Challenges and Opportunities of Using Data Fusion Methods for Travel Time Estimation

Collection and analysis of traffic data are the most critical challenges in traffic control in transportation networks. The dramatic growth of new technologies such as tiny devices equipped with elements of computing, sensors, and actuators in the smart environment of the internet of things (IoT) has led to the collection of large volumes of data. Identifying and combining data obtained from various smart devices plays a key role in data processing. Hence, data fusion is required to collect and extract usable data from numerous sources. This study discusses the challenges and opportunities of using data fusion to estimate travel time under wireless sensor networks (WSNs) in the Intelligent Transportation Systems (ITS). For this purpose, the three main categories of data fusion, namely the probability-based method, the artificial intelligence-based method, and the evidence theory-based method, were considered. Consequently, discussing the advantages and drawbacks of data fusion techniques can give researchers a better perspective of travel time estimation with better accuracy.