Fine-Tuning Time-of-Day Transitions for Arterial Traffic Signals

Zheng, Jianfeng; Liu, Henry; Misgen, Steve

doi:10.3141/2488-04

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…The TS-Diagram is a convenient and popular tool for traffic engineers to evaluate performance of signal coordination, and to fine-tune signal settings if necessary. The procedure to construct TS-Diagram is based on Zheng et al (2015), and the result is shown in Fig. 15 for the selected corridor with the 5 intersections for time period 8 AM-9 AM.…”

Section: Mapementioning

confidence: 99%

Estimating traffic volumes for signalized intersections using connected vehicle data

Zheng

Liu

2017

Transportation Research Part C: Emerging Technologies

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190

113

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Mobile data GPS trajectory Traffic signal Vehicle-to-infrastructure communication Traffic volume estimation Safety Pilot Model Deployment (SPMD) project a b s t r a c tRecently connected vehicle (CV) technology has received significant attention thanks to active pilot deployments supported by the US Department of Transportation (USDOT). At signalized intersections, CVs may serve as mobile sensors, providing opportunities of reducing dependencies on conventional vehicle detectors for signal operation. However, most of the existing studies mainly focus on scenarios that penetration rates of CVs reach certain level, e.g., 25%, which may not be feasible in the near future. How to utilize data from a small number of CVs to improve traffic signal operation remains an open question. In this work, we develop an approach to estimate traffic volume, a key input to many signal optimization algorithms, using GPS trajectory data from CV or navigation devices under low market penetration rates. To estimate traffic volumes, we model vehicle arrivals at signalized intersections as a time-dependent Poisson process, which can account for signal coordination. The estimation problem is formulated as a maximum likelihood problem given multiple observed trajectories from CVs approaching to the intersection. An expectation maximization (EM) procedure is derived to solve the estimation problem. Two case studies were conducted to validate our estimation algorithm. One uses the CV data from the Safety Pilot Model Deployment (SPMD) project, in which around 2800 CVs were deployed in the City of Ann Arbor, MI. The other uses vehicle trajectory data from users of a commercial navigation service in China. Mean absolute percentage error (MAPE) of the estimation is found to be 9-12%, based on benchmark data manually collected and data from loop detectors. Considering the existing scale of CV deployments, the proposed approach could be of significant help to traffic management agencies for evaluating and operating traffic signals, paving the way of using CVs for detector-free signal operation in the future.

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Section: Mapementioning

confidence: 99%

Estimating traffic volumes for signalized intersections using connected vehicle data

Zheng

Liu

2017

Transportation Research Part C: Emerging Technologies

Self Cite

190

113

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“…Several studies have addressed the retiming of traffic signals during the last few decades; we give a detailed review of those studies in the following section. A novel trend, called Automated Traffic Signal Performance Measures advocates recording and collecting traffic signal data at a higher resolution (logs recorded with 10 Hz frequency) for improving the monitoring of traffic signals and understanding their operations (12)(13)(14). Such high-resolution (HR) signal data show great promise in modifying the way we optimize traffic signals.…”

mentioning

confidence: 99%

Modifying Signal Retiming Procedures and Policies by Utilizing High-Fidelity Modeling with Medium-Resolution Traffic Data

Dobrota

Stevanovic

Mitrović³

2021

Transportation Research Record: Journal of the Transportation R

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Current signal retiming policies are deficient in recognizing the potential of emerging traffic datasets and simulation tools to improve signal timings. Consequently, current practice advocates the use of periodically collected (low-resolution) traffic datasets and deterministic (low-fidelity) simulation tools. When deployed in the field, such signal timings require excessive fine-tuning. The most recent trends promote the use of high-resolution data collected at 10 Hz frequency. While such an approach shows promise, the process heavily relies on specific data sets that are neither widely available nor clearly integrated into the existing signal retiming practices and procedures. Interestingly, data collected in an ongoing fashion and aggregated in several-minute bins (referred to here as medium-resolution) have not received much attention in the traditional retiming procedures. This study examines traditional signal retiming practices to provide a contextual framework for the other retiming alternatives. The authors define and classify different resolutions of various traffic data used in the signal retiming process and propose a signal retiming procedure based on widely available medium-resolution data and high-fidelity simulation modeling. The authors apply the traditional (low-resolution and low-fidelity) and a proposed (medium-resolution and high-fidelity) approach to a 28-intersection corridor in southeastern Florida. Signal timing plans developed from the proposed approach outperformed current plans from field and those plans developed in the traditional approach by reducing the average delay anywhere between 6.5 and 26%. With regard to the number of stops, changes for the traditional and proposed approaches were of much lower significance when compared with the field signal timings. The traveling speeds have been increased by 4.1%–18% by the proposed signal timings and delay was not transferred onto the neighboring streets, as was the case for plans developed by the traditional approach. Development, calibration, and validation of models within the proposed approach are more time-consuming and challenging than the modeling needs of the traditional approach. One direction of future research should address the automation of calibration and validation procedures. The other direction for future research should be related to the field evaluation of proposed signal timing plans.

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Fine-tuning time-of-day partitions for signal timing plan development: revisiting clustering approaches

Chen

Zheng

Sun³

et al. 2019

Transportmetrica A: Transport Science

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Fine-Tuning Time-of-Day Transitions for Arterial Traffic Signals

Cited by 4 publications

References 7 publications

Estimating traffic volumes for signalized intersections using connected vehicle data

Estimating traffic volumes for signalized intersections using connected vehicle data

Modifying Signal Retiming Procedures and Policies by Utilizing High-Fidelity Modeling with Medium-Resolution Traffic Data

Fine-tuning time-of-day partitions for signal timing plan development: revisiting clustering approaches

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