Transportation Research Part C-Emerging Technologies最新文献

{"title":"Optimal decentralized signal control for platooning in connected vehicle networks","authors":"The Anh Hoang ,&nbsp;Neil Walton ,&nbsp;Hai L. Vu","doi":"10.1016/j.trc.2024.104832","DOIUrl":"10.1016/j.trc.2024.104832","url":null,"abstract":"<div><p>In the last decade, pressure-based schemes such as Back Pressure and Max Weight algorithms have been widely researched and applied for traffic signal control due to their simplicity and proven throughput maximization. In such algorithms, the next chosen signal phase at an intersection in a road network is the one with the highest measured weight, representing the pressure of traffic movements at the intersection, determined based on a single characteristic of the traffic flow or vehicles’ state at that intersection. This paper develops a new optimal Max Weight control mechanism to enhance the network throughput and reduce vehicle delays in a network using a concept of platooning enabled by Connected Vehicles (CVs). To this end, we propose a new proven optimal Max Weight control scheme where the weight consists of several features including the platoon delay, as well as the speed and position of vehicles within the platoon. To the best of our knowledge, this work is the first to propose a platoon pressure-based concept considering multiple configurable attributes in formulating the pressure. Furthermore, we provide a rigorous stability proof that ensures the throughput optimality of the proposed control scheme. In addition, we also develop a machine learning procedure in this paper to optimize the weighting parameter of each attribute contributing to the total pressure enabling its seamless deployment in practice. A number of simulation results demonstrate the feasibility of the learning procedure and show that our Max Weight platoon pressure-based scheme outperforms the state-of-the-art and well-known existing pressure-based algorithms.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104832"},"PeriodicalIF":7.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X2400353X/pdfft?md5=5c26070b6fc2ef68ee46359e5617ddf4&pid=1-s2.0-S0968090X2400353X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142122206","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":"Decentralized human-like control strategy of mixed-flow multi-vehicle interactions at uncontrolled intersections: A game-theoretic approach","authors":"Dian Jing,&nbsp;Enjian Yao,&nbsp;Rongsheng Chen","doi":"10.1016/j.trc.2024.104835","DOIUrl":"10.1016/j.trc.2024.104835","url":null,"abstract":"<div><p>A critical challenge that future autonomous driving systems face is improving the ability to cope with complex real-world interaction scenarios such as uncontrolled intersections. In the near future, a mixed traffic flow of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) will coexist in transport networks, which motivates us to explore the interaction between HDVs and CAVs to improve traffic efficiency and safety. To help CAVs better interact with HDVs and adapt to the mixed-flow environment, we propose a human-like decentralized control strategy for CAVs. First, a game-theoretic framework is proposed to model multi-vehicle interactions (including HDV-CAV, CAV-CAV interactions) in the mixed-flow environment. The existence of solutions is proven to ensure the feasibility of the proposed game-theoretic model. Next, a driving style recognition algorithm is embedded into the proposed model to help CAVs understand and predict human drivers’ actions. The proposed model is calibrated via a real-world dataset and used to simulate traffic in several testing scenarios. Real-world vehicle trajectories are used to verify the accuracy of generated vehicle trajectories in simulations. Experimental results indicate that 1) CAVs can take more reasonable actions to determine whether to yield while ensuring safety when competing for the right of way with HDVs using the proposed method compared with conservative driving strategies, 2) a higher penetration rate of CAVs can significantly enhance travel efficiency and lower collision risk at uncontrolled intersections.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104835"},"PeriodicalIF":7.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117612","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":"On the impact of co-optimizing station locations, trip assignment, and charging schedules for electric buses","authors":"Rito Brata Nath ,&nbsp;Tarun Rambha ,&nbsp;Maximilian Schiffer","doi":"10.1016/j.trc.2024.104839","DOIUrl":"10.1016/j.trc.2024.104839","url":null,"abstract":"<div><p>As many public transportation systems around the world transition to electric buses, the planning and operation of fleets can be improved via tailored decision-support tools. In this work, we study the impact of jointly locating charging facilities, assigning electric buses to trips, and determining when and where to charge the buses. We propose a mixed integer linear program that co-optimizes planning and operational decisions jointly and an iterated local search heuristic to solve large-scale instances. Herein, we use a concurrent scheduler algorithm to generate an initial feasible solution, which serves as a starting point for our iterated local search algorithm. In the sequential case, we first optimize trip assignments and charging locations. Charging schedules are then determined after fixing the optimal decisions from the first level. The joint model, on the other hand, integrates charge scheduling within the local search procedure. The solution quality of the joint and sequential iterated local search models are compared for multiple real-world bus transit networks. Our results demonstrate that joint models can help further improve operating costs by 14.1% and lower total costs by about 4.1% on average compared with sequential models. In addition, energy consumption costs and contracted power capacity costs have been reduced significantly due to our integrated planning approach.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104839"},"PeriodicalIF":7.6,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096209","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":"Modelling reservation strategies for managing peak-hour stranding on an oversaturated metro line","authors":"Jie Tang ,&nbsp;Jianjun Wu ,&nbsp;Ping Zhang ,&nbsp;Yufeng Zhang ,&nbsp;Jinxin Cao","doi":"10.1016/j.trc.2024.104819","DOIUrl":"10.1016/j.trc.2024.104819","url":null,"abstract":"<div><p>Metro is the main travel model for urban commuters in many metropolises around the world. During peak hours, large numbers of passengers pour into metro stations for rail services, but some are unable to board the trains in time and left stranded on the platform or even queuing outside the stations. The trip reservation (TR) strategy, where passengers preplan their trips and reserve their entry time to the stations. This paper develops an entry reservation strategy (ERS) to optimize the commuter flow during peak hours, and construct a multi-objective passenger flow joint optimization model based on many-to-many passenger demand to minimize the total trip cost of passengers at reservation station and the number of stranded passengers at intermediate stations. The passenger flow optimization problem is formulated as a mixed-integer non-linear programming (MINLP) model. We design an iterative sequential search algorithm combined with the GUROBI solver to obtain the parameters of the optimal ERS and the passenger flow distribution in the metro system after disaggregated reformulation of the complex constraints of the model. We also demonstrate the accuracy and effectiveness of the proposed method with two experiments – an illustrative example and a large-scale case study of Beijing Metro. The results of Beijing Metro experiment show that the joint optimization model with entry reservation strategy (JO-ERS) reduces the number of stranded passengers by 88.46 % compared with the original passenger flow from the AFC.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104819"},"PeriodicalIF":7.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087799","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":"Distributional equity impacts of automated vehicles: A disaggregated approach","authors":"Zhiwei Chen ,&nbsp;Amy L. Stuart ,&nbsp;Yujie Guo ,&nbsp;Yu Zhang ,&nbsp;Xiaopeng Li","doi":"10.1016/j.trc.2024.104828","DOIUrl":"10.1016/j.trc.2024.104828","url":null,"abstract":"<div><p>Better understanding of the equity impacts of automated vehicles (AV) is needed to design equitable deployment plans for AV technology. The goal of this study was to develop and demonstrate a modeling framework to support distributional equity assessments of AV systems prior to their wide adoption. This modeling framework takes a disaggregated approach that integrates agent-based simulation, diverse transportation outcomes, and equity analyses. The framework uses individual-level data to capture detailed disparities in transportation outcomes, applies a state-of-the-art multi-agent traffic simulation model (MATSim) to simulate privately-owned and shared AVs simultaneously, and considers distributional equity from multiple perspectives. To test and demonstrate the framework, a case study was conducted for the Tampa Bay region. Five scenarios were considered with different AV market shares and integration strategies based on scenario planning by the U.S. Federal Highway Administration. Results reveal that high AV penetration rates were required for substantial reductions in inequality. The introduction of AVs led to a more even distribution of accessibility, but slightly more uneven distribution of traditional mobility and affordability. Impacts of disparities in outcomes for disadvantaged groups were mixed. These results suggest that AVs will likely perpetuate existing inequity in the transportation system as long as its fundamental structure remains the same as today. Results highlight the importance of planning and design strategies that directly address the distributional impacts to ensure that AV technology is deployed equitably.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104828"},"PeriodicalIF":7.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087800","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":"Bayesian inference for estimating the penetration rate of probe vehicles from interdetection times only","authors":"Rafael Delpiano ,&nbsp;Juan Carlos Herrera","doi":"10.1016/j.trc.2024.104836","DOIUrl":"10.1016/j.trc.2024.104836","url":null,"abstract":"<div><p>Data provided by floating vehicles have the same limitation as that provided by an imperfect stationary detector that undercounts vehicles: they provide data from an unknown proportion of the total flow. Since the knowledge of this proportion is critical for some applications, its value is usually estimated by complementing with data provided by other monitoring infrastructure. However, this type of infrastructure is not the norm in many cities, particularly in developing countries. This paper extends a previously proposed method to estimate this penetration solely from a series of inter-detection times. The improved method is based on Bayesian inference and supports a series of passage times coming from multilane infrastructure, albeit still requiring an uninterrupted traffic regime. The method was applied to actual RFID interdetection times from a four-lane weaving urban freeway segment in Santiago, Chile. The resulting estimated rates are consistent with those reported by the freeway operator, showing the method’s capabilities. Thus, the method is instrumental in successfully taking full advantage of technology already in place in many cities without needing new investments in monitoring infrastructure.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104836"},"PeriodicalIF":7.6,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084070","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 homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios","authors":"Yuning Wang,&nbsp;Jinhao Li,&nbsp;Tianqi Ke,&nbsp;Zehong Ke,&nbsp;Junkai Jiang,&nbsp;Shaobing Xu,&nbsp;Jianqiang Wang","doi":"10.1016/j.trc.2024.104833","DOIUrl":"10.1016/j.trc.2024.104833","url":null,"abstract":"<div><p>Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104833"},"PeriodicalIF":7.6,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142076832","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":"Carsharing adoption dynamics considering service type and area expansions with insights from a Montreal case study","authors":"Cen Zhang ,&nbsp;Jan-Dirk Schmöcker ,&nbsp;Martin Trépanier","doi":"10.1016/j.trc.2024.104810","DOIUrl":"10.1016/j.trc.2024.104810","url":null,"abstract":"<div><p>Carsharing operators (CSOs) are adapting their service over time to meet changing demands and grow their market share. Service areas are enlarged and, in some cities, “dual-mode settings” evolve, incorporating free-floating carsharing (FFcs) as a new service alongside existing station-based carsharing (SBcs). This paper proposes a methodology to discuss adoption dynamics in such a context, specifically focusing on the impact of existing services and service extensions on the adoption of the new service. We propose a framework, comprising of two parts: a potential market assessment and an adoption model. The potential market assessment focuses on establishing the relationships between the local population, carsharing memberships and Points of Interest (POIs) within the given service area. The adoption model then describes the likelihood of consumers adopting the FFcs service. By combining these two models, the effects of service extensions can be assessed. We evaluate the framework using a nearly six year dataset from Communauto, Montreal. The first 35 months of data are set as training data, while the subsequent 33 months are used for validation of predictive performance. Results demonstrate that the proposed model accurately predicts adoption dynamics. Prior experience of SBcs and initial information spread are found to be key parameters for demand prediction determining early adoption peaks and, due to follower effects, also impact long-term demand. Additionally, we quantify the importance of covering residential areas and points of interests in the service area, highlighting the synergy effects of service area expansions.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104810"},"PeriodicalIF":7.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003310/pdfft?md5=4508e8606298777af82cee5b12ebbb1a&pid=1-s2.0-S0968090X24003310-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049278","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 hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach","authors":"Shan Jiang ,&nbsp;Xiangdong Chen ,&nbsp;Xi Lin ,&nbsp;Meng Li","doi":"10.1016/j.trc.2024.104824","DOIUrl":"10.1016/j.trc.2024.104824","url":null,"abstract":"<div><p>Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104824"},"PeriodicalIF":7.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049280","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":"Ensemble methods for route choice","authors":"Haotian Wang,&nbsp;Emily Moylan,&nbsp;David Levinson","doi":"10.1016/j.trc.2024.104803","DOIUrl":"10.1016/j.trc.2024.104803","url":null,"abstract":"<div><p>Understanding travellers’ route preferences allows for the calculation of traffic flow on network segments and helps in assessing facility requirements, costs, and the impact of network modifications. Most research employs logit-based choice methods to model the route choices of individuals, but machine learning models are gaining increasing interest. However, all of these methods typically rely on a single ‘best’ model for predictions, which may be sensitive to measurement errors in the training data. Moreover, predictions from discarded models might still provide insights into route choices. The ensemble approach combines outcomes from multiple models using various pattern recognition methods, assumptions, and/or data sets to deliver improved predictions. When configured correctly, ensemble models offer greater prediction accuracy and account for uncertainties. To examine the advantages of ensemble techniques, a data set from the I-35 W Bridge Collapse study in 2008, and another from the 2011 Travel Behavior Inventory (TBI), both in Minneapolis–St. Paul (The Twin Cities) are used to train a set of route choice models and combine them with ensemble techniques. The analysis considered travellers’ socio-demographics and trip attributes. The trained models are applied to two datasets, the Longitudinal Employer-Household Dynamics (LEHD) commute trips and TBI morning peak trips, for validation. Predictions are also compared with the loop detector records on freeway links. Traditional Multinomial Logit and Path-Size Logit models, along with machine learning methods such as Decision Tree, Random Forest, Extra Tree, AdaBoost, Support Vector Machine, and Neural Network, serve as the foundation for this study. Ensemble rules are tested in both case studies, including hard voting, soft voting, ranked choice voting, and stacking. Based on the results, heterogeneous ensembles using soft voting outperform the base models and other ensemble rules on testing sets.</p></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"167 ","pages":"Article 104803"},"PeriodicalIF":7.6,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0968090X24003243/pdfft?md5=38db06abbc21d7a29bd4a8c9436738ca&pid=1-s2.0-S0968090X24003243-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142049279","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}