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.
Highlights
With the rapid growth of private vehicle ownership, the corresponding transportation infrastructure is insufficiently supplied, traffic congestion has become more serious, affecting people’s travel and limiting the city’s economic stable development [1], [2]
This study focuses on the discrimination and prediction of traffic congestion states on weekdays, so the data of 21 working days are selected for analysis, totaling more than 600 million data, with a storage scale of about 65GB
We believe that it is essential to decrease the impact of accidental conditions such as traffic accidents and severe weather on Travel Time Index (TTI) value
Summary
With the rapid growth of private vehicle ownership, the corresponding transportation infrastructure is insufficiently supplied, traffic congestion has become more serious, affecting people’s travel and limiting the city’s economic stable development [1], [2]. The most influential cities in China suffer economic losses of $1 billion every year [3]. The European Commission calculates that the annual cost related to traffic congestion is about 100 billion euros (1% of GDP) [4]. Patterns of traffic congestion and predicting the short-term traffic state are significant urban management challenges [6]. Despite much research on route optimization and congestion prediction, there is still an inadequate understanding of the whole road network’s spatiotemporal congestion patterns. Assessing the spatiotemporal characteristics of urban traffic congestion and deeply mining its complex operation regularity has important theoretical significance and practical value for improving traffic operation efficiency and intelligent traffic management technology [7]
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