Tuesday, 26 November 2019
Austroads has published a report which considers how to more accurately model heavy vehicle movements during interrupted traffic flows.
Heavy vehicle traffic on the arterial road network is increasing. The length and low acceleration capability of heavy vehicles can reduce traffic flows, especially at intersections.
Road agencies are concerned that traffic modelling software such as SIDRA, LINSIG, AIMSUN and VISSIM cannot correctly calculate the capacity of arterial roads when there are many heavy vehicles, resulting in inaccurate scenario testing and policy analysis.
Austroads commissioned a research project to develop parameters that could accurately model heavy vehicle movements during interrupted traffic flows in Australia and New Zealand.
“To collect the data to serve as a basis for parameter development, we conducted field surveys, including video surveys, at four intersections with signals and one intersection without signals in Perth and Melbourne,” said Dr Young Li, Senior Professional, Future Transport Systems, ARRB and a co-author of the report. “We surveyed vehicle length, clearance space, acceleration, start-up and saturation headways, through and turning speeds, critical gap and follow-up headway.
We monitored five heavy vehicle types: rigid trucks, single articulated trucks, B-double trucks, double road trains and triple road trains.
We developed a set of heavy vehicle parameters that can be used as a guide when road agencies are applying them to similar modelling conditions used in the study, that is, arterial roads that are relatively flat and in speed limit zones of between 60 to 70 kilometres an hour. Application of the parameters to dissimilar traffic and highway conditions should be reviewed using field observations,” Dr Li said.
Report recommendations include testing the parameters under more varied conditions and at different sites to improve confidence in them, and testing more specific vehicle types.
The methodology in this report can be used as a guide to further refine and expand the scope of the model parameters.