Journal of Geo-information Science >
Study on the Method of Identifying the Characteristics of the Traffic Violation Behavior based on the Spatial and Temporal Hotspot Analysis Approach
Received date: 2021-10-01
Revised date: 2022-01-10
Online published: 2022-09-25
Supported by
National Key R & D Program of China(2017YFB0503500)
China Postdoctoral Science Foundation(2019M652244)
The Central Guided Local Development of Science and Technology Project of Fujian(2020L3005)
Fujian Cooperation Project between Universities and Enterprises(2021H6004)
Urban traffic violation behavior plays an important role in traffic accidents. Analyzing the spatial and temporal distribution of traffic violation behavior can support related decision makings for traffic management and the optimization of the surroundings of the hotspots. Due to the limitation in data acquisition, existing studies paid little attention to the variation of the spatial and temporal patterns between different violation behavior types. There is a lack of analysis framework to support the decision makings in traffic violation behavior treatment. In this study, we propose a traffic-violation-behavior treatment-oriented analysis framework based on the spatial and temporal hotspot approach. Two analyses are designed and conducted to support the traffic violation behavior treatment: (1) analyzing the temporal pattern of each spatial hotspot to support the reasoning analysis and precise treatment policy makings at the local scale; (2) analyzing the spatial pattern of the hotspots during typical periods (e.g., morning and evening rush hours) to support the reasoning analysis and the optimization of the allocation of police resources on a global scale. We use a dataset of Fuzhou city acquired in 2017 to verify the proposed method. The spatial and temporal patterns of the motor traffic violation behavior and the non-motor type are analyzed and compared. We find that: (1) the traffic violation behavior exhibits a double peak hourly pattern at 9:00 am and 4:00 pm during a day, respectively. The morning peak is obviously higher than the evening peak. The traffic violation behavior more likely happens during weekdays than weekends; (2) the traffic violation behavior mainly concentrates at the core-built area within the second ring highway and several hotspots in the suburban area including the shopping mall of Cangshan Wanda and the exit of the Kuiqi tunnel oriented to Mawei; (3) motor and non-motor traffic violation exhibit different temporal and spatial patterns. Non-motor traffic violation frequencies exhibit both larger hourly and weekday-weekend differences, and mainly concentrates at the road crosses with big traffic volume of both motor cars and e-bikes/pedestrian. While the motor traffic violation exhibits more stable patterns across the hours in a day and the days in a week, and mainly happens around the critical places such as large hospitals, shopping malls, and complex overpasses; (4) the spatial scales affect the patterns of the spatial hotspots of the traffic violation behavior. The spatial autocorrelation of the traffic violation increases with the scale size rapidly before 1500 m and keeps around 0.6 afterward. Motor traffic violation exhibits lower spatial autocorrelation than the non-motor. The above findings validate the effectiveness of the proposed method. It can help to guide the construction of the traffic violation behavior treatment platform and further optimize the allocation of the police resources and improve the effectiveness of the law enforcement for the traffic violation behavior.
ZHAO Zhiyuan , HUANG Yonggang , WU Sheng , WU Qunyong , WANG Yanxia . Study on the Method of Identifying the Characteristics of the Traffic Violation Behavior based on the Spatial and Temporal Hotspot Analysis Approach[J]. Journal of Geo-information Science, 2022 , 24(7) : 1312 -1325 . DOI: 10.12082/dqxxkx.2022.210599
表1 交通违法数据示例Tab. 1 Examples of traffic violation records |
ID | 违法地址 | 违法内容 | 违法时间 |
---|---|---|---|
000034*** | 卢滨路金洲路口北向 | 在高速公路或城市快速路以外的道路上行驶时,驾驶人未按规定使用安全带的 | 2017-10-07 17:20 |
00003D*** | 华林路五四路口 | 非机动车未在非机动车道内行驶 | 2017-11-29 9:45 |
…… | …… | …… | …… |
SIRC*** | 福飞路新园路口南向 | 行机动车通过有灯 控路口时,不按所需行进方向驶入导向车道 | 2017-01-26 8:41 |
表2 各违法类型记录次数Tab. 2 Statistics of the violation records by the type |
类型 | 次数/(万次) | 占比/(%) | 主要违法行为 |
---|---|---|---|
机动车违法行为 | 26.40 | 49.20 | 机动车违反禁令标志、禁止标线;机动车违反规定停放;机动车行驶中存在安全隐患;驾驶机动车违反道路交通信号灯通行等 |
非机动车违法行为 | 27.27 | 50.80 | 非机动车未在非机动车道内行驶;行人和非机动车违反交通信号灯通行;非机动车行驶中存在安全隐患;非机动车逆向行驶等 |
图5 机动车违法热点一天的时间分布Fig. 5 Hourly distribution of motor vehicle violation hotspots during a day |
图6 机动车违法热点一周的时间分布Fig. 6 Daily distribution of motor vehicle violation hotspots during a week |
表3 机动车违法行为最频繁的5个区域Tab. 3 Top 5 areas of the motor vehicle violations |
序号 | 地点名称 |
---|---|
M1 | 三环魁岐1#隧道往马尾方向出口处 |
M2 | 金山大道新展城口东向 |
M3 | 仓前路三县洲大桥下监控范围 |
M4 | 金洲南路金港路口南向 |
M5 | 福州机场前高架桥上 |
表4 非机动车违法行为最频繁的5个区域Tab. 4 Top 5 areas of non-motor vehicle violations |
序号 | 地点名称 |
---|---|
N1 | 华林路五四路口 |
N2 | 北二环路华林路口 |
N3 | 工业路西二环中路口 |
N4 | 浦上大道尤溪洲大桥西口 |
N5 | 江滨中大道(六一中路至长乐南路段) |
图7 非机动车违法热点一天的时间分布Fig. 7 Hourly distribution of non-motor vehicle violation hotspots during a day |
表5 交通违法行为异常聚集原因分类及说明Tab. 5 The potential reasons and instructions of traffic violation behavior |
类型 | 原因分析 | 说明 | 时空间分布特点及示例 |
---|---|---|---|
机动车 | 局部道路环境设计不合理 | 因道路标线、交通标志以及其他道路设施设置不合理引发 | 主要分布在主城区外围道路主要分叉口,例如三环魁岐隧道出口马尾方向 |
多样化驾驶行为集中同现 | 因多样化的出行目的在局部区域集中产生的减速、转向等行为引起 | 主要分布在主城区大型场所附近的路口,例如附近有福建医科大学附属协和医院、附属第一人民医院和福建省儿童医院等大型医院的八一七路口 | |
非机动车 | 人车混行路口出行流量增加激化行人与机动车之间的矛盾 | 流量激增,普遍存在机动车道和非机动车道相互占用情况,极大激化两者之间的出行冲突 | 主要分布在早高峰期间城市中心区人车混行的路口,例如早高峰期间白马南路和工业路口以及西二环路乌山路口处;晚高峰期间国货东路和六一中路口以及梅峰路西二环北路口处 |
执法点有偏 | 现场执法点位置违法行为多 | 本文缺少充分数据进行论证,不做深入讨论 |
[1] |
许洪国, 周立, 鲁光泉. 中国道路交通安全现状、成因及其对策[J]. 中国安全科学学报, 2004, 14(8):34-38.
[
|
[2] |
戴帅. 我国城市道路交通安全问题及对策[J]. 综合运输, 2015, 37(7):9-12,21.
[
|
[3] |
|
[4] |
梁娟珠, 徐森. 福州市城市交通违法行为的空间分布规律分析[J]. 华侨大学学报(自然科学版), 2021, 42(1):128-134.
[
|
[5] |
徐森, 梁娟珠. 基于福州市交通违法数据的时空关联规则挖掘研究[J]. 地理信息世界, 2020, 27(5):46-51.
[
|
[6] |
|
[7] |
|
[8] |
|
[9] |
|
[10] |
|
[11] |
蒋贤才, 黄科, 汪贝, 等. 电子执法环境下交通违法行为影响因素分析[J]. 哈尔滨工业大学学报, 2013, 45(8):84-89.
[
|
[12] |
|
[13] |
|
[14] |
付川云, 刘华, 周悦, 等. 基于电子抓拍数据的交通违法行为影响因素研究[J]. 武汉理工大学学报(交通科学与工程版), 2019, 43(6):985-990.
[
|
[15] |
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
薛涛. “限电令”能否破解城市交通管理难题?[EB/OL]. http://www.xinhuanet.com/2020-06/25/c_1126158582.htm 2021年9月30获取).
[
|
[21] |
|
[22] |
徐冲, 柳林, 周素红, 等. DP半岛街头抢劫犯罪案件热点时空模式[J]. 地理学报, 2013, 68(12):1714-1723.
[
|
[23] |
孙小芳. 夜光遥感支持下的城市人口核密度空间化及自相关分析[J]. 地球信息科学学报, 2020, 22(11):2256-2266.
[
|
[24] |
吴康敏, 王洋, 叶玉瑶, 等. 广州市零售业态空间分异影响因素识别与驱动力研究[J]. 地球信息科学学报, 2020, 22(6):1228-1239.
[
|
[25] |
|
[26] |
曾滢. 城市路网窄密程度评估方法研究——以广州为例[J]. 城市建筑, 2020, 17(19):53-58.
[
|
[27] |
GB/50220-1995,城市道路交通规划设计规范[S]. 北京: 中国标准出版社, 1995.
[GB/50220-1995,Code for transport planning on urban road[S]. Beijing: China Standard Press, 1995. ]
|
[28] |
陈超. 小尺度街区模式研究——对重庆悦来生态城规划的分析评价[D]. 重庆: 重庆大学, 2015.
[
|
[29] |
|
[30] |
|
[31] |
|
[32] |
|
[33] |
杜挺, 谢贤健, 梁海艳, 等. 基于熵权TOPSIS和GIS的重庆市县域经济综合评价及空间分析[J]. 经济地理, 2014, 34(6):40-47.
[
|
[34] |
李雅箐, 李小娟, 王彦兵. 北京市房山区农村经济发展空间格局分析[J]. 地球信息科学学报, 2011, 13(3):391-400.
[
|
[35] |
崔娜娜, 冯长春, 宋煜. 北京市居住用地出让价格的空间格局及影响因素[J]. 地理学报, 2017, 72(6):1049-1062.
[
|
[36] |
福州市交通运输局. 《福州市交通运输发展“十三五”规划》[EB/OL]. http://fzjt.fuzhou.gov.cn/zz/zwgk/ghjh_33476/201609/t20160914_1618278.htm 2016年9月14获取).
[ Fuzhou Municipal Transportation Bureau. "The 13th five-year plan for the development of transportation in Fuzhou"[EB/OL]. http://fzjt.fuzhou.gov.cn/zz/zwgk/ghjh_33476/201609/t20160914_1618278.htm last accessed on September 14th 2016). ]
|
/
〈 |
|
〉 |