Although speed is considered to be one of the main crash contributory factors, research findings are inconsistent. Independent of the robustness of their statistical approaches, crash frequency models typically employ crash data that are aggregated using spatial criteria (e.g., crash counts by link termed as a link-based approach). In this approach, the variability in crashes between links is explained by highly aggregated average measures that may be inappropriate, especially for time-varying variables such as speed and volume. This paper re-examines crash-speed relationships by creating a new crash data aggregation approach that enables improved representation of the road conditions just before crash occurrences. Crashes are aggregated according to the similarity of their pre-crash traffic and geometric conditions, forming an alternative crash count dataset termed as a condition-based approach. Crash-speed relationships are separately developed and compared for both approaches by employing the annual crashes that occurred on the Strategic Road Network of England in 2012. The datasets are modelled by injury severity using multivariate Poisson lognormal regression, with multivariate spatial effects for the link-based model, using a full Bayesian inference approach. The results of the condition-based approach show that high speeds trigger crash frequency. The outcome of the link-based model is the opposite; suggesting that the speed-crash relationship is negative regardless of crash severity. The differences between the results imply that data aggregation is a crucial, yet so far overlooked, methodological element of crash data analyses that may have direct impact on the modelling outcomes.

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Citation: IMPRIALOU, M-I., QUDDUS, M.A. and PITFIELD, D.E., 2016. Predicting the safety impact of a speed limit increase using condition-based multivariate Poisson lognormal regression. Transportation Planning and Technology, 39 (1), pp. 3-23.

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.Keywords: crash location, crash mapping, metropolitan/urban networks, multilevel logistic regression Imprialou M.-I.M., Quddus M., Pitfield D.A. 3 INTRODUCTIONSustainable road safety programmes require constant enhancements to crash prevention policies and countermeasures. The precautionary measures should focus on the network areas where many crashes occur and the driving attitudes that are considered mostly responsible for crashes. Analysis of crash data aims to identify and explain the factors that lead to traffic crashes. The quality and reliability of the crash data that are used as an input for spatial crash analyses (e.g. identification of black spots, spatial crash modelling etc.) is closely related to the validity of their outcomes (e.g. [1][2][3][4][5]. The spatial nature of crashes makes crash locations as one of the most important and informative attributes of crash databases (5-7) that at the same time are very likely to be inaccurately reported (1-4, 7). Therefore, the refinement of the crash locations gives to the crash analyses the potential to improve in quality.Traffic crashes create major problems to society as they are related to personal injuri...