An analyses of pedestrian and bicycle casualties using regional panel data

An analysis is presented of pedestrian and bicycle casualties by using cross-sectional time series data for the regions of Great Britain. A fixed-effect negative binomial model is used that accounts for heterogeneity in the data and the distributional properties of count data. Various factors associated with those killed and seriously injured as well as with slight injuries are examined. These include the average age of vehicles in the region, the road length of various road classes, vehicle ownership in the region, per capita income, per capita expenditure on alcohol, age cohorts, and various proxies for medical technology improvements. Various specifications of the models are estimated. Generally, it is found that more serious pedestrian injuries are associated with lower-income areas, increases in percent of local roads, increased per capita expenditure on alcohol, and total population. Statistical effects are more difficult to detect in models with serious injuries for bicyclists, but alcohol expenditure is strongly associated with increased injuries. This work has implications for transport policy aimed at increasing the modal share of pedestrians and bicyclists. Further research is needed to clearly understand some of the trends found in the analysis, especially the effect of changes in medical care and technology on total injuries

Congestion and safety: a spatial analysis of London

A disaggregate spatial analysis, using enumeration district data for London was conducted with the aim of examining how congestion may affect traffic safety. It has been hypothesized that while congested traffic conditions may increase the number of vehicle crashes and interactions, their severity is normally lower than crashes under uncongested free flowing conditions. This is primarily due to the slower speeds of vehicles when congestion is present. Our analysis uses negative binomial count models to examine whether factors affecting casualties (fatalities, serious injuries and slight injuries) differed during congested time periods as opposed to uncongested time periods. We also controlled for congestion spatially using a number of proxy variables and estimated pedestrian casualty models since a large proportion of London casualties are pedestrians. Results are not conclusive. Our results suggest that road infrastructure effects may interact with congestion levels such that in London any spatial differences are largely mitigated. Some small differences are seen between the models for congested versus uncongested time periods, but no conclusive trends can be found. Our results lead us to suspect that congestion as a mitigator of crash severity is less likely to occur in urban conditions, but may still be a factor on higher speed roads and motorways

Flow improvements and vehicle emissions: effects of trip generation and emission control technology

This paper examines whether road schemes that increase the availability of road space or which smooth the flow of traffic result in increased vehicle pollution. Economic theory indicates that increases in road space and the consequent decreases in travel time will tend to increase total vehicular travel, an effect known as induced travel. The net impacts on vehicle pollution have largely been a matter of conjecture with some arguing that policies to reduce congestion (by adding more road space) will reduce pollution by smoothing the flow of traffic and reducing stop and go traffic, while others argue that induced traffic will overwhelm this effect. This paper uses a micro-simulation model (VISSIM), integrated with a modal emissions model (CMEM), to evaluate the overall strategic policy question of how changes in available road capacity affects vehicle emissions. The analysis examines alternative vehicle fleets, ranging from a fleet with no emission control technology to relatively clean Tier 1 vehicles. Results show emission break-even points for CO, HC, NOx, fuel consumption and CO2. Increased traffic is found to quickly diminish any initial emission reduction benefits

A spatially disaggregate analysis of road casualties in England

Spatially disaggregate ward level data for England is used in an analysis of various area-wide factors on road casualties. Data on 8414 wards was input into a geographic information system that contained data on land use types, road characteristics and road casualties. Demographic data on area-wide deprivation (the index of multiple deprivation) for each ward was also included. Negative binomial count data models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Results suggest that urbanized areas are associated with fewer casualties (especially fatalities) while areas of higher employment density are associated with more casualties. More deprived areas tend to have higher levels of casualties, though not of motorized casualties (except slight injuries). The effect of road characteristics are less significant but there are some positive associations with the density of “A” and “B” level roads

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

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

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