Geoscience Reference
In-Depth Information
Detecting Spatial and Temporal Route
Information of GPS Traces
Tao Feng and Harry J.P. Timmermans
Abstract This paper aims at detecting route information of GPS traces to represent
spatial and temporal information of trips. A Bayesian belief network model is used
to calculate the probability of a road matching a GPS log point. The algorithm
incorporates road network topology, distance from trace nodes to road segments,
the angle between two lines, direction difference, accuracy of measured GPS log
point, and position of roads. GPS data collected in the Eindhoven region, The
Netherlands, is used to examine the performance of this algorithm. Results based on
a small sample show that the algorithm has a good performance in both processing
ef
ciency and prediction accuracy of correctly identi
ed instances. Prediction
accuracy using a small sample is 87.02 %.
Keywords Bayesian belief network
GPS
Map matching
Road network
1 Introduction
Detecting route information of GPS traces has been an important research topic in
recent years in transportation. Various map matching algorithms have been pro-
posed with the aim to match some geographical locations or points with the existing
network data. For vehicle-based traf
c research, it can provide microscopic spatial-
temporal
cial to the
requirements of many research topics such as route choice simulation, emission and
energy consumption analysis, travel demand forecasting, travel behaviour analysis,
etc. Especially, with the increasing applications of new technologies such as GPS,
information for a speci
c vehicle. This would be bene
Search WWH ::
Custom Search