Experienced Travel Time Prediction for Freeway Systems

Travel time is considered as one of the most important performance measures for roadway systems, and dissemination of travel time information can help travelers to make reliable travel decisions such as route choice or time departure. Since the traffic data collected in real time reflects the past or the current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow essentials (e.g. shockwave analysis, bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.


Published in:
2012 15Th International Ieee Conference On Intelligent Transportation Systems (Itsc), 570-575
Presented at:
15th International IEEE Conference on Intelligent Transportation Systems (ITSC)
Year:
2012
Publisher:
New York, Ieee
ISSN:
2153-0009
ISBN:
978-1-4673-3063-3
Laboratories:




 Record created 2013-03-28, last modified 2018-03-17


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