Abstract
The prediction of the uncertainty of route travel time predictions for all possible routes in an urban road network is of importance for example for logistics. Such predictions need to take the essential features of the data set as well as the underlying traffic dynamics into account.In this paper a large floating taxi data set is used in order to derive predictions of route travel time uncertainty based on link travel time uncertainty predictions. Prediction errors, that is actual travel times minus predicted travel times, are differentiated from model errors, that is measured travel times minus predicted travel times. These two errors are related, but not identical, as model errors contain measurement noise while the prediction errors do not. Detailed models for the variance of the link travel time prediction errors as well as the correlation between the model errors for different links are derived. The models are validated in depth using two different validation data sets.Estimates for the variance of prediction errors are obtained. The standardized model error distributions show a remarkable stability, such that modelling the variance appears to be sufficient for quantifying the uncertainty of the model errors.Furthermore we show that the model errors for adjacent links are highly correlated but correlations fade with increasing distance. Additionally usage of the road network plays a role with high correlation for links along common routes and low correlations for links along seldom used routes. We assume identical features for the prediction errors which is partly validated based on additional data.The paper provides a way to estimate the complete distribution of route travel time prediction errors for any given route in the street network.
Highlights
1 Introduction The quantification of route travel time uncertainty is of importance for logistics applications as well as for publicly available routing services
We present a model for the variances of the measured link travel times and discuss how to construct estimates for the variance of the link travel time predictions from this model and suitable estimates of the measurement error variance
Note that we model the correlation of the normalized model errors, whereas for the estimation of the uncertainty of the route travel times the correlations of the normalized prediction errors would be needed
Summary
The quantification of route travel time uncertainty is of importance for logistics applications as well as for publicly available routing services (see [3] for a survey of studies dealing with valuing reliability; compare [14]). For logistic applications planning usually involves pre trip decisions, in many cases several hours or even days before the trips are executed. Both private individuals as well as logistics companies typically have asymmetric costs with being late implying higher penalties than being early. ’shortest’ is to be understood in a broad sense and could involve predictions of link travel times for a particular departure time For such predictions a huge amount of different methods based on a large number of different data sets have been obtained, first for highways (compare the papers contained in the compendium [2]), subsequently for general road networks. In these approaches the predicted route travel time is obtained as the sum of the predicted link travel times
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