Dynamic price prediction in ride-on-demand service with multi-source urban data

Abstract

Ride-on-demand (RoD) services such as Uber and Didi (in China) are becoming increasingly popular, and in these services dynamic price plays an important role in balancing the supply (i.e., the number of cars) and demand (i.e., the number of passenger requests) to benefit both drivers and passengers. However, the dynamic price also creates concerns for passengers: the "unpredictable" prices sometimes prevent them from making quick decisions at ease. One may wonder if it is possible to get a lower price if s/he chooses to wait a while. Giving passengers more information helps to tackle this concern, and predicting the prices is a possible solution. In this paper we perform dynamic price prediction based on multi-source urban data. Price prediction helps passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is based on urban data from multiple sources, including the RoD service itself, taxi service, public transportation, weather, the map of a city, etc. The rationale behind using multi-source urban data is that the dynamic price in RoD may be influenced by different factors found in different data sources. We train a neural network to perform the prediction, and evaluate the prediction accuracy of using different combinations of multi-source urban data. Our results show that using multi-source urban data indeed helps improve the prediction accuracy, and different datasets may have varying influences on the dynamic prices.