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":"2026-02-06","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":"A foundational dwell time model for regional railways","authors":"Kenneth Ng,&nbsp;Nirajan Shiwakoti,&nbsp;Peter Stasinopoulos","doi":"10.1016/j.multra.2026.100290","DOIUrl":"10.1016/j.multra.2026.100290","url":null,"abstract":"<div><div>This study presents a novel foundational Regional Dwell Time (RDT) Model tailored for regional railway networks, addressing the gap of existing dwell time models specifically designed for this type of railway. Using video-based observations from Victoria, Australia, empirical data were collected and analysed to develop a statistical regression model integrating passenger flow dynamics and operational constants such as door operation and train dispatch procedures. The proposed RDT Model was calibrated and validated against 398 regional train services at two stations. The model was compared with established statistical dwell time models in the literature. The RDT Model demonstrated superior predictive accuracy, with significantly lower error metrics (RMSE, MAPE, MAE) compared to alternative models. The study emphasizes the significance of operational time in dwell time estimations and highlights opportunities for reducing dwell time through improved operational procedures. Findings suggest the RDT Model's adaptability to various other regional railways, provided operational constants are recalibrated. This model serves as a proof-of-concept and a novel framework for dwell time modelling in peri‑urban regional railways, with broader applicability contingent on future multi-corridor validation. Future research on regional railways should aim to build on this type of model and explore real-time data integration and machine learning techniques to enhance predictive capabilities and network efficiency.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100290"},"PeriodicalIF":0.0,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187565","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":"2025-12-25","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":"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":"2025-12-11","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}

{"title":"A demand response to line planning","authors":"Prasetyaning Diah Rizky Lestari ,&nbsp;Ronghui Liu ,&nbsp;Richard Batley","doi":"10.1016/j.multra.2025.100285","DOIUrl":"10.1016/j.multra.2025.100285","url":null,"abstract":"<div><div>As a linking factor between supply and demand, line planning is a crucial aspect of long-term railway planning. It aims to create a viable line system that optimises specific objectives by determining stop patterns and service frequencies to meet the predicted passenger demand. In the proposed model, railway operators can choose from different line plan designs based on line planning outcomes. The objective functions include economic objectives, focusing on either cost or capacity. We introduce a model to examine how demand responds to different line plans. This study focuses on stop patterns and service frequencies as the primary factors of line plan-related service quality. The model is intended as a tool for policymaking and evaluating attractiveness through metrics including generalised journey time (GJT) and/or generalised cost. The changes in demand for different line plan designs are simulated using the proposed metrics until equilibrium between the assignment and demand model is reached. This proposed model represents a significant advancement, providing reliable line plans for decision-making. It applies a similar approach commonly used for timetable-related service quality, enhancing railway planning effectiveness. To validate the model, we present a case study of a small-scale future network for Indonesia’s Jakarta-Surabaya semi-high-speed train.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100285"},"PeriodicalIF":0.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799758","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":"2025-12-08","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":"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":"2025-12-07","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":"Corrigendum to Autonomous fleet management system in smart ports: Practical design and analytical considerations [Multimodal Transp. 4 (2025) /100211]","authors":"Rui Chen ,&nbsp;Jing Zhang ,&nbsp;Hua Wang","doi":"10.1016/j.multra.2025.100267","DOIUrl":"10.1016/j.multra.2025.100267","url":null,"abstract":"","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"4 4","pages":"Article 100267"},"PeriodicalIF":0.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790179","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":"2025-11-28","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":"A bi-objective slot allocation model under airport capacity and resource utilization","authors":"Priyadarshan Loshan,&nbsp;Loshaka Perera","doi":"10.1016/j.multra.2025.100273","DOIUrl":"10.1016/j.multra.2025.100273","url":null,"abstract":"<div><div>Most airports operate below their declared capacity, yet expansion through costly infrastructure development remains the primary strategy for meeting the rising demand for flights. Inefficient slot allocation, underutilised airside resources, and a lack of detailed demand–capacity analysis hinder performance, leading to rejected slot requests that may cost the industry billions of dollars annually. This research proposes a practical alternative: optimizing existing capacity before pursuing expansion using a bi-objective mathematical model. This model simultaneously maximizes the utilisation of runway, apron, and terminal gate capacity through revised slot scheduling, and incorporates real-time operational constraints to minimise delay propagation while maintaining separation minima. The model was validated using real data from Bandaranaike International Airport-Colombo (BIA). The proposed linear programming model demonstrated increased average airside resource utilisation on peak days, from 44.9% to 62.7%, while ensuring that the current schedule peak traffic intensities are maintained. Through delay optimization, the proposed schedule is capable of reducing congestion and cumulative delays compared to the non-optimized schedule, mainly when delays are propagated due to uncertainties. With an average delay reduction of 140.80 min per scenario, the model's validity was confirmed, providing strong evidence of its robustness and reliability. These results demonstrate the potential of optimized slot allocation as a decision-support tool, enabling fairer access for new entrants, reducing delays, and enhancing efficiency across existing operations.</div></div>","PeriodicalId":100933,"journal":{"name":"Multimodal Transportation","volume":"5 2","pages":"Article 100273"},"PeriodicalIF":0.0,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623482","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}