Abstract
In recent years, the use of advanced technologies such as wireless communication and sensors in intelligent transportation systems has made a significant increase in traffic data available. With this data, traffic prediction has the ability to improve traffic conditions and to reduce travel delays by facilitating better utilization of available capacity. This paper presents a real-time transportation prediction system named VTraffic for Vermont Agencies of Transportation by integrating traffic flow theory, advanced sensors, data gathering, data fusion, and data mining and visualization technologies to estimate and visualize the current and future traffic. In our VTraffic system, acoustic sensors were installed to monitor and to collect real-time data. Reliable predictions can be obtained from historical data and be verified and refined by the current and near future real-time data.
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Li, H., Li, Z., White, R.T., Wu, X. (2012). A Real-Time Transportation Prediction System. In: Jiang, H., Ding, W., Ali, M., Wu, X. (eds) Advanced Research in Applied Artificial Intelligence. IEA/AIE 2012. Lecture Notes in Computer Science(), vol 7345. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31087-4_8
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DOI: https://doi.org/10.1007/978-3-642-31087-4_8
Publisher Name: Springer, Berlin, Heidelberg
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