Information collection is a new traffic flow information collection technology developed with ITS. To ensure the operation safety and scheduling efficiency of the rearview mirror camera , GPS devices are equipped. These devices send the status information of the vehicles to the management centre as certain frequency. This information records into log files, which contain the trajectories of the vehicles. Through vehicle trajectory analysis, not only improve the scheduling efficiency of the taxi dispatch system, but also provide road traffic conditions for the relevant departments, give them the decision support of traffic control. The obtaining of real-time traffic information has become an important part of the Intelligent Transportation Systems (ITS). With the key technologies development of ITS, it’s possible to offer real-time, dynamic and predicted traffic information for travellers by information collection, processing and analysis. The major difference between one GPS device and another is often in the features that it offers prospective buyers, and some of the features on the latest generation of GPS navigators are pretty amazing: voice recognition, real-time traffic data, red light and speed trap warnings, trip logs that record your progress, upgradeable maps, and useful extras like Bluetooth integration, MP3 players and picture viewers.
The feasibility of road traffic flow monitoring and prediction based on the car Android rear view camera
has been validated through a series of experiments. In 2008, Saurabh and his partner carried out the ‘‘Mobile century’’ field experiments. The experiment confirms that GPS-enabled cell phones can realistically be used as traffic sensors to detect the traffic flow, while preserving individuals’ privacy. In 2009, Calabrese used the taxi GPS data in Shenzhen to do the city-wide traffic modeling and traffic flow analysis and forecast the traffic conditions about the city’s daily movements. Fabritiis’ paper proposed two algorithms, pattern matching and Artificial Neural Network (ANN), which used for short-term (15–30 min) road travel time prediction. Daniel used the experiment data from ‘‘Mobile Century’’, put forward an Ensemble Kalman Filtering (EnKF) approach to highway traffic estimation using GPS enabled mobile devices.The floating wireless GPS tracker for car
processing is the main work in traffic flow analysis. Map matching is one of the key technologies. We can mine individual life pattern based on location history data, so as the taxi travel pattern. During the data processing, the data can be used to mine the floating car’s frequent pattern and build the trajectory model. It is a great improvement for data processing.
The source data involved in traffic flow analysis and prediction process consists of two parts: the floating car GPS log files and road network. The camera rear view mirror
log file is a summary of all the floating car location information sent in accordance with the time sequence. Its major fields include device terminal id, car state, position information, message record time, car velocity and direction. Road network is from maps. It consists of three granularities: road section, road, road network. If your ride didn’t come with an in-dash navigation system consider investing in a standalone GPS. Sure, your trusty smartphone could do it all, but you’re already draining its battery using it as a DJ and a camera. Plus, mapping apps are known data suckers and if you lose cell service, you could be stranded on a remote road.