Transportation Research Interdisciplinary Perspectives最新文献

{"title":"The assessment of fire damaged street furniture in Lahaina, Maui, using YOLOv12 and 360° imagery","authors":"Karl Kim ,&nbsp;Eric Yamashita ,&nbsp;Jaeho Choi ,&nbsp;Michael Vorce","doi":"10.1016/j.trip.2026.102027","DOIUrl":"10.1016/j.trip.2026.102027","url":null,"abstract":"<div><div>Using 360° imagery before and after the 2023 Lahaina fire disaster, damage to street furniture including traffic signs, control devices, street lighting, utility poles, and other objects, is assessed. In addition to describing the collection of panoramic 360° images using a Mosaic X camera, the detection and characterization of objects using YOLOv12 and other software are described. Accuracy, reliability, and bias associated with data collection and analysis are discussed for the most common types of street furniture. The research and machine vision tools are helpful for emergency management ane for routine and repair and maintenance operations. The technologies and processes contribute to the development of digital roadway twins and novel applications for transportation planning, operations, repair, and construction of critical roadway infrastructure.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102027"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858520","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":"What factors influence driver behaviour characteristics when pedestrians cross the road? Insights from the UDRIVE naturalistic driving study","authors":"Silvia F. Varotto ,&nbsp;Girish Kumaar Srinivasan Ravi Kumar ,&nbsp;Ruomei Liu ,&nbsp;Matheus H.W. Stratermans ,&nbsp;Reyzha Wikki Zaninshi ,&nbsp;Eleonora Papadimitriou ,&nbsp;Meng Wang","doi":"10.1016/j.trip.2026.102013","DOIUrl":"10.1016/j.trip.2026.102013","url":null,"abstract":"<div><div>In urban areas, drivers frequently interact with vulnerable road users. On-road studies have shown that drivers are more likely to have safety-relevant interactions with pedestrians when they are inattentive and when pedestrians behave unexpectedly. Notwithstanding these behavioural effects, most microscopic traffic flow models do not accurately describe driver response to pedestrian crossing behaviour.</div><div>This study investigates the factors influencing driver behaviour characteristics when pedestrians cross the road in front of the vehicle. The data were collected in the UDRIVE naturalistic driving study in France and the UK. The interactions with pedestrians in daylight were identified using the MobilEye® smart camera. The minimum time to zebra and the maximum deceleration during each interaction were investigated in regression models.</div><div>The results showed that, controlling for the initial speed of the subject vehicle, the minimum time to zebra during interactions was significantly shorter when the pedestrian crossed while the driver had a green traffic light, the vehicle segment was medium, and other pedestrians had already crossed. Controlling for initial speed and acceleration, the maximum deceleration during interactions was lower when the pedestrian crossed while the driver had a green traffic light, no other pedestrians had already crossed, the pedestrian was not a child, teenager or elderly person, and the pedestrian did not glance toward the vehicle. These factors can be incorporated into traffic simulations to describe driver responses more realistically. Further research is needed to understand the influence of the driver’s state because most drivers looked toward pedestrians.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102013"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802360","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":"Equity in network design: a latent class choice model for prioritising investment decisions","authors":"Ali Najmi ,&nbsp;S.Travis Waller ,&nbsp;Taha H. Rashidi","doi":"10.1016/j.trip.2026.102039","DOIUrl":"10.1016/j.trip.2026.102039","url":null,"abstract":"<div><div>The allocation of resources in transport network investments is central to shaping accessibility, sustainability, and equity within infrastructure systems. Yet, balancing diverse public preferences with equity considerations remains a persistent challenge, often resulting in decisions that fail to adequately reflect the needs of different societal groups. This study addresses this gap by investigating public preferences for transport network investments in Australia through a discrete choice experiment with 2,050 respondents. Using a Latent Class Choice Model (LCCM), we capture heterogeneity in preferences and identify two distinct groups. The first, labelled <em>Environmental, Societal, and Governance (ESG)-centric</em>, prioritises environmentally friendly, safe, and inclusive transport options. The second, labelled <em>Self-centric</em>, favours affordability, reduced travel times, and maximisation of personal benefits. By distinguishing these classes, this study contributes new evidence on the trade-offs that shape investment preferences and offers policy recommendations that emphasise accessibility, safety, sustainability, and innovative funding models. The findings provide actionable insights for designing transport policies that are both equitable and responsive to diverse societal needs.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102039"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858515","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":"MetaCountRegressor: A package for extensive analysis and estimation of advanced count data models","authors":"Zeke Ahern ,&nbsp;Paul Corry ,&nbsp;Alexander Paz","doi":"10.1016/j.trip.2026.101969","DOIUrl":"10.1016/j.trip.2026.101969","url":null,"abstract":"<div><div>Analyzing and modeling rare events in count data presents significant challenges due to the scarcity of observations and the complexity of underlying processes, which often can be overlooked by analysts due to limitations in time, resources, knowledge, and experience. This paper provides details and instructions to use MetaCountRegressor, a Python package designed to facilitate predictive count modeling of rare events using advanced optimization algorithms. MetaCountRegressor offers a wide range of capabilities specifically tailored for the unique characteristics of rare event analysis and prediction. Metaheuristic algorithms efficiently generate and evaluate model specifications, thereby facilitating extensive hypothesis testing, the discovery of insights, and parameter tuning without overfitting. These algorithms are specifically engineered to deal with the inherent challenges of modeling rare events for predictive purposes, and capturing causative effects that are easily interpretable. State-of-the-art models are produced by coupling analysts knowledge and preferences with decision-based optimization framework. This includes the ability to capture unobserved heterogeneity through random parameters as well as correlated and grouped random parameters. It also supports a range of distributions for the random parameters, and can capture heterogeneity in the means. The package also supports panel data, among other features, and serves as a systematic framework for analysts to discover optimization-driven results, saving time, reducing biases, and minimizing the need for extensive prior knowledge. The experimental results highlight the effectiveness of MetaCountRegressor in optimizing complex count models using synthetic and real crash data, supported by illustrative examples on fitting models in order to discover the functional form respective of the problem instance. The framework achieves significant reductions in the Bayesian Information Criterion (BIC) and Root Mean Squared Prediction Error (RMSE) compared to analysts proposed models, all while maintaining computational efficiency across diverse test cases. By eliminating the need for manual iterative processes, these findings demonstrate the vast solution space and underscore the importance of enhanced tools for model selection. Results and resources are provided to illustrate the capabilities and advantages of using the proposed modeling framework implemented in MetaCountRegressor.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 101969"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802330","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":"Longitudinal modeling and velocity control of autonomous electric vehicles with energy optimization using non-linear autoregressive model with exogenous inputs (NLARX) system identification method","authors":"R. Prasanna ,&nbsp;S. Raja ,&nbsp;Maher Ali Rusho ,&nbsp;Shubham Sharma ,&nbsp;T. Ramachandran ,&nbsp;Krishna Prakash Arunachalam ,&nbsp;Ghanshyam G. Tejani ,&nbsp;Krishnaraj Ramaswamy","doi":"10.1016/j.trip.2026.101944","DOIUrl":"10.1016/j.trip.2026.101944","url":null,"abstract":"<div><div>Precise modeling and control of vehicle longitudinal dynamics are critical to guaranteeing the safety, efficiency, and real time performance of autonomous electrical driving systems. This paper suggests a non-linear autoregressive model with exogenous inputs (NLARX) for longitudinal velocity estimation. MATLAB vehicle dynamics blockset was used to generate high fidelity simulation data for three degrees of freedom dual track configuration of the Urban Dynamometer Driving Schedule (UDDS) drive cycle. The suggested framework combines five input states, i.e., tyre forces and steering commands, and three output states, thus allowing detailed capture of dynamic interaction. Validation results demonstrate a substantial improvement in predictive accuracy compared with a long short term memory (LSTM) network and a third order transfer function model indicate superior accuracy of the NLARX model with highest fit percentage and lowest root mean square error (RMSE). For longitudinal velocity, the NLARX model achieved a fit of 99.01% with an RMSE of 0.0552 m/s, compared to 78.29% (0.1967 m/s) for LSTM and 69.30% (0.5820 m/s) for the transfer function model. Averaged over all three outputs, the NLARX attained 98.54% fit with an average RMSE of 0.0333, significantly outperforming LSTM <strong>(</strong>80.49%, 0.2114<strong>)</strong> and the transfer-function model (67.54%, 1.5170 m/s). Frequency domain analysis further confirmed smooth gain and phase characteristics of the NLARX model, indicating superior bandwidth suitability and dynamic consistency. A PID velocity tracking controller, optimized through the continuous oscillation method, was applied and tested for various road grade conditions, showing negligible overshoot, zero steady state error, and robust tracking performance. Frequency domain analysis also provided further evidence of model stability and bandwidth suitability for real time implementation. The suggested NLARX based approach thus bring forth a twofold benefit such that it offers enhanced tracking performance and energy efficient operation, which is of utmost concern to electric power and energy networks.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 101944"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802351","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":"Determinants of commuter usage in multimodal public transport: An empirical study in Jakarta, Indonesia","authors":"I Wayan Koko Suryawan ,&nbsp;Sapta Suhardono ,&nbsp;Chun-Hung Lee ,&nbsp;Ari Rahman ,&nbsp;Nova Ulhasanah","doi":"10.1016/j.trip.2026.101971","DOIUrl":"10.1016/j.trip.2026.101971","url":null,"abstract":"<div><div>This study examines the factors that influence the use of multimodal public transportation by commuters in Jakarta. It specifically focuses on service satisfaction, environmental attitudes, demographic characteristics, and social equity. Using Exploratory Factor Analysis (EFA), cluster analysis, and binary logistic regression, the research identifies key determinants of transportation behavior among a diverse group of commuters. The results indicate that service quality, environmental awareness, travel time, and multimodal connectivity are significant drivers of multimodal commuting. Income also emerges as an important predictor, while the effects of gender, education, marital status, age, and administrative area are mixed and less consistent. These findings suggest that improving service reliability, enhancing connectivity across modes, and promoting environmental awareness can effectively encourage multimodal commuting in Jakarta. The study contributes new evidence from a Global South megacity, emphasizing that interventions should not only enhance efficiency but also address socio-economic disparities to ensure inclusive and equitable access to public transport.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 101971"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802354","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":"Understanding mobility choices in Australia’s driverless future: A mixed logit modelling study","authors":"Asif Faisal ,&nbsp;Tan Yigitcanlar ,&nbsp;Sheikh Islam ,&nbsp;Niklas Tilly ,&nbsp;Alexander Paz","doi":"10.1016/j.trip.2026.102032","DOIUrl":"10.1016/j.trip.2026.102032","url":null,"abstract":"<div><div>The anticipated introduction of driverless vehicle mobility in Australia from 2027 underscores the need to better understand user preferences and evolving travel behaviours. Conventional classifications such as ‘owned only’, ‘shared only’, or ‘bus riding only’ often fail to capture the hybrid and overlapping nature of future mobility choices. This study reconceptualises autonomous mobility choice (AMC) into three integrated modes: private autonomous mobility (PAM), shared autonomous mobility (SAM), and public autonomous mobility (PUAM), enabling a more holistic understanding of multi-modal travel. Using survey data from 985 respondents across Brisbane, Melbourne, and Sydney, a Mixed Logit (MXL) model was employed to examine determinants of AMC preferences. Descriptive analysis reveals strong interest in PAM (22%) and moderate engagement with SAM (3%), while traditional private vehicles continue to dominate (35%). The MXL model identifies significant predictors influencing AMC choices, including age, vehicle value, use of driving aids, trip purpose, adoption timeframe, current commuting mode, travel distance, and relocation intentions linked to AV adoption. Marginal effects indicate younger adults exhibit lower interest in PAM, whereas mid-value car owners show stronger preferences for PUAM and SAM. Long-distance commuters are more likely to adopt SAM, while those considering relocation from urban centres tend to prefer PAM. Overall, the findings demonstrate the value of the proposed unified AMC framework in capturing complex, hybrid mobility preferences and providing a more robust basis for designing adaptive, inclusive, and forward-looking policy frameworks to guide the integration of autonomous mobility into future transport systems.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102032"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802359","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":"Enhancing safety in automated ports: a virtual reality study of pedestrian–autonomous vehicle interactions under time pressure, visual constraints, and varying vehicle size","authors":"Yuan Che ,&nbsp;Mun On Wong ,&nbsp;Xiaowei Gao ,&nbsp;Haoyang Liang ,&nbsp;Yun Ye","doi":"10.1016/j.trip.2026.102041","DOIUrl":"10.1016/j.trip.2026.102041","url":null,"abstract":"<div><div>Autonomous driving improves traffic efficiency but presents substantial safety challenges in complex port environments, where pedestrians and autonomous vehicles often interact amid low lighting, adverse weather, stacked containers, heavy machinery, and large industrial vehicles operating in confined shared spaces. This study investigates how environmental factors, traffic characteristics, and pedestrian attributes influence interaction safety between autonomous vehicles and pedestrians in ports. Using virtual reality simulations of typical port scenarios, 33 participants completed pedestrian crossing tasks under varying visibility, vehicle sizes, and time pressure conditions. Results indicate that low-visibility conditions, partial occlusions, and larger vehicle sizes significantly increase perceived risk, prompting pedestrians to wait longer and accept larger gaps. Specifically, pedestrians tended to accept larger gaps and waited longer when interacting with large autonomous truck platoons, reflecting heightened caution due to their perceived threat. However, local obstructions also reduce post-encroachment time, compressing safety margins. Individual attributes such as age, gender, and driving experience further shape decision-making, while time pressure undermines compensatory behaviors and increases risk. Based on these findings, safety strategies are proposed, including installing wide-angle cameras at multiple viewpoints, enabling real-time vehicle-infrastructure communication, enhancing port lighting and signage, and strengthening pedestrian safety training. This study offers practical recommendations for improving the safety and deployment of vision-based autonomous systems in port settings.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102041"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858519","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":"Hearing acceleration: enhancing vehicle sound for earlier detection of acceleration in pedestrians","authors":"Mengwen Chen,&nbsp;Jingyi Tian,&nbsp;Yu Chen,&nbsp;Meng Liu,&nbsp;Xiangling Zhuang","doi":"10.1016/j.trip.2026.102021","DOIUrl":"10.1016/j.trip.2026.102021","url":null,"abstract":"<div><div>Acceleration serves as both an implicit cue of a vehicle’s non-yielding intention and a necessary maneuver following a yielding action in autonomous vehicles (AVs). However, vehicle acceleration is difficult for pedestrians to accurately perceive. This study proposed and evaluated an auditory external interface for AVs that enhances the vehicle’s engine sound to facilitate pedestrians’ perception of acceleration. In a virtual reality environment, we measured pedestrians’ time delay in detecting vehicle acceleration under four sound conditions: untreated, amplitude-enhanced, frequency-enhanced, and combined amplitude-frequency enhancement. Results indicated that all three enhancement methods could speed up acceleration perception compared to the untreated condition, with amplitude enhancement producing the weakest effect. The combined enhancement proved most effective, offering a slight advantage over frequency enhancement. These findings provide insights into effectively enhancing auditory characteristics that may inform future research on vehicle–pedestrian interaction.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102021"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858779","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":"Effects of urban design elements on pedestrian wayfinding behavior and stress in a train station: A virtual reality study","authors":"Céleste Richard ,&nbsp;Yan Feng","doi":"10.1016/j.trip.2026.101999","DOIUrl":"10.1016/j.trip.2026.101999","url":null,"abstract":"<div><div>This study employed Virtual Reality (VR) and physiological sensors to study the impact of different urban elements on pedestrian wayfinding behavior and physiological responses during outdoor-to-indoor transitions in a train station context. Three urban elements — greenery, water, and leading pavement — were placed either indoors, outdoors, both, or neither, creating four experimental scenarios. In total, 35 participants completed wayfinding tasks across all four scenarios. Behavioral, physiological, and eye-tracking data were collected and analyzed. The results revealed that outside-only placement of urban elements was associated with the worst wayfinding performance across all metrics, performing worse than even the control condition with no urban elements. Eye-tracking analysis demonstrated that outside-only placement of urban elements actively distracted participants from the indoor navigation path. Inside-only placement supported the most efficient navigation in terms of travel time, while the scenario with elements in both locations produced the most focused gaze behavior and the highest subjective comfort ratings. These findings highlight that the placement location of urban elements is more critical than their mere presence for supporting outdoor-to-indoor wayfinding, with important implications for human-centered design in transport hubs.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 101999"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802357","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}