A spatial and temporal analysis of seat-belt usage and seat-belt laws

Seat-belt usage has increased significantly in the US since the introduction of mandatory seat-belt usage laws in the 1980`s. This paper analyzes the impact of these laws on increasing seat-belt usage while controlling for other state-specific variables. Spatial analyses techniques are employed to further explain these effects. Spatial autocorrelation is found in the data but diminishes over time. Spatial correlation also exhibits a clear east-west direction. Results clearly show that both secondary and primary seat-belt laws have been effective at increasing seat-belt usage. In addition, our spatial analysis suggests that these effects cross state boundaries, implying an even larger level of effectiveness than direct measurement would suggest. Despite this some unexplained spatial correlation remains.

A spatial and temporal analysis of seat-belt usage and seat-belt laws

Seat-belt usage has increased significantly in the US since the introduction of mandatory seat-belt usage laws in the 1980`s. This paper analyzes the impact of these laws on increasing seat-belt usage while controlling for other state-specific variables. Spatial analyses techniques are employed to further explain these effects. Spatial autocorrelation is found in the data but diminishes over time. Spatial correlation also exhibits a clear east-west direction. Results clearly show that both secondary and primary seat-belt laws have been effective at increasing seat-belt usage. In addition, our spatial analysis suggests that these effects cross state boundaries, implying an even larger level of effectiveness than direct measurement would suggest. Despite this some unexplained spatial correlation remains

Nuclear ribosomal DNA diversity of a cotton pest (Rotylenchulus reniformis) in the United States

The reniform nematode (Rotylenchulus reniformis) has emerged as a major cotton pest in the United States. A recent analysis of over 20 amphimictic populations of this pest from the US and three othercountries has shown no sequence variation at the nuclear ribosomal internal transcribed spacer (ITS) despite the region’s usual variability. We investigated this unexpected outcome by amplifying, cloningand sequencing two regions of the nuclear ribosomal DNA (18S, ITS1) to ascertain whether any variation occurred within and among populations of reniform nematodes in Alabama, US. Both thenrITS1 and the relatively conserved 18S region showed a fairly substantial amount of variation among populations. The identity among ITS sequences ranged from 1.00 to 0.86, while sequence identity at the18S ranged from 1.00 to 0.948. We conclude that variation does exist in these sequences in reniform nematodes, and the earlier report showing no ribosomal ITS variation in this pest might have beencaused by preferential amplification of a conserved ITS paralog. Current and future application towards resistance in cotton varieties to this pest requires reliable information on the molecular variability of thenematode in cotton-growing areas

Long-range collision avoidance for shared space simulation based on social forces

Shared space is an innovative approach to improve environments where both pedestrians and vehicles are present, with integrated layouts to balance priority. The Social Force Model (SFM) was used to visualise pedestrian and car trajectories so that peaks of density and pressure at critical locations are avoided. This paper extends the SFM to consider a long-range collision detection and collision resolution strategy. The determination of potential conflicts is enhanced using principle component analysis for a set of agent's prior speeds and directions. This long-range collision avoidance strategy results in more realistic SFM-based trajectories for pedestrians and cars in shared spaces

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