Improvements in medical care and technology and reductions in traffic-related fatalities in Great Britain

Traffic-related fatalities in the UK have fallen dramatically over the last 30 years by about 50%. This decline has been observed in many other developed countries with similar rates of reduction. Many factors have been associated with this decline, including safer vehicle design, increased seat-belt use, changing demographics, and improved infrastructure. One factor not normally considered is the role that improved medical technology may have in reducing total traffic-related fatalities. This study analyzed cross-sectional time-series data in the UK to examine this relationship. Various proxies for medical technology improvement were included in a fixed effects negative binomial model to assess whether they are associated with reductions in traffic-related fatalities. Various demographic variables, such as age cohorts, GDP and changes in per-capita income are also included. The statistical methods employed control for heterogeneity in the data and therefore other factors that may affect the dependent variable for which data are not available do not need to be considered. Results suggest a strong relationship between improved medical technology and reductions in traffic-related fatalities as well as expected relationships with demographic factors. These results could imply that continued reductions in UK fatalities may be more difficult to achieve if medical technology improvements are diminishing, however, demographic changes will likely contribute to a further downward trend.

Analysing parking search (‘cruising’) time using generalised multilevel structural equation modelling

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the URI link.The aim of this paper is to identify factors influencing parking search (cruising) time. A revealed-preference on-street parking survey was undertaken with individual drivers in four UK cities to investigate the influence of personal, trip, socio-economic, physical, time49 related, and price-related variables on parking search. In order to address the potential endogeneity problems between the factors (e.g. parking fee and parking search time) and hierarchical issues in the survey data, a generalised multilevel structural equation model was applied. It was revealed that cruising time could be reduced by seeking drivers to pay for parking as a way of improving social welfare

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.

Effects of geodemographic profiles of drivers on their injury severity from traffic crashes using multilevel mixed-effects ordered logit model

The purpose of this paper is to examine various geodemographic factors on the levels of driver injury severity using a statistical model. A driver’s geodemographic profile with respect to the involvement in a traffic crash consists of variables from multiple hierarchical levels such as drivers who are nested within crashes and crashes that are clustered within areas. A geodemographic profile of a driver therefore contains factors such as age, gender, residence of driver, social deprivation, and the distance from home to crash locations (at the driver-level); land-use patterns of crash location, casualties per crash and vehicles involved in the crash (at the crash- level); and vehicles per 1,000 population and population density (at the area-level). This implies that driver-level observations are correlated rather than independent as assumed in many injury severity modelling. In order to capture within-group and between-group correlations among observations a multilevel mixed-effects ordered logit model has been employed in this research. Mixed-effects allows some variables to vary by observations (i.e. random parameters). The analysis is based on UK national traffic crash data between 2009 to 2011 consisting of 271,654 drivers from 217,523 traffic crashes occurring across 27,773 different census areas. Data on area deprivation, Census, and land-use patterns were collected from multiple sources and integrated using a GIS framework. The results indicate that the severity of injuries sustained by urban drivers involved in crashes increases if they travel to rural areas; the level of driver injury severity also increases if traffic crashes occur in areas with high car ownership per capita; and drivers from more disadvantaged areas would sustain, if all else are equal, more severe injuries. The findings from this study would be useful to the Department for Transport and Local Authorities in formulating safety policies aimed at enhancing driver education, training and licensing programmes

Gasoline Price Effects on Traffic Safety in Urban and Rural Areas: Evidence from Minnesota, 1998-2007.

A large literature base has found that economic factors have important effects on traffic crashes. A small but growing branch of literature also examines the role that gasoline prices play in the occurrence of traffic crashes. However, no studies have investigated the possible difference of these effects between urban and rural areas. In this study, we used the monthly traffic crash data from 1998–2007 at the county level in Minnesota to investigate the possibly different effects gasoline prices may have on traffic crashes in urban versus rural areas. The results indicate significant difference of gasoline price effects on total crashes in urban versus rural areas. Gasoline prices also significantly affect the frequency of injury crashes in both urban and rural areas; however, the difference is not significant. Gasoline prices have no significant effects on the frequency of fatal crashes in urban and rural areas. Traffic volume plays a bigger role on the incidence of injury and fatal crashes. The results concerning the differences between urban and rural areas have important policy implications for traffic safety planners and decision makers.gasoline prices, traffic incidents, traffic safety, age, gender

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

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

TDMP-Reliable Target Driven and Mobility Prediction based Routing Protocol in Complex VANET

Vehicle-to-everything (V2X) communication in the vehicular ad hoc network (VANET), an infrastructure-free mechanism, has emerged as a crucial component in the advanced Intelligent Transport System (ITS) for special information transmission and inter-vehicular communications. One of the main research challenges in VANET is the design and implementation of network routing protocols which manage to trigger V2X communication with the reliable end-to-end connectivity and efficient packet transmission. The organically changing nature of road transport vehicles poses a significant threat to VANET with respect to the accuracy and reliability of packet delivery. Therefore, a position-based routing protocol tends to be the predominant method in VANET as they overcome rapid changes in vehicle movements effectively. However, existing routing protocols have some limitations such as (i) inaccurate in high dynamic network topology, (ii) defective link-state estimation (iii) poor movement prediction in heterogeneous road layouts. In this paper, a target-driven and mobility prediction (TDMP) based routing protocol is therefore developed for high-speed mobility and dynamic topology of vehicles, fluctuant traffic flow and diverse road layouts in VANET. The primary idea in TDMP is that the destination target of a driver is included in the mobility prediction to assist the implementation of the routing protocol. Compared to existing geographic routing protocols which mainly greedily forward the packet to the next-hop based on its current position and partial road layout, TDMP is developed to enhance the packet transmission with the consideration of the estimation of inter-vehicles link status, and the prediction of vehicle positions dynamically in fluctuant mobility and global road layout.Comment: 35 pages,16 Figure

Crash data quality for road safety research: current state and future directions

Crash databases are one of the primary data sources for road safety research. Therefore, their quality is fundamental for the accuracy of crash analyses and, consequently the design of effective countermeasures. Although crash data often suffer from correctness and completeness issues, these are rarely discussed or addressed in crash analyses. Crash reports aim to answer the five “W” questions (i.e. When?, Where?, What?, Who? and Why?) of each crash by including a range of attributes. This paper reviews current literature on the state of crash data quality for each of these questions separately. The most serious data quality issues appear to be: inaccuracies in crash location and time, difficulties in data linkage (e.g. with traffic data) due to inconsistencies in databases, severity misclassification, inaccuracies and incompleteness of involved users’ demographics and inaccurate identification of crash contributory factors. It is shown that the extent and the severity of data quality issues are not equal between attributes and the level of impact in road safety analyses is not yet entirely known. This paper highlights areas that require further research and provides some suggestions for the development of intelligent crash reporting systems