Next-gen traffic surveillance: AI-assisted mobile traffic violation detection system

Road traffic accidents pose a significant global public health concern, leading to injuries, fatalities, and vehicle damage. Approximately 1,3 million people lose their lives daily due to traffic accidents [World Health Organization, 2022]. Addressing this issue requires accurate traffic law violation detection systems to ensure adherence to regulations. The integration of Artificial Intelligence algorithms, leveraging machine learning and computer vision, has facilitated the development of precise traffic rule enforcement. This paper illustrates how computer vision and machine learning enable the creation of robust algorithms for detecting various traffic violations. Our model, capable of identifying six common traffic infractions, detects red light violations, illegal use of breakdown lanes, violations of vehicle following distance, breaches of marked crosswalk laws, illegal parking, and parking on marked crosswalks. Utilizing online traffic footage and a self-mounted on-dash camera, we apply the YOLOv5 algorithm's detection module to identify traffic agents such as cars, pedestrians, and traffic signs, and the strongSORT algorithm for continuous interframe tracking. Six discrete algorithms analyze agents' behavior and trajectory to detect violations. Subsequently, an Identification Module extracts vehicle ID information, such as the license plate, to generate violation notices sent to relevant authorities

Detection and recognition of illegally parked vehicles based on an adaptive gaussian mixture model and a seed fill algorithm.

In this paper, we present an algorithm for the detection of illegally parked vehicles based on a combination of some image processing algorithms. A digital camera is fixed in the illegal parking region to capture the video frames. An adaptive Gaussian mixture model (GMM) is used for background subtraction in a complex environment to identify the regions of moving objects in our test video. Stationary objects are detected by using the pixel-level features in time sequences. A stationary vehicle is detected by using the local features of the object, and thus, information about illegally parked vehicles is successfully obtained. An automatic alarm system can be utilized according to the different regulations of different illegal parking regions. The results of this study obtained using a test video sequence of a real-time traffic scene show that the proposed method is effective

Illegal parking detection using Gaussian mixture model and kalman filter

Automatic analysis of videos for traffic monitoring has been an area of significant research in the recent past. In this paper, we proposed a system to detect and track illegal vehicle parking using Gaussian Mixture Model and Kalman Filter. i-LIDS dataset is used to test and evaluate the algorithm by comparing the results with the ground truth provided, we have tested the system using 4 full videos from i-LIDS to detect parked vehicle whiten specific area. Region of interest has been used to detect Vehicle parks in a no parking zone over sixty seconds and remains stationary.Within the scope of this work, we highlighted the components of an automated traffic surveillance system, including background modeling, foreground extraction, Kalman filter and Gaussian mixture model. © 2017 IEEE

Evaluation of the implementation process of urban road pricing schemes in the United Kingdom and Italy

This paper is based upon detailed research that has taken place in the UK and Italy, on the implementation strategies for urban road pricing schemes. In the UK, both in London and Durham, the Road User Charging schemes required new legislation, and were implemented rapidly. The time from announcement to implementation took three years and the schemes were introduced after short periods of intensive planning, consultations and stakeholder networking. In Italy, the situation has been very different. The road pricing schemes in Rome and Genoa were not introduced under specific legislation but rather evolved from access control zones originally implemented in historic urban centres. The incremental introduction of the Italian road pricing experiments has taken approximately ten years. The paper undertakes a comparison of these different strategies to introduce urban road pricing and the lessons they contain for the development of similar measures elsewhere. The comparison of the different implementing experiences is undertaken using Strategic Policy Niche Management, a method designed to explore, among other factors, the dynamics of the stakeholder networks involved in planning, introducing, marketing and managing radical urban Travel Demand Management policies

Security and Safety-Based Parking Area Monitoring System

Security became a major concern these days in parking areas. Nowadays vehicles are rapidly increasing due to the rapid increase in parking traffic. Vehicle-safe parking has become a serious problem for organizations and Universities. Some vehicles do not register them legally or utilize the license plates of other vehicles. Those license plates can be used to determine the identification of a vehicle accused of committing a crime around the organization. So only detecting the number plate as the Vehicle identification at the parking entrance is not safe. For that proposing a novelty-based Smart Parking Area Monitoring System to overcome this problem. Here, train the vehicle model using the neural network transfer learning technique to identify the vehicle model and classify the vehicles. The entrance of the organization detects and compares the vehicle models with number plate details and operates the barrier system based on the vehicle’s authorization status. Nowadays parking systems detect wrong-parked vehicles using sensors in every parking slot. It is very costly and not efficiently working. This research proposes a wrong parking detection system by using only the CCTV cameras of parking areas. Here using Yolo object detection and OpenCV line detection algorithms to detect parking slots and wrong-parked vehicles

Action Research on Development and Application of AIoT Traffic Solution

AIoT solution based on the AI (Artificial Intelligent) and IoT (Internet of Things) is considered state-of-the-art technology and has emerged in various business environments. To enhance intelligent traffic quality, maximize energy saving and reduce carbon emission, this study applied an AIoT technology based on traffic counting modules and people behavior modules as traffic inference systems. Applications of the IoT technology based on WiFi, 3G/4G and NB-IoT (Narrowband IoT) was conducted gradually in key demonstration roads and cities worldwide, and the development and evaluation results were aligned to an action research framework. The five phases in the action research included designing, collecting data, analyzing data, communicating outcome, and acting phases. During the first two phases, problems of functional operations in traffic were verified and designed for network services by ICT (Information and Communication Technology) and IoT technologies to collection traffic big data. In the third phase, stakeholders may use basic statistic or further deep learning methods to solve traffic scheduling, order and road safety issues. During the fourth and fifth phases, the roles and benefits of stakeholders participating in the service models were evaluated, and issues and knowledge of the whole application process were respectively derived and summarized from technological, economic, social and legal perspectives. From an action research approach, AIoT-based intelligent traffic solutions were developed and verified and it enables MOTC (Ministry of Transportation and Communications) and stakeholders to acquire traffic big data for optimizing traffic condition in technology enforcement. With its implementation, it will ultimately be able to go one step closer to smart city vision. The derived service models could provide stakeholders, drivers and citizens more enhanced traffic services and improve policies’ work more efficiency and effectiveness

Summary of the Application of Automobile Electronic Logo in Traffic Management

With the rapid development of China's economy, people's living standards have been continuously improved, and people's pursuit of a better life has promoted the vigorous development of China's automobile industry. Nowadays, no matter in China or other countries, as people's means of transportation, cars play an increasingly important role in people's lives. However, in recent years, due to the explosive growth of the number of cars, the traffic congestion is becoming more and more serious,Only relying on manpower and traditional intelligent management is not enough to support the normal operation of traffic. Therefore, in order to solve the traffic problems, people have researched and innovated a new way of collecting traffic information-automobile electronic identification

Review of Western Australian drug driving laws

In 2007, the Western Australian Road Traffic Act 1974 was amended to allow for new police enforcement practices designed to reduce the incidence of drug driving. The Road Traffic Amendment (Drugs) Act 2007 made provision for two new offences: driving with the presence of a prescribed illicit drug in oral fluid or blood, and driving while impaired by a drug. The prescribed drugs were methamphetamine, methylenedioxymethamphetamine (MDMA or ecstasy) and delta-9-tetrahydrocannabinol (THC, the psychoactive compound in cannabis). As part of the new laws, statute 72A was inserted into the Act requiring that the Western Australian State Government undertake a review of the amended legislation after 12 months of operation. This report provides a review of the amended legislation and the associated drug driving law enforcement. It includes a process review of the roadside oral fluid testing and drug impaired driving enforcement programs; an analysis of testing, offence detection and legal penalty data pertaining to the first year of operation of the new drug enforcement measures; and a report on consultations with various stakeholders. These form the basis for recommendations on possible improvements to the processes and legislation related to the deterrence of driving after drug use among Western Australian drivers.J.E. Woolley and M.R.J. Baldoc