Abstract
Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.
Highlights
Real-time estimation of the travel time between locations in city can help the individuals and transporters to plan their trips more accurately
We proposed a novel trip travel time forecasting algorithm based on selective forgetting extreme learning machine (SF-extreme learning machine (ELM))
Since road network components such as traffic signals have significant effects on travel times and these factors are difficult to integrate into road link-based prediction model, our trip based model can indirectly reflect the trip conditions change and our methods are simple and practicable and can be used in engineering
Summary
Real-time estimation of the travel time between locations in city can help the individuals and transporters to plan their trips more accurately. People are more likely to choose public transportation if they can know in advance that the practically quickest driving route to a destination would be still slower than the public transportation such as subway. It may affect their travels and schedules very much. Taxi drivers are experienced in finding the quickest driving routes based on their knowledge and they generally know the routes between any two locations and often follow the same routes [2, 3]. Historically recorded taxi trips should contain abundant information for predicting the duration for a future trip
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