Evaluation of Taxi Service with Regard to the Drivers Income Using Simulation Support

Abstract

Taxi is a popular service in many cities and tends to improve mobility without being the costumer directly charged by the vehicle maintenance. A company or a self-employed person (i.e., the taxi driver) is the one in charge with vehicle maintenance, fuel payment, insurance, etc. The taxi service request is mainly made through mobile applications, where costumers select payment method, origin and destination, and an information system, aware of the taxi and costumer locations, associates the closest taxi to the customer request. Typically, this service is analyzed from the costumer side mainly looking for travel time and fare reductions. There is a lack of research that investigates the taxi drivers side. In this work, we evaluate the taxi service from the taxi drivers point of view. We have already published part of this study with the focus on the taxi driver income on previous work and here we extended the research to assess other features such as the impact of the vehicle type engine (electric, ethanol, gasoline and CNG) in taxi drivers income and expenses, the percentage of the fleet occupation given different service demand, and the difference between traveled occupied distance and total distance. Given a demand and costs, a simulation is proposed to detail and evaluate the appropriate balance between drivers income and demand scheme to keep the service viable to the drivers. Simulation was conducted with the support of SUMO traffic simulator using as scenario, a medium-sized city in Southern Brazil. Based on literature, city hall documentation and Internet news, input values were chosen to make the simulation as realistic as possible. As conclusion, we found that the city town hall must define a maximum number of taxi licenses for feasible taxi service. The vehicle type with regard to energy source has a major impact in the taxi driver’s profit. Despite of high acquisition cost, electric vehicles have a lower cost per km driven in comparison to other vehicles. If the daily traveled distance increases, the difference between electric vehicles and others decreases, making electric vehicles more advantageous. Fleet occupancy also impacts driver’s profit. With regard to the fleet occupancy, as demand grows, the fleet occupancy levels rise and as well as the driver’s profit. Finally, we found that difference between total traveled distance and occupied distance increases as the number of travel runs increases.