Research on the Application of Multi-Source Data in Bus Route Network Optimization

Abstract

This research aims to optimize the bus route network in Shiyan City using bus Origin-Destination (OD) data. By integrating multi-source data, including GDP, population, and bus OD big data from 2018 to 2022, short-term and long-term indicators are forecasted by time-series methods. Shiyan city is divided into 77 Traffic Analysis Zones (TAZs) considering its mountainous terrain, population-industry distribution, and urban planning. A conventional four-stage traffic demand model is applied, calibrated with bus OD data in 2021. The investigation reveals peak-hour bus passenger travel demand of 29,700 short-term and 31,800 long-term person-trips in the city center and key corridors. Bus passenger travel forms a four-vertical and five-horizontal layout in the short term, evolving to a five-vertical, five-horizontal, and two wings pattern in the long term with eastward urban expansion. Accordingly, an optimized and upgraded bus route improvement strategy is devised. In the short term, there are seventy existing bus routes that are adjusted, creating a five-layer bus route network with diverse functions. Long-term plans involve optimizing twenty bus routes and adding eight new routes to align with urban development. This research not only aids an integrated bus route network optimization framework using bus OD data in a time-consuming way, but also provides a sample of bus route network adjustment for a typical mountainous city.