Research on Traffic Situation Analysis for Urban Road Network Through Spatiotemporal Data Mining: A Case Study of Xi’an, China
Ruiyu Zhou, Hong Chen, Hengrui Chen, Enze Liu, Shangjing Jiang
Abstract
Severe traffic congestion has promoted the development of the Intelligent Transportation System (ITS). Accurately analyzing and predicting the traffic states of the urban road networks has important theoretical significance and practical value for improving traffic efficiency and formulating ITS scheme according to local conditions. This study aims to identify and predict the traffic operation status in the road network within the Third Ring Road in Xi'an and explore spatiotemporal patterns of traffic congestion. In this paper, firstly, we discriminated the traffic status of the urban road network used the GPS data of floating vehicles (e.g., taxis and buses) in Xi'an by the Travel Time Index (TTI). Secondly, we used the emerging hot spot analysis method to locate different hot spot patterns. The time series clustering method was used to divide the whole road network's locations into distinct clusters with similar spatiotemporal characteristics. Thirdly, we applied three different time series forecasting models, including Curve Fit Forecast (CFF), Exponential Smoothing Forecast (ESF), Forest-based Forecast (FBF), to predict the traffic operation status. Finally, we summarized the spatiotemporal characteristics of the whole-network congestion. The results of this study can contribute some helpful insights for alleviating traffic congestion. For instance, it is essential to speed up the construction of urban traffic microcirculation and increase the road network density. Moreover, it is crucial to adhere to the urban public transport priority development strategy and increase public transportation travel sharing.