Abstract
Crash severity prediction has been raised as a key problem in traffic accident studies. Thus, to progress in this area, in this study, a thorough artificial neural network combined with an improved metaheuristic algorithm was developed and tested in terms of its structure, training function, factor analysis, and comparative results. Data from I5, an interstate highway in the Washington State during the period of 2011–2015, were used for fitting and prediction, and after setting the theoretical three-layer neural network (NN), an improved Particle Swarm Optimization (PSO) method with adaptive inertial weight was offered to optimize the NN, and finally, a comparison among different adaptive strategies was conducted. The results showed that although the algorithms produced almost the same accuracy in their predictions, a backpropagation method combined with a nonlinear inertial weight setting in PSO produced fast global and accurate local optimal searching, thereby demonstrating a better understanding of the entire model explanation, which could best fit the model, and at last, the factor analysis showed that non-road-related factors, particularly vehicle-related factors, are more important than road-related variables. The method developed in this study can be applied to a big data analysis of traffic accidents and be used as a fast-useful tool for policy makers and traffic safety researchers.
Highlights
The related road factors such as curve and gradient variables have the least importance, and the only important factor contributing to the severity prediction is the road functional class, which indicates whether a crash occurred on a certain type of road. us, it can be concluded that the relative road variables contribute less than the relative nonroad variables, compared with the relative vehicle factors
We can see that the most severe crashes occur during the winter in the Washington State (December, January, and February), and drivers below the age of 25 and above the age of 60 are more prone to encountering severe injury crashes. e month may account for the rainy season in the mountainous Seattle area, whereas age may be derived from the fact that younger people and older people are more prone to making severe mistakes
Besides the test of different types of training structure and methods, more importantly, a nonlinear adaptive Particle Swarm Optimization (PSO) optimization method was proposed in order to solve the tradeoff problem between the global and local search ability among the previous studies. e detail test of different algorithm confirmed our hypothesis. e additional contributing factor analysis offers a different point of view compared with former statistical analysis. e main conclusions can be concluded as follows: (1) e number 12 hidden layer nodes fit the model developed in this paper well; and the BP method (Levenberg–Marquardt) can be better utilized when aided by fast hardware
Summary
Traffic safety is a challenging task to be accomplished and has been identified as crash hotspots around the world. E total number of fatal crashes in the U.S increased to around 35,000 in 2016. According to the Washington State Collision Summary report, a total of 117,053 crashes were identified in the Washington State, including 499 fatal collisions, 36,531 injury collisions, and 77,358 property-damage-only collisions, indicating a crash occurred every 4.5 min and a person died in a crash every 16 hours [1]. Billions of dollars in personal and property damage are wasted in traffic crashes each year around the world [2]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
