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

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

A time-series analysis of motorway collisions in England considering road infrastructure, socio-demographics, traffic and weather characteristics

Traffic injuries on motorways are a public health problem worldwide. Collisions on motorways represent a high injury rate in comparison to the entire national network. Furthermore, collisions that occur on the hard–shoulder are even more severe than those that happen on the main carriageway. The purpose of this paper is to explore motorway safety through the identification of patterns in the sequence of monthly hard–shoulder and main carriageway collisions separately over a long period of time (1993– 2011) by using reported collision data from British motorways. In order to examine the trends of hard– shoulder and motorway collisions over the same period, a Vector Autoregressive (VAR) model is developed; this allows the inclusion of two time-series in the same model and the examination of the effect of one series on the other and vice-versa. Exogenous variables are also added in order to explore the long-term factors that might affect the occurrence of collisions. The factors considered are related to the infrastructure (e.g. length of motorways), socio-demographics (e.g. percentage of young drivers), traffic (e.g. percentage of vehicle-miles travelled by Heavy Goods Vehicles) and weather (e.g. precipitation). The results suggest different patterns in the sequences in terms of the lingering effects of preceding observations for the two time-series. In terms of the significance of exogenous variables, it is suggested that main carriageway collision frequency is affected by weather conditions and the presence of Heavy Goods Vehicles, while hard–shoulder collisions are decreased by the presence of Motorway Service Areas, which allow a safe exit off the motorway to stop and rest in case of fatigue

Exploring the factors affecting motorway accident severity in England using the generalised ordered logistic regression model

Problem The severity of motorway accidents that occurred on the hard shoulder (HS) is higher than for the main carriageway (MC). This paper compares and contrasts the most important factors affecting the severity of HS and MC accidents on motorways in England. Method Using police reported accident data, the accidents that occurred on motorways in England are grouped into two categories (i.e., HS and MC) according to the location. A generalized ordered logistic regression model is then applied to identify the factors affecting the severity of HS and MC accidents on motorways. The factors examined include accident and vehicle characteristics, traffic and environment conditions, as well as other behavioral factors. Results Results suggest that the factors positively affecting the severity include: number of vehicles involved in the accident, peak-hour traffic time, and low visibility. Differences between HS and MC accidents are identified, with the most important being the involvement of heavy goods vehicles (HGVs) and driver fatigue, which are found to be more crucial in increasing the severity of HS accidents. Practical applications Measures to increase awareness of HGV drivers regarding the risk of fatigue when driving on motorways, and especially the nearside lane, should be taken by the stakeholders

British airways’ move to Terminal 5 at London Heathrow airport: A statistical analysis of transfer baggage performance

This article was published in the serial, Journal of Air Transport Management [© Elsevier]. The definitive version is available at: http://www.sciencedirect.com/science/article/pii/S0969699710000876This paper investigates transfer baggage performance when British Airways’ occupancy of Terminal 5 at London Heathrow Airport took place. Operational data on transfer baggage performance are collated from BA performance scorecards and the Gini coefficient is used as a measure of consolidation of flight operations within a single terminal and in the investigation of correlation of consolidated flights in Terminal 5 with transfer baggage performance variation. The relationship between consolidation of operations in the terminal and improving transfer baggage performance is found to be significant. In addition, there is evidence of significant changes in transfer baggage performance on switch phases of flights as they were moved to Terminal 5 in steps. The exclusive use of a terminal gives improved performance

Aeronautical charging policy incentive schemes for airlines at European airports

This article was accepted for publication in the of Air Transport Management. The definitive published version is available at: http://dx.doi.org/10.1016/j.jairtraman.2013.06.009This paper introduces the concept of incentive schemes that may accompany airports’ aeronautical charging policies and develops a taxonomy of such schemes based on an analysis of data for 46 European Airports held in the RDC Aviation database.1 This taxonomy details the different types of incentive schemes that in are operation. It is clear their use is widespread and that the magnitude of the incentive is often significant. A financial benchmarking analysis is undertaken for four selected airports to illustrate the extent of the variations between airports both in terms of the basic characteristics of the incentive schemes and in the periods over which the discounts are available

Some insights into competition between low-cost airlines

The phenomenon of the wide world growth in low-cost airlines has resulted in a focus on their pricing strategies, on issues of cost recovery and on their impact on the traffic and market shares of legacy carriers or other low-cost carriers when they are in competition, either directly or at adjacent airports. This paper provides a brief overview of the characteristics of these low-cost carriers as well as their history and geography. It goes on to outline ways in which these carriers compete and manage demand, ranging from price competition to advertising; some of these methods directly reflect their special characteristics. Some empirical evidence is presented which indicates a correlation in fare setting behaviour between competitors and insights are offered on cost recovery. The impact of the start-up of low-cost carriers is also analysed, focusing on their impacts on other low-cost carriers. The case of Ryanair competing with easyJet on London-Venice is examined along with Southwest and Frontier on Denver-Las Vegas.Low-cost airlines Pricing strategy Competition