The main work content of this project includes: operation and maintenance of the license plate recognition system and notification of inspections, publicity related to mobile pollution sources, statistical analysis data screening and feedback, development of technological images and technology of the license plate recognition system, strengthening the ban and management of highly polluting vehicles, etc. , the work content is summarized as shown in Table 1. The implementation period of the plan is from February 8, 2020 to February 7, 2013. The monthly implementation results of each work are shown in Table 2.
1. Project background
The number of registered motorcycles in Tainan City is 1,355,839, and the number of vehicles that should be regularly inspected is 936,331. There are 328 motorcycle exhaust inspection stations in the jurisdiction. The number of electric motorcycles increased from 34,777 in 2010 to 61,160 in 2012, an increase of 75.9%. There are 67,220 diesel vehicles registered in the top three regions: Yongkang District 10,339 vehicles, Annan District 6,955 vehicles, and Eastern District 5,345 vehicles. The largest vehicle type is small trucks (43%), followed by passenger cars (25%) and large trucks. Trucks (19%).
2. Working methods
The locations for erecting motorcycle identification systems are mainly based on the inaccurate vehicle registration data for unregular inspections released by the Environmental Protection Agency every month, and the areas where unregular inspection vehicles are concentrated are identified as locations for erecting vehicle identification systems. The location for erecting diesel vehicle identification equipment is mainly on the roads surrounding the industrial area and at locations provided by the diesel vehicle project.
3. Description of results
During the project period, 120,000 vehicles need to undergo license plate recognition. This project has completed license plate recognition on a total of 120,180 vehicles, a total of 76,441 motorcycles and 43,739 diesel vehicles. During the planning period, 45,000 regular inspection notices for locomotives and 6,500 inspection notices for diesel cars need to be sent, and 45,000 regular inspection notices for locomotives and 6,663 inspection notices for diesel cars have been sent.
Locomotive identification:
A total of 17 administrative districts have been completed so far. In terms of unscheduled inspection rate, the top five administrative districts are Rende District (9.9%)>East District (7.7%)>Yujing District (6.0%)>Xinshi District (5.7%)>Guanmiao District (5.5%).
In terms of the unscheduled inspection rate of old cars, the top five administrative districts are Rende District (31.3%)>Eastern District (23.7%)>Southern District (21.1%)>Nanhua District (13.5%)=Guanmiao District (13.5%) .
In terms of unregistered traffic volume (vehicles/day), the top five administrative districts are Eastern District (53) > Annan District (31) > Rende District (20) > Guanmiao District (13) = North District (13).
A total of 46 road sections have been completed so far. In terms of unscheduled inspection rate, the top five road sections are Section 2 of Zhongzheng Road in Rende District (12.2%), Yunong Road in East District (9.5%), and Section 2 of Dongmen Road in East District (7.1%). , Delun Road in Rende District (6.9%) and a section of Linsen Road in East District (6.8%).
In terms of the unscheduled inspection rate of old cars, the top five are Yunong Road in East District (65.8%), Section 2 of Zhongzheng Road in Rende District (43.4%), Wanli Road in South District (21.8%), and Delun Road in Rende District (21.7%) and Wenxin Road, Rende District (19.5%).
In terms of unregistered vehicle traffic volume (vehicles/day), the top five road sections are Section 2 of Dongmen Road in East District (93), Yunong Road in East District (91), Section 2 of Haidian Road in Annan District (44), and Section 2 of Annan Road in Annan District. Section 3 of Zhong Road (40) and Section 2 of Zhongzheng Road in Rende District (38).
Overall, Annan District and Rende District have a larger flow of unscheduled vehicles, so these two administrative districts should be prioritized for inspections of vehicles in use.
Diesel vehicle identification:
Classified by administrative region: the first three areas with higher proportion of vehicles in Phases 1-3 are Shan District 37.5%, East District 29.4% and Jiali District 28.0%; the areas with higher number of uninspected vehicles in Phases 1-3 are Jiali District 16 vehicles/day, 11 vehicles/day in Xinying District, and 10 vehicles/day in East District.
Classified by road section: The three areas with the highest number of vehicles in Phases 1-3 are: Section 1 of Zhonghua West Road in the Southern District 40 vehicles/day, Section 2 of Yongcheng Road in the South District 27 vehicles/day, and Zhongzheng North Road in Yongkang District 27 vehicles/day and Section 2 of Datong Road in East District, 25 vehicles/day.
Classified by uninspected: Phases 1-3 have higher number of vehicles. The previous three major areas are South District Zhonghua West Road Section 13 vehicles/day, Jiali District South 28 County Road 12 vehicles/day, and Shan District South 178A 11 vehicles/day. .
Overall, the overall uninspected proportion of South 178A in Shanshan District is higher (37.6%) and the number is larger (11 vehicles/day), while the uninspected proportion of Tai 19A in Xincheng District and Delun Road in Rende District reaches 40 More than %, but the number is small. Therefore, in terms of road sections, the number of uninspected old diesel vehicles in Shan District South 178A is more likely to be detected, followed by Kaiyuan Road in Xinying District at 29.1% (10 vehicles/day)
Pollution map:
Locomotives are drawn based on the distribution of old vehicles, inspection concentration and distribution of old vehicles that have not been regularly inspected. Diesel vehicles are drawn based on uninspected vehicles and classified according to administrative areas and road sections. Microsoft big data software Power Bi is used for data collection and graphical presentation. It can be formulated Simple conditional switching of pollution map data.
Equipment updates:
The accuracy of identifying old vehicles is about 55% for locomotives and about 95% for diesel vehicles; the accuracy of identifying new vehicles is about 95% for locomotives and about 97% for diesel vehicles. The accuracy of motorcycle identification has been improved the most (40%).
Regarding sending notifications:
Locomotives: The return inspection rate of vehicles in use is relatively high, and notifications should be sent to vehicles in use first. Diesel vehicles: There is little difference in the average return inspection rate of each vehicle type. However, vehicles without vehicle identification will not be subject to inspection this year. It is recommended that notifications be sent to diesel vehicles with vehicle identification next year.
