Development of a cell transmission model using crowdsourced data for expressways

Abstract

The demand for expressways in Sri Lanka is rapidly increasing with the increasing traffic congestion in alternate roads. Traffic management strategies have to be considered with further increase in demand for expressways. Deviating from traditional expensive methods of traffic data collection, a more economical and reliable data collection method is needed for developing countries. This study aims to develop a Cell Transmission Model using crowdsourced traffic data collected by Google Distance Matrix API. An expressway section was selected and divided into number of cells. The average speed of each cell was collected from Google maps using the M-TRADA platform for every 5-minutes interval. The speed data collected were represented in a spatiotemporal graph. The cell lengths were varied to identify the optimum cell lengths for the model. It was found that the vehicle flow in the selected section is significantly lower than the capacity of the expressway. Therefore, significant speed drops are not frequent. This model is more useful for expressways when it's at higher demand. A user interface is proposed for a web application that can be developed using this model for real-time traffic monitoring purposes, which even non-expert users will be able to because of the simplicity.