A Partitioning Algorithm for Bi-modal Road Networks

The recent extension of a macroscopic fundamental diagram (MFD) into a bi-modal MFD (or called 3D-MFD) provides the relationship among the total network circulating flows and the accumulations of private vehicles and public buses. 3D-MFD reveals the significance of large occupancy vehicles such as buses contributing to passenger flows. A lot of bi-modal traffic management techniques are introduced based on 3D-MFD to improve the urban traffic efficiency without using detailed origin-destination (OD) information. However, similar to MFD, 3D-MFD is also highly affected by the heterogeneity of a road network. In order to form 3D-MFDs with low scatter to be utilized in the further bi-modal traffic management, this paper proposes a partition method to cluster road links into several homogeneous regions for a bi-modal urban network. This bi-modal partition is comprised of three layers named as initial partition, merging, and boundary adjusting. At the initial partition layer, Seeded Region Growing (SRG) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are integrated to obtain a number of subregions. A modified Genetic Algorithm (GA) is developed to merge the subregions into larger regions at the merging layer. Then, boundary adjustment is applied by changing the region to which a boundary belongs to get the optimal result. Multi-sensor data collected from Shenzhen, China are utilized to verify the effectiveness of the proposed partition method.