Transportation Research Part B-Methodological最新文献

We study a variant of dynamic pickup and delivery crowd-shipping operation for delivering online orders within a few hours from a brick-and-mortar store. This crowd-shipping operation is subject to a high degree of uncertainty due to the stochastic arrival of online orders and crowd-shippers that impose several challenges for efficient matching of orders to crowd-shippers. We formulate the problem as a Markov decision process and develop an Approximate Dynamic Programming (ADP) policy using value function approximation for obtaining a highly scalable and real-time matching strategy while considering temporal and spatial uncertainty in arrivals of online orders and crowd-shippers. We incorporate several algorithmic enhancements to the ADP algorithm, which significantly improve the convergence. We compare the ADP policy with an optimization-based myopic policy using various performance measures. Our numerical analysis with varying parameter settings shows that ADP policies can lead to up to 25.2% cost savings and a 9.8% increase in the number of served orders. Overall, we find that our proposed framework can guide crowd-shipping platforms for efficient real-time matching decisions and enhance the platform delivery capacity.

This study investigates the theoretical properties of a departure time choice problem considering commuters’ heterogeneity with respect to the value of schedule delay in corridor networks. Specifically, we develop an analytical method to solve the dynamic system optimal (DSO) and dynamic user equilibrium (DUE) problems. To derive the DSO solution, we first demonstrate the bottleneck-based decomposition property, i.e., the DSO problem can be decomposed into multiple single bottleneck problems. Subsequently, we obtain the analytical solution by applying the theory of optimal transport to each decomposed problem and derive optimal congestion prices to achieve the DSO state. To derive the DUE solution, we prove the queue replacement principle (QRP) that the time-varying optimal congestion prices are equal to the queueing delay in the DUE state at every bottleneck. This principle enables us to derive a closed-form DUE solution based on the DSO solution. Moreover, as an application of the QRP, we prove that the equilibrium solution under various policies (e.g., on-ramp metering, on-ramp pricing, and its partial implementation) can be obtained analytically. Finally, we compare these equilibria with the DSO state.

Random utility maximization-based discrete choice models involve utility functions that are typically specified with explanatory variables representing alternative-specific attributes. It may be useful to specify some alternative-specific attributes as stochastic in situations when the analyst cannot accurately measure the attribute values considered by the decision maker. In addition, the parameters representing decision makers’ response to the attributes may have to be specified as stochastic to recognize response heterogeneity in the population. Ignoring either of these two sources of stochasticity can lead to biased parameter estimates and distorted willingness-to-pay estimates. Further, in some situations the analyst may not even have access to measurements of important alternative-specific attributes to include them in the utility specification. In this study, we explore the feasibility of simultaneously inferring alternative attributes and the corresponding coefficients, as well as stochasticity in both – without the help of external measurement data on alternative attributes – using mixed logit models on pooled revealed preference (RP) and stated preference (SP) choice datasets. To do so, we first theoretically examine parameter identifiability for different specifications and distributional forms of alternative attributes and their coefficients. Next, we illustrate this through simulation experiments in a travel mode choice setting and demonstrate the conditions under which pooled RP-SP data can help disentangle stochastic alternative attributes from random coefficients. In addition, an empirical application is presented in the context of commute mode choice in Bengaluru, India, to demonstrate the importance of recognizing stochasticity in mode-specific in-vehicle travel times along with the random coefficient on in-vehicle travel times.

Estimation of traffic volumes between each origin and destination of travel is a standard practice in transport engineering. Commonly the available data constitute traffic counts at various locations on the network, supplemented by a survey-based prior estimate of mean origin–destination traffic volumes. Statistical inference in this type of network tomography problem is known to be challenging. Moreover, the difficulties are increased in practice by the presence of a large number of nuisance parameters corresponding to route choice probabilities, for which we have no direct prior information. Working in a Bayesian framework, we determine these parameters using a stochastic user equilibrium route choice model. We develop an MCMC algorithm for model fitting. This requires repeated computation of stochastic user equilibrium flows, and so we develop a computationally cheap emulator. Our methods are tested on numerical examples based on a section of the road network in the English city of Leicester.

We developed an integer programing extreme value (IPEV) model that accounts the integer property of trip data and has the same advantages as the multiple discrete–continuous extreme value choice (MDCEV) model. The proposed model is consistent with utility theory and provides a single structural framework for simultaneously modeling the choice of alternatives and quantity decisions with the constraint of the integer value of consumption. We demonstrate that the proposed model has a closed-form probability expression. Finally, we apply the proposed model to the recreation demand for national parks in Japan. The empirical results suggest that the proposed model provides a better fit for the data than the previous model and that ignoring the integer property of demand might cause an underestimation of the welfare loss.

Perimeter control is a traffic management approach aimed at regulating vehicular accumulation within urban regional networks by managing flows on all border-crossing roads. Methods based on the macroscopic fundamental diagram (MFD) fall short in providing specific metering for individual roads. Recent advancements in the cell transmission model (CTM) have attempted to address this limitation but are hindered by their reliance on centralized control, which requires the availability of full information and authority over traffic generation sites. Our study proposes an innovative decentralized, game-theoretical framework for perimeter control to address these practical challenges. It is structured around two key groups of agents: perimeter agents, tasked with managing border roads, and interior agents, focused on traffic within generation sites. The framework also incorporates mechanisms for interactions between these agents and the road network, aiming to optimize their individual utilities. Additionally, we have developed a multi-agent reinforcement learning (RL) algorithm, extending the mean-field theory concept, to address the complexity of simultaneous learning by multiple agents.

Promising the seamless integration of multiple transportation modes, Mobility-as-a-Service (MaaS) has gained popularity over the years, yet its effectiveness in enticing private car users and improving travel efficiency remains uncertain. This study explores the competitiveness of MaaS-enabled multi-modal travel options versus private car usage through equilibrium analysis. In addition to pricing that affects the fixed cost for travel, we examine the often overlooked inconvenience cost associated with multi-modal trips. We establish our analysis for the commuting problem of a one-origin-one-destination network, where a highway and a mass transit line connect the residential area and the central business district (CBD) area. Travelers choose their departure time and travel mode among auto, Park-and-Ride (PnR), ride-hailing and transit (RnT) to minimize their total travel cost. Inconvenience costs associated with searching for parking and waiting for ride-hailing are explicitly modeled. We analytically provide the mode share and departure time windows under all possible equilibria. Our findings reveal the complex nature of mode choice, distinctly affected by fixed and inconvenience costs, with demand playing an even more significant role. Notably, fixed costs set an entry fee to adopt different modes, and the feature of inconvenience costs affects the utilization of available transportation resources. More importantly, to benefit the overall system, we encourage maintaining a balanced mode share by implementing pricing and capacity strategies rather than aiming for a completer transition of private car users to MaaS.

We estimate the switching costs created by tier levels, one of the main components of airline frequent flier programs, by exploiting discrete tier thresholds. We first demonstrate that travellers increase their demand to reach a higher tier level just before the end of the calendar year when tier levels are determined, but do not manipulate demand in earlier months. This allows for a novel fuzzy discontinuity approach to identify causal demand effects of higher tier levels, from which we derive the airline switching costs. While the lowest level creates only negligible switching costs, the switching costs associated with the highest tier level are in the range of 30%–41% of the price of a ticket, representing an important determinant of travel behaviour in airline markets. These results especially provide evidence of the use of tier levels to induce loyalty from high-frequency travellers for whom free flight awards alone would not create substantial switching costs.

This paper introduces a spatial queueing model for a single bottleneck during morning peak hours. Utilizing the logistic function and after appropriate calibration, it articulates the arrival and departure flows in continuous, differentiable terms. By validating the model across different peak periods and locations, the demand model’s robustness is superior to other commonly used functions. This model also incorporates constant or varying capacity scenarios. It effectively captures key aspects of morning peak traffic, including the emergence of hysteresis loops in fundamental diagrams (FDs) of density and flow. The model’s multi-stage approach recognizes three distinct phases in traffic flow: freeflow, transition, and queued segments, ensuring spatial consistency in flow and density across these stages. It accounts for the growth of the queued segment and vehicle spillback under various bottleneck intensities, with the resulting FDs for speed and density also displaying hysteresis loops. The calibration of model parameters utilizes time-series data of traffic flow and density space–time maps derived from real-world data. The validation results accurately reflect real traffic scenarios, emulating the counterclockwise hysteresis loops observed in density and its heterogeneity, and provide both planar and three-dimensional FDs at different points along the traffic link, each mirroring real-life traffic patterns. Additionally, a comparison with the cell transmission model (CTM) reveals that the proposed model exhibits superior generalization and robustness.

The Electric Autonomous Dial-A-Ride Problem (E-ADARP) consists in scheduling a fleet of electric autonomous vehicles to provide ride-sharing services for customers that specify their origins and destinations. The E-ADARP considers the following perspectives: (i) a weighted-sum objective that minimizes both total travel time and total excess user ride time; (ii) the employment of electric autonomous vehicles and a partial recharging policy. This paper presents the first labeling algorithm for a path-based formulation of the DARP/E-ADARP, where the main ingredient includes: (1) fragment-based representation of paths, (2) a novel approach that abstracts fragments to arcs while ensuring excess-user-ride-time optimality, (3) construction of a sparser new graph with the abstracted arcs, which is proven to preserve all feasible routes of the original graph, and (4) strong dominance rules and constant-time feasibility checks to compute the shortest paths efficiently. This labeling algorithm is then integrated into Branch-and-Price (B&P) algorithms to solve the E-ADARP. In the computational experiments, the B&P algorithm achieves optimality in 71 out of 84 instances. Remarkably, among these instances, 50 were solved optimally at the root node without branching. We identify 26 new best solutions, improve 30 previously reported lower bounds, and provide 17 new lower bounds for large-scale instances with up to 8 vehicles and 96 requests. In total 42 new best solutions are generated on previously solved and unsolved instances. In addition, we analyze the impact of incorporating the total excess user ride time within the objectives and allowing unlimited visits to recharging stations. The following managerial insights are provided: (1) solving a weighted-sum objective function can significantly enhance the service quality, while still maintaining operational costs at nearly optimal levels, (2) the relaxation on charging visits allows us to solve all instances feasibly and further reduces the average solution cost.