Pub Date : 2024-01-20DOI: 10.1177/03611981231217741
Jin Wang, Ziang Song, Ziyi Zhang, Yanyan Chen, Niuqi Xu
It is essential to delineate visual occlusions of head-on traffic signboards under varying available sight distances (ASDs) to improve road safety and guide maintenance. However, the status of sight occlusions in actual road scenarios is unclear because of the lack of an automatic and quantitative evaluation approach. This study presents a new cluster attention traffic-sign occlusion (CASO) method with a cluster attention traffic-sign network (CASNet) to automatically classify road infrastructures, and an occlusion delineating module to dynamically describe the occlusions of head-on traffic signboards using point clouds. CASNet consists of a cluster module to alleviate the interference of redundant object features and an attention module to focus on learning local features of small samples on road scenes. The occlusion delineating module is investigated to rapidly construct a signboard-related oblique cone and dynamically assess occlusions. The sight occlusions change with varying ASDs and are delineated by two indices: the degree of shaded signboard area, and the degree of occlusion volume from the driver’s dynamic perspective to the head-on signboard. Compared with the state-of-the-art networks, the experimental extraction results achieved for signboards show an overall improved performance. The degree of occlusion volumes toward head-on signboards fluctuates between 26.75% and 36.70%, and the degree of shaded areas on signboards increases from 22.75% to 59.63%, with ASDs varying from 20 to 75 m. This research contributes to evaluating road safety in intelligent transportation systems and accurately guiding the allocation of maintenance budgets to the heavily occluded road sections.
{"title":"Delineating Sight Occlusions of Head-On Traffic Signboards under Varying Available Sight Distances Using LiDAR Point Clouds","authors":"Jin Wang, Ziang Song, Ziyi Zhang, Yanyan Chen, Niuqi Xu","doi":"10.1177/03611981231217741","DOIUrl":"https://doi.org/10.1177/03611981231217741","url":null,"abstract":"It is essential to delineate visual occlusions of head-on traffic signboards under varying available sight distances (ASDs) to improve road safety and guide maintenance. However, the status of sight occlusions in actual road scenarios is unclear because of the lack of an automatic and quantitative evaluation approach. This study presents a new cluster attention traffic-sign occlusion (CASO) method with a cluster attention traffic-sign network (CASNet) to automatically classify road infrastructures, and an occlusion delineating module to dynamically describe the occlusions of head-on traffic signboards using point clouds. CASNet consists of a cluster module to alleviate the interference of redundant object features and an attention module to focus on learning local features of small samples on road scenes. The occlusion delineating module is investigated to rapidly construct a signboard-related oblique cone and dynamically assess occlusions. The sight occlusions change with varying ASDs and are delineated by two indices: the degree of shaded signboard area, and the degree of occlusion volume from the driver’s dynamic perspective to the head-on signboard. Compared with the state-of-the-art networks, the experimental extraction results achieved for signboards show an overall improved performance. The degree of occlusion volumes toward head-on signboards fluctuates between 26.75% and 36.70%, and the degree of shaded areas on signboards increases from 22.75% to 59.63%, with ASDs varying from 20 to 75 m. This research contributes to evaluating road safety in intelligent transportation systems and accurately guiding the allocation of maintenance budgets to the heavily occluded road sections.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139524352","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 : 2024-01-17DOI: 10.1177/03611981231220632
Bowen Cai, Xuesong Wang
This study is about smallest acceptable sample size determination in experimental design studies involving a driving simulator. The smallest acceptable sample size should be specified so researchers can make accurate inferences about their studied populations. However, the number of samples typically collected is largely subject to the expense of data collection. Working out the methodology of estimating the required number of subjects based on an initially small number is a better way for researchers to determine the smallest acceptable sample size in the experiment. Predictor estimate precision and prediction accuracy are major factors for conducting experiments. Accordingly, this study estimates the smallest acceptable sample size, with emphasis on coefficient estimation and prediction accuracy for selected significant variables. The smallest acceptable sample size is chosen to be the maximum value returned by both coefficient estimation calculation and accuracy prediction calculation approaches. This methodology is flexible and scalable, and can be tailored to other experimental situations. To validate the appropriateness of this procedure, a more than sufficient sample of 50 drivers was recruited. The smallest acceptable sample size was determined backwardly, based on the variable coefficient convergence trends of the mean squared error (MSE) curves of the significant variables. Both the clear converging trends of the MSE curves and the proposed method indicated that 30 was an acceptable sample size.
{"title":"Estimation of the Smallest Acceptable Sample Size in Bilateral Approaches to Coefficient Estimation and Accuracy Prediction","authors":"Bowen Cai, Xuesong Wang","doi":"10.1177/03611981231220632","DOIUrl":"https://doi.org/10.1177/03611981231220632","url":null,"abstract":"This study is about smallest acceptable sample size determination in experimental design studies involving a driving simulator. The smallest acceptable sample size should be specified so researchers can make accurate inferences about their studied populations. However, the number of samples typically collected is largely subject to the expense of data collection. Working out the methodology of estimating the required number of subjects based on an initially small number is a better way for researchers to determine the smallest acceptable sample size in the experiment. Predictor estimate precision and prediction accuracy are major factors for conducting experiments. Accordingly, this study estimates the smallest acceptable sample size, with emphasis on coefficient estimation and prediction accuracy for selected significant variables. The smallest acceptable sample size is chosen to be the maximum value returned by both coefficient estimation calculation and accuracy prediction calculation approaches. This methodology is flexible and scalable, and can be tailored to other experimental situations. To validate the appropriateness of this procedure, a more than sufficient sample of 50 drivers was recruited. The smallest acceptable sample size was determined backwardly, based on the variable coefficient convergence trends of the mean squared error (MSE) curves of the significant variables. Both the clear converging trends of the MSE curves and the proposed method indicated that 30 was an acceptable sample size.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139526896","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 : 2024-01-17DOI: 10.1177/03611981231222234
Yuze Shang, Fei Liu, Ping Qin, Zhizhong Guo, Zhe Li
In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.
{"title":"Energy-Efficient Speed Planning for Autonomous Driving in Dynamic Traffic Scenarios","authors":"Yuze Shang, Fei Liu, Ping Qin, Zhizhong Guo, Zhe Li","doi":"10.1177/03611981231222234","DOIUrl":"https://doi.org/10.1177/03611981231222234","url":null,"abstract":"In the field of autonomous driving, velocity planning is of paramount importance for handling dynamic obstacle scenarios. To avoid unnecessary acceleration and deceleration, self-driving vehicles need to find an energy-optimized velocity trajectory. Moreover, in complex traffic environments, the vehicle trajectory must consider the spatio-temporal coupling problem to avoid unrealistic driving paths. To address these challenges, this paper proposes a hierarchical planner that first plans the path and then performs speed planning based on the already planned path. Specifically, we focus on the energy consumption factor and use dynamic programming for speed planning while combining safety and comfort considerations. The optimal energy-saving trajectory is obtained by combining the speed profile with the optimal path. To cope with complex scenarios on real roads, we propose an adaptive trajectory adjustment strategy based on model predictive control to track by adaptively selecting tracking modes. Finally, hardware-in-the-loop experimental validation demonstrates that our proposed method significantly reduces energy consumption compared with the traditional decoupling method while ensuring that the autonomous vehicle adapts well to complex traffic scenarios.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527716","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 : 2024-01-17DOI: 10.1177/03611981231213079
Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou
Traffic state estimation is a challenging task because of the collection of sparse and noisy measurements from fixed points in the traffic network. Induction loops, as they are non-intrusive, can observe any area of the traffic network on demand and provide accurate traffic density and speed measurements. Our main contribution is the development of an optimization framework where small parts of the traffic network are monitored by Unmanned Aerial Vehicles (UAVs) and accurate estimates of traffic density and mean speeds for every region in the traffic network are returned in real-time. Assuming regional-based traffic dynamics, a cyclical UAV flight path is defined for each region. One UAV is assigned to each flight path and monitors a small area of the region below. The UAV-based traffic measurements are expressed as moving averages to smooth out fluctuations in traffic density and mean speed. A moving horizon optimization problem is formulated, which minimizes the estimation and process errors over a moving time window. The problem is non-convex and challenging to solve, because of the presence of nonlinear traffic dynamics. By considering free-flow conditions, the optimization problem is recast to a quadratic program that returns density estimations for each region of the traffic network in real-time. Simulation results compare our UAV framework to an alternative, where the whole traffic network is monitored by UAVs. Both frameworks obtain similar results, despite the alternative framework using more UAVs than our framework.
{"title":"Real-Time Unmanned Aerial Vehicle-Based Traffic State Estimation for Multi-Regional Traffic Networks","authors":"Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou","doi":"10.1177/03611981231213079","DOIUrl":"https://doi.org/10.1177/03611981231213079","url":null,"abstract":"Traffic state estimation is a challenging task because of the collection of sparse and noisy measurements from fixed points in the traffic network. Induction loops, as they are non-intrusive, can observe any area of the traffic network on demand and provide accurate traffic density and speed measurements. Our main contribution is the development of an optimization framework where small parts of the traffic network are monitored by Unmanned Aerial Vehicles (UAVs) and accurate estimates of traffic density and mean speeds for every region in the traffic network are returned in real-time. Assuming regional-based traffic dynamics, a cyclical UAV flight path is defined for each region. One UAV is assigned to each flight path and monitors a small area of the region below. The UAV-based traffic measurements are expressed as moving averages to smooth out fluctuations in traffic density and mean speed. A moving horizon optimization problem is formulated, which minimizes the estimation and process errors over a moving time window. The problem is non-convex and challenging to solve, because of the presence of nonlinear traffic dynamics. By considering free-flow conditions, the optimization problem is recast to a quadratic program that returns density estimations for each region of the traffic network in real-time. Simulation results compare our UAV framework to an alternative, where the whole traffic network is monitored by UAVs. Both frameworks obtain similar results, despite the alternative framework using more UAVs than our framework.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139527068","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 : 2024-01-13DOI: 10.1177/03611981231220656
Nihal Abuzinadah, Turki Aljrees, Xiaoyuan Chen, Muhammad Umer, Omar Ibrahim Aboulola, Saba Tahir, Ala’ Abdulmajid Eshmawi, Khaled Alnowaiser, Imran Ashraf
Although there have been improvements in traffic safety measures, the frequency of traffic accidents continues to persist. Developing countries experience a significant impact from traffic accidents with respect to fatalities and property damage. Traffic accidents happen for multiple reasons, involving traffic conditions, driving violations, driver misjudgments, and so forth. Severe casualties may lead to fatalities; therefore, accident severity prediction might help reduce the chances of fatalities. This research makes use of a U.S. road accident dataset that contains the most relevant 32 factors related to accidents. For obtaining accurate prediction of traffic accident severity, this research proposes a solution based on an ensemble of random forest and support vector classifiers that is trained using deep convoluted features. Features are extracted from the road accident dataset using a convolutional neural network (CNN). The performance of models using original features and CNN features is analyzed that shows the superiority of convoluted features. Experimental results involving the use of several well-known machine learning models indicate that the proposed model can obtain an accuracy of 99.99% for traffic accident severity prediction. The efficacy of the proposed model is validated against existing state-of-the-art approaches.
{"title":"Improving Traffic Accident Severity Prediction Using Convoluted Features and Decision-Level Fusion of Models","authors":"Nihal Abuzinadah, Turki Aljrees, Xiaoyuan Chen, Muhammad Umer, Omar Ibrahim Aboulola, Saba Tahir, Ala’ Abdulmajid Eshmawi, Khaled Alnowaiser, Imran Ashraf","doi":"10.1177/03611981231220656","DOIUrl":"https://doi.org/10.1177/03611981231220656","url":null,"abstract":"Although there have been improvements in traffic safety measures, the frequency of traffic accidents continues to persist. Developing countries experience a significant impact from traffic accidents with respect to fatalities and property damage. Traffic accidents happen for multiple reasons, involving traffic conditions, driving violations, driver misjudgments, and so forth. Severe casualties may lead to fatalities; therefore, accident severity prediction might help reduce the chances of fatalities. This research makes use of a U.S. road accident dataset that contains the most relevant 32 factors related to accidents. For obtaining accurate prediction of traffic accident severity, this research proposes a solution based on an ensemble of random forest and support vector classifiers that is trained using deep convoluted features. Features are extracted from the road accident dataset using a convolutional neural network (CNN). The performance of models using original features and CNN features is analyzed that shows the superiority of convoluted features. Experimental results involving the use of several well-known machine learning models indicate that the proposed model can obtain an accuracy of 99.99% for traffic accident severity prediction. The efficacy of the proposed model is validated against existing state-of-the-art approaches.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531782","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 : 2024-01-13DOI: 10.1177/03611981231222238
Shivam Khaddar, Mahmudur Rahman Fatmi
COVID-19 mitigation measures triggered a sharp increase in the adoption of teleshopping and telecommuting activities. However, there is a need to understand the extent to which past frequencies and experiences will affect post-pandemic teleactivity behavior. Moreover, teleshopping and telecommuting are interconnected, and a relationship may exist between them in the post-pandemic world. This study investigates post-pandemic preferences toward online grocery shopping, online food ordering, and working from home by using a multivariate ordered probit (MVOP) model. The data come from a web-based survey conducted for the Central Okanagan region of Canada. Model results confirm the presence of unobserved factors influencing telecommuting and teleshopping choices. Looking at endogeneity, working from home after the pandemic revealed a positive effect on online grocery shopping. However, results were not the same for post-pandemic online food ordering. Model results also confirm the significant impact of past teleactivity frequencies and experiences on post-pandemic preferences. Overall, the findings provide important insights into post-pandemic activity and travel patterns which can be used for robust policymaking.
{"title":"Modeling Telecommuting and Teleshopping Preferences in the Post-Pandemic Era","authors":"Shivam Khaddar, Mahmudur Rahman Fatmi","doi":"10.1177/03611981231222238","DOIUrl":"https://doi.org/10.1177/03611981231222238","url":null,"abstract":"COVID-19 mitigation measures triggered a sharp increase in the adoption of teleshopping and telecommuting activities. However, there is a need to understand the extent to which past frequencies and experiences will affect post-pandemic teleactivity behavior. Moreover, teleshopping and telecommuting are interconnected, and a relationship may exist between them in the post-pandemic world. This study investigates post-pandemic preferences toward online grocery shopping, online food ordering, and working from home by using a multivariate ordered probit (MVOP) model. The data come from a web-based survey conducted for the Central Okanagan region of Canada. Model results confirm the presence of unobserved factors influencing telecommuting and teleshopping choices. Looking at endogeneity, working from home after the pandemic revealed a positive effect on online grocery shopping. However, results were not the same for post-pandemic online food ordering. Model results also confirm the significant impact of past teleactivity frequencies and experiences on post-pandemic preferences. Overall, the findings provide important insights into post-pandemic activity and travel patterns which can be used for robust policymaking.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139531240","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 : 2024-01-13DOI: 10.1177/03611981231214523
Pengcheng Yuan, Mingliang Luo, Ge Miao, Jilin Li
In view of the current problem of non-emergency patient transport route planning, the research is mainly carried out on fuel vehicles, and in the related research on the vehicle routing problem of electric vehicles, the relevant research results considering flexible charging strategies of different charging technologies are relatively weak. Therefore, this paper studies the non-emergency patient transport route planning problem based on pure electric vehicles, introduces a flexible charging strategy, constructs a mathematical model with the goal of maximizing the average patient satisfaction and minimizing the total cost of patient transport, and designs a hybrid heuristic algorithm to solve the problem. Then, different algorithms are used to solve the mathematical model considering patient satisfaction and the mathematical model not considering patient satisfaction. The feasibility and applicability of the mathematical model and the hybrid algorithm are verified by comparing and analyzing the solution results of different algorithms. Finally, based on the hybrid heuristic algorithm, the mathematical model of non-emergency patient transport route planning considering different charging strategies is solved. The applicability and reliability of the hybrid algorithm are further verified by comparing the solution results of different charging strategies, which shows that the flexible charging strategy can not only achieve a better balance between patient satisfaction and the total cost of patient transport, but also effectively improve the utilization efficiency of the remaining power of the vehicle.
{"title":"Problem of Patient Transport Route Planning for Battery Electric Vehicles Considering a Flexible Charging Strategy","authors":"Pengcheng Yuan, Mingliang Luo, Ge Miao, Jilin Li","doi":"10.1177/03611981231214523","DOIUrl":"https://doi.org/10.1177/03611981231214523","url":null,"abstract":"In view of the current problem of non-emergency patient transport route planning, the research is mainly carried out on fuel vehicles, and in the related research on the vehicle routing problem of electric vehicles, the relevant research results considering flexible charging strategies of different charging technologies are relatively weak. Therefore, this paper studies the non-emergency patient transport route planning problem based on pure electric vehicles, introduces a flexible charging strategy, constructs a mathematical model with the goal of maximizing the average patient satisfaction and minimizing the total cost of patient transport, and designs a hybrid heuristic algorithm to solve the problem. Then, different algorithms are used to solve the mathematical model considering patient satisfaction and the mathematical model not considering patient satisfaction. The feasibility and applicability of the mathematical model and the hybrid algorithm are verified by comparing and analyzing the solution results of different algorithms. Finally, based on the hybrid heuristic algorithm, the mathematical model of non-emergency patient transport route planning considering different charging strategies is solved. The applicability and reliability of the hybrid algorithm are further verified by comparing the solution results of different charging strategies, which shows that the flexible charging strategy can not only achieve a better balance between patient satisfaction and the total cost of patient transport, but also effectively improve the utilization efficiency of the remaining power of the vehicle.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139530849","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 : 2024-01-12DOI: 10.1177/03611981231213084
Tony Diana
The implementation of new performance-based navigation procedures at an East Coast airport in 2016 required the airport authority to step up its engagement with airport community residents. This case study leverages natural language processing to explain changes in the sentiments of airport community residents from 2015 to 2021. Natural language processing algorithms made it possible to create a community engagement grid that highlights issues identified in digital prints and social media and allows decision-makers to prioritize them based on awareness and urgency.
{"title":"Can We Leverage Text Analysis to Inform Policy Priorities Through a Community Engagement Grid?","authors":"Tony Diana","doi":"10.1177/03611981231213084","DOIUrl":"https://doi.org/10.1177/03611981231213084","url":null,"abstract":"The implementation of new performance-based navigation procedures at an East Coast airport in 2016 required the airport authority to step up its engagement with airport community residents. This case study leverages natural language processing to explain changes in the sentiments of airport community residents from 2015 to 2021. Natural language processing algorithms made it possible to create a community engagement grid that highlights issues identified in digital prints and social media and allows decision-makers to prioritize them based on awareness and urgency.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139532135","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 : 2024-01-11DOI: 10.1177/03611981231217504
Jonathan Aguero-Valverde, Dario Vargas-Aguilar
Spatial correlation models have been traditionally used in road safety to account for spatial effects resulting from unmeasured or unknown risk factors that induce spatial correlation between neighboring areas. In transportation, the interaction between neighboring areas is highly influenced by the number of roads that connect those areas and the importance of those roads. This paper proposes an approach in which the weights of the spatial interaction (and therefore the spatial correlation) between areas depends on the number of road connections between those areas and the importance of those connections. The results using districts in Costa Rica show that the inclusion of road network connectivity in the models of spatial correlation significantly improves model fit, even after accounting for model complexity using the deviance information criterion (DIC) and widely applicable information criterion (WAIC). The inclusion of higher weights for national roads compared to municipal or local roads further improved the model fit. The best three models with respect to the posterior deviance, DIC, and WAIC are those that give at least three times more weight to national roads compared to local roads. With respect to site ranking, those three models present similar results, which also highlights the consistency among those models.
{"title":"Incorporating Road Network Connectivity in Neighboring Structures for Crash Prediction Models at the Area Level","authors":"Jonathan Aguero-Valverde, Dario Vargas-Aguilar","doi":"10.1177/03611981231217504","DOIUrl":"https://doi.org/10.1177/03611981231217504","url":null,"abstract":"Spatial correlation models have been traditionally used in road safety to account for spatial effects resulting from unmeasured or unknown risk factors that induce spatial correlation between neighboring areas. In transportation, the interaction between neighboring areas is highly influenced by the number of roads that connect those areas and the importance of those roads. This paper proposes an approach in which the weights of the spatial interaction (and therefore the spatial correlation) between areas depends on the number of road connections between those areas and the importance of those connections. The results using districts in Costa Rica show that the inclusion of road network connectivity in the models of spatial correlation significantly improves model fit, even after accounting for model complexity using the deviance information criterion (DIC) and widely applicable information criterion (WAIC). The inclusion of higher weights for national roads compared to municipal or local roads further improved the model fit. The best three models with respect to the posterior deviance, DIC, and WAIC are those that give at least three times more weight to national roads compared to local roads. With respect to site ranking, those three models present similar results, which also highlights the consistency among those models.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533831","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 : 2024-01-11DOI: 10.1177/03611981231220635
Yu (Fred) Song
Truck platooning is a promising solution for enhancing efficiency and reducing fuel consumption in freight transportation. However, overtaking truck platoons poses safety challenges that need to be addressed. This paper employs an overtaking model incorporating lane change dynamics, and a collision risk assessment to evaluate the safety risks involved in overtaking truck platoons. The safety assessment is presented through a numerical analysis that considers three aspects: roadway geometry, traffic conditions, and driver behavior. Scenarios representing different truck platoon lengths, driver behavior, and opposing traffic conditions are evaluated, highlighting their impact on safety. Two major findings from the assessment are that the overtaking distance and time increase linearly as the truck platoon length increases, and potential driver hesitation and the presence of opposing vehicles are shown to increase collision risks. Safety implications are that the length of truck platoons needs to be regulated on two-lane undivided highways; and driver behavior should be considered in the safety assessment and regulation of truck platooning, but further investigations are needed. From the perspective of overtaking, this paper emphasizes the need for safety guidelines and regulations for truck platooning. Policymakers, transportation agencies, and industry stakeholders may utilize the findings to establish standardized safety measures and protocols.
{"title":"Integrated Overtaking Model and Safety Analysis for Truck Platooning Requirements on Two-Lane Undivided Highways","authors":"Yu (Fred) Song","doi":"10.1177/03611981231220635","DOIUrl":"https://doi.org/10.1177/03611981231220635","url":null,"abstract":"Truck platooning is a promising solution for enhancing efficiency and reducing fuel consumption in freight transportation. However, overtaking truck platoons poses safety challenges that need to be addressed. This paper employs an overtaking model incorporating lane change dynamics, and a collision risk assessment to evaluate the safety risks involved in overtaking truck platoons. The safety assessment is presented through a numerical analysis that considers three aspects: roadway geometry, traffic conditions, and driver behavior. Scenarios representing different truck platoon lengths, driver behavior, and opposing traffic conditions are evaluated, highlighting their impact on safety. Two major findings from the assessment are that the overtaking distance and time increase linearly as the truck platoon length increases, and potential driver hesitation and the presence of opposing vehicles are shown to increase collision risks. Safety implications are that the length of truck platoons needs to be regulated on two-lane undivided highways; and driver behavior should be considered in the safety assessment and regulation of truck platooning, but further investigations are needed. From the perspective of overtaking, this paper emphasizes the need for safety guidelines and regulations for truck platooning. Policymakers, transportation agencies, and industry stakeholders may utilize the findings to establish standardized safety measures and protocols.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139533600","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}
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