Introducing drivers' collaboration network: A two-layers social network perspective in road transportation system analysis

Abstract

Drivers can make significant impacts on transportation systems. They can leave important information due to their social behaviors. But, the role of drivers has been overlooked yet. In this paper, for the first time, drivers' collaboration network is introduced. The network is considered in a heterogenous form, because of existence multiple relationships between drivers in the real-world situation. Since drivers do not belong to only one community, the overlapping of communities is considered and a new overlapping community detection algorithm is developed to discover the hidden structure of the network. Also, we present a new overlapping score to improve the community detection algorithm, using the adjacencies among non-memeber neighbor nodes and communities. Solving the algorithm will lead to discovering dense communities of drivers that have meaningful relationships with each other. This will result in a better understanding of the transportation network and also improving the overall performance of the system. A comparison of the developed algorithm with the others demonstrates the effectiveness of the algorithm. To evaluate the applicability of the algorithm, a real drivers' collaboration network is presented and the developed algorithm is applied to derive insights.