Abstract
Currently, there are kinds of algorithms in order to detect real-time urban traffic condition. Most of these approaches consider speed of vehicles as a main metric to describe traffic situation. In this paper, we find out two important observations through several experiments. (1) In urban city, the speed of vehicles is influenced significantly by some factors such as traffic lights delay and road condition. The actual situation rarely satisfy hypothesis required for these solutions. Therefore, these traditional algorithms do not work well in practical environment. (2) Traffic volume on a road segment shows strong pattern and changes smoothly at adjacent time. This feature of traffic volume inspires us to define a metric: traffic-rate, which is used to detect traffic condition in real time. In our solution, we develop a novel traffic-detection algorithm based on originaldestination (OD) matrix. We illustrate our approach and measure its performance in real environment. The performance evaluations confirm the effectiveness of our algorithm.
Highlights
Nowadays, traffic congestion has been a serious problem in many urban cities
We develop a novel traffic-detection algorithm based on originaldestination (OD) matrix
Element f (i, j) in the OD matrix means the number of vehicles from road segment i to road segment j .traditional algorithms to construct OD matrix do not work well in real world, since their solutions do not consider features of real traffic condition
Summary
Traffic congestion has been a serious problem in many urban cities. In China, it caused about 5%-8% GDP wasted each year. Governments cost million dollars to build Intelligent Transportation System (ITS). In response to these challenges, OriginalDestination (OD) matrix is often required as application input to provide service for transportation. Element f (i, j) in the OD matrix means the number of vehicles from road segment i to road segment j .traditional algorithms to construct OD matrix do not work well in real world, since their solutions do not consider features of real traffic condition. The traditional algorithms of traffic detection have limitations when runs in real environment. In their solutions, speed of vehicles is considered as a main metric to identify traffic states. We find that speed is influenced significantly by traffic lights
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