Managing Traffic Flow Based on Predictive Data Analysis
In this paper, we propose a solution to reduce traffic congestion by utilizing the existing traffic technologies and social data. The amount of social data will increase in the upcoming years as mobile devices and Internet begin serving a larger population. The availability of the driver’s location data from mobile devices and feeds from micro-blogging sites play a key role in arriving at the solution. The concepts used to implement the solution are image processing algorithms, making efficient use of traffic camera feeds and social data. This solution will govern the existing traffic signal system and adapt it in real-time to streamline the traffic flow. The main challenge solved by this mechanism is to create smooth flowing traffic between two traffic signals while maintaining the fairness to other traffic conditions, thus improving the average traffic speed. This approach evaluates traffic variables obtained from the real time camera feed with sentiment analysis on social-data to create dynamic traffic timing. The end result of implementing the proposed solution will help in reducing travel time, vehicle idling time and reduce accident occurrences arising due to driver’s mental fatigue.
KeywordsSocial data machine learning image processing traffic jams Data Mining Data Science
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- 1.Tubaishat, M., Shang, Y., Shi, H.: Adaptive Traffic Light Control with Wireless Sensor Networks. University of Missouri, ColumbiaGoogle Scholar
- 2.Zeller, K., Hinz, S., Rosenbaum, D., Leitloff, J., Reinartz, P.: Traffic Monitoring Without Single Car Detection From Optical Airborne ImagesGoogle Scholar
- 3.Go, A., Bhayani, R., Huang, L.: Twitter Sentiment Classification using Distant SupervisionGoogle Scholar
- 4.Hinz, S., Lenhart, D., Leitloff, J.: detection and tracking of vehicles in low framerate aerial image Sequences. ArticleGoogle Scholar