Pub Date : 2024-05-20DOI: 10.1177/03611981241242079
Evelyn M. Schneider, L. D’Ambrosio, Chaiwoo Lee, Joseph F. Coughlin
Despite concerns over distracted driving, many Americans still engage in risky activities while driving, leading to crashes and fatal outcomes. This study aims to investigate the impact of individual risk attitudes and in-vehicle technologies on various types of distracted driving behaviors (DDB), providing insights into the factors that contribute to an increased likelihood of DDB and enhancing an understanding of the effects of advanced vehicle technologies (AVT) on driver behavior. The analysis leverages self-reported survey questionnaire data from a nationally representative sample of participants. To assess the relationships between the variables, exploratory factor analysis and multiple linear regression analysis were used. The findings revealed that the presence of AVT and individual risk attitudes each predicted DDB. The presence of driver-assist and safety features did, however, lead to some degree of decreased distracted driving. Convenience features, such as Wi-Fi and Bluetooth, were most likely to increase DDB, highlighting the need for the design of AVT systems to minimize distracted driving while leveraging the benefits of technology. The data also indicate that other factors affect DDB. Notably, younger individuals engaged in more DDB compared with older individuals, and individuals who drive more frequently and for longer distances also exhibited a higher frequency of DDB. Factors such as driving experience and exposure also affected DDB, with driving exposure having a more substantial influence.
{"title":"Impacts of Advanced Vehicle Technologies and Risk Attitudes on Distracted Driving Behaviors","authors":"Evelyn M. Schneider, L. D’Ambrosio, Chaiwoo Lee, Joseph F. Coughlin","doi":"10.1177/03611981241242079","DOIUrl":"https://doi.org/10.1177/03611981241242079","url":null,"abstract":"Despite concerns over distracted driving, many Americans still engage in risky activities while driving, leading to crashes and fatal outcomes. This study aims to investigate the impact of individual risk attitudes and in-vehicle technologies on various types of distracted driving behaviors (DDB), providing insights into the factors that contribute to an increased likelihood of DDB and enhancing an understanding of the effects of advanced vehicle technologies (AVT) on driver behavior. The analysis leverages self-reported survey questionnaire data from a nationally representative sample of participants. To assess the relationships between the variables, exploratory factor analysis and multiple linear regression analysis were used. The findings revealed that the presence of AVT and individual risk attitudes each predicted DDB. The presence of driver-assist and safety features did, however, lead to some degree of decreased distracted driving. Convenience features, such as Wi-Fi and Bluetooth, were most likely to increase DDB, highlighting the need for the design of AVT systems to minimize distracted driving while leveraging the benefits of technology. The data also indicate that other factors affect DDB. Notably, younger individuals engaged in more DDB compared with older individuals, and individuals who drive more frequently and for longer distances also exhibited a higher frequency of DDB. Factors such as driving experience and exposure also affected DDB, with driving exposure having a more substantial influence.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141120686","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 : 2024-05-19DOI: 10.1177/03611981241242771
S. Click, M. Mohebbi, Ruth Steiner, Virginia P. Sisopiku, Mohammed Hadi, Dimitra Michalaka, Muhammed Sherif, James B. Martin, Jeremy Griffith
This project addresses the contemporary challenges faced by the transportation workforce, influenced by demographic shifts, labor market fluctuations, and the growing demand for interdisciplinary skills. Using a case study of the southeastern United States, five main objectives guided the project: a) synthesizing the current state of workforce development practices, b) identifying key challenges in the transportation workforce, c) defining the term “workforce development” within this context, d) exploring potential roles of University Transportation Centers (UTCs) in tackling these challenges, and e) offering actionable recommendations for enhancing transportation workforce development. The research used findings from a literature review, stakeholder meetings, a survey of transportation professionals, and personal interviews with selected experts. The findings were integrated to derive conclusive results instead of independently interpreting each dataset. The study revealed that workforce development hinges on stakeholders, recruitment strategies, educational aspects, and diversity initiatives. The most pressing challenges involved changing labor market trends, demographic shifts, and the necessity for interdisciplinary skills. Workforce development was conceptualized as strategic measures for recruiting, retaining, educating, and training the present and future transportation labor force to meet identified challenges and needs. The UTCs’ potential contributions were identified in facilitating recruitment, inspiring interest in transportation careers, and creating resources for continuous education and training. Key recommendations involve interdisciplinary educational initiatives, specialized training, and resource development to assess and enhance existing training strategies.
{"title":"Framework for the Development of a Diverse Transportation Workforce in the Southeast Region","authors":"S. Click, M. Mohebbi, Ruth Steiner, Virginia P. Sisopiku, Mohammed Hadi, Dimitra Michalaka, Muhammed Sherif, James B. Martin, Jeremy Griffith","doi":"10.1177/03611981241242771","DOIUrl":"https://doi.org/10.1177/03611981241242771","url":null,"abstract":"This project addresses the contemporary challenges faced by the transportation workforce, influenced by demographic shifts, labor market fluctuations, and the growing demand for interdisciplinary skills. Using a case study of the southeastern United States, five main objectives guided the project: a) synthesizing the current state of workforce development practices, b) identifying key challenges in the transportation workforce, c) defining the term “workforce development” within this context, d) exploring potential roles of University Transportation Centers (UTCs) in tackling these challenges, and e) offering actionable recommendations for enhancing transportation workforce development. The research used findings from a literature review, stakeholder meetings, a survey of transportation professionals, and personal interviews with selected experts. The findings were integrated to derive conclusive results instead of independently interpreting each dataset. The study revealed that workforce development hinges on stakeholders, recruitment strategies, educational aspects, and diversity initiatives. The most pressing challenges involved changing labor market trends, demographic shifts, and the necessity for interdisciplinary skills. Workforce development was conceptualized as strategic measures for recruiting, retaining, educating, and training the present and future transportation labor force to meet identified challenges and needs. The UTCs’ potential contributions were identified in facilitating recruitment, inspiring interest in transportation careers, and creating resources for continuous education and training. Key recommendations involve interdisciplinary educational initiatives, specialized training, and resource development to assess and enhance existing training strategies.","PeriodicalId":309251,"journal":{"name":"Transportation Research Record: Journal of the Transportation Research Board","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141123952","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 : 2024-01-20DOI: 10.1177/03611981231216977
Mark T. Kotwicz Herniczek, Brian J. German, Lukas Preis
An understanding of fleet size and vertiport size sensitivity to demand and operational parameters is necessary to quantify the scalability of urban air mobility (UAM) services. In this work, we implement a bilevel rolling window fleet scheduling formulation that includes vertiport area as a secondary objective. We also present a simple vertiport area estimation methodology that leverages the fleet scheduling results and provides a lower bound on vertiport infrastructure area requirements. Lastly, we explore the sensitivity of fleet size and vertiport infrastructure requirements to several vehicle and operational parameters, including geographical demand distribution, daily passenger volume, vehicle passenger capacity, passenger aggregation window, battery charge rate, pad separation, and pad size. We find that, although the fleet size is reasonable for a UAM commuting service scaled to serve 10,000 passengers per day, vertiport area requirements are likely problematic under current sizing guidance from the Federal Aviation Administration, particularly area requirements for vertiports that serve as workplace hubs located in dense urban centers.
{"title":"Fleet and Vertiport Sizing for an Urban Air Mobility Commuting Service","authors":"Mark T. Kotwicz Herniczek, Brian J. German, Lukas Preis","doi":"10.1177/03611981231216977","DOIUrl":"https://doi.org/10.1177/03611981231216977","url":null,"abstract":"An understanding of fleet size and vertiport size sensitivity to demand and operational parameters is necessary to quantify the scalability of urban air mobility (UAM) services. In this work, we implement a bilevel rolling window fleet scheduling formulation that includes vertiport area as a secondary objective. We also present a simple vertiport area estimation methodology that leverages the fleet scheduling results and provides a lower bound on vertiport infrastructure area requirements. Lastly, we explore the sensitivity of fleet size and vertiport infrastructure requirements to several vehicle and operational parameters, including geographical demand distribution, daily passenger volume, vehicle passenger capacity, passenger aggregation window, battery charge rate, pad separation, and pad size. We find that, although the fleet size is reasonable for a UAM commuting service scaled to serve 10,000 passengers per day, vertiport area requirements are likely problematic under current sizing guidance from the Federal Aviation Administration, particularly area requirements for vertiports that serve as workplace hubs located in dense urban centers.","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":"139523708","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 : 2024-01-20DOI: 10.1177/03611981231215338
Liang Lu, Fangfang Zheng, Xiaobo Liu
This paper proposes a stochastic model for mixed traffic consisting of human-driven vehicles (HVs), connected automated vehicles (CAVs), and degraded connected automated vehicles (DCAVs). The model addresses the issue that most of the current literature ignores: the degradation of CAVs, and the heterogeneity and uncertainty of HVs, CAVs, and DCAVs. The source of uncertainty was the heterogeneous behavior of HVs, CAVs, and DCAVs, captured using vehicle-specific car-following relations, that is, parametric uncertainty. The proposed model allowed for the explicit investigation of the uncertainty, efficiency, and stability of mixed traffic under various CAV penetration rates, different positions of CAVs in the traffic stream, and the different degradation levels of CAVs. The numerical experiment results showed that a larger CAV penetration rate helped to reduce uncertainty and improve the efficiency and stability of traffic flow. Furthermore, we investigated the impact of different position combinations of CAVs in the mixed traffic stream on traffic performance under four scenarios: 1) CAVs randomly distributed in the traffic stream, 2) CAVs forming a platoon traveling in the front of the traffic stream, 3) CAVs forming a platoon traveling in the middle of the traffic stream, and 4) CAVs forming a platoon traveling in the rear of the traffic stream. The results demonstrated that Scenario 2 gave the best performance in reducing uncertainty and improving efficiency and stability under different CAV penetration rates, whereas Scenario 4 performed the worst. Moreover, increasing degradation levels of CAVs negatively affected the reduction of uncertainty and improvement of efficiency and stability.
{"title":"Uncertainty, Efficiency, and Stability of Mixed Traffic Flow: Stochastic Model-Based Analyses","authors":"Liang Lu, Fangfang Zheng, Xiaobo Liu","doi":"10.1177/03611981231215338","DOIUrl":"https://doi.org/10.1177/03611981231215338","url":null,"abstract":"This paper proposes a stochastic model for mixed traffic consisting of human-driven vehicles (HVs), connected automated vehicles (CAVs), and degraded connected automated vehicles (DCAVs). The model addresses the issue that most of the current literature ignores: the degradation of CAVs, and the heterogeneity and uncertainty of HVs, CAVs, and DCAVs. The source of uncertainty was the heterogeneous behavior of HVs, CAVs, and DCAVs, captured using vehicle-specific car-following relations, that is, parametric uncertainty. The proposed model allowed for the explicit investigation of the uncertainty, efficiency, and stability of mixed traffic under various CAV penetration rates, different positions of CAVs in the traffic stream, and the different degradation levels of CAVs. The numerical experiment results showed that a larger CAV penetration rate helped to reduce uncertainty and improve the efficiency and stability of traffic flow. Furthermore, we investigated the impact of different position combinations of CAVs in the mixed traffic stream on traffic performance under four scenarios: 1) CAVs randomly distributed in the traffic stream, 2) CAVs forming a platoon traveling in the front of the traffic stream, 3) CAVs forming a platoon traveling in the middle of the traffic stream, and 4) CAVs forming a platoon traveling in the rear of the traffic stream. The results demonstrated that Scenario 2 gave the best performance in reducing uncertainty and improving efficiency and stability under different CAV penetration rates, whereas Scenario 4 performed the worst. Moreover, increasing degradation levels of CAVs negatively affected the reduction of uncertainty and improvement of efficiency and stability.","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":"139523798","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 : 2024-01-20DOI: 10.1177/03611981231223194
Md Mahmud Hossain, Mohammad Reza Abbaszadeh Lima, Huaguo Zhou
Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.
{"title":"Severity Analysis of Secondary Crashes on High-Speed Roadways: Pattern Recognition Using Association Rule Mining","authors":"Md Mahmud Hossain, Mohammad Reza Abbaszadeh Lima, Huaguo Zhou","doi":"10.1177/03611981231223194","DOIUrl":"https://doi.org/10.1177/03611981231223194","url":null,"abstract":"Secondary crashes (SCs) are a major concern, posing additional safety threats to both non-involved vehicles and incident responders. The objective of this study was to identify the affiliated factors contributing to SCs on roadways with a speed limit of 55 mph or above. Traditional police-investigated crash dataset was analyzed, spanning more than four years (January 2016–February 2020) for the entire state of Alabama. As the crash database did not directly include information on SCs and did not allow for linking a primary crash with a subsequent SC, a data extraction process was developed to identify SCs and understand their characteristics. Association rule mining (ARM) was applied to identify crash patterns based on maximum injury severity levels. The generated rules were filtered based on support, confidence, and lift, and then validated by the lift increase criterion. The results revealed complex relationships between risk factors and severity of SCs. In relation to SCs with injuries, single-vehicle crashes were frequently observed during peak hours and when drivers swerved to avoid objects/persons/vehicles. In contrast, concerning SCs with possible/no injuries, single-vehicle collisions were more likely to occur when drivers failed to notice objects/persons/vehicles and were involved in speeding. On urban interstates, single-vehicle SCs were frequently associated with injuries, while rear-end SCs were often linked to possible/no injuries. The findings of this study can be helpful in enhancing existing traffic incident management programs to mitigate the occurrence of SCs.","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":"139523549","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 : 2024-01-20DOI: 10.1177/03611981231222232
Zhiyong Cui, Meng-Ju Tsai, Meixin Zhu, H. Yang, Chenxi Liu, Shuyi Yin, Yinhai Wang
Measuring traffic performance is critical for public agencies which manage traffic and individuals who. This is the topic which the authors attempt to emphasize. One potential challenge for traffic prediction tasks is that short-term-incident-induced traffic pattern changes cannot be timely detected and the deployed model cannot adapt to the new traffic pattern. As for encountering long-term incidents, such as during COVID-19, traffic patterns are gradually changing, and the prediction model also needs to be periodically updated to avoid the so-called out-of-distribution problem. Therefore, the online training and predicting mechanisms can facilitate model updates, deployment of traffic prediction applications, and the planning of trips, especially when special events happen, such as the long-lasting COVID-19 pandemic. However, most existing traffic performance metrics narrowly focus on one aspect of the impacts but not comprehensive changes to the network. Further, during the pandemic, urban traffic patterns and travelers’ trip planning were dramatically affected and, thus, network-wide online traffic prediction became an urgent but more complicated task. To overcome such challenges, this study proposes a traffic performance score (TPS) incorporating multiple parameters for measuring both urban and freeway network-wide traffic performance. The TPS is compared with other metrics to show its superiority. To solve the challenging network-wide online traffic prediction task, this study also proposes an online training and updating strategy to predict network-wide traffic performance. Experimental results indicate that the proposed model with the online learning strategy outperforms existing methods in prediction accuracy and learning efficiency. In addition, the TPS measurement and its related online prediction functions are implemented on a publicly accessible platform and applied in real practice, which is another contribution of this work.
{"title":"Traffic Performance Score: Measuring Urban Mobility and Online Predicting of Near-Term Traffic, like Weather Forecasting","authors":"Zhiyong Cui, Meng-Ju Tsai, Meixin Zhu, H. Yang, Chenxi Liu, Shuyi Yin, Yinhai Wang","doi":"10.1177/03611981231222232","DOIUrl":"https://doi.org/10.1177/03611981231222232","url":null,"abstract":"Measuring traffic performance is critical for public agencies which manage traffic and individuals who. This is the topic which the authors attempt to emphasize. One potential challenge for traffic prediction tasks is that short-term-incident-induced traffic pattern changes cannot be timely detected and the deployed model cannot adapt to the new traffic pattern. As for encountering long-term incidents, such as during COVID-19, traffic patterns are gradually changing, and the prediction model also needs to be periodically updated to avoid the so-called out-of-distribution problem. Therefore, the online training and predicting mechanisms can facilitate model updates, deployment of traffic prediction applications, and the planning of trips, especially when special events happen, such as the long-lasting COVID-19 pandemic. However, most existing traffic performance metrics narrowly focus on one aspect of the impacts but not comprehensive changes to the network. Further, during the pandemic, urban traffic patterns and travelers’ trip planning were dramatically affected and, thus, network-wide online traffic prediction became an urgent but more complicated task. To overcome such challenges, this study proposes a traffic performance score (TPS) incorporating multiple parameters for measuring both urban and freeway network-wide traffic performance. The TPS is compared with other metrics to show its superiority. To solve the challenging network-wide online traffic prediction task, this study also proposes an online training and updating strategy to predict network-wide traffic performance. Experimental results indicate that the proposed model with the online learning strategy outperforms existing methods in prediction accuracy and learning efficiency. In addition, the TPS measurement and its related online prediction functions are implemented on a publicly accessible platform and applied in real practice, which is another contribution of this work.","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":"139524464","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 : 2024-01-20DOI: 10.1177/03611981231218008
Xiqiong Chen, Yanni Jiu, Dawei Hu
The integrated service mode of in-store pick-up and delivery has become common in the post-epidemic period owing to the combined online and offline purchases of perishable products. This study investigates the diverse requirements of in-store pick-up and delivery customers. Then, it establishes a two-echelon location–routing model for a perishable food distribution network to minimize total cost as an objective. An adaptive large neighborhood search (ALNS) algorithm was also developed to solve the foregoing problem. To test the algorithm, instances from those of Solomon are derived. The proposed ALNS algorithm was found to achieve satisfactory performance with respect to speed and accuracy by comparing its results with those of the CPLEX software for a 12-node small-scale instance. The applicability and stability of the ALNS algorithm were further verified using different types of instances with more nodes. Different proportions of in-store pick-up and delivery customers were set, and the total cost of location–routing schemes under these proportions was compared. The results show that an integrated service type compared with the single delivery service mode and single in-store pick-up service mode can save 7.98% and 11.44% of the total cost, respectively.
{"title":"Two-Echelon Location–Routing Problem of Perishable Products Based on the Integrated Mode of In-Store Pick-Up And Delivery","authors":"Xiqiong Chen, Yanni Jiu, Dawei Hu","doi":"10.1177/03611981231218008","DOIUrl":"https://doi.org/10.1177/03611981231218008","url":null,"abstract":"The integrated service mode of in-store pick-up and delivery has become common in the post-epidemic period owing to the combined online and offline purchases of perishable products. This study investigates the diverse requirements of in-store pick-up and delivery customers. Then, it establishes a two-echelon location–routing model for a perishable food distribution network to minimize total cost as an objective. An adaptive large neighborhood search (ALNS) algorithm was also developed to solve the foregoing problem. To test the algorithm, instances from those of Solomon are derived. The proposed ALNS algorithm was found to achieve satisfactory performance with respect to speed and accuracy by comparing its results with those of the CPLEX software for a 12-node small-scale instance. The applicability and stability of the ALNS algorithm were further verified using different types of instances with more nodes. Different proportions of in-store pick-up and delivery customers were set, and the total cost of location–routing schemes under these proportions was compared. The results show that an integrated service type compared with the single delivery service mode and single in-store pick-up service mode can save 7.98% and 11.44% of the total cost, respectively.","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":"139524740","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 : 2024-01-20DOI: 10.1177/03611981231223190
Akshat Bansal, Kezhe Perumpadappu Anoop, N. Rangaraj
This paper addresses the crew scheduling for long-distance passenger train services. A heuristic with bin packing features is developed to generate repeatable crew schedules that satisfy the operational and crew allocation rules. By ensuring the connectivity of crew duties that can be repeated over periodic train schedules, a better estimate of the crew requirement in a region is also obtained. Further, the heuristic ensures a fair division of the total workload and creates long duty cycles, which also makes the process of cyclic rostering easier. The paper also presents an exact approach for crew scheduling using a combination of constraint programming and set covering formulations. The exact approach is not computationally viable for practical scale problem instances, but the heuristic generates good quality solutions (often very close to optimal) even on large data sets. We illustrate the approach on data from the Mumbai Division in Indian Railways and the computational results show that there is potential to reduce the total number of crew duties in the region by around 12%. The heuristic approach provides an efficient way to generate improved crew schedules every time there is a change in the train timetable.
{"title":"Heuristic for Railway Crew Scheduling With Connectivity of Schedules","authors":"Akshat Bansal, Kezhe Perumpadappu Anoop, N. Rangaraj","doi":"10.1177/03611981231223190","DOIUrl":"https://doi.org/10.1177/03611981231223190","url":null,"abstract":"This paper addresses the crew scheduling for long-distance passenger train services. A heuristic with bin packing features is developed to generate repeatable crew schedules that satisfy the operational and crew allocation rules. By ensuring the connectivity of crew duties that can be repeated over periodic train schedules, a better estimate of the crew requirement in a region is also obtained. Further, the heuristic ensures a fair division of the total workload and creates long duty cycles, which also makes the process of cyclic rostering easier. The paper also presents an exact approach for crew scheduling using a combination of constraint programming and set covering formulations. The exact approach is not computationally viable for practical scale problem instances, but the heuristic generates good quality solutions (often very close to optimal) even on large data sets. We illustrate the approach on data from the Mumbai Division in Indian Railways and the computational results show that there is potential to reduce the total number of crew duties in the region by around 12%. The heuristic approach provides an efficient way to generate improved crew schedules every time there is a change in the train timetable.","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":"139524453","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 : 2024-01-20DOI: 10.1177/03611981231217743
Seyed Farhad Abdollahi, Poornachandra Vaddy, M. Emin Kutay
Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.
{"title":"Development of a Mechanistic-Empirical-Based Highway Cost Allocation Model for Flexible Pavements","authors":"Seyed Farhad Abdollahi, Poornachandra Vaddy, M. Emin Kutay","doi":"10.1177/03611981231217743","DOIUrl":"https://doi.org/10.1177/03611981231217743","url":null,"abstract":"Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.","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":"139524064","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 : 2024-01-20DOI: 10.1177/03611981231223193
Jiale Lu, Baofeng Pan, Quan Liu, Pengfei Liu, M. Oeser
Pavement intelligent management systems have attracted considerable interest from researchers. However, various service conditions of pavement surface concerning the pavement type, texture service age, and so forth, inhibit a universal algorithm that is feasible for all cases. In this regard, the automatic classification of pavement type and service age is an essential premise to unblock the bottleneck stated above. Based on the surface texture data, a pilot study of the automatic classification approach to identify pavement surface textures using convolutional neural networks (CNNs) is presented. For comparison, the efficiency of the support vector machine (SVM) is also investigated. In total, three cases, (i) pavement types, (ii) texture service ages, and (iii) a combination of (i) and (ii), are involved in the automatic classification. The results indicate that the CNN outperforms the SVM, and the CNN models show a favorable classification accuracy for the above three cases with 93.0%, 81.1%, and 83.8%, respectively. In conclusion, the CNN demonstrates a high capability in expressing the pavement texture features and achieves satisfactory identification results for pavement surface types, but is inferior for texture service age. It is promising that the presented results could serve as a foundational exploration in the automatic identification of texture service conditions benchmarked with standard texture databases to facilitate pavement management systems.
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