Drivers' Behavior Analysis Under Reduced Visibility Condition Using A Driving Simulator
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ProgramMaster of Science
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This study involves the analysis of drivers' behavior under foggy and rainy conditions using a driving simulator; it is aimed at determining factors that affect drivers' speeds under such conditions. The study is divided into two parts: 1. The analysis of drivers' behavior in FOGGY conditions 2. The analysis of drivers' behavior in RAINY conditions For the analysis concerning FOG, a 400-square kilometer road network southwest of the Baltimore metro area is assimilated in a UC-win/Road driving simulator; the research is based on a study on over 100 participants. Off-peak traffic conditions were simulated to solely observe the effects of FOG on drivers' behavior. The average speeds of the participants are compared in three different areas- BEFORE encountering the fog, WITHIN the fog area, and AFTER exiting the fog; this comparison was conducted using hypothesis testing (F-Test and T-Test). A multinomial logistic model was used to examine factors affecting speed variances. For the analysis concerning RAIN, another 400 square kilometers is considered. The start point is on I-95 southbound just outside Baltimore, and the end point is Downtown Rockville. There are three routes of choice for the participants from the origin to the destination: Maryland 198, Maryland 200, and I-495 with speed limits set at 35 mph, 55 mph, and 65 mph respectively. In this aspect of the study also, the average speeds of participants are compared in the same order- BEFORE encountering rain, IN rain, and AFTER exiting rain, utilizing hypothesis testing method (F-Test and T-Test). A further study was conducted on the segments of rain; this time, an analysis was performed on the speeds of drivers on the same segment but without rain this time. A regression analysis was performed to analyze the factors affecting speed change among participants. The results, in the case of FOG, illustrate that there is significant difference in average speeds BEFORE the onset of fog and WITHIN the fog. However, when it comes to the comparison of average speeds WITHIN the fog and AFTER the fog, the difference in average speeds is trivial. Thus, the fog accounted for the reduction of speed when it was encountered, but in its aftermath, the average speeds were below expectation. The correlation test shows that drivers' characteristics such as age, gender, household size, and work status turn out to be significantly associated with speed variations. The results from the multinomial logistic model indicate that the variations in speed have a close relationship with only gender among all variables. Thus, it reveals that reduction of speed due to fog is more prevalent amongst women than men. The results, in the case of RAIN, illustrate that there is a difference in average speeds before the beginning of rain and within; there is also a difference, to the same degree, in average speeds within the rain and after the rain. The difference in speed in this part of the study is not as significant as in the fog analysis. Also in the analysis of average speeds in the same road segments WITH and WITHOUT rain, there is a significant difference in average speeds; this signifies that RAIN is the only factor that affected average speeds in the selected segments of study. The results of the binary logistic model reveal that speed change is related with only age amongst all the tested variables. The increase in speed after exiting the rain rear is less probable amongst older participants.