Intelligent Traffic Management Systems Using Deep Learning

TrafficSense: A Machine Learning - Based Intelligent Traffic Management System

Conventional traffic management systems mainly depend on decision-tree and rule-based methods for controlling traffic flow. These methods generally provide only moderate accuracy and are often slow in identifying traffic conditions in real time. In addition, they have limited capability to adapt to changing traffic patterns and face difficulties when processing large volumes of data. To overcome these drawbacks, an Intelligent Traffic Management System can be developed using Artificial Intelligence (AI) and Internet of Things (IoT) technologies. Sensors and cameras installed on roads continuously collect data related to traffic flow, vehicle density, and congestion levels. Based on the collected information, traffic signals can be adjusted dynamically to improve vehicle movement and reduce delays. This helps in lowering fuel consumption and minimizing air pollution caused by traffic congestion. The system can also provide signal priority for emergency vehicles such as ambulances and suggest alternate routes for other vehicles. Further improvement can be achieved by applying deep learning techniques, which offer better vehicle detection accuracy, faster processing, and effective real-time operation. These techniques also support scalability and adaptability in changing traffic environments. As a result, the proposed system can reduce congestion, improve road safety, and contribute to efficient urban transportation management.

Real-Time Intelligent Traffic Congestion Prediction and Management using Machine Learning

Artificial Intelligence Models for Smart Traffic Management and Road Safety

An overview of the most prominent types of AI models employed in smart traffic control and road safety, such as supervised and unsupervised machine learning, deep learning, computer vision, and reinforcement learning, and their applications for traffic flow prediction, adaptive traffic signal control, vehicle and pedestrian detection, accident prediction, driver behaviour monitoring, and prioritisation of emergency vehicles are discussed.

Big Data Analytics for Traffic Flow Prediction

The paper is a detailed research of how big data analytics have been used to predict traffic flow and analyses sources of data, analytics, machine learning and deep- learning models and scalable processing frameworks in modern traffic prediction systems.

Intelligent Traffic Management using Machine Learning for Peak and Off-Peak Hour Prediction

Traffic monitoring in cities is highly significant in transportation planning, in determining the extent of traffic conditions, and the operation of smart cities. However, conventional approaches, such as counting manually and relying on sensors installed in the infrastructure, have some significant issues. Manual techniques are also labor-intensive and difficult to maintain with time. Sensor-based systems, although automated, are expensive to install and maintain over time and these systems lack scalability. To avoid these issues, this study proposes an automated vehicle traffic analysis system, which applies deep learning and video analytics. Input video clips in the system are processed using the YOLO object detection model on a continuous live stream. Video frames are processed sequentially one after the other and combining the identified vehicles over time, in order to determine the density of the traffic and peak traffic periods, low traffic activity periods. This methodology involves live streaming recording, segmenting video into smaller clips, identifying frames, matching them in temporal sequence and statistical analysis to view traffic patterns. The data is also presented in the form of graphs and summary statistics to better demonstrate how the traffic flow varies with the time windows. The framework is easy to scale and affordable alternative to the conventional techniques, yet its functionality can be influenced by factors, such as video quality and the surroundings. Nevertheless, the experimental results show that the proposed methodology is competent when it comes to identifying the pattern of traffic activity based on the long-duration surveillance videos and, thus, makes it possible to make data-driven decisions in the smart transportation systems.

Review of Deep Learning Traffic Congestion Control in Intelligent Transportation Systems

A critical review of the available literature underscores the potential of DL to improve congestion management and provides important pointers for future development in order to make it more applicable to sustainable and intelligent transportation systems.