The effects of navigation sensors and spatial road network data quality on the performance of map matching algorithms

Map matching algorithms are utilised to support the navigation module of advanced transport telematics systems. The objective of this paper is to develop a framework to quantify the effects of spatial road network data and navigation sensor data on the performance of map matching algorithms. Three map matching algorithms are tested with different spatial road network data (map scale 1:1,250; 1:2,500 and 1:50,000) and navigation sensor data (global positioning system (GPS) and GPS augmented with deduced reckoning) in order to quantify their performance. The algorithms are applied to different road networks of varying complexity. The performance of the algorithms is then assessed for a suburban road network using high precision positioning data obtained from GPS carrier phase observables. The results show that there are considerable effects of spatial road network data on the performance of map matching algorithms. For an urban road network, the results suggest that both the quality of spatial road network data and the type of navigation system affect the link identification performance of map matching algorithms

Advanced Map Matching Technologies and Techniques for Pedestrian/Wheelchair Navigation

Due to the constantly increasing technical advantages of mobile devices (such as smartphones), pedestrian/wheelchair navigation recently has achieved a high level of interest as one of smartphones’ potential mobile applications. While vehicle navigation systems have already reached a certain level of maturity, pedestrian/wheelchair navigation services are still in their infancy. By comparing vehicle navigation systems, a set of map matching requirements and challenges unique in pedestrian/wheelchair navigation is identified. To provide navigation assistance to pedestrians and wheelchair users, there is a need for the design and development of new map matching techniques. The main goal of this research is to investigate and develop advanced map matching technologies and techniques particular for pedestrian/wheelchair navigation services. As the first step in map matching, an adaptive candidate segment selection algorithm is developed to efficiently find candidate segments. Furthermore, to narrow down the search for the correct segment, advanced mathematical models are applied. GPS-based chain-code map matching, Hidden Markov Model (HMM) map matching, and fuzzy-logic map matching algorithms are developed to estimate real-time location of users in pedestrian/wheelchair navigation systems/services. Nevertheless, GPS signal is not always available in areas with high-rise buildings and even when there is a signal, the accuracy may not be high enough for localization of pedestrians and wheelchair users on sidewalks. To overcome these shortcomings of GPS, multi-sensor integrated map matching algorithms are investigated and developed in this research. These algorithms include a movement pattern recognition algorithm, using accelerometer and compass data, and a vision-based positioning algorithm to fill in signal gaps in GPS positioning. Experiments are conducted to evaluate the developed algorithms using real field test data (GPS coordinates and other sensors data). The experimental results show that the developed algorithms and the integrated sensors, i.e., a monocular visual odometry, a GPS, an accelerometer, and a compass, can provide high-quality and uninterrupted localization services in pedestrian/wheelchair navigation systems/services. The map matching techniques developed in this work can be applied to various pedestrian/wheelchair navigation applications, such as tracking senior citizens and children, or tourist service systems, and can be further utilized in building walking robots and automatic wheelchair navigation systems

Development of a weight-based topological map-matching algorithm and an integrity method for location-based ITS services

The main objective of this research is to enhance navigation modules of location-based Intelligent Transport Systems (ITS) by developing a weight-based topological map-matching algorithm and a map-aided integrity monitoring process. Map-matching (MM) algorithms integrate positioning data from positioning sensors with spatial road network data to identify firstly, the road link on which a vehicle is travelling from a set of candidate links; and secondly, to determine the vehicle s location on that segment. A weight-based topological MM algorithm assigns weights for all candidate links based on different criteria such as the similarity in vehicle movement direction and link direction and the nearness of the positioning point to a link. The candidate link with the highest total weighting score is selected as the correct link. This type of map-matching algorithm is very popular due to its simplicity and speediness in identifying the correct links. Existing topological map-matching algorithms however have a number of limitations: (1) employing a number of thresholds that may not be transferable, (2) assigning arbitrary weighting coefficients to different weights, (3) not distinguishing among different operational environments (i.e., urban, suburban and rural) when determining the relative importance of different weights and (4) not taking into account all available data that could enhance the performance of a topological MM algorithm. In this research a novel weight-based topological map-matching algorithm is developed by addressing all the above limitations. The unique features of this algorithm are: introducing two new weights on turn restrictions and connectivity at junctions to improve the performance of map-matching; developing a more robust and reliable procedure for the initial map-matching process; performing two consistency checks to minimise mismatches and determining the relative importance of different weights for specific operational environments using an optimisation technique. Any error associated with either the raw positioning data (from positioning sensors) or spatial road network, or the MM process can lead to incorrect road link identification and inaccurate vehicle location estimation. Users should be notified when the navigation system performance is not reliable. This is referred to as an integrity monitoring process. In this thesis, a user-level map-aided integrity method that takes into account all error sources associated with the three components of a navigation system is developed. Again, the complexity of the road network is also considered. Errors associated with a spatial road map are given special attention. Two knowledge-based fuzzy inference systems are employed to measure the integrity scale, which provides the level of confidence in map-matching results. Performance of the new MM algorithm and the integrity method was examined using a real-world field data. The results suggest that both the algorithm and the integrity method have the potential to support a wide range of real-time location-based ITS services. The MM algorithm and integrity method developed in this research are simple, fast, efficient and easy to implement. In addition, the accuracy offered by the enhanced MM algorithm is found to be high; it is able to identify the correct links 97.8% of the time with an horizontal accuracy of 9.1 m. This implies that the developed algorithm has high potential to be implemented by industry for the purpose of supporting the navigation modules of location-based intelligent transport systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo