Data Analysis and State Estimation using WiFi-based Traffic Sensor Technology

Source: https://wsai.iitm.ac.in/projects/data-analysis-and-state-estimation-using-wifi-based-traffic-sensor-technology/ Parent: https://wsai.iitm.ac.in/projects/

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Data Analysis and State Estimation using WiFi-based Traffic Sensor Technology

Investigators

Bhargava Rama Chilukuri Lelitha Devi Vanajakshi

Tags

traffic management data analytics sensor-based data collection

With increasing traffic congestion and the associated pollution in the Indian cities, there is a greater interest to develop smart traffic management strategies. These strategies will only be effective when the traffic state is estimated accurately and the latter is only possible with reliable traffic stream data. Traditional traffic data collection technologies such as location-based sensors (e.g. video cameras, inductive loops, Bluetooth, etc.) and mobile sensors (e.g. GPS) have well-known limitations. While the former gives point-space and time-continuous traffic stream data, the latter give space-time-continuous data of only the vehicle on which the sensor is mounted (traffic stream characteristics need to be estimated based on limited vehicle data). To overcome these limitations, this research proposes to a) study the feasibility of using WiFi sensors for traffic data collection, b) field deploy WiFi sensors along a selected corridor to collect reliable traffic stream data, c) develop a repository of the corridor-level traffic data and do data analytics, and d) develop a methodology for delay characterization at intersections/junctions in the corridor. This research has an enormous potential to positively impact the state-of-the-practice data collection since the proposed sensor technology will collect richer traffic data compared to the traditional methods with lesser infrastructure. The unique MAC IDs, detected with the proposed sensor, matched at different locations will facilitate extracting reliable travel patterns and can be used to characterize delay for real-time traffic management applications.