An Investigation of Real-Time Dynamic Data Driven Transportation Simulation

Widespread deployment of sensors in roadways and vehicles is creating new challenges in effectively exploiting the wealth of real-time transportation system data. However, the precision of the real-time data varies depending on the level of data aggregation. For example, minute-by-minute data are more precise than hourly average data. This paper explores the ability to create an accurate estimate of the evolving state of transportation systems using real-time roadway data aggregated at various update intervals. It is found that simulation based on inflow data aggregated over a short time interval is capable of providing a superior representation of the real world over longer aggregate intervals. However, the perceived improvements are minimal under congested conditions and most pronounced under un-congested conditions. In addition, outflow constraints should be considered during congested flow periods, otherwise significant deviation from the real world performance may arise