Traffic congestion-aware graph-based vehicle rerouting framework from aerial imagery (original) (raw)

Delays in transportation due to congestion generated by public and private transportation are common in many urban areas of the world. To make transportation systems more efficient, intelligent transportation systems (ITS) are currently being developed. One of the objectives of ITS is to detect congested areas and redirect vehicles away from them. However, most existing approaches only react once the traffic jam has occurred and, therefore, the delay has already spread to more areas of the traffic network. We propose a vehicle redirection system to avoid congestion that uses a model based on deep learning to predict the future state of the traffic network. The model uses the information obtained from the previous step to determine the zones with possible congestion, and redirects the vehicles that are about to cross them. Alternative routes are generated using the entropy-balanced k Shortest Path algorithm (EBkSP). The proposal uses information obtained in real time by a set of prob...

Spatio-temporal transportations have various issues like traffic congestion, weather and wind direction. The measure problem is to prevent from traffic based accidents. The traffic may be in homogenous and heterogeneous format. In this paper the complete focus is based on heterogeneous traffic flow. In first stage we studied various research and we studied deep convolution network for identifying and measures the traffic accidents and developed a unique spatiotemporal graph-based model for predicting the probability of future traffic accidents. We used hybrid approach to improve the reliability and sustainability of large-scale networks through improving both recurrent and non-recurrent traffic conditions.

In most major areas traffic has been one of the largest challenges. Classification of traffic flow is crucial in evaluating the management and strategic planning of traffic enforcement. Traffic jams has an effect on society, as it take a lot of time even though it is essential to manage the traffic jams. Through classifying we could get to recognize which pathway has congestion, by which we would then test the causes for congestion and make suitable conclusions to optimize the effectiveness. Traffic video data is an appropriate source for monitoring the traffic. Traffic congestion generally produces additional gasoline pollution which is injurious to people and then also generates a series of trouble in the maintenance of road transport and sinks a lot of deadly time and energy waste. Way to diagnose traffic congestion results to a big issue and such traffic congestion allows vehicles to take various shortcuts in breach of the traffic laws which cause dangerous crashes. A lot of research has gone into alert system for traffic collision on the street. Though other research is capable of achieving impressive outcomes, the major issue of detecting traffic collisions seems far from being fully resolved because of the complicated circumstances like the climatic conditions, a vehicular traffic simultaneously. As the first step to manage the traffic and reduce accidents, the traffic congestion should be detected as soon as possible and measures should be taken to clear it early. This paper discusses about the traffic congestion detection and traffic management through Convolution Neural Network (CNN) techniques.

Annually, a large number of injuries and deaths around the world are related to motor vehicle accidents. This value has recently been reduced to some extent, via the use of driver-assistance systems. Developing driver-assistance systems (i.e., automated driving systems) can play a crucial role in reducing this number. Estimating and predicting surrounding vehicles' movement is essential for an automated vehicle and advanced safety systems. Moreover, predicting the trajectory is influenced by numerous factors, such as drivers' behavior during accidents, history of the vehicle's movement and the surrounding vehicles, and their position on the traffic scene. The vehicle must move over a safe path in traffic and react to other drivers' unpredictable behaviors in the shortest time. Herein, to predict automated vehicles' path, a model with low computational complexity is proposed, which is trained by images taken from the road's aerial image. Our method is based on...

Emergency Traffic Management (ETM) is one of the main problems in smart urban cities. This paper focuses on selecting an appropriate object detection model for identifying and counting vehicles from closed-circuit television (CCTV) images and then estimating traffic flow as the first step in a broader project. Therefore, a case is selected at one of the busiest roads in Christchurch, New Zealand. Two experiments were conducted in this research; 1) to evaluate the accuracy and speed of three famous object detection models namely faster R-CNN, mask R-CNN and YOLOv3 for the data set, 2) to estimate the traffic flow by counting the number of vehicles in each of the four classes such as car, bus, truck and motorcycle. A simple Region of Interest (ROI) heuristic algorithm is used to classify vehicle movement direction such as "left-lane" and "right-lane". This paper presents the early results and discusses the next steps.

In the traffic engineering realm, queue length estimation is considered one of the most critical challenges in the Intelligent Transportation System (ITS). Queue lengths are important for determining traffic capacity and quality, such that the risk for blockage in any traffic lane could be minimized. The Vision-based sensors show huge potentials compared to fixed or moving sensors as they offer flexibility for data acquisition due to large-scale deployment at a huge pace. Compared to others, these sensors offer low installation/maintenance costs and also help with other traffic surveillance related tasks. In this research, a CNN-based approach for estimation of vehicle queue length in an urban traffic scenario using low-resolution traffic videos is proposed. The system calculates queue length without the knowledge of any camera parameter or onsite calibration information. The estimation in terms of the number of cars is considered a priority as compared to queue length in the number...