Transportation Research Part C-Emerging Technologies最新文献

{"title":"A theory-informed multivariate causal framework for trustworthy short-term urban traffic forecasting","authors":"Panagiotis Fafoutellis,&nbsp;Eleni I. Vlahogianni","doi":"10.1016/j.trc.2024.104945","DOIUrl":"10.1016/j.trc.2024.104945","url":null,"abstract":"<div><div>Traffic forecasting using Deep Learning has been a remarkably active and innovative research field during the last decades. However, there are still several barriers to real-world, large-scale implementation of Deep Learning forecasting models, including their data requirements, limited explainability and low efficiency. In this paper, we propose a novel theory-driven framework that is based on a Granger causality-inspired feature selection method and a multitask LSTM to jointly predict two traffic variables. Traffic flow theory intuition is induced in the training process by an enhanced Traffic Flow Theory-Informed loss function (TFTI loss), which includes the divergence of the joint prediction of two traffic variables from the actual fundamental diagram of the corresponding location. The theory-informed, Granger causal, multitask LSTM is trained for one step ahead volume and speed forecasting using loop detector data coming from the extended Athens road network (Greece). Findings indicate that the models trained using the TFTI loss and a reduced input space, which includes only causal information, achieve a significantly improved performance, compared to the models using the classic Mean Squared Error loss function. Moreover, we introduce a dedicated trustworthiness evaluation framework that indicates that the proposed approach enhances the trustworthiness of the predictions, as well as the models’ transparency and resilience to noisy data.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104945"},"PeriodicalIF":7.6,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706435","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":"Mitigating stop-and-go traffic congestion with operator learning","authors":"Yihuai Zhang ,&nbsp;Ruiguo Zhong ,&nbsp;Huan Yu","doi":"10.1016/j.trc.2024.104928","DOIUrl":"10.1016/j.trc.2024.104928","url":null,"abstract":"<div><div>This paper presents a novel neural operator learning framework for designing boundary control to mitigate stop-and-go congestion on freeways. The freeway traffic dynamics are described by second-order coupled hyperbolic partial differential equations (PDEs), i.e. the Aw–Rascle–Zhang (ARZ) macroscopic traffic flow model. The proposed framework learns feedback boundary control strategies from the closed-loop PDE solution using backstepping controllers, which are widely employed for boundary stabilization of PDE systems. The PDE backstepping control design is time-consuming and requires intensive depth of expertise, since it involves constructing and solving backstepping control kernels. Existing machine learning methods for solving PDE control problems, such as physics-informed neural networks (PINNs) and reinforcement learning (RL), face the challenge of retraining when PDE system parameters and initial conditions change. To address these challenges, we present neural operator (NO) learning schemes for the ARZ traffic system that not only ensure closed-loop stability robust to parameter and initial condition variations but also accelerate boundary controller computation. The first scheme embeds NO-approximated control gain kernels within a analytical state feedback backstepping controller, while the second one directly learns a boundary control law from functional mapping between model parameters to closed-loop PDE solution. The stability guarantee of the NO-approximated control laws is obtained using Lyapunov analysis. We further propose the physics-informed neural operator (PINO) to reduce the reliance on extensive training data. The performance of the NO schemes is evaluated by simulated and real traffic data, compared with the benchmark backstepping controller, a Proportional Integral (PI) controller, and a PINN-based controller. The NO-approximated methods achieve a computational speedup of approximately 300 times with only a 1% error trade-off compared to the backstepping controller, while outperforming the other two controllers in both accuracy and computational efficiency. The robustness of the NO schemes is validated using real traffic data, and tested across various initial traffic conditions and demand scenarios. The results show that neural operators can significantly expedite and simplify the process of obtaining controllers for traffic PDE systems with great potential application for traffic management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104928"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706436","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":"The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions","authors":"Weiquan Wang ,&nbsp;Yossiri Adulyasak ,&nbsp;Jean-François Cordeau ,&nbsp;Guannan He","doi":"10.1016/j.trc.2024.104932","DOIUrl":"10.1016/j.trc.2024.104932","url":null,"abstract":"<div><div>This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a heterogeneous fleet of electric vehicles, utilizing multiple charging modes, and accounting for time-dependent waiting time functions at charging stations. The problem is modeled using a path-based mixed-integer linear programming formulation. To solve this problem, we present an algorithmic framework that alternates between two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function, which obtains local optimal solutions and generates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of routes from the pool, which is constructed based on reduced costs, into a feasible solution. We design HEVRP-NL benchmark instances based on the publicly available electric fleet size and mix vehicle routing problem instances, which are used to evaluate our methods. For small-scale HEVRP-NL instances, the proposed model can be employed in a general-purpose mixed integer programming solver to achieve optimal solutions or find good upper bounds. This exact approach serves as an evaluation of our heuristic algorithm’s ability to attain optimal solutions rapidly. Extensive computational results on large-scale HEVRP-NL instances illustrate the advantages of considering non-linear charging functions and show the impact of waiting time at the charging stations. Finally, we conduct experiments on 120 benchmark instances for the E-VRP-NL and 168 benchmark instances for the E-FSMFTW-PR, which are the special cases of our problem. The results indicate that our algorithm outperforms existing approaches from the literature and identifies 32 new best solutions for the E-VRP-NL and 33 new best solutions for the E-FSMFTW-PR, respectively.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104932"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706468","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":"Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach","authors":"Qingyun Tian ,&nbsp;Yun Hui Lin ,&nbsp;David Z.W. Wang ,&nbsp;Kaidi Yang","doi":"10.1016/j.trc.2024.104938","DOIUrl":"10.1016/j.trc.2024.104938","url":null,"abstract":"<div><div>Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104938"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706469","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":"Reducing the blocking effect in the airport slot allocation problem with seasonal flexibility","authors":"David Melder ,&nbsp;John H. Drake ,&nbsp;Sha Wang ,&nbsp;Edmund K. Burke","doi":"10.1016/j.trc.2024.104917","DOIUrl":"10.1016/j.trc.2024.104917","url":null,"abstract":"<div><div>Capacity limitations, combined with increased air-traffic, continue to drive the need for better resource management at airports. At congested airports, the allocation of resources for flights to take-off and land is governed through the use of slots. Slots are allocated twice a year according to the Worldwide Airport Slot Guidelines (WASG). A principle of WASG is to allocate slots to a request at the same time and day of the week over a given period, to maintain regularity in a schedule. Adhering to this principle can result in <em>blocking</em>, where requests are displaced to an alternative time period due to lack of available capacity. This paper addresses blocking by allowing a request to be allocated to slightly different times on different operating days. We present a novel integer linear programming (ILP) model and develop a two-stage framework, incorporating the Late Acceptance Hill Climbing metaheuristic, to increase flexibility across a complete scheduling season to mitigate the effect of blocking on three real-world congested airport instances. Results show that exact methods are not tractable for larger problems, as they are unable to find a solution in reasonable computational time, resulting in the need for heuristic methods to solve such problems in practice. Schedules created using the proposed framework are directly compared to an existing state-of-the-art model which aims to mitigate the effect of blocking through seasonal segmentation. The proposed approach is demonstrated to be able to reduce the effect of blocking, reduce the total number of displaced requests, and the maximum displacement within slot schedules when compared with non-flexible schedules.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104917"},"PeriodicalIF":7.6,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706437","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":"A link-based flow model with turn-level queue transmission and time-varying free-flow speed for urban road networks","authors":"Lei Wei ,&nbsp;S. Travis Waller ,&nbsp;Yu Mei ,&nbsp;Peng Chen ,&nbsp;Yunpeng Wang ,&nbsp;Meng Wang","doi":"10.1016/j.trc.2024.104937","DOIUrl":"10.1016/j.trc.2024.104937","url":null,"abstract":"<div><div>Macroscopic link-based flow models are efficient for simulating flow propagation in urban road networks. Existing link-based flow models described traffic states of a link with two state variables of link inflow and outflow and assumed homogeneous traffic states within a whole link. Consequently, the turn-level queue length change within the link cannot be captured accurately, resulting in underrepresented queue spillback. Moreover, a constant link free-flow speed was assumed to formulate models, restricting their applicability in modeling phenomena involving time-varying free-flow speed. This study proposed a new link-based flow model by introducing an additional state variable of link queue inflow and adapting the link outflow to be free-flow speed-dependent. In our model, the vehicle propagation within each link is described by the link inflow, queue inflow, and outflow, which depends on the link free-flow speed changes and signal control. A node model is further defined to capture the presence of potential queue spillback, which estimates the constrained flow propagation between adjacent road segments. Simulation experiments were conducted on a single intersection and on networks to verify the proposed model performance. Results demonstrate the predictive power of the proposed model in simulating traffic proppagations for networks with multiple turning movements and time-varying free-flow speed. Our model outperforms the baseline link-based flow model while preserving the computational tractability property of link-based flow models.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104937"},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706473","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":"Lane-free intersection control for connected automated vehicles prioritizing vulnerable road users","authors":"Patrick Malcolm,&nbsp;Klaus Bogenberger","doi":"10.1016/j.trc.2024.104918","DOIUrl":"10.1016/j.trc.2024.104918","url":null,"abstract":"<div><div>With the advent of connected autonomous vehicles (CAVs) come many possibilities for improving the capacity and level of service of roadways and intersections. Two such concepts are automated intersection management (AIM) and lane-free traffic. Previous research on lane-free AIM approaches is scant and often overlooks vulnerable road users (VRUs) such as pedestrians and cyclists. We present a novel first-come, first-served lane-free AIM algorithm specifically designed for urban environments and to give VRUs high priority. Simulation results show that our lane-free approach provides an excellent level of service for VRUs while also outperforming both a conventional traffic signal control design and a lane-based AIM approach with respect to CAV level of service, meaning that the deployment of lane-free AIMs could result in improved traffic flow for all road users while maintaining or reducing the amount of street space needing to be dedicated to vehicles. We also investigate the influence of the introduction of narrow vehicles and find that significant capacity improvements can be achieved with our lane-free approach even for relatively small market penetration rates.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104918"},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706472","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":"Safety-aware human-lead vehicle platooning by proactively reacting to uncertain human behaving","authors":"Jia Hu ,&nbsp;Shuhan Wang ,&nbsp;Yiming Zhang ,&nbsp;Haoran Wang ,&nbsp;Zhilong Liu ,&nbsp;Guangzhi Cao","doi":"10.1016/j.trc.2024.104941","DOIUrl":"10.1016/j.trc.2024.104941","url":null,"abstract":"<div><div>Human-Lead Cooperative Adaptive Cruise Control (HL-CACC) is regarded as a promising vehicle platooning technology in real-world implementation. By utilizing a Human-driven Vehicle (HV) as the platoon leader, HL-CACC reduces the cost and enhances the reliability of perception and decision-making. However, state-of-the-art HL-CACC technology still has a great limitation on driving safety due to the lack of considering the leading human driver’s uncertain behavior. In this study, a HL-CACC controller is designed based on Stochastic Model Predictive Control (SMPC). It is enabled to predict the driving intention of the leading Connected Human-Driven Vehicle (CHV). The proposed controller has the following features: (i) enhanced perceived safety in oscillating traffic; (ii) guaranteed safety against hard brakes; (iii) computational efficiency for real-time implementation. The proposed controller is evaluated on a PreScan&amp;Simulink simulation platform. Real vehicle trajectory data is collected for the calibration of the simulation. Results reveal that the proposed controller: (i) improves perceived safety by 19.17 % in oscillating traffic; (ii) enhances actual safety by 7.76 % against hard brakes; (iii) is confirmed with string stability. The computation time is approximately 3.2 ms when running on a laptop equipped with an Intel i5-13500H CPU. This indicates the proposed controller is ready for real-time implementation.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104941"},"PeriodicalIF":7.6,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706474","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":"Failed lane-changing detection and prediction using naturalistic vehicle trajectories","authors":"Yingying Xing ,&nbsp;Yutong Wu ,&nbsp;Hongwei Wang ,&nbsp;Ling Wang ,&nbsp;Lun Li ,&nbsp;Yichuan Peng","doi":"10.1016/j.trc.2024.104939","DOIUrl":"10.1016/j.trc.2024.104939","url":null,"abstract":"<div><div>Lane-changing maneuvers are crucial driving behaviors closely linked to various collisions, such as rear-end and sideswipe collisions. Precisely predicting lane-changing maneuvers can aid drivers in making informed decisions, thus enhancing driving safety. However, existing research primarily focuses on successful lane-changing maneuvers, neglecting failed ones. This study comprehensively investigates the detection and prediction of failed lane-changing maneuvers in discretionary scenarios using naturalistic vehicle trajectory data. The Mexican hat wavelet (MHW) is employed to accurately detect key time points in successful or failed lane-changing events, including the start, occurrence of failure, and end of the lane-changing maneuver. Subsequently, a failed lane-changing prediction model based on GA-XGBoost is developed to proactively perceive whether a lane-changing maneuver will succeed before it initiates. Moreover, the Shapley Additive exPlanations (SHAP) technique assesses feature importance and interaction effects between features in the prediction model. The results demonstrate that MHW effectively identifies critical time points in lane-changing events. The proposed GA-XGBoost model achieves an impressive 94.55% accuracy in predicting failed lane-changing maneuvers before the driver initiates the lane-changing maneuver. SHAP values reveal that failed lane-changing maneuvers often result from a high collision risk between the subject vehicle (SV) and the following vehicle in the target lane (FVT). Consequently, drivers should pay increased attention to approaching vehicles from behind. Moreover, accelerating during lane-changing helps maintain a safe distance from FVT, improving the likelihood of a successful lane-changing. Integrating the proposed model into advanced driver-assistance systems or autonomous driving systems has the potential to significantly enhance driving safety.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104939"},"PeriodicalIF":7.6,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706471","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":"Characterization of traffic dynamics in non-equilibrium ride-hailing mobility networks: A mesoscopic approach","authors":"Hai-Hong Xu ,&nbsp;Feixiong Liao ,&nbsp;Ren-Yong Guo","doi":"10.1016/j.trc.2024.104895","DOIUrl":"10.1016/j.trc.2024.104895","url":null,"abstract":"<div><div>Ride-hailing vehicles, private vehicles, and passengers are integral components of ride-hailing markets. Accurately characterizing the traffic dynamics driven by the spatio-temporal variations of these traffic flows is crucial for formulating operational strategies to realize sustainable ride-hailing services. From the mesoscopic perspective, we develop an integrated simulation model with high spatio-temporal resolutions. In a multi-class cell transmission model, we embed aggregate-ratio based decision-making mechanisms and bilateral matching between waiting passengers and idle vehicles in a large-scale non-equilibrium ride-hailing mobility network. At the individual level, the simulation model can capture the entire trip chain of passengers. Simultaneously, it can describe the cruising strategy of idle vehicles and the routing strategy of reserved/occupied/private vehicles. At the network level, it can depict the real-time space distribution of these multi-class traffic flows in the ride-hailing mobility network. We use empirical data, including road network density data, ride-hailing order, and trajectory data, to calibrate and verify the proposed simulation model. Sensitivity analyses based on simulation experiments indicate that the matching strategy, fleet size, and background traffic have diverse and significant effects on the operation performance of ride-hailing services.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"170 ","pages":"Article 104895"},"PeriodicalIF":7.6,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706470","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}