Transportation Research Part C-Emerging Technologies最新文献

{"title":"Developing a personalised end-to-end optimisation algorithm for smart parking systems","authors":"Jingshuo Qiu ,&nbsp;Yuxiang Feng ,&nbsp;Simon Dale ,&nbsp;Mohammed Quddus ,&nbsp;Mireille Elhajj ,&nbsp;Washington Yotto Ochieng","doi":"10.1016/j.trc.2026.105548","DOIUrl":"10.1016/j.trc.2026.105548","url":null,"abstract":"<div><div>Rapid economic growth and technological advancement have fostered increased car ownership around the world. Despite the critical role of vehicles in modern life, parking-related challenges persist, leading to negative externalities such as delays, fuel consumption, and environmental impacts. Although Smart Parking Systems (SPSs) have been developed to address these parking issues, they typically only provide available parking spaces with direct walking access from a car park to a destination, thereby restricting the range of parking options available to drivers. By expanding the parking allocation framework to consider the entire journey from origin to destination rather than solely to a car park, a wider range of available parking options can be explored, which may yield more optimal solutions for reducing negative externalities. In addition, SPSs usually assume uniform parking preferences among drivers, which may not reflect the diverse preferences observed in real-world scenarios. To accommodate varying individual preferences, a personalised parking solution is preferred to optimise parking allocation with a particular focus on alleviating negative externalities. Therefore, this paper develops a personalised end-to-end parking allocation algorithm using Multi-Agent Reinforcement Learning (MARL) to broaden the search for available parking spaces and provide intermodal travel solutions to help drivers reach their destinations from car parks. Real-world data from Nottingham, UK, are used to calibrate the simulation model which is employed to evaluate the learning performance of MARL algorithms, including Deep Q-Network (DQN) and Advantage Actor-Critic (A2C). Additionally, as two commonly-used methods for multi-attribute decision making problems, Grey Relational Analysis (GRA) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are compared in this paper for their effectiveness in modelling personalised parking profiles. The results demonstrate the superiority of the A2C-GRA algorithm, with a significant average total reward improvement of 19% over benchmark models at 95% confidence interval. On average, the travel time and distance optimised by the A2C-GRA algorithm are 39 min and 6.6 km, respectively, representing reductions of 5.22% and 3.62% compared to the benchmark models.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105548"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135135","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":"CurricuVLM: Towards safe autonomous driving via personalized safety-critical curriculum learning with vision-language models","authors":"Zihao Sheng ,&nbsp;Zilin Huang ,&nbsp;Yansong Qu ,&nbsp;Yue Leng ,&nbsp;Sruthi Bhavanam ,&nbsp;Sikai Chen","doi":"10.1016/j.trc.2026.105549","DOIUrl":"10.1016/j.trc.2026.105549","url":null,"abstract":"<div><div>Ensuring safety in autonomous driving systems remains a critical challenge, particularly in handling rare but potentially catastrophic safety-critical scenarios. While existing research has explored generating safety-critical scenarios for autonomous vehicle (AV) testing, there is limited work on effectively incorporating these scenarios into policy learning to enhance safety. Furthermore, developing training curricula that adapt to an AV’s evolving behavioral patterns and performance bottlenecks remains largely unexplored. To address these challenges, we propose CurricuVLM, a novel framework that leverages Vision-Language Models (VLMs) to enable personalized curriculum learning for autonomous driving agents. Our approach uniquely exploits VLMs’ multimodal understanding capabilities to analyze agent behavior, identify performance weaknesses, and dynamically generate tailored training scenarios for curriculum adaptation. Through a comprehensive analysis of unsafe driving situations with narrative descriptions, CurricuVLM performs in-depth reasoning to evaluate the AV’s capabilities and identify critical behavioral patterns. The framework then synthesizes customized training scenarios targeting these identified limitations, enabling effective and personalized curriculum learning. Extensive experiments on the Waymo Open Motion Dataset show that CurricuVLM outperforms state-of-the-art baselines across both regular and safety-critical scenarios, achieving superior performance in terms of navigation success, driving efficiency, and safety metrics. Further analysis reveals that CurricuVLM serves as a general approach that can be integrated with various RL algorithms to enhance autonomous driving systems. The code and demo video will be available at: <span><span>https://zihaosheng.github.io/CurricuVLM/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105549"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152650","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":"An agent-based approach for travel mode choice equilibrium problem in MaaS considering heterogeneous users","authors":"Yifan Zhang ,&nbsp;Meng Xu ,&nbsp;Wenxiang Wu","doi":"10.1016/j.trc.2026.105534","DOIUrl":"10.1016/j.trc.2026.105534","url":null,"abstract":"<div><div>Accompanying the rising applications of Mobility as a Service (MaaS), it is crucial for understanding user mode choices. This paper defines the travel mode choice equilibrium of heterogeneous users in MaaS and derives its existence conditions. It further identifies cutoff value of time (VOT) thresholds that induce mode shifts, mainly influenced by the prices of different modes and the transfer time difference. The relationship between cutoff VOTs and boundary VOTs is analyzed to clarify their roles in determining the mode choice equilibrium. A heterogeneous agent-based mode choice (HAMC) approach is proposed to approximate mode choice equilibrium of heterogeneous users in MaaS, handling the inherent non-convexity. A case study using real Beijing MaaS data demonstrates that the proposed approach converges to the mode choice equilibrium and reveals that price interventions and transfer time-related interventions primarily influence high-VOT car-owning users and low- to medium-VOT non-car-owning users, respectively.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105534"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146135146","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":"Evaluating passenger flow variations across an urban rail network induced by new lines using spatio-temporal transfer learning method","authors":"Yutao Ye ,&nbsp;Pengling Wang ,&nbsp;Jianrui Miao ,&nbsp;Pieter Vansteenwegen","doi":"10.1016/j.trc.2026.105550","DOIUrl":"10.1016/j.trc.2026.105550","url":null,"abstract":"<div><div>In many major cities worldwide, the expansion and construction of new rail transit lines are actively pursued to alleviate operational pressures on existing networks. Evaluating the impacts of new lines on existing ones, particularly through network-wide Origin–Destination (OD) passenger flow forecasting that accounts for newly constructed lines, is crucial for efficient line planning and network operations. However, OD flow prediction faces significant challenges due to the absence of historical passenger flow data for new lines and the changes they introduce to overall passenger volumes and distribution. This study presents a transfer learning-based hypergraph approach to represent OD flow data, tackling computational challenges in megacities with hundreds of urban rail stations. In this model, OD pairs serve as vertices, while their spatiotemporal similarities are captured by hyperedges. Spatial features are extracted from geographical data, while temporal features of existing OD pairs are learned from historical passenger flows. For new OD pairs lacking historical data, transfer learning infers temporal features from spatially similar pairs. These spatiotemporal similarities are then used to construct the hypergraph. Then, the hypergraph convolution is applied to extract high-order spatiotemporal features from the proposed hypergraph model, enabling the prediction of OD flow changes in the expanded urban rail transit network. A logit-based passenger assignment model is adopted to estimate how passengers redistribute across the network in response to the introduction of new lines. The effectiveness and accuracy of the proposed method are validated using real-world data from the Shanghai urban rail network. Results demonstrate that the modeling framework enables detailed analysis of station- and section-level passenger flow changes across the entire network. The integrated prediction–assignment framework presented in this study offers a novel and practical tool to support data-driven planning and operational decision-making in large urban rail systems.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105550"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146160930","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":"Real-time identification of cooperative perception necessity in road traffic scenarios","authors":"Chengyuan Ma ,&nbsp;Hangyu Li ,&nbsp;Keke Long ,&nbsp;Hang Zhou ,&nbsp;Zhaohui Liang ,&nbsp;Pei Li ,&nbsp;Hongkai Yu ,&nbsp;Xiaopeng Li","doi":"10.1016/j.trc.2026.105547","DOIUrl":"10.1016/j.trc.2026.105547","url":null,"abstract":"<div><div>Cooperative perception (CP) has shown great potential in enhancing traffic safety with Vehicle-to-Everything (V2X) communications. However, its substantial communication burden makes resource-efficient CP crucial, especially when a single vehicle’s intelligence with adequate perception is sufficient to handle most traffic scenarios. Therefore, to reduce the resource consumption of CP, it is essential to identify the traffic conditions under which CP should be applied. This paper addresses this issue by identifying the necessity of CP among road users, and evaluating whether their sensory information is adequate for ensuring traffic safety. We propose a practical framework to assess CP necessity by leveraging bird’s-eye view data from roadside cameras. The framework begins with video-based object localization and tracking to identify the position and movement of each road user. Next, we use a stochastic motion prediction model to analyze the collision risks between pairs of road users. Simultaneously, pairwise perception analysis is used to assess the probability of one road user perceiving another, determining if a road user falls within a blind spot. Road users with both collision risk and potential perception blind spots are identified as requiring CP. Field tests are conducted using real-world scenarios at two complex intersections in Madison, WI, which include a diverse range of road users, such as various vehicles and vulnerable pedestrians and cyclists. The results demonstrate that the proposed framework can effectively identify the safety-challenging scenarios that require CP in complex traffic environments. With only 0.1% of situations in our field test requiring CP, implementing the proposed framework can save a significant amount of communication bandwidth and computational costs while ensuring the same level of safety. Our code and data will be made available upon the acceptance of this paper.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105547"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146102561","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 foundational individual mobility prediction model based on open-source large language models","authors":"Zhenlin Qin ,&nbsp;Leizhen Wang ,&nbsp;Yancheng Ling ,&nbsp;Francisco Camara Pereira ,&nbsp;Zhenliang Ma","doi":"10.1016/j.trc.2026.105562","DOIUrl":"10.1016/j.trc.2026.105562","url":null,"abstract":"<div><div>Individual mobility prediction plays a key role in urban transport, enabling personalized service recommendations and effective travel management. It is widely modeled by data-driven methods such as machine learning, deep learning, as well as classical econometric methods to capture key features of mobility patterns. However, such methods are hindered in promoting further transferability and robustness due to limited capacity to learn mobility patterns from different data sources, predict in out-of-distribution settings (a.k.a “zero-shot”). To address this challenge, this paper introduces MoBLLM, a foundational model for individual mobility prediction that aims to learn a shared and transferable representation of mobility behavior across heterogeneous data sources. Based on a lightweight open-source large language model (LLM), MoBLLM employs Parameter-Efficient Fine-Tuning (PEFT) techniques to create a cost-effective training pipeline, avoiding the need for large-scale GPU clusters while maintaining strong performance. We conduct extensive experiments on six real-world mobility datasets to evaluate its accuracy, robustness, and transferability across varying temporal scales (years), spatial contexts (cities), and situational conditions (e.g., disruptions and interventions). MoBLLM achieves the best F1 score and accuracy across all datasets compared with state-of-the-art deep learning models and shows better transferability and cost efficiency than commercial LLMs. Further experiments reveal its robustness under network changes, policy interventions, special events, and incidents. These results indicate that MoBLLM provides a generalizable modeling foundation for individual mobility behavior, enabling more reliable and adaptive personalized information services for transportation management.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105562"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146152644","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":"Street design and driving behavior: Evidence from a large-scale study in Milan and Amsterdam","authors":"Giacomo Orsi ,&nbsp;Titus Venverloo ,&nbsp;Andrea La Grotteria ,&nbsp;Umberto Fugiglando ,&nbsp;Fábio Duarte ,&nbsp;Paolo Santi ,&nbsp;Carlo Ratti","doi":"10.1016/j.trc.2026.105554","DOIUrl":"10.1016/j.trc.2026.105554","url":null,"abstract":"<div><div>In recent years, cities have increasingly reduced speed limits from 50 km/h to 30 km/h to enhance road safety, reduce noise pollution, and promote sustainable modes of transportation, such as walking and cycling. However, achieving compliance with these new limits remains a key challenge for urban planners, as adherence is crucial to achieving the intended benefits.</div><div>This study investigates drivers’ compliance with the 30 km/h speed limit (ca. 20 mph) in Milan and examines how street characteristics influence driving behavior. Using 51 million telemetry data points from vehicles, we analyze speed profiles across the city and employ an observational study to estimate the causal effect of speed limit reductions. Our findings indicate that the average vehicle speed in streets with a limit of 30 km/h is only 2.29 km/h lower than in those with a limit of 50 km/h with similar street layout. This suggests that the mere introduction of lower speed limits is not sufficient to reduce driving speeds effectively, highlighting the need to understand how street design can improve speed limit adherence. To comprehend this relationship, we apply computer vision-based semantic segmentation models to Google Street View images of Milan’s streets. A large-scale analysis reveals that narrower streets and densely built environments are associated with lower speeds, whereas roads with greater visibility and larger sky views encourage faster driving.</div><div>To evaluate the influence of the local context on the urban design features that influence drivers’ compliance, we apply the developed methodological framework to the city of Amsterdam, which, similar to Milan, is a historic European city not originally developed for cars. The results of the analyses largely confirm the findings obtained in Milan, which demonstrates the broad applicability of the road design guidelines for driver speed compliance identified in this paper.</div><div>Finally, we develop a machine learning model to predict driving speeds based on street characteristics. We showcase the model’s predictive power by estimating the compliance with speed limits in Milan if the city were to adopt a 30 km/h speed limit city-wide. The tool provides actionable insights for urban planners, supporting the design of interventions to improve speed limit compliance, optimize traffic management strategies, and create safer urban environments.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"185 ","pages":"Article 105554"},"PeriodicalIF":7.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146153275","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":"NeuralMOVES: A lightweight and microscopic vehicle emission estimation model based on reverse engineering and surrogate learning","authors":"Edgar Ramirez-Sanchez ,&nbsp;Catherine Tang ,&nbsp;Yaosheng Xu ,&nbsp;Nrithya Renganathan ,&nbsp;Vindula Jayawardana ,&nbsp;Zhengbing He ,&nbsp;Cathy Wu","doi":"10.1016/j.trc.2026.105530","DOIUrl":"10.1016/j.trc.2026.105530","url":null,"abstract":"<div><div>The transportation sector accounts for nearly one-quarter of global greenhouse gas (GHG) emissions. Emerging technologies-such as eco-driving, connected vehicle control, and others-offer significant potential for emission reduction; however, officially validated, yet optimization-ready emission models are essential for guiding their design, deployment, and evaluation. The U.S. EPA’ Motor Vehicle Emission Simulator (MOVES) is the validated regulatory and industry standard for vehicle emissions in the U.S. Yet, its complexity, macroscopic focus, and high computational demands make it unsuitable and incompatible with control and optimization applications, and burdensome even for traditional analyses. Furthermore, its reliance on location-specific inputs limits its applicability beyond the U.S. As a result, researchers often resort to alternative models, leading to emission estimates that are neither comparable nor officially validated. To address this gap, we introduce <strong>NeuralMOVES</strong>, an open-source, lightweight surrogate model for CO₂ emissions with near-MOVES fidelity. NeuralMOVES transforms MOVES from a multi-software system requiring specialized expertise and hours of computation into a 2.4 MB Python package that runs in milliseconds and integrates seamlessly into optimization frameworks. Developed by reverse-engineering MOVES through over 200 million batch queries to generate a comprehensive microscopic emission dataset (MOVES_RE, 9.89 GB), NeuralMOVES uses machine learning to compress this dataset by over 4,000× while enabling continuous, differentiable, and real-time emission estimation. An extensive validation shows a mean absolute percentage error of 6.013% across over two million test scenarios, each representing a complete driving trajectory evaluated under specific environmental and vehicle conditions. We demonstrate NeuralMOVES in a dynamic eco-driving case study, showing that it integrates seamlessly into optimization pipelines, leads to different trajectories than alternative models, and captures parameter sensitivities that alternative models overlook. NeuralMOVES enables regulatory-grade, microscopic emission modeling for emerging transportation technologies worldwide and is available at: <span><span>https://github.com/edgar-rs/neuralMOVES</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105530"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078148","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":"U-Aerodrome: Data-driven and risk-bounded airspace reconfiguration for safe integration of urban air mobility at aerodrome","authors":"Xinting Hu ,&nbsp;Bizhao Pang ,&nbsp;Sameer Alam ,&nbsp;Mir Feroskhan","doi":"10.1016/j.trc.2025.105506","DOIUrl":"10.1016/j.trc.2025.105506","url":null,"abstract":"<div><div>Urban Air Mobility (UAM) offers promising solutions for alleviating urban congestion and enabling seamless air transportation. However, its integration near aerodromes is limited by static no-fly zones and traditional airspace management practices. Existing boundary-setting methods often depend on oversimplified assumptions about trajectory distributions or apply rigid spatial constraints, which can lead to safety risks and inefficient airspace utilization. To address these limitations, this study introduces U-Aerodrome, a data-driven and risk-bounded airspace reconfiguration framework designed to support the safe and flexible integration of UAM operations near controlled aerodromes. The approach employs procedure-based trajectory classification and equal-altitude sampling to ensure equitable and non-biased representation of flight patterns. It further incorporates probabilistic boundary estimation that accommodates both Gaussian and non-Gaussian distributions, as well as a time-dependent boundary update mechanism responsive to dynamic traffic demand. The framework is validated using real-world data collected from Singapore Changi Airport. Results show that U-Aerodrome reduces missed detections and conservative volume compared to a purely Gaussian baseline, yielding 30.95 % average safety improvement and 15.25 % higher availability. The time-dependent mechanism further reduces unnecessary restrictions by an additional 20.02 % on average compared with baselines assuming static boundaries. The framework supports flexible and statistically grounded planning for safe UAM access near aerodromes.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105506"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927844","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":"CONTINA: Confidence interval for traffic demand prediction with coverage guarantee","authors":"Chao Yang ,&nbsp;Xiannan Huang ,&nbsp;Shuhan Qiu ,&nbsp;Yan Cheng","doi":"10.1016/j.trc.2025.105502","DOIUrl":"10.1016/j.trc.2025.105502","url":null,"abstract":"<div><div>Accurate short-term traffic demand prediction is critical for the operation of traffic systems. Besides point estimation, the confidence interval of the prediction is also of great importance. Many models for traffic operations, such as shared bike rebalancing and taxi dispatching, take into account the uncertainty of future demand and require confidence intervals as the input. However, existing methods for confidence interval modeling rely on strict assumptions, such as unchanging traffic patterns and correct model specifications, to guarantee enough coverage. Therefore, the confidence intervals provided could be invalid, especially in a changing traffic environment. To fill this gap, we propose an efficient method, CONTINA (Conformal Traffic Intervals with Adaptation) to provide interval predictions that can adapt to external changes. By collecting the errors of interval during deployment, the method can adjust the interval in the next step by widening it if the errors are too large or shortening it otherwise. Furthermore, we theoretically prove that the coverage of the confidence intervals provided by our method converges to the target coverage level. Experiments across four real-world datasets and prediction models demonstrate that the proposed method can provide valid confidence intervals with shorter lengths. Our method can help traffic management personnel develop a more reasonable and robust operation plan in practice. And we release the code, model and dataset in <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":54417,"journal":{"name":"Transportation Research Part C-Emerging Technologies","volume":"184 ","pages":"Article 105502"},"PeriodicalIF":7.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886225","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}