Transportation Research Part C-Emerging Technologies最新文献

{"title":"A probabilistic approach for queue length estimation using license plate recognition data: Considering overtaking in multi-lane scenarios","authors":"Lyuzhou Luo ,&nbsp;Hao Wu ,&nbsp;Jiahao Liu ,&nbsp;Keshuang Tang ,&nbsp;Chaopeng Tan","doi":"10.1016/j.trc.2025.105029","DOIUrl":"10.1016/j.trc.2025.105029","url":null,"abstract":"<div><div>Multi-section license plate recognition (LPR) data has emerged as a valuable source for lane-based queue length estimation, providing both input–output information and sampled travel times. However, existing studies often rely on restrictive assumptions such as the first-in-first-out (FIFO) rule and uniform arrival processes, which fail to capture the complexity of multi-lane scenarios, particularly regarding overtaking behaviors and traffic flow variations. To address this issue, we propose a probabilistic approach to derive the stochastic queue length by constructing a conditional probability model of <em>no-delay arrival time</em> (NAT), i.e., the arrival time of vehicles without experiencing any delay, based on multi-section LPR data. First, the NAT conditions for all vehicles are established based on upstream and downstream vehicle departure times and sequences. To reduce the computational dimensionality and complexity, a dynamic programming (DP)-based algorithm is developed for vehicle group partitioning based on potential interactions between vehicles. Then, the conditional probability of NATs of each vehicle group is derived and a Markov Chain Monte Carlo (MCMC) sampling method is employed for calculation. Subsequently, the stochastic queue profile and maximum queue length for each cycle can be derived based on the NATs of vehicles. Eventually, we extend our approach to multi-lane scenarios, where the problem can be converted to a weighted general exact coverage problem and solved by a backtracking algorithm with heuristics. Empirical and simulation experiments demonstrate that our approach outperforms the baseline method, demonstrating significant improvements in accuracy and robustness across various traffic conditions, including different V/C ratios, matching rates, miss detection rates, and FIFO violation rates. The estimated queue profiles demonstrate practical value for offset optimization in traffic signal control, achieving a 6.63% delay reduction compared to the conventional method.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105029"},"PeriodicalIF":7.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463925","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":"Virtual car following based cooperative control of connected automated vehicles in complex scenarios: A roundabout example","authors":"Meng Li ,&nbsp;Soyoung Ahn ,&nbsp;Yang Zhou ,&nbsp;Sixu Li","doi":"10.1016/j.trc.2025.105062","DOIUrl":"10.1016/j.trc.2025.105062","url":null,"abstract":"<div><div>This paper proposes a generic cooperative control framework for connected and automated vehicles (CAVs) for compound maneuvers of car-following, merging, and diverging on curved paths. The proposed framework is illustrated through a roundabout example. The framework is based on the “virtual car-following” concept, where multiple conflicting streams of CAVs are transformed onto a single, straight virtual axis. With this transformation, the control of compound maneuvers is simplified into a car-following and lane-keeping control problem. This is facilitated by firstly applying curvilinear coordinates to each approach to transform the vehicle kinematic motions along a curved path into longitudinal and lateral movements on a straight path. Then the transformed paths for all the conflicting vehicle streams are rotated onto the same virtual axis with respect to the merging/diverging point to form a virtual car-following stream. Traffic conflicts are then resolved through virtual car-following control, paired with lane-keeping control. Specifically, a serial distributed model predictive control (MPC) with a time-varying horizon is designed to fulfill these control tasks simultaneously. Numerical simulations are conducted for a single-lane roundabout scenario. The results showed that the proposed strategy is capable of actively generating gaps for vehicles to safely merge, reducing voids, and dampening traffic disturbances as manifested by speed variations.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105062"},"PeriodicalIF":7.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463926","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":"Environmental impacts and emission reduction methods of vehicles equipped with driving automation systems: An operational-level review","authors":"Haoran Jiang ,&nbsp;Kui Xia ,&nbsp;Yingying Zhao ,&nbsp;Zhihong Yao ,&nbsp;Yangsheng Jiang ,&nbsp;Zhengbing He","doi":"10.1016/j.trc.2024.104996","DOIUrl":"10.1016/j.trc.2024.104996","url":null,"abstract":"<div><div>Driving automation systems offer both opportunities and challenges in addressing environmental issues. It is essential to clarify: (1) the environmental impacts of driving automation systems-equipped vehicles (referred to as automated vehicles, i.e., AVs in this review) and (2) the methods for leveraging AVs to improve the environment. To answer these questions, this study systematically reviews relevant literature from the past decade, focusing on the air environment and traffic operation. We also pay additional attention to the link between environmental impacts and improvement methods. The findings reveal that the environmental impact of AVs is two-sided, and it is primarily determined by connectivity, penetration rate, and traffic demand. When traffic demand is low, integrating AVs into traffic can lead to environmental improvements. However, under high traffic demand, the ability of AVs to improve the environment depends on their connectivity and the scale to form a multi-vehicle cooperation. Both micro-level traffic parameters and macro-level traffic scenarios affect the environmental benefits of AVs. While micro-parameters can be adjusted by altering the settings of AVs to realize positive environmental effects, macro-scenario conditions must be adapted by AVs through control and management methods to achieve ecological goals. Several representative methods from both vehicle control and traffic management perspectives are discussed. Generally, the existing control and management methods can be categorized into four phases: individual AV control, cooperative control of multiple AVs, facility-based management methods, and network-based ecological schemes. Finally, this study outlines five potential research directions for the sustainable development of AVs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 104996"},"PeriodicalIF":7.6,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143463927","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":"Reliable deployment of automatic vehicle identification sensors for origin-destination matrix observation","authors":"Hessam Arefkhani,&nbsp;Yousef Shafahi","doi":"10.1016/j.trc.2025.105045","DOIUrl":"10.1016/j.trc.2025.105045","url":null,"abstract":"<div><div>An Origin-Destination Matrix (ODM) is fundamental in transportation studies, as it provides essential insights into travel patterns and demand. The ODM can be constructed using data collected from Automatic Vehicle Identification (AVI) sensors strategically installed on network links. However, the quality of the ODM can be significantly affected by the fact that sensors are subject to failure in real-world scenarios. This issue underscores the importance of ODM reliability because it can significantly influence the study outcomes. Some researchers focused on incorporating sensor failure considerations into the Sensor Location Problem for ODM observation. One common approach is to consider a predefined level of reliability for ODM and try to find a sensor deployment with the minimum number of sensors that meet the reliability constraint. In this study, we first show by a counter-example that the most recently developed reliable sensor location model for ODM observation using the mentioned approach does not guarantee the predefined reliability level for ODM. Second, we introduce a new reliability term for ODM observation and incorporate it into our new reliable AVI sensor location models specifically designed to observe ODM and route flows. Additionally, we develop reliable AVI sensor location models that accommodate partial observations of ODM and route flows while adhering to budget constraints. Third, a greedy algorithm and a Genetic-Based Algorithm (GBA) are developed to solve the proposed models for middle to large-scale networks. Finally, the proposed models are applied to some numerical examples to illustrate their applicability and effectiveness. The numerical examples revealed the models’ capability to identify optimal sensor locations for reliable observation of ODM and route flows considering sensor failure. Moreover, the results highlighted the efficiency of the GBA as an efficient solution method, especially for medium and large-scale networks.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105045"},"PeriodicalIF":7.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143454857","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":"Deep imitative reinforcement learning with gradient conflict-free for decision-making in autonomous vehicles","authors":"Zitong Shan ,&nbsp;Jian Zhao ,&nbsp;Wenhui Huang ,&nbsp;Yang Zhao ,&nbsp;Linhe Ge ,&nbsp;Shouren Zhong ,&nbsp;Hongyu Hu ,&nbsp;Chen Lv ,&nbsp;Bing Zhu","doi":"10.1016/j.trc.2025.105047","DOIUrl":"10.1016/j.trc.2025.105047","url":null,"abstract":"<div><div>As autonomous driving technology advances, researchers are focusing on utilizing expert priors to improve the agents for learning-based decision-making in autonomous vehicles. Expert priors have various carriers, and the existing technology primarily utilizes expert priors derived from demonstration data and interaction data. This paper proposed a deep imitative reinforcement learning method for decision-making in autonomous vehicles, synergizing the expert priors in both demonstration data and interaction data. The gradient projection technique was adopted to mitigate gradient conflicts between the demonstration and interaction data during the training phase, thus preventing learning stagnation and enhancing agent performance. Furthermore, we deployed the proposed decision-making method on real autonomous vehicles. An augmented reality experiment was conducted with random virtual traffic flows from the simulator. The simulation and experiment results demonstrated that the proposed method enhanced training efficiency and safety performance, and preliminarily overcame sim-to-real challenges.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"173 ","pages":"Article 105047"},"PeriodicalIF":7.6,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143445544","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":"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}