Accident; analysis and prevention
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Emergency vehicles, such as police, ambulances, and fire vehicles, need to arrive at the scene of emergencies as quickly as possible, and thus they often travel in emergency mode - using their lights and sirens and often bypassing traffic signals. We examined whether travelling in emergency mode increased crash risk among police, ambulance and fire vehicles. ⋯ Crash risk increased when police vehicles drove with lights and sirens but did not increase for ambulance and fire vehicles. Further research is necessary to develop and evaluate strategies to mitigate crash risk among police vehicles. Cultural approaches which prioritize transportation safety in conjunction with reaching the scene as quickly as possible may be warranted.
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Highway workers frequently work in close proximity of live traffic in highway work zones, traffic accidents therefore have devastating effects on worker safety. In order to reduce the potential for such accidents, methods involving use of advisory signs and police presence have been used to mitigate accident risks and improve safety for highway workers. This research evaluates the magnitude of the speeding problem in highway work zones and the effects of four levels of police presence on improving work zone safety. ⋯ This paper analyzes this data using statistical methods to evaluate the effectiveness of these different methods of speed control on the safety of the work zone. Four Measures of Effectiveness (MOE) were used in this evaluation consisting of average speed reduction, speed variance, 85th percentile speed, and proportion of high speed vehicles. The results indicate that all levels of police presence provided statistically significant improvements in one or more of the MOEs.
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In Thailand, red light running is considered as one of the most dangerous behaviors at intersection. Red light running (RLR) behavior is the failure to obey the traffic control signal. However, motorcycle riders and car drivers who are running through red lights could be influenced by human factors or road environment at intersection. ⋯ The results from this study can help to understand the characteristics of red light runners and factors affecting them to run red lights. For motorcycle riders and car drivers, age, gender, occupation, driving license, helmet/seatbelt use, and the probability to be penalized when running the red light significantly affect RLR behavior. In addition, the results indicated that vehicle travelling direction, time of day, existence of turning lane, number of lanes, lane width, intersection sight distance, type of traffic signal pole, type of traffic signal operation, length of yellow time interval, approaching speed, distance from intersection warning sign to stop line, and pavement roughness significantly affect RLR rates.
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This study analyzes rear-end collision risk of cars and heavy vehicles on freeways using a surrogate safety measure. The crash potential index (CPI) was modified to reflect driver's reaction time and estimated by types of lead and following vehicles (car or heavy vehicle). CPIs were estimated using the individual vehicle trajectory data from a segment of the US-101 freeway in Los Angeles, U. ⋯ The result also shows that rear-end collision risk is lower for heavy vehicles than cars in the crash case due to their shorter reaction time and lower speed when spacing is shorter. Thus, it is important to reflect the differences in driver behavior and vehicle performance characteristics between cars and heavy vehicles in estimating surrogate safety measures. Lastly, it was found that the CPI-based crash prediction model can correctly identify the crash and non-crash cases at higher accuracy than the other crash prediction models based on detectors.
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Although a growing body of literature focuses on the relationship between the built environment and pedestrian crashes, limited evidence is provided about the relative importance of many built environment attributes by accounting for their mutual interaction effects and their non-linear effects on automobile-involved pedestrian crashes. This study adopts the approach of Multiple Additive Poisson Regression Trees (MAPRT) to fill such gaps using pedestrian collision data collected from Seattle, Washington. Traffic analysis zones are chosen as the analytical unit. ⋯ More importantly, some factors show clear non-linear relationships with pedestrian crash frequency, challenging the linearity assumption commonly used in existing studies which employ statistical models. With various accurately identified non-linear relationships between the built environment and pedestrian crashes, this study suggests local agencies to adopt geo-spatial differentiated policies to establish a safe walking environment. These findings, especially the effective ranges of the built environment, provide evidence to support for transport and land use planning, policy recommendations, and road safety programs.