TY - JOUR
T1 - Network-level turning movement counts estimation using traffic controller event-based data
AU - Xu, Peipei
AU - Li, Xiaofeng
AU - Wu, Yao Jan
AU - Noh, Hyunsoo
N1 - Funding Information: The authors gratefully acknowledge the funding and data support received from the Pima Association of Governments. We would like to thank the City of Tucson for providing the traffic controller event-based data. We would also like to thank James Tokishi, Ryan Hatch, Josh Pope, and Paul Casertano for providing valuable advice and technical support in this project. Also, we want to thank David Klebosky for collecting the infrastructure data. Special thanks to Lilly Cottam and Adrian Cottam for their assistance in English proofreading. Publisher Copyright: © 2022 Taylor & Francis Group, LLC.
PY - 2022
Y1 - 2022
N2 - Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies.
AB - Accurate turning movement counts (TMCs) data collected from regional-wide signalized intersections is critical to regional transportation planning and simulation modeling. A variety of existing traffic sensors, configured at intersections for traffic detection and signal control, can generate a large amount of real-time high-resolution event-based data from traffic controllers but few of these sensors are configured to collect TMC. This paper proposes a methodology for estimating network-level TMC using existing traffic controller event-based data without installing additional sensors. First, relevant features that can indicate traffic arrival are extracted from existing event-based data, including detector occupancy time, detector-triggered count, and green time duration. With these features, a multi-output multilayer neural network model is developed to estimate TMC. To further improve network-level TMC estimation accuracy, intersection infrastructure data and point-of-interest (POI) data are included as exogenous variables for the proposed model. Ninety-three signalized intersections are chosen from the Pima County region, Arizona, to calibrate and verify the developed model. The validation results show that the proposed model can accurately estimate TMC, as indicated by a median Root Mean Square Error (RMSE) of 41 veh/15 min, 11 veh/15 min, and 12 veh/15 min for through movement, left-turn movement, and right-turn movement volume estimation, respectively. This research provides a new possibility of utilizing existing data sources to obtain network-level TMC data without additional infrastructure and labor costs for transportation agencies.
KW - multi-output multilayer neural network model
KW - signalized intersections
KW - traffic controller event-based data
KW - turning movement counts
UR - http://www.scopus.com/inward/record.url?scp=85131156847&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85131156847&partnerID=8YFLogxK
U2 - https://doi.org/10.1080/15472450.2022.2075701
DO - https://doi.org/10.1080/15472450.2022.2075701
M3 - Article
SN - 1547-2450
JO - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
JF - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
ER -