Exploiting Trip Patterns in Passenger Trajectory Streams for Bus Scheduling Optimization in Real Time

Analyzing and mining trajectories of moving objects (such as persons or vehicles) in the cities bring a promising way to discover the potential knowledge and therefore can foster diversified applications, including personalized travel services, intelligent transportation systems (ITSs), and etc. For increasing the intelligence of current public transit systems, the paper proposes to discover and utilize the patterns in passenger trajectory streams to optimize bus scheduling. More specifically, the paper first analyzes the real world data from bus smart cards so as to fully understand the nature of passenger trajectories and bus operations. Based on it, the paper defines a new trip pattern, i.e., the frequent bus passenger trip pattern for bus scheduling (the FBPT4BS pattern in short). Then, the paper proposes an approach. The approach gives the procedure of discovering FBPT4BS patterns from passenger trajectory streams and finds the bus lines whose capacities are not enough to satisfy the passengers' travel demands. Further, the approach gives the suggestion on the corresponding scheduling adjustment strategy for bus lines. Experiments are conducted on the data from the Beijing Public Transport Group. The experimental results show that the proposed approach can efficiently decrease the travel times of passengers.