The use of simulation in the design of a road transport incident detection algorithm

Automatic incident detection is becoming one of the core tools of urban traffic management, enabling more rapid identification and response to traffic incidents and congestion. Existing traffic detection infrastructure within urban areas (often installed for traffic signal optimization) provides urban traffic control systems with a near continuous stream of data on the state of traffic within the network. The creation of a simulation to replicate such a data stream therefore provides a facility for the development of accurate congestion detection and warning algorithms. This paper describes firstly the augmentation of a commercial traffic model to provide an urban traffic control simulation platform and secondly the development of a new incident detection system (RAID-Remote Automatic Incident Detection), with the facility to use the simulation platform as an integral part of the design and calibration process. A brief description of a practical implementation of RAID is included along with summary evaluation results

Traffic monitoring using image processing : a thesis presented in partial fulfillment of the requirements for the degree of Master of Engineering in Information and Telecommunications Engineering at Massey University, Palmerston North, New Zealand

Traffic monitoring involves the collection of data describing the characteristics of vehicles and their movements. Such data may be used for automatic tolls, congestion and incident detection, law enforcement, and road capacity planning etc. With the recent advances in Computer Vision technology, videos can be analysed automatically and relevant information can be extracted for particular applications. Automatic surveillance using video cameras with image processing technique is becoming a powerful and useful technology for traffic monitoring. In this research project, a video image processing system that has the potential to be developed for real-time application is developed for traffic monitoring including vehicle tracking, counting, and classification. A heuristic approach is applied in developing this system. The system is divided into several parts, and several different functional components have been built and tested using some traffic video sequences. Evaluations are carried out to show that this system is robust and can be developed towards real-time applications

A machine learning approach with verification of predictions and assisted supervision for a rule-based network intrusion detection system

Network security is a branch of network management in which network intrusion detection systems provide attack detection features by monitorization of traffic data. Rule-based misuse detection systems use a set of rules or signatures to detect attacks that exploit a particular vulnerability. These rules have to be handcoded by experts to properly identify vulnerabilities, which results in misuse detection systems having limited extensibility. This paper proposes a machine learning layer on top of a rule-based misuse detection system that provides automatic generation of detection rules, prediction verification and assisted classification of new data. Our system offers an overall good performance, while adding an heuristic and adaptive approach to existing rule-based misuse detection systems

Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV

The automatic detection of traffic accidents is a significant topic in traffic monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, improve traffic management, and encourage safer driving practices. Computer vision can be a promising technique for automatic accident detection because it provides a reliable, automated, and speedy accident detection system that can improve emergency response times and ultimately save lives. This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. The model framework's robustness is evaluated using YouTube video sequences with various lighting circumstances. The proposed model has been trained using the open-source dataset Crash Car Detection Dataset, and its produced precision, recall, and mAP are 93.8% and 98%, 96.1%, respectively, which is a significant improvement above the prior precision, recall, and mAP figures of 91.3%, 87.6%, and 93.8%. The effectiveness of the proposed approach in real-time traffic surveillance applications is proved by experimental results using actual traffic video data

Car Traffic Sign Annunciator

Automatic detection and recognition of traffic signs is an essential part of automated driver assistance systems which contribute to the safety of the drivers, pedestrians and vehicles. This paper presents the advanced driver assistance system (ADAS) based on Raspberry pi for traffic sign detection, recognition and annunciation. Such a system presents a vital support for driver assistance in an intelligent automotive. The proposed algorithm is implemented in a real time embedded system using OpenCV library. Proposed method introduced a new method for detection and recognition of traffic signs. Firstly, Potential traffic signs regions are detected by colour segmentation method, then classified using HOG features and a linear SVM classifier to identify the traffic sign class. The proposed system shows good recognition rate under complex challenging lighting and weather conditions. Experimental results on the accuracy of the road sign detection are reported in this paper

Automatic Vehicle Accident Detection and Traffic Control System

The main objective of the project is to find the accident location by means of GPS and GSM technology. The system also controls the traffic density in the specified location. This system also provide vehicle tracking facility through GPS. The main purpose is to provide the information about the vehicle conditions to the authorized persons. In this project LPC2129 microcontroller is used as the main heart of the system and it is also cost effective and very easy to understand. The GSM and GPS module are integrated to the microcontroller which helps in the tracking of the vehicle. The exact location is represented through longitude and latitude values and those values are sent to the registered mobile numbers through GSM modem. This system plays an important role in avoiding the road accident and also helps in the tracking the accidental victims. Safety is very important in today’s world. Right use of technology will save lives of the people. DOI: 10.17762/ijritcc2321-8169.15060

Integration of Lane-Specific Traffic Data Generated from Real-Time CCTV Videos into INDOT\u27s Traffic Management System

The Indiana Department of Transportation (INDOT) uses about 600 digital cameras along populated Indiana highways in order to monitor highway traffic conditions. The videos from these cameras are currently observed by human operators looking for traffic conditions and incidents. However, it is time-consuming for the operators to scan through all video data from all the cameras in real-time. The main objective of this research was to develop an automatic and real-time system and implement the system at INDOT to monitor traffic conditions and detect incidents automatically. The Transportation and Autonomous Systems Institute (TASI) of the Purdue School of Engineering and Technology at Indiana University-Purdue University Indianapolis (IUPUI) and the INDOT Traffic Management Center have worked together to research and develop a system that monitors the traffic conditions based on the INDOT CCTV video feeds. The proposed system performs traffic flow estimation, incident detection, and the classification of vehicles involved in an incident. The goal was to develop a system and prepare for future implementation. The research team designed the new system, in­cluding the hardware and software components, the currently existing INDOT CCTV system, the database structure for traffic data extracted from the videos, and a user-friendly web-based server for identifying individual lanes on the highway and showing vehicle flowrates of each lane automatically. The preliminary prototype of some system components was implemented in the 2018–2019 JTRP projects, which provided the feasibility and structure of the automatic traffic status extraction from the video feeds. The 2019–2021 JTRP project focused on developing and improving many features’ functionality and computation speed to make the program run in real-time. The specific work in this 2021–2022 JTRP project is to improve the system further and implement it on INDOT’s premises. The system has the following features: vehicle-detection, road boundary detection, lane detection, vehicle count and flowrate detection, traffic condition detection, database development, web-based graphical user interface (GUI), and a hardware specification study. The research team has installed the system on one computer in INDOT for daily road traffic monitoring operations