Transportation Research Part B-Methodological最新文献

We investigate the value of time as a resource (VOTR) and the value of childcare time saving (VOCTS) for a married couple with children by life cycle stage. Extending the framework of DeSerpa (1971), we develop a novel intertemporal utility-maximization model that can represent trade-offs within an individual and within a couple between different activities in their life stages based on a household lifetime equilibrium, and we derive wives’ and husbands’ time values when their first child is of pre-school age and after their first child reaches school age. Applying the model to the 2004–2018 Japan Household Panel Survey, we analyze couples in two life stages to empirically find the value of time by gender. The results show that the wives’ average VOTR is greater than 4400 yen/hour with statistical significance when their first child is of pre-school age; the value, however, drastically drops to around 400 yen/hour with statistical insignificance after their first child reaches school age. Conversely, the magnitudes of the husbands’ VOTRs do not change much in different life stages. In the background mechanisms, the wives’ high and low VOTRs reflect their short and long work and commute hours, respectively, whereas the husbands reduce their work and commute hours only slightly over time. For the dual-income households that only spend the minimum required time on childcare, VOCTS is statistically insignificant when their first child is of pre-school age but is greater than 28,000 yen/hour after their first child reaches school age. Using the estimated time values for urban and transport policy simulations, we find that enabling work flexibility could help households increase welfare more compared to transportation improvement and childcare support services.

We consider a ride-hail system in which a third-party integrator receives ride requests and allocates them to ride service platforms. The ride allocation problem (RAP) is modeled as a Stackelberg game. The integrator, as the leader, chooses the allocation that maximizes its profit, by pricing the rides such that no platform (i.e., follower) can find a more profitable allocation. In pursuit of self-interest, the integrator may refuse to match as many rides as the platforms are willing to serve, thereby injecting an artificial scarcity into the system. To protect the platforms from over exploitation, an exogenous reserve price is introduced to bound their per capita profit from below. We formulate RAP as a bilevel pricing problem, and convert it to a single-level problem by dualizing the lower level. When artificial scarcity is eliminated and all reserve prices are set to zero, we prove the single-level problem can be turned into a mixed integer-linear program that equals its linear relaxation, thus becoming polynomially solvable. Moreover, this version of RAP is shown to be related to cooperative assignment games. Numerical experiments confirm that artificial scarcity negatively affects matching productivity and social welfare. The integrator is favored to take most profits, and leveraging artificial scarcity strengthens its dominance. Moreover, the tighter the supply, the more the integrator benefit from artificial scarcity. The reserve price helps redistribute benefits from the integrator to the platforms. However, demanding an excessively large reserve price may depress the platforms’ profits, while undermining system efficiency.

Container ports serve as crucial logistics hubs in global supply chains, but navigating ships within such ports is complex due to restricted waterways. Tugboats play a critical role in ensuring safety and efficiency by escorting and towing ships under these conditions. However, the tugboat deployment and scheduling problem has received little attention. To fill the research gap, we propose a new research problem - the dynamic tugboat deployment and scheduling problem, in which not all requests are confirmed initially but dynamically confirmed over time and future tugging demands need to be anticipated when managing the utilization of tugboats. To formulate the problem, we propose an extended Markov decision process (MDP) that incorporates both reactive task assignment decisions and proactive tugboat waiting decisions, creating a reactive and proactive MDP. To solve the advanced MDP model efficiently for real-time decisions, we develop an anticipatory approximate dynamic programming method that incorporates appropriate task assignment and waiting strategies for deploying and scheduling a heterogeneous tugboat fleet and embed the method into an improved rollout algorithm to anticipate future scenarios. The effectiveness, efficiency, and performance sensitivity of the developed modeling and solution methods are demonstrated via extensive numerical experiments for the Singapore container port.

Mobility-as-a-Service (MaaS) systems are two-sided markets, with two mutually exclusive sets of agents, i.e., travelers/users and operators, forming a mobility ecosystem in which multiple operators compete or cooperate to serve customers under a governing platform provider. This study proposes a MaaS platform equilibrium model based on many-to-many assignment games incorporating both fixed-route transit services and mobility-on-demand (MOD) services. The matching problem is formulated as a convex multicommodity flow network design problem under congestion that captures the cost of accessing MOD services. The local stability conditions reflect a generalization of Wardrop's principles that include operators’ decisions. Due to the presence of congestion, the problem may result in non-stable designs, and a subsidy mechanism from the platform is proposed to guarantee local stability. A new exact solution algorithm to the matching problem is proposed based on a branch and bound framework with a Frank-Wolfe algorithm integrated with Lagrangian relaxation and subgradient optimization, which guarantees the optimality of the matching problem but not stability. A heuristic which integrates stability conditions and subsidy design is proposed, which reaches either an optimal MaaS platform equilibrium solution with global stability, or a feasible locally stable solution that may require subsidy. For the heuristic, a worst-case bound and condition for obtaining an exact solution are both identified. Two sets of reproducible numerical experiments are conducted. The first, on a toy network, verifies the model and algorithm, and illustrates the differences local and global stability. The second, on an expanded Sioux Falls network with 82 nodes and 748 links, derives generalizable insights about the model for coopetitive interdependencies between operators sharing the platform, handling congestion effects in MOD services, effects of local stability on investment impacts, and illustrating inequities that may arise under heterogeneous populations.

The phenomenon of transportation service providers (TSPs) engaging in both competition and cooperation, commonly referred to as coopetition, has become increasingly prevalent in the transportation market. This is driven by the rapid advancements in information technology and sharing economy. In practice, competitive TSPs can engage in a resource exchange scheme to share their resources to improve their service quality. However, such a resource exchange scheme may not be sustained since the service prices and profits will be further changed due to the competition in the end market. This study proposes a two-stage sequential-move game to characterize the coopetition problem between TSPs, wherein the first-stage resource exchange problem is modeled with a Nash bargaining game, and the second-stage pricing problem is modeled with a non-cooperative Nash game. Different from prior studies, our model incorporates the supply–demand congestion effects and the asymmetric bargaining power of TSPs. The subsequent impacts on social welfare, TSPs, and end users are investigated. Analytical results show that only when the unit price of the exchanged resources decreases in the exchanged resource quantity will the resource-exchange scheme succeed. Furthermore, we find that TSPs prefer to leave some “buffer zone” in between to avoid fierce competition with price wars.

In practice, road disruptions occur frequently, interrupting multiple bus routes at the same time and causing widespread passenger delays. Typically, these disrupted roads are repaired sequentially and then gradually put into service. In response to such time-varying road disruptions, this paper aims to assist bus operators in developing effective alternative service networks for passengers. The proposed approach involves the joint optimization of service-based route adjustments, bus timetables, and passenger assignment to minimize the total passenger cost and weighted bus operation time. Specifically, a novel service-based adjustment strategy is introduced to flexibly adapt each bus service to time-varying road disruptions. An integer programming model is built for the studied problem based on the set of passengers’ time-space itineraries. To efficiently generate these time-space itineraries and solve models for large-scale problems, this paper develops a hierarchical solution framework. The framework consists of three key parts: (1) a column generation procedure to iteratively explore passengers’ spatial paths; (2) a customized extension algorithm to extend these spatial paths to time-space itineraries; and (3) a tailored adaptive large neighbourhood search heuristic to solve the final itinerary-based model. After that, the overall methodology is tested with both an illustrative example and a real-world example in Beijing. Experimental results show that our methodology produces a high-performance solution with only 7.3% of unserved passengers. Besides, compared to the two benchmark adjustment strategies, our service-based adjustment strategy reduces the average itinerary cost for all passengers by 27.0% and 43.3%, respectively.

Based on Wardrop’s first principle, the perfectly rational dynamic user equilibrium is widely used to study dynamic traffic assignment problems. However, due to imperfect travel information and a certain “inertia” in decision-making, the boundedly rational dynamic user equilibrium is more suitable to describe realistic travel behavior. In this study, we consider the departure time choice problem incorporating the concept of bounded rationality. The continuum modeling approach is applied, in which the road network within the modeling region is assumed to be sufficiently dense and can be viewed as a continuum. We describe the traffic flow with the reactive dynamic continuum user equilibrium model and formulate the boundedly rational departure time problem as a variational inequality problem. We prove the existence of the solution to our boundedly rational reactive dynamic continuum user equilibrium model under particular assumptions and provide an intuitive and graphical illustration to demonstrate the non-uniqueness of the solution. Numerical examples are conducted to demonstrate the characteristics of this model and the non-uniqueness of the solution.

This paper addresses the sustainable design of hub networks under uncertainty in the context of less-than-truckload transportation, taking into account factors related to carbon pricing. The problem is modeled to maximize profits in a stochastic demand environment, where a portion of the demand may remain unserved depending on the trade-off between profits, costs, and carbon emissions. The model explicitly incorporates a carbon tax into the objective function, along with transportation and hub operation costs. To ensure compliance with the carbon cap, a constraint is incorporated to limit the emissions across the entire transportation network. The carbon emission on each arc of the network is modeled using a generic convex function that depends on the total demand routed on the arc which is then approximated by a piecewise linear function to derive a mixed-integer stochastic formulation. A Benders-decomposition-based algorithm coupled with a sample average approximation scheme is developed to solve the stochastic model. The algorithm is enhanced with acceleration techniques to solve large-scale instances. Extensive computational experiments are conducted to evaluate the efficiency of the proposed algorithm and also to analyze the impact of incorporating carbon pricing factors on optimal hub networks. Computational results provide insights into sustainable hub network designs.

The majority of the past research dealing with lane-changing controller design of autonomous vehicles ($AV$s) is based on the assumption of full knowledge of the model dynamics of the $AV$ and the surrounding vehicles. However, in the real world, this is not a very realistic assumption as accurate dynamic models are difficult to obtain. Also, the dynamic model parameters might change over time due to various factors. Thus, there is a need for a learning-based lane change controller design methodology that can learn the optimal control policy in real time using sensor data. In this paper, we have addressed this need by introducing an optimal learning-based control methodology that can solve the real-time lane-changing problem of $AV$s, where the input-state data of the $AV$ is utilized to generate a near-optimal lane-changing controller by approximate/adaptive dynamic programming (ADP) technique. In the case of this type of complex lane-changing maneuver, the lateral dynamics depend on the longitudinal velocity of the vehicle. If the longitudinal velocity is assumed constant, a linear parameter invariant model can be used. However, assuming constant velocity while performing a lane-changing maneuver is not a realistic assumption. This assumption might increase the risk of accidents, especially in the case of lane abortion when the surrounding vehicles are not cooperative. Thus, in this paper, the dynamics of the $AV$ are assumed to be a linear parameter-varying system. Thus we have two challenges for the lane-changing controller design: parameter-varying, and unknown dynamics. With the help of both gain scheduling and ADP techniques combined, a learning-based control algorithm that can generate a near-optimal lane-changing controller without having to know the accurate dynamic model of the $AV$ is proposed. The inclusion of a gain scheduling approach with ADP makes the controller applicable to non-linear and/or parameter-varying $AV$ dynamics. The stability of the learning-based gain scheduling controller has also been rigorously proved. Moreover, a data-driven lane-changing decision-making algorithm is introduced that can make the $AV$ perform a lane abortion if safety conditions are violated during a lane change. Finally, the proposed learning-based gain scheduling controller design algorithm and the lane-changing decision-making methodology are numerically validated using MATLAB, SUMO simulations, and the NGSIM dataset.

In this paper, a class of single-allocation hub location problems is investigated from the perspective of upgrading. The latter is understood as an improvement of a set of edges to increase their individual performance, e.g., a decreased unit transportation cost. The goal is to obtain an improved optimal solution to the problem compared to that obtained if upgrading was not done. A budget constraint is assumed to limit the upgrading operations. A flow-based formulation is initially proposed that extends a classical model for uncapacitated single-allocation hub location with complete hub networks. Nevertheless, the fact that the unit costs after upgrading may violate the triangle inequality needs to be accounted for. Since the proposed formulation has a high computing burden, different possibilities are discussed for enhancing it. This leads to devising an efficient branch-and-cut algorithm with different variants. Additionally, a formulation based on the discrete ordered median function is also introduced that is also enhanced and embedded into a branch-and-cut algorithm again with several variants. All models and algorithms are also adapted to problems embedding hub network design decisions. Extensive computational tests were conducted to assess the methodological contributions proposed.