Congestion and Safety: A Spatial Analysis of London

Spatially disaggregate Enumeration District (ED) level data for London is used in an analysis of various area-wide factors on road casualties. Data on 15335 EDs was input into a geographic information system (GIS) that contained data on road characteristics, public transport accessibility, information of nearest hospital location, car ownership and road casualties. Demographic data for each ED was also included. Various count data models e.g., negative binomial or zero-inflated Poisson and negative binomial models were used to analyze the associations between these factors with traffic fatalities, serious injuries and slight injuries. Different levels of spatial aggregation were also examined to determine if this affected interpretation of the results. Different pedestrian casualties were also examined. Results suggest that dissimilar count models may have to be adopted for modeling different types of accidents based on the dependent variable. Results also suggest that EDs with more roundabouts are safer than EDs with more junctions. More motorways are found to be related to fewer pedestrian casualties but higher traffic casualties. Number of households with no car seems to have more traffic casualties. Distance of the nearest hospital from EDs tends to have no significant effect on casualties. In all cases, it is found that EDs with more employees are associated with fewer casualties.

Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data

Count models such as negative binomial (NB) regression models are normally employed to establish a relationship between area-wide traffic crashes and the contributing factors. Since crash data are collected with reference to location measured as points in space, spatial dependence exists among the area-level crash observations. Although NB models can take account of the effect of unobserved heterogeneity (due to omitted variables in the model) among neighbourhoods, such models may not account for spatial correlation areas. It is then essential to adopt an econometric model that takes account of both spatial dependence and uncorrelated heterogeneity simultaneously among neighbouring units. In studying the spatial pattern of traffic crashes, two types of spatial models may be employed: (i) classical spatial models for higher levels of spatial aggregation such as states, counties, etc. and (ii) Bayesian hierarchical models for all spatial units, especially for smaller scale area-aggregations. Therefore, the primary objectives of this paper is to develop a series of relationships between area-wide different traffic casualties and the contributing factors associated with ward characteristics using both non-spatial models (such as NB models) and spatial models and to identify the similarities and differences among these relationships. The spatial units of the analysis are the 633 census wards from the Greater London metropolitan area. Ward-level casualty data are disaggregated by severity of the casualty (such as fatalities, serious injuries, and slight injuries) and by severity of the casualty related to various road users. The analysis implies that differentward-level factors affect traffic casualties differently. The results also suggest that Bayesian hierarchical models aremore appropriate indeveloping a relationship between areawide traffic crashes and the contributing factors associated with the road infrastructure, socioeconomic and traffic conditions of the area. This is because Bayesian models accurately take account of both spatial dependence and uncorrelated heterogeneity

Time series count data models: an empirical application to traffic accidents

Count data are primarily categorised as cross-sectional, time series, and panel. Over the past decade, Poisson and Negative Binomial (NB) models have been used widely to analyse cross-sectional and time series count data, and random effect and fixed effect Poisson and NB models have been used to analyse panel count data. However, recent literature suggests that although the underlying distributional assumptions of these models are appropriate for cross-sectional count data, they are not capable of taking into account the effect of serial correlation often found in pure time series count data. Real-valued time series models, such as the autoregressive integrated moving average (ARIMA) model, introduced by Box and Jenkins have been used in many applications over the last few decades. However, when modelling non-negative integer-valued data such as traffic accidents at a junction over time, Box and Jenkins models may be inappropriate. This is mainly due to the normality assumption of errors in the ARIMA model. Over the last few years, a new class of time series models known as integer-valued autoregressive (INAR) Poisson models, has been studied by many authors. This class of models is particularly applicable to the analysis of time series count data as these models hold the properties of Poisson regression and able to deal with serial correlation, and therefore offers an alternative to the real-valued time series models. The primary objective of this paper is to introduce the class of INAR models for the time series analysis of traffic accidents in Great Britain. Different types of time series count data are considered: aggregated time series data where both the spatial and temporal units of observation are relatively large (e.g., Great Britain and years) and disaggregated time series data where both the spatial and temporal units are relatively small (e.g., congestion charging zone and months). The performance of the INAR models is compared with the class of Box and Jenkins real-valued models. The results suggest that the performance of these two classes of models is quite similar in terms of coefficient estimates and goodness of fit for the case of aggregated time series traffic accident data. This is because the mean of the counts is high in which case the normal approximations and the ARIMA model may be satisfactory. However, the performance of INAR Poisson models is found to be much better than that of the ARIMA model for the case of the disaggregated time series traffic accident data where the counts is relatively low. The paper ends with a discussion on the limitations of INAR models to deal with the seasonality and unobserved heterogeneity

Renewable energy RD&D expenditure and CO2 emissions in 15 European countries

Purpose: Renewable energy is an important component to the complex portfolio of technologies that have the potential to reduce CO2 emissions and to enhance the security of energy supplies. Despite RE’s potential to reduce CO2 emissions, the expenditure on renewable energy research, development & demonstration (RERD&D) as a percentage of total government energy research, development & demonstration (ERD&D) investment remains low in developed countries. The declining ERD&D expenditure prompted this research to explore the relationship between CO2 emissions per capita and RERD&D as opposed to ERD&D. Methodology: An econometric analysis of annual CO2 emissions per capita during the period 1990 – 2004 for the 15 pre-2004 European Union (EU15) countries was carried out. It was hypothesized that the impact of RERD&D expenditure on the reduction of CO2 emissions would be higher than that of ERD&D expenditure, primarily due to several RE technologies being close to carbon neutral. Country-level GDP per capita and an index of the ratio between industry consumption and industrial production (IICIP) were introduced in the analysis as proxies to control for activities that generate CO2 emissions. A number of panel data econometric models that are able to take into account both country- and time-specific unobserved effects were explored. Findings: It was found that random effect models were more appropriate to examine the study hypothesis. The results suggest that expenditure on RERD&D is statistically significant and negatively associated with CO2 emissions per capita in all models, whereas expenditure on ERD&D is statistically insignificant (ceteris paribus). Originality: The findings of this paper provided useful insight into the effectiveness of renewable energy RD&D investment in reducing CO2 emissions and are of value in the development of policies for targeted RD&D investment to mitigate the impacts of climate change

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

High accuracy tightly-coupled integrity monitoring algorithm for map-matching

A map-matching algorithm employs data from Global Positioning System (GPS), a Geographic Information System (GIS)-based road map and other sensors to first identify the correct link on which a vehicle travels and then to determine the physical location of the vehicle on the link. Due to uncertainties associated with the raw measurements from GPS/other sensors, the road map and the related methods, it is essential to monitor the integrity of map-matching results, especially for safety and mission-critical intelligent transport systems such as positioning and navigation of autonomous and semi-autonomous vehicles. Current integrity methods for map-matching are inadequate and unreliable as they fail to satisfy the integrity requirement due mainly to incorrect treatment of all the related uncertainties simultaneously. The aim of this paper is therefore to develop a new tightly-coupled integrity monitoring method for map-matching by properly treating the uncertainties from all sources concurrently. In this method, the raw measurements from GPS, low-cost Dead-Reckoning (DR) sensors and Digital Elevation Model (DEM) are first integrated using an extended Kalman Filter to continuously obtain better position fixes. A weight-based topological map-matching process is then developed to map-match position fixes onto the road map. The accuracy of the map-matching process is enhanced by employing a range of network features such as grade separation, traffic flow directions and the geometry of road link. The Receiver Autonomous Integrity Monitoring (RAIM) technique, which has been successfully applied to monitor the integrity of aircraft navigation, is modified and enhanced so as to apply it to monitor the quality of map-matching. In the enhanced RAIM method, two modifications are made: (1) a variable false alarm rate (as opposed to a constant false alarm rate) is considered to improve the fault detection performance in selecting the links, especially near junctions. (2) a sigma inflation for a non-Gaussian distribution of measurement noises is applied for the purpose of satisfying the integrity risk requirement. The implementation and validation of the enhanced RAIM method is accomplished by utilising the required navigation performance parameters (in terms of accuracy, integrity and availability) of safety and mission-critical intelligent transport systems. The required data were collected from Nottingham and central London. In terms of map-matching, the results suggest that the developed map-matching method is capable of identifying at least 97.7% of the links correctly in the case of frequent GPS outages. In terms of integrity, the enhanced RAIM method provides better the fault detection performance relative to the traditional RAIM

Injury severity analysis of accidents involving young male drivers in Great Britain

Young male drivers are over-represented in traffic accidents; they were involved in 14% of fatal accidents from 1991 to 2003 while holding only 8% of all drivers licenses in the UK. In this study, a subset of the UK national road accident data from 1991 to 2003 has been analyzed. The primary aim is to determine how to best use monetary and progressive resources to understand how road safety measures will reduce the severity of accidents involving young male drivers in both London and Great Britain. Method: Ordered probit models were used to identify specific accident characteristics that increase the likelihood of one of three categorical outcomes of accident severity: slight, serious, or fatal. Results: Characteristics found to lead to a higher likelihood of serious and fatal injuries are generally similar across Great Britain and London but are different from those predicted to lead to a higher likelihood of slight injuries. Those characteristics predicted to lead to serious and fatal injuries include driving in darkness, between Friday and Sunday, on roads with a speed limit of 60 mph, on single carriageways, overtaking, skidding, hitting an object off the carriageway, and when passing the site of a previous accident. Characteristics predicted to lead to slight injuries include driving in daylight, between Monday and Thursday, on roads with a speed limit of 30 mph or less, at a roundabout, waiting to move, and when an animal is on the carriageway. Impact on Industry: These results aid the selection of policy options that are most likely to reduce the severity of accidents involving young male drivers

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

Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England

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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