Experimental and Empirical Investigations of Traffic Flow Instability

Jiang, Rui; Jin, Cheng-Jie; Zhang, H.M.; Huang, Yong-Xian; Tian, Junfang; Wang, Wei; Hu, Mao-Bin; Wang, Hao; Jia, Bin

doi:10.1016/j.trpro.2017.05.010

Cited by 34 publications

(11 citation statements)

References 41 publications

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“…Finally, for each paired comparison of scenarios, the percent changes in the four indicators were calculated based on the four simulations, including the average passing vehicle speed (Vavg), standard deviation of the passing vehicle speed (Vsd), LAeq, and L10-L90. The parameter Vsd reflects the traffic flow stability(Jiang et al, 2017). Larger Vsd values result in lower traffic flow stability and vice versa.…”

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confidence: 99%

Influence of urban road characteristics on traffic noise

Lü

Kang

Zhu

et al. 2019

Transportation Research Part D: Transport and Environment

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Urban road networks significantly influence traffic noise. However, existing studies have neglected the causal chain between road characteristics and traffic noise; thus, clarity on their influencing mechanisms is lacking. In this study, structural equation models were developed to explore the mediated effect of road characteristics on traffic noise through traffic flow using data from field measurement in Dalian City, China; paired comparisons of scenarios though microscopic and macroscopic traffic simulations were performed for further analysis. The results show that lane number influences traffic noise mainly in terms of the number of vehicles in a group (NVG). More lanes indicate increased traffic demand due to connected urban land, which increases the NVG and, in turn, increases noise intensity but decreases noise amplitude. The influence of road segment length (RSL) on traffic noise mainly depends on the suppression effect. A longer RSL allows for higher vehicle speeds, leading to increased noise intensity and reduced noise amplitude. This also indicates that traffic flows disperse more easily, decreasing the NVG and, in turn, reducing noise intensity and increasing noise amplitude. Road junctions (RJ), which are classified according to the presence or absence of traffic lights, have significant direct effects on both noise intensity and noise amplitude, which are both likely to increase as drivers accelerate or decelerate in the middle of the road segment. These findings provide a reference for local governments and urban planners when working to improve quality of life in urban areas.

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confidence: 99%

Influence of urban road characteristics on traffic noise

Lü

Kang

Zhu

et al. 2019

Transportation Research Part D: Transport and Environment

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“…However, the disadvantage of GPS receivers is that they only provide vehicle-specific data. Due to the requirements of equipment installation, it is difficult to collect car-following trajectory data extensively [29].…”

Section: A Related Workmentioning

confidence: 99%

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

et al. 2020

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This study proposes the design, implementation, and application of a new car-following data collecting method based on binocular stereo vision system. The car-following data consist of car-following distance, velocity, relative velocity and driving trajectory of a following vehicle. Specifically, triangulation principle is used to measure car-following distance. Robust locally weighted regression smoothing method is applied to automatically filter out the outliers caused by disturbance in real-world driving condition. Real-world experiments indicate that binocular cameras with 25mm focal length have advantages in traffic conditions with long car-following distance and high driving speed, and that binocular cameras with12mm focal length are suitable to measure on urban roads, where car-following distance is short and more driving conditions are needed to be observed. In 0-60m distance measuring range, it is verified that the mean absolute percentage error (MAPE) of binocular cameras with 25mm focal length is 3.2% and the MAPE of binocular cameras with 12mm focal length is 6.3%. The data collecting system helps to advance car following models due to its high accuracy and effective cost. INDEX TERMS Binocular stereo vision, car-following, data collecting method, robust locally weighted regression.

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“…To measure the effect of AVs on dampening traffic oscillations in the stochastic dynamic process, traffic speed was chosen as the performance indicator, as suggested by Jiang et al ( 32 ), who demonstrate via experiments that traffic speed is a better indicator of traffic instability than vehicle spacing. Four performance metrics are defined, namely, the ensemble-averaged spatial-temporal average speed (EnAve), the ensemble-averaged standard deviation of all vehicles over all time intervals (EnStd), the standard deviation of EnAve, and the standard deviation of the EnStd.…”

Section: Numerical Experimentsmentioning

confidence: 99%

Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles

Zheng

et al. 2019

Transportation Research Record

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The phenomenon of stop-and-go waves is frequently observed in congested traffic. With the development of connected and autonomous vehicle (CAV) technologies, it is possible to reduce traffic oscillation via control of CAVs in a mixed traffic flow with both human drivers and autonomous vehicles (AVs). This paper introduces a stochastic Lagrangian model which is capable of simulating stop-and-go traffic considering the heterogeneity of drivers. The sample paths of the stochastic process are smooth without aggressive oscillation. The model is further extended to the mixed traffic flow condition, considering stochastic human driving behavior and deterministic behavior of AVs. With the proposed model, the variation of performance of AV control strategies can be quantified in addition to the average performance. A numerical example with a single lane circular road is used to investigate the impact of the AV control strategy on mitigating stop-and-go waves. Both qualitative and quantitative results show that the phenomenon of stop-and-go waves can be reduced significantly with only one AV, while the increase of AVs from 10% (two AVs) to 50% (10 AVs) offers just marginal improvement in relation to the ensemble-averaged performance and 95% confidence interval of the ensemble-averaged performance. The proposed simulation approach based on the stochastic Lagrangian model can effectively investigate the impact of AV control strategies on traffic oscillation, considering in particular the uncertainty of human driver behavior.

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Experimental and Empirical Investigations of Traffic Flow Instability

Cited by 34 publications

References 41 publications

Influence of urban road characteristics on traffic noise

Influence of urban road characteristics on traffic noise

A Car-Following Data Collecting Method Based on Binocular Stereo Vision

Traffic Oscillation using Stochastic Lagrangian Dynamics: Simulation and Mitigation via Control of Autonomous Vehicles

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