Transportation Research Part E-Logistics and Transportation Review最新文献

The advent of autonomous vehicles (AVs) with self-parking capability puts forward new requirements for parking policy and management. In a transportation system with both general and dedicated AV lanes, this work aims to examine and compare the effectiveness of different parking policies on the morning commute. To be specific, three policies of parking space allocation, namely, first-come-first-service (FCFS), advanced reservation (AR), and their combination (CB), are proposed to guide the commuters’ travel choices. Commuters can drive regular vehicles (RVs) alone on the general lane or ridesharing shared autonomous vehicles (SAVs) on the dedicated AV lane. The occurrence conditions and the resultant SAV penetration rates for various scenarios under three parking policies are revealed. The area division for various scenarios with respect to two key parameters is presented, and the typical departure patterns are plotted as well. The numerical results clarify that the CB policy always has a better effect on the total travel cost than FCFS and AR policies. The optimal CB policy is quantified in terms of the optimal amount of all parking spaces and the proportion of unreserved parking spaces. These findings help to improve the spatial and temporal distribution of traffic flow and allocate transportation resources efficiently in the coming era of autonomous driving.

For Mobility-on-Demand systems, the imbalance between vehicle supply and demand is a long-standing challenge, leading to losses of orders and long waiting times. Relocating idle vehicles to high-demand regions can enhance system efficiency, thus improving the quality of service. Enforcing vehicle relocation via either link-node or grid-based representation makes it hard to capture the interrelated dynamics with private vehicles while being computationally intensive. The macroscopic fundamental diagram (MFD) provides a powerful tool to model the interrelated dynamics while individual vehicle details may be absent in the regional-level representation. Therefore, we propose a bi-level rebalancing scheme to maximize the served orders in the system. The urban area is first partitioned into several subregions. For the upper level, the interrelated dynamics of private vehicles and on-demand vehicles are modeled based on the MFD. Then a stochastic programming problem is formulated and solved using Approximate Dynamic Programming (ADP) to determine the number of desired vehicles in each subregion and cross-border. For the lower level, a Voronoi-based distributed coverage control algorithm is implemented by each vehicle to obtain position guidance efficiently. The bi-level framework is evaluated on a simulator of the real road network of Shenzhen, China. Simulation results demonstrate that, compared to other policies, the proposed approach can serve more requests with less waiting time.

The topic of net-zero shipping is emerging as a global priority. Given the projected increase in the shipping sector’s contributions to climate change, understanding the emerging research challenges for enabling net-zero shipping is critical. We address this research need by outlining key techno-economic and policy research priorities for achieving effective and equitable net-zero shipping. We use a three-step approach to achieve it. We begin by translating contemporary media issues to techno-economic and policy research challenges, followed by corroborating these identified research challenges with academic literature, and finally prioritizing them by consulting an array of maritime experts in policy, technology, fuel, and infrastructure fields. Our results delineate eleven overarching themes, among which the top four prioritized research challenges based on expert input are: (i) cost-benefit analyses of port initiatives aimed at facilitating maritime climate action; (ii) the investment and techno-economic aspects of onboard carbon capture and alternative shipping fuels, along with green shipping corridors’ potential to facilitate alternative fuel adoption, and (iii & iv) the complex interaction of climate, economic, and socio-political factors in shipping carbon pricing implementation as part of ongoing negotiations at the International Maritime Organization (IMO) and shipping’s inclusion in the EU Emissions Trading System (ETS). The outlined priorities are significant as they could guide future industrial and academic research in generating actionable recommendations for policymakers and industry stakeholders, thereby expediting the formulation of focused approaches for an effective and fair transition to net-zero shipping. In terms of future research, the dynamic nature of emerging issues in achieving net-zero shipping suggests that findings may necessitate ongoing updates.

The emergence of generative AI presents numerous potential solutions to address challenges in sustainable supply chain management (SCM). However, not all firms can effectively master the methods of using generative AI and realize potential benefits. To address this dilemma, we adopt a practice-based view (PBV) to examine generative AI usage’s effect on sustainable supply chain performance (SSCP). Analyzing survey data from 213 Chinese manufacturing firms, we identify a positive relationship between generative AI usage and SSCP. Moreover, two types of sustainable supply chain practices—green supply chain collaboration (GSCC) and circular economy implementation (CEI)——emerge as serial mediators connecting this relationship. We contribute to existing AI-enabled SCM research by elucidating the potential mediation mechanisms underlying the link between generative AI usage and SSCP. We also offer insightful implications for firms adapting to new norms in global SCM.

This paper aims to study and address the problem of completely re-scheduling high-speed freight train line plans under the conditions of a large-scale network, particularly for direct freight trains between central network nodes. First, we constructed a line pool of candidate trains. Then, considering constraints such as flow balance, station capacity, and train transport capacity, we formulated an integer programming model with 0–1 variables. The objective is to minimize train operation costs and freight transfer fees, determining the origin and destination stations and the operating frequency of freight trains. To address the structural characteristics of the model, an integer Benders decomposition-based branch-and-cut algorithm (IBD-BCA) is proposed. This algorithm, within the framework of branch-and-bound, solves the master problem decomposed by Benders and adds two sets of integer Benders cuts to achieve the optimal solution of the model. To demonstrate the effectiveness and performance of the model and algorithm, numerical experiments were conducted based on actual data from the main high-speed railway network in China. The results show that the IBD-BCA in this study can obtain optimal solutions within a reasonable time and requires adding a relatively small number of cuts during the search process. Compared with branch-and-bound algorithms and direct solving using Gurobi, the IBD-BCA proposed maintains sufficient efficiency when dealing with large-scale problems. Additionally, sensitivity analyses of parameters such as capacity and costs validate the robustness and scalability of the presented model and algorithm.

The rapid expansion of express delivery volume necessitates the development of logistics centers and the optimization of parcel transportation routes between cities within an extensive express transportation network. This study addresses the intercity express transportation network optimization problem (IETNP), which integrates the hub location problem with the multi-commodity flow problem. An integer linear programming model is introduced to represent the IETNP. To leverage the decomposable structure of the IETNP model, an improved Alternating direction method of multipliers (ADMM)-based algorithm is developed for solving the IETNP. A novel dual decomposition strategy is proposed to mitigate the negative effects of numerous coupling constraints on achieving high-quality upper-bound solutions. This strategy, incorporating penalty-term-reduction and multiplier-replacement methods, diminishes the number of penalty terms and the search space, thus enhancing computational efficiency while maintaining solution quality. A Lagrangian relaxation (LR)-based algorithm is employed to generate lower-bound solutions that assess the quality of the upper-bound solutions. Auxiliary constraints are integrated into the dualized formulation to enhance these lower-bound solutions. The effectiveness and efficiency of the improved ADMM-based algorithm are validated using over 100 artificial instances with 10–500 nodes and a realistic instance involving 338 cities. Comparative analysis with an off-the-shelf solver and existing ADMM- and LR-based algorithms reveals that the improved ADMM-based algorithm reduced the upper-bound values by 11.44% on average and by up to 22.09%.

In traditional traffic equilibrium models, travelers typically rely on link flow information to estimate link/route costs. However, an often-overlooked aspect in prior research is that travelers can easily acquire information about the route choices of other travelers within the same OD pair. The presentation of route flow information, combined with the influence of travelers’ herd behavior, results in novel and intriguing conclusions distinct from conventional equilibrium models. Specifically, this study demonstrates that by considering the influence of the herd effect in route choice within the stochastic user equilibrium model, it is possible to address three critical problems that remain inadequately resolved in the literature: (1) The stability of stochastic user equilibrium (SUE) in the presence of route flow information. (2) The existence of meaningful link tolls capable of steering the SUE flow pattern toward system optimum (SO). (3) The design of a trial-and-error congestion pricing scheme with disequilibrium observed network flow patterns. To tackle these problems, we present a logit SUE flow evolution process with herd effect and propose a corresponding trial-and-error congestion pricing scheme. Rigorous proofs of related convergence theorems will be provided. The findings of this study support the assertion that “herd effect can offset travelers’ perception errors”, carrying significant policy implications for leveraging herd effect in the design of navigation software and congestion pricing strategies.

An extension of the Share-a-Ride Problem is proposed, which explores the coordination of ride-hailing vehicles (RVs) and logistic vehicles (LVs) for integrated passenger and parcel service. The goal is to investigate the extent to which RVs can increase profits and logistic companies can reduce operational costs, via information sharing and vehicle coordination. A new optimization problem, termed the Share-a-Ride problem with ride-hailing and logistic vehicles (SARP-RL), simultaneously determines the LV fleet size and passenger/parcel request assignment, assuming that passenger requests can only be served by RVs while parcel requests can be served by both RVs and LVs. The goal is to maximize the total RV profits while minimizing logistic costs. An exact solution framework is proposed by (1) generating all trips for the entire set of passenger and parcel requests via an efficient enumeration method; and (2) finding Pareto-optimal solutions of the bi-objective problem via an $\varepsilon$-constraint method. A case study of the Manhattan network demonstrates the solution characteristics of SARP-RL. The results indicate that: (a) the SARP-RL can increase total RV profits by 11.93%–27.65% and reduce logistic costs by 47.63%–81.06%. (b) The novel enumeration method achieves computational savings by over 80% compared to Alonso-Mora et al. (2017) approach. (c) Key factors influencing the performance of SARP-RL include the RV fleet size, spatial distribution of parcel requests, passenger/parcel request ratio, unit price of transport service, vehicle capacity, and passenger service requirements, which are quantitatively analyzed to offer managerial insights for real-world implementation.

South Korea, an island-like nation divided by the demilitarized zone within the Korean Peninsula, facilitates cargo transportation for Central Asia through a sea-rail combined transportation system (SRTS) between Korea, China, and Russia. This system effectively utilizes the China Railway Express and the trans-Siberian railway. The objectives of this research are three-fold: first, to scrutinize the research of the SRTS by applying text mining technique to the collected papers between 2013–2023; second, to investigate the operational and managerial aspects of the system and the problems encountered in the system from its user’s perspective, i.e., major Korean logistics companies; and lastly, to propose policy recommendations and suggestions to aimed at promoting the SRTS. Extensive interviews and field trips were conducted with prominent logistics companies in Korea, CRE platforms, international inland (dry) ports, and cross-border regions in China to accomplish the objectives. Notably, these interviews and field trips took place before and after the COVID-19 pandemic, thereby examining the pandemic’s impact on the SRTS in both South Korea and China. The key findings of the interviews reveal significant issues such as infrastructure bottlenecks, policy and subsidy challenges, and the need for a unified information-sharing platform.

With the growing demand for high-quality mobility services, transportation service providers need to offer transit services that not only fulfill passengers’ basic travel needs but also ensure an appealing quality of service. During rush hours, fleet sizes are often insufficient to cater to all passenger preferences on service quality, such as ride time and number of co-riders, leading to the sacrifice of service quality for some passengers. Motivated by these practices, we investigate a first-mile ridesharing problem incorporating passenger service quality preferences. This problem involves intricate decisions about the match between requests and vehicles, vehicle routing, and route schedules. To solve this problem, we first develop an arc-based mixed-integer linear programming (MILP) model for this problem. For obtaining near-optimal solutions within practical computation time requirements, we reformulate the MILP model as a trip-based set-partitioning model and propose a math-heuristic algorithm. This algorithm builds upon the column-generation algorithm and tailored bidirectional labeling algorithms with novel dominance rules. Additionally, we introduce a proposition to determine the best schedule for each ridesharing route. To obtain the optimal solution for large-scale instances, we introduce a branch-and-price exact algorithm. Computational experiments based on real-world road networks and randomly generated instances confirm the effectiveness and efficiency of the proposed approaches, demonstrating that the proposed matheuristic finds near-optimal solutions within 40 s for all instances. The results also show that the presented approach significantly improves the quality of first-mile services for prioritized riders, with the ratio of satisfied requests increasing by 23% even when the fleet is generally insufficient.