Proactive congestion management via data-driven methods and connected vehicle-based microsimulation

Abstract

Traffic congestion is a phenomenon that has been extensively explored by researchers due to its impact on reliability and safety. This research is focused on proactively detecting and mitigating congestion on freeways by fuzing conventional traffic data obtained from radar and loop detectors with newer sources, such as Bluetooth and connected vehicles (CV). Data-driven and signal-processing techniques are explored to develop algorithms that use near- or real-time traffic measurements to predict the onset and intensity level of traffic congestion. The developed algorithm can be applied to both conventional and low penetration CV-based datasets to identify four types of congestion, that is, normal, recurring, other non-recurring, and incident. This research also demonstrates the advantage of using CV-based travel time estimates to calibrate microsimulation models over fixed point-based derivations of travel time from spot speeds. Finally, a set of mitigation strategies consisting of speed harmonization and dynamic rerouting are implemented in the calibrated simulation network to demonstrate their effectiveness in proactively reducing recurring and non-recurring congestion. The final derived algorithm is effective in proactively predicting the onset of congestion and its intensity level, with an overall mean prediction error of 30.2%. A limitation to the algorithm’s methodology is that it cannot disentangle the type of congestion when two or more are occurring simultaneously and only predicts/classifies the anticipated highest level. However, this does not impair the user’s ability to readily deploy appropriate mitigation strategies to alleviate the predicted intensity of congestion.

Keywords:

• congestion
• connected vehicles
• data fusion
• microsimulation
• signal processing

Acknowledgements

The authors would like to thank THEA, Texas A&M Transportation Institute, and Dr. Vince Bernardin (Caliper Corporation) for their technical support during the project.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions (e.g., anonymized data). (Tampa CV pilot dataset: Anonymized data available from ITS Connected Vehicle Pilot Sandbox; Simulation models: Proprietary)

Additional information

Funding

This work is supported by the USDOT under the National Institute for Congestion Reduction (NICR) program [grant number: 69A3551947136] with a full match from Tampa Hillsborough Expressway Authority (THEA).