104.txt

Faculty of Computers and Artificial Intelligence
Cairo University
A Framework for Monitoring Road
Traffic using Fog Computing
By:
Ahmed Ramzy Ahmed Ahmed Negm
Under Supervision of:
Prof. Ehab E. Hassanein
Assoc. Prof. Ahmed H. Awad
Abstract
Total number of vehicles in most of the cities around the world has
increased during past decade along with the population growth. Traffic
monitoring in this situation is a big challenge. Various traffic monitoring
approaches have been researched and developed to handle this increase in
traffic density. Also various Traffic Monitoring Services (TMS) have been
proposed to provide strong and encompassing online platform to observe
the roads and the traffic status and predict the arrival time of the driver.
Arrival time prediction provided by most of navigation systems is
affected by several factors, such as road condition, travel time, weather
condition, vehicle speed, etc. Systems that provide near real-time road
condition updates, e.g. Google Maps, depend on crowdsourcing GPS data
from vehicles or mobile devices on the road. GPS data thus has a long journey
to travel from their sources to the analytics engine on the cloud before a
status update is sent back to the client. Between the time taken for GPS data
to be broadcast, received and processed, significant changes in road
conditions can take place and would still be unreported, leading to wrong
decisions on the route to choose.
Road condition, especially average speed of vehicles, monitoring is of
a local and continuous nature. It needs to be accomplished near GPS stream
data sources to reduce latency and increase the accuracy of reporting.
Solutions based on geo-distributed road monitoring, using the Fog-
computing paradigm, provide lower latency and higher accuracy than
centralized (cloud-based) approaches. Yet, they require a heavy investment
VIIand a large infrastructure, which might be a limit for its utility in some
countries, e.g. Egypt. In this thesis, we propose a more dynamic approach to
continuously update average speed on the road. The computation is done
locally on the client device, e.g. the traveling vehicle or the mobile device of
the traveler. Our implementation protects the privacy of the participating
vehicle drivers and don’t store the identity and the travel history of the
mobile device owner. We compare, through simulation, our proposed
approach to the fog-computing-based traffic monitoring. Simulation result
give an empirical evidence on the correctness of our results compared to fog-
based speed calculation.