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Recently Published
Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested networkSimulation Modelling Practice and Theory 86:169-186
https://www.sciencedirect.com/science/article/pii/S1569190X18300558 Abstract. Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes. |
We develop advanced dynamic models of large-scale networks, specifically simulation-based dynamic traffic assignment with advanced features such as road pricing and perimeter control.
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Changing Melbourne 2.0 uses visual analytics to demonstrate how socio-demographic characteristics of Melbourne are changing over time using data from ABS Census 2006 and 2011.
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800,000 people moving around Melbourne everyday. This project provides insights into the spatial-temporal distribution of pedestrian activities and crashes in Melbourne.
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This project builds modeling tools to evaluate the impact of different congestion pricing schemes on Melbourne network traffic to inform transport pricing policies.
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In this project, we visualize 5 years' worth of bicycle crashes combined with the Strava Global Heatmap to provide insights into bicycle safety in Melbourne.
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Places by Metro is an interactive application reinventing public transport users experience, connecting businesses to travelers across Melbourne.
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This project aims to develop a new model to measure housing and transport affordability in Australia, with greater Melbourne selected as the pilot study area.
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Large scale demand for travel between origins and destinations in a city can be visualized and analyzed as a complex network.
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The map visualizes the spatial distribution of Average Daily Truck Traffic (ADTT) in Melbourne in 2012.
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