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Estimating travel times from static map attributes
Estimating accurate travel times on road networks is a prerequisite for many mobility related applications such as transportation planning, dynamic intermodal routing or logistics. Two widely used methods are (1) the trivial but inaccurate calculation of travel times using static speeds taken from road maps and (2) the use of historic time series calculated from rich data sets. For the second method extensive measurement campaigns are required. In this paper we present a novel approach to estimate realistic travel times exclusively from static map coefficients without the need for further data collection. Our method uses a linear regression model to estimate the diurnal variation of travel times for cars in urban and interurban areas. We discuss the model which has been developed and calibrated for the city of Vienna, Austria, and demonstrate the transferability of the model to a different city.
Estimating travel times from static map attributes
Estimating accurate travel times on road networks is a prerequisite for many mobility related applications such as transportation planning, dynamic intermodal routing or logistics. Two widely used methods are (1) the trivial but inaccurate calculation of travel times using static speeds taken from road maps and (2) the use of historic time series calculated from rich data sets. For the second method extensive measurement campaigns are required. In this paper we present a novel approach to estimate realistic travel times exclusively from static map coefficients without the need for further data collection. Our method uses a linear regression model to estimate the diurnal variation of travel times for cars in urban and interurban areas. We discuss the model which has been developed and calibrated for the city of Vienna, Austria, and demonstrate the transferability of the model to a different city.
Estimating travel times from static map attributes
Leodolter, Maximilian (author) / Koller, Hannes (author) / Straub, Markus (author)
2015-06-01
362612 byte
Conference paper
Electronic Resource
English
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