An application of the Sequential Monte Carlo to increase the accuracy of travel time estimation in urban areas

This paper presents an application of the Sequential Monte Carlo that will help to increase the accuracy of travel time estimations in our historical data. Our estimation filter is based on the Monte Carlo Method and was modeled in such a way as to be applicable to our new kind of data in order to estimate travel time per section of road. We took into consideration the delay time while changing the sections to symbolize the delay due to traffic lights or crossroads. We worked on an urban zone of Rouen, a French city, to evaluate our application. In this application, information is collected from a specific GPS system that warns drivers of the location of both fixed and mobile speed radars. Unlike the classical GPS system, this system is characterized by the data flow frequency where the GPS data is received from the probe vehicles at one minute intervals. After receiving the data we apply the map matching method in order to correct the GPS errors. Also, our geo-referencing system has special features; each road or section of road is formed by nodes and segments, and the intersection between each section is called a PUMAS points. The PUMAS Points are GPS coordinate points on a digital map which can be propagated or moved without cost, providing total flexibility to mesh a city or rural area. Over all the performance of the filter estimator is around 85% if we set our threshold at 50%.