A machine learning-based approach to time-dependent shortest path queries

• المؤلف الرئيسي: Wei, Yumou
• مؤلفون آخرون: Xiao Xiaokui
• التنسيق: Final Year Project
• اللغة: English
• منشور في: 2017
• الموضوعات: DRNTU::Engineering::Computer science and engineering
• الوصول للمادة أونلاين: http://hdl.handle.net/10356/70473
• المؤسسة: Nanyang Technological University

Road traffic is known to be time-dependent. The travel time of a road varies at different times of the day. Many algorithms have been proposed for finding a shortest path in a time-dependent road network. In this project, I explored an alternative approach that leveraged on GPS trajectories collected from thousands of taxis. Each GPS trajectory was mapped to a set of real road segments. An abstract landmark graph was built to represent the city’s road network and a machine learning-based approach was proposed to estimate the travel time of each edge. The estimates made by this approach were compared against real-time estimates made by existing online mapping services to evaluate its accuracy. A modified Dijkstra’s algorithm was presented to calculate a shortest path in a time-dependent landmark graph, based on the travel time estimates.