Communications in Transportation Research最新文献

{"title":"Bidirectional Q-learning for recycling path planning of used appliances under strong and weak constraints","authors":"Yang Qi ,&nbsp;Jinxin Cao ,&nbsp;Baijing Wu","doi":"10.1016/j.commtr.2024.100153","DOIUrl":"10.1016/j.commtr.2024.100153","url":null,"abstract":"<div><div>With the continuous innovation in household appliance technology and the improvement of living standards, the production of discarded household appliances has rapidly increased, making their recycling increasingly significant. Traditional path planning algorithms encounter difficulties in balancing efficiency and constraints in addressing the multi-objective, multi-constraint challenge posed by discarded household appliance recycling routes. To tackle this issue, this study introduces a bi-directional <em>Q</em>-learning-based path planning algorithm. By developing a bi-directional <em>Q</em>-learning mechanism and enhancing the initialization method of <em>Q</em>-learning, the algorithm aims to achieve efficient and effective optimization of discarded household appliance recycling routes. It implements bidirectional updates of the state-action value function from both the starting point and the target point. Additionally, a hierarchical reinforcement learning strategy and guided rewards are introduced to minimize blind exploration and expedite convergence. By decomposing complex recycling tasks into multiple sub-tasks and seeking paths with superior performance at each sub-task level, the initial exploratory blindness is reduced. To validate the efficacy of the proposed algorithm, gridbased modeling of real-world environments is utilized. Comparative experiments reveal significant improvements in iteration counts and path lengths, thereby validating its practical applicability in path planning for recycling initiatives.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100153"},"PeriodicalIF":12.5,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization model of autonomous minibus considering passenger arrival reliability and travel risk","authors":"Zhicheng Jin ,&nbsp;Haoyang Mao ,&nbsp;Di Chen ,&nbsp;Hao Li ,&nbsp;Huizhao Tu ,&nbsp;Ying Yang ,&nbsp;Maria Attard","doi":"10.1016/j.commtr.2024.100152","DOIUrl":"10.1016/j.commtr.2024.100152","url":null,"abstract":"<div><div>The advancement of self-driving technologies facilitates the emergence of autonomous minibuses (ABs) in public transportation, which could provide flexible, reliable, and safe mobility services. This study develops an AB routing and scheduling model considering each passenger’s arrival reliability and travel risk. Firstly, to guarantee each passenger’s arrival on time, the arrival reliability (a predetermined threshold of on-time arrival probability of <em>α</em> ​= ​0.9) is included in the constraints. Secondly, three objectives, including system costs, greenhouse gas (GHG) emissions, and travel risk, are optimized in the model. To assess the travel risk of ABs, an enhanced method based on kernel density estimation (KDE) is proposed. Thirdly, an advanced multi-objective adaptive large neighborhood search algorithm (MOALNS) is designed to find the Pareto optimal set. Finally, experiments are conducted in Shanghai to validate model performance. Results show that it can decrease GHG emissions (−2.12%) and risk (−9.47%), while only increasing costs by 2.02%. Furthermore, the proposed arrival reliability constraint can improve an average of 14.70% of passengers to meet their arrival reliability requirement (<em>α</em> ​= ​0.9).</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100152"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling the clustering strength of connected autonomous vehicles and its impact on mixed traffic capacity","authors":"Peilin Zhao,&nbsp;Yiik Diew Wong,&nbsp;Feng Zhu","doi":"10.1016/j.commtr.2024.100151","DOIUrl":"10.1016/j.commtr.2024.100151","url":null,"abstract":"<div><div>In a mixed traffic environment consisting of connected autonomous vehicles (CAVs) and human-driven vehicles (HVs), platooning intensity serves as a critical metric, quantifying the strength of CAV clustering, with inherent ramifications for traffic flow efficiency. While various definitions of platooning intensity are found in existing literature, many fall short in effectively capturing the strength of CAV clustering in mixed traffic. To address the gap, this study models the vehicle stream of mixed traffic on the single-lane road as a binary sequence and proposes the autocorrelation-based platooning intensity (API) metric. Through theoretical analysis, the proposed API is shown to be an effective indicator for measuring the clustering strength of CAVs. The probability distribution of API through fisher transformation is also derived. This study then moves on to formulate the capacity of mixed traffic, taking into account CAV penetration rate, API, and stochastic headway. Numerical verification of the estimated mixed traffic capacity reveals a negligible error (less than 1%) compared to simulated capacity. Marginal analysis confirms the validity of related propositions, notably that stronger CAV clustering does not always improve traffic capacity due to headway stochasticity. The outcome of this study contributes to the understanding of CAV platooning intensity and offers valuable insights for advancing mixed traffic modeling and management.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100151"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated operator and user-based rebalancing and recharging in dockless shared e-micromobility systems","authors":"Elnaz Emami,&nbsp;Mohsen Ramezani","doi":"10.1016/j.commtr.2024.100155","DOIUrl":"10.1016/j.commtr.2024.100155","url":null,"abstract":"<div><div>This study proposes a rebalancing method for a dockless e-micromobility sharing system, employing both trucks and users. Platform-owned trucks relocate and recharge e-micromobility vehicles using battery swapping technology. In addition, some users intending to rent an e-micromobility vehicle are offered incentives to end their trips in defined locations to assist with rebalancing. The integrated formulation of rebalancing and recharging accounts for each e-micromobility vehicle's characteristics, such as location and charge level. The problem is formulated as a mixed binary problem, which minimizes operational costs and total unmet demand while maximizing the system's profit. To solve the optimization problem, a Branch and Bound method is employed. Rebalancing decisions and routing plans of each truck are obtained by solving the optimization problem. We simulate an on-demand shared e-micromobility system with the proposed integrated rebalancing method and conduct numerical studies. The results indicate that the proposed method enhances system performance and user travel times.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100155"},"PeriodicalIF":12.5,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142722281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling the determinants of battery electric vehicle performance: A systematic review and meta-analysis","authors":"Fangjie Liu ,&nbsp;Muhammad Shafique ,&nbsp;Xiaowei Luo","doi":"10.1016/j.commtr.2024.100148","DOIUrl":"10.1016/j.commtr.2024.100148","url":null,"abstract":"<div><div>The transition toward battery electric vehicles (BEVs) is a critical element in the global shift toward sustainable transportation. This meta-analysis delves into the multifaceted factors influencing BEV performance, including environmental, technological, behavioral, and political-economic determinants. The purpose of this review is to systematically organize and assess how these factors impact BEV efficiency and sustainability across various operational scenarios, such as driving, charging, and decommissioning. By examining a wide range of literature, this study constructs a comprehensive framework that categorizes the primary components and performance metrics, revealing complex relationships and potential causal connections. The findings highlight that although technological advancements and regulatory frameworks are the predominant drivers of BEV performance, environmental conditions and user behaviors also play significant roles. The key emerging topics identified suggest further research avenues, particularly in optimizing battery technology and expanding policy support. Additionally, the analysis provides new and systematic insights compared with previous reviews, offering a clearer understanding of the determinants, their impacts, and the interactions between them. These insights are crucial for developing a transparent evaluation system for future research and policy formulation. This comprehensive synthesis not only aids in understanding the current landscape but also in directing future scholarly and practical endeavors in electric vehicle research.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100148"},"PeriodicalIF":12.5,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142707036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning and extreme value theory for estimating crash frequency-by-severity via AI-based video analytics","authors":"Fizza Hussain ,&nbsp;Yuefeng Li ,&nbsp;Md Mazharul Haque","doi":"10.1016/j.commtr.2024.100147","DOIUrl":"10.1016/j.commtr.2024.100147","url":null,"abstract":"<div><div>Traffic conflict techniques rely heavily on the proper identification of conflict extremes, which directly affects the prediction performance of extreme value models. Two sampling techniques, namely, block maxima and peak over threshold, form the core of these models. Several studies have demonstrated the inefficacy of extreme value models based on these sampling approaches, as their crash estimates are too imprecise, hindering their widespread practical use. Recently, anomaly detection techniques for sampling conflict extremes have been used, but their application has been limited to estimating crash frequency without considering the crash severity aspect. To address this research gap, this study proposes a hybrid model of machine learning and extreme value theory within a bivariate framework of traffic conflict measures to estimate crash frequency by severity level. In particular, modified time-to-collision (MTTC) and expected post-collision change in velocity (Delta-<em>V</em> or Δ<em>V</em>) have been proposed in the hybrid modeling framework to estimate rear-end crash frequency by severity level. Rear-end conflicts were identified through artificial intelligence-based video analytics for three four-legged signalized intersections in Brisbane, Australia, using four days of data. Non-stationary bivariate hybrid generalized extreme value models with different anomaly detection/sampling techniques (isolation forest and minimum covariance determinant) were developed. The non-stationarity of traffic conflict extremes was handled by parameterizing model parameters, including location, scale, and both location and scale parameters simultaneously. The results indicate that the bivariate hybrid models can estimate severe and non-severe crashes when compared with historical crash records, thereby demonstrating the viability of the proposed approach. A comparative analysis of two anomaly techniques reveals that the isolation forest model marginally outperforms the minimum covariance determinant model. Overall, the modeling framework presented in this study advances conflict-based safety assessment, where the severity dimension can be captured via bivariate hybrid models.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100147"},"PeriodicalIF":12.5,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bridging the gap: Toward a holistic understanding of shared micromobility fleet development dynamics","authors":"Shahnaz N. Fuady ,&nbsp;Paul C. Pfaffenbichler ,&nbsp;Yusak O. Susilo","doi":"10.1016/j.commtr.2024.100149","DOIUrl":"10.1016/j.commtr.2024.100149","url":null,"abstract":"<div><div>Rapid urbanization and shifting demographics worldwide necessitate innovative urban transportation solutions. Shared micromobility systems, such as bicycle- and scooter-sharing programs, have emerged as promising alternatives to traditional urban mobility challenges. This study delves into the complexity of shared micromobility fleet development, focusing on the interplay between fleet size, user demand, regulatory frameworks, economic viability, and public engagement. By employing a system dynamics modeling approach that incorporates causal loop diagrams (CLDs) and stock and flow models (SFMs), we explore various policy scenarios to optimize micromobility management systems. Our findings reveal that financial incentives, such as fee reductions and government subsidies, significantly increase user adoption and profitability, whereas increased operational fees necessitate a delicate balance between cost management and service attractiveness. Sensitivity and uncertainty analyses highlight critical parameters for effective fleet management. This research offers actionable insights for policymakers and operators, promoting sustainable urban transport systems.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100149"},"PeriodicalIF":12.5,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modular flying vehicles: Scheduling modes, social benefits, and challenges","authors":"Di Lv,&nbsp;Yuhao Wang,&nbsp;Liang Wang,&nbsp;Yang Fei,&nbsp;Kai Wang,&nbsp;Xiaobo Qu","doi":"10.1016/j.commtr.2024.100144","DOIUrl":"10.1016/j.commtr.2024.100144","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100144"},"PeriodicalIF":12.5,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142661683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing multimodal large language models for traffic knowledge graph generation and decision-making","authors":"Senyun Kuang,&nbsp;Yang Liu,&nbsp;Xin Wang,&nbsp;Xinhua Wu,&nbsp;Yintao Wei","doi":"10.1016/j.commtr.2024.100146","DOIUrl":"10.1016/j.commtr.2024.100146","url":null,"abstract":"","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100146"},"PeriodicalIF":12.5,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Controllability test for nonlinear datatic systems","authors":"Yujie Yang ,&nbsp;Letian Tao ,&nbsp;Likun Wang,&nbsp;Shengbo Eben Li","doi":"10.1016/j.commtr.2024.100143","DOIUrl":"10.1016/j.commtr.2024.100143","url":null,"abstract":"<div><div>Controllability is a fundamental property of control systems, serving as the prerequisite for controller design. While controllability test is well established in modelic (i.e., model-driven) control systems, extending it to datatic (i.e., data-driven) control systems is still a challenging task due to the absence of system models. In this study, we propose a general controllability test method for nonlinear systems with datatic description, where the system behaviors are merely described by data. In this situation, the state transition information of a dynamic system is available only at a limited number of data points, leaving the behaviors beyond these points unknown. Different from traditional exact controllability, we introduce a new concept called <em>ϵ</em>-controllability, which extends the definition from point-to-point form to point-to-region form. Accordingly, our focus shifts to checking whether the system state can be steered to a closed state ball centered on the target state, rather than exactly at that target state. Given a known state transition sample, the Lipschitz continuity assumption restricts the one-step transition of all the points in a state ball to a small neighborhood of the subsequent state. This property is referred to as one-step controllability backpropagation, i.e., if the states within this neighborhood are <em>ϵ</em>-controllable, those within the state ball are also <em>ϵ</em>-controllable. On its basis, we propose a tree search algorithm called maximum expansion of controllable subset (MECS) to identify controllable states in the dataset. Starting with a specific target state, our algorithm can iteratively propagate controllability from a known state ball to a new one. This iterative process gradually enlarges the <em>ϵ</em>-controllable subset by incorporating new controllable balls until all <em>ϵ</em>-controllable states are searched. Besides, a simplified version of MECS is proposed by solving a special shortest path problem, called Floyd expansion with radius fixed (FERF). FERF maintains a fixed radius of all controllable balls based on a mutual controllability assumption of neighboring states. The effectiveness of our method is validated in three datatic control systems whose dynamic behaviors are described by sampled data.</div></div>","PeriodicalId":100292,"journal":{"name":"Communications in Transportation Research","volume":"4 ","pages":"Article 100143"},"PeriodicalIF":12.5,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142578752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}