Detecting and correcting map-matching errors in location-based Intelligent
Transport Systems
Nagendra R. Velaga*, Mohammed A. Quddus, Abigail L. Bristow
Transport Studies Group, Department of Civil and Building Engineering, Loughborough University,
Leicestershire, LE11 3TU, UK.
(*Email: n.r.velaga@lboro.ac.uk)
Abstract:
The navigation modules of location-based Intelligent Transport Systems (ITS) are normally supported by a
range of positioning systems - such as Global Navigation Satellite Systems (e.g., GPS), dead-reckoning
(DR) systems, and inertial navigation systems (INS) - and a digital map. Map-matching (MM) algorithms
integrate positioning data with the digital map in order to identify firstly, the road segment on which a
vehicle is travelling from a set of candidate segments; and secondly, determine the vehicle’s location on that
segment. These MM algorithms are categorised into: geometric, topological, probabilistic and advanced
algorithms. Though the probabilistic and the advanced algorithms perform better, they are generally slow
and difficult to be implemented in real-time. The topological MM (tMM) algorithms, that use road
geometric and topological information, are simple, fast and easy to implement in real-time. The accuracy
offered by such algorithms is, however, low relative to that of probabilistic and advanced algorithms.
Due to the errors in positioning sensors, digital maps and map-matching methods, existing tMM algorithms
sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is
known as mismatching. The wrong road link identification could mislead the users and make the ITS
service ineffective. Therefore, the objective of this research is to further improve a tMM algorithm by
analysing each mismatching case, identifying different ways to improve the algorithm, modifying the
algorithm accordingly, and finally re-evaluating the algorithm’s performance using an independent dataset.
In this study, a weight-based tMM algorithm developed by the authors in their earlier work is used for the
error detection, correction and the performance re-evaluation process. This algorithm selects the correct link
from a set of candidate links using a weight scheme. Four weights (i.e., heading, proximity, connectivity
and turn restriction weights) were used. The relative importance of these weights in different operational
environments were determined by an optimisation technique. Although the performance of this tMM
algorithm was found to be better than other existing tMM algorithms, the algorithm incorrectly identified
road segments 4.1% of the time in the urban area of Washington, DC, and 3.3 % of the time in a suburban
area of London.
A series of controlled experiments in three different countries (UK, USA and India) were conducted to
collect 62,887 positioning fixes for the purpose of identifying mismatches in the weight-based tMM
algorithm. A total of 2,926 mismatches were then discovered. Each mismatching case was individually
examined in order to find out the reason(s) of mismatching. A thematic analysis of the reasons was then
carried out resulting in a number of key reasons: (1) errors due to positioning sensor (2) errors due to digital
map and (3) errors in the map-matching process. Afterwards, a number of strategies were identified to
correct these mismatches. This includes: (1) re-examining the optimal weight scores using a Genetic
Algorithm (GA) optimisation technique, here the sample size is also increased, (2) using a lookup table to
identify the weight scores corresponding to the operational environment (e.g., urban, sub-urban and rural),
(3) checking the threshold values used in the algorithm, (4) using a routing algorithm in the map-matching
process while identifying the correct link from the candidate links. Then, the tMM algorithm is enhanced
accordingly, and its performance is re-evaluated.
An independent dataset (sample size 5,256 GPS observations) collected in central and suburban areas of
Nottingham, UK, was used to re-evaluate the performances of the enhanced tMM algorithm. A reference
(true) trajectory was obtained from a carrier-phase GPS receiver integrated with a high-grade INS. The
enhanced tMM algorithm correctly identified road segments 98.7% of the time. With the same positioning
data, the success rate was found to be 96.5% before the enhancement. The enhanced tMM algorithm
developed in this research is simple, fast, efficient and easy to implement. Since, the accuracy offered by
the enhanced algorithm is found to be high, the developed algorithm has high potential to be implemented
by industry for real-time ITS applications.