Traffic congestion and ride-hailing booking: Evidence from Hanoi, Vietnam

Several studies have empirically investigated the association between traffic congestion and ride-hailing (RH) services from the perspective of users' booking behavior, except in a limited number of countries such as the United States and China. To add new evidence and expand research, this study investigated the association between traffic congestion and booking orders of RH service users in Hanoi, Vietnam. A total of 50,767 booking records on RH users of a private operator in Hanoi were used for empirical analysis. The study established the following four hypotheses: (1) the frequency of RH bookings in congested areas is lower than that in less congested areas; (2) the frequency of RH bookings using an app-based method in congested areas is higher than that in less congested areas during evening-peak hours; (3) the effects of built environment, sociodemographics, and transportation facilities on the frequency of RH bookings in congested areas are less significant than those in less congested areas during evening-peak hours; and (4) the effects of built environment, sociodemographics, and transportation facilities on the frequency of RH bookings are lower in congested than in less congested areas. After identifying more and less congested areas, the four hypotheses were statistically tested with grid-zone-based data matched using the Mahalanobis distance method between the two areas. The results of the statistical tests showed that Hypotheses (1) and (2) were supported by empirical data. Next, the estimation results of the regression models to explain RH service bookings with built environment, sociodemographic, and transportation facilities factors by time period reveal that Hypotheses (3) and (4) were also supported. Finally, policy implications were presented from the findings, including (i) requiring RH service provides to improve their service quality and attract more users during periods of serious traffic congestion to promote a modal shift from private modes to RH services and thus reduce traffic congestion; (ii) providing technical support to traditional taxi companies to use web-based service apps so that the disparity between traditional taxi and RH services could be mitigated; and (iii) identifying the heterogeneity in RH service demand across urban characteristics for urban travel demand forecasting.