The Effect of Lane Change Volatility on Real Time Accident Prediction
نام عام مواد
[Thesis]
نام نخستين پديدآور
Tesheira, Hamilton
نام ساير پديدآوران
Mahgoub, Imad
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
Florida Atlantic University
تاریخ نشرو بخش و غیره
2019
مشخصات ظاهری
نام خاص و کميت اثر
67
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
Florida Atlantic University
امتياز متن
2019
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
According to a March 2019 publication by the National Highway Transportation Safety Administration (NHTSA), 62% of all police-reported accidents in the United States between 2011 and 2015 could have been prevented or mitigated with the use of ve groups of collision avoidance technologies in passenger vehicles: (1) forward collision prevention, (2) lane keeping, (3) blind zone detection, (4) forward pedestrian impact, and (5) backing collision avoidance. These technologies work mostly by reducing or removing the risks involved in a lane change maneuver; yet,the Broward transportation management system does not directly address these risk. Therefore, we are proposing a Machine Learning based approach to real-time accident prediction for Broward I-95 using the C5.1 Decision Tree and the Multi-Layer Perceptron Neural Network to address them. To do this, we design a new measure of volatility, Lane Change Volatility (LCV), which measures the potential for a lane change in a segment of the highway. Our research found that LCV is an important predictor of accidents in an exit zone and when considered in tandem with current system variable, such as lighting conditions, the machine learning classiers are able to predict accidents in the exit zone with an accuracy rate of over 98%
موضوع (اسم عام یاعبارت اسمی عام)
موضوع مستند نشده
Artificial intelligence
موضوع مستند نشده
Computer engineering
موضوع مستند نشده
Transportation
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )