Examining the factors influencing microtransit users’ next ride decisions using Bayesian networks

Abstract

Abstract The progress of microtransit services across the world has been slower than expected due to institutional, operational, and financial barriers. However, how users' ride experiences and system attributes affects their future ride decisions remain an important issue for successful deployment. A Bayesian network approach is proposed to infer users’ next ride decisions on a microtransit service based on historical ride data from Kussbus, a pilot microtransit system operating in the Belgium–Luxembourg cross-border areas in 2018. The results indicate that the proposed Bayesian network approach could reveal a plausible causal relationship between different dependent factors compared to the classical multinomial logit modeling approach. By examining public transport coverage in the study area, we find that Kussbus complements the existing public transport and provides an effective alternative to personal car use.

Keywords

• Microtransit
• Demand-responsive transport
• On-demand mobility
• Bayesian network
• Next ride occurrence