Comparing & Combining Existing & Emerging Data Collection & Modeling Strategies in Support of Signal Control Optimization & Management
For decades, traffic signal management agencies have used signal timing optimization tools combined with fine-tuning of signal timing based on field observations in their updates of time-of-day signal timing plans. These traditional signal optimization methods and tools use very limited amount of data and depend on default values in the signal timing optimization/simulation tools to estimate network performance under different signal optimization strategies. In recent years, new data collection technologies are emerging including high resolution controller data, more advanced detection technologies such as video image detection that are based on vehicle tracking and possible integration with microwave detectors, automatic vehicle-based identification technologies, third party crowdsourcing data, connected vehicles, and connected automated vehicles data. The objective of the study is to propose methods and algorithms to combine data collected from existing and emerging sources with enhanced models and optimization algorithms to optimize and manage signal operations. The study developed a method for the calibration and validation of microscopic simulation models of arterial networks utilizing high-resolution controller data combined with a two-level unsupervised clustering technique and multi-objective optimization for simulation model calibration. The study demonstrated the benefits of this methodology. Based on the results from this calibration, the study compared the performance of two signal timing optimization methods based on macroscopic simulation and microscopic simulation with and without fine-tuning their parameters based on high-resolution controller data. The next step was to use a combination of two artificial intelligence approaches, namely Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to recommend modifications to signal timings during non-recurrent events such as incidents, construction, surge in demands, and device malfunctions. An important aspect of the methodology was the calibration of the utilized mesoscopic simulation-based Multi-Resolution Modeling (MRM) based on the increase in demands and travel times on alternative routes using data from third party vendors. Another important aspect was the use of microscopic simulation-based optimization of signal timing utilizing a multi-objective optimization that jointly minimizes the delays and maximizes the throughputs considering the whole intersections as well the specific impacted movements on the alternative routes.
- Record URL:
- Summary URL:
- Summary URL:
- Record URL:
-
Supplemental Notes:
- This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
-
Corporate Authors:
Florida International University, Miami
Department of Civil and Environmental Engineering
Miami, FL United States 33174University of Alabama, Birmingham
Birmingham, AL United States 35294Southeastern Transportation Research, Innovation, Development and Education Center (STRIDE)
University of Florida
365 Weil Hall
Gainesville, FL United States 32611Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Authors:
- Hadi, Mohammed
- Sisiopiku, Virginia
- Tariq, Mosammat Tahnin
- Saha, Rajib Chandra
- Wang, Tao
- Pacal, Gokmen
- Publication Date: 2021-9-14
Language
- English
Media Info
- Media Type: Digital/other
- Edition: Final Report
- Features: Figures; Maps; References; Tables;
- Pagination: 145p
Subject/Index Terms
- TRT Terms: Artificial intelligence; Data collection; Detection and identification systems; Optimization; Traffic signal timing; Traffic simulation
- Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;
Filing Info
- Accession Number: 01785218
- Record Type: Publication
- Report/Paper Numbers: M2, UAB UTC Project 930-998
- Contract Numbers: 69A3551747104
- Files: UTC, NTL, TRIS, ATRI, USDOT
- Created Date: Oct 22 2021 9:18AM