Pub Date : 2024-08-09DOI: 10.1177/03611981241266838
Debashis Ray Sarkar, K. Ramachandra Rao, Niladri Chatterjee
A generalized, reliable unmanned aerial vehicle (UAV) system for visual tracking and detection of road vehicles from aerial videography would outperform traditional traffic monitoring systems, providing extensive coverage and optimal study area perspectives. The combination of UAV technology for data collection and advanced video processing tools for visual tracking would assist traffic engineers in a detailed spatial and temporal utilization analysis with accurate traffic characteristics. Initially, traffic conflicts were determined by post encroachment time from visual data at unsignalized intersection. But a new concept (known as “required post encroachment time”) has been proposed to differentiate between critical and non-critical conflicts among road users. Finally, by extracting the information of vehicle trajectories, we have also developed a “collision probability evaluation model” to determine the severity level of critical conflicts in heterogeneous traffic conditions. Our numerical results show the high precision of our suggested model with regard to risk recognition when evaluating the collision probability at the study intersection. This research utilizes vehicle trajectories to evaluate driving risk at intersections through automatic traffic safety analysis.
{"title":"Automatic Traffic Safety Analysis using Unmanned Aerial Vehicle Technology at Unsignalized Intersections in Heterogeneous Traffic","authors":"Debashis Ray Sarkar, K. Ramachandra Rao, Niladri Chatterjee","doi":"10.1177/03611981241266838","DOIUrl":"https://doi.org/10.1177/03611981241266838","url":null,"abstract":"A generalized, reliable unmanned aerial vehicle (UAV) system for visual tracking and detection of road vehicles from aerial videography would outperform traditional traffic monitoring systems, providing extensive coverage and optimal study area perspectives. The combination of UAV technology for data collection and advanced video processing tools for visual tracking would assist traffic engineers in a detailed spatial and temporal utilization analysis with accurate traffic characteristics. Initially, traffic conflicts were determined by post encroachment time from visual data at unsignalized intersection. But a new concept (known as “required post encroachment time”) has been proposed to differentiate between critical and non-critical conflicts among road users. Finally, by extracting the information of vehicle trajectories, we have also developed a “collision probability evaluation model” to determine the severity level of critical conflicts in heterogeneous traffic conditions. Our numerical results show the high precision of our suggested model with regard to risk recognition when evaluating the collision probability at the study intersection. This research utilizes vehicle trajectories to evaluate driving risk at intersections through automatic traffic safety analysis.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141922609","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}
Pub Date : 2024-08-09DOI: 10.1177/03611981241262315
Ahmed Hossain, Xiaoduan Sun, A. S. Hasan, M. Jalayer, Julius Codjoe
The present trend in the United States suggests that one in five pedestrian fatalities in motor vehicle crashes involves a hit-and-run, a serious traffic safety concern. The over-representation of pedestrian hit-and-run collisions necessitates a systemic data-driven investigation to uncover the contributing factors that cause fatalities or serious injuries. This study addressed two research questions (RQ), RQ1: What factors contribute to pedestrian hit-and-runs? RQ2: What causes hit-and-run pedestrian fatalities? This study addresses the RQs using the XGBoost algorithm (RQ1) and binary logistic regression model (RQ2) to analyze police-reported pedestrian crashes (2015–2019) in Louisiana. The XGBoost model was used to classify pedestrian hit-and-run crashes (hit-and-run = yes/no) and identified critical factors as predictors of pedestrian hit-and-run crashes including: primary contributing factors (pedestrian action, pedestrian violation, prior movement, pedestrian condition); settings (dark-with-streetlight, posted speed limit of > 55 mph, two-way road with physical separation); pedestrian characteristics (younger and older pedestrians, male gender, presence of dark clothing); and weekend. The binary logistic regression model was further used to identify critical high-risk hit-and-run scenarios resulting in fatal or severe injury of pedestrians. Some of the identified top factors are posted speed limit of 55 mph or higher (OR = 12.74), pedestrian impairment (OR = 4.77), older pedestrians (OR = 2.68), younger pedestrians (OR = 1.79), and dark-no-streetlight conditions (OR = 2.91). Both models showed strong relationships between pedestrian hit-and-run crashes and fatal or severe injuries (e.g., dark-with-streetlight, high-speed settings, older pedestrians, and pedestrian actions). Identifying these critical links can help policymakers, law enforcement agencies, and transportation authorities develop targeted interventions and strategies to address the risk factors.
{"title":"Comprehensive Investigation of Pedestrian Hit-and-Run Crashes: Applying XGBoost and Binary Logistic Regression Model","authors":"Ahmed Hossain, Xiaoduan Sun, A. S. Hasan, M. Jalayer, Julius Codjoe","doi":"10.1177/03611981241262315","DOIUrl":"https://doi.org/10.1177/03611981241262315","url":null,"abstract":"The present trend in the United States suggests that one in five pedestrian fatalities in motor vehicle crashes involves a hit-and-run, a serious traffic safety concern. The over-representation of pedestrian hit-and-run collisions necessitates a systemic data-driven investigation to uncover the contributing factors that cause fatalities or serious injuries. This study addressed two research questions (RQ), RQ1: What factors contribute to pedestrian hit-and-runs? RQ2: What causes hit-and-run pedestrian fatalities? This study addresses the RQs using the XGBoost algorithm (RQ1) and binary logistic regression model (RQ2) to analyze police-reported pedestrian crashes (2015–2019) in Louisiana. The XGBoost model was used to classify pedestrian hit-and-run crashes (hit-and-run = yes/no) and identified critical factors as predictors of pedestrian hit-and-run crashes including: primary contributing factors (pedestrian action, pedestrian violation, prior movement, pedestrian condition); settings (dark-with-streetlight, posted speed limit of > 55 mph, two-way road with physical separation); pedestrian characteristics (younger and older pedestrians, male gender, presence of dark clothing); and weekend. The binary logistic regression model was further used to identify critical high-risk hit-and-run scenarios resulting in fatal or severe injury of pedestrians. Some of the identified top factors are posted speed limit of 55 mph or higher (OR = 12.74), pedestrian impairment (OR = 4.77), older pedestrians (OR = 2.68), younger pedestrians (OR = 1.79), and dark-no-streetlight conditions (OR = 2.91). Both models showed strong relationships between pedestrian hit-and-run crashes and fatal or severe injuries (e.g., dark-with-streetlight, high-speed settings, older pedestrians, and pedestrian actions). Identifying these critical links can help policymakers, law enforcement agencies, and transportation authorities develop targeted interventions and strategies to address the risk factors.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924341","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}
Pub Date : 2024-08-09DOI: 10.1177/03611981241265846
Jianming Cai, Zixin Zhou, Zhiqiang Zhao, Yaxin Wang
The growth of the electric vehicle (EV) market is significantly influenced by the development of EV charging infrastructure. In China, the surge in private charging piles has led to the promotion of the private charging pile sharing model (PCPSM) as a strategic solution to overcome infrastructure challenges. This research develops a tripartite evolutionary game model among pile owners, property companies, and EV users to explore the promotion of the sharing model. Innovatively, it integrates prospect theory to capture the decision-making psychology of the participants. Using system dynamics and numerical simulation, an in-depth analysis is conducted on the effects of 15 key factors influencing strategic decisions, culminating in the formulation of feasible incentive mechanisms. The research reveals that: 1) Exclusive reliance on private pile sharing between pile owners and EV users is unstable, highlighting the need for greater involvement from property companies; 2) Managing crucial factors, including property management costs, charging pile usage prices, and profit-sharing ratios, within appropriate limits is essential for the sustainable growth of PCPSM; 3) Enhancing players’ awareness of potential losses and decreasing their risk preference are effective in encouraging proactive strategy adoption; and 4) The practice of pile owners contributing a specific proportion of management fees to property companies, along with dynamic government incentives, considerably elevates the propensity of property companies to engage actively in the sharing model. This study provides novel insights into enhancing PCPSM, with wide-reaching implications for the sustainability of the EV sector and urban transportation systems.
{"title":"Insights for Sustainable Urban Transport via Private Charging Pile Sharing in the Electric Vehicle Sector","authors":"Jianming Cai, Zixin Zhou, Zhiqiang Zhao, Yaxin Wang","doi":"10.1177/03611981241265846","DOIUrl":"https://doi.org/10.1177/03611981241265846","url":null,"abstract":"The growth of the electric vehicle (EV) market is significantly influenced by the development of EV charging infrastructure. In China, the surge in private charging piles has led to the promotion of the private charging pile sharing model (PCPSM) as a strategic solution to overcome infrastructure challenges. This research develops a tripartite evolutionary game model among pile owners, property companies, and EV users to explore the promotion of the sharing model. Innovatively, it integrates prospect theory to capture the decision-making psychology of the participants. Using system dynamics and numerical simulation, an in-depth analysis is conducted on the effects of 15 key factors influencing strategic decisions, culminating in the formulation of feasible incentive mechanisms. The research reveals that: 1) Exclusive reliance on private pile sharing between pile owners and EV users is unstable, highlighting the need for greater involvement from property companies; 2) Managing crucial factors, including property management costs, charging pile usage prices, and profit-sharing ratios, within appropriate limits is essential for the sustainable growth of PCPSM; 3) Enhancing players’ awareness of potential losses and decreasing their risk preference are effective in encouraging proactive strategy adoption; and 4) The practice of pile owners contributing a specific proportion of management fees to property companies, along with dynamic government incentives, considerably elevates the propensity of property companies to engage actively in the sharing model. This study provides novel insights into enhancing PCPSM, with wide-reaching implications for the sustainability of the EV sector and urban transportation systems.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924408","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}
Pub Date : 2024-08-09DOI: 10.1177/03611981241255901
Kirsten J. Tilleman, S. Chowdhury
Women frequently face gender-based harassment when using public transport and adjust their travel behavior as a result. The present study focuses on how the presence of bystanders influences women’s sense of security and self-efficacy while using public transport. The study assesses the impact community support and social norms, perceived responsibilities of authority, and environmental factors have on women’s perception of security in the context of harassment. We conducted an online survey in Auckland, New Zealand ( n = 524). We analyzed results for differences in responses by gender and intersectional identities such as ethnicity and LGBTQ+. We used common factor analysis to uncover hypothesized latent variables that affect women’s perceptions of security and expectations of bystanders. The analysis produced a four-factor model for women+. The strongest factor in the women+ model was Community, followed by Authority, Confidence, then Vigilance. The women+ model suggests bystander and community support is an important expectation for women using public transport, affecting their perception of security and self-efficacy. For comparison and to gain insight into the role men may have as bystanders, we performed factor analysis on responses from men. The resulting three-factor model included factors for Confidence, Authority, and Vigilance. The strength of the Confidence factor for men suggests there is space for calling men in as bystanders who are informed and willing to act. Overall, study findings indicate that anti-harassment strategies can be strengthened by building an active bystander community, bolstering support for vulnerable riders, and helping establish harassment as an unacceptable form of passenger behavior.
{"title":"Role of Bystanders on Women’s Perception of Personal Security When Using Public Transport","authors":"Kirsten J. Tilleman, S. Chowdhury","doi":"10.1177/03611981241255901","DOIUrl":"https://doi.org/10.1177/03611981241255901","url":null,"abstract":"Women frequently face gender-based harassment when using public transport and adjust their travel behavior as a result. The present study focuses on how the presence of bystanders influences women’s sense of security and self-efficacy while using public transport. The study assesses the impact community support and social norms, perceived responsibilities of authority, and environmental factors have on women’s perception of security in the context of harassment. We conducted an online survey in Auckland, New Zealand ( n = 524). We analyzed results for differences in responses by gender and intersectional identities such as ethnicity and LGBTQ+. We used common factor analysis to uncover hypothesized latent variables that affect women’s perceptions of security and expectations of bystanders. The analysis produced a four-factor model for women+. The strongest factor in the women+ model was Community, followed by Authority, Confidence, then Vigilance. The women+ model suggests bystander and community support is an important expectation for women using public transport, affecting their perception of security and self-efficacy. For comparison and to gain insight into the role men may have as bystanders, we performed factor analysis on responses from men. The resulting three-factor model included factors for Confidence, Authority, and Vigilance. The strength of the Confidence factor for men suggests there is space for calling men in as bystanders who are informed and willing to act. Overall, study findings indicate that anti-harassment strategies can be strengthened by building an active bystander community, bolstering support for vulnerable riders, and helping establish harassment as an unacceptable form of passenger behavior.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923443","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}
Pub Date : 2024-08-08DOI: 10.1177/03611981241247047
James Giller, Mischa Young, Giovanni Circella
The availability of ridehailing services, such as Uber and Lyft, affects the way people choose to travel and can enable travel opportunities that were previously suppressed, leading to additional trips. Previous studies have investigated the modal substitution and induced travel caused by ridehailing, yet few have investigated the factors associated with these travel behaviors. Accordingly, this study examines the personal and trip characteristics associated with ridehailing users’ decisions to substitute other modes of travel or conduct new trips by ridehailing. Using detailed survey data collected in three California metropolitan regions from 2018 and 2019, we estimated an error components logit model of ridehailing users’ choice of an alternative travel option if ridehailing services were unavailable. We found that over 50% of ridehailing trips in our sample were replacing more sustainable modes (i.e., public transit, active modes, and carpooling) or were creating new vehicle miles, with a 5.8% rate of induced travel, with public transit being the most frequently substituted mode. Respondents without a household vehicle and who use pooled services were more likely to replace transit. Longer-distance ridehailing trips were less likely to replace walking, biking, or transit trips. Respondents identifying as a racial or ethnic minority or lacking a household vehicle were least likely to cancel a trip were ridehailing unavailable, suggesting their use of ridehailing for essential rather than discretionary purposes. Together, these findings provide valuable insights for policy makers seeking to address the environmental and equity issues associated with ridehailing.
{"title":"Correlates of Modal Substitution and Induced Travel of Ridehailing in California","authors":"James Giller, Mischa Young, Giovanni Circella","doi":"10.1177/03611981241247047","DOIUrl":"https://doi.org/10.1177/03611981241247047","url":null,"abstract":"The availability of ridehailing services, such as Uber and Lyft, affects the way people choose to travel and can enable travel opportunities that were previously suppressed, leading to additional trips. Previous studies have investigated the modal substitution and induced travel caused by ridehailing, yet few have investigated the factors associated with these travel behaviors. Accordingly, this study examines the personal and trip characteristics associated with ridehailing users’ decisions to substitute other modes of travel or conduct new trips by ridehailing. Using detailed survey data collected in three California metropolitan regions from 2018 and 2019, we estimated an error components logit model of ridehailing users’ choice of an alternative travel option if ridehailing services were unavailable. We found that over 50% of ridehailing trips in our sample were replacing more sustainable modes (i.e., public transit, active modes, and carpooling) or were creating new vehicle miles, with a 5.8% rate of induced travel, with public transit being the most frequently substituted mode. Respondents without a household vehicle and who use pooled services were more likely to replace transit. Longer-distance ridehailing trips were less likely to replace walking, biking, or transit trips. Respondents identifying as a racial or ethnic minority or lacking a household vehicle were least likely to cancel a trip were ridehailing unavailable, suggesting their use of ridehailing for essential rather than discretionary purposes. Together, these findings provide valuable insights for policy makers seeking to address the environmental and equity issues associated with ridehailing.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141926713","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}
Pub Date : 2024-08-08DOI: 10.1177/03611981241254389
Aurore Sallard, François Hublet
With the deepening of European integration, Western Europe has witnessed the emergence of highly interconnected cross-border living areas. So far, these areas have received rather limited attention from both quantitative research and public policy. The COVID-19 pandemic dramatically exposed the limitations of the status quo: with travel restrictions imposed at administrative borders and limited cross-border crisis management, the daily life of people in border regions was affected in a disproportionate way. In an effort to better understand the geography of cross-border communities, this paper presents the first large-scale quantitative analysis of cross-border communities in Western Europe. We apply the Louvain community detection algorithm to a transnational, fine-grained dataset gathering commuter flows across 10 Western European countries. This allows us to produce the first comprehensive transnational mapping of communities in these countries and identify five main cross-border living areas. Based on these findings, we put forward policy recommendations aimed at improving the design of mobility censuses and developing new institutional frameworks in cross-border regions.
{"title":"Where the Borders Lie: Mapping Cross-Border Communities in 10 Western European Countries","authors":"Aurore Sallard, François Hublet","doi":"10.1177/03611981241254389","DOIUrl":"https://doi.org/10.1177/03611981241254389","url":null,"abstract":"With the deepening of European integration, Western Europe has witnessed the emergence of highly interconnected cross-border living areas. So far, these areas have received rather limited attention from both quantitative research and public policy. The COVID-19 pandemic dramatically exposed the limitations of the status quo: with travel restrictions imposed at administrative borders and limited cross-border crisis management, the daily life of people in border regions was affected in a disproportionate way. In an effort to better understand the geography of cross-border communities, this paper presents the first large-scale quantitative analysis of cross-border communities in Western Europe. We apply the Louvain community detection algorithm to a transnational, fine-grained dataset gathering commuter flows across 10 Western European countries. This allows us to produce the first comprehensive transnational mapping of communities in these countries and identify five main cross-border living areas. Based on these findings, we put forward policy recommendations aimed at improving the design of mobility censuses and developing new institutional frameworks in cross-border regions.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928654","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}
Pub Date : 2024-08-08DOI: 10.1177/03611981241260696
Jiyong Zhang, Bo Wang, Hamad Naeem, Shengxin Dai
Lane lines are frequently interrupted in autonomous driving environments because of some objective conditions, such as occlusion or congestion, which often lead to the decreased detection performance of a model. Current detection methods relying on spatial information struggle to detect complete lane lines in such conditions. In this paper, we build a robust lane detection model by fusing spatiotemporal information and dilated convolution. The proposed model is aided by the dilated convolution, which expands the scope of convolutional processes to extract more lane feature information from various perception environments. Convolutional gate recurrent units (ConvGRUs) are employed at the high-level semantic phase to aid the proposed model to get more effective lane feature information by dealing with the spatiotemporal information of consecutive frames. Compared with models FCN, DeepLabv3, RefineNet, SCNN, Cheng-DET, LDNet, SegNet, SegNet-Ego-Lane, Res18, Res34, ResNet-18-SAD, ResNet-34-SAD, ENet-SAD, ReNet-101, R-18-E2E, R-34-E2E, R-101-SAD, R-101-E2E, ResNet34-Qin, LaneNet, PINET(64x32), UNet_ConvLSTMSegNet_ConvLSTM, LDSTNet, extensive experiments on three well-known lane detection benchmarks prove the usefulness of the proposed model, achieving robust results and competitive performance.
{"title":"Robust Spatiotemporal Lane Detection Model","authors":"Jiyong Zhang, Bo Wang, Hamad Naeem, Shengxin Dai","doi":"10.1177/03611981241260696","DOIUrl":"https://doi.org/10.1177/03611981241260696","url":null,"abstract":"Lane lines are frequently interrupted in autonomous driving environments because of some objective conditions, such as occlusion or congestion, which often lead to the decreased detection performance of a model. Current detection methods relying on spatial information struggle to detect complete lane lines in such conditions. In this paper, we build a robust lane detection model by fusing spatiotemporal information and dilated convolution. The proposed model is aided by the dilated convolution, which expands the scope of convolutional processes to extract more lane feature information from various perception environments. Convolutional gate recurrent units (ConvGRUs) are employed at the high-level semantic phase to aid the proposed model to get more effective lane feature information by dealing with the spatiotemporal information of consecutive frames. Compared with models FCN, DeepLabv3, RefineNet, SCNN, Cheng-DET, LDNet, SegNet, SegNet-Ego-Lane, Res18, Res34, ResNet-18-SAD, ResNet-34-SAD, ENet-SAD, ReNet-101, R-18-E2E, R-34-E2E, R-101-SAD, R-101-E2E, ResNet34-Qin, LaneNet, PINET(64x32), UNet_ConvLSTMSegNet_ConvLSTM, LDSTNet, extensive experiments on three well-known lane detection benchmarks prove the usefulness of the proposed model, achieving robust results and competitive performance.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928755","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}
Pub Date : 2024-07-27DOI: 10.1177/03611981241255905
Songha Lee, Juneyoung Park, Mohamed Abdel-Aty
Traffic crash-post management is very important for transportation agencies. Delays in clearing the scene after a crash can directly increase the likelihood of a secondary crash and cause more serious traffic congestion. To optimize the management strategies for non-recurrent congestion, it is important to understand the factors that affect incident clearance times. This paper develops a model to analyze the duration time on highways using various types of datasets, including real-time data at the time of or immediately before the crash, detailed time variables, and crash type, with an accelerated failure time model. The model includes the three parametric distributions and assumed randomness, which is called unobserved heterogeneity, and can parametrically estimate the time to hazard to provide the conditional probability that the crash will be resolved. The results show that the Weibull distribution model with random parameters was suitable for both injury and non-injury crashes. Specifically, factors such as whether a truck was involved, temporal speed difference, rain, and rollover status are related to the increase in the duration time. Also, when the weighted length of the response time and detection time are applied to the duration time, the shorter the response time, the shorter the duration time for injury crashes. If there are no injuries, the faster it will be detected and help arrive at the scene. On this result, it is expected that it will be possible to develop a highly accurate clearance time prediction model with artificial intelligence techniques by using more data samples or high-resolution vehicle trajectory data.
{"title":"Hazards-Based Duration Time Model with Priorities Considering Unobserved Heterogeneity Using Real-Time Traffic and Weather Big Data","authors":"Songha Lee, Juneyoung Park, Mohamed Abdel-Aty","doi":"10.1177/03611981241255905","DOIUrl":"https://doi.org/10.1177/03611981241255905","url":null,"abstract":"Traffic crash-post management is very important for transportation agencies. Delays in clearing the scene after a crash can directly increase the likelihood of a secondary crash and cause more serious traffic congestion. To optimize the management strategies for non-recurrent congestion, it is important to understand the factors that affect incident clearance times. This paper develops a model to analyze the duration time on highways using various types of datasets, including real-time data at the time of or immediately before the crash, detailed time variables, and crash type, with an accelerated failure time model. The model includes the three parametric distributions and assumed randomness, which is called unobserved heterogeneity, and can parametrically estimate the time to hazard to provide the conditional probability that the crash will be resolved. The results show that the Weibull distribution model with random parameters was suitable for both injury and non-injury crashes. Specifically, factors such as whether a truck was involved, temporal speed difference, rain, and rollover status are related to the increase in the duration time. Also, when the weighted length of the response time and detection time are applied to the duration time, the shorter the response time, the shorter the duration time for injury crashes. If there are no injuries, the faster it will be detected and help arrive at the scene. On this result, it is expected that it will be possible to develop a highly accurate clearance time prediction model with artificial intelligence techniques by using more data samples or high-resolution vehicle trajectory data.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798304","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}
Pub Date : 2024-07-27DOI: 10.1177/03611981241260700
Fesih Keskin, Melih Yıldız, Bircan Arslannur
This paper presents a methodology for generating realistic driving cycles through a combination of Markov chain modeling, Monte Carlo simulation, and dynamic time warping. The study is focused on the construction of a representative driving cycle for the city of Iğdır in Turkey, taking into account its unique traffic characteristics. The methodology involves two main stages: first, determining reference segments partitioned from original driving datasets based on traffic conditions and road types, using the dynamic time warping technique based on the similarity between each segment time series. The second stage is to stochastically generate a representative driving cycle by employing a combination of Markov chain and Monte Carlo simulation, producing variability and randomness. In this stage, the best driving cycle segment of each segment group from among the generated driving segments utilizing Markov chain modeling and Monte Carlo simulation was selected using the dynamic time warping techniques, considering the reference segments. Finally, a representative driving cycle was constructed by stitching each segment. To assess the generated representative cycle, commonly used kinematic parameters were compared with real-world driving cycle data for Iğdır. The results show that the proposed methodology provides an advanced algorithm for generating a reasonable representative driving cycle, which can contribute to energy consumption analysis, vehicle performance, and emission evaluation. The comprehensive approach provided by the proposed methodology enables an accurate understanding of driving patterns, promoting the development of sustainable mobility solutions.
{"title":"A Data-Driven Framework for Driving Cycle Generation and Analysis","authors":"Fesih Keskin, Melih Yıldız, Bircan Arslannur","doi":"10.1177/03611981241260700","DOIUrl":"https://doi.org/10.1177/03611981241260700","url":null,"abstract":"This paper presents a methodology for generating realistic driving cycles through a combination of Markov chain modeling, Monte Carlo simulation, and dynamic time warping. The study is focused on the construction of a representative driving cycle for the city of Iğdır in Turkey, taking into account its unique traffic characteristics. The methodology involves two main stages: first, determining reference segments partitioned from original driving datasets based on traffic conditions and road types, using the dynamic time warping technique based on the similarity between each segment time series. The second stage is to stochastically generate a representative driving cycle by employing a combination of Markov chain and Monte Carlo simulation, producing variability and randomness. In this stage, the best driving cycle segment of each segment group from among the generated driving segments utilizing Markov chain modeling and Monte Carlo simulation was selected using the dynamic time warping techniques, considering the reference segments. Finally, a representative driving cycle was constructed by stitching each segment. To assess the generated representative cycle, commonly used kinematic parameters were compared with real-world driving cycle data for Iğdır. The results show that the proposed methodology provides an advanced algorithm for generating a reasonable representative driving cycle, which can contribute to energy consumption analysis, vehicle performance, and emission evaluation. The comprehensive approach provided by the proposed methodology enables an accurate understanding of driving patterns, promoting the development of sustainable mobility solutions.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141798570","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}
Pub Date : 2024-07-27DOI: 10.1177/03611981241260688
Zhongyi Liu, Travis Shoemaker, E. Tutumluer, Y. Hashash
The development of a large-scale high-fidelity model of train, rail, crosstie, and ballast offers a virtual laboratory for studying train–track dynamics. Currently, Train–Track (TT) models integrate the whole train and track system together, but lack explicit representation of ballast particles and simplify them as one-degree-of-freedom mass blocks only moving vertically, whereas models based on Discrete Element Method (DEM) for detailed ballast granular mechanics rarely include detailed representations of the rail and train because these multi-body systems are difficult to model within a DEM framework. To overcome these shortcomings, a large-scale TT-DEM coupled model with more than 480,000 polyhedron ballast particles was established to simulate track dynamic responses. To make this size model feasible with available computing resources, the TT and DEM models were coupled with a proportional–integral–derivative (PID) algorithm to eliminate the need for iteration within each time step. Additionally, the DEM time step was increased, cross-software communication was streamlined, and DEM data extraction was improved. Collectively, these improvements resulted in a model speed-up of about 200 times. The proposed TT-DEM model was validated by comparing predicted and field measured crosstie displacements. These comparisons showed that the TT-DEM model more closely represents the nonlinear system behavior than the conventional TT model and offers the advantage of studying the ballast at the particle level. A study of the thirty-crosstie TT-DEM ballast particle response to train track loading identified significant horizontal ballast forces that are not included in the TT model or single-crosstie TT-DEM models.
大规模高保真列车、轨道、横梁和道碴模型的开发为研究列车-轨道动力学提供了一个虚拟实验室。目前，列车-轨道（TT）模型将整个列车和轨道系统集成在一起，但缺乏对道碴颗粒的明确表示，并将其简化为仅垂直运动的单自由度质量块，而基于离散元素法（DEM）的详细道碴颗粒力学模型很少包含轨道和列车的详细表示，因为这些多体系统很难在 DEM 框架内建模。为了克服这些缺点，我们建立了一个包含超过 480,000 个多面体道碴颗粒的大型 TT-DEM 耦合模型，用于模拟轨道动态响应。为使这一规模的模型在现有计算资源条件下可行，TT 和 DEM 模型采用了比例-积分-派生（PID）算法进行耦合，以消除每个时间步长内的迭代需要。此外，还增加了 DEM 时间步长，简化了跨软件通信，并改进了 DEM 数据提取。总之，这些改进使模型速度提高了约 200 倍。通过比较预测和现场测量的横梁位移，对所提出的 TT-DEM 模型进行了验证。比较结果表明，TT-DEM 模型比传统的 TT 模型更贴近地反映了非线性系统行为，并具有从颗粒层面研究道碴的优势。通过研究 30 根横梁的 TT-DEM 有砟轨道颗粒对列车轨道荷载的响应，发现了 TT 模型或单横梁 TT-DEM 模型中未包含的显著水平有砟力。
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