Abstract
Traffic crashes in Riyadh city cause losses in the form of deaths, injuries and property damages, in addition to the pain and social tragedy affecting families of the victims. In 2005, there were a total of 47,341 injury traffic crashes occurred in Riyadh city (19% of the total KSA crashes) and 9% of those crashes were severe. Road safety in Riyadh city may have been adversely affected by: high car ownership, migration of people to Riyadh city, high daily trips reached about 6 million, high rate of income, low-cost of petrol, drivers from different nationalities, young drivers and tremendous growth in population which creates a high level of mobility and transport activities in the city. The primary objective of this paper is therefore to explore factors affecting the severity and frequency of road crashes in Riyadh city using appropriate statistical models aiming to establish effective safety policies ready to be implemented to reduce the severity and frequency of road crashes in Riyadh city. Crash data for Riyadh city were collected from the Higher Commission for the Development of Riyadh (HCDR) for a period of five years from 1425H to 1429H (roughly corresponding to 2004-2008). Crash data were classified into three categories: fatal, serious-injury and slight-injury. Two nominal response models have been developed: a standard multinomial logit model (MNL) and a mixed logit model to injury-related crash data. Due to a severe underreporting problem on the slight injury crashes binary and mixed binary logistic regression models were also estimated for two categories of severity: fatal and serious crashes. For frequency, two count models such as Negative Binomial (NB) models were employed and the unit of analysis was 168 HAIs (wards) in Riyadh city. Ward-level crash data are disaggregated by severity of the crash (such as fatal and serious injury crashes). The results from both multinomial and binary response models are found to be fairly consistent but the results from the random parameters model seem more reasonable. Age and nationality of the driver, excessive speed, wet road surface and dark lighting conditions and single vehicle crashes are associated with increased probability of fatal crashes. More specifically, the probability of having a fatal crash increases with the age of the driver and Saudi drivers (relative to non-Saudi drivers) are associated with the probability of fatal crashes (relative to serious injury crashes). A crash involving a single vehicle is found to be more severe than a crash involving a multiple vehicles. The results from the frequency models suggest that percentage of non-Saudi found positively associated with serious injury crashes; percentage of illiterate people and the income per capita found to be positively significant with the frequency of fatal and serious injury crashes; and the increased residential, transport, and educational areas of land use is associated with the decreased level of fatal and serious injury crashes occurrences. Based on the findings, a range of countermeasures are proposed to reduce the severity and frequency of traffic crashes in Riyadh city.