School of Civil Engineering

Projects in transport engineering

Project 1. Traffic monitoring with GPS data

Supervisor:             Dr Mehmet Yildirimoglu m.yildirimoglu@uq.edu.au

In this project, we utilize a unique dataset from a large number of probe vehicles that provide a GPS signal every few seconds in a Chinese megacity: Shenzhen. Using these signals, one can estimate traffic conditions in the network and track the evolution of congestion. Traffic congestion appears with different shapes and patterns and might propagate in particular directions varying from day to day in urban networks. In this project, we will formulate the problem of finding connected congested parts of networks mathematically and observe how congestion propagates during the peak hours. These findings are critical to have a better understanding of traffic congestion in urban networks. 

Prerequisite:            Basic programming skills in MATLAB or similar (e.g., R, Python, C/C++).

 

Project 2. Modelling network dynamics through microscopic simulation models.

Supervisor:             Dr Mehmet Yildirimoglu m.yildirimoglu@uq.edu.au

Congestion in urban networks is a substantial problem. Transportation researchers and practitioners turn to complex models in order to trrack congestion propagation in these networks. A key concept enabling the development of low-complexity aggregated models of large-scale urban networks is the macroscopic fundamental diagram (MFD), which provides a unimodal, low-scatter, and demand-insensitive relationship between accumulation and trip completion flow of an urban region. This project focuses on the estimation of MFD in a real network modelled in microscopic simulation environment, and evaluates the network performance under certain management scenarios.

Prerequisite:            Basic programming skills in MATLAB or similar (e.g., R, Python, C/C++).

 

Project 3. Automated and cooperative merging at freeway on ramps

Supervisor:             Dr Mehmet Yildirimoglu m.yildirimoglu@uq.edu.au

The emerging technology of connected and automated vehicles (CAV) is gaining momentum. CAVs can improve both transportation network efficiency and safety through control algorithms that can harmonically use all existing information to coordinate the vehicles. This paper addresses the problem of optimally coordinating CAVs at merging roadways to achieve smooth traffic flow without stop-and-go driving. The strategies will be tested in microscopic simulation environment.

Prerequisite:            Basic programming skills in Python or C/C++.