Transportation Research Part C-Emerging Technologies最新文献

{"title":"Efficient pedestrian and bicycle traffic flow estimation combining mobile-sourced data with route choice prediction","authors":"Simanta Barman ,&nbsp;Michael W. Levin ,&nbsp;Raphael Stern ,&nbsp;Greg Lindsey","doi":"10.1016/j.trc.2025.105046","DOIUrl":"10.1016/j.trc.2025.105046","url":null,"abstract":"<div><div>Accurate estimates of traffic flow measures like annual average daily traffic (AADT) are crucial for roadway planning, safety, maintenance, and operation. Due to resource constraints and high costs of traditional monitoring methods, we develop a methodology to estimate pedestrian and bicyclist traffic flows using mobile data sources, avoiding privacy issues of household surveys. The methodology is general, and potentially could be used with any reasonably comprehensive mobile source data set. To deal with erroneous and high variability data from mobile data sources we use different techniques to estimate and keep improving an origin–destination (OD) matrix constructed using the observed link flows to ultimately obtain reasonable approximations of actual link flows. We provide a non-linear optimization formulation along with a projected gradient descent based solution algorithm to solve this problem. Furthermore, we present the performance of the solution algorithm for several networks including the Twin Cities’ bicycle and pedestrian networks. We also compare the accuracy of our estimate with manually collected AADB and AADP counts from Minnesota Department of Transportation monitoring stations. For the Sioux-Falls network, the highest error from our model was less than 1%. These estimates can be improved by using existing methods to improve the quality of mobile sourced data as a pre-processing step to our method.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105046"},"PeriodicalIF":7.6,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding the capacity of airport runway systems","authors":"Kailin Chen ,&nbsp;Anupriya ,&nbsp;Prateek Bansal ,&nbsp;Richard J. Anderson ,&nbsp;Nicholas S. Findlay ,&nbsp;Daniel J. Graham","doi":"10.1016/j.trc.2025.104998","DOIUrl":"10.1016/j.trc.2025.104998","url":null,"abstract":"<div><div>Runway systems are often the primary bottlenecks in airport operations. Thus, understanding their capacity is of critical importance to airport operators. However, developing this understanding is not straightforward because, unlike demand or throughput, runway system capacity (RSC) remains unobserved. Moreover, the complex interactions of the physical runway system infrastructure with underlying operating conditions (such as weather) and the airspace result in different capacities under different airport operational scenarios, thereby making the measurement of RSC more complicated. Both analytical and simulation-based approaches need extensive efforts for customization according to specific runway configurations. Analytical models with a moderate level of fidelity are often used to support strategic capacity decisions. In contrast, high-fidelity simulation-based approaches are more appropriate for accommodating wide-ranging operational scenarios and providing accurate RSC estimates to support short-term capacity decisions, though they tend to be resource-intensive. To that end, the availability of granular data on day-to-day runway operations facilitates the development of statistical model that can offer a standardized model specification with minimal customization and provide a precise estimation of RSC for short-term capacity decisions. However, the exercise is empirically challenging due to statistical biases that emerge via the above-mentioned interactions between air traffic flow and control at airports and in the airspace and RSC. This paper develops a novel causal statistical framework based on a confounding-adjusted Stochastic Frontier Analysis (SFA) to deliver estimates of RSC and its parameters that are robust to such biases and are therefore suitable to inform airport operations and planning. The model captures the key factors and interactions affecting RSC in a computationally efficient manner. The performance of the model is benchmarked via a Monte Carlo simulation and further by comparing the estimated capacities of five major multi-runway airports with their representative estimates from the literature.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 104998"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time system optimal traffic routing under uncertainties — Can physics models boost reinforcement learning?","authors":"Zemian Ke ,&nbsp;Qiling Zou ,&nbsp;Jiachao Liu ,&nbsp;Sean Qian","doi":"10.1016/j.trc.2025.105040","DOIUrl":"10.1016/j.trc.2025.105040","url":null,"abstract":"<div><div>System optimal traffic routing can mitigate congestion by assigning routes for a portion of vehicles so that the total travel time of all vehicles in the transportation system can be reduced. However, achieving real-time optimal routing poses challenges due to uncertain demands and unknown system dynamics, particularly in expansive transportation networks. While physics model-based methods are sensitive to uncertainties and model mismatches, model-free reinforcement learning struggles with learning inefficiencies and interpretability issues. Our paper presents TransRL, a novel algorithm that integrates reinforcement learning with physics models for enhanced performance, reliability, and interpretability. TransRL begins by establishing a deterministic policy grounded in physics models, from which it learns from and is guided by a differentiable and stochastic teacher policy. During training, TransRL aims to maximize cumulative rewards while minimizing the Kullback–Leibler (KL) divergence between the current policy and the teacher policy. This approach enables TransRL to simultaneously leverage interactions with the environment and insights from physics models. We conduct experiments on three transportation networks with up to hundreds of links. The results demonstrate TransRL’s superiority over traffic model-based methods for being adaptive and learning from the actual network data. By leveraging the information from physics models, TransRL consistently outperforms state-of-the-art reinforcement learning algorithms such as proximal policy optimization (PPO) and soft actor-critic (SAC). Moreover, TransRL’s actions exhibit higher reliability and interpretability compared to baseline reinforcement learning approaches like PPO and SAC.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105040"},"PeriodicalIF":7.6,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization model for electric aircraft tow tractors scheduling under operator cooperation","authors":"Dan-Wen Bao ,&nbsp;Jia-Yi Zhou ,&nbsp;Di Kang ,&nbsp;Zhuo Chen","doi":"10.1016/j.trc.2025.105032","DOIUrl":"10.1016/j.trc.2025.105032","url":null,"abstract":"<div><div>Collaborating among operators can significantly reduce transportation costs—a concept already proven in the logistics industry. With growing transportation demand and the added complexity of electric vehicle (EV) charging times, airport ground support services face increasing pressure to optimize operations. This study introduces a novel concept of operator-cooperate mode for airport ground support services for the first time, where operators share vehicle fleets to enhance efficiency. This paper develops vehicle scheduling and cost allocation methods under the cooperation framework. Two models are established for scheduling electric tow tractors: one for the traditional operator-separate mode and another for the operator-cooperate mode. Using an adaptive large neighborhood search framework, algorithms are designed to generate scheduling plans that minimize costs and delays. To support cooperation, the study proposes a cost allocation method that considers differentiated unit delay costs and level of sharing among operators to ensure the feasibility and fairness of cooperation. Finally, numerical experiments are conducted based on one day of flight schedule data from a major international airport, validating the effectiveness of the algorithm and cost allocation method across 21 experimental scenarios. The results show that the algorithm delivers solutions faster than traditional solvers while keeping the weighted objective function gap within 2%.Moreover, the improved cost allocation method ensures greater fairness than the traditional Shapley method. The numerical experiments indicate that cooperation can save 5–16% in operating costs and 15–33% in delay times for airports, with the savings varying based on the sharing parameters. The study also uses sensitivity analysis and other quantitative methods to examine changes in overall and individual cooperated utility changes. It provides recommendations and decision-making strategies for configuring and managing airport ground operations.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105032"},"PeriodicalIF":7.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143422690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint optimization of train stop planning and timetabling with time-dependent passenger and freight demands in high-speed railway","authors":"Jianguo Qi ,&nbsp;Yaru Zhou ,&nbsp;Fanting Meng ,&nbsp;Lixing Yang ,&nbsp;Qin Luo ,&nbsp;Chuntian Zhang","doi":"10.1016/j.trc.2025.105025","DOIUrl":"10.1016/j.trc.2025.105025","url":null,"abstract":"<div><div>To effectively utilize the remaining transportation capacity and increase the revenue of railway transport enterprises during periods of lower passenger demand, this study proposes a general framework for the joint optimization of train stop planning and timetabling problems based on a flexible train composition mode for passenger and freight co-transportation on high-speed railways. This framework aims to simultaneously provide different transport services for time-dependent passenger and freight demands. With the basic services being satisfied, a quadratic programming model is formulated to maximize the transportation revenue of railway companies by considering different operation costs of different types of train composition modes and minimizing the time deviations of passenger and freight compared with their desired service times. To effectively solve the proposed model, a heuristic approach combining the variable neighborhood search (VNS) and the commercial solver CPLEX is designed to search for high-quality solutions. Finally, the performance and effectiveness of the proposed approaches are verified using a small-scale example and a real case study of the Wuhan-Guangzhou high-speed railway.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105025"},"PeriodicalIF":7.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A heuristic-attention method for location-routing problems with shared pick-up stations in green last-mile delivery","authors":"Zhonghao Zhao ,&nbsp;Carman K.M. Lee ,&nbsp;Yung Po Tsang ,&nbsp;Xinsheng Xu","doi":"10.1016/j.trc.2025.105031","DOIUrl":"10.1016/j.trc.2025.105031","url":null,"abstract":"<div><div>This paper investigates the location-routing problem for a green last-mile delivery system (LRP-GLD) with shared pick-up stations (PUSs). LRP-GLD first requires determining the locations of a set of opened PUSs, followed by solving the capacitated delivery routing problem given the spatial layout of the PUSs. The overall objective is to minimize the sum of the fixed PUS opening cost, service cost, and delivery costs while satisfying the load capacity and battery capacity constraints of the electric delivery vehicles (EDVs). To effectively address the LRP-GLD with a combinatorially large solution space, we develop a two-stage method that combines simulated annealing algorithm and attention mechanism (SA-AM). At the lower operational stage, an attention model with an encoder-decoder architecture and a customized embedding strategy is trained to solve the delivery routing problem. The attention parameters are updated and optimized through a policy gradient method with an input-dependent baseline function. At the upper strategic planning stage, we employ simulated annealing (SA) to address the PUS location problem, where the performance of the location solution for the routing problem is evaluated by iteratively invoking the pre-trained attention model. Numerical experiments are conducted on randomly generated delivery networks to examine the efficiency and feasibility of the proposed solution method. A comprehensive analysis is also performed to explore the impacts of the designed delivery system and several key parameters on the system performance and provide managerial insights for decision-makers.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105031"},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-rank approximation of path-based traffic network models","authors":"Pengji Zhang ,&nbsp;Sean Qian","doi":"10.1016/j.trc.2025.105027","DOIUrl":"10.1016/j.trc.2025.105027","url":null,"abstract":"<div><div>Traffic network models have been widely used in transportation planning and management. However, the use of path sets in certain models introduces practical issues to the applications of those models in real world, due to the large number of paths in a network that usually cannot be explicitly enumerated. To reduce the size of the path set and thus make such network models more applicable in practice, we propose a novel method to generate a small hypothetical path set tailored for network flow models in a way analogous to low-rank matrix approximation. The generated hypothetical path set are no longer based on the topological structure of the network, but on traffic flows in the network, making it possible to represent the information of the original path set in the context of network modeling with a much smaller set. The hypothetical path set is created through fitting a surrogate traffic assignment model, which could be employed as a replacement of the original model in a variety of transportation network design problems. In addition, the reduced hypothetical path set itself, even though does not fully preserve the physical information of the original path set, may also be directly used in network modeling. As an example we show an efficient way to estimate origin–destination travel demands with the reduced hypothetical path set. Proposed methods are examined on four toy networks and one real-world network. Numerical experiments show the proposed methods could well approximate the original network models, and meanwhile bring nontrivial improvement in run time efficiency and large-scale data calibration of path-based network models.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105027"},"PeriodicalIF":7.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Dynamic Traveling Salesman Problem with Time-Dependent and Stochastic travel times: A deep reinforcement learning approach","authors":"Dawei Chen ,&nbsp;Christina Imdahl ,&nbsp;David Lai ,&nbsp;Tom Van Woensel","doi":"10.1016/j.trc.2025.105022","DOIUrl":"10.1016/j.trc.2025.105022","url":null,"abstract":"<div><div>We propose a novel approach using deep reinforcement learning to tackle the Dynamic Traveling Salesman Problem with Time-Dependent and Stochastic travel times (DTSP-TDS). The main goal is to dynamically plan the route with the shortest tour duration that visits all customers while considering the uncertainties and time-dependence of travel times. We employ a reinforcement learning approach to dynamically address the stochastic travel times to observe changing states and make decisions accordingly. Our reinforcement learning approach incorporates a Dynamic Graph Temporal Attention model with multi-head attention to dynamically extract information about stochastic travel times. Numerical studies with varying amounts of customers and time intervals are conducted to test its performance. Our proposed approach outperforms other benchmarks regarding solution quality and solving time, including the rolling horizon heuristics and other existing reinforcement learning approaches. In addition, we demonstrate the generalization capability of our approach in solving the various DTSP-TDS in various scenarios.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105022"},"PeriodicalIF":7.6,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cooperative adaptive cruise platoon controller design considering switching control and stability","authors":"Tiancheng Ruan ,&nbsp;Yujia Chen ,&nbsp;Gengyue Han ,&nbsp;Jian Wang ,&nbsp;Xiaopeng Li ,&nbsp;Rui Jiang ,&nbsp;Wei Wang ,&nbsp;Hao Wang","doi":"10.1016/j.trc.2025.105024","DOIUrl":"10.1016/j.trc.2025.105024","url":null,"abstract":"<div><div>With the advancement of Cooperative Adaptive Cruise Control (CACC) technology, the CACC platoon control strategy has emerged as a significant innovation in enhancing road capacity, stability, and safety while reducing emissions. The advantages of CACCs can be further enhanced by enlarging the platoon size. With a larger platoon size, the CACC platoon can reduce the desired time gap and increase capacity while maintaining string stability. However, altering the desired time gap during operations due to changes in platoon size necessitates controller switching, highlighting the absence of an efficacious strategy to guarantee smooth switching and stability. To address these challenges, this paper introduces a novel switching control strategy named Cooperative Adaptive Cruise Platoon Control (CACPC). This strategy considers the CACC platoon as the control object and dynamically adjusts to variations in platoon size, thereby enhancing the utilization of CACC technology. Employing the Youla-Kučera (YK) parameterization, a class of controllers is developed that encompasses all controllers capable of stabilizing the CACC platoon, thereby ensuring stability during controller transitions triggered by changes in platoon size. Prior to switching, YK parameterization operates under a feedback control mode, stabilizing the platoon based on current operating conditions. Upon switching, YK transitions into an adaptive control mode, adjusting the controller to accommodate changes in platoon size while maintaining string stability. Additionally, a tuning function is formulated as the switching signal for the controller class via YK parameterization, making CACPC adaptable to diverse platoon sizes. Through comprehensive numerical analyses and simulations, the impact of CACPC on the dynamic performance of the platoon was evaluated. The results demonstrate that CACPC effectively maintains string stability across various platoon sizes and significantly attenuates spontaneous perturbations induced by controller switching due to platoon size alterations, notably reducing the perturbation amplitude from 2 m/s² to 0.011 m/s².</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105024"},"PeriodicalIF":7.6,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143378494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning the on-demand adaptable matching range with a reinforcement learning","authors":"Yuhan Liu ,&nbsp;Siyuan Feng ,&nbsp;Yue Bao ,&nbsp;Hai Yang","doi":"10.1016/j.trc.2025.105018","DOIUrl":"10.1016/j.trc.2025.105018","url":null,"abstract":"<div><div>Ride-sourcing services have reshaped urban transportation, providing greater convenience and efficiency for city commuters. At the core of these services is the matching process, which directly impacts service efficiency, passenger satisfaction, and overall platform profitability. Consequently, developing highly effective matching algorithms, especially under imbalanced supply–demand conditions, is of utmost importance. In existing matching algorithms, the matching range is a key factor. A larger matching range can result in longer pickup waiting times, potentially leading passengers to abandon their requests. Conversely, a smaller matching range may shorten waiting times but can also reduce the overall matching rate. Previous research on optimizing the matching range has often overlooked future information, leading to short-term improvements. In this paper, we propose a generalized, on-demand adaptable matching range technique based on reinforcement learning framework, designed to optimize decision-making from a long-term perspective while accounting for future information. Additionally, we develop a flexible framework adaptable to different kinds of matching modes. To evaluate the effectiveness of our approach, we implement our strategy with real-world supply and demand data and conduct a series of sensitivity analyses. The experimental results demonstrate that our method can achieve improvements in terms of the platform’s revenue and passengers’ satisfaction simultaneously compared with benchmark algorithms.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"172 ","pages":"Article 105018"},"PeriodicalIF":7.6,"publicationDate":"2025-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}