Determinants of Microtransit Demand: Insights from Five Microtransit Systems in North Carolina

Abstract: Microtransit is an emerging mode of public transportation expected to significantly improve access to employment and other essential destinations for disadvantaged populations living in low-density areas. Although many transit agencies in the United States have implemented microtransit pilots in recent years, our understanding of these systems is limited due to the lack of empirical research. Utilizing empirical trip microdata from five microtransit systems in North Carolina, this study makes an important contribution to the emerging set of literature on microtransit by examining the relationship between number of microtransit passengers originating from (arriving at) a census block and the socioeconomic and built environment that generates (attracts) these passenger trips. To do so, we estimate hurdle negative binomial models on the number of passengers traveling from and arriving at a census block in a specific hour of a weekday, aggregated over months for each of the five microtransit systems in North Carolina. We also combine data from the different service areas to incorporate their distinct service characteristics as independent variables in our models, to enhance our understanding of service characteristics that would contribute to greater demand. The models estimated separately for the five microtransit systems indicate that neighborhood socioeconomic characteristics have a significant effect on microtransit demand. More specifically, areas with a higher percentage of female, younger populations between age groups 18-44, and careless populations contribute to greater demand across multiple service areas. In addition, the number of low-wage and healthcare jobs is also positively correlated with an increased number of passengers traveling from or arriving in a census block. For instance, a 1% increase in the number of low-wage jobs in a census block is associated with upto 0.109% greater probability of a passenger pick-up and upto 0.105% increase in number of passenger pick-ups. Moreover, we also find that higher residential density in a census block contributes to higher demand across multiple microtransit systems. The econometric analysis of the pooled data from all microtransit services reveals that, after controlling for temporal, socioeconomic, and built environment factors, service characteristics — such as fare, acceptable wait times, pick-up/drop-off locations, and monthly service hours, are important determinants of microtransit demand. Among these variables, acceptable waiting times set by the transit agency displays the greatest effect (elasticity). For example, a 1% increase in acceptable waiting time is associated with a 0.188% decrease in the number of passengers pick-ups. Furthermore, the results also suggest that, if other factors are held constant, a microtransit service with door to door pick-up or drop-off service would lead to greater passenger demand. In summary, our findings identify common socioeconomic and built environment factors that drive higher microtransit demand across different service areas and also offer valuable insights about the service characteristics that would contribute to higher demand. The findings of this study will assist transit agencies in evaluating the suitability of microtransit for their jurisdictions and in designing service characteristics to successfully increase ridership.