#GOOGLE MAPS TRAFFIC HISTORICAL DATA BLUETOOTH#

#GOOGLE MAPS TRAFFIC HISTORICAL DATA BLUETOOTH#

Park, H., Haghani, A.: Optimal number and location of Bluetooth sensors considering stochastic travel time prediction. Yuan, W., Deng, P., Taleb, T., Wan, J., Bi, C.: An unlicensed taxi identification model based on big data analysis. 15(4), 639–661 (2015)ĭiaz Alvarez, A., Serradilla Garcia, F., Naranjo, J.E., Anaya, J.J., Jimenez, F.: Modeling the driving behavior of electric vehicles using smartphones and neural networks. I figured out I could take an independent screenshot of the Google maps at regular intervals and animate the same using magick package to evaluate compliance and compare across countries. You control what types of data appear on the map by selecting the Layers legend. Unfortunately, Google Maps does not provide historical traffic data beyond traffic information on a Typical (specified) day and time. 142(6), 1–10 (2016)ĭeng, B., Denman, S., Zachariadis, V., Jin, Y.: Estimating traffic delays and network speeds from low-frequency GPS taxis traces for urban transport modelling. The interactive map allows you to view traffic data across the province. Yu, B., Song, X., Guan, F., Yang, Z., Yao, B.: k-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. In: Proceedings of the International Joint Conference on Neural Networks (2013)

Park, J., Murphey, Y.L., Kristinsson, J., McGee, R., Kuang, M., Phillips, T.: Intelligent speed profile prediction on urban traffic networks with machine learning. Orange Lines: This shows that your roads have medium traffic. Green Lines: This indicates there are no traffic delays. Check traffic now & later To reach your destination as quickly as possible, check typical traffic before you drive. You’ll see your streets and roads highlighted with one of these color lines. Further, a more detailed study by type of road and by time period was conducted, showing an overall accuracy of 95.8% in fluid traffic situations. 01:13 What Do the Colors Mean in Google Maps To show you the different levels of traffic, Google Maps uses various color codes. The overall accuracy reaches 74.9% on week days and 83.3% on weekend days. Then, using data gathered through our Google Traffic-based method for several roads in Rabat (Morocco), we evaluate the accuracy of traffic prediction based on historical average. The traffic data obtained from Google Traffic are validated with a traffic data set collected by sensors installed in Paris. Since Google Traffic maps are provided in the form of rendered images, we apply image processing on Google Traffic maps to extract traffic data that are suitable for processing and analysis. This paper addresses this challenge by leveraging ‘real-time’ Google Traffic maps which show the state of the traffic on different road segments using four different colors. This is a challenge mainly because of the high financial cost that the installation of a large number of sensors would incur. The main challenge is the acquisition of real-time large-scale urban traffic data at a sufficient spatio-temporal resolution. Urban traffic management uses increasingly sophisticated methods to overcome the many challenges involved in the development of traffic forecasting solutions.

Google maps traffic historical data