Transportation Research Interdisciplinary Perspectives最新文献

{"title":"Typology of resistant adopters and policy implications for autonomous shipping: Evidence from the Rhine River","authors":"Dhaneswara Al Amien ,&nbsp;Roberto Rivas Hermann ,&nbsp;Oleg Nenadić ,&nbsp;Terje Andreas Mathisen","doi":"10.1016/j.trip.2026.102017","DOIUrl":"10.1016/j.trip.2026.102017","url":null,"abstract":"<div><div>Autonomous shipping is expected to transform inland waterway transport, yet empirical evidence on how shipowners differ in their adoption positions remains limited. This article investigates adoption dynamics in the Rhine River corridor using a mixed-method design. Semi-structured interviews informed survey development, and survey responses from inland shipowners <span><math><mrow><mo>(</mo><mi>N</mi><mo>=</mo><mn>47</mn><mo>)</mo></mrow></math></span> were analysed using Multiple Correspondence Analysis.</div><div>The analysis identifies three adopter typologies: early adopters, a silent majority, and laggards, which are positioned along a continuum of openness and resistance. The results suggest that resistance is not uniform but varies with organizational capacity, fleet retrofit constraints, perceived regulatory, and operational uncertainty. Adoption therefore appears fragmented and gradual, rather than consistent with a conventional S-shaped diffusion pattern.</div><div>Building on these findings, the article develops a stage-based interpretative framework based on empirical data. This framework links adopter heterogeneity to the literature on resistant innovation and diffusion. Early diffusion aligns with niche-oriented vertical cooperation among pioneers. Broader diffusion increases the emphasis on visibility, collective learning, coordination, and standardisation. Rather than prescribing specific instruments, the article illustrates how policy and governance approaches may align differently with heterogeneous adoption capacities as innovation matures.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102017"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147858514","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":"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":"Greener logistics: analyzing the impact of truck characteristics and load management for fuel efficiency","authors":"Maryrose Vallejos ,&nbsp;Alexis Fillone","doi":"10.1016/j.trip.2026.102025","DOIUrl":"10.1016/j.trip.2026.102025","url":null,"abstract":"<div><div>Fuel efficiency in road freight transport is a critical factor influencing both environmental sustainability and economic performance, as fuel costs represent a substantial share of logistics expenditures. While prior studies have examined vehicle and operational factors affecting fuel consumption, much of the existing literature relies on modeled or aggregated data. This study analyzes data from 296 freight trips in Bataan, Philippines, using descriptive statistics, correlation analysis, and ordinary least squares with heteroskedasticity-robust (HC3) standard errors. The final regression model, based on a log-transformed specification, demonstrates good explanatory power (R² = 0.605). Results indicate that travel distance and payload weight are the primary statistically significant determinants of fuel efficiency, measured as fuel consumption intensity (L/ton-km). Longer trips and higher payloads are associated with lower fuel consumption per unit of freight, suggesting improved operational efficiency and capacity utilization. In contrast, vehicle-related variables exhibit more nuanced effects. The number of axles is statistically significant and positively associated with fuel consumption intensity, while load type (FTL vs. LTL) does not show a significant independent effect after controlling for other factors. The findings highlight the dominant role of operational variables in shaping freight fuel performance. Although results are associational due to the cross-sectional design, the study provides empirical evidence from observed trip-level data and supports the need for future research using higher-resolution operational and vehicle data.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102025"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802352","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":"Exploring the relationships between individual cultural values and initial trust in automated driving systems: a separate examination of cognition-based and affect-based trust","authors":"Zixin Cui ,&nbsp;Makoto Itoh","doi":"10.1016/j.trip.2026.102024","DOIUrl":"10.1016/j.trip.2026.102024","url":null,"abstract":"<div><div>Initial trust prior to actual interaction influences the acceptance and subsequent use of automated driving systems (ADSs) and is potentially associated with individuals’ individual cultural values (ICVs). Extending prior work that examined the separate associations between selected ICVs drawn from Hofstede’s cultural framework and initial trust in automation, this study aims to construct a unified analytical model to examine the joint effects of all Hofstede’s cultural dimensions on initial trust in ADS, while controlling for the potential confounding effects of several sociodemographic factors and personality traits. An online survey with 638 participants was conducted, and hierarchical regression analyses were employed. Results revealed that a higher level of affect-based initial trust was associated with greater uncertainty avoidance, indulgence, and collectivism. In contrast, cognition-based initial trust was positively associated only with uncertainty avoidance and indulgence. Power distance, motivation toward achievement and success, and long-term orientation showed no significant associations with either affect-based or cognition-based initial trust. These findings extend the existing literature by providing new insights into the relationships between ICV and trust in ADS. Trust management strategies in both policy and practice may benefit from being tailored to individuals with varying levels of uncertainty avoidance, indulgence, and collectivism.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"37 ","pages":"Article 102024"},"PeriodicalIF":3.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147802355","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}