Pub Date : 2024-08-17DOI: 10.1177/03611981241255359
Maged Gouda, Karim El-Basyouny
This work aims to assess the occlusion of traffic signs for autonomous vehicles (AVs) using point cloud data, while addressing the limitations and recommendations of previous studies. Dense point cloud data are used to create a digital twin of existing roads and simulate a set of AV sensors within this environment. Convex polyhedrons or hulls with an octree data structure and semantic segmentation were used to assess traffic sign occlusion. Using the developed method, several case studies are presented to identify locations with occluded traffic signs for AVs. This work can help infrastructure operators and AV professionals make data-driven decisions about smart physical infrastructure investments for AVs.
{"title":"Evaluation of Traffic Sign Occlusion for Autonomous Vehicles Using Point Cloud Data","authors":"Maged Gouda, Karim El-Basyouny","doi":"10.1177/03611981241255359","DOIUrl":"https://doi.org/10.1177/03611981241255359","url":null,"abstract":"This work aims to assess the occlusion of traffic signs for autonomous vehicles (AVs) using point cloud data, while addressing the limitations and recommendations of previous studies. Dense point cloud data are used to create a digital twin of existing roads and simulate a set of AV sensors within this environment. Convex polyhedrons or hulls with an octree data structure and semantic segmentation were used to assess traffic sign occlusion. Using the developed method, several case studies are presented to identify locations with occluded traffic signs for AVs. This work can help infrastructure operators and AV professionals make data-driven decisions about smart physical infrastructure investments for AVs.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195995","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 : 2024-08-17DOI: 10.1177/03611981241257514
Amjid Pervez, Naveed Farooz Marazi, Bandhan Bandhu Majumdar, Mao Ruizhi, Jaeyoung Lee, Prasanta K. Sahu
Autonomous vehicle technologies are anticipated to transform road transportation systems, promising enhanced traffic safety and efficiency across different modes, including public buses (PB) and ride-pooling services (RPS). However, in India, there is a growing security concern/fear of crime concerning conventional PB and RPS because of the recent rise in crimes committed on them. Moreover, the introduction of driverless modes of PB and RPS may further heighten commuters’ crime concerns on such services because of the absence of a driver. Thus, this study investigates the acceptance of autonomous public buses (APB) and autonomous ride-pooling services (ARPS), as well as how commuters’ characteristics influence the perceived risks of crime and victimization and their willingness to use the modes. To achieve this, a stated preference survey was designed and conducted across India. The survey resulted in 732 complete responses. The results show that socioeconomic attributes, vehicle automation, and security-related measures significantly influence commuters’ perceived fear of crime and willingness to use APB and ARPS in India. More specifically, young commuters demonstrate higher willingness to use APB and ARPS, while females exhibit lower willingness to use APB and ARPS. In addition, the presence of a security officer on these modes decreases commuters’ concerns about crime. Moreover, travel distance is positively associated with commuters’ perceived level of crime and victimization, while it has a negative relationship with their unwillingness to use APB and ARPS. APB and ARPS are yet to be introduced in India, and Indian commuters have not experienced the security concerns associated with them; thus, the results of this study can serve as the base for guideline formulation for security concerns in India. Based on the results of this study, a set of policy implications, such as female-only transit units, enhancing security measures on the automated modes, and design framework and infrastructure, were proposed. These policy implications can be instrumental in increasing the acceptability of APB and ARPS in India and other countries with similar characteristics.
{"title":"Investigating Indian Commuters’ Perceived Crime Risk on Autonomous Public Buses and Ride-Pooling Services","authors":"Amjid Pervez, Naveed Farooz Marazi, Bandhan Bandhu Majumdar, Mao Ruizhi, Jaeyoung Lee, Prasanta K. Sahu","doi":"10.1177/03611981241257514","DOIUrl":"https://doi.org/10.1177/03611981241257514","url":null,"abstract":"Autonomous vehicle technologies are anticipated to transform road transportation systems, promising enhanced traffic safety and efficiency across different modes, including public buses (PB) and ride-pooling services (RPS). However, in India, there is a growing security concern/fear of crime concerning conventional PB and RPS because of the recent rise in crimes committed on them. Moreover, the introduction of driverless modes of PB and RPS may further heighten commuters’ crime concerns on such services because of the absence of a driver. Thus, this study investigates the acceptance of autonomous public buses (APB) and autonomous ride-pooling services (ARPS), as well as how commuters’ characteristics influence the perceived risks of crime and victimization and their willingness to use the modes. To achieve this, a stated preference survey was designed and conducted across India. The survey resulted in 732 complete responses. The results show that socioeconomic attributes, vehicle automation, and security-related measures significantly influence commuters’ perceived fear of crime and willingness to use APB and ARPS in India. More specifically, young commuters demonstrate higher willingness to use APB and ARPS, while females exhibit lower willingness to use APB and ARPS. In addition, the presence of a security officer on these modes decreases commuters’ concerns about crime. Moreover, travel distance is positively associated with commuters’ perceived level of crime and victimization, while it has a negative relationship with their unwillingness to use APB and ARPS. APB and ARPS are yet to be introduced in India, and Indian commuters have not experienced the security concerns associated with them; thus, the results of this study can serve as the base for guideline formulation for security concerns in India. Based on the results of this study, a set of policy implications, such as female-only transit units, enhancing security measures on the automated modes, and design framework and infrastructure, were proposed. These policy implications can be instrumental in increasing the acceptability of APB and ARPS in India and other countries with similar characteristics.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195994","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 : 2024-08-14DOI: 10.1177/03611981241262298
Ziyan Ju, Muqing Du
In the context of the trend for green travel, the emergence of shared mobility, represented by e-hailing and ridesharing, provides behavioral richness for travelers. Consequently, the authorities that devote themselves to balancing supply and demand in multimodal networks are concerned with incorporating shared mobility into transportation planning and formulating reasonable policy measures. A novel capacity model incorporating a policy mix is developed here as a bi-level programming problem in which the lower-level model is a combined modal split and traffic assignment considering elastic demand (CMSTA-ED) problem, while the upper-level model maximizes the origin–destination (OD) demand multiplier. Integrating the capacity model (the effectiveness index) with social welfare (the implementability index) can account for the synergy, facilitation, and potential contradiction of policy mixes. Numerical experiments validate the characteristics of shared mobility as a supplement to poorly performing public transit under its poor performance. It also examines the policy mix between a public-transit priority subsidy, parking charging, and the shared-mobility subsidy to discover the positive coordination that any individual policy cannot satisfy. This study provides implementable insights for further formulating rational policy-mix strategies on shared mobility to promote the sustainable usage of shared mobility.
{"title":"Effect of Policy Mix on Urban Road Network Capacity Assessment Considering Shared Mobility","authors":"Ziyan Ju, Muqing Du","doi":"10.1177/03611981241262298","DOIUrl":"https://doi.org/10.1177/03611981241262298","url":null,"abstract":"In the context of the trend for green travel, the emergence of shared mobility, represented by e-hailing and ridesharing, provides behavioral richness for travelers. Consequently, the authorities that devote themselves to balancing supply and demand in multimodal networks are concerned with incorporating shared mobility into transportation planning and formulating reasonable policy measures. A novel capacity model incorporating a policy mix is developed here as a bi-level programming problem in which the lower-level model is a combined modal split and traffic assignment considering elastic demand (CMSTA-ED) problem, while the upper-level model maximizes the origin–destination (OD) demand multiplier. Integrating the capacity model (the effectiveness index) with social welfare (the implementability index) can account for the synergy, facilitation, and potential contradiction of policy mixes. Numerical experiments validate the characteristics of shared mobility as a supplement to poorly performing public transit under its poor performance. It also examines the policy mix between a public-transit priority subsidy, parking charging, and the shared-mobility subsidy to discover the positive coordination that any individual policy cannot satisfy. This study provides implementable insights for further formulating rational policy-mix strategies on shared mobility to promote the sustainable usage of shared mobility.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195998","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 : 2024-08-12DOI: 10.1177/03611981241270163
Yang Zhou, Quan Yuan, Chao Yang, Tangyi Guo, Xiaoyi Ma, Wenyong Sun, Tianren Yang
Residence-workplace identification is a fundamental task in mobile phone data analysis, but it faces certain challenges in sparse data processing and results validation because of the lack of ground-truth labels. Previous studies have generally relied on frequency-based methods for inference and trip-based metrics for validation, posing limitations in reliability and efficiency. This paper aims to fill this gap by developing a systematic approach that ranges from data error categorization and processing, feature relevance examination and parameter optimization, and the development of performance metrics considering both residence and workplace validation. For residence-workplace identification, we use a spatiotemporal closeness criterion to deal with the sparsity of data and develop effective dwelling time to enhance frequency-based methods, using one-month cellular signaling records from nine cities in the Yangtze River Delta urban agglomeration in China. For validation, we propose a residence-workplace pair metric based on the population-adjusted number of users, enabling more efficient evaluation of home and work locations than trip-based metrics. Results show that the mean absolute percentage errors (MAPEs) for the Nanjing and Shanghai cases are 5.04% and 8.46%, respectively. Adopted and verified in the large-scale urban agglomeration, the proposed method would be reliable for extracting residence and workplace from low-resolution mobile phone data and contributing to a more accurate identification of urban commuting patterns.
{"title":"Residence-Workplace Identification and Validation Based on Mobile Phone Data: A Case Study in a Large-Scale Urban Agglomeration in China","authors":"Yang Zhou, Quan Yuan, Chao Yang, Tangyi Guo, Xiaoyi Ma, Wenyong Sun, Tianren Yang","doi":"10.1177/03611981241270163","DOIUrl":"https://doi.org/10.1177/03611981241270163","url":null,"abstract":"Residence-workplace identification is a fundamental task in mobile phone data analysis, but it faces certain challenges in sparse data processing and results validation because of the lack of ground-truth labels. Previous studies have generally relied on frequency-based methods for inference and trip-based metrics for validation, posing limitations in reliability and efficiency. This paper aims to fill this gap by developing a systematic approach that ranges from data error categorization and processing, feature relevance examination and parameter optimization, and the development of performance metrics considering both residence and workplace validation. For residence-workplace identification, we use a spatiotemporal closeness criterion to deal with the sparsity of data and develop effective dwelling time to enhance frequency-based methods, using one-month cellular signaling records from nine cities in the Yangtze River Delta urban agglomeration in China. For validation, we propose a residence-workplace pair metric based on the population-adjusted number of users, enabling more efficient evaluation of home and work locations than trip-based metrics. Results show that the mean absolute percentage errors (MAPEs) for the Nanjing and Shanghai cases are 5.04% and 8.46%, respectively. Adopted and verified in the large-scale urban agglomeration, the proposed method would be reliable for extracting residence and workplace from low-resolution mobile phone data and contributing to a more accurate identification of urban commuting patterns.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196004","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 : 2024-08-12DOI: 10.1177/03611981241264276
Natchaphon Leungbootnak, Kevin Heaslip
One of the most discussed infrastructure issues of our time is cybersecurity. A transportation system that connects not only people but also logistics to make the community around the world closer is one of the critical infrastructures requiring cybersecurity to perform its functions. Transportation systems include aviation, maritime, pipeline, railroad, and road networks. This study focuses only on the roadway network system. Cutting-edge technologies related to highway networks, such as variable message signs, vehicular ad hoc networks, and in-vehicle networks, have been developed to improve safety and efficiency. Those technologies make transportation systems more complex and integrated, bringing many potential vulnerabilities and cyber risks. This can attract an adversary to attack and exploit the system. The number of cybersecurity attacks on transportation systems has been growing for many years. However, it is not feasible to protect against all cybersecurity attacks in the system. The risk assessment concept is proposed to prioritize risk resulting from attacks to support decision-makers in formulating appropriate policies or countermeasures. This study reviews risk assessment methods for cybersecurity attacks on road networks and intelligent transportation system applications. Three potential risk assessment methods are examined for road network systems: the National Institute of Standards and Technology Special Publication 800-30, Attack Potential and Damage Potential, and the fuzzy analytic hierarchy process.
{"title":"Review of Risk Assessment Methods for Cybersecurity Attacks on Road Network and Intelligent Transportation System Applications","authors":"Natchaphon Leungbootnak, Kevin Heaslip","doi":"10.1177/03611981241264276","DOIUrl":"https://doi.org/10.1177/03611981241264276","url":null,"abstract":"One of the most discussed infrastructure issues of our time is cybersecurity. A transportation system that connects not only people but also logistics to make the community around the world closer is one of the critical infrastructures requiring cybersecurity to perform its functions. Transportation systems include aviation, maritime, pipeline, railroad, and road networks. This study focuses only on the roadway network system. Cutting-edge technologies related to highway networks, such as variable message signs, vehicular ad hoc networks, and in-vehicle networks, have been developed to improve safety and efficiency. Those technologies make transportation systems more complex and integrated, bringing many potential vulnerabilities and cyber risks. This can attract an adversary to attack and exploit the system. The number of cybersecurity attacks on transportation systems has been growing for many years. However, it is not feasible to protect against all cybersecurity attacks in the system. The risk assessment concept is proposed to prioritize risk resulting from attacks to support decision-makers in formulating appropriate policies or countermeasures. This study reviews risk assessment methods for cybersecurity attacks on road networks and intelligent transportation system applications. Three potential risk assessment methods are examined for road network systems: the National Institute of Standards and Technology Special Publication 800-30, Attack Potential and Damage Potential, and the fuzzy analytic hierarchy process.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196002","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 : 2024-08-12DOI: 10.1177/03611981241265849
Wooseok Do, Nicolas Saunier, Luis Miranda-Moreno
The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.
{"title":"Evaluation of Conventional Surrogate Indicators of Safety for Connected and Automated Vehicles in Car Following at Signalized Intersections","authors":"Wooseok Do, Nicolas Saunier, Luis Miranda-Moreno","doi":"10.1177/03611981241265849","DOIUrl":"https://doi.org/10.1177/03611981241265849","url":null,"abstract":"The driving behaviors of connected and automated vehicles (CAVs) will differ from those of human-driven vehicles (HDVs) because the CAVs’ driving decisions are controlled by computers. Because of the limited amount of crash data for CAVs, researchers have relied on surrogate measures of safety to assess their safety impacts. However, they often use the same safety indicators for CAVs that were used for HDVs, raising questions about the adequacy of the safety indicators for CAVs. This study aims to investigate the suitability of using conventional safety indicators for CAVs. To achieve this, we evaluated eight safety indicators used for CAVs in the literature: time-to-collision (TTC), post-encroachment time (PET), time-exposed TTC, time-integrated TTC, deceleration rate to avoid a crash (DRAC), crash-potential index, rear-end-collision risk index, and potential index for collision with urgent deceleration (PICUD). For the evaluation, we first simulate CAVs on an approaching lane of signalized intersections using the acceleration-control algorithm. The algorithm replaces the HDV trajectories with CAVs for mixed simulations where HDVs and CAVs coexist. Analyzing the simulation output, we examined the safety indicators for the various car-following scenarios and the CAV proportions. The findings suggest that PET and PICUD can yield different safety implications for CAVs because of their small-gap car-following characteristics. Ignoring such characteristics may lead to interpreting the small-gap car-following situations as simply dangerous traffic interactions for CAVs. The car-following experiments indicate that TTC, PET, and DRAC are insufficient in measuring the safety implications when successive vehicles operate at similar speeds for CAVs.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196003","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 : 2024-08-12DOI: 10.1177/03611981241264275
Christian Hecht, Surya Teja Swarna, Parth Bhavsar, Yusuf Mehta, Taha Bouhsine
One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.
{"title":"Cost-Effective Pavement Condition Survey for Municipal Road Networks","authors":"Christian Hecht, Surya Teja Swarna, Parth Bhavsar, Yusuf Mehta, Taha Bouhsine","doi":"10.1177/03611981241264275","DOIUrl":"https://doi.org/10.1177/03611981241264275","url":null,"abstract":"One of the most challenging issues for municipalities in the U.S. is to secure federal funding, state funding, or both, for local roadway improvement. Existing frameworks such as manual data collection, light detection and ranging have proven to be expensive and cumbersome. In this paper, a low-cost pavement management framework is proposed using artificial intelligence (AI). AI has solidified itself across industries as a revolutionary advancement that can automate many tasks that were performed by humans. AI has the potential to make roadway assessment easier and more cost-effective than ever, but this application has been hindered by dataset quality and quantity. Roadway datasets are often imbalanced, containing many more images of certain deformations than others. This decreases the performance of AI models. In this paper, different methods of pavement dataset labeling are tested to gain an understanding of which is best for pavement distress detection using a classification neural network. An AI-friendly pavement condition index is designed to give a clear indicator of the current pavement condition and provide a metric by which to rank the roads based on the need to repair them. The best-performing AI model is incorporated into the low-cost pavement management framework.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196007","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 : 2024-08-12DOI: 10.1177/03611981241270166
Suxia Gong, Ismaïl Saadi, Jacques Teller, Mario Cools
Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.
{"title":"Tensor Decomposition for Spatiotemporal Mobility Pattern Learning with Mobile Phone Data","authors":"Suxia Gong, Ismaïl Saadi, Jacques Teller, Mario Cools","doi":"10.1177/03611981241270166","DOIUrl":"https://doi.org/10.1177/03611981241270166","url":null,"abstract":"Detecting urban mobility patterns is crucial for policymakers in urban and transport planning. Mobile phone data have been increasingly deployed to measure the spatiotemporal variations in human mobility. This work applied non-negative Tucker decomposition (NTD) to mobile phone-based origin–destination (O-D) matrices to explore mobility patterns’ latent spatial and temporal relationships in the province of Liège, Belgium. Four [Formula: see text] traffic tensors have been built for one regular weekday, one regular weekend day, one holiday weekday, and one holiday weekend day, respectively. The proposed method inferred spatial clusters and temporal patterns while interpreting the correlation between spatial clusters and temporal patterns through geographical visualization. As a result, we found the similarity of O-D and destination–origin (D-O) patterns and the symmetry for the trips of the temporal patterns with evening peak and morning peaks on the weekday. Moreover, we investigated the attraction of different spatial clusters with two temporal patterns on a regular weekday and validated the reconstructed demand using population counts and commuting matrices. Finally, the differences in spatial and temporal interactions have been addressed in detail.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196000","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 : 2024-08-12DOI: 10.1177/03611981241270155
Oleksandr Rossolov, José Holguín-Veras, Yusak O. Susilo
This paper explores aspects of the post-purchase trips generated by the necessity to return purchased clothing to the shops. Particular focus is given to online shopping that has become common for clothing purchases. This study’s novelty lies in delving into the underlying reasons behind post-purchase trips, particularly those initiated when customers seek to return clothing to retailers. It examines the impact of free and paid return delivery options as key factors driving consumers’ decisions in relation to clothing returns. The study consists of two branches and leverages the random utility maximization theory. The first branch focuses on the impact of free and paid return options on the preferred shopping channels by utilizing a stated preference dataset collected from 507 US online shoppers. The second branch of this study employs the revealed preference dataset and aims to explore the return trip behavior. The willingness-to-pay values estimated for the free return delivery option are higher among female online shoppers compared with males—$7.42 and $6.65 per delivery, respectively. It was found that among the identified “returners,” 84.62% of males and 79.91% of females showed a strong reliance on private cars for their return trips. The potential environmental consequences of return trips were evaluated, focusing on the case of the USA. Additionally, the estimated marginal probability effect revealed that factors such as an aging population, car ownership, and number of children in households positively influence car usage for post-purchase trips. The study’s implications for stakeholders are discussed.
{"title":"Post-Purchase Trip Heterogeneity: Exploring the Impact of Free and Paid Return Deliveries on Shopping and Transport Mode Choices in the USA","authors":"Oleksandr Rossolov, José Holguín-Veras, Yusak O. Susilo","doi":"10.1177/03611981241270155","DOIUrl":"https://doi.org/10.1177/03611981241270155","url":null,"abstract":"This paper explores aspects of the post-purchase trips generated by the necessity to return purchased clothing to the shops. Particular focus is given to online shopping that has become common for clothing purchases. This study’s novelty lies in delving into the underlying reasons behind post-purchase trips, particularly those initiated when customers seek to return clothing to retailers. It examines the impact of free and paid return delivery options as key factors driving consumers’ decisions in relation to clothing returns. The study consists of two branches and leverages the random utility maximization theory. The first branch focuses on the impact of free and paid return options on the preferred shopping channels by utilizing a stated preference dataset collected from 507 US online shoppers. The second branch of this study employs the revealed preference dataset and aims to explore the return trip behavior. The willingness-to-pay values estimated for the free return delivery option are higher among female online shoppers compared with males—$7.42 and $6.65 per delivery, respectively. It was found that among the identified “returners,” 84.62% of males and 79.91% of females showed a strong reliance on private cars for their return trips. The potential environmental consequences of return trips were evaluated, focusing on the case of the USA. Additionally, the estimated marginal probability effect revealed that factors such as an aging population, car ownership, and number of children in households positively influence car usage for post-purchase trips. The study’s implications for stakeholders are discussed.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142195999","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}
Infrastructure systems play important roles in economic development and the social quality of life. Interdependencies exist between infrastructure systems: a functional disruption in one system can affect dependent systems, thereby escalating the impacts. It is vital to properly model interdependencies to understand the full impacts of disruptive events on infrastructure systems. Quantitative data on infrastructure interdependency is often difficult to obtain or unavailable for a variety of reasons. To overcome quantitative data scarcity issues, qualitative subject expert knowledge has been used in interdependency analysis, primarily in the form of linguistic responses. Linguistic data is susceptible to uncertainties arising from variations in intended meanings, which may yield inaccurate results. This paper proposes a framework to address this problem using two fuzzy inference systems to model event-specific, network-wide infrastructure failures. The first fuzzy inference system models the damage induced by interdependencies using verbal descriptions. The second fuzzy inference system accounts for synergistic, compounding effects of multiple incidences of indirect damage caused by interdependencies. A case study is conducted to demonstrate the applicability of the proposed methodology using electric and gas distribution networks in the United Kingdom. Sensitivity analyses are performed to show the flexibility of the fuzzy inference systems. The results show that the proposed method can model the interdependency and vulnerability of infrastructure systems using fuzzy inference systems to handle imprecise input. The proposed framework may assist practitioners in better understanding the interdependency and vulnerability of infrastructure systems, and in making more informed decisions to reduce losses resulting from disruptive events.
{"title":"Modeling Interdependent Infrastructure System Vulnerability with Imprecise Information Using Two Fuzzy Inference Systems","authors":"Shidong Pan, Kyle Bathgate, Zhe Han, Jingran Sun, Zhanmin Zhang","doi":"10.1177/03611981241270153","DOIUrl":"https://doi.org/10.1177/03611981241270153","url":null,"abstract":"Infrastructure systems play important roles in economic development and the social quality of life. Interdependencies exist between infrastructure systems: a functional disruption in one system can affect dependent systems, thereby escalating the impacts. It is vital to properly model interdependencies to understand the full impacts of disruptive events on infrastructure systems. Quantitative data on infrastructure interdependency is often difficult to obtain or unavailable for a variety of reasons. To overcome quantitative data scarcity issues, qualitative subject expert knowledge has been used in interdependency analysis, primarily in the form of linguistic responses. Linguistic data is susceptible to uncertainties arising from variations in intended meanings, which may yield inaccurate results. This paper proposes a framework to address this problem using two fuzzy inference systems to model event-specific, network-wide infrastructure failures. The first fuzzy inference system models the damage induced by interdependencies using verbal descriptions. The second fuzzy inference system accounts for synergistic, compounding effects of multiple incidences of indirect damage caused by interdependencies. A case study is conducted to demonstrate the applicability of the proposed methodology using electric and gas distribution networks in the United Kingdom. Sensitivity analyses are performed to show the flexibility of the fuzzy inference systems. The results show that the proposed method can model the interdependency and vulnerability of infrastructure systems using fuzzy inference systems to handle imprecise input. The proposed framework may assist practitioners in better understanding the interdependency and vulnerability of infrastructure systems, and in making more informed decisions to reduce losses resulting from disruptive events.","PeriodicalId":517391,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":"68 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196001","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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