Service quality of transit and demand-supply forecasting for ride-hailing in the Jakarta Greater Area, Indonesia

  • Gito Sugiyanto Jenderal Soedirman University
  • Yanto Yanto Jenderal Soedirman University
  • Aris Wibowo PT Nur Straits Engineering (NSE) Bandung and PT Rekabhumi Segarayasa Bestari, East Java, Indonesia
  • Teguh Wiji Astoto PT Rekabhumi Segarayasa Bestari, East Java, Indonesia


The extensive use of smartphones by individuals has led innovators to develop application-based transportation services that efficiently link passengers to drivers. Ride-hailing systems have been extensively operated in more than 600 cities worldwide. Ride-hailing services can provide not only a very personalized mobility experience but also ensure efficiency. Identifying factors that influence the demand of taxi and ride-hailing is very important for understanding where and when people use taxis or ride-hailing services and how the quality of taxi and ride-hailing service. The aims of this research are to identify the factors that influence demand for taxis and ride-hailing and forecasting the demand of taxis and ride-hailing using demand-supply model. The study identified socio-demographic and trip characteristics from 949 respondents in the Jakarta Greater area, Indonesia. Respondents interviewed about the waiting time, travel time and travel costs for the origin-destination of trips that are most often done using taxi, ride-hailing, and bus. The service quality of ride-hailing and taxi was analyzed based on the respondent’s preferences from important-performance analysis survey. Factors that influence on taxi and ride-hailing demand are number of trips per day, average vehicle occupancy factors, mode share percentage, number of operating hours per day, waiting time of passengers, travel time, and waiting time of driver. The forecasting demand of taxi and ride-hailing in the Jakarta Greater area using demand-supply model is 71,660 vehicle units. The research findings are service quality of ride-hailing is better than conventional taxis based on waiting time, travel time, and travel cost variable. The next research is modeling taxi and ride-hailing demand using dynamic model that added variable service area, peak hour factor, and average vehicles speed that represent characteristics of traffic in field.

Civil Engineering