Accuracy Of Traffic Estimation Research Articles

Cloud-Assisted Mobile Crowd Sensing for Route and Congestion Monitoring

Accurate and reliable real-time urban traffic management can benefit urban citizens’ daily life by reducing stress, travel time and carbon footprint. The provision of reliable and accurate traffic information has however proven to be a major challenge in intelligent transportation systems. Citizens carrying smartphones can be exploited as an important provider of traffic information and the mobile crowd sensing paradigm can be used as a solution to this challenge. In this paper, an urban traffic monitoring system which exploits the power of participatory sensing and cloud messaging is proposed. Crowd intelligence that is used to estimate traffic congestion levels, arrival times, and average road speed is harvested from crowd sensed data. Traffic congestion control at route level is implemented with a route guidance system. Proactive warnings or recommendations to drivers in the vicinity of or on the route to reported events are provided. The drivers can also report short-term traffic events and physical road conditions for road monitoring. Real-world experiments are conducted with a prototype implementation and the results demonstrates both system feasibility and traffic estimation accuracy.

Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform

Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper.

Mining Road Network Correlation for Traffic Estimation via Compressive Sensing

This paper presents a transport traffic estimation method which leverages road network correlation and sparse traffic sampling via the compressive sensing technique. Through the investigation on a traffic data set of more than 4400 taxis from Shanghai city, China, we observe nontrivial traffic correlations among the traffic conditions of different road segments and derive a mathematical model to capture such relations. After mathematical manipulation, the models can be used to construct representation bases to sparsely represent the traffic conditions of all road segments in a road network. With the trait of sparse representation, we propose a traffic estimation approach that applies the compressive sensing technique to achieve a city-scale traffic estimation with only a small number of probe vehicles, largely reducing the system operating cost. To validate the traffic correlation model and estimation method, we do extensive trace-driven experiments with real-world traffic data. The results show that the model effectively reveals the hidden structure of traffic correlations. The proposed estimation method derives accurate traffic conditions with the average accuracy as 0.80, calculated as the ratio between the number of correct traffic state category estimations and the number of all estimation times, based on only 50 probe vehicles' intervention, which significantly outperforms the state-of-the-art methods in both cost and traffic estimation accuracy.

Adaptive random sampling for load change detection

Timely detection of changes in traffic load is critical for initiating appropriate traffic engineering mechanisms. Accurate measurement of traffic is essential since the efficacy of change detection depends on the accuracy of traffic estimation. However, precise traffic measurement involves inspecting every packet traversing a link, resulting in significant overhead, particularly on high speed links. Sampling techniques for traffic load estimation are proposed as a way to limit the measurement overhead. In this paper, we address the problem of bounding sampling error within a pre-specified tolerance level and propose an adaptive random sampling technique that determines the minimum sampling probability adaptively according to traffic dynamics. Using real network traffic traces, we show that the proposed adaptive random sampling technique indeed produces the desired accuracy, while also yielding significant reduction in the amount of traffic samples. We also investigate the impact of sampling errors on the performance of load change detection.