Empirical analysis and simulation of the concave growth pattern of traffic oscillations

Tian, Junfang; Jiang, Rui; Jia, Bin; Gao, Ziyou; Ma, Shoufeng

doi:10.1016/j.trb.2016.08.001

Cited by 87 publications

(20 citation statements)

References 29 publications

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“…In this section, we simulate platoon car-following experiments and test which of the three possible oscillation mechanisms, namely instability, noise, or action points, or which combinations thereof, allows for reproducing the empirical findings of a concave increase of the speed fluctuation amplitude as a function of the platoon vehicle number [25]). Moreover, by simulating these mechanisms with three underlying models (the IDM, the FVDM and the PCF model), we test to which extent the results are universal, i.e., independent of the specific car-following model.…”

Section: Vehicular Traffic: Platoon Experimentsmentioning

confidence: 99%

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

Treiber

Kesting

2018

Transportation Research Part B: Methodological

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Traffic flow oscillations, including traffic waves, are a common yet incompletely understood feature of congested traffic. Possible mechanisms include traffic flow instabilities, indifference regions or finite human perception thresholds (action points), and external acceleration noise. However, the relative importance of these factors in a given situation remains unclear. We bring light into this question by adding external noise and action points to the Intelligent Driver Model and other car-following models thereby obtaining a minimal model containing all three oscillation mechanisms. We show analytically that even in the subcritical regime of linearly stable flow (order parameter ǫ < 0), external white noise leads to spatiotemporal speed correlations "anticipating" the waves of the linearly unstable regime. Sufficiently far away from the threshold, the amplitude scales with (−ǫ) −0.5 . By means of simulations and comparisons with experimental car platoons and bicycle traffic, we show that external noise and indifference regions with action points have essentially equivalent effects. Furthermore, flow instabilities dominate the oscillations on freeways while external noise or action points prevail at low desired speeds such as vehicular city or bicycle traffic. For bicycle traffic, noise can lead to fully developed waves even for single-file traffic in the subcritical regime.

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Section: Vehicular Traffic: Platoon Experimentsmentioning

confidence: 99%

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

Treiber

Kesting

2018

Transportation Research Part B: Methodological

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“…This 'concavity' in the oscillation growth was first revealed in a 25-vehicle car-following platoon experiment (Jiang et al 2014); Figure 4 shows two sample experiments. Tian et al (2016) used both Jiang's and NGSIM trajectory data and found that when the leader speed is in between 30 and 55 km/h, the oscillation growth is well approximated by a single concave function. These findings are consistent with Li and Ouyang (2011), who show that oscillation amplitude exhibits a similar growth pattern.…”

Section: Platoon Oscillation Growthmentioning

confidence: 99%

A geometric Brownian motion car-following model: towards a better understanding of capacity drop

Yuan

Laval

Knoop

et al. 2018

Transportmetrica B: Transport Dynamics

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A geometric Brownian motion car-following model: towards a better understanding of capacity drop, Transportmetrica B: Transport Dynamics, 7:1, 915-927, ABSTRACTTraffic flow downstream of the congestion is generally lower than the pre-queue capacity. This phenomenon is called the capacity drop. Recent empirical observations show a positive relationship between the speed in congestion and the queue discharge rate. Literature indicates that variations in driver behaviors can account for the capacity drop. However, to the best of authors' knowledge, there is no solid understanding of what and how this variation in driver behaviors lead to the capacity drop, especially without lane changing. Hence, this paper fills this gap. We incorporate the empirically observed desired acceleration stochasticity into a car-following model. The extended parsimonious car-following model shows different capacity drop magnitudes in different traffic situations, consistent with empirical observations. All results indicate that the stochasticity of desired accelerations is a significant reason for the capacity drop. The new insights can be used to develop and test new measures in traffic control. ARTICLE HISTORY

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“…The authors have shown that the proposed discrete IDM with the randomized desired time gap in equation (1) can replicate the synchronized traffic flow patterns. Latter on this improved model has been used by Tian et al (2016b) to simulate a concave growth of traffic oscillations. In general, it has been shown that allowing the desired time-headway to change over time (e.g.…”

Section: Introductionmentioning

confidence: 99%

Langevin method for a continuous stochastic car-following model and its stability conditions

Ngoduy

Treiber

Keyvan‐Ekbatani

et al. 2019

Transportation Research Part C: Emerging Technologies

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In car-following models, the driver reacts according to his physical and psychological abilities which may change over time. However, most car-following models are deterministic and do not capture the stochastic nature of human perception. It is expected that purely deterministic traffic models may produce unrealistic results due to the stochastic driving behaviors of drivers. This paper is devoted to the development of a distinct car-following model where a stochastic process is adopted to describe the time-varying random acceleration which essentially reflects the random individual perception of driver behavior with respect to the leading vehicle over time. In particular, we apply coupled Langevin equations to model complex human driver behavior. In the proposed model, an extended Cox-Ingersoll-Ross (CIR) stochastic process will be used to describe the stochastic speed of the follower in response to the stimulus of the leader. An important property of the extended CIR process is to enhance the non-negative properties of the stochastic traffic variables (e.g. nonnegative speed) for any arbitrary model parameters. Based on stochastic process theories, we derive stochastic linear stability conditions which, for the first time, theoretically capture the effect of the random parameter on traffic instabilities. Our stability results conform to the empirical results that the traffic instability is related to the stochastic nature of traffic flow at the low speed conditions, even when traffic is deemed to be stable from deterministic models.

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Empirical analysis and simulation of the concave growth pattern of traffic oscillations

Cited by 87 publications

References 29 publications

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations

A geometric Brownian motion car-following model: towards a better understanding of capacity drop

Langevin method for a continuous stochastic car-following model and its stability conditions

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