The new era of sharing information and "big data" has raised our expectations to make mobility more predictable and controllable through a better utilization of data and existing resources. The realization of these opportunities requires going beyond the e ...
Complex network theory describes network performance mostly based on topological characteristics, like betweenness centrality. This work integrates concepts from complex networks and traffic engineering. We propose a new measure for spatial networks and, i ...
Accurate traffic density estimations is essential for numerous purposes like the developing successful transit policies or to forecast future traffic conditions for navigation. Current developments in the machine learning and computer systems bring the tra ...
Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time ...
In this paper, we macroscopically describe the traffic dynamics in heterogeneous transportation urban networks by utilizing the Macroscopic Fundamental Diagram (MFD), a widely observed relation between network-wide space-mean flow and density of vehicles. ...
Travel time is considered as one of the most important performance measures for roadway systems, and dissemination of travel time information can help travelers to make reliable travel decisions such as route choice or time departure. Since the traffic dat ...
This research deals with self-supplied navigation systems (SNS), a combination of the technology of gathering travel time using floating car data (FCD) and dynamic route guidance (DRG). Using similar on-board equipment, these two systems can easily be comb ...
