Exploring Potential Customized Bus Passengers Across Private Car Trajectory Data

Customized bus is considered an effective means to alleviate traffic congestion and reduce traffic-related environmental pollution caused by the increasing number of private cars. Exploring potential passenger information as the first stage of customized bus service has become a popular topic. Unlike manual investigation and passenger request methods, current studies utilize data mining methods to actively explore potential passengers from various historical travel data. However, the existing data mining methods only consider the spatiotemporal features of potential passengers and neglect the semantic features related to customized bus services, which play an important role in determining whether passengers are willing to use services. In this paper, we treated the exploration of potential customized bus passengers as a binary classification problem based on private car trajectory data. Then, we propose a novel data mining method, named iTrAdaboost-DTCN, which combines the strengths of deep learning and transfer learning. In detail, it integrates state-of-the-art deep neural networks by constructing a deep trajectory classification network (DTCN), which can automatically extract semantic feature representations to help improve classification accuracy. Due to the lack of city-wide labeled customized bus passenger information in practice, it also integrates instance-based transfer learning through improved TrAdaboost, which solves the learning problem of the target classification domain with limited labeled samples. Experimental results demonstrate that our method can explore potential passengers more effectively than other baseline methods. Furthermore, we apply our method to real-world scenarios and compare three travel characteristics of identified customized and non-customized bus passengers.