Developing a new real-time traffic safety management framework for urban expressways utilizing reinforcement learning tree

Kui Yang, Mohammed Quddus, Constantinos Antoniou

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

One of the main objectives of an urban traffic control system is to reduce the crash frequency and the loss caused by these crashes on urban expressways. Real-time crash risk prediction (RTCRP) is an essential technique to identify crash precursors so as to take proactive measures to smooth traffic fluctuations. In addition, automatic incident detection (AID) is another important approach to timely detect an incident so as to design countermeasures that reduce any negative impacts on traffic dynamics. With the introduction of disruptive technologies in transport, highly disaggregated large datasets have started to emerge for modelling while existing modelling techniques utilized in RTCRP and AID may not be able to accurately predict traffic crashes in real-time. Therefore, this paper proposes a state-of-the-art reinforcement learning tree (RLT) approach to develop RTCRP model and automatic crash detection (ACD) model similar to AID, and further utilizes it to build a real-time traffic safety management framework for urban expressways with the input of online traffic data streaming. Recorded traffic flow data and historical crash data were extracted and integrated to develop and implement both RTCRP models and ACD models. The prediction results were compared with the frequently used logistic regression (LR), support vector machine (SVM) and deep neural network (DNN) and a sensitivity analysis for variable effects was conducted. The results confirm that RLT outperforms LR, SVM and DNN in developing RTCRP and ACD models. At the cost of 10% false-alarm rate, about 96% of the crashes were predicted or detected correctly by the proposed framework. The results also indicate that: i) collecting more data is helpful to improve the predictive performance and approximatively a minimum sample size of 20 observations per variable is reasonable for training RLT models; and ii) obtaining more factors is beneficial to improve the predictive performance. With the RLT approach, it was demonstrated that selected important variables also have the capability to provide reasonable predictive performance.

Original languageEnglish
Article number106848
JournalAccident Analysis and Prevention
Volume178
DOIs
StatePublished - Dec 2022

Keywords

  • Automatic crash detection
  • Real-time crash risk prediction
  • Reinforcement learning tree
  • Traffic safety
  • Urban expressways

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