This study aims at identifying crash-influencing factors by facility type of Nagoya Urban Expressway, considering the interaction of geometry, traffic flow, and ambient conditions. Crash rate （CR） model is firstly d...This study aims at identifying crash-influencing factors by facility type of Nagoya Urban Expressway, considering the interaction of geometry, traffic flow, and ambient conditions. Crash rate （CR） model is firstly developed separately at four facility types： basic, merge, and diverge segments and sharp curve. Traffic flows are thereby categorized, and based on the traffic categories, the significances of factors affecting crashes are analyzed by principal component analysis. The results reveal that, the CR at merge segment is significantly higher than those at basic and diverge segments in uncongested flow, while the value is not significantly different at the three facility types in congested flow. In both un- and congested flows, sharp curve has the worst safety performance in view of its highest CR. Regarding influencing factors, geometric design and traffic flow are most significant in un- and congested flows, respectively. As mainline flow increases, the effect of merging ratio affecting crash is on the rise at basic and merge segments as opposed to the decreasing significance of diverging ratio at diverge segment. Mean- while, longer acceleration and deceleration lanes are adverse to safety in uncongested flow, while shorter acceleration and deceleration lanes are adverse in congested flow. Due to its special geometric design, crashes at sharp curve are highly associated with the large centrifugal force and heavy restricted visibility.展开更多
Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with...Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.展开更多
基金support of Nagoya Expressway Public Corporation for the data provision
文摘This study aims at identifying crash-influencing factors by facility type of Nagoya Urban Expressway, considering the interaction of geometry, traffic flow, and ambient conditions. Crash rate （CR） model is firstly developed separately at four facility types： basic, merge, and diverge segments and sharp curve. Traffic flows are thereby categorized, and based on the traffic categories, the significances of factors affecting crashes are analyzed by principal component analysis. The results reveal that, the CR at merge segment is significantly higher than those at basic and diverge segments in uncongested flow, while the value is not significantly different at the three facility types in congested flow. In both un- and congested flows, sharp curve has the worst safety performance in view of its highest CR. Regarding influencing factors, geometric design and traffic flow are most significant in un- and congested flows, respectively. As mainline flow increases, the effect of merging ratio affecting crash is on the rise at basic and merge segments as opposed to the decreasing significance of diverging ratio at diverge segment. Mean- while, longer acceleration and deceleration lanes are adverse to safety in uncongested flow, while shorter acceleration and deceleration lanes are adverse in congested flow. Due to its special geometric design, crashes at sharp curve are highly associated with the large centrifugal force and heavy restricted visibility.
基金supported by the National Natural Science Foundation (71301119)the Shanghai Natural Science Foundation (12ZR1434100)
文摘Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines （SVMs） technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on real- time safety management on freeways.
