Real-time Intelligent Map-matching Algorithms for Advanced Transport Telematics Systems (RiMATTS)

Lead Research Organisation: Loughborough University
Department Name: Civil and Building Engineering


A variety of transport applications and services such as pay-as-you-drive insurance scheme, navigation and route guidance, accident and emergency responses (enhanced 999 emergency services), bus arrival information at bus stops and fleet management require spatial and temporal location, and time information. One of the important components of such services is the navigation module which provides the required positioning data. Many commercial devices are available to support navigation modules of such transport systems. In recent years, most commercial devices use GPS technology for acquiring such positioning data. Since GPS suffers both systematic errors and noise, the required positioning accuracies of many transport services cannot be achieved by such devices. Moreover, such devices do not provide integrity (the level of confidence) of position solutions which is very important for liability and safety critical applications such as pay-as-you-drive insurance schemes (due to the possibility of billing incorrectly) and responses to emergency 999 calls. A map matching algorithm that integrates the locational data (from GPS or other sensors) with the spatial road network data needs to be employed. Map matching not only enables the physical location of the vehicle to be identified but also improves the positioning accuracy if a good digital map is available. Current map matching algorithms are not capable of supporting the navigation modules of certain transport systems in some operational environments (specifically in dense urban areas) due to the inherent limitations and constraints associated with them. In addition to this, a single map matching algorithm cannot optimally support the navigation module of a transport system in different operational environments. Therefore, there is a distinct need to select a set of representative map matching algorithms. The detailed characterisation of these algorithms through experiments is essential to evaluate their performance in the operational environments for which they were designed and to identify their limitations. This representative set of existing map matching algorithms with further enhancements, along with a new map matching algorithm that can take into account limitations and constraints of existing map matching algorithms, could optimally support the navigation modules of most transport systems in most operational environments. Therefore, the main objectives of this research project are to (1) identify a set of representative map matching algorithms from existing algorithms, (2) develop a new map matching algorithm and to address any gaps identified in objective 1 both in terms of applications and operational environments, (3) develop a knowledge-based intelligent map matching (iMM) technique to identify the best map matching algorithm (achieved in objectives 1 and 2 above) suitable for an operational environment, and (4) demonstrate a potential application of iMM in different operational environments. Several criteria will be defined for use with the iMM technique to select the best algorithm for a particular service in a given operational environment. Such criteria will include the geographic characteristics of the operational environment (such as land-use, road network density, and building height information) and others (such as complexity, and cost) if required. The exploitation of this proposed research would be in two levels: (1) the algorithms, (2) the actual navigation system which incorporates the algorithms and the navigation sensors. The expectation is that the cost associated with the actual navigation system will be relatively low (at the level of 500 per unit). This is expected to fall as the price of navigation sensor chips and MEMS technology-based sensors reduce over time.


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Description Under this project, a range of map-matching algorithms that integrate data from GPS, vehicle-based sensors and digital map were developed. This opens up the door of implementing many new intelligent transport systems (ITS) such as path planning for intelligent vehicles. The important component of these algorithms is 'integrity monitoring' - providing the level of confidence in the output. This is fundamental for safety-of-life critical ITS applications.
Exploitation Route Through research articles (both conference and journal papers)
Sectors Digital/Communication/Information Technologies (including Software),Transport

Description The algorithms developed under this grant were implemented by the UK Highways Agency (HA) in mapping traffic crashes onto their strategic road network with the aim of investigating network performance, identifying crash hot-spots through risk mapping and other various strategic decisions. In addition, an insurance telematics company in Australia has been implementing one of the algorithms in their device. Renault - a car manufacturer has implemented some components of the algorithms developed under this grant
First Year Of Impact 2012
Sector Digital/Communication/Information Technologies (including Software),Transport
Impact Types Economic

Description Safety Strategy - Highways Agency
Amount £90,000 (GBP)
Organisation Department of Transport 
Department Highways Agency
Sector Public
Country United Kingdom
Start 03/2012 
End 04/2015
Description Link with Harbin Engineering University China 
Organisation Harbin Engineering University
Department College of Automation
Country China 
Sector Academic/University 
PI Contribution Loughborough research team was closely working with a researcher from Harbin Engineering University who spent one year at Loughborough. We provided the background research material, the experimental set up and shared some of our algorithms.
Collaborator Contribution Liang Li from Harbin Engineering University China awarded a scholarship from Chinese Government to spend one year with us to work on map-matching algorithms. He was an outstanding researcher who was closely working with our team at Loughborough and contributed significantly in the project. He developed two new map-matching algorithms.
Impact Two journal papers: Li, L., Quddus, M.A., Zhao, L. (2013) High accuracy tightly-coupled integrity monitoring algorithm for map-matching, Transportation Research Part C: Emerging Technologies, 35, 13-26. Li, L, Quddus, M., Ison, S., Zhao, L. (2012) Multiple reference consistency check for LAAS: a novel position domain approach, GPS Solutions, 16(2), 209-222.
Start Year 2010