{"title":"COVID-19大流行期间自动连接班车(ACS)的有效性","authors":"Shanjeeda Akter, HM Abdul Aziz","doi":"10.1145/3486629.3490694","DOIUrl":null,"url":null,"abstract":"The COVID-19 pandemic had significantly impacted the public transit system in most cities across the world. Factors including physical distancing, remote working, distance education, and COVID-risk perception due to exposure have contributed to reducing public transport ridership. Automated Connected shuttles (ACS) services can be an effective alternative to regular buses with substantially reduced break-out risks and efficient operations. Our goal is to assess the mobility and energy impacts of ACS deployments when deployed as a replacement of standard (human-driven) buses within the context of the COVID-19 pandemic accounting for adjustment in passenger demand and capacity. To accomplish this purpose, we used a traffic microsimulation tool---PTV VISSIM---to simulate the behavior of buses and ACS units. We designed and simulated hypothetical scenarios in a New York City, NY, network. The scenarios are designed based on different COVID-19 restrictions, and the performances are compared to measure ACS effectiveness over regular buses. The results showed that ACS units are more effective than regular buses when they operate at business-as-usual capacity. Further, ACS services are more energy-efficient during physical distancing restrictions than bus services based on the emissions and energy estimates using the EPA-MOVES tool.","PeriodicalId":263760,"journal":{"name":"Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Effectiveness of automated connected shuttles (ACS) during COVID-19 pandemic\",\"authors\":\"Shanjeeda Akter, HM Abdul Aziz\",\"doi\":\"10.1145/3486629.3490694\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The COVID-19 pandemic had significantly impacted the public transit system in most cities across the world. Factors including physical distancing, remote working, distance education, and COVID-risk perception due to exposure have contributed to reducing public transport ridership. Automated Connected shuttles (ACS) services can be an effective alternative to regular buses with substantially reduced break-out risks and efficient operations. Our goal is to assess the mobility and energy impacts of ACS deployments when deployed as a replacement of standard (human-driven) buses within the context of the COVID-19 pandemic accounting for adjustment in passenger demand and capacity. To accomplish this purpose, we used a traffic microsimulation tool---PTV VISSIM---to simulate the behavior of buses and ACS units. We designed and simulated hypothetical scenarios in a New York City, NY, network. The scenarios are designed based on different COVID-19 restrictions, and the performances are compared to measure ACS effectiveness over regular buses. The results showed that ACS units are more effective than regular buses when they operate at business-as-usual capacity. Further, ACS services are more energy-efficient during physical distancing restrictions than bus services based on the emissions and energy estimates using the EPA-MOVES tool.\",\"PeriodicalId\":263760,\"journal\":{\"name\":\"Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3486629.3490694\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3486629.3490694","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effectiveness of automated connected shuttles (ACS) during COVID-19 pandemic
The COVID-19 pandemic had significantly impacted the public transit system in most cities across the world. Factors including physical distancing, remote working, distance education, and COVID-risk perception due to exposure have contributed to reducing public transport ridership. Automated Connected shuttles (ACS) services can be an effective alternative to regular buses with substantially reduced break-out risks and efficient operations. Our goal is to assess the mobility and energy impacts of ACS deployments when deployed as a replacement of standard (human-driven) buses within the context of the COVID-19 pandemic accounting for adjustment in passenger demand and capacity. To accomplish this purpose, we used a traffic microsimulation tool---PTV VISSIM---to simulate the behavior of buses and ACS units. We designed and simulated hypothetical scenarios in a New York City, NY, network. The scenarios are designed based on different COVID-19 restrictions, and the performances are compared to measure ACS effectiveness over regular buses. The results showed that ACS units are more effective than regular buses when they operate at business-as-usual capacity. Further, ACS services are more energy-efficient during physical distancing restrictions than bus services based on the emissions and energy estimates using the EPA-MOVES tool.