Rapid Mobility Planning
Origin to Destination (OD) matrices that describe the travel demand patterns among different zones in the transport network serve as the main input to traffic assignment models. Ground truth of OD matrices for large scale road networks cannot be directly measured on a day-to-day basis. Traditionally, representative OD matrices are obtained from Household travel surveys, which are both time consuming and expensive to conduct at a large scale. A few alternative data sources such as loop detectors, call data records, Bluetooth data, and probe vehicles have been used by researchers to “infer” OD patterns. However, such datasets have the limitation of spatial coverage, location accuracy, and transferability.
This project involves developing Rapidex, a novel OD demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables fetching travel time data from pervasive traffic data providers, such as TomTom and Google. Then Rapidex uses a machine learning model to derive the OD demand pattern. The tool produces critical outputs such as traffic volumes, road travel times, OD travel times, average trip length and duration, and congestion levels. Finally, changes to the network and/or demand data can be made, and the subsequent impacts on performance metrics can be identified using Rapidex.
The pervasive traffic platforms have wide spatial coverage, fine temporal resolution and are cost-effective compared to traditional data sources. Moreover, the proposed approach is scalable, transferable, and solves the OD estimation problem of any given city within few hours, thus enabling rapid decision making for strategic planning and operational purposes. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.
Paper: Waller, S.T.; Chand, S.; Zlojutro, A.; Nair, D.; Niu, C.; Wang, J.; Zhang, X.; Dixit, V.V. Rapidex: A Novel Tool to Estimate Origin–Destination Trips Using Pervasive Traffic Data. Sustainability 2021, 13, 11171. https://doi.org/10.3390/su132011171
For more information
Prof S. Travis Waller – s.waller@unsw.edu.au
Dr Sai Chand – saichand.transport@gmail.com
Prof Vinayak Dixit – v.dixit@unsw.edu.au
