Machine Learning-Based Data-Driven Traffic Flow Estimation from Mobile Data

Sammanfattning: Comprehensive information on traffic flow is essential for vehicular emission monitoring and traffic control. However, such information is not observable everywhere and anytime on the road because of high installation costs and malfunctions of stationary sensors. In order to compensate for stationary sensors’ weakness, this thesis analyses an approach for inferring traffic flows from mobile data provided by INRIX, a commercial crowd-sourced traffic dataset with wide spatial coverage and high quality. The idea is to develop Artificial Neural Network (ANN)-based models to automatically extract relations between traffic flow and INRIX measurements, e.g., speed and travel time, from historical data considering temporal and spatial dependencies. We conducted experiments using four weeks of data from INRIX and stationary sensors on two adjacent road segments on the E4 highway in Stockholm. Models are validated via traffic flow estimation based on one week of INRIX data. Compared with the traditional approach that fits the stationary flow-speed relationship based on the multi-regime model, the new approach greatly improves the estimation accuracy. Moreover, the results indicate that the new approach’s models have better resistance to the drift of input variables and can decrease the deterioration of estimation accuracy on the road segment without a stationary sensor. Hence, the new approach may be more appropriate for estimating traffic flows on the nearby road segments of a stationary sensor. The approach provides a highly automated means to build models adaptive to datasets and improves estimation and imputation accuracy. It can also easily integrate new data sources to improve the models. Therefore, it is very suitable to be applied to Intelligent Transport Systems (ITS) for traffic monitor and control in the context of the Internet of Things (IoT) and Big Data.