Real-time Traffic Prediction Research Articles

Sensor Coverage and Location for Real-Time Traffic Prediction in Large-Scale Networks

The ability to observe flow patterns and performance characteristics of dynamic transportation systems remains an important challenge for transportation agencies, notwithstanding continuing advances in surveillance and communication technologies. As these technologies continue to become more reliable and cost-effective, demand for travel information is also growing, as are the potential and the ability to use sensor and probe information in sophisticated decision support systems for traffic systems management. This paper focuses on improving the efficiency of data collection in transportation networks by studying how sensor placement affects network observability. The objective of this study is to identify a set of sensor locations that optimize the coverage of origin–destination demand flows of the road network and maximize the information gains through observation data over the network, while minimizing the uncertainties of the estimated origin–destination demand matrix. The proposed sensor models consider problems where the numbers of sensors are limited and unlimited. The paper also provides several examples to illustrate the relative effectiveness of the proposed methodologies.

Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction: Sensitivity Analysis

Installing and maintaining sensors in a transportation network can be expensive. The motivation for this research is finding the best way to deploy finite resources and generate a network detection system in a manner that produces minimal estimation errors. The analysis uses a simulation-based real-time network traffic estimation and prediction system based on dynamic traffic assignment (DTA) methodology to analyze different levels of detection and different sensor locations in a portion of the Chesapeake Highway Advisories Routing Traffic (CHART) network (between Washington, D.C., and Baltimore, Maryland). This study provides a conceptual framework of the sensor location problem and a theoretical description of the objectives associated with the sensor location problem. A sensitivity analysis of the estimation and prediction quality with the DYNASMART-X real-time DTA system in relation to sensor number and location is conducted in the Maryland CHART network. The analysis considers both randomly generated location scenarios and scenarios based on engineering judgment. The analysis reveals the importance of providing detection in specific locations of the network and the dependence of the value of additional detection on the specific location selected.

A Timescale Decomposition Approach to Network Traffic Prediction

The presence of the complex scaling behavior in network traffic makes accurate traffic prediction a challenging task. Some conventional prediction tools such as the recursive least square method are not appropriate for network traffic prediction. In this paper we propose a timescale decomposition approach to real time traffic prediction. The raw traffic data is first decomposed into multiple timescales using the a trous Haar wavelet transform. The wavelet coefficients and the scaling coefficients at each scale are predicted independently using the ARIMA model. The predicted wavelet coefficients and scaling coefficient are then combined to give the predicted traffic value. This timescale decomposition approach can better capture the correlation structure of the traffic caused by different network mechanisms, which may not be obvious when examining the raw data directly. The proposed prediction algorithm is applied to real network traffic. It is shown that the proposed algorithm outperforms traffic prediction algorithms in the literature and gives more accurate results.