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

Abstract

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.

Assessing Safety Benefits of Variable Speed Limits

“…Since the values of the log-likelihood ratio χ 2 were found to be low with p-values close to 1 in all cases, the model fit was adequately high at a 95% confidence level (α = 0.05). This modified model displayed slightly better performance than the model we developed in the previous study (6). As expected, high-level crash precursors generally contribute to high crash frequency.…”

“…In our previous studies (5,6), the following three crash precursor variables were identified: (1) the coefficient of variation of speed (which is equal to the standard deviation of speed divided by the average speed) upstream of a specific location (CVS), (2) average density (D), and (3) average speed difference between the upstream and downstream of a specific location (Q). The details of model structure, the calculation of precursors including the determination of observation time slice duration (time offset), the categorization of precursor variables, and the calibration of parameters using real traffic data are discussed in Lee et al (5,6). …”

Real-time traffic safety management model on motorways

“…) was modified in 2003 [15], and then in 2004 [16] by the author and implemented in the simulation of a smaller transport network, 2.5 km long, which contains four inductive loops, three variable message signs and three traffic lanes. As a conclusion of the said simulation, the obtained results show that the average crash potential is reduced by approximately 25% by using the system of variable traffic signs and the crash prevention model.…”

Research on Prediction Method of Freeway Operation Situation Based on Short-Term Traffic Flow Multi-Parameters Regression

“…The United States and Canada began to carry out researches on the traffic accident detection algorithms and the traffic flow harbinger characteristics before the traffic accident from 1990s [8][9][10][11][12][13][14][15]. Among them, Chris adopted the speed differences between upstream and downstream, and the variances of the cross-section speed as the characterization factors of the traffic flow real-time risk discrimination [9], the results of which was referenced by Kansas state highway agency of USA in 2006.…”