Calculation of Average Road Speed Based on Car-to-Car Messaging
Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Arrival time prediction provided by most of naviga tion systems is affected by several factors, such as road condition,
travel time, weather condition, car speed, etc. These predictions
are mainly based on historical data. Systems that provide near real-time road condition updates, e.g. Google Maps, depend on
crowdsourcing GPS data from cars 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 cars, 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 Fog-computing paradigm, provide lower
latency and higher accuracy than centralized (cloud-based)
approaches. Yet, they require a heavy investment and a large
infrastructure, which might be a limit for its utility in some
countries, e.g. Egypt. In this paper, 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
car or the mobile device of the traveler. We compare, through
simulation, our proposed approach to the fog-computing-based
traffic monitoring. Simulation results give an empirical evidence
on the correctness of our results compared to fog-based speed
calculation.
Index Terms—Traffic Monitoring; D2D Communication;
Crowdsourcing
Description
Keywords
Traffic Monitoring, D2D Communication, Crowdsourcing
Citation
Ramzy, A. et al. (2019) “Calculation of Average Road Speed Based on Car-to-Car Messaging,” in 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 1–8.