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A non‐recurring incident often negatively affects traffic, which is represented as non‐recurrent congestion. However, travellers can usually perceive congestion without knowing the underlying reasons. Accordingly, this paper proposes a data‐driven framework for non‐recurrent congestion detection and interpretation analysis. First, a statistical algorithm named generalized extreme studentized deviate is introduced to detect non‐recurrent congestion by comparing the current traffic speed with the speed threshold learned from historical data. The case study in Beijing shows that the proposed generalized extreme studentized deviate outperforms other prevailing algorithms in terms of detection rate, false alarm rate, and mean detection time. Second, data mining and natural language processing technologies are implemented on data collected from Sina Weibo, a Chinese microblog site akin to Twitter, to classify non‐recurring incidents that may be associated with non‐recurrent congestion, including traffic accident, road construction, concert, special sport (marathon), and commercial activity. Results show that overall classification accuracy reaches 95%. Finally, the association relationship between the detected non‐recurrent congestions and incidents is established via spatiotemporal information matching. This information matching provides a bidirectional verification. On the one hand, nearly 58% of non‐recurrent congestion can be explained by incident‐related (IR) microblogs. On the other hand, an average of 62% of IR microblogs can be traced by nearby non‐recurrent congestions. This paper suggests that social media can be used as a secondary source and integrated with traffic data to enhance the understanding of non‐recurrent congestion. [ABSTRACT FROM AUTHOR]

With the rapid growth of car ownership, traffic congestion has become one of the most serious social problems. For us, accurate real-time travel time predictions are especially important for easing traffic congestion, enabling traffic control and management, and traffic guidance. In this paper, we propose a method to predict urban road travel time by combining XGBoost and LightGBM machine learning models. In order to obtain a relatively complete data set, we mine the GPS data of Beijing and combine them with the weather feature to consider the obtained 14 features as candidate features. By processing and analyzing the data set, we discussed in detail the correlation between each feature and the travel time and the importance of each feature in the model prediction results. Finally, the 10 important features screened by the LightGBM and XGBoost models were used as key features. We use the full feature set and the key feature set as input to the model to explore the effect of different feature combinations on the prediction accuracy of the model and then compare the prediction results of the proposed fusion model with a single model. The results show that the proposed fusion model has great advantages to urban travel time prediction. [ABSTRACT FROM AUTHOR]

This study aimed to analyze traffic congestion in urban road networks. The speed performance index was adopted to evaluate the existing road network conditions of congestion, then road segment and network congestion indexes were introduced to respectively measure the congestion levels of urban road segment and network. This study also carried out a traffic congestion analysis for Beijing expressway network, based on the speed performance data collected from January 1 to November 1, 2012, by Beijing Traffic Management Bureau (BTMB). Based on these analyses the proposed congestion indexes can well assess the traffic congestion conditions of urban road networks, more importantly, such an assessment study provides traffic control and management agencies an accurate and clear understanding of operation status of traffic networks. [ABSTRACT FROM AUTHOR]