The deployment of vehicle location services generates increasingly massive vehicle
trajectory data, which incurs high storage and transmission costs. A range of studies target …

In this thesis, we study the problem of choosing among partitioning strategies in distributed
graph processing systems. To this end, we evaluate and characterize both the performance …

Accurate network-wide real time traffic forecasting is essential for next generation smart
cities. In this context, we study a novel and complex traffic data set and explore the potential …

Empirical studies have demonstrated the existence of a well-defined Network Macroscopic
Fundamental Diagram (NMFD). Well-shaped NMFD, however, may not be a universal law …

Road traffic networks are rapidly growing in size with increasing complexities. To simplify
their analysis in order to maintain smooth traffic, a large urban road network can be …

The urban road networks undergo frequent traffic congestions during the peak hours and
around the city center. Capturing the spatiotemporal evolution of the congestion scenario in …

Large‐scale urban networks are usually loaded heterogeneously with a polycentric
congestion pattern, resulting in a highly scattered network macroscopic fundamental …

The spatial partitioning problem of urban traffic network sub-regions has been mainly
researched in static mode by considering traffic conditions at a certain time. Nevertheless …

Urban traffic management is increasingly critical in the future to ensure the livability,
efficiency, and sustainability of the city. Urban road network partition is a fundamental step in …

Deep neural networks are being increasingly used for short-term traffic flow prediction,
which can be generally categorized as convolutional (CNNs) or graph neural networks …