VSP‐based emission factor calibration and signal timing optimisation for arterial streets

Yao, Ronghan; Sun, Li; Meng, Linghui

doi:10.1049/iet-its.2018.5066

Cited by 7 publications

(2 citation statements)

References 24 publications

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“…Since the detection of on-road high-emitting vehicles is related to the percentage concentration of normalCnormalO,normalHnormalC,normalNnormalO and vehicle specific power (VSP), three-dimensional and histogram statistics were adopted to analyze the relationships between these four parameters, as shown in Figure 2 and Figure 3. VSP is calculated by the IPCs by the follow Equation [33], VSP=v×false[a×false(1.1+9.8×θfalse)+0.132false]+0.000302×v3 where v is vehicle speed, a is vehicle acceleration and θ is the gradient.…”

Section: Preliminariesmentioning

confidence: 99%

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Xie

Zhang

et al. 2019

Sensors

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Optical remote sensing systems (RSSs) for monitoring vehicle emissions can be installed on any road and provide non-contact on-road measurements, that allow law enforcement departments to monitor emissions of a large number of on-road vehicles. Although many studies in different research fields have been performed using RSSs, there has been little research on the automatic recognition of on-road high-emitting vehicles. In general, high-emitting vehicles and low-emitting vehicles are classified by fixed emission concentration cut-points, that lack a strict scientific basis, and the actual cut-points are sensitive to environmental factors, such as wind speed and direction, outdoor temperature, relative humidity, atmospheric pressure, and so on. Besides this issue, single instantaneous monitoring results from RSSs are easily affected by systematic and random errors, leading to unreliable results. This paper proposes a method to solve the above problems. The automatic and fast-recognition method for on-road high-emitting vehicles (AFR-OHV) is the first application of machine learning, combined with big data analysis for remote sensing monitoring of on-road high-emitting vehicles. The method constructs adaptively updates a clustering database using real-time collections of emission datasets from an RSS. Then, new vehicles, that pass through the RSS, are recognized rapidly by the nearest neighbor classifier, which is guided by a real-time updated clustering database. Experimental results, based on real data, including the Davies-Bouldin Index (DBI) and Dunn Validity Index (DVI), show that AFR-OHV provides faster convergence speed and better performance. Furthermore, it is not easily disturbed by outliers. Our classifier obtains high scores for Precision (PRE), Recall (REC), the Receiver Operator Characteristic (ROC), and the Area Under the Curve (AUC). The rates of different classifications of excessive emissions and self-adaptive cut-points are calculated automatically in order to provide references for law enforcement departments to establish evaluation criterion for on-road high-emitting vehicles, detected by the RSS.

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Section: Preliminariesmentioning

confidence: 99%

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Xie

Zhang

et al. 2019

Sensors

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“…However, speed signal controls can mitigate overall pollutant emissions. Yao et al [7] refined emission coefficients for green-and red-light periods using a vehicle-specific power (VSP) model. By factoring in turning movements and varied traffic flows, they optimized signal control on major roads, targeting reduced vehicle delays and emissions.…”

Section: Introductionmentioning

confidence: 99%

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

Wang,

Xu,

2023

Sustainability

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To alleviate intersection traffic congestion and reduce carbon emissions at intersections, research on exploiting reinforcement learning for intersection signal control has become a frontier topic in the field of intelligent transportation. This study utilizes a deep reinforcement learning algorithm based on the D3QN (dueling double deep Q network) to achieve adaptive control of signal timings. Under a mixed traffic environment with connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), this study constructs a reward function (Reward—CO2 Reduction) to minimize vehicle waiting time and carbon dioxide emissions at the intersection. Additionally, to account for the spatiotemporal distribution characteristics of traffic flow, an adaptive-phase action space and a fixed-phase action space are designed to optimize action selections. The proposed algorithm is validated in a SUMO simulation with different traffic volumes and CAV penetration rates. The experimental results are compared with other control strategies like Webster’s method (fixed-time control). The analysis shows that the proposed model can effectively reduce carbon dioxide emissions when the traffic volume is low or medium. As the penetration rate of CAVs increases, the average carbon dioxide emissions and waiting time can be further reduced with the proposed model. The significance of this study lies in its dual achievement: by presenting a flexible strategy that not only reduces the environmental impact by lowering carbon dioxide emissions but also enhances traffic efficiency, it provides a tangible example of the advancement of green intelligent transportation systems.

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Review and prospect of research on road traffic flow energy model

Sun

Zhang

Chao

et al. 2022

Environ Sci Pollut Res

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VSP‐based emission factor calibration and signal timing optimisation for arterial streets

Cited by 7 publications

References 24 publications

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Automatic and Fast Recognition of On-Road High-Emitting Vehicles Using an Optical Remote Sensing System

Carbon Dioxide Emission Reduction-Oriented Optimal Control of Traffic Signals in Mixed Traffic Flow Based on Deep Reinforcement Learning

Review and prospect of research on road traffic flow energy model

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