Calibrating a Real-Time Traffic Crash-Prediction Model Using Archived Weather and ITS Traffic Data

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

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Sensitivity of a real-time freeway crash prediction model to calibration optimality

European Transport Research Review ◽

10.1007/s12544-012-0072-y ◽

2012 ◽

Vol 4 (3) ◽

pp. 167-174 ◽

Cited By ~ 1

Author(s):

Amir Samimi ◽

Bruce Hellinga

Keyword(s):

Prediction Model ◽

Real Time ◽

Crash Prediction ◽

Crash Prediction Model

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Real-Time Crash Prediction Model for Application to Crash Prevention in Freeway Traffic

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1840-08 ◽

2003 ◽

Vol 1840 (1) ◽

pp. 67-77 ◽

Cited By ~ 155

Author(s):

Chris Lee ◽

Bruce Hellinga ◽

Frank Saccomanno

Keyword(s):

Prediction Model ◽

Real Time ◽

Traffic Flow ◽

Flow Characteristics ◽

Previous Model ◽

Traffic Condition ◽

Freeway Traffic ◽

Crash Prediction ◽

Crash Prediction Model ◽

Time Basis

The likelihood of a crash or crash potential is significantly affected by the short-term turbulence of traffic flow. For this reason, crash potential must be estimated on a real-time basis by monitoring the current traffic condition. In this regard, a probabilistic real-time crash prediction model relating crash potential to various traffic flow characteristics that lead to crash occurrence, or “crash precursors,” was developed. In the development of the previous model, however, several assumptions were made that had not been clearly verified from either theoretical or empirical perspectives. Therefore, the objectives of the present study were to ( a) suggest the rational methods by which the crash precursors included in the model can be determined on the basis of experimental results and ( b) test the performance of the modified crash prediction model. The study found that crash precursors can be determined in an objective manner, eliminating a characteristic of the previous model, in which the model results were dependent on analysts’ subjective categorization of crash precursors.

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