{"title":"What the heck is a choice rider? A theoretical framework and empirical model","authors":"E. Guerra","doi":"10.5198/jtlu.2022.2096","DOIUrl":null,"url":null,"abstract":"As local, state, and federal agencies began investing substantial resources into subsidizing transit in the 1960s and ‘70s, public documents argued that transit agencies should focus on attracting choice riders instead of dependent riders, who have no alternatives and use transit regardless of service quality. After six decades, the definitions, uses, and implications of the terms choice and dependent rider have remained consistent in the academic and professional literature. These definitions, however, lack a strong theoretical grounding or empirical evidence to support them. Using travel diary data from the Philadelphia region, I estimate discrete choice models to identify choice riders, who I define as those who have close to a 50% probability of choosing between a car or transit for a given trip. The Philadelphia region, which has a diverse range of transit users and transit services, is an ideal place to develop and fit an empirical model of choice ridership. Attributes assumed to be associated with dependent riders, such as lack of a car, low income, and being a racial or ethnic minority, are much more prevalent among choice riders than the general metropolitan population. Choice riders are also diverse, with a mix of racial backgrounds, income levels, educational attainment, and access to private cars. Transit dependency, by contrast, is rare. The lowest and highest income residents generally only choose transit when service quality is high, and transit is cost and time competitive with the car.","PeriodicalId":47271,"journal":{"name":"Journal of Transport and Land Use","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Transport and Land Use","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.5198/jtlu.2022.2096","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 1
Abstract
As local, state, and federal agencies began investing substantial resources into subsidizing transit in the 1960s and ‘70s, public documents argued that transit agencies should focus on attracting choice riders instead of dependent riders, who have no alternatives and use transit regardless of service quality. After six decades, the definitions, uses, and implications of the terms choice and dependent rider have remained consistent in the academic and professional literature. These definitions, however, lack a strong theoretical grounding or empirical evidence to support them. Using travel diary data from the Philadelphia region, I estimate discrete choice models to identify choice riders, who I define as those who have close to a 50% probability of choosing between a car or transit for a given trip. The Philadelphia region, which has a diverse range of transit users and transit services, is an ideal place to develop and fit an empirical model of choice ridership. Attributes assumed to be associated with dependent riders, such as lack of a car, low income, and being a racial or ethnic minority, are much more prevalent among choice riders than the general metropolitan population. Choice riders are also diverse, with a mix of racial backgrounds, income levels, educational attainment, and access to private cars. Transit dependency, by contrast, is rare. The lowest and highest income residents generally only choose transit when service quality is high, and transit is cost and time competitive with the car.
期刊介绍:
The Journal of Transport and Land Usepublishes original interdisciplinary papers on the interaction of transport and land use. Domains include: engineering, planning, modeling, behavior, economics, geography, regional science, sociology, architecture and design, network science, and complex systems. Papers reporting innovative methodologies, original data, and new empirical findings are especially encouraged.