Empirical analysis of the causes of stop‐and‐go waves at sags

Ros, B. Goñi; Knoop, Victor L.; Arem, Bart van; Hoogendoorn, Serge P.

doi:10.1049/iet-its.2013.0102

Cited by 18 publications

(10 citation statements)

References 14 publications

(45 reference statements)

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“…Vehicle trajectory data from the Yamato sag (Tomei Expressway, Japan) are available (see reference [7]). However, data from additional sites will also be necessary.…”

Section: Discussionmentioning

confidence: 99%

“…In general, the bottleneck is located 500 to 1000 m downstream of the bottom of the sag [3]. The factors reducing the capacity of sags seem to be related primarily to two changes in car-following behavior that occur when vehicles go through the vertical curve: i) drivers tend to reduce speed [1,3,4]; and ii) drivers tend to keep longer headways than expected given their speed [5,6,7]. These changes in car-following behavior seem to be unintentional [6].…”

Section: Causes Of Congestion At Sagsmentioning

confidence: 99%

“…Consequently, sags often cause congestion in high traffic demand conditions [1]. The main reason why the freeway capacity decreases at sags is related to local changes in car-following behavior [3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Traffic at Sags

et al. 2014

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Freeway capacity decreases at sags due to local changes in car-following behavior. Consequently, sags are often bottlenecks in freeway networks. This article presents a microscopic traffic model that reproduces traffic flow dynamics at sags. The traffic model includes a new car-following model that takes into account the influence of freeway gradient on vehicle acceleration. The face-validity of the traffic model is tested by means of a simulation study. The study site is a sag of a Japanese freeway. The simulation results are compared to empirical traffic data presented in previous studies. We show that the model is capable of reproducing the key traffic phenomena that cause the formation of congestion at sags, including the lower capacity compared to normal sections, the location of the bottleneck around the end of the vertical curve, and the capacity drop induced by congestion. Furthermore, a sensitivity analysis indicates that the traffic model is robust enough to reproduce those phenomena even if some inputs are modified to some extent. The sensitivity analysis also shows what parameters need to be calibrated more accurately for real world applications of the model.

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“…Vehicle trajectory data from the Yamato sag (Tomei Expressway, Japan) are available (see reference [7]). However, data from additional sites will also be necessary.…”

Section: Discussionmentioning

confidence: 99%

Section: Causes Of Congestion At Sagsmentioning

confidence: 99%

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Modeling Traffic at Sags

et al. 2014

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“…First, drivers tend to reduce speed (1,4). Second, drivers tend to keep longer distance headways than expected given their speed (12,13). These local changes in driving behavior seem to be caused by the fact that drivers are unable to accelerate sufficiently and compensate for the increase in resistance force resulting from the increase in slope (14).…”

Section: Sags As Freeway Bottlenecksmentioning

confidence: 99%

Mainstream Traffic Flow Control at Sags

Ros

Knoop

Arem

et al. 2014

Transportation Research Record

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Sags are freeway sections along which the gradient changes significantly from downwards to upwards. The capacity of sags is considerably lower than the capacity of normal sections. Consequently, sags are often freeway bottlenecks. Recently, several control measures have been proposed to improve traffic flow efficiency at sags. Those measures generally aim to increase the capacity of the bottleneck and/or to prevent traffic flow perturbations in nearly-saturated conditions. This paper presents an alternative type of measure based on the concept of mainstream traffic flow control. The proposed control measure regulates the traffic density at the bottleneck area in order to keep it below the critical density, hence preventing traffic from breaking down while maximizing outflow. Density is regulated by means of a variable speed limit section that regulates the inflow to the bottleneck. Speed limits are selected based on a feedback control law. We evaluate the effectiveness of the proposed control strategy by means of a simple case study using microscopic traffic simulation. The results show a significant increase in bottleneck outflow, particularly during periods of high demand, which leads to a considerable decrease in total delay. This finding suggests that mainstream traffic flow control strategies using variable speed limits have the potential to substantially improve the performance of freeway networks containing sags.

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“…Freeway performance is often hindered by the 'capacity drop' phenomenon, where discharge rates from fixed bottlenecks (or moving jams) diminish significantly (10-30%) upon onset of queue (Banks, 1991;Bertini and Leal, 2005;Cassidy and Bertini, 1999;Hall and Hall, 1990) presumably due to lane changing (Cassidy and Rudjanakanoknad, 2005;Laval and Daganzo, 2006;Leclercq et al, 2011;Zhao et al, 2013), time-dependent driver car-following characteristics (Chen et al, 2014b;Goñi Ros et al, 2014), or rubbernecking induced by incidents (Knoop et al, 2008). Variable speed limit (VSL) control, developed originally to improve safety, has emerged as a promising method to mitigate capacity drop.…”

Section: Introductionmentioning

confidence: 99%

Variable speed limit control at fixed freeway bottlenecks using connected vehicles

Han

Chen

Ahn

2017

Transportation Research Part B: Methodological

108

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The connected vehicle (CV) technology is applied to develop VSL strategies to improve bottleneck discharge rates and reduce system delays. Three VSL control strategies are developed with different levels of complexity and capabilities to enhance traffic stability using: (i) only one CV (per lane) (Strategy 1), (ii) one CV (per lane) coupled with variable message signs (Strategy 2), and (iii) multiple CVs (Strategy 3). We further develop adaptive schemes for the three strategies to remedy potential control failures in real time. These strategies are designed to accommodate different queue detection schemes (by CVs or different sensors) and CV penetration rates. Finally, probability of control failure is formulated for each strategy based on the stochastic features of traffic instability to develop a general framework to (i) estimate expected delay savings, (ii) assess the stability of different VSL control strategies, and (iii) determine optimal control speeds under uncertainty. Compared to VMS-only strategies, the CVbased strategies can effectively impose dynamic control over continuous time and space, enabling (i) faster queue clearance around a bottleneck, (ii) less restrictive control with higher control speed (thus smoother transition), and (iii) simpler control via only one or a small number of CVs.

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Empirical analysis of the causes of stop‐and‐go waves at sags

Cited by 18 publications

References 14 publications

Modeling Traffic at Sags

Modeling Traffic at Sags

Mainstream Traffic Flow Control at Sags

Variable speed limit control at fixed freeway bottlenecks using connected vehicles

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