{"title":"Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition","authors":"","doi":"10.1016/j.ijtst.2023.10.005","DOIUrl":"10.1016/j.ijtst.2023.10.005","url":null,"abstract":"<div><div>Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 298-318"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135849512","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":"Asphalt pavement surface repair area detection based on smartphone sensors","authors":"","doi":"10.1016/j.ijtst.2023.10.003","DOIUrl":"10.1016/j.ijtst.2023.10.003","url":null,"abstract":"<div><div>Asphalt pavement repair areas affect pavement performance and service levels. It is necessary to distinguish the repair areas from normal sections. Based on vehicle vibration signals, this study identified ten pavement repair areas and divided them into four cases by factors including length and form in conjunction with the driving approach. Additionally, time domain analysis, frequency analysis, and probability distribution analysis were used to form the characteristics of the repair cases as well as the normal sections. It was found that the maximum value, extreme deviation, standard deviation in the time domain, maximum amplitude in the frequency domain, and peak of the probability density curve would serve as judgment indexes. A framework for identifying the repair areas was also established based on the five indexes. By validation, the overall accuracy can reach 95.0%, demonstrating a strong generalization capability.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 271-283"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135706613","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":"Empirical investigation of shared space traffic: A comparison to conventional urban road environment","authors":"","doi":"10.1016/j.ijtst.2023.08.001","DOIUrl":"10.1016/j.ijtst.2023.08.001","url":null,"abstract":"<div><div>Shared space is an unconventional concept that is not based on formal rules and standards, as it encourages road users to share the same road space with little physical or visual separation. Consequently, this concept creates intriguing research questions that have not been fully answered yet, i.e., a) can a shared space road section produce more pedestrian crossings? b) what is the relationship between pedestrian crossings and traffic speeds? and c) what are the differences with a conventional road when motorizing traffic dominates in shared space? This study examines traffic conditions in shared space by answering these research questions. More specifically, it uses Amalias Street in Nafplio Greece as a case study. This road is divided into two main sections, namely: the conventional road section and the shared space road section, allowing meaningful comparisons. The collected data are further analyzed by developing multiple linear regression models that predict pedestrian crossings and mean car speeds in both sections. This study discusses model outputs with the literature to export valid conclusions. The results show that pedestrian crossings were increased in shared space when vehicle headways were high. Shared space results in a significant drop in car speeds that is confirmed by previous studies; surprisingly, the variance of car speeds was also reduced, leading to a more homogenous driving behavior. Pedestrian crossing rate significantly influences car speeds in shared space, while this relationship was not significant in the conventional road section. Shared space seems to calm traffic speed and allow coexistence even when motorizing traffic dominates.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 122-135"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46610333","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":"Risk quantification and prediction of non-driving-related tasks on drivers' critical intervention behavior in autonomous driving scenarios","authors":"","doi":"10.1016/j.ijtst.2023.06.003","DOIUrl":"10.1016/j.ijtst.2023.06.003","url":null,"abstract":"<div><div>For autonomous driving, drivers’ intervention may be required when vehicles fail or are in a dilemma to detect emergent and unprogrammed events. In such situations, non-driving related tasks may have a great impact on the safety of drivers’ critical intervention behavior thus leading to traffic accidents. Therefore, exploring the impacts of non-driving-related tasks on drivers’ critical intervention behavior, quantifying and predicting the corresponding risks have become important. In this paper, driving simulation experiments are carried out to obtain the vehicle driving state data and visual behavior information of drivers during the autonomous driving scenarios that require critical interventions. To construct the risk quantification model for drivers’ critical intervention behavior, the fuzzy comprehensive evaluation method and the criteria importance though intercriteria correlation (CRITIC) weighting method are employed. Then, for risk prediction, a model is constructed based on the visual behavior information before the occurrences of intervention. Multivariate logistic regression (MLR) and support vector machine are compared. The results show that non-driving tasks significantly postpone driver's critical intervention responses, increasing crash risks of the driving. For prediction, SVM performs better than the MLR in terms of metrics including the precision, the recall, and the overall accuracy. This paper examines the risks during situations requiring drivers’ critical intervention, associated with different non-driving tasks, which has remained much unexplored in the previous research. The methodology of this paper can be applied to smart vehicle systems in alerting vehicles for take-over reactions, with recognizing and predicting potential risks.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 1-23"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44685480","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":"Mechanistic evaluation of segregation in HMA mixtures","authors":"","doi":"10.1016/j.ijtst.2023.08.006","DOIUrl":"10.1016/j.ijtst.2023.08.006","url":null,"abstract":"<div><div>Segregation in hot mix asphalt (HMA) mixtures is defined as the separation of the coarse aggregate particles in the mixtures from the rest of the mass. Segregation can be a result of aggregate stockpiling and handling, production, storage, truck loading practices, construction practices, and equipment adjustments. Segregation is usually evaluated visually, which is considered as a subjective method with no definite limits and depends on the evaluator’s opinion.</div><div>This study uses two mechanistic surface texture indicators, i.e., mean texture depth (MTD) that is measured using sand patch method and mean profile depth (MPD) using laser texture profilometer to evaluate if a road section is segregated or not.</div><div>The sand patch method is standardized in ASTM E965 -15 (2019) “for Measuring Pavement Macrotexture Depth Using Volumetric Technique”. MPD is covered by the international standards ASTM E1845-15 “Standard Practice for Calculating Pavement Macrotexture Mean Profile Depth”.</div><div>Using both measured MTD values at grid point crossings, and average MPD values at 25 m intervals in the wheel paths, in addition to the use of statistical analysis of the obtained data, assuming that the obtained data are normally distributed and finding the 95% probability limits of the MTD and MPD values, it is possible to prove the closeness of the obtained texture depth indicator data, homogeneity of the road section, and that the segregation is only present at very limited localized locations.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 198-210"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43354363","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 exploration of the preferences and mode choice behavior between autonomous demand-responsive transit and traditional buses","authors":"","doi":"10.1016/j.ijtst.2023.07.004","DOIUrl":"10.1016/j.ijtst.2023.07.004","url":null,"abstract":"<div><div>With the advancement in autonomous driving techniques, autonomous demand-responsive transit (ADRT) is a newly emerging sustainable transport mode for the future, which will provide more flexible services to public users. ADRT offers benefits such as flexible stops and routes and comfortable seats, but it also involves risks due to the vehicles being driverless. This paper particularly investigates users’ preferences and attitudes towards ADRT, and mode choice behavior between ADRT buses and traditional buses. A survey with Likert scale statements and stated preference (SP) choice scenarios is designed and conducted to explore users’ attitudes towards the safety risks of autonomous vehicles (AVs), social concerns, service flexibility concerns when using AVs, interest in new things, and shuttle mode choices. An integrated choice and latent variable (ICLV) model is adopted to explore users’ psychological factors through latent variables and to integrate them into mode choice behavioral modeling. Estimated results indicate that users’ attitudes towards AV safety risks, their social concerns, and their flexibility concerns with ADRT strongly influence their mode choices and are strongly related to sociodemographic and travel-related factors such as age, gender, income, education, number of family members. In general, a young age, a high education level, a higher income, private car ownership, and better knowledge of AVs are positively related to attitudes towards ADRT. Females, users from large families, and users with driving licenses or long commuting times are less willing to adopt ADRT. The study's outcomes highlight significant heterogeneities among users and can be highly valuable for policymakers, such as government authorities, in providing social support and designing policies targeting specific population groups. This will be beneficial in attracting more users to this emerging mobility service and contributing to sustainable urban development.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 81-101"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41498776","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":"Connected vehicle enabled hierarchical anomaly behavior management system for city-level networks","authors":"","doi":"10.1016/j.ijtst.2023.06.004","DOIUrl":"10.1016/j.ijtst.2023.06.004","url":null,"abstract":"<div><div>Drivers who are distracted cannot operate their vehicles appropriately, which leads to error-prone behavior on the roads. This behavior increases the risk of collisions for both themselves and surrounding vehicles, making it urgent to manage anomalous vehicles with distracted drivers and mitigate their impacts on driving safety. To address this problem, this paper presents an anomaly behavior management system that leverages connected vehicles to improve the safety performance for both individual vehicles and the whole network. The proposed system integrates a hierarchical architecture that reduces the risk of collisions caused by anomalous vehicles in large-scale road networks. Connected vehicles monitor anomalous vehicles and estimate speed and lane-changing instructions to avoid dangerous behaviors. The benefits of the proposed system are evaluated using microscopic traffic simulation, which shows a reduction in the risk of collisions and improved mobility for both connected vehicles and the entire network. The paper also conducts a sensitivity analysis of the market penetration rates of connected vehicles and traffic demand levels to understand the system’s reliability at different development stages of connected vehicles and traffic congestion.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 24-34"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42150381","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 freight truck-related traffic crash hazards with uncertainties: A framework of interpretable Bayesian neural network with stochastic variational inference","authors":"","doi":"10.1016/j.ijtst.2023.08.005","DOIUrl":"10.1016/j.ijtst.2023.08.005","url":null,"abstract":"<div><div>Due to the increasing demand for goods movement, externalities from freight mobility have attracted much concern among local citizens and policymakers. Freight truck-related crash is one of these externalities and impacts urban freight transportation most drastically. Previous studies have mainly focused on correlation analyses of influencing factors based on crash density/count data, but have paid little attention to the inherent uncertainties of freight truck-related crashes (FTCs) from a spatial perspective. While establishing an interpretable analysis model for freight truck-related accidents that considers uncertainties is of great significance for promoting the robust development of urban freight transportation systems. Hence, this study proposes the concept of FTC hazard (FTCH), and employs the Bayesian neural network (BNN) model based on stochastic variational inference to model uncertainty. Considering the difficulty in interpreting deep learning-based models, this study introduces the local interpretable modelagnostic explanation (LIME) model into the analysis framework to explain the results of the neural network model. This study then verifies the feasibility of the proposed analysis framework using data from California from 2011 to 2020. Results show that FTCHs can be effectively modeled by predicting confidence intervals for effects of built environment factors, in particular demographics, land use, and road network structure. Results based on LIME values indicate the spatial heterogeneity in influence mechanisms on FTCHs between areas within the metropolitan regions and alongside the freeways. These findings may help transport planners and logistic managers develop more effective measures to avoid potential negative effects brought by FTCHs in local communities.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 181-197"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46036046","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":"Operational measures to maintaining physical distancing at railway stations","authors":"","doi":"10.1016/j.ijtst.2023.08.004","DOIUrl":"10.1016/j.ijtst.2023.08.004","url":null,"abstract":"<div><div>The corona virus disease 2019 (COVID-19) pandemic has increased awareness towards maintaining physical distancing during transportation-related activities. This study presents a microsimulation model to explore operational measures to maintain physical distance among railway station passengers. The secondary and primary data obtained from field surveys are utilized to construct and calibrate the model. The peak hour data is employed to investigate the worst-case conditions. The calibrated model is then utilized to evaluate several operational measures, i.e., changing the headway, increasing the train capacity, increasing the train door duration, and changing the train door rules. From the simulation, it is found that changing the train door rules was ineffective if it was individually implemented. It is concluded that a combination of operational measures provides additional benefits for maintaining physical distancing among passengers.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 170-180"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42327219","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":"Evaluation of aircraft random vibration under roughness excitation during taxiing","authors":"","doi":"10.1016/j.ijtst.2023.07.003","DOIUrl":"10.1016/j.ijtst.2023.07.003","url":null,"abstract":"<div><div>The assessment of runway smoothness or roughness is intimately tied to the vibrational response of aircraft during taxiing. In this study, employing the pseudo excitation method (PEM) based on random vibration analysis, we unearthed the relationship between the random vibrations of five distinct aircraft types and runway irregularities. Initially, we established two three-dimensional (3D) models of aircraft taxiing vibration and derived the response output under roughness excitation. Subsequently, we employed MATLAB to analyze the power spectral characteristics of the vibrational response in different parts of the aircraft. Lastly, we examined the effects of taxiing speed, aircraft type, runway roughness, and lift on the aircraft's vibration. Our findings indicate that the distribution of vibration power spectral density (PSD) exhibits multiple peaks, correlating with the degrees of freedom of the aircraft. We further note that the frequency that aligns most closely with the response peak should be the focus of investigation. High-frequency excitation impacts the pilot and nose landing gear more significantly than the passenger and main landing gear. Absent the consideration of lift, increased taxiing speed amplifies the impact of roughness excitation on aircraft taxiing safety. Larger aircrafts are more sensitive to long-wave roughness. With lift in consideration, all aircraft types exhibit a speed sensitivity to vibration, which should be the primary concern in runway roughness evaluations.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 65-80"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45840002","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}