Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.10.003
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.08.001
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.
共享空间是一个非常规的概念，它不以正式的规则和标准为基础，因为它鼓励道路使用者共享同一道路空间，几乎没有物理或视觉分隔。因此，这一概念产生了一些有趣的研究问题，但这些问题尚未得到充分解答，例如：a) 共享空间路段是否会产生更多的行人过街现象；b) 行人过街与车速之间的关系是什么；以及 c) 在共享空间中，机动车交通占主导地位时，与传统道路的区别是什么？本研究通过回答这些研究问题来考察共享空间的交通状况。更具体地说，它以希腊纳夫普里奥的 Amalias 街为案例进行研究。这条道路被分为两个主要路段，即：传统路段和共享空间路段，以便进行有意义的比较。通过建立多元线性回归模型，对收集到的数据进行进一步分析，预测两个路段的行人过街情况和平均车速。本研究将模型输出结果与文献进行讨论，以得出有效结论。结果表明，当车辆间隔较长时，共享空间内的行人过街人数有所增加。共享空间导致车速显著下降，这一点已被先前的研究证实；令人惊讶的是，车速的方差也减小了，从而导致驾驶行为更加单一。在共享空间中，行人过街率对车速有明显影响，而在传统路段中，这种关系并不明显。共享空间似乎能降低车速，即使在机动车占主导地位的情况下也能实现共存。
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.06.003
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.08.006
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.
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.
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”.
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.07.004
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.06.004
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.07.003
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.
{"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":null,"pages":null},"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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.08.003
The constraints of transportation networks are fundamental to disaster planning. Having the capability of evaluating the emergent dynamics of such networks in the context of large traffic incidents can inform the design of traffic management strategies. On February 7, 2019, the Richmond-San Rafael Bridge in the San Francisco Bay Area, connecting multiple cities and carrying over 100 000 vehicles daily, had to be suddenly closed for over 9 hours due to a structural failure of its upper deck. This incident caused major disruptions in the region as the typical traffic was interrupted and detoured as travelers found alternate routes. In this study, we demonstrate the capability of large-scale traffic impact assessments of major network disruptions using the Richmond-San Rafael Bridge closure as a case study. Using a high-performance, parallel-discrete event traffic simulation, we assess the traffic impacts resulting from the bridge closure at both the regional system and city levels. Our model estimates that the region incurred an additional 14 000 vehicle hours of delay and 600 000 vehicle miles in distance due to the bridge closure. The incident affected over 55 000 trips; certain trips experienced an increase of 46 min in delay and 26 miles in travel distance. The median traffic volume on neighborhood streets in San Francisco, Vallejo, and San Rafael increased by 30%, 22%, and 13%, respectively. The results suggest that the cities’ local roads provided the additional adaptive capacity to disperse the traffic. With large-scale modeling of a critical network disruption using dynamic rerouting capability, complete road network, and full demand, we provide valuable insights into the response dynamics of this specific event. In doing so, the value of such regional analyses to incident and disaster planning is demonstrated.
{"title":"Evaluating the impacts of major transportation disruptions – San Francisco Bay Area case study","authors":"","doi":"10.1016/j.ijtst.2023.08.003","DOIUrl":"10.1016/j.ijtst.2023.08.003","url":null,"abstract":"<div><div>The constraints of transportation networks are fundamental to disaster planning. Having the capability of evaluating the emergent dynamics of such networks in the context of large traffic incidents can inform the design of traffic management strategies. On February 7, 2019, the Richmond-San Rafael Bridge in the San Francisco Bay Area, connecting multiple cities and carrying over 100 000 vehicles daily, had to be suddenly closed for over 9 hours due to a structural failure of its upper deck. This incident caused major disruptions in the region as the typical traffic was interrupted and detoured as travelers found alternate routes. In this study, we demonstrate the capability of large-scale traffic impact assessments of major network disruptions using the Richmond-San Rafael Bridge closure as a case study. Using a high-performance, parallel-discrete event traffic simulation, we assess the traffic impacts resulting from the bridge closure at both the regional system and city levels. Our model estimates that the region incurred an additional 14 000 vehicle hours of delay and 600 000 vehicle miles in distance due to the bridge closure. The incident affected over 55 000 trips; certain trips experienced an increase of 46 min in delay and 26 miles in travel distance. The median traffic volume on neighborhood streets in San Francisco, Vallejo, and San Rafael increased by 30%, 22%, and 13%, respectively. The results suggest that the cities’ local roads provided the additional adaptive capacity to disperse the traffic. With large-scale modeling of a critical network disruption using dynamic rerouting capability, complete road network, and full demand, we provide valuable insights into the response dynamics of this specific event. In doing so, the value of such regional analyses to incident and disaster planning is demonstrated.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43217649","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}
Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.08.005
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.
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Pub Date : 2024-09-01DOI: 10.1016/j.ijtst.2023.08.004
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.
{"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":null,"pages":null},"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}
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