Motorcycle Crash Prediction Model for Non-Signalized Intersections

Harnen, S; Wong, Shaw Voon; Umar, Radin Sohadi Radin; Hashim, Wan Ibrahim Wan

doi:10.1016/s0386-1112(14)60144-8

Cited by 39 publications

(21 citation statements)

References 5 publications

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“…Several studies (Poch and Mannering, 1996;Sayed and Rodriguez, 1999;Harnen et al, 2003;and Salifu, 2004) presented the empirical relationships obtained through the Poisson regression analyses and/or NB techniques, relating the crashes with traffic flow, traffic control, and key highway geometric design variables. The results indicated that roadway geometric, vehicular, and operational features had an effect on crash frequency.…”

Section: Intersectionsmentioning

confidence: 99%

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

Lu¹

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During the time of my doctoral study at Florida International University, he helped me become a professional, responsible, and confident person. Dr. Gan's influence on my life has spanned many levels, at times as a professor, at other times, like a caring father.Dr. Gan taught me invaluable wisdom and problem-solving skills, both of which have been of great benefit to me during my studies, and will certainly continue to do so as I enter the engineering profession. He even took time out of his busy schedule to show me how to write professionally, from emails to technical papers. With Dr. Gan's help, I received many university, state, and international honors and awards. He has shown me that nothing is impossible if I simply believe and try my best. I am deeply honored to have been one of Dr. Gan's students. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs.In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from [2007][2008][2009][2010] for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs vii for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models.The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors.The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests,represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R 2 (R 2 FT ), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibr...

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Section: Intersectionsmentioning

confidence: 99%

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

Lu¹

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“…Reference [13] developed a prediction model for motorcycle crashes which focused on non-signalized intersections on urban roads in Malaysia. They found that speed, lane width, number of lanes, shoulder width and land use are significant in explaining motorcycle crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Fatality prediction model for motorcycle accidents in Malaysia

Nasaruddin

Voon

Wah

et al. 2012

2012 International Conference on Statistics in Science, Business and Engineering (ICSSBE)

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This paper involves building a fatality predictive model for motorcycle accidents data in Malaysia. The number of registered motorcycles in Malaysia has increased four-fold compared to the last 20 years. Thus, the motorcycle accidents rate and fatality rates among riders and pillion in Malaysia has also increased dramatically. However, results show that when taken into account the numbers of fatalities per 10,000 registered motorcycles, the fatality rate shows a decreasing trend starting from 1996 onwards. The motorcycle accident data for the period of 1996 to 2010 was analyzed using Smeed's Law and regression method. The results show that regression method approach gives better estimates of fatality rate than Smeed's equation.

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“…The road environment (road type, road geometry, roadside installations etc.) plays a very significant role in PTW accident risk (Haque et al, 2009;Haque et al, 2010;Harnen et al, 2003;Kasantikul et al, 2005;Wanvik, 2009;Daniello and Gabler, 2011). However, the effect of traffic and weather characteristics has not been extensively explored (Branas and Knudson, 2001;Houston and Richardson, 2008;Xuequn et al, 2011;Abdul Manan and Várhelyi, 2012).…”

Section: Introductionmentioning

confidence: 99%