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Real-time travel time prediction models in routing for car navigation applications

100%

Gaweł P. , Dembczyński K. , Kotłowski W. , Jaszkiewicz A.

2013

tom Vol. 6, iss. 4

4--8

We consider the problem of using real-time floating car data to construct vehicle travel time prediction models meant to be used as input to routing algorithms for finding the fastest (time-shortest) path in the traffic network. More specifically we target the on-line car navigation systems. The travel time estimates for such a system need to be computed efficiently and provided for all short segments (links) of the roads network. We compare several fast real-time methods such as last observation, moving average and exponential smoothing, each combined with a historical traffic pattern model. Through a series of large-scale experiments on real-world data we show that the described approach yields promising results and conclude that specific prediction function form may be less important than a proper control of bias-variance trade-off (achieved by historical and real-time models combination). In addition, we consider two different settings for testing the prediction quality of the models. The first setting concerns measuring the prediction error on short road segments, while the second on longer paths through the traffic network. We show the quality and model parameters vary depending on the assessment method.

Ensembles of decision rules

88%

Błaszczyński J. , Dembczyński K. , Kotłowski W. , Słowiński R. , Szeląg M.

tom Vol. 31, No. 3-4

221-232

In most approaches to ensemble methods, base classifiers are decision trees or decision stumps. In this paper, we consider an algorithm that generates an ensemble of decision rules that are simple classifiers in the form of logical expression: if [conditions], then [decision]. Single decision rule indicates only one of the decision classes. If an object satisfies conditions of the rule, then it is assigned to that class. Otherwise the object remains unassigned. Decision rules were common in the early machine learning approaches. The most popular decision rule induction algorithms were based on sequential covering procedure. The algorithm presented here follows a different approach to decision rule generation. It treats a single rule as a subsidiary, base classifier in the ensemble. First experimental results have shown that the presented algorithm is competitive with other methods. Additionally, generated decision rules are easy in interpretation, which is not the case of other types of base classifiers.