Transportation Research Interdisciplinary Perspectives最新文献

{"title":"The effects of split sleep on sleepiness on performance: A train simulator study","authors":"Herman Rahadian Soetisna,&nbsp;Sevty Auliani,&nbsp;Ludfi Pratiwi Bowo,&nbsp;Hardianto Iridiastadi","doi":"10.1016/j.trip.2026.102090","DOIUrl":"10.1016/j.trip.2026.102090","url":null,"abstract":"<div><div>Fatigue and sleepiness are critical safety risks in train operations, particularly among train drivers. One contributing factor is poor sleep quality, which may result from split or fragmented sleep patterns due to operational schedules and personal time-use constraints. This study aimed to investigate the effects of split sleep on fatigue and sleepiness during simulated train operations. Fifteen male participants completed a 2.5-hour train-driving simulation under three sleep conditions: split sleep (05:00 a.m. – 10:00 a.m. and 00:00p.m. – 3:00p.m.), consolidated daytime sleep (05:00 a.m. – 01:00p.m.), and baseline nighttime sleep (09:00p.m. – 05:00 a.m.). Sleepiness was assessed using ocular measures (blink duration, blink frequency, and microsleep per minute), subjective video rating, EEG parameters (alpha and theta wave power), and the Karolinska Sleepiness Scale (KSS). Results showed that the split sleep condition led to a substantial increase in fatigue indicators, with ocular and facial measures increasing by 15–70% compared to baseline. The consolidated sleep condition produced moderate increases (9–31%). No significant differences were observed in EEG parameters, and subjective KSS scores showed only marginal changes. These findings indicate that, despite an equivalent total sleep duration, split sleep patterns significantly impair alertness. The results highlight the need for careful scheduling and the provision of appropriate rest facilities in operational settings to mitigate fatigue-related risks.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102090"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling driver behaviour and the interplay of demographic and environmental factors in traffic violations: A bayesian network approach","authors":"Cinzia Carrodano","doi":"10.1016/j.trip.2026.102114","DOIUrl":"10.1016/j.trip.2026.102114","url":null,"abstract":"<div><div>Road traffic accidents remain a leading cause of mortality worldwide, with over 1.3 million preventable deaths annually and millions more injured (WHO). Traditional research approaches often fail to capture the complex interplay between demographic, behavioral, and environmental factors contributing to crash outcomes. This study addresses these gaps by employing a Bayesian Network (BN) model to analyze real-world data from Tempe, Arizona, spanning a two-year period. The model integrates variables such as behavioral factors, demographic attributes, and environmental conditions to assess their compound impact on crash severity.</div><div>The BN model allows for both predictive analyses and causal inferences, providing actionable insights for targeted interventions. Sensitivity analyses highlight key contributors to crash outcomes, with violations like speeding and impaired driving identified as critical behavioral risks, often exacerbated by environmental conditions like low visibility. The study also provides a granular analysis with age-specific risk patterns, emphasizing the need for tailored safety strategies for young, adult, and senior drivers.</div><div>This research demonstrates the use of Bayesian Networks in capturing risk interactions, offering a comprehensive methodology and a granular driving risk analysis for policymakers and urban planners. By addressing the limitations of traditional approaches, the study contributes to the development of data-driven strategies to mitigate traffic accidents and enhance road safety and propose a novel BN model considering behavioral, demographic and environmental risk factors.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102114"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting specific injury patterns from crash data: a GIDAS-based and manufacturer-independent logistic regression approach for next generation eCall","authors":"Michael Hetz,&nbsp;Angela Schubert,&nbsp;Thomas Unger,&nbsp;Henrik Liers,&nbsp;Klaus-Dieter Schaser Prof. Dr. med.,&nbsp;Christian Kleber","doi":"10.1016/j.trip.2026.102109","DOIUrl":"10.1016/j.trip.2026.102109","url":null,"abstract":"<div><h3>Background</h3><div>While prehospital traffic fatalities remain a critical challenge, current eCall systems transmit limited injury-relevant data, and existing prediction models often fail to support targeted prehospital triage. This study developed and validated a manufacturer-independent, data-driven framework to predict tactically relevant, region-specific injury patterns to enhance early rescue chain activation.</div></div><div><h3>Methods</h3><div>Logistic regression models were developed using GIDAS data for front-row occupants, stratified by impact direction. Clinically relevant injury categories were defined based on prehospital tactical criteria. Predictors included crash severity (Energy Equivalent Speed, EES), restraint use, and vehicle characteristics. Performance was assessed via cross-validation and external validation on a recent GIDAS subset, utilizing metrics robust to class imbalance.</div></div><div><h3>Results</h3><div>Across all injury groups, the models demonstrated high discrimination (AROC &gt; 0.85 for most groups), robust calibration, and biomechanically plausible associations. Energy-equivalent speed (EES) consistently emerged as the strongest and most reliable predictor of injury severity. Optimized classification thresholds supported the prediction of both global and region-specific trauma. External validation confirmed reproducibility for thorax, head, and overall severe injuries, maintaining high sensitivity and specificity under optimized thresholds.</div></div><div><h3>Conclusion</h3><div>Ultimately, this study provides a scientifically robust framework for predicting region-specific injury patterns, with results that can be translated into next-generation eCall systems to inform prehospital triage and decision-support. Clinically, this predictive framework translates crash data into triage intelligence, enabling emergency dispatchers to allocate specialized trauma resources more precisely. While the models demonstrate strong predictive performance on the GIDAS dataset, prospective multicenter validation is ongoing to assess their real-world operational utility.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102109"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MaaS bundle uptake among university students: A cross-European survey","authors":"Willy Kriswardhana,&nbsp;Michela Le Pira,&nbsp;Giuseppe Inturri,&nbsp;Nejc Geržinič,&nbsp;Niels van Oort,&nbsp;Grzegorz Sierpiński,&nbsp;Adrian Barchański,&nbsp;Domokos Esztergár-Kiss","doi":"10.1016/j.trip.2026.102125","DOIUrl":"10.1016/j.trip.2026.102125","url":null,"abstract":"<div><div>Considering current trends in innovative mobility solutions and the shifts in young generations’ travel behavior, this study investigates university students’ Mobility as a Service (MaaS) bundle uptake. Travel behavior of the younger generation has been widely explored in the literature, but their uptake of MaaS bundles has received limited attention. The data are derived from stated preference surveys conducted among the students of four European universities, where a mixed logit model is performed to calculate the parameters. The descriptive statistics confirm that people using public transport (PT) are potential adopters of MaaS bundles, whereas car users exhibit less interest. The results indicate differences in travel characteristics among specific locations in Europe. Students in Budapest are mostly PT users, and they show the highest interest in adopting MaaS bundles. Furthermore, students in Delft cycle more often and show disinclination toward bundles containing bike-sharing services. Cars are popular among students in Catania and Katowice, and they have interest in purchasing bundles including car-sharing services. The current research finds that providing incentives in MaaS bundles could potentially support their uptake. The interaction analysis indicates that high-income students are more inclined to have e-scooter-sharing and car-sharing within MaaS bundles. The findings shed light on the preferences toward MaaS among university students with different travel characteristics, which are relevant in addressing policies to promote more sustainable mobility options for residents. The results can potentially counterbalance skepticism that MaaS is not a sustainable mobility tool by demonstrating how context-specific bundle customization can bridge the wider adoption.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102125"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STFusioner: Bidirectional spatiotemporal fusion via dual-branch cross-attention transformers for traffic flow prediction","authors":"Fuwen Deng,&nbsp;Xiaobo Chen,&nbsp;Jiandong Jin","doi":"10.1016/j.trip.2026.102115","DOIUrl":"10.1016/j.trip.2026.102115","url":null,"abstract":"<div><div>This paper presents STFusioner, a novel traffic flow prediction model that leverages spatiotemporal feature decoupling encoding and a bidirectional cross-attention-based feature mixing mechanism (Bi-STCA). The model introduces an innovative spatiotemporal representation learning framework following a “decoupling-coupling” paradigm, designed to achieve highly accurate traffic flow predictions in large-scale road networks. Specifically, it first disentangles spatial and temporal features through parallel multi-head self-attention modules, followed by dynamic feature fusion via the proposed Bi-STCA module, which effectively captures complex spatiotemporal interactions inherent in traffic flow. This paradigm enables the implicit learning of empirical traffic dynamics, specifically the propagation of shockwaves, thus facilitating accurate prediction of the evolving dynamics of the traffic network. Extensive experiments on real-world datasets demonstrate that STFusioner achieves state-of-the-art performance in most cases, outperforming existing models with average relative improvements of 1.34% (MAE), 0.77% (RMSE), and 1.24% (MAPE). Ablation studies confirm the Bi-STCA module’s pivotal role in feature fusion and performance gains. Due to its generalizability, STFusioner can be easily adapted to practical traffic flow prediction tasks, positioning it as a versatile solution for real-world applications.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102115"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating the spatial spread and containment of a pandemic in a multimodal tourism district using agent-based modeling","authors":"Farnaz Kaviari,&nbsp;Karl Kim,&nbsp;Eric Yamashita","doi":"10.1016/j.trip.2026.102111","DOIUrl":"10.1016/j.trip.2026.102111","url":null,"abstract":"<div><div>The spatial dynamics of human mobility play a critical role in shaping disease transmission and containment policies, particularly in densely populated urban areas with significant tourism and transport activity. This study presents an Agent-Based Model to simulate the spread and containment of COVID-19 in Waikiki, Hawaii, a high-density resort district characterized by diverse transport modes and pedestrian interactions. The model simulates movements of pedestrians, motorists, and public transit users, using high‑resolution built-environment data to capture where and when contacts occur, and links interactions among infectious and susceptible individuals to infection risks under varying interventions. Intervention scenarios, including mask usage, testing/contact tracing, and vaccination, are simulated, and model outcomes are validated against observed case data (R2 = 0.864). Results demonstrate that enhanced contact tracing was the most effective intervention, reducing cumulative reported cases by 18.9 % and community transmission by 44 % compared to the realistic baseline. Higher vaccination coverage yielded a 4.5 % reduction in cases and a 15.8 % reduction in hospitalizations, highlighting the vaccine’s role in mitigating severe outcomes. Conversely, reducing contact tracing below baseline levels increased cumulative cases by 22.5 % and community cases by 53.2 %, while low mask use increased overall cases by 4.8 %, highlighting risks of relaxing interventions. Persistence of transmission, even with high vaccination rates, highlights the importance of policy measures, particularly rapid case identification and isolation in high‑mobility resort districts. These findings demonstrate the value of ABM as a decision‑support tool bridging transportation planning, urban epidemiology, and public health policy, an integration essential for resilient community management.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102111"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An extended integrated TAM–TPB–IDT model for explaining new energy vehicle purchase intentions among China’s Generation Z","authors":"Jianhua Zhang,&nbsp;Ruonan Sun,&nbsp;Terefe Alemu,&nbsp;Mengke Zhang,&nbsp;Kaixuan Shi,&nbsp;Muhammad Noman,&nbsp;Tianqi Wu","doi":"10.1016/j.trip.2026.102031","DOIUrl":"10.1016/j.trip.2026.102031","url":null,"abstract":"<div><div>This study investigates the determinants of New Energy Vehicle (NEV) purchase intention among Generation Z consumers in China, against the backdrop of an ongoing transition of the transport sector toward low-carbon mobility. To elucidate the behavioural mechanisms underlying NEV adoption, an extended integrated framework is developed by synthesising the Technology Acceptance Model (TAM), the Theory of Planned Behaviour (TPB) and Innovation Diffusion Theory (IDT), and by incorporating context-specific factors that capture China’s policy environment, charging infrastructure and digital mobility ecosystem. Using survey data from 487 young adults, Partial Least Squares Structural Equation Modelling (PLS-SEM) is applied to examine how technological perceptions, attitudinal and control beliefs, innovation attributes and external enabling conditions jointly shape NEV purchase intention. The results show that perceived usefulness, attitude, perceived behavioral control, environmental consciousness, relative advantage, compatibility, charging infrastructure and smart networked technology exert significant positive effects on purchase intention, whereas perceived risk does not constitute a salient barrier for this cohort. Attitude mediates the effects of perceived usefulness and perceived ease of use on intention, while government policy support and social media influence, respectively, strengthen the impacts of attitude and subjective norms. These findings provide a nuanced characterisation of NEV adoption behaviour among emerging young consumers in the world’s largest NEV market and yield policy-relevant insights for the design of integrated instrument packages, the planning of charging infrastructure and the use of digital communication strategies to accelerate NEV diffusion within sustainable transport systems.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102031"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From queueing performance evaluation to policy guidance: A new modeling framework for escalator systems","authors":"Fahan Chen,&nbsp;Xinyu Wang,&nbsp;Chao Guo,&nbsp;Moshe Zukerman","doi":"10.1016/j.trip.2026.102073","DOIUrl":"10.1016/j.trip.2026.102073","url":null,"abstract":"<div><div>Escalators play a critical role in vertical transportation in urban transit systems such as metro and subway stations, yet their efficiency is often constrained by informal usage norms such as the “walk left, stand right” rule. This study presents a new framework to characterize passenger boarding behaviors and evaluate the resulting delays on escalators. In particular, we focus on the average passenger queueing delay before boarding the escalator and define it as our performance metric designated as Average Delay Before Escalator (ADBE). Two distinct arrival processes, Poisson and Single Batch, are considered to estimate lower and upper bounds for the ADBE. A probabilistic output process is used to simulate boarding decisions under varying traffic and behavior scenarios. We analyze three boarding policies: (1) all-standing, (2) walk-left-stand-right with two variants, and (3) a hybrid strategy reflecting real-world behavior. Four passenger types are defined to emulate behavioral diversity, reflecting differing urgency and space preferences. Simulation results reveal that the all-standing policy significantly reduces the ADBE and increases throughput, especially under congestion. Our model offers actionable insights for transit planners and metro system operators, providing a quantitative basis for optimizing escalator usage policies and enhancing both throughput and safety.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102073"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A general data-driven methodology for predicting future air traffic distributions around airports","authors":"Lorenzo Dorbolò,&nbsp;Marco Pretto,&nbsp;Pietro Giannattasio","doi":"10.1016/j.trip.2026.102108","DOIUrl":"10.1016/j.trip.2026.102108","url":null,"abstract":"<div><div>A reliable forecast of future low-altitude air traffic distributions is essential for the prediction of aircraft noise and pollutant emissions in airport areas. This work proposes a novel methodology for generating aircraft movements around airports based on historical flight tracking data. These data are exploited to derive representative flight paths via unsupervised machine learning and typical airport operations via statistical analysis. These outcomes feed a custom-made algorithm that generates future traffic distributions, requiring only traffic intensity and forecast timeframe as inputs. The methodology, tested at Stockholm Arlanda Airport, uses 2022 tracking data to predict the traffic distribution in 2024 and is validated comparing simulated and actual 2024 day-evening-night noise levels <span><math><msub><mi>L</mi><mrow><mi>DEN</mi></mrow></msub></math></span>. An accurate prediction is achieved, as moderate <span><math><msub><mi>L</mi><mrow><mi>DEN</mi></mrow></msub></math></span> differences involve regions under 45 dB(A) while <span><math><mrow><msub><mi>L</mi><mrow><mi>DEN</mi></mrow></msub><mo>≥</mo><mn>45</mn></mrow></math></span> dB(A) contour areas are overestimated by less than 6 %. Errors come mostly from changes in airport operations, to be addressed in future developments.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102108"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-objective mathematical model for hub location-allocation with inspection under uncertainty: a benders decomposition approach","authors":"Ali Heidari,&nbsp;Mohammad Khalilzadeh,&nbsp;Fariborz Jolai,&nbsp;Matineh Ziari","doi":"10.1016/j.trip.2026.102112","DOIUrl":"10.1016/j.trip.2026.102112","url":null,"abstract":"<div><div>The growing demand for rail transportation has intensified the need for designing an optimal and sustainable network to facilitate freight movement with minimal cost, time, and environmental impact. A key challenge in this domain is wagon failures and the resulting delays, underscoring the critical importance of timely and preventive maintenance. In this study, major stations are considered as hubs where defective wagons are consolidated and repaired. To address the multifaceted requirements of rail transport systems, a robust multi-objective mathematical model has been developed, incorporating economic and environmental objectives alongside social responsibility components, specifically, enhancing customer satisfaction (by reducing transit time) and improving employment opportunities as primary decision-making criteria. For solving the model, an enhanced epsilon-constraint method is employed for small-scale instances, while a combination of the weighted-sum approach and the Benders decomposition algorithm is applied to large-scale problems. Subsequently, a sensitivity analysis is conducted to evaluate the stability of solutions against variations in key parameters. Sensitivity analysis shows that wagon capacity is the most influential parameter, as increasing it significantly reduces infrastructure, transportation, inspection, and reward costs. Demand is another highly sensitive factor, directly affecting the number of hubs, locomotives, and the overall network size. Within the tested ranges, the model remained stable and no infeasibility was observed. Numerical results demonstrate that the proposed model not only exhibits high efficacy in managing rail transport networks but also simultaneously fulfills sustainability, economic, and social objectives.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"38 ","pages":"Article 102112"},"PeriodicalIF":4.8,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148476599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}