TY - GEN
T1 - On the Effect of Traffic and Road Conditions on the Drivers' Behavior
T2 - 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
AU - Abdelrahman, Abdalla
AU - Abu-Ali, Najah
AU - Hassanein, Hossam S.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/28
Y1 - 2018/8/28
N2 - In the last decade, naturalistic driving studies (NDSs) have given researchers an unprecedented way to study the behavior of drivers through the deployment of, and capturing the data from, on-board vehicle sensors and cameras. The ability to determine the dominant driving risk facto rs can play an essenti al role in shaping transportation policies and education programs for drivers. This paper presents a cohort study statistical analysis to determine the risks associated with traffic and road surface conditions, quanti fied in terms of crash and near crash events. Two risk quantification measures, odds ratio (OR) and relative risk (RR, are utilized to signify the associated risk. For this research we used the 100-CAR data set, with a total of 829 crash and near crash and 19616 baseline events, which are driving events captured randomly in normal driving episodes. In the 100-CAR data set, traffic density is divid ed into six levels according to the traffic flow con dition. Similarly, road su rface condition is divided into four categories. To quantify the statistical significance of the results, measures such as the p-value are employed. The results show that icy roads with level-of-service (LOS) A, wet roads with LOS D, and dry roads with LOS D have the highest risk for crashes and near crashes. These results are proven to be of statistical significance.
AB - In the last decade, naturalistic driving studies (NDSs) have given researchers an unprecedented way to study the behavior of drivers through the deployment of, and capturing the data from, on-board vehicle sensors and cameras. The ability to determine the dominant driving risk facto rs can play an essenti al role in shaping transportation policies and education programs for drivers. This paper presents a cohort study statistical analysis to determine the risks associated with traffic and road surface conditions, quanti fied in terms of crash and near crash events. Two risk quantification measures, odds ratio (OR) and relative risk (RR, are utilized to signify the associated risk. For this research we used the 100-CAR data set, with a total of 829 crash and near crash and 19616 baseline events, which are driving events captured randomly in normal driving episodes. In the 100-CAR data set, traffic density is divid ed into six levels according to the traffic flow con dition. Similarly, road su rface condition is divided into four categories. To quantify the statistical significance of the results, measures such as the p-value are employed. The results show that icy roads with level-of-service (LOS) A, wet roads with LOS D, and dry roads with LOS D have the highest risk for crashes and near crashes. These results are proven to be of statistical significance.
KW - Naturalistic driving studies (NDSs)
KW - data driven applications
KW - driving behavior
KW - driving risk management
KW - intelligent transportation systems (ITS)
UR - http://www.scopus.com/inward/record.url?scp=85053927929&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85053927929&partnerID=8YFLogxK
U2 - 10.1109/IWCMC.2018.8450504
DO - 10.1109/IWCMC.2018.8450504
M3 - Conference contribution
AN - SCOPUS:85053927929
SN - 9781538620700
T3 - 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
SP - 892
EP - 897
BT - 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 June 2018 through 29 June 2018
ER -