Optimizing travel routes using temporal networks constructed from global positioning system data in kyoto tourism

Owing to the complexity of urban transportation networks and temporal changes in traffic conditions, the assessment of real-time traffic situations is a challenge. However, the development of mobile information devices using the global positioning system (GPS) has made it easier to obtain personal mobility information. In this study, we developed a method for evaluating the mobility of people in a city using GPS data. We applied two methods: evaluating human mobility using temporal networks constructed from GPS data, and searching for the shortest path by constructing and solving the time-dependent traveling salesman problem (TDTSP). The estimation is expected to be more realistic if transportation delays from congestion are considered. This study makes two major contributions. First, we propose a new method for estimating the time weights of edges in temporal networks using probability density functions for the travel time. Second, to apply ant colony optimization to the TDTSP, we propose a new method for estimating the congestion level from GPS data and calculating the transition probability using the estimated congestion level. As a case study, we conducted a human mobility analysis in Kyoto City.

Automated summary

This study develops a method for evaluating the mobility of people in a city using GPS data, including evaluating human mobility using temporal networks and searching for the shortest path by solving the time-dependent traveling salesman problem. The results show that considering congestion leads to more realistic estimations.