An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario

Abstract

Featured Application: The proposed algorithm and architecture has the capability of estimating the queue length of waiting vehicles at a signalized intersection, where traffic cameras, edge server, and some connected vehicles are available. This method can resolve the queue length estimation problem in a mixed traffic scenario, especially when there are variable types of vehicles. It can provide key information for traffic lights control and improve the traffic efficiency.Abstract: Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm.

Improving the performance of unsignalized t-intersections within CAVs mixed traffic

“…These wireless technologies would create a link between vehicles to form a network of shared information such as speed, location and traffic control, etc. [13]. This shared link includes "Vehicle to Infrastructure" (V2I), "Vehicle to Vehicle" (V2V), and "Vehicle to X (other connected devices)" (V2X), which enables data communications among them [14].…”

A Study on Correlation of Traffic Accident Tendency with Driver Characters Using In-Depth Traffic Accident Data

“…Figure 5 shows the distribution of driving styles of the accident vehicle drivers, which were recorded for each accident case in the CIDAS database, but the driving styles were declared by the driver himself. In the CIDAS database, the driving styles include three types that are adventure style, conservative style, and general style [34].…”

The Real-World Effects of Route Familiarity on Drivers’ Eye Fixations at Urban Intersections in Changsha, China

“…Intersections are among the most important places in a city and therefore are the main focus of researchers [ 23 , 24 , 25 ]. More than 60% of severe injuries in traffic accidents occur at intersections in European countries [ 26 ].…”