Abstract：To quantitatively analyze the road congestion around primary and secondary schools and improve the congestion around schools, the traffic congestion data in school season and non-school season were compared. The congestion index of the road around school was established, and the congestion evaluation function was constructed. The influence degree of each road by school travel was analyzed, and the key congested roads were identified. An algorithm for obtaining traffic big data based on the map open platform was proposed. The roads around 79 primary and secondary schools in the main urban area of Nanjing were analyzed, and the congestion value of each road affected by school travel was given. The results show that the overall congestion degree in school season is higher than that in non-school season, and the average congestion index is 1.973 in school season, which is 18.57% higher than that of 1.664 in non-school season.
LU M, SUN C, ZHENG S Q. Congestion and pollution consequences of driving-to-school trips: a case study in Beijing[J]. Transportation Research Part D:Transport and Environment, 2017,50:280-291.
［3］
LIU Y, JI Y J, SHI Z B, et al. Investigating the effect of the spatial relationship between home, workplace and school on parental chauffeurs′ daily travel mode choice[J]. Transport Policy,2018,69:78-87.
PANG M B, PEI Y N, ZHANG N.Integrated simulation of mixed traffic flow on elementary and middle school gate′s road when students going to school[J]. Journal of System Simulation,2018,30(3):1162-1170,1177.（in Chinese）
YANG X F, LIU H, HE K B. The significant impacts on traffic and emissions of ferrying children to school in Beijing[J]. Transportation Research Part D: Transport and Environment, 2016,47:265-275.
［7］
ZHANG Q Q,XU H L. Data driven analytics for school location impacting urban traffic congestion[C]∥2017 4th International Conference on Transportation Information and Safety.Piscataway，USA:IEEE,2017:883-888.
［8］
CHEN Y Y,CHEN C,WU Q,et al. Spatial-temporal traffic congestion identification and correlation extraction using floating car data[J]. Journal of Intelligent Transportation Systems, 2021,25(3):263-280.
ZHANG X Y,SONG G H,ZHU L,et al. FCD-based identification method for urban recurrent traffic congestions[J].Journal of Transport Information and Safety, 2014,32(1):5-9,15.（in Chinese）
［10］
SHEN Q,BAN X J,GUO C. Urban traffic congestion evaluation based on kernel the semi-supervised extreme learning machine[J]. Symmetry,2017,9(5):1-12.
［11］
ZHAO P J, HU H Y. Geographical patterns of traffic congestion in growing megacities: big data analytics from Beijing[J]. Cities,2019,92:164-174.
ZHANG J X,GUO L W.Congestion status recognition of road network based on traffic situation analysis of online map[J]. Journal of Transportation Systems Engineering and Information Technology, 2018,18(5):75-81.（in Chinese）
