The purpose of this study was to offer a comprehensive econometrical framework based on a multilevel random effect logistic model that could highlight important contributors to carpool users among different cities with various attributes. The data was collected from the three cities of Tucson, AZ, USA; El Paso, TX, USA; and Austin, TX, USA and was based on register-based travel trip data from the Metropia platform and American Community Survey information from 2016 to 2017. The empirical results indicated there were statistically significant differences among carpool users in different cities due to the transportation mode, number of vehicles available, total number of males driving alone, and number of single-parent households. The individual level result showed that incentives had a significant effect on the promotion of carpool passenger and driver behavior. In addition, the time of finding the parking space at work, living situation of the household, flexibility to change departure times, gender, and age could effectively increase the possibility of carpool usage. The results of this study give a better understanding of the events in the initial factors of carpooling behavior and can be used by the government or commercial company to design an effective solution for traffic congestion.
- Drive-up occupancy
- Multilevel random effect logistic regression model
ASJC Scopus subject areas
- Geography, Planning and Development
- Renewable Energy, Sustainability and the Environment
- Management, Monitoring, Policy and Law