TY - GEN
T1 - Modeling and characterization of vehicular density at scale
AU - Thakur, Gautam S.
AU - Hui, Pan
AU - Helmy, Ahmed
PY - 2013
Y1 - 2013
N2 - Future vehicular networks shall enable new classes of services and applications for car-to-car and car-to-roadside communication. The underlying vehicular mobility patterns significantly impact the operation and effectiveness of these services, and hence it is essential to model and characterize such patterns. In this paper, we examine the mobility of vehicles as a function of traffic density of more than 800 locations from six major metropolitan regions around the world. The traffic densities are generated from more than 25 million images and processed using background subtraction algorithm. The resulting vehicular density time series and distributions are then analyzed. It is found using the goodness-of-fit test that the vehicular density distribution follows heavy-tail distributions such as Log-gamma, Log-logistic, and Weibull in over 90% of these locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators signifies that the traffic patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding, which will influence the design and deployment of the next generation vehicular network and also aid in the development of opportunistic communication services and applications for the vehicles. In addition, it shall provide a much needed input for the development of smart cities.
AB - Future vehicular networks shall enable new classes of services and applications for car-to-car and car-to-roadside communication. The underlying vehicular mobility patterns significantly impact the operation and effectiveness of these services, and hence it is essential to model and characterize such patterns. In this paper, we examine the mobility of vehicles as a function of traffic density of more than 800 locations from six major metropolitan regions around the world. The traffic densities are generated from more than 25 million images and processed using background subtraction algorithm. The resulting vehicular density time series and distributions are then analyzed. It is found using the goodness-of-fit test that the vehicular density distribution follows heavy-tail distributions such as Log-gamma, Log-logistic, and Weibull in over 90% of these locations. Moreover, a heavy-tail gives rise to long-range dependence and self-similarity, which we studied by estimating the Hurst exponent (H). Our analysis based on seven different Hurst estimators signifies that the traffic patterns are stochastically self-similar (0.5 ≤ H ≤ 1.0). We believe this is an important finding, which will influence the design and deployment of the next generation vehicular network and also aid in the development of opportunistic communication services and applications for the vehicles. In addition, it shall provide a much needed input for the development of smart cities.
UR - http://www.scopus.com/inward/record.url?scp=84883090241&partnerID=8YFLogxK
U2 - 10.1109/INFCOM.2013.6567126
DO - 10.1109/INFCOM.2013.6567126
M3 - Conference contribution
AN - SCOPUS:84883090241
SN - 9781467359467
T3 - Proceedings - IEEE INFOCOM
SP - 3129
EP - 3134
BT - 2013 Proceedings IEEE INFOCOM 2013
T2 - 32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013
Y2 - 14 April 2013 through 19 April 2013
ER -