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Route choice modeling under travel time uncertainty is essential for analyzing travelers’ mobility patterns. This paper investigates the impact of travel time uncertainty on route choice behavior in user equilibrium models based on a generalized mean-variance metric (GMV). This model can capture the influence of risk attitudes and schedule unpunctuality on route choice using a generalization of expected travel time, variance, and expected early or late arrival penalties, of which travelers are assumed to minimize the GMV of trips considering a certain on-time arrival probability. This paper establishes the properties of GMV and formulates the GMV-based static user equilibria as a variational inequality (VI) problem, for which the existence and uniqueness of the solutions are also analyzed. An effective traffic assignment algorithm without path enumeration is developed to solve the proposed user equilibrium problem. Numerical examples are conducted to demonstrate the properties of the proposed model.KEYWORDS: Travel time uncertainty, route choice, generalize mean-variance, user equilibrium

To verify the effects of Wuhan highway lockdown measures on the spread of COVID-19 across China cities, we extracted the vehicle outflow from Wuhan to 245 cities from the Chinese highway toll system. A dynamic exponential risk model that considered the vehicle outflow, city gross domestic product, city population, and distance between two cities was established to characterize the spread of pandemics and quantify the blocking effects. Results showed that an early highway lockdown measure could indeed reduce the confirmed cases and vehicles with 1-9 seats played a leading role. The confirmed cases in Guangxi, Henan, and Shanxi could be reduced by more than 50%, as well as Hubei by 20% if the highway was closed 3 days in advance. The blocking effects on Fujian, Jiangxi, Guangdong, Hunan, and Shandong were not obvious, where the number of confirmed cases only decreased by a small proportion (below 10%). The findings could be used to help each provincial government to adjust policies properly and improve the effectiveness of epidemic control and prevention. Moreover, the proposed method could also be applied to various countries or regions affected by COVID-19, as well as other similar pandemics.

The popularity of smartphones and the advent of GPS positioning and wireless communication technologies in the recent decade have facilitated large-scale implementations of dynamic ridepooling services, such as Uber Pool, Lyft Line, and Didi Pinche. As in such services trips usually start before the appearance of pooling partners, knowing the probability of getting matching with another order (i.e., matching probability), the expected detour distance, and the expected shared distance before the start of each trip is essential for passengers to evaluate their willingness to pool and for ridepooling platforms to offer attractive discounts. In this paper, assuming that every ridepooling passenger shares vehicle space with at most one another during the entire trip, and ridepooling orders in each (origin-destination) OD pair appear following a Poisson process with a given rate, we propose a mathematical modeling approach to predict the matching probability, the expected ride distance, and the expected shared distance of each order under a first-come-first-serve strategy in dynamic ridepooling service. The method defines unmatched passengers at different locations along the exclusive-riding path of each OD pair into different seeker- and taker-states, formulates the complex interdependency of the matching probabilities, matching rates and arrival rates of (unmatched) passengers in different states into a system of nonlinear equations, and generates the matching probabilities and expected ride/shared distances of all OD pairs simultaneously. Under the same first-come-first-serve strategy, we simulated the occurrence, movements and state transitions of ridepooling orders based on a 30*30 grid network and the real network of Haikou City in China. In comparison with simulation results, we show that the method proposed in this paper can generate fairly satisfactory predictions under diverse matching conditions and demand intensities.

Uncertainty in transportation systems can incur additional travel costs, but this adverse effect can be mitigated by providing travel information (Lindsey et al., 2014). It is not clear, however, if providing information always reduces system travel costs, particularly when the information provided is not one hundred percent accurate. This paper studies the welfare effects resulting from providing pre-trip information to morning commuters in a two-route network with bottlenecks, where the bottleneck capacity and free-flow travel time on each route are stochastic. We derive the expected travel costs at user equilibrium (UE) under stochastic conditions without and with pre-trip information first on one route, and later on two routes. Under this model, we examine how accurate and inaccurate information affect commuting costs. We find that full and accurate information is welfare-improving under stochastic bottleneck capacity and deterministic free-flow travel time. However, when bottleneck capacity and free-flow travel time are both stochastic, full and accurate information can be welfare-reducing for the two-route scenario. We show that the degree of correlation between routes in traffic conditions, the frequency and severity of bottleneck capacity drops, and the relationship between free-flow travel time and bottleneck capacity significantly affect the welfare effects of pre-trip information. Furthermore, when bottleneck capacity experiences severe drops, full information is more likely to be welfare-improving, even if the provided information is not completely accurate. These theoretical results are supplemented by numerical examples that show the welfare effects of providing pre-trip information to morning commuters.

Electric vehicles (EVs) have drawn much attention in recent years due to the advantage of zero on-road carbon emission. The government subsidies and rapid development of electricity charging technologies promote the roll-out of EVs. The boost of EVs brings challenges to the deployment of EV charging services. Given that travelers may need to charge their EVs while conducting out-of-home activities, this study analyzes the activity duration-related charging behavioral responses of private EV travelers to charging services in an equilibrium model. The charging services concern the operating policies related to the spatial allocation of charging opportunities and charging pricing. Considering the trade-offs between travel and activity duration-related charging choices, a boundedly rational dynamic user equilibrium (BR-DUE) model is proposed to evaluate the two operating policies. The case study of the Eindhoven network demonstrates that the operating policies, if appropriately set up, can improve the accessibility to charging services with improved charging service rate and decreased queuing time.

The wide-spread application of automatic train operation (ATO) system on metro lines allows short service headways, high-density operations and high operation efficiency. This paper addresses real-time train control for ATO when faced with disturbances or disruptions in its operations. More specifically, the paper focuses on the design of integrated train dwell time regulation and speed profile generation in real-time and in response to dynamic changes in the operation environment. A nonlinear optimal control model is formulated in a rolling horizon scheme that incorporates three key operating elements: train timetable, passenger load and train speed profile. The objective is to simultaneously improve headway regularity and reduce the total energy consumptions. To satisfy the real-time control requirement for ATO system, a decomposition method based on the alternating direction method of multipliers (ADMM) is designed to divide the original optimization problem into many sub-problems, one for each train, which can then be computed in a distributed manner. Moreover, to address the non-convexity issue, a relax-round-polish process is developed to deal with the formulated nonlinear optimal control problem with convex objective over non-convex constraints in order to find the approximate solutions quickly for the embedded applications. The combined result is an ADMM-based heuristic algorithm. The effectiveness of the proposed model and solution algorithm is demonstrated using real-world data from the Changping Line of Beijing Metro. The results show that the proposed distributed and embedded optimization algorithm is able to significantly enhance the robustness and reliability of real-time train control in automated high-density metro lines.

In this paper, we attempt to address the issue of controlling the sensitivity parameters (or control gains) of automated driving vehicles in an open heterogeneous traffic flow system. The automated...

Previous studies have shown traffic oscillations can be induced by special network topology. In the simplest case, a network of two intersections connected by two parallel roads would produce oscillatory traffic, when the split of drivers between the two roads falls into certain range. To understand how traffic information may affect such oscillations, a subset of drivers is allowed to be “reactive” in this study; that is, their route choice varies according to information about prevailing traffic conditions on the roads. We show that, depending on the ratio of reactive drivers, the system displays six new decaying, periodic oscillatory, or stable patterns. All solutions are obtained analytically in closed form and validated by macroscopic traffic simulation. Of all the solutions discovered, only one both is stable and fully utilizes the road space between the two intersections, and hence it is more desirable than the other solutions. The findings reveal the link between information provision and topology-induced oscillations, which may help practitioners design strategies that contribute to mitigating the adverse impact of such oscillations.

With the rapid increase in travel demands in urban areas, large passenger flow becomes a common phenomenon in the metro system of some large cities. To ensure the safety and improve the operational...

The rapidly-increasing urban transportation contributes to a number of externalities such as congestion and pollution; moreover, environmental uncertainties widely exist and bring challenges to cre...

This paper analyzes the car following behavioral stochasticity based on two sets of field experimental trajectory data by measuring the wave travel time series τ ˜ n ( t ) of vehicle n. The analysis shows that (i) No matter the speed of leading vehicle oscillates significantly or slightly, τ ˜ n ( t ) might change significantly; (ii) A follower's τ ˜ n ( t ) can vary from run to run even if the leader travels at the same stable speed; (iii) Sometimes, even if the leader's speed fluctuates significantly, the follower can keep a nearly constant value of τ ˜ n ( t ) . The Augmented Dickey-Fuller test indicates that the time series ξ n ( t ) = d τ ˜ n ( t ) / d t follows a mean reversion process, no matter the oscillations fully developed or not. Based on the finding, a simple stochastic Newell model is proposed. The concave growth pattern of traffic oscillations has been derived analytically. Furthermore, simulation results demonstrate that the new model well captures both macroscopic characteristic of traffic flow evolution and microscopic characteristic of car following.

Existing literature has confirmed that severe weather events have a negative impact on travel behavior. Considering the features of long distances and long durations, intercity travel may be affected by the weather at the origin (O) point and at the destination (D) point. This paper aims to reveal the relationship between weather and OD volume of intercity travel by additional considerations of the weather difference between OD points and the lagging effect of various severe weather conditions. Travel data from toll collection system of freeway network in Shandong Province of China is extracted to investigate the weather effect on intercity travel demand. Three types of real-time weather variables (continuous variables of basic weather, dummy variables of severe weather, and weather difference variables between OD points) and two types of non-real-time weather variables (lagging variables and advance variables of severe weather) are combined to systematically analyze the weather effect. The results show that, temperature value and wind speed have no significant impact on intercity travel. Hot day, cold day, foggy day, strong breeze, heavy rain, rainfall, snowfall and visibility have significant impacts on freeway OD volume. Among these weather events, fog, heavy rain and snow usually have the most severe impact. Except for the weather at the O point, the differences in rainfall and snowfall between OD points have significant impacts on intercity travel demand, while the differences in temperature, wind speed, visibility between OD points do not. In addition, snow and heavy rain have the lagging and advance effects on freeway OD volume. The advance effect of snow and heavy rain lasts for one day and the lagging effect lasts for two days. Intercity travel demand is very sensitive to adverse weather on weekend days and during the afternoon. Comparative analysis of multiple OD pairs indicates that intercity travelers with less relevant experience are more sensitive to adverse weather. Findings from the paper will provide transport institutions with a practical guide for systematically investigating the weather effect on intercity travel demand. It can be applied in the intelligent transportation system to provide long-distance travelers with more accurate weather information and traffic guidance services.

Conflicts between crowds are considered to be critical sources of safety incidents, and choosing a side is a common strategy for conflict resolution. Here, circle antipode experiments, which create...

This paper introduces the stochastic multi-modal hub location problem with direct link strategy and multiple capacity levels for cargo delivery systems under demand uncertainty. For capturing the u...

To improve the accessibility of the metro network during night operations, this study aims to investigate a collaborative optimization for the last train timetable in an urban rail transit network. By using a space-time network framework, all the involved transportation activities are well characterized in an extended space-time network, in which the train space-time travel arcs, passenger travel arcs, transfer arcs, etc., are all taken into account. Two performance measures are proposed to evaluate the network-based timetable of the last trains. Through considering the route choice behaviors, the problem of interest is formulated as 0–1 linear programming models from the perspective of a space-time network design. To effectively solve the proposed models, we dualize the hard constraints into the objective function to produce the relaxed models by introducing a set of Lagrangian multipliers. Then, the sub-gradient algorithm is proposed to iteratively minimize the gap of the lower and upper bounds of the primal models. Finally, two sets of numerical experiments are implemented in an illustrative network and the Beijing metro network, respectively, and experimental results demonstrate the efficiency and performance of the proposed methods.

Train scheduling and maintenance planning compete for the resources in a railway network. A commonly used way is dealing with maintenance planning first and then train scheduling, or vice versa. In this paper, we propose a joint optimization model for the two problems in a railway network with double-track, where the upstream and downstream trains are independent and a maintenance task on a section cannot be split or disrupted. In order to solve the model, a heuristic algorithm using Lagrangian relaxation is developed. Due to the large number of constraints, we use a dynamic constraint-generation technique in the iterations of the sub-gradient optimization procedure. We apply the model and algorithm to a practical problem in the Chinese railway network, in which some additional trains are inserted into a fixed existing timetable and the maintenance plan on the involved high-speed railway sections is adjusted. The computational results illustrate the effectiveness and efficiency of the proposed model and algorithm.

Complex passenger demand and electricity transmission processes in metro systems cause difficulties in formulating optimal timetables and train speed profiles, often leading to inefficiency in energy consumption and passenger service. Based on energy-regenerative technologies and smart-card data, this study formulates an optimization model incorporating energy allocation and passenger assignment to balance energy use and passenger travel time. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is applied and the core components are redesigned to obtain an efficient Pareto frontier of irregular timetables for maximizing the use of regenerative energy and minimizing total travel time. Particularly, a parallelogram-based method is developed to generate random feasible timetables; crossover and local-search-driven mutation operators are proposed relying on the graphic representations of the domain knowledge. The suggested approach is illustrated using real-world data of a bi-directional metro line in Beijing. The results show that the approach significantly improves regenerative energy use and reduces total travel time compared to the fixed regular timetable.

Focusing on reducing the traffic congestion on an oversaturated urban metro system, this study investigates the collaborative passenger flow control problem for a metro line. By introducing the tim...

This paper focuses on designing robust bus bridging service in response to the rail transit system disruptions. We firstly develop a path-based multi-commodity flow formulation to bus bridging service design. Then its robust counterpart is formulated to incorporate bus travel time uncertainty. The column generation procedure is devised to solve this problem efficiently. At last, we carry out case studies to demonstrate its applicability and promising effects. The results reveal that our approach can significantly reduce the total cost and number of stranded passengers in disruption events. Besides, the rise of bus travel time variation will deteriorate the performance of bus bridging service.

Public and private roads co-exist in a practical transport network system, and this in general is known as a public–private mixed road network. Two stakeholders, that is, the government and a priva...

Understanding the mechanisms responsible for the emergence and evolution of oscillations in traffic flow has been a subject of intensive research by numerous scholars. In our previous work, we proposed a new mechanism to explain the generation of traffic oscillations: traffic instability caused by the competition between speed adaptation and stochastic effects. By conducting a closer examination of car following data obtained in a 25-car platoon experiment, we discovered in this work that the speed difference plays a more important role on car-following dynamics than the spacing, and when its amplitude is small, the growth of oscillations is mainly determined by the stochastic factors that follow the mean reversion process; when its amplitude increases, the growth of the oscillations is determined by the competition between the stochastic factors and the speed difference. An explanation is then provided, based on the above findings, to why the speed variance in the oscillatory traffic grows in a concave way along the platoon. We also proposed a mode-switching stochastic car-following model that incorporates the speed adaptation and spacing indifference behavior of drivers, which captures the observed characteristics of oscillation and discharge rate. Finally, our sensitivity analysis shows that the influence on oscillation growth and discharge rate is small by the reaction delay but large by the indifference region boundary.

The oversaturated situation is now very common in the metro system of some megacities due to large commuting demands in peak hours, which leads to passenger accumulation on platforms and causes potential accident risks. To improve the transport efficiency and passenger accumulation safety at each station, this paper proposes a cooperative passenger flow control optimization in a specific metro network, in which each passenger can freely switch trains between different metro lines with a single ticket. Through considering the dynamic characteristics and transfer behaviors of passengers, an effective bi-objective integer linear programming model is formulated to characterize the passenger control process, in which the objectives are to minimize the total passenger waiting time and passenger accumulation risks at all the involved stations. To solve the proposed model conveniently, the above model is transformed into a single-objective model through reformulating the risk objective as a threshold-based constraint. Finally, two sets of numerical experiments, including a small-scale case and a real-world instance with operation data of the Beijing metro system, are implemented to demonstrate the performance and effectiveness of the proposed approaches.

The effect of environmental uncertainty on equilibrium patterns is of vital importance to understand travel choice behavior. This paper conducted a laboratory experiment to investigate the effects of stochastic bottleneck capacity on departure time choice behavior. In the experiment, the bottleneck capacity varied stochastically from round to round, and two different scenarios with different information feedback were investigated. Our experimental results showed that the relationship between the mean travel cost and the standard deviation of travel cost on each departure time was fitted approximately linearly with a positive slope λ∗, indicating subjects were more likely to minimize their travel cost budget rather than their mean travel cost. Also, we found that the feedback on costs of all departure times resulted in a smaller λ∗ than the feedback on the subjects’ own travel cost only. We propose a reinforcement learning model to reproduce the main experimental findings.

Urban rail transit (URT) has been considered an effective means of addressing urban congestion problems in metropolises. The operations of a URT system involve high energy consumption and its trade-off with passenger travel times. Existing energy-efficient timetabling studies have predominately focused on single URT lines and thus are incapable of accurately modeling the energy consumption in a URT network with transfer opportunities and synchronization between the URT lines. To extend the energy-efficient timetabling from one single line to multiple interlinked lines, we propose a bi-level model incorporating the operator’s decision on a regular timetable and passengers’ path choice in a URT network. The objective of energy consumption and timetable constraints of the upper level are linearized and formulated as mixed-integer linear programming. The lower level captures the user equilibrium based path choice behavior responding to the timetable. We develop a heuristic algorithm for the bi-level model that produces near-optimal timetable solutions in a relaxation process. The proposed model and solution algorithm are validated in the URT network of Xi’an (China). It is found that the energy consumption is considerably reduced, compared with using the current timetable, at the expense of an acceptable increase in the average travel time.

The metro systems of some megacities are facing serious oversaturation problem due to the heavy passenger flow during high peak hours. We consider the bus transit network design problem based on an existing metro network that can balance the modal split between metro and bus transit systems. The challenges facing this problem lie in that passengers have a different preference between metro and bus services, and the bus travel time and passenger demand may exhibit significant variations. This paper develops a two-step model framework to determine a bus transit network and departure frequency with consideration of travel time and passenger demand uncertainties. Firstly, we develop a column generation method to identify the candidate set of bus transit lines and passenger paths. Then a stochastic linear programming model is developed to optimize the bus line frequency and passenger path flow under demand and bus travel time uncertainty. To solve this model, a primal-dual online algorithm based on the online convex optimization theory is built to obtain the optimal solution with a theoretical performance guarantee. Finally, we implement the developed framework into an illustrative network and a real-world Beijing Second Ring public transit network to demonstrate its applicability and promising effects. The computational results show that the method can provide significant benefits for public transit systems.

We organized a series of novel circle antipode experiments in which pedestrians are uniformly initialized on the circle and they are required to leave for their antipodal positions simultaneously. In the experiments, a conflicting area is naturally formulated in the center region due to the converged shortest routes, so the practical motion navigation strategies, as well as the conflict avoidance behaviors, can be explored and summarized. Besides, the symmetric experimental conditions of pedestrians, e.g., symmetric starting points, symmetric destination points, and symmetric surroundings, lay the foundation for further quantitative comparisons among participants. In the paper, the pedestrian trajectories are recognized, and the pedestrian dynamics are accordingly investigated from the perspective of macroscopic indexes, i.e., route length, route potential, travel time, velocity and time-series. It is found that the route length has a log-normal distribution, the route potential obeys the exponential distribution, and the travel time is normally distributed as well as the speed. Furthermore, a model evaluation based on the experimental results is performed, using four distribution indexes and two time series indexes in both space and time dimensions. We graded the simulated distribution indexes by applying a Kolmogorov-Smirnov (K-S) test based method and the simulated time series indexes by a Dynamic Time Warping (DTW) based method. We evaluated two models: the traditional social force model, the modified model with default parameters and with calibrated parameters. The evaluation results showed that the two models cannot well reproduce the six macroscopic indexes, which indicates that the models do not capture the route choice and conflict avoidance behaviors of pedestrians. Our study is expected to shed light on the pedestrian modeling. Specifically, it indicates that apart from classical indexes such as a fundamental diagram, more comprehensive indexes are needed to evaluate the pedestrian flow models.

When traffic control is demand-responsive, a bottleneck can have a queue-length-dependent capacity. Motivated by this fact, we studied the morning commute problem in which the bottleneck capacity increases from s1 to s2 as the queue length exceeds a threshold D1 and decreases back to s1 as the queue length reduces and becomes smaller than another threshold D2 (D2 ≤ D1). It has been found that multiple equilibria exist when D2

This paper proposes an optimal urban expressway system model with consideration to the interaction equilibrium between transportation and land use. In the suggested model, three kinds of economic a...

The column generation (CG) algorithm has been widely applied to traffic assignment problems due to its capability of circumventing path enumeration. Incorporating bounded rationality (BR) and dynamics, this paper proposes four tolerance-based strategies for extending the CG algorithm to the bounded rational dynamic user equilibrium model (BR-DUE): (i) a tolerance-based minimum disutility path search strategy is developed to allow travelers seeking satisfactory paths; (ii) a self-adjusted convergence threshold strategy is applied for fast convergence at the intermediate iterations; (iii) a varied temporal resolution scheme, combining exploration and exploitation, is suggested to assign flows to narrow time regions rather than to the whole time horizon; and (iv) a path search skipping strategy is introduced by comparing the lower bound of travel disutility and the minimum disutility between the OD pairs. With these strategies, an efficient tolerance-based column generation (TBCG) algorithm for BR-DUE is developed. Numerical examples are provided to demonstrate that the TBCG algorithm leads to significant computation time reductions without the expense of solution quality, of which the speedup factors are around two compared with using the original CG algorithm.

To investigate the car-following behavior under high speed driving conditions, we performed a set of 11-car-platoon experiments on Hefei airport highway. The formation and growth of oscillations have been analyzed and compared with that in low speed situations. It was found that there is considerable heterogeneity for the same driver over different runs of the experiment. This intra-driver heterogeneity was quantitatively depicted by a new index and incorporated in an enhanced two-dimensional intelligent driver model. Using both the new high-speed and the previous low-speed experimental data, the new and three existing models were calibrated. Simulation results show that the enhanced model outperforms the three existing car-following models that do not take into account this intra-driver heterogeneity in reproducing the essential features of the traffic in the experiments.

In designing the automatic train regulation strategy of metro networks subject to frequent disturbances, it is essential to coordinate the trains at the transfer stations among different lines to facilitate passengers transferring. In this paper, we systematically investigate the distributed optimal control method framework for automatic train regulation of large-scale complex urban metro networks with the transfer coordination constraints. A dynamic train traffic model of metro networks is elaborately developed in the form of the state-space equation. In case frequent disturbances happen, a dynamic optimization problem is developed to minimize the timetable and headway deviations for each line of the metro network under the interaction constraints of different lines on the transfer coordination. By regarding each line as a subsystem of the whole network, the optimization problem is formulated to coordinate a number of subsystems coupled by the state constraints. To satisfy the real-time control requirement, according to the dual decomposition technique, a new distributed optimal control method based on the distributed message passing mechanism is designed, which effectively decomposes the original large-scale problem spatially and temporally into multiple small-scale optimization subproblems that can be computed completely in parallel on a single computing platform to speed up the solution procedure, and thereby reduces the computational burden of centralized implementations for the large-scale urban metro networks. Numerical examples are given to illustrate the effectiveness of the proposed method.

Intracity heavy truck freight trips are basic data in city freight system planning and management. In the big data era, massive heavy truck GPS trajectories can be acquired cost effectively in real-time. Identifying freight trip ends (origins and destinations) from heavy truck GPS trajectories is an outstanding problem. Although previous studies proposed a variety of trip end identification methods from different perspectives, these studies subjectively defined key threshold parameters and ignored the complex intracity heavy truck travel characteristics. Here, we propose a data-driven trip end identification method in which the speed threshold for identifying truck stops and the multilevel time thresholds for distinguishing temporary stops and freight trip ends are objectively defined. Moreover, an appropriate time threshold level is dynamically selected by considering the intracity activity patterns of heavy trucks. Furthermore, we use urban road networks and point-of-interest (POI) data to eliminate misidentified trip ends to improve method accuracy. The validation results show that the accuracy of the method we propose is 87.45%. Our method incorporates the impact of the city freight context on truck trajectory characteristics, and its results can reflect the spatial distribution and chain patterns of intracity heavy truck freight trips, which have a wide range of practical applications.

Automotive roll-on/roll-off (Ro-Ro) transportation is an efficient and competitive method for the large-scale transshipment of commercial cars. However, the low-efficiency operations and insufficient storage resources of automotive Ro-Ro terminals have constrained the development of Ro-Ro transportation. This paper investigates the storage location assignment problem (SLAP) for the arrival of cars at the yard, and it aims to improve the ship-loading efficiency and contribute to efficient storage at Ro-Ro terminals. Two deadlock situations resulting from blocked routes are analyzed in detail. Based on the Ro-Ro ship stowage plan, a car group concept is proposed to reflect the loading sequence of cars into a Ro-Ro ship. The dispersion degree is defined to describe the centralized layout of every car group in the yard. A linear 0-1 integer programming model is formulated to minimize the total dispersion degrees of all car groups. Furthermore, an indicator called the attraction degree is presented to quantify the preferred degree of each location for storing different groups of cars. A hierarchical two-stage exchange strategy (HTSES) is designed to obtain the car layout with the minimum total dispersion degree. To reduce the scale of the solved problem, a rolling-horizon heuristic approach based on closed-loop (positive and negative) feedback is proposed. Positive feedback based on the guidance mechanism describes the guidance provided by the existing car layout to arriving cars, while negative feedback based on the reformulation mechanism represents the influence of arriving cars on the car layout. A series of numerical experiments show that the proposed method can effectively produce a satisfactory car assignment plan for the management of automotive Ro-Ro terminals.

Subway lines connecting different urban functional zones in large cities have direction-dependent and time-variant passenger demand, namely, asymmetry in passenger demand. Most existing studies adopt a symmetric strategy to design operations in both directions and sequentially optimize the different problems associated with operations, thereby failing to meet the asymmetry in passenger demand. This study formulates an asymmetric operation strategy as an integrated mixed-integer non-linear model to optimize the entire operational process of rolling stock from the perspective of service quality and operating costs. Based on the proposed model, an exact algorithm is proposed with speed-up techniques to quickly generate an optimal solution. To this end, the original model is decomposed into several sub-problems that can be exactly solved by using a forward dynamic programming algorithm. Based on actual data from the Beijing subway's Yizhuang line, numerical experiments are conducted to investigate the effectiveness of the asymmetric operation strategy, to identify managerial insights on the integrated optimization, and to evaluate the performance of the proposed methodology.

For urban rail transit (URT) operation and management, it is important to accurately estimate the spatial and temporal passenger flow distribution spreading all over the network. Unfortunat...

The intercity freight trips of heavy trucks are important data for transportation system planning and urban agglomeration management. In recent decades, the extraction of freight trips from GPS data has gradually become the main alternative to traditional surveys. Identifying the trip ends (origin and destination, OD) is the first task in trip extraction. In previous trip end identification methods, some key parameters, such as speed and time thresholds, have mostly been defined on the basis of empirical knowledge, which inevitably lacks universality. Here, we propose a data-driven trip end identification method. First, we define a speed threshold by analyzing the speed distribution of heavy trucks and identify all truck stops from raw GPS data. Second, we define minimum and maximum time thresholds by analyzing the distribution of the dwell times of heavy trucks at stop location and classify truck stops into three types based on these time thresholds. Third, we use highway network GIS data and freight-related points-of-interest (POIs) data to identify valid trip ends from among the three types of truck stops. In this step, we detect POI boundaries to determine whether a heavy truck is stopping at a freight-related location. We further analyze the spatiotemporal characteristics of intercity freight trips of heavy trucks and discuss their potential applications in practice.

This paper studies a bottleneck model in which the capacity of the bottleneck is constant within a day but changes stochastically from day-to-day between a designed value (good condition) and a degraded one (bad condition). The study relates the travel cost variability due to stochastic capacity with commuters' departure time choice behaviors. We postulate that commuters acquire the variability of travel cost based on past experiences and factor such variability into their departure time choice consideration by minimizing their travel cost budget (TCB), defined as a weighted average of mean travel cost and standard deviation of travel cost. We show that the consideration of TCB yields seven possible equilibrium patterns. Closed form solutions to all possible equilibrium patterns and their corresponding parameter ranges are derived. The rationality of the patterns has been investigated. Dependence of travel cost and the duration of peak hours on the commuters' risk attitude has also been derived in each equilibrium pattern. Finally, numerical studies have been conducted to illustrate the properties.

Boundedly rational dynamic activity-travel assignment (BR-DATA) endogenously integrates activity-travel scheduling and dynamic traffic assignment to determine the interaction between land use transport supplies and activity-travel demands of boundedly rational travelers. The combinatorial explosion of activity-travel patterns (ATPs) involving multi-dimensional choice facets poses severe challenges to the model applicability in large networks. This study refines a tolerance-based column generation (TBCG) algorithm for solving BR-DATA problems in multi-state supernetworks without ATP enumeration. The refined TBCG algorithm employs spatial-temporal exploration to allocate activity-travel flows only to potential ATPs in the intermediate assignment process. The spatial-temporal exploitation intensifies ATP generation and network loading, which results in fewer iterations and ultimately substantial speedups compared with the original column generation algorithm. We prove that the TBCG algorithm is capable of finding solutions that satisfy the BR-DATA user equilibrium conditions. A series of numerical examples demonstrate that the TBCG algorithm has a speedup factor larger than two whilst producing approximately the same BR-DATA solutions as the original column generation algorithm.