Journal
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
Volume 20, Issue 5, Pages 438-448Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15472450.2016.1149700
Keywords
heterogeneous traffic data; nonparametric model; travel time prediction
Funding
- National Natural Science Foundation of China [51178032]
- Beijing Municipal Commission of Education [T13H100020]
- Fundamental Research Funds for the Central Universities [2015JBM048]
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Providing reliable travel time prediction is very much needed for commuters for their upcoming trips to reduce travel time and relieve traffic congestion. This article proposes an integrated model for path and multi-step-ahead travel time prediction on freeways using both historical and real-time heterogeneous traffic and weather data. The model's performance is investigated in a case study under various traffic scenarios. Results indicate that the proposed model provides satisfactory prediction results in various performance tests. For practical purposes, general guidelines for selecting the model's parameter sets as well as the efficient size of historical data are also presented.
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