Transport Data Analytics With Selection of Tools and Techniques

Background

Recent advancements in Intelligent Transport Systems (ITS) have revolved the transport industry nationwide across the globe to serve the public in better way. Traffic flow congestion estimation and management on highways is always in demand worldwide across all nations for safe and hassle-free travel. Traffic congestion problem is inherent in travel time decisions and solving such problem is essential for travel guidance especially during peak hours of a journey. In early 1990’s automated traffic controller was introduced (Bauer, 2009; Batz et al., 2010, 2013) and the two broad spectrums of research in transportation that is everlasting in infrastructure planning are (i) Shortest path computation problem and (ii) Time series traffic forecasting. In spite of technology driven traffic management using IoV (Internet of Vehicles), monitoring and control of vehicular traffic remains a serious issue in real time thus, a fully automated traffic management system is not feasible. Therefore, it is essential to manage traffic flow congestion systematically as it cannot be avoided but can be mitigated (Chen et al., 2014; Wu et al., 2016). In this view, a speedup technique is necessary to bridge the gap between traffic flow estimation and path routing of vehicular traffic between origin and destination (OD). In a spatially connected network, it is essential to analyze spatial dependency of road segment with respect to upstream and downstream traffic flow thereby, road segments (a path) that are connecting the source and destination in preceding time instances are analyzed in successive time instances for re-establishing connectivity. In this way, when traffic congestion is detected, the path is reconnected based on spatial – temporal traffic information. In the interest of reducing travel delay, Spatial-TemporAl Re-connect (STAR) algorithm is proposed in which traffic flow is estimated by sequencing spatial and temporal traffic information. The traffic information in upstream and downstream road segments represents spatial traffic information, while traffic flow at each time instance forms temporal information. The interest of this work is to formulate speedup technique by estimating traffic flow at each of arterial junctions between OD pair and re-establish path connectivity of road network based on spatial and temporal traffic sequences.