Transportation Research Part C-Emerging Technologies最新文献

{"title":"Harmonizing recurring patterns and non-recurring trends in traffic datasets for enhanced estimation of missing information","authors":"Shubham Sharma ,&nbsp;Richi Nayak ,&nbsp;Ashish Bhaskar","doi":"10.1016/j.trc.2025.105083","DOIUrl":"10.1016/j.trc.2025.105083","url":null,"abstract":"<div><div>Traffic datasets commonly comprise missing information due to sensor malfunctions, environmental conditions, security concerns, and technical/data quality issues. These challenges are inherent in real-world traffic data collection systems. Despite numerous imputation algorithms proposed in the literature, concerns persist about selecting a reliable algorithm that consistently performs well across diverse missing data scenarios. This is crucial for two main reasons. Firstly, real-world traffic datasets often exhibit a range of missing gaps with varying temporal durations, encompassing both short and long gaps within a single dataset. Secondly, in spatio-temporal traffic datasets, both recurring and non-recurring traffic conditions coexist. Since different data imputation principles reported in the literature suit either type of missing data (short or long gaps) and traffic conditions (recurring or non-recurring) better than others, algorithms often output sub-optimal estimates for network-wide datasets characterized by multiple types of missing gaps and traffic conditions.</div><div>To address the issue, this paper proposes a tensor decomposition algorithm named SINTD (Stochastic Informed Non-Negative Tensor Decomposition) and logically integrates it with a spline regression model in a novel data imputation framework called SPRINT (Spline-powered Informed Non-negative Tensor Decomposition). Where SINTD mines dominant patterns in the traffic datasets, effective in estimating missing gaps under recurring traffic conditions, integration of spline with tensor decomposition helps <em>a) capturing the time-localized trends unaccounted by tensor decomposition, aiding in approximating better the non-recurring component of the traffic states</em>, and <em>b) complementing SINTD for improved mining of recurring patterns in the subsequent iterations</em> of SPRINT. Although the two algorithms have distinct limitations when used separately, their harmonization allows us to effectively utilize their respective strengths and overcome individual limitations. This paper, through extensive experimentation on six traffic datasets and benchmarking against nine baseline algorithms, demonstrates the efficacy of SPRINT in consistently producing high-accuracy missing data estimates across five diverse missing data scenarios. These include a) experiments on datasets exhibiting a mix of short and long-duration missing gaps—mimicking the intricate missing data structure of real-world traffic datasets, and b) a Logan City (Australia) case study highlighting the imputation of missing data under potential non-recurring traffic conditions resulting from road incidents.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105083"},"PeriodicalIF":7.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680483","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 container drayage problem for electric trucks with charging resource constraints","authors":"Liyang Xiao ,&nbsp;Luxian Chen ,&nbsp;Peng Sun ,&nbsp;Gilbert Laporte ,&nbsp;Roberto Baldacci","doi":"10.1016/j.trc.2025.105100","DOIUrl":"10.1016/j.trc.2025.105100","url":null,"abstract":"<div><div>Amidst the ongoing green transformation in transportation, the electrification of trucks has emerged as a pivotal strategy to address climate-related issues. This paper introduces the container drayage problem for electric trucks, considering the charging resource constraints. Electric trucks are assigned to serve a series of origin–destination tasks between terminals and customers. Each truck can opt between battery swapping and two charging modes: normal and fast, each featuring a nonlinear charging process. The paper addresses the charging queueing problem arising from limitations in charging resources, presenting a novel mixed integer programming model tailored to container drayage challenges for electric trucks. To tackle this challenging problem, we propose an enhanced adaptive large neighborhood search algorithm that integrates an exact method. In the first stage, routes are generated based on customized procedures without considering queueing charging to minimize overall operation costs. The second stage is triggered by the call frequency and condition coefficient, utilizing CPLEX to optimize further queueing charging strategies. The algorithm is applied to instances based on real-world task data obtained from logistics companies. A series of comparative experiments are conducted to validate the efficacy and ascertain the parameter configuration of the algorithm. Furthermore, we examine the influence of charge levels and numbers of replaceable batteries on overall expenses and conduct a comprehensive analysis of the application influence of electric trucks compared to conventional fuel trucks in terms of cost and emissions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105100"},"PeriodicalIF":7.6,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680484","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":"Alert modalities in connected and smart work zones to enhance workers’ safety from traffic accidents using virtual reality (VR) experiments","authors":"Gajanand Sharma ,&nbsp;Sabyasachee Mishra","doi":"10.1016/j.trc.2025.105085","DOIUrl":"10.1016/j.trc.2025.105085","url":null,"abstract":"<div><div>Connected and Smart Work Zones (CSWZ) represent the future of work zones, utilizing technology to enhance safety, efficiency, and productivity among workers. However, research on leveraging technologies to communicate hazards and systematically evaluate different alert modalities to enhance safety remains limited. Assessing and understanding such hazards from the worker’s perspective is highly challenging, making virtual reality (VR) a promising solution that provides flexibility in assessing complex and influencing factors. In this paper, a set of 12 VR evaluation tasks for alert technology-assisted work zones are developed, encompassing various attributes by applying orthogonal design principles. Participants are exposed to these VR evaluation tasks, generating a dataset comprising 222 distinct scenarios. A proximity-based threshold is used to quantify a critical event based on worker and vehicle interaction orientations. The impact of different alert modalities (no alert, centralized, and personalized alert systems) on critical safety outcomes (reaction time and reaching safe region) using discrete choice modelling framework and kinematic behavior (temporal variation of evasive speed and relative distance) are comprehensively evaluated. The personalized alert consistently demonstrated its effectiveness by facilitating faster reactions and more effective evasive actions, significantly improving safety outcomes through a three-phase sequential motion pattern of acceleration, speed maintenance, and deceleration. This study provides a comprehensive assessment of the effectiveness of different alert systems, offering valuable insights for mitigating risks associated with intruding vehicles, especially in scenarios involving high vehicle speeds and worker involvement levels.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105085"},"PeriodicalIF":7.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680482","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 train rescheduling and speed management in a railway network: A meso-micro approach based on direct multiple shooting and alternating direction method of multipliers","authors":"Cong Xiu ,&nbsp;Jinyi Pan ,&nbsp;Andrea D’Ariano ,&nbsp;Shuguang Zhan ,&nbsp;Tao Feng ,&nbsp;Qiyuan Peng","doi":"10.1016/j.trc.2025.105076","DOIUrl":"10.1016/j.trc.2025.105076","url":null,"abstract":"<div><div>The performance of high-speed railway systems is often affected by unavoidable disruptions, which impact the reliability of train operations and passenger satisfaction. In contrast to most existing studies, which focus on either train rescheduling or speed management in separate or sequential frameworks, this paper addresses the integrated train rescheduling and speed management problem during severe disruptions, considering power supply constraints on a bidirectional railway network. Specifically, this problem incorporates detailed train speed control into the rescheduling process and involves train rerouting strategies and flexible stops to mitigate disruption effects. To characterize the integrated problem, we develop a three-dimensional space–time-state network, where each arc corresponds to a detailed driving strategy. We then propose a mixed-integer nonlinear programming (MINLP) model to simultaneously optimize the train schedule (i.e., train order, departure and arrival times, and routes) and train speed profiles, with the goal of reducing both total passenger delays and train energy consumption. To efficiently solve the integrated model, we propose a two-stage approach based on the direct multiple shooting method and the alternating direction method of multipliers (ADMM). This approach is implemented by combining offline and online computing to meet real-time requirements. The effectiveness and efficiency of the proposed model and algorithm are verified through numerous experiments using real-world data from Chinese high-speed railways. Experimental results demonstrate that our integrated approach improves energy efficiency by an average of 19.40% in complete section blockage scenarios and 7.69% in temporary speed restriction scenarios, compared to methods that do not incorporate speed management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105076"},"PeriodicalIF":7.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680627","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 novel AVI sensor location model for individual vehicle path reconstruction on urban road networks","authors":"Keshuang Tang ,&nbsp;Qiushan Zhang ,&nbsp;Yumin Cao ,&nbsp;Jiahao Liu ,&nbsp;Junping Xiang ,&nbsp;Hong Zhu","doi":"10.1016/j.trc.2025.105103","DOIUrl":"10.1016/j.trc.2025.105103","url":null,"abstract":"<div><div>Advances in traffic detection technology have enabled the transition from traditional counting detectors to vehicle identification detectors in many cities across the world. This shift has facilitated the collection of high-dimensional network traffic data through Automatic Vehicle Identification (AVI) systems, significantly advancing areas such as path flow estimation and individual vehicle path reconstruction. However, due to the high cost of fully deploying AVI sensors across urban road networks, reconstructing individual vehicle paths with limited sensor data remains a significant challenge. While existing research has focused primarily on Network Sensor Location Problem (NSLP) models for path flow estimation, their application to individual vehicle path reconstruction is less explored. Inspired by system entropy theory in information science, this study introduces a novel metric called Path Reconstruction Entropy (PRE) to quantify the uncertainty in vehicle path choices as observed by AVI sensors. Leveraging this metric, we propose a novel non-linear integer programming AVI-NSLP model that follows the Minimum PRE principle, optimizing the placement of AVI sensors for enhanced vehicle path reconstruction. The model incorporates a composite objective function that considers network benefits, including the uncertainty of vehicle path reconstruction for observed vehicles and coverage of observed vehicles, across two scenarios: with and without prior path flow information. Numerical studies conducted on two networks—a small-sized Sioux Falls network and a large-sized Xujiahui network in Shanghai—demonstrate that the proposed model consistently outperforms existing models in terms of PRE and other sensor deployment metrics on the smaller network and shows a modest advantage on the larger network. The experimental results demonstrate that the AVI sensor placement strategy developed by the proposed model can significantly improve the accuracy of individual vehicle path reconstruction on urban road networks.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105103"},"PeriodicalIF":7.6,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680626","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":"Multi-element probabilistic collocation solution for dynamic continuum pedestrian models with random inputs","authors":"Zepeng Liu ,&nbsp;S.C. Wong ,&nbsp;Liangze Yang ,&nbsp;Chi-Wang Shu ,&nbsp;Mengping Zhang","doi":"10.1016/j.trc.2025.105104","DOIUrl":"10.1016/j.trc.2025.105104","url":null,"abstract":"<div><div>This study focuses on dynamic continuum pedestrian flow models with random inputs, which can be represented by sets of partial differential equations with some modeling parameters being randomized. Under random conditions, the model outputs are no longer fixed and may differ appreciably from their respective average levels. Simulating the resulting distribution is important as it helps quantify the effects of uncertainties on traffic behaviors when evaluating walking facilities. Through two examples based on continuum models, the effect of random inputs on pedestrian flow propagation is qualitatively analyzed. Crowd evacuation is found to be effective in reducing the variation and risk produced by randomness, while congestion is observed to significantly increase the uncertainty within the system. For a general system without an explicitly known exact solution, an existing efficient solver — the multi-element probabilistic collocation method (ME-PCM) — is introduced to derive the solution distribution numerically. The ME-PCM is non-intrusive and flexible and has no limitations in terms of governing partial differential equations and the numerical schemes for solving them. The ME-PCM’s use of element-wise local orthogonal polynomials to represent the solution enables it to converge efficiently even if shocks occur during the modeling period. As a demonstration case, the well-known Hughes model is applied in a numerical example with a corridor and an obstacle. The demand at the inflow boundary is randomized to a lognormal distribution that represents day-to-day demand stochasticity. The results indicate that the ME-PCM’s solution converges more rapidly than those of the Monte Carlo and generalized polynomial chaos methods. Statistical information on pedestrian density is derived from the ME-PCM solution and can be used to identify the locations in walking facilities where the average pedestrian density is moderate but where exceptional congestion with a large variance can occur. This successful application shows the possibility of quantifying the uncertainty in pedestrian flow models using the ME-PCM. The proposed approach can also be applied to models with other similar random inputs, given that a well-established algorithm for deterministic cases is available.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105104"},"PeriodicalIF":7.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643006","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":"Sparse Gaussian process-based strategies for two-layer model predictive control in autonomous vehicle drifting","authors":"Cheng Hu ,&nbsp;Yangyang Xie ,&nbsp;Lei Xie ,&nbsp;Michele Magno","doi":"10.1016/j.trc.2025.105065","DOIUrl":"10.1016/j.trc.2025.105065","url":null,"abstract":"<div><div>Vehicle safety is paramount in autonomous driving, particularly when managing vehicles at extreme side-slip angles—a challenge often overlooked by conventional controllers. Recent studies have focused on vehicle drift control under such extreme conditions. However, tracking complex trajectories while drifting is challenging, especially in the presence of a model mismatch. This paper proposes a two-layer model predictive controller based on sparse variational Gaussian processes. The first layer is responsible for computing the optimal drift equilibrium points, while the second layer is tasked with tracking these points. A variational free energy-based Gaussian process is utilized to compensate for errors in the upper-layer drift equilibrium point calculations and mismatches in the lower-layer controller model. Moreover, the vehicle’s state is determined to be either in transit drift or deep drift based on whether the slip angle and steering angle have reached critical values. Gaussian models are established for each state to enhance prediction accuracy. The effectiveness of the controller is demonstrated through joint simulations on MATLAB and CarSim platforms. First, the proposed two-layer model predictive controller was compared with three state-of-the-art drift controllers, demonstrating at least a 48.64% reduction in average lateral error when tracking trajectories with varying curvature. Second, when combined with sparse Gaussian processes, the controller’s learning ability was validated in scenarios with a 5% to 20% friction coefficient mismatch. Specifically, in the scenario with a 20% friction coefficient mismatch, its average lateral error was reduced by 95.09% after model error learning. Additionally, the controller was compared with both Fully Independent Training Conditional (FITC) GP-based MPC and Full GP-based MPC controllers, demonstrating better trajectory tracking capability and model error learning ability.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105065"},"PeriodicalIF":7.6,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143680625","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 multi-layer multiclass cell transmission model for modeling heterogeneous traffic flow","authors":"Jialin Liu ,&nbsp;Rui Jiang ,&nbsp;Yang Liu ,&nbsp;Shiteng Zheng ,&nbsp;Bin Jia ,&nbsp;Hao Ji","doi":"10.1016/j.trc.2025.105099","DOIUrl":"10.1016/j.trc.2025.105099","url":null,"abstract":"<div><div>Modeling heterogeneous traffic with different free-flow speeds poses theoretical challenges for the existing multiclass cell transmission models (MCTMs) with identical cells. Specifically, the existing MCTMs face two challenges due to the numerical diffusion: delaying flow and rushing flow. These challenges arise because slow vehicles cannot travel through a cell within a time interval under free-flow conditions, leading to inaccurate estimates of cell occupancy and travel time. To address these challenges, we propose a Multi-layer Multiclass Cell Transmission Model (MMCTM) with multi-size cells. Firstly, a road is divided into a multi-layer multi-size cell network based on different free-flow speeds of multiclass vehicles. Class-specific vehicles move within a class-specific cell network, while other class vehicles are equivalently projected into class-specific cells using density mutual projection formulas. Secondly, we formulate the flow propagation rules for multiclass vehicles based on the original rules of CTM and conversion coefficients of different classes. We prove the capability of the MMCTM by showing that it can avoid unrealistic situations where the densities in the cells are negative or exceed the maximum density. Finally, numerical experiments demonstrate that our proposed model can effectively address the two challenges and reproduce essential phenomena of mixed traffic flow, such as the moving bottleneck effect of slow vehicles, shockwave propagation, overtaking, FIFO, and oscillatory waves. In particular, the MMCTM can reproduce the drop and resurge of the discharge rate of fast vehicles. Furthermore, we calibrate and validate the MMCTM using NGSIM I-80 dataset and the I-24 MOTION dataset. The results indicate that (1) our proposed model improves the estimation accuracy of travel time and cell occupancy for multiclass vehicles; (2) the MMCTM outperforms the general MCTM with identical cells (GMCTM) under traffic congestion conditions.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105099"},"PeriodicalIF":7.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143643005","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":"Integrating battery-related decisions into truck-drone tandem delivery problem with limited battery resources","authors":"Zhongshan Liu ,&nbsp;Bin Yu ,&nbsp;Tingting Chen ,&nbsp;Li Zhang","doi":"10.1016/j.trc.2025.105082","DOIUrl":"10.1016/j.trc.2025.105082","url":null,"abstract":"<div><div>The truck-drone tandem delivery mode provides a promising application in last-mile package delivery but is limited by the duration of drones. The battery swap strategy is a widely adopted approach to extend the cruising ranges of drones, ensuring that depleted batteries can be swapped for fully charged ones in a matter of minutes. However, most existing studies assume that there are sufficient batteries available at the depot, which is impractical as storing a large number of batteries is expensive. To bridge this gap, this paper considers the truck-drone tandem delivery problem with a battery swap strategy, under the condition of a limited number of batteries. To address the challenges posed by the practical limitation of the number of batteries, we propose a joint optimization problem integrating two types of interdependent decisions, i.e., battery-related decisions and route-related decisions. The battery-related decisions identify which batteries to be installed on drones and establish optimal battery charging schedules at the depot. And the route-related decisions determine the truck-drone tandem delivery routes. The studied joint optimization problem is formulated as a mixed integer linear programming model, and this model is integrated into a well-designed adaptive large neighborhood search algorithm to determine the two types of decisions. Specifically, on the basis of traditional operators, we design a series of depot-related operators tailored to the feature of route-related decisions. Furthermore, regarding the features of battery swapping and charging schedules, we introduce a novel battery operator to determine optimal battery-related decisions. The numerical experiments show that introducing the battery-related decisions can bring flexible battery schedules when the total number of batteries is limited. The effects of battery capacity, charging rate, charging cost, drone speed, and the number of batteries and drones are analyzed to provide practical suggestions for companies.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105082"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631945","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":"Scaling laws of dynamic high-capacity ride-sharing","authors":"Wang Chen ,&nbsp;Linchuan Yang ,&nbsp;Xiqun Chen ,&nbsp;Jintao Ke","doi":"10.1016/j.trc.2025.105064","DOIUrl":"10.1016/j.trc.2025.105064","url":null,"abstract":"<div><div>This study discovers a few scaling laws that can effectively capture the key performance of dynamic high-capacity ride-sharing through extensive experiments based on real-world mobility data from ten cities. These scaling laws are concise and contain only one dimensionless variable named system load that reflects the relative magnitude of demand versus supply. The scaling laws can accurately measure how key performance metrics such as passenger service rate and vehicle occupancy rate change with the system load. The scaling laws strongly agree with experimental results, with the values of <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> exceeding 0.95 across all scenarios. In addition, the scaling laws can accurately reproduce experimental results of dynamic high-capacity ride-sharing involving different road networks, supply–demand patterns, vehicle capacities, and matching algorithms, indicating these scaling laws could be general and applied to other cities. These scaling laws provide a reference for transportation network companies and governments to efficiently manage dynamic ride-sharing services. For example, according to these scaling laws, when the demand is relatively high, e.g., system load equals 3, ride-sharing services with a capacity of 2 passengers can only accommodate 50% of demand. In comparison, high-capacity ride-sharing services with a capacity of 4 passengers can satisfy 72% of demand. The findings provide valuable insights into the expected performance of ride-sharing, informing decisions about how to operate a fleet to improve transportation efficiency.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"174 ","pages":"Article 105064"},"PeriodicalIF":7.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631946","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}