDOI
TL;DR: The derivations of mean and variance–covariance of the stochastic passenger flows and dis-utility terms involved in the route/mode choice model under the common-line framework are provided.
Abstract: This paper proposes a multi-modal transport network assignment model considering uncertainties in both demand and supply sides of the network. These uncertainties are due to adverse weather conditions with different degrees of impacts on different modes. The paper provides the derivations of mean and variance–covariance of the stochastic passenger flows and dis-utility terms involved in the route/mode choice model under the common-line framework. The risk-averse travelers are assumed to consider both the mean and variance of the random perceived travel time on each multi-modal path in their path choice decisions. The model also considers travelers’ perception errors by using a Probit stochastic user equilibrium framework which is formulated as fixed point problem. A heuristic solution algorithm is proposed to solve the fixed point problem. Numerical examples are presented to illustrate the applications of the proposed model.

Journal ArticleDOI
TL;DR: The authors develop a stochastic optimization model under demand uncertainty, where the inherent risk is modeled by scenarios, and propose solution methods for the stochastically optimization problem based on L-shaped algorithm within an e-optimality framework.
Abstract: The authors consider the design of a two-echelon production distribution network with multiple manufacturing plants, customers and a set of candidate distribution centers. The main contribution of the study is to extend the existing literature by incorporating the demand uncertainty of customers within the distribution center location and transportation mode allocation decisions, as well as providing a network design satisfying the both economical and service quality objectives of the decision maker within two levels supply network setting. The authors formulate the problem as two stage integer recourse problem to find a set of optimal network configuration and assignment of transportation modes and the respective flows in order to minimize total cost and total service time, simultaneously. The authors develop a stochastic optimization model under demand uncertainty, where the inherent risk is modeled by scenarios. Finally, they propose solution methods for our stochastic optimization problem based on L-shaped algorithm within an e-optimality framework and present numerical results demonstrating the computational effectiveness.

Journal ArticleDOI
TL;DR: In this paper, a self-learning approach is proposed to determine optimal pricing strategies for high-occupancy/toll lane operations, which learns recursively motorists' willingness to pay by mining the loop detector data, and then specifies toll rates to maximize the freeway's throughput while ensuring a superior travel service to the users of the toll lanes.
Abstract: This paper proposes a self-learning approach to determine optimal pricing strategies for high-occupancy/toll lane operations. The approach learns recursively motorists’ willingness to pay by mining the loop detector data, and then specifies toll rates to maximize the freeway’s throughput while ensuring a superior travel service to the users of the toll lanes. In determination of the tolls, a multi-lane hybrid traffic flow model is used to explicitly consider the impacts of the lane-changing behaviors before the entry points of the toll lanes on throughput and travel time. Simulation experiments are conducted to demonstrate and validate the proposed approach, and provide insights on when to convert high-occupancy lanes to toll lanes.

Journal ArticleDOI
TL;DR: The goal of this article is to provide the theoretical basis for enabling tractable solutions to the "arriving on time" problem and enabling its use in real-time mobile phone applications and to present an efficient algorithm for finding an optimal routing policy with a well bounded computational complexity.
Abstract: The goal of this article is to provide the theoretical basis for enabling tractable solutions to the "arriving on time" problem and enabling its use in real-time mobile phone applications. Optimal routing in transportation networks with highly varying traffic conditions is a challenging problem due to the stochastic nature of travel-times on links of the network. The definition of optimality criteria and the design of solution methods must account for the random nature of the travel-time on each link. Most common routing algorithms consider the expected value of link travel-time as a sufficient statistic for the problem and produce least expected travel-time paths without consideration of travel-time variability. However, in numerous practical settings the reliability of the route is also an important decision factor. In this article, the authors consider the following optimality criterion: maximizing the probability of arriving on time at a destination given a departure time and a time budget. The authors present an efficient algorithm for finding an optimal routing policy with a well bounded computational complexity, improving on an existing solution that takes an unbounded number of iterations to converge to the optimal solution. A routing policy is an adaptive algorithm that determines the optimal solution based on en route travel-times and therefore provides better reliability guarantees than an a-priori solution. Novel speed-up techniques to efficiently compute the adaptive optimal strategy and methods to prune the search space of the problem are also investigated. Finally, an extension of this algorithm which allows for both time varying traffic conditions and spatio-temporal correlations of link travel-time distributions is presented. The dramatic runtime improvements provided by the algorithm are demonstrated for practical scenarios in California.

Journal ArticleDOI
TL;DR: The authors formulate two mixed-integer linear programming (MILP) models for this problem which they refer to as the selective MDVRP with pricing and propose a tabu search based heuristic method to solve medium and large-sized instances.
Abstract: Firms in the durable goods industry occasionally launch trade-in or buyback campaigns to induce replacement purchases by customers. As a result of this, used products (cores) quickly accumulate at the dealers during the campaign periods. We study the reverse logistics problem of such a firm that aims to collect cores from its dealers. Having already established a number of collection centers where inspection of the cores can be performed, the firm’s objective is to optimize the routes of a homogeneous fleet of capacitated vehicles each of which will depart from a collection center, visit a number of dealers to pick up cores, and return to the same center. We assume that dealers do not give their cores back free of charge, but they have a reservation price. Therefore, the cores accumulating at a dealer can only be taken back if the acquisition price announced by the firm exceeds the dealer’s reservation price. However, the firm is not obliged to visit all dealers; vehicles are dispatched to a dealer only if it is profitable to do so. The problem we focus on becomes an extension of the classical multi-depot vehicle routing problem (MDVRP) in which each visit to a dealer is associated with a gross profit and an acquisition price to be paid to take the cores back. We formulate two mixed-integer linear programming (MILP) models for this problem which we refer to as the selective MDVRP with pricing. Since the problem is NP -hard, we propose a Tabu Search based heuristic method to solve medium and large-sized instances. The performance of the heuristic is quite promising in comparison with solving the MILP models by a state-of-the-art commercial solver.

Journal ArticleDOI
TL;DR: It is deduced that it is zero speed that induces jam density in traffic rather than vice versa, so that the direction of causality between speed and density differs according to circumstances.
Abstract: We develop the relationship between speed and density to analyse the flow of traffic under the operation of variable speed limits. By statistical analysis of traffic data from the UK motorway network, we find that the functional form preferred for this does not have an explicit jam density that will induce zero speed in traffic. We deduce that it is zero speed that induces jam density in traffic rather than vice versa, so that the direction of causality between speed and density differs according to circumstances. We develop an approach to modelling traffic in light of this. We apply this to analyse speed control as a traffic management measure and show how it can be used to estimate the effect of speed management on road capacity.

Journal ArticleDOI
TL;DR: In this article, a cost-flow network problem with an NP-hard complexity level is formulated, and a heuristic algorithm based on the Deficit Function theory is developed to solve it.
Abstract: The public-transport (transit) operation planning process commonly includes four basic activities, usually performed in sequence: (1) network route design, (2) timetable development, (3) vehicle scheduling, and (4) crew scheduling. The purpose of this work is to address the vehicle scheduling problem, while taking into account the association between the characteristics of each trip (urban, peripheral, inter-city, etc.) and the vehicle type required for the particular trip. The problem is based on given sets of trips and vehicle types, where the categories are arranged in decreasing order of vehicle cost. Therefore, each trip can be carried out by its vehicle type, or by other types listed in prior order. This problem can be formulated as a cost-flow network problem with an NP-hard complexity level. Thus, a heuristic algorithm is developed in this work, based on the Deficit Function theory. Two examples are used as an expository device to illustrate the procedures developed, along with a real-life example of a bus company.