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原文传递 Network-level turning movement counts estimation using traffic controller event-based data

题名：	Network-level turning movement counts estimation using traffic controller event-based data
正文语种：	eng
作者：	Peipei Xu;Xiaofeng Li;Yao-Jan Wu;Hyunsoo Noh
作者单位：	Department of Civil and Architectural Engineering and Mechanics The University of Arizona Tucson AZ USA;Department of Civil and Architectural Engineering and Mechanics The University of Arizona Tucson AZ USA;Department of Civil and Architectural Engineering and Mechanics The University of Arizona Tucson AZ USA;Pima Association of Governments Tucson AZ USA
关键词：	multi-output multilayer neural network model; signalized intersections; traffic controller event-based data; turning movement counts
摘要：	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/15min, 11 veh/15min, and 12 veh/15min 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.
出版年：	2023
期刊名称：	Journal of Intelligent Transportation Systems
卷：	27
期：	1/6
页码：	677-691