A high accuracy fuzzy logic based map matching algorithm for road transport

Recent research on map matching algorithms for land vehicle navigation has been based on either a conventional topological analysis or a probabilistic approach. The input to these algorithms normally comes from the global positioning system (GPS) and digital map data. Although the performance of some of these algorithms is good in relatively sparse road networks, they are not always reliable for complex roundabouts, merging or diverging sections of motorways, and complex urban road networks. In high road density areas where the average distance between roads is less than 100 m, there may be many road patterns matching the trajectory of the vehicle reported by the positioning system at any given moment. Consequently, it may be difficult to precisely identify the road on which the vehicle is travelling. Therefore, techniques for dealing with qualitative terms such as likeliness are essential for map matching algorithms to identify a correct link. Fuzzy logic is one technique that is an effective way to deal with qualitative terms, linguistic vagueness, and human intervention. This article develops a map matching algorithm based on fuzzy logic theory. The inputs to the proposed algorithm are from GPS augmented with data from deduced reckoning sensors to provide continuous navigation. The algorithm is tested on different road networks of varying complexity. The validation of this algorithm is carried out using high precision positioning data obtained from GPS carrier phase observables. The performance of the developed map matching algorithm is evaluated against the performance of several well-accepted existing map matching algorithms. The results show that the fuzzy logic-based map matching algorithm provides a significant improvement over existing map matching algorithms both in terms of identifying correct links and estimating the vehicle position on the links

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

Context-aware GPS Integrity Monitoring for Intelligent Transport Systems (ITS)

The integrity of positioning systems has become an increasingly important requirement for location-based Intelligent Transports Systems (ITS). The navigation systems, such as Global Positioning System (GPS), used in ITS cannot provide the high quality positioning information required by most services, due to the various type of errors from GPS sensor, such as signal outage, and atmospheric effects, all of which are difficult to measure, or from the map matching process. Consequently, an error in the positioning information or map matching process may lead to inaccurate determination of a vehicle’s location. Thus, the integrity is require when measuring both vehicle’s positioning and other related information such as speed, to locate the vehicle in the correct road segment, and avoid errors. The integrity algorithm for the navigation system should include a guarantee that the systems do not produce misleading or faulty information; as this may lead to a significant error arising in the ITS services. Hence, to achieve the integrity requirement a navigation system should have a robust mechanism, to notify the user of any potential errors in the navigation information. The main aim of this research is to develop a robust and reliable mechanism to support the positioning requirement of ITS services. This can be achieved by developing a high integrity GPS monitoring algorithm with the consideration of speed, based on the concept of context-awareness which can be applied with real time ITS services to adapt changes in the integrity status of the navigation system. Context-aware architecture is designed to collect contextual information about the vehicle, including location, speed and heading, reasoning about its integrity and reactions based on the information acquired. In this research, three phases of integrity checks are developed. These are, (i) positioning integrity, (ii) speed integrity, and (iii) map matching integrity. Each phase uses different techniques to examine the consistency of the GPS information. A receiver autonomous integrity monitoring (RAIM) algorithm is used to measure the quality of the GPS positioning data. GPS Doppler information is used to check the integrity of vehicle’s speed, adding a new layer of integrity and improving the performance of the map matching process. The final phase in the integrity algorithm is intended to verify the integrity of the map matching process. In this phase, fuzzy logic is also used to measure the integrity level, which guarantees the validity and integrity of the map matching results. This algorithm is implemented successfully, examined using real field data. In addition, a true reference vehicle is used to determine the reliability and validity of the output. The results show that the new integrity algorithm has the capability to support a various types of location-based ITS services.Saudi Arabia Cultural Burea

Integrity of map-matching algorithms

Map-matching algorithms are used to integrate positioning data with digital road network data so that vehicles can be placed on a road map. However, due to error associated with both positioning and map data, there can be a high degree of uncertainty associated with the map-matched locations. A quality indicator representing the level of confidence (integrity) in map-matched locations is essential for some Intelligent Transport System applications and could provide a warning to the user and provide a means of fast recovery from a failure. The objective of this paper is to determine an empirical method to derive the integrity of a map-matched location for three previously developed algorithms. This is achieved by formulating a metric based on various error sources associated with the positioning data and the map data. The metric ranges from 0 to 100 where 0 indicates a very high level of uncertainty in the map-matched location and 100 indicates a very low level of uncertainty. The integrity method is then tested for the three map-matching algorithms in the cases when the positioning data is from either a stand-alone global positioning system (GPS) or GPS integrated with deduced reckoning (DR) and for map data from three different scales (1:1250, 1:2500, and 1:50 000). The results suggest that the performance of the integrity method depends on the type of map-matching algorithm and the quality of the digital map data. A valid integrity warning is achieved 98.2% of the time in the case of the fuzzy logic map-matching algorithm with positioning data come from integrated GPS/DR and a digital map data with a scale of 1:2500

An Interval Type-2 Fuzzy Logic Based Map Matching Algorithm for Airport Ground Movements

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimisation. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this paper, we aim to develop a new interval type-2 fuzzy logic based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimisation algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong Airport, we compared the developed algorithm with other well-known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time

Performance of a New Enhanced Topological Decision-Rule Map-Matching Algorithm for Transportation Applications

Indexación: Web of Science; ScieloMap-matching problems arise in numerous transportation-related applications when spatial data is collected using inaccurate GPS technology and integrated with a flawed digital roadway map in a GIS environment. This paper presents a new enhanced post-processing topological decision-rule map-matching algorithm in order to address relevant special cases that occur in the spatial mismatch resolution. The proposed map-matching algorithm includes simple algorithmic improvements: dynamic buffer that varies its size to snap GPS data points to at least one roadway centerline; a comparison between vehicle heading measurements and associated roadway centerline direction; and a new design of the sequence of steps in the algorithm architecture. The original and new versions of the algorithm were tested on different spatial data qualities collected in Canada and United States. Although both versions satisfactorily resolve complex spatial ambiguities, the comparative and statistical analysis indicates that the new algorithm with the simple algorithmic improvements outperformed the original version of the map-matching algorithm.El problema de la ambigüedad espacial ocurre en varias aplicaciones relacionadas con transporte, específicamente cuando existe inexactitud en los datos espaciales capturados con tecnología GPS o cuando son integrados con un mapa digital que posee errores en un ambiente SIG. Este artículo presenta un algoritmo nuevo y mejorado basado en reglas de decisión que es capaz de resolver casos especiales relevantes en modo post-proceso. El algoritmo propuesto incluye las siguientes mejoras algorítmicas: un área de búsqueda dinámica que varía su tamaño para asociar puntos GPS a al menos un eje de calzada, una comparación entre el rumbo del vehículo y la dirección del eje de calzada asignada, y un nuevo diseño de la secuencia de pasos del algoritmo. Tanto el algoritmo original como el propuesto fueron examinados con datos espaciales de diferentes calidades capturados en Canadá y Estados Unidos. Aunque ambas versiones resuelven satisfactoriamente el problema de ambigüedad espacial, el análisis comparativo y estadístico indica que la nueva versión del algoritmo con las mejoras algorítmicas entrega resultados superiores a la versión original del algoritmo.http://ref.scielo.org/9mt55

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

Speed Aware – a mobile app prototype for the promotion of responsible driving

This paper addresses the promotion and awareness of responsible driving and road safety through the development of a very simple to use mobile application prototype, Speed Aware. This application provides users with speed limit information on roads they are travelling on, together with a journey logging feature that allows off-line self-review of driving behaviour. Tracked journeys can be displayed on a map and the trace shown as a heatmap, comparing the vehicle speed to the road speed limit. Furthermore, an audible alarm is emitted whenever the vehicle is travelling at a speed higher than the legal limit. At the heart of this app is a map matching algorithm, which matches raw Global Positioning System (GPS) data to the road network. Five map matching algorithms are implemented and compared on the basis of real-time performance and accuracy. A ground truth dataset of GPS traces in dense, urban, and sub-urban environments, together with TraceView, a trace visualisation and management tool, were developed. A modified version of a weight-based topological algorithm achieved accuracy of 94.9% at a GPS sampling frequency of 1Hz. This algorithm, together with three of the reviewed map matching algorithms, were implemented on a mobile device and subjectively tested for realtime performance.peer-reviewe