RESEARCH ON SPATIOTEMPORAL PREDICTION OF DEMAND SPOTS FOR DEMAND TAXIS USING DEEP LEARNING
2022 Volume 3 Issue J2 Pages 1091-1098
Details
2022 Volume 3 Issue J2 Pages 1091-1098
In recent years, the aging of Japan's population has made it a major challenge to secure means of transportation in areas where public transportation is not available. Demand-responsive transportation, which is more responsive to users' needs than local buses and community buses, is expected to become increasingly important in the future to solve this problem. Higashimatsuyama City in Saitama Prefecture introduced a demand cab service in December 2015 to provide a means of transportation in areas with public transportation vacancies. Six years have passed since the start of the service, and while demand for the service has taken root, there are also issues such as requests for service improvement and increased project costs.
In this study, a demand heat map was created using the accumulated usage data, and a model was developed to predict demand spots in space and time by applying deep learning image recognition technology using a neural network.
