摘要：
为有效采集城市道路交通事故数据,分析交通事故时空特征,辨识交通事故原因,利用手机APP方法采集了宁波市鄞州区2016年第四季度的37 654起交通事故数据.鉴于传统K-means聚类算法收敛慢和精度低的缺陷,建立改进的K-means聚类算法以消除孤立点对聚类结果的影响,对研究范围内的交通事故黑点进行识别及分析.结果表明:基于手机APP采集的有效事故数据样本量占实际警情的比例为96.4％,能满足事故数据分析的精度和质量要求;近4年每个季度的事故变化趋势呈现明显的锯齿状变化;机动车与机动车事故数量最大,机动车与非机动车事故数量次之,事故比例分别为58.4％和15.8％;时间特征方面,周一发生的事故数量最大,周四最低,事故比例分别为15.4％和13.2％;空间特征方面,道路交通事故发生地点主要集中在地面路段、交叉口、停车场等,事故比例分别为77.4％、11.6％和7.0％,居民小区和高架事故比例较低,分别为3.2％和0.9％;事故原因方面,跟车距离过近、转弯未让直行、违法变更车道、超速行驶等驾驶行为是引发交叉口机动车与机动车类型事故的主要原因,事故比例分别为28.8％、22.9％、15.6％和7.6％;机动车转弯未让直行非机动车、非机动车闯红灯、机动车与非机动车相互占用车道和非机动车逆向行驶,是诱发交叉口机动车与非机动车类型事故的主要原因,事故比例分别为36.6％、16.6％、9.9％和7.3％.%To collect the data of urban road traffic accidents effectively and analyze the time-space characteristics and the cause of traffic accidents, data of 37654accidents in the district of Yinzhou, Ningbo in the fourth quarter of 2016, were collected by a smart mobile application (APP). In view of the drawback of the traditional K-means clustering algorithm, i. e. , slow convergence and low accuracy, an improved K-means clustering algorithm was proposed to eliminate the influence of outliers on the clustering results and identify the accident black spots automatically. The results show that the effective accident data sampled based on the mobile phone APP account for 96. 4％of the actual alarm amount, which can meet the requirement of the accuracy and quality for the accident data analysis;the change trend of each quarter over the last four years' accidents exhibits an obvious erratic change. The number of accidents between motor vehicles was the highest, followed by the accidents involving motor vehicles and non-motorized vehicles, and the proportions were 58. 4％ and 15. 8％ respectively. In terms of temporal characteristics, the number of accidents on a Monday is the highest with the rate of 15. 4％, the lowest on a Thursday with the rate of 13. 2％;in terms of space characteristics, the locations of road traffic accidents are primarily concentrated in the ground sections, road intersections, and parking lot, where the accident rates are 77. 4％, 11. 6％, and 7. 0％respectively. The accidents that occurred in the residential area and the elevated road are relatively low, with the rates of 3. 2％ and 0. 9％ respectively. In terms of the cause of accidents, driving behaviors such as following the front car closely, turning a corner without avoiding straight vehicles, illegal lane changing, and speeding are the primary reasons for accidents between two motor vehicles, with the rates of 28. 8％, 22. 9％, 15. 6％, and 7. 6％respectively. Turning a corner without avoiding the oncoming non-motor vehicles, running the red light of non-motor vehicles, occupying the wrong lanes, and reverse driving of non-motor vehicles are the primary reasons for accidents between motor vehicles and non-motor vehicles, with the rates of 36. 6％, 16. 6％, 9. 9％, and 7. 3％respectively.
