Multimodal Transportation最新文献

{"title":"Integrating artificial intelligence and blockchain for the resilience of sustainable multimodal transport: A systematic review","authors":"Badr Machkour ,&nbsp;Naoufal Rouky ,&nbsp;Ahmed Abriane","doi":"10.1016/j.multra.2026.100298","DOIUrl":"10.1016/j.multra.2026.100298","url":null,"abstract":"<div><div>This article investigates the integration of Artificial Intelligence and blockchain within multimodal transport, with the objective of assessing their combined contribution to resilience and sustainability, while proposing an integrative conceptual model. The methodology relies on a PRISMA 2020–compliant systematic literature review covering 2019–2024. Searches were performed in Scopus, Web of Science, Cairn, IEEE Xplore, and ScienceDirect for peer-reviewed journal articles, reviews, and conference papers published in English, French, or Spanish. Titles/abstracts and full texts were screened independently by two reviewers; data were extracted using a pilot-tested coding sheet; and study quality was appraised using JBI (quantitative), CASP (qualitative), and MMAT (mixed methods). Evidence was synthesized through a SWiM-oriented thematic narrative approach and systematically reported in three evidence categories (AI-only, Blockchain-only, and AI+Blockchain). The analysis focused specifically on multimodal transport, examining the interplay between AI, blockchain, resilience, and sustainability. The review includes eighty-three studies and indicates a marked growth in publications from 2019 onward, with geographical predominance in Europe, Asia, and North America, and a prevalence of exploratory qualitative approaches centered on case studies. Four thematic axes emerge: AI’s contributions to optimization, adaptive responses, and prediction; blockchain’s role in traceability and smart contracts; joint integration logics; and conceptual frameworks of resilience and sustainability. The proposed model establishes a connection between AI–blockchain capabilities and organizational mediators such as visibility, coordination, trust, and automation. These mediators exert a direct influence on resilience dimensions, which include anticipation, absorption, adaptation, and acceleration, as well as on sustainability dimensions that encompass economic, environmental, and social aspects. The entire framework is shaped by contextual factors, notably interoperability, governance, skills, and the institutional environment. This study exposes the limits of the current literature, characterized by a lack of quantitative evidence and limited transferability, and outlines a future research agenda based on mixed methods and validated instruments. It advocates gradual adoption trajectories while emphasizing the need for strengthened data governance and the development of interoperability standards.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 3","pages":"Article 100298"},"PeriodicalIF":0.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DrivNet: Adaptive driving behavior prediction for improving the safety of multimodal transportation in autonomous vehicle systems","authors":"J Robert Theivadas,&nbsp;Dr Suresh Ponnan","doi":"10.1016/j.multra.2025.100266","DOIUrl":"10.1016/j.multra.2025.100266","url":null,"abstract":"<div><div>The safety and efficiency of autonomous vehicles depend on their ability to react swiftly and appropriately to dynamic driving environments. This paper presents DrivNet (Adaptive Driving Network), an adaptive algorithm, DrivNet, aimed at enhancing the reaction time and decision-making capabilities of autonomous vehicles under varying traffic conditions and times of day. DrivNet integrates vehicle parameters and driving behavior with physiological features to improve forward movement within situational awareness environments. The algorithm is validated using a real-time driving dataset collected from expert drivers, capturing both vehicle and driving behavior data under heavy and normal traffic conditions, as well as day and night scenarios. Distinct driving behavior patterns are generated for three key situational awareness conditions: accelerating on clear roads, navigating through critical situations, and maintaining fuel efficiency. These patterns serve as the basis for adapting vehicle control decisions. To validate the effectiveness of these driving behavior patterns, a Recurrent Neural Network (RNN) architecture is employed, enabling the detection and classification of psychological features such as mental workload, stress, and fatigue. The proposed DrivNet algorithm offers valuable insights into distinguishing safe from unsafe driving modes, thereby supporting an intelligent control mechanism that enhances the overall safety of autonomous transportation systems. The results demonstrate the potential of DrivNet to improve autonomous vehicle performance, contributing to the future of safe and efficient self-driving technologies.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Path planning methods for autonomous vehicles at intersections: A review","authors":"Akhil Vinayak ,&nbsp;Muhammad Aizzat Zakaria ,&nbsp;Maryam Younus ,&nbsp;Mohamad Heerwan Peeie ,&nbsp;M. Izhar Ishak","doi":"10.1016/j.multra.2025.100286","DOIUrl":"10.1016/j.multra.2025.100286","url":null,"abstract":"<div><div>Autonomous vehicles (AVs) rapidly transform transportation, potentially enhancing safety, efficiency, and traffic flow. However, intersections remain a critical challenge for AVs due to the complex interactions with other vehicles, pedestrians, and dynamic traffic signals. Effective path planning at intersections is essential for AVs to navigate these environments safely and efficiently. Although numerous reviews have been published on path planning, limited attention has been devoted specifically to intersections. This review paper presents a comprehensive analysis of major path-planning methods used in Autonomous Vehicle (AV) navigation at intersections, including graph-based, sampling-based, curve-based, optimization-based, and machine learning–based approaches, while also examining emerging AI-driven path planners to better understand their capabilities. Each method is analysed in terms of its strengths, limitations, and applicability to real-world scenarios, focusing on the specific demands of intersection navigation. Furthermore, the review highlights key challenges such as handling dynamic multi-agent environments, managing interactions with human-driven vehicles, and balancing computational efficiency with path optimality and discusses potential solutions through adaptive, real-time algorithms, cooperative planning, and predictive modelling. Overall, this review aims to support the development of AV path planning, ultimately contributing to safer and more efficient autonomous systems in urban environments.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100286"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic workforce allocation for train-based crowd-shipping: Multi-period optimization incorporating transit discomfort costs","authors":"Qiuhong Huang,&nbsp;Shinya Hanaoka","doi":"10.1016/j.multra.2025.100269","DOIUrl":"10.1016/j.multra.2025.100269","url":null,"abstract":"<div><div>Last-mile parcel delivery faces challenges such as rising costs, labor shortages, and environmental concerns, especially in urban areas. This paper tackles these issues with a new rolling horizon framework that uses public train networks. We create a dynamic model that assigns delivery tasks to crowd-shippers (commuters) and part-time workers based on real-time data, considering train occupancy and commuter discomfort costs. Our results show that strategic delays in parcel delivery can offer economic benefits, with a delayed end-of-day approach saving 6.3% to 9.1% compared to the faster mixed-workforce approach we studied, which involved both part-timers and crowd-shippers. Additionally, we find that crowdsourced delivery is mainly cost-effective during off-peak transit hours, while part-time staff are more economical during peak times. These findings support adopting a flexible, hybrid workforce strategy that adjusts to changing transit conditions over time.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Catboost - SHapley Additive exPlanations (SHAP) car following model: Explaining model features in mixed traffic conditions","authors":"Tianyang Cui ,&nbsp;D. J. Wu ,&nbsp;Zejiang Wang","doi":"10.1016/j.multra.2025.100284","DOIUrl":"10.1016/j.multra.2025.100284","url":null,"abstract":"<div><div>Car-following behavior is critical for traffic management, road design, and the development of advanced driver assistance systems (ADAS) and autonomous vehicles (AVs). Traditional theory-based car-following models are widely used in traffic simulations but rely on simplified assumptions, limiting their ability to capture the complexity of real-world driving. In contrast, machine learning (ML) models can leverage large datasets to uncover complex driving behaviors. However, a major limitation of ML models is their lack of interpretability. Moreover, the rise of AVs has introduced mixed-traffic environments where AVs and human-driven vehicles share the road. Understanding different interaction scenarios—such as AVs following human drivers (AH), human drivers following AVs (HA), and human drivers following other humans (HH)—is essential for accurate modeling and safe AV deployment. To address these challenges, we propose a car-following modeling framework that integrates the CatBoost algorithm with SHapley Additive exPlanations (SHAP). CatBoost handles both numerical and categorical data, enabling the development of scenario-specific models (AH, HA, HH) and a unified car-following model incorporating scenario type as a feature. SHAP enhances interpretability by quantifying the contribution of each model feature, e.g., speed and inter-vehicle distance, across scenarios. We apply this framework to the Lyft Level-5 dataset to analyze feature importance and evaluate how scenario type moderates driving behavior. The insights derived from our analysis support the design of more adaptive AV control strategies and inform transportation policies for the safe integration of AVs into modern traffic systems.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100284"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “A Method for Determining Pickup and Delivery Locations of Intercity Customized Bus based on Passenger Demand and POIs” [Multimodal Transp. 2(2026) /100270]","authors":"Yueying Huo ,&nbsp;Huijuan Zhou ,&nbsp;Feng Hao ,&nbsp;Man Zhang ,&nbsp;Yachao Liu","doi":"10.1016/j.multra.2025.100283","DOIUrl":"10.1016/j.multra.2025.100283","url":null,"abstract":"","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100283"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145750072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time delay prediction for single-track intercity railways","authors":"Ratthaphong Meesit,&nbsp;Rathathammanoon Suwannasri","doi":"10.1016/j.multra.2025.100288","DOIUrl":"10.1016/j.multra.2025.100288","url":null,"abstract":"<div><div>Thailand’s intercity railway system is dominated by single-track operations, where limited passing loops, priority rules and aging infrastructure make services highly vulnerable to delay propagation. Existing analytical and simulation-based methods struggle to capture complex, non-linear interactions between operational factors, infrastructure constraints, and real-time information. This study introduces a real-time delay prediction framework specifically designed for single-track intercity railways, integrating both scheduled features and evolving en-route conditions. A cross-validated random forest model, enhanced with explainable SHAP analysis, is developed to generate real-time predictions of arrival delays at destination stations. The model demonstrates strong predictive accuracy (R² = 0.84; MAE = 2.53 min), with 87.5% of forecasts fall within 5 min of actual arrival delay and 94.9% fall within 10 min, confirming its suitability for operational decision support. SHAP interpretation reveals that real-time delay propagation variables dominate prediction outcomes, while service characteristics and infrastructure factors contribute secondary but meaningful effects. The proposed framework provides practical, real-time value for dispatchers, enabling proactive routing decisions, congestion mitigation, and improved passenger communication. This work offers one of the first explainable machine-learning delay prediction models tailored to single-track railway operations and presents insights applicable to similarly constrained rail systems worldwide.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards autonomy: A comprehensive technical and ethical review of automated vehicle safety","authors":"Morteza Soleimani,&nbsp;Ayse Aysu Sari","doi":"10.1016/j.multra.2025.100272","DOIUrl":"10.1016/j.multra.2025.100272","url":null,"abstract":"<div><div>This review paper explores the dynamic landscape of Automated Vehicles (AVs), with safety as a top priority. It discusses various dimensions, encompassing the pivotal role of safety in AV development, regulatory frameworks guiding their operation, intricacies of software architecture designed to uphold safety standards, and societal perceptions. The review scrutinizes diverse methodologies employed in ensuring vehicle safety, advocating for a holistic integration of safety and security. It calls for collaborative efforts to enhance the safety and security of AVs even more. Addressing current gaps in safety regulations, the paper outlines new safety approaches designed specifically for AVs. Furthermore, it discusses expert-recommended strategies for strong software engineering in AV systems, aimed at improving reliability. The review also considers the profound implications of AV integration in society and legal frameworks. By bringing together different viewpoints, it aims to build a clear understanding of AV safety while identifying new challenges in this fast-changing field of engineering.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating jaywalking behaviour in adaptive traffic signal control using a novel deep reinforcement learning approach","authors":"Lok Sang Chan,&nbsp;Xiaocai Zhang,&nbsp;Neema Nassir,&nbsp;Majid Sarvi","doi":"10.1016/j.multra.2025.100271","DOIUrl":"10.1016/j.multra.2025.100271","url":null,"abstract":"<div><div>This paper presents a deep reinforcement learning based adaptive traffic signal control framework that explicitly models jaywalking at urban intersections. We integrate a behaviourally grounded Jaywalking Decision model, which endogenises red light violations through waiting time and dynamic gap acceptance, with a Branching Double Deep Q-Network and a comprehensive hybrid action space that controls both phase selection and subphase timing. A multiobjective reward balances delay and jaywalking related safety risk, enabling the controller to respond to non-compliance as it emerges. The framework is evaluated in a multimodal microsimulation of a real intersection in Melbourne across four naturalistic demand scenarios and against both actuated control and established reinforcement learning baselines. Results show that the proposed approach reduces observed pedestrian delay and jaywalking relative to actuated control while achieving a more balanced safety and efficiency profile than single objective or alternative learning architectures. The analysis highlights context-dependent trade-offs that are relevant for policy, since the controller can adapt timing to mitigate non-compliance without assuming full pedestrian obedience. The contributions are threefold: a realistic jaywalking model linked to observable states, a high-granularity action space for multimodal control, and an integrated learning framework that jointly manages delay and safety risk. The proposed framework not only facilitates a more equitable traffic operation system but also offers the first scalable approach to managing risky behaviours in urban traffic environments.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating multimodal integration of metro and feeder bus services in Bengaluru using integrated analytical and perception based methods","authors":"Veeresh Kori ,&nbsp;Seelam Srikanth ,&nbsp;Subhashish Dey","doi":"10.1016/j.multra.2025.100287","DOIUrl":"10.1016/j.multra.2025.100287","url":null,"abstract":"<div><div>Rapid urban growth in Bengaluru has increased the need for well-integrated metro rail and feeder bus systems to improve accessibility and support sustainable urban mobility. This study aimed to assess the current level of integration and identify priority strategies for improvement using a combination of quantitative and perception-based methods. Data were collected through structured surveys at three major metro stations such as Baiyappanahalli, Majestic and Yeshwantpur covering commuter perceptions across 18 service indicators. The Sustainability Integration Index (SII) showed that Majestic station achieved the highest integration score (77.23%), followed by Baiyappanahalli (72.42%) and Yeshwantpur (65.04%). Policy analysis indicated that increasing bus frequency produced the greatest improvement in integration (+4.66%). Exploratory Factor Analysis (EFA) identified seven latent factors explaining 70.41% of the variance in perceptions, with frequency, reliability, and safety emerging as the most influential dimensions. Importance-Performance Analysis (IPA) revealed that while speed and reliability were perceived as strengths, waiting conditions had the largest negative performance gap (–0.814), highlighting a critical area for improvement. The Analytic Hierarchy Process (AHP) was applied to rank policy interventions, with experts assigning the highest priority to increasing bus frequency (49.06%) over implementing a single ticketing system and relocating bus stops. Overall, the findings confirm that enhancing service frequency, improving operational reliability, and simplifying fare systems are essential strategies to strengthen multimodal integration and encourage greater use of public transport in Bengaluru.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100287"},"PeriodicalIF":0.0,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145839186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}