Transportation Research Part C-Emerging Technologies最新文献

{"title":"Individual response prediction and personalized guidance strategy optimization in urban rail transit networks","authors":"Xueqin Wang ,&nbsp;Xinyue Xu ,&nbsp;Junyi Zhang ,&nbsp;Jun Liu","doi":"10.1016/j.trc.2024.104875","DOIUrl":"10.1016/j.trc.2024.104875","url":null,"abstract":"<div><div>Advanced travel information systems play a crucial role in alleviating network-wide congestion in urban rail transit. However, existing studies overlook the heterogeneity of passengers’ compliance with information and their personalized requirements, leading to inefficiencies in guidance. To address these limitations, this paper explores the personalized guidance problem. An integrated framework is proposed to strategically provide passengers with differential route suggestions, ultimately minimizing systematic cost. The framework includes two modules, the first of which predicts individual route decisions under information. Passenger preferences are incorporated into the gradient boosting decision tree model to capture the heterogeneity of compliance with information. Additionally, this module integrates automated fare collection data with stated preference data, thereby avoiding the large-scale and costly data collection. The second module formulates and solves the personalized guidance problem. The problem is modeled as a Markov decision process encompassing an extensive solution space. Moreover, the deep deterministic policy gradient approach is utilized to overcome the dynamicity and dimensional disaster of the problem. A case study of the Beijing Subway is provided to highlight the effectiveness of the proposed framework. The findings show that the guidance strategy significantly decreases the network-wide generalized travel cost by 18.7%, with considerable benefits in overcrowded regions by guiding passengers toward less crowded areas. Moreover, the proposed framework accurately predicts individual behavior responses in route choice, reducing the mean squared error by at least 18.5 %. This study offers valuable information for subway managers to effectively organize passenger flow and improve the quality of passenger travel.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425636","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":"Unraveling stochastic fundamental diagrams with empirical knowledge: Modeling, limitations, and future directions","authors":"Yuan-Zheng Lei,&nbsp;Yaobang Gong,&nbsp;Xianfeng Terry Yang","doi":"10.1016/j.trc.2024.104851","DOIUrl":"10.1016/j.trc.2024.104851","url":null,"abstract":"<div><div>Traffic flow modeling relies heavily on fundamental diagrams. However, deterministic fundamental diagrams, such as single or multi-regime models, cannot capture the underlying uncertainty in traffic flow. To address this limitation, this study proposes a non-parametric Gaussian process model to formulate the stochastic fundamental diagram. Unlike parametric models, the non-parametric approach is insensitive to parameters, flexible, and widely applicable. The computational complexity and high memory requirements of Gaussian process regression are also mitigated by introducing sparse Gaussian process regression. This study also examines the impact of incorporating empirical knowledge into the prior of the stochastic fundamental diagram model and assesses whether such knowledge can enhance the model’s robustness and accuracy. By using several well-known single-regime fundamental diagram models as priors and testing the model’s performance with different sampling methods on real-world data, this study finds that empirical knowledge benefits the model only when small inducing samples are used with a relatively clean and large dataset. In other cases, a purely data-driven approach is sufficient to estimate and describe the density–speed relationship pattern.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425635","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":"Feedback–feedforward signal control with exogenous demand estimation in congested urban road networks","authors":"Leonardo Pedroso ,&nbsp;Pedro Batista ,&nbsp;Markos Papageorgiou","doi":"10.1016/j.trc.2024.104863","DOIUrl":"10.1016/j.trc.2024.104863","url":null,"abstract":"<div><div>To cope with uncertain traffic patterns and traffic models, traffic-responsive signal control strategies in the literature are designed to be robust to these uncertainties. These robust strategies still require sensing infrastructure to implement traffic-responsiveness. In this paper, we take a novel perspective and show that it is possible to use the already necessary sensing infrastructure to estimate the uncertain quantities in real time. Specifically, resorting to the store-and-forward model, we design a novel network-wide traffic-responsive strategy that estimates the occupancy and exogenous demand in each link, i.e., entering (exiting) vehicle flows at the origins (destinations) of the network or within links, in real time. Borrowing from optimal control theory, we design an optimal linear quadratic control scheme, consisting of a linear feedback term, of the occupancy of the road links, and a feedforward component, which accounts for the varying exogenous vehicle load on the network. Thereby, the resulting control scheme is a simple feedback–feedforward controller, which is fed with occupancy and exogenous demand estimates, and is suitable for real-time implementation. Numerical simulations for the urban traffic network of Chania, Greece, show that, for realistic surges in the exogenous demand, the proposed solution significantly outperforms tried-and-tested solutions that ignore the exogenous demand.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425738","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":"Reliability modeling for perception systems in autonomous vehicles: A recursive event-triggering point process approach","authors":"Fenglian Pan ,&nbsp;Yinwei Zhang ,&nbsp;Jian Liu ,&nbsp;Larry Head ,&nbsp;Maria Elli ,&nbsp;Ignacio Alvarez","doi":"10.1016/j.trc.2024.104868","DOIUrl":"10.1016/j.trc.2024.104868","url":null,"abstract":"<div><div>Ensuring the reliability of sensor-fusion-based perception systems is crucial for the safe deployment of autonomous vehicles. Such systems function through a sequence of interconnected stages, where errors in upstream stages may propagate to downstream stages and trigger additional errors. The cross-stage error propagation conceptually exists and makes errors in different stages, not independent, posing model challenges, estimation challenges, and data challenges for reliability modeling. The existing methods cannot be applied to address all these challenges. Thus, this paper presents a recursive event-triggering point process to explicitly consider the error propagation based on the simulated data. The data are simulated from a proposed error injection framework, which can generate various errors from a sequence of interconnected stages in a perception system. The latent and probabilistic error propagation information is incorporated into a modified expectation–maximization (EM) algorithm for parameter estimation. The numerical and physics-based simulation case studies demonstrate the prediction accuracy and interpretability of the proposed modeling methodology.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425634","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 scalable macro–micro approach for cooperative platoon merging in mixed traffic flows","authors":"Weiming Zhao,&nbsp;Mehmet Yildirimoglu","doi":"10.1016/j.trc.2024.104859","DOIUrl":"10.1016/j.trc.2024.104859","url":null,"abstract":"<div><div>This study presents a new approach to efficiently coordinate platoon movements in mixed traffic environments. The approach synergises macroscopic, network-wide traffic control models with microscopic, individual vehicle dynamics. This overcomes the limitations of using either strategy in isolation: macroscopic models lack detailed vehicle interactions, while microscopic models do not scale. By combining both strategies, the framework provides a scalable solution for mixed traffic networks.</div><div>At the macroscopic level, the framework dynamically optimises speed limits and ramp metering rates to reduce total travel time and queue lengths, establishing a high-level control mechanism that facilitates microscopic implementations. At the microscopic level, the strategy focuses on the trajectory planning of automated vehicles (AVs). Vehicles are organised into platoons with an AV leader, enabling cooperative behaviour in mixed traffic. The number of platoons and their entry into the merging area are carefully regulated to match macroscopic control references. In addition, we propose a novel passing sequence rule for platoons in the merge area. This is further supported by a virtual platooning method for trajectory planning of AVs.</div><div>The effectiveness of the integrated approach is demonstrated through rigorous microscopic simulations. Our method reduces the total travel time by more than 30% with a 20% AV penetration rate compared to the no-control scenario. It also outperforms the existing macroscopic approach even at a 10% AV penetration rate. Furthermore, it balances queue lengths across multiple merging areas. Our integrated control strategy facilitates the integration of AVs into existing transport systems, resulting in a more efficient, coordinated and adaptable system.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425632","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":"Predicting flight arrival times with deep learning: A strategy for minimizing potential conflicts in gate assignment","authors":"Feng Cao ,&nbsp;Tieqiao Tang ,&nbsp;Yunqi Gao ,&nbsp;Oliver Michler ,&nbsp;Michael Schultz","doi":"10.1016/j.trc.2024.104866","DOIUrl":"10.1016/j.trc.2024.104866","url":null,"abstract":"<div><div>Air transportation is frequently disrupted by factors such as weather and air traffic control, making it difficult for flights to strictly adhere to schedules, leading to frequent early arrivals or delays. These disruptions pose challenges to airport operations management, particularly in gate assignments, where potential conflicts and adjustments are often required. Unlike traditional methods that focus on enhancing robustness to reduce conflicts, this study adopts a Predict-then-Optimize (PO) framework, using predicted flight arrival times for gate assignments to avoid the need for robustness-related objectives. In the prediction phase, a CNN-LSTM-Attention deep learning model is developed to predict flight arrival times based on the historical data of a single airport, enhancing data availability and model practicality. In the optimization phase, a bi-objective gate assignment model is constructed, using predicted arrival times instead of scheduled times as input. An epsilon-constrained branch-and-price algorithm is developed to obtain non-dominated Pareto optimal solutions. Analysis using actual operational data from Beijing Capital International Airport shows that the prediction model achieves an accuracy of 93.27% for early arrivals and 83.6% for on-time flights. The epsilon-constrained branch-and-price algorithm outperforms heuristic algorithms in both the quantity and quality of Pareto solutions. Notably, the gate assignment strategy based on predicted arrival times significantly reduces potential conflicts, with a maximum reduction of 25.33% compared to the schedule-based strategy. This study demonstrates that the proposed gate assignment method, based on flight arrival time prediction, effectively mitigates the impact of arrival time uncertainty on gate assignments, providing a new approach to reducing potential conflicts without relying on robustness.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425633","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":"MetaFollower: Adaptable personalized autonomous car following","authors":"Xianda Chen ,&nbsp;Kehua Chen ,&nbsp;Meixin Zhu ,&nbsp;Hao (Frank) Yang ,&nbsp;Shaojie Shen ,&nbsp;Xuesong Wang ,&nbsp;Yinhai Wang","doi":"10.1016/j.trc.2024.104872","DOIUrl":"10.1016/j.trc.2024.104872","url":null,"abstract":"<div><div>Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing research interest in recent decades. In this study, we propose an adaptable personalized car-following framework —– MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425851","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":"Bi-objective robust nonlinear decision approach for en-route bus speed control considering implementation errors and traffic uncertainties","authors":"Pengjie Liu ,&nbsp;Liang Zheng ,&nbsp;Nan Zheng","doi":"10.1016/j.trc.2024.104870","DOIUrl":"10.1016/j.trc.2024.104870","url":null,"abstract":"<div><div>This study proposes a bi-objective robust nonlinear decision mapping (Bi-RNDM) approach for en-route bus speed control, aiming to enhance bus service level and reliability. Through a two-stage procedure, the proposed approach addresses the challenges due to traffic flow uncertainties and implementation errors from bus drivers. In the first stage, a bi-objective nonlinear programming model (Bi-NLPM) is built and solved to collect labeled data, which are then used to pre-train the mapping relationship between bus system states and optimal bus control speeds using support vector machines (SVM). This results in a bi-objective pre-trained nonlinear decision mapping (Bi-PNDM) consisting of an SVM-based classifier and an SVM-based regressor. In the second stage, a bi-objective robust critical parameter simulation-based optimization (BRCPSO) model is built within the min–max expectation framework, and it is solved using a modified bi-objective robust simulation-based optimization (MBORSO) algorithm to optimize the critical parameters of Bi-PNDM. The resulting Bi-RNDM improves the operation performance by reducing the deviation in service headway as well as the deviation from service schedule, considering the existence of traffic uncertainties and implementation errors from bus drivers. Numerical experiments are conducted based on the case study of the bus line 406 in Changsha, China, to demonstrate the efficiency of the MBORSO algorithm and the superior bus service level and robustness of the Bi-RNDM method. Results show that the proposed Bi-RNDM method can effectively balance the two competitive objectives, and the produced speed control is implementable for only about 20% of the operation period, suggesting high practicality. The proposed framework is not only applicable in the bus speed control problems, as it promises for addressing other complex multi-objective online optimal decision-making problems that are under various uncertainties and resolvable through data-driven nonlinear decision mapping.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142425852","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 Dial-a-Ride problem with meeting points: A problem formulation for shared demand–responsive transit","authors":"L.E. Cortenbach ,&nbsp;K. Gkiotsalitis ,&nbsp;E.C. van Berkum ,&nbsp;E. Walraven","doi":"10.1016/j.trc.2024.104869","DOIUrl":"10.1016/j.trc.2024.104869","url":null,"abstract":"<div><div>In this paper, a formulation for the Dial-a-Ride Problem with Meeting Points (DARPmp) is introduced. The problem consists of defining routes that satisfy trip requests between pick-up and drop-off points while complying with time window, ride time, vehicle load, and route duration constraints. A set of meeting points is defined, and passengers may be asked to use these meeting points as alternative pickup or drop-off points if this results in routes with lower costs. Incorporating meeting points into the DARP is achieved by formulating a mixed-integer linear program. Two preprocessing steps and three valid inequalities are introduced, which improve the computational performance when solving the DARPmp to global optimality. Two versions of the Tabu Search metaheuristic are proposed to approximate the optimal solution in large-scale networks due to the NP-hardness of DARPmp. Performing numerical experiments with benchmark instances, this study demonstrates the benefits of DARPmp compared to DARP in terms of reducing vehicle running costs.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359136","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":"Integrated optimization of lane allocation and adaptive signal control at isolated intersection under stochastic demand","authors":"Wei Huang ,&nbsp;Haofan Cheng ,&nbsp;Guoyu Huang ,&nbsp;Lubing Li","doi":"10.1016/j.trc.2024.104862","DOIUrl":"10.1016/j.trc.2024.104862","url":null,"abstract":"<div><div>Traffic arrival rate at intersections is usually fluctuating and random. This paper proposes an integrated optimization method for lane allocation and signal control under stochastic demand using the concept of level-of-service (LOS) reliability. First, we develop a two-stage stochastic programming model. In the first stage, a base lane allocation plan that takes account of the arrival uncertainty is derived. The objective is to minimize the expected average delay of the intersection. At the second stage, upon realizations of the random arrivals, the signal timing plan is adjusted in respond to the varying traffic conditions and address the occasional overflows, which may occur with the base lane allocation plan. In view of the intertwined two-stage decisions, we introduce the LOS reliability to decouple the model into two independent subproblems for solution efficiency. A decoupled model is then reconstructed with a LOS reliability constraint, which is associated with a fixed traffic arrival pattern. Then, a gradient descent algorithm is developed to solve the optimal LOS reliability level. Numerical experiments on both a test intersection and a real intersection are conducted and two benchmark methods are introduced for comparison. Experimental results demonstrate the effectiveness of the proposed LOS reliability-based optimization method in terms of reducing the expected average delay. With the benefits from a robust base lane allocation plan, the optimal signal timing plans can better adapt to random traffic demands.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":null,"pages":null},"PeriodicalIF":7.6,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142359135","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}