12 research outputs found
Editorial: Special Issue: Intelligent Vehicle Navigation (iVN)
Editorial: Special Issue: Intelligent Vehicle Navigation (iVN
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
The effect of the London congestion charge on road casualties: an intervention analysis
The introduction of the congestion charge in central London on the 17th of
February, 2003, led to a reduction in congestion. One factor that has not been fully
analysed is the impact of the congestion charge on traffic casualties in London. Less car
travel within the charging zone may result in fewer traffic collisions, however, as the
number of pedestrians, cyclists, and motorcyclists increased after the introduction of the
congestion charge, the number of traffic casualties associated with these groups may also
have increased. Reductions in congestion can also lead to faster speeds. Therefore, there
could be increases in injury severity for those crashes that do occur. An intervention
analysis was conducted to investigate the effect of the congestion charge on traffic casualties
for motorists, pedestrians, cyclists, and motorcyclists, both within the charging zone
and in areas of London outside the zone. This was done for killed and serious injuries
(known as KSI in British terminology) and for slight injuries to examine whether there
were any shifts in severity outcomes. Our results suggest no statistically significant effect
for total casualties in London, but within the charging zone there has been a statistically
significant drop in motorist casualties, and possibly an increase in cyclist casualties. There
is an associated effect of an increase in casualties of motorcyclists and cyclists in some
areas outside the charging zone, suggesting that changes in the design of the congestion
charge may be needed to achieve reductions in casualties
Map-matching in complex urban road networks
Global Navigation Satellite Systems (GNSS) such as GPS and digital road maps can be used for land vehicle navigation
systems. However, GPS requires a level of augmentation with other navigation sensors and systems such as Dead
Reckoning (DR) devices, in order to achieve the required navigation performance (RNP) in some areas such as urban
canyons, streets with dense tree cover, and tunnels. One of the common solutions is to integrate GPS with DR by
employing a Kalman Filter (Zhao et al., 2003). The integrated navigation systems usually rely on various types of
sensors. Even with very good sensor calibration and sensor fusion technologies, inaccuracies in the positioning sensors
are often inevitable. There are also errors associated with spatial road network data. This paper develops an improved
probabilistic Map Matching (MM) algorithm to reconcile inaccurate locational data with inaccurate digital road network
data. The basic characteristics of the algorithm take into account the error sources associated with the positioning
sensors, the historical trajectory of the vehicle, topological information on the road network (e.g., connectivity and
orientation of links), and the heading and speed information of the vehicle. This then enables a precise identification of
the correct link on which the vehicle is travelling. An optimal estimation technique to determine the vehicle position on
the link has also been developed and is described. Positioning data was obtained from a comprehensive field test carried
out in Central London. The algorithm was tested on a complex urban road network with a high resolution digital road
map. The performance of the algorithm was found to be very good for different traffic maneuvers and a significant
improvement over using just an integrated GPS/DR solution
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
Positioning algorithms for transport telematics applications
This paper develops two integrated positioning algorithms
for transport telematics applications and services. The
first is an Extended Kalman Filter (EKF) algorithm for
the integration of GPS and low cost DR sensors to
provide continuous positioning in built-up areas. The
second takes this further by integrating the GPS/DR
output with map data in a novel a map-matching process
to both identify the physical location of a vehicle on the
road network and improve positioning capability. The
proposed MM algorithm is validated using a higher
accuracy reference (truth) of the vehicle trajectory as
determined by high precision positioning achieved by the
carrier phase observable from GP
Validation of map matching algorithms using high precision positioning with GPS
Map Matching (MM) algorithms are usually employed for a range of transport telematics applications to correctly identify the physical location of a vehicle travelling on a road network. Two essential components for MM algorithms are (1) navigation sensors such as the Global Positioning System (GPS) and dead reckoning (DR), among others, to estimate the position of the vehicle, and (2) a digital base map for spatial referencing of the vehicle location. Previous research by the authors (Quddus et al., 2003; Ochieng et al., 2003) has developed improved MM algorithms that take account of the vehicle speed and the error sources associated with the navigation sensors and the digital map data previously ignored in conventional MM approaches. However, no validation study assessing the performance of MM algorithms has been presented in the literature. This paper describes a generic validation strategy and results for the MM algorithm previously developed in Ochieng et al. (2003). The validation technique is based on a higher accuracy reference (truth) of the vehicle trajectory as determined by high precision positioning achieved by the carrier-phase observable from GPS. The results show that the vehicle positions determined from the MM results are within 6 m of the true positions. The results also demonstrate the importance of the quality of the digital map data to the map matching process
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
A general map matching algorithm for transport telematics applications
This paper describes a map-matching
algorithm designed to support the navigational
functions of a real-time vehicle performance and
emissions monitoring system currently under
development, and other transport telematics
applications. The algorithm is used together with the
outputs of an extended Kalman filter formulation for
the integration of GPS and dead reckoning data, and
a spatial digital database of the road network, to
provide continuous, accurate and reliable vehicle
location on a given road segment. This is
irrespective of the constraints of the operational
environment, thus alleviating outage and accuracy
problems associated with the use of stand-alone
location sensors. The map-matching algorithm has
been tested using real field data and has been found
to be superior to existing algorithms, particularly in
how it performs at road intersections
An extended Kalman filter algorithm for integrating GPS and low cost dead reckoning system data for vehicle performance and emissions monitoring
This paper describes the features of an extended Kalman filter algorithm designed to support
the navigational function of a real-time vehicle performance and emissions monitoring
system currently under development. The Kalman filter is used to process global positioning
system (GPS) data enhanced with dead reckoning (DR) in an integrated mode, to provide
continuous positioning in built-up areas. The dynamic model and filter algorithms are
discussed in detail, followed by the findings based on computer simulations and a limited
field trial carried out in the Greater London area. The results demonstrate that use of the
extended Kalman filter algorithm enables the integrated system employing GPS and
low cost DR devices to meet the required navigation performance of the device under
development