Integrated Network Optimization Method for Joint Services of RT and DRT in Low-Density Areas

This study addresses the issues of low efficiency and poor flexibility in conventional public transport services in low-density areas and proposes an integrated network optimization method for regular transit (RT) and demand-responsive transit (DRT) in an effort to achieve a balance between the operational supply costs and passenger travel demand costs in public transport operations. Considering the fleet size required for the lines, passenger walking distances, and the impact of transfers, the cost components of the joint service are derived, and a multi-objective joint optimization model is established. The model optimizes the routes of RT, the service areas of DRT, and the overall fleet size and identifies three different benefit preference plans: service priority, cost priority, and balanced approach for RT line schemes. Based on the spatial distribution of passenger travel and the desired time windows, clustering is performed to determine the optimal clustering points as temporary stops. The Multiple-Chain Chromosome Genetic Algorithm (MCGA) is then applied to further solve for the optimal service routes of DRT. Finally, the effectiveness and adaptability of the proposed method are verified using the Qilitang neighborhood area in Hefei as a case study. The results indicate that compared to the initial scheme, the optimized scheme under the balanced approach reduces passenger travel costs by 13%, lowers operational supply costs by 5%, and comprehensively enhances service levels while improving operational economy.