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

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

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

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

Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression

Speed limit changes are considered to lead to proportional changes in the number and severity of crashes. To predict the impact of a speed limit alteration, it is necessary to define a relationship between crashes and speed on a road network. This paper examines the relationship of crashes with speed, as well as with other traffic and geometric variables, on the UK motorways in order to estimate the impact of a potential speed limit increase from 70 mph to 80 mph on traffic safety. Full Bayesian multivariate Poisson lognormal regression models are applied to a dataset aggregated using the condition-based approach for crashes by vehicle (i.e. single-vehicle and multiple-vehicle) and severity (i.e. fatal or serious and slight). The results show that single-vehicle crashes of all severities and fatal or serious injury crashes involving multiple vehicles increase at higher speed conditions and particularly when these are combined with lower volumes. Slight injury multiple-vehicle crashes are found not to be related with high speeds, but instead with congested traffic. Using the speed elasticity values derived from the models the predicted annual increase in crashes after a speed limit increase on the UK motorway is found to be 6.2-12.1 % for fatal or serious injury crashes and 1.3-2.7% for slight injury, or else up to 167 more crashes

Impact of combined alignments on lane departure: A simulator study for mountainous freeways

Lane departures are responsible for many side-swipe, rear-end and single-vehicle run-off-road crashes. There is a dearth of research, however, on how lane departures are impacted by roadway alignments. The objective of this paper is to examine which geometric design characteristics, including road alignment at the current segment and the adjacent segments, have significant influence on lane departure. Lane departure data from a total 30 drivers were collected from a driving simulator study of a four-lane (two lanes in each direction) divided mountainous freeway. Lane departures were classified into lane keeping, lane departure to the left and lane departure to the right for all-alignments (Dataset I), and lane keeping, lane departure to the inside and lane departure to the outside for curves-only (Dataset II). A mixed multinomial logit model for each dataset was employed to examine the contributory factors. This approach allows for the possibility that the estimated model parameters can vary randomly to account for unobserved effects potentially relating to heterogeneous driver behaviors. Fixed parameters that had a significant increase on lane departure were horizontal curvature at the current segment, and the difference (max-min) in horizontal curvature within the 300-m adjacent upstream alignment. Downward slope and upward slope with fixed parameters significantly decreased lane departure. Estimated parameters related to the direction of the curve, driving lane (bordering median or hard shoulder) and driving speed had found to have randomly distributed over the drivers. This indicates that driver behavior is not consistent in the effect of these three variables on lane departure. These results can assist engineers in designing safer mountainous freeways

A spatio-temporal analysis of the impact of congestion on traffic safety on major roads in the UK

A spatio-temporal analysis has been conducted aiming to explore the relationship between traffic congestion and road accidents based on the data on the M25 motorway and its surrounding major roads in England during the period 2003-2007. It was hypothesised that increased traffic congestion may be beneficial to road safety as the number of fatal and serious injury (KSI) accidents would be less due to low average speed when congestion is present. If this is confirmed then it poses a potential dilemma for transport policy makers. A series of classical count outcome models (random-effects Negative Binomial models) and spatial models using a full Bayesian hierarchical approach have been developed in this study in order to examine whether congestion has any effect on the frequency of accidents. The results suggest that increased traffic congestion is associated with more KSI accidents and traffic congestion has little impact on slight injury accidents. This may be due to the higher speed variance among vehicles within and between lanes and worse driving behaviour in the presence of congestion. In addition, traffic speeds even within congested situations are likely to be relatively high on major roads compared to other parts of the road network. Some strategies are then proposed to optimise traffic flow which would be beneficial to both congestion and accident reduction

Multilevel logistic regression modelling for crash mapping in metropolitan areas

The spatial nature of traffic crashes makes crash locations one of the most important and informative attributes of crash databases. It is however very likely that recorded crash locations in terms of easting and northing coordinates, distances from junctions, addresses, road names and types are inaccurately reported. Improving the quality of crash locations therefore has the potential to enhance the accuracy of many spatial crash analyses. The determination of correct crash locations usually requires a combination of crash and network attributes with suitable crash mapping methods. Urban road networks are more sensitive to erroneous matches due to high road density and inherent complexity. This paper presents a novel crash mapping method suitable for urban and metropolitan areas that matched all the crashes that occurred in London from 2010-2012. The method is based on a hierarchical data structure of crashes (i.e. candidate road links are nested within vehicles and vehicles nested within crashes) and employs a multilevel logistic regression model to estimate the probability distribution of mapping a crash onto a set of candidate road links. The road link with the highest probability is considered to be the correct segment for mapping the crash. This is based on the two primary variables: (a) the distance between the crash location and a candidate segment and (b) the difference between the vehicle direction just before the collision and the link direction. Despite the fact that road names were not considered due to limited availability of this variable in the applied crash database, the developed method provides a 97.1% (±1%) accurate matches (N=1,000). The method was compared with two simpler, non-probabilistic crash mapping algorithms and the results were used to demonstrate the effect of crash location data quality on a crash risk analysis

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds