CONNECTED VEHICLE Q&A
Data dreamer One of the promises of connected vehicle data is that it could do away with the need for any other type of road sensor. That dream has not yet been realised, but US researcher Hesham A Rakha has been investigating machine learning algorithms that could one day help to do just that
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Words | Anthony James
nowing how many cars are on a given part of the road is critical for traffic management, especially at intersections with traffic lights, where estimates of traffic density are used as inputs for the timing of traffic signals. In theory, when every vehicle is a smart vehicle, able to communicate with infrastructure, estimating traffic density will be far easier and more accurate. It will also be cheaper, as modellers will no longer have to rely on expensive embedded loop or other stationary sensors to capture vehicle data. With this future scenario in mind, a team from the Urban Mobility & Equity Center – a federally funded research consortium in the USA, led by Morgan State University, with support from the University of Maryland and Virginia Tech – has investigated and tested several approaches to estimating traffic stream density on roads with traffic signals using data from connected vehicles, in downtown Blacksburg, Virginia, USA. The findings have been published in a report, entitled, Estimating Traffic Stream Density Using Connected Vehicle Data. The report found that the most accurate estimates resulted from using a linear Kalman filter – a type of algorithm. The research also involved determining the level of market penetration of connected
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Intertraffic World | Annual Showcase 2022
vehicles, which the researchers did by developing a machine-learning model. As that level increases, so too will the accuracy of the estimates, the report found. Intertraffic World spoke to Hesham A Rakha, the principal investigator on the research project, which was completed in April 2021, to discover more.
What is the problem you are trying to solve?
Traffic stream density is a critical input to various advanced traffic management systems. However, measuring traffic density in the field is difficult since it is categorized as a spatial measurement. In our work, several estimation approaches are developed to provide real-time estimates of the traffic stream density on signalised approaches using connected vehicle (CV) data. The estimates are then input to adaptive traffic signal controllers to improve intersection performance, namely its efficiency, mobility, and environmental impacts. Past research has used different data sources, such as stationary sensors (e.g. loop detectors) or fused data (combining two different sources of data) to estimate the traffic stream density along traffic roadways. However, stationary sensors suffer from poor detection accuracy and have high installation and maintenance costs. The fusion of data from multiple sources requires considerable
