Enhancing Traffic Enforcement of Highway Patrol by Using License Plate Recognition System

• Main Author: 黃修哲
• Other Authors: Pin-Yi Tseng、Henry Chung-Jen Chao
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/7ga3y3

碩士 === 中央警察大學 === 交通管理研究所 === 100 === The criminal cases use vehicles as instruments of crime or criminal purpose types are increasing daily and diversification. To fight against and prevent vehicles larcener, the police expect disposing checking point; they also plan various tasks positively by advance technology. The Criminal Investigation Bureau built Stolen Vehicle Investigation Network System in 2004, and built Involved In Vehicle Monitoring and Investigation Network System in other to strengthen investigation in 2008. In other to help the police investigate vehicles larceners, the system installs automatic license plate recognition system at the important intersection and the toll station on the freeway. However to the department of freeway police force, whether the system is effective to enforcement (administer justice) since it worked till today? It is an essential research topic, so this study will set on of the tool station as our research target. To know the fact and problems of using the system to investigate vehicles larceners on the department of freeway police force. We also hope it can be the referral to advancing the enforcement of law enforcement agency in the future. According to the data of Sep.2009, the system said that there were 1,471,841 vehicles were identified, 816 were stolen, 2,659 were the vehicle identification stolen, 22 were involved in criminal case. The police intercepted 24 times, and succeed 6 times.To seek an appropriate strategy and establish the arrest plan of ALPR reference template, the study use analyzing the key factor to check the progressing, task planning, promulgating and dispatching of law enforcement agency. We propose three traffic enforcement models to estimate the ALPR enforcement effects. Model 1: model 1 is for two patrol cars. Situation 1 is for two cars drive parallel; situation 2 is for two cars drive reversely; situation 3 is for two cars stop at the rode side and checks the passing cars (there are no patrol cars on line). Model 2: model 2 is for three patrol cars. Situation 1 is for two cars drive parallel and the other drive reversely; situation 3 is for one cars drive parallel and the other two drive reversely; situation 3 is for one cars stop at the toll station and one cars drive parallel and the other drive reversely. Model 3: model 3 is for four patrol cars. Situation 1 is for two cars drive parallel and the other two drives reversely; situation 2 is for one car stop at the toll station, one cars drive parallel and the other two drive reversely; situation 3 is for one cars stop at the rode side and check the passing cars (there are two patrol cars on line). According to the test of the Yuanin team during Apr.1 to Apr.30, there were 80 cases identified, 51 cases were miss identified, 29 cases were promulgated to arrest. If we don’t classified the arrested cases, the arrest rate was 37%, better than 25% before. The mean arrest area were the Yuanlin toll station to Yuanlin intersection, Yuanlin intersection to Puyen intersection, so the patrol cars just near the toll station and patrol cars can arrest parallel easily. On the other hand, the enforcement model, model 2 situation 1 and model 3 situation 1 would be better.