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Saudi Arabia is one of the countries with the highest number of road accidents and associated fatalities in the world. Speeding has been identified as an important cause of increased traffic accidents, which also aggravate their severity. Road safety improvement strategies are primarily based on the accurate identification of accident hotspots. Installing speed cameras at a network level is an expensive road safety measure, and its spatiotemporal effectiveness should be assessed. In this study, a traffic accident risk assessment framework has been developed and implemented on the 84 km long Buraydah Ring Road in the Qassim region of Saudi Arabia. The selected highway was divided into 42 (×2 km long) segments using the ArcGIS software. A risk scoring scheme was developed to incorporate both the frequency and severity of road accidents. Speed cameras installation at various segments showed a 70% decline in total accident counts, 53% in accidents with property damage, 84% decline in accidents causing injury, and complete absence of accidents with fatalities. The 48% segments were identified as hotspots with risk level ≥ medium, while the speed cameras installation completely eliminated the hotspots from the study area. The proposed framework can be implemented on major high-speed highways, accommodating high traffic volumes, for hotspot identification and evaluation of various road safety measures in Saudi Arabia and elsewhere.

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Motorization has been escalating rapidly in developing countries, posing a severe challenge to sustainable urban mobility. In the past two decades, car-sharing has emerged as one of the most prominent alternatives to facilitate smart mobility solutions, thereby helping to reduce air pollution and traffic congestion. However, before its full-scale deployment, it is essential to understand the consumers’ acceptance of car-sharing systems. This study aimed to assess the public perception and acceptance of the car-sharing system through a stated preference (SP) questionnaire in the city of Lahore, Pakistan. The collected data contained detailed information on various service attributes of three alternative modes (car-sharing, private car, and taxi) in addition to the sociodemographic attributes of respondents. Data analysis and interpretation were performed using econometric models such as the Multinomial Logit Model (MNL), the Nested Logit Model (NL), and the Random Parameter Logit Model (RPL). Study findings revealed that several generic attributes such as travel time, travel cost, waiting time, and privacy were predicated as significant influential factors towards the adoption of car-sharing. Sociodemographic attributes, including age, education, monthly income, the individuals who had driver’s licenses, and frequency of travel in a week, were also found to be significant. The findings of the current study can provide valuable insights to stakeholders and transportation planners in formulating effective policies for car-sharing.