BusNav: Multi-Objective Bus Routing Algorithm for Intelligent Transportation Networks

Abstract

With the rapid development of vehicle intelligence and the Internet of Vehicles industry, passengers’ demand for travel experiences continues to increase. To provide more comfortable and convenient intelligent transportation services, it is necessary to effectively arrange routes that meet demand. However, the actual traffic environment is complex and changeable, and traditional path planning methods are no longer adaptable. To cope with the complex and changeable traffic environment, a new bus operation model called “flexible bus” is proposed. The flexible bus system is a bus model that responds to demand. Passengers can reserve shuttle buses according to their needs, and buses depart after a certain number of passengers is reached. Flexible bus vehicles are scheduled and planned in real time based on passenger needs and driving routes to improve operational efficiency and passengers’ boarding experiences. To achieve the goal of reducing bus operating costs and improving passengers’ boarding experience, a heuristic path planning algorithm called BusNav is proposed. BusNav includes two procedures: path extraction and trip allocation, which can efficiently dispatch buses to meet different operating costs and passengers’ boarding experience needs. Compared with traditional path planning methods, experimental results show that the BusNav algorithm performs well in terms of reduce operating costs and improve passengers’ boarding experience.

Keywords

• Multi-objective
• bus routing
• flexible bus system
• vehicle intelligence