This study proposes a conflict-based traffic safety assessment method by associating conflict frequency and severity with shorttermtraffic characteristics. Instead of analysing historical crash data, this study employ...This study proposes a conflict-based traffic safety assessment method by associating conflict frequency and severity with shorttermtraffic characteristics. Instead of analysing historical crash data, this study employs microscopic trajectory data to quantify therelationship between conflict risk and traffic characteristics. The time-to-collision (TTC) index is used to detect conflicts, and a severityindex (SI) is proposed on the basis of time-integrated TTC. With SI, the k-means algorithm is applied to classify the conflict severitylevel. Then the severity of regional conflict risk is split to three levels. Zero truncated Poisson regression and ordered logit regressionmethods are employed to estimate the effects of short-term traffic characteristics on conflict frequency and severity, respectively.Furthermore, the copula-based joint modelling method is applied to explore the potential non-linear dependency of conflict riskoutcomes. A total of 18 copula models are tested to select the optimal ones. The HighD dataset from Germany is utilized to examinethe proposed framework. Both between-lane and within-lane factors are considered. Results show that the correlations betweentraffic characteristics and conflict risk are significant, and the dependency of conflict outcomes varies among different severity levels.The difference of speed variation between lanes significantly influences the conflict frequency and severity simultaneously. Findingsindicate that the proposed method is practicable to assess real-time traffic safety within a specific region by using short-term (30-second time interval) traffic characteristics. This study also contributes to develop targeted proactive safety strategies by evaluatingroad safety based on conflict risk, and considering different severity levels.展开更多
基金the National Natural Science Foundation of China(Grant Nos.71901223,71971222)the Natu-ral Science Foundation of Hunan Province(Grant No.2021JJ40746)the Fundamental Research Funds for the Central Universities of Central South University(Grant No.1053320214771)。
文摘This study proposes a conflict-based traffic safety assessment method by associating conflict frequency and severity with shorttermtraffic characteristics. Instead of analysing historical crash data, this study employs microscopic trajectory data to quantify therelationship between conflict risk and traffic characteristics. The time-to-collision (TTC) index is used to detect conflicts, and a severityindex (SI) is proposed on the basis of time-integrated TTC. With SI, the k-means algorithm is applied to classify the conflict severitylevel. Then the severity of regional conflict risk is split to three levels. Zero truncated Poisson regression and ordered logit regressionmethods are employed to estimate the effects of short-term traffic characteristics on conflict frequency and severity, respectively.Furthermore, the copula-based joint modelling method is applied to explore the potential non-linear dependency of conflict riskoutcomes. A total of 18 copula models are tested to select the optimal ones. The HighD dataset from Germany is utilized to examinethe proposed framework. Both between-lane and within-lane factors are considered. Results show that the correlations betweentraffic characteristics and conflict risk are significant, and the dependency of conflict outcomes varies among different severity levels.The difference of speed variation between lanes significantly influences the conflict frequency and severity simultaneously. Findingsindicate that the proposed method is practicable to assess real-time traffic safety within a specific region by using short-term (30-second time interval) traffic characteristics. This study also contributes to develop targeted proactive safety strategies by evaluatingroad safety based on conflict risk, and considering different severity levels.
