Gregory D. Erhardt , Vedant S. Goyal , Josephine Kressner , Simon J. Berrebi , Candace Brakewood , Kari E. Watkins
{"title":"模拟通过重新分配公交服务来增加公交乘客量的战略效果:两个案例研究","authors":"Gregory D. Erhardt , Vedant S. Goyal , Josephine Kressner , Simon J. Berrebi , Candace Brakewood , Kari E. Watkins","doi":"10.1016/j.jpubtr.2023.100080","DOIUrl":null,"url":null,"abstract":"<div><p>We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.</p></div>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1077291X23000413/pdfft?md5=b4ca2b55ee95272129193dfbe790fe89&pid=1-s2.0-S1077291X23000413-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Simulating the effect of strategies to increase transit ridership by reallocating bus service: Two case studies\",\"authors\":\"Gregory D. Erhardt , Vedant S. Goyal , Josephine Kressner , Simon J. Berrebi , Candace Brakewood , Kari E. Watkins\",\"doi\":\"10.1016/j.jpubtr.2023.100080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.</p></div>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1077291X23000413/pdfft?md5=b4ca2b55ee95272129193dfbe790fe89&pid=1-s2.0-S1077291X23000413-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1077291X23000413\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1077291X23000413","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
Simulating the effect of strategies to increase transit ridership by reallocating bus service: Two case studies
We evaluate three strategies that transit operators might consider to increase ridership: a) increasing service on bus routes serving the highest share of low-income riders, b) increasing service on those bus routes with the highest ridership, and c) further providing the high-ridership routes identified in strategy (b) with exclusive bus lanes. In each scenario, we double the service frequency of buses on the focus routes and reduce the frequency on all other routes to maintain the total vehicle revenue miles, making the changes roughly cost-neutral. We tested these scenarios for Oshkosh, Wisconsin, and Atlanta, Georgia, using a modeling framework that combines CityCast, a commercially available data-driven planning tool to replicate observed travel patterns, and MATSim to simulate how travelers would change the route, mode, and time-of-day of the trips they make in response to the service changes. The results show substantial ridership gains for all but one scenario, suggesting that these strategies may provide a promising, low-cost means of increasing transit ridership in some contexts. However, impacts varied across the two case studies, indicating that local conditions play a role.