Spatio-temporal traffic queue detection for uninterrupted flows

Bae, Bumjoon; Liu, Yuandong; Lee, Han; Bozdogan, Hamparsum

doi:10.1016/j.trb.2019.09.001

Cited by 17 publications

(8 citation statements)

References 28 publications

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“…For further assessment, the proposed algorithm is compared with the speed threshold algorithm developed in a previous study ( 35 ). The speed threshold method is often employed as a benchmarking method in traffic queue detection algorithms ( 7 ). The thresholds of this algorithm have been identified as a range between 30 mph and 40 mph for the freeways in Portland and San Diego, U.S., respectively ( 35 , 36 ).…”

Section: Resultsmentioning

confidence: 99%

“…Prior research has been conducted to identify or predict queue movement in real time with a focus on signalized intersections and freeway work zones (3)(4)(5). Limited studies have been dedicated to the more general analysis of freeway traffic queues (6,7). Most of the developed methodologies rely on fixed traffic sensor data to estimate the temporal and spatial extent of congestion and identify queue locations (7)(8)(9).…”

mentioning

confidence: 99%

“…Limited studies have been dedicated to the more general analysis of freeway traffic queues ( 6 , 7 ). Most of the developed methodologies rely on fixed traffic sensor data to estimate the temporal and spatial extent of congestion and identify queue locations ( 7–9 ). The detection accuracy of those methodologies is restricted by the spatial and temporal resolutions of traffic sensors.…”

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confidence: 99%

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Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Liu

Zhang

Lee

et al. 2021

Transportation Research Record: Journal of the Transportation R

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Traffic queues, especially queues caused by non-recurrent events such as incidents, are unexpected to high-speed drivers approaching the end of queue (EOQ) and become safety concerns. Though the topic has been extensively studied, the identification of EOQ has been limited by the spatial-temporal resolution of traditional data sources. This study explores the potential of location-based crowdsourced data, specifically Waze user reports. It presents a dynamic clustering algorithm that can group the location-based reports in real time and identify the spatial-temporal extent of congestion as well as the EOQ. The algorithm is a spatial-temporal extension of the density-based spatial clustering of applications with noise (DBSCAN) algorithm for real-time streaming data with an adaptive threshold selection procedure. The proposed method was tested with 34 traffic congestion cases in the Knoxville,Tennessee area of the United States. It is demonstrated that the algorithm can effectively detect spatial-temporal extent of congestion based on Waze report clusters and identify EOQ in real-time. The Waze report-based detection are compared to the detection based on roadside sensor data. The results are promising: The EOQ identification time of Waze is similar to the EOQ detection time of traffic sensor data, with only 1.1 min difference on average. In addition, Waze generates 1.9 EOQ detection points every mile, compared to 1.8 detection points generated by traffic sensor data, suggesting the two data sources are comparable in respect of reporting frequency. The results indicate that Waze is a valuable complementary source for EOQ detection where no traffic sensors are installed.

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Section: Resultsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Liu

Zhang

Lee

et al. 2021

Transportation Research Record: Journal of the Transportation R

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“…In the analysis of traffic congestion, scholars have built various models to reveal the essence of congestion. At the macrolevel [20][21][22][23][24], traffic congestion is generally regarded as a process of compression, blockage, and spread of traffic flow. At the microlevel [25][26][27][28][29], more attention is paid to conflicts, queuing, and easing of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evolution Mechanism of Congestion in the Entrances and Exits of Parking Facilities Based on the Improved Spatial Autoregressive Model

Deng

et al. 2021

Journal of Advanced Transportation

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The entrance and exit area of parking facilities has the characteristics of high concentration of urban traffic and prominent traffic intertwining phenomenon, which easily induces rapid congestion of mixed heterogeneous traffic at specific times and local locations and quickly spreads to the entire road section or even a larger area. In order to better understand the congestion distribution characteristics and propagation effects of access section of the parking entrance and exit from the mid and microperspective, a 5 m ∗ lane width pixel grid is used to divide the frontage road research. It also proposes a spatially robust autoregressive model and complex network tools suitable for analysis of local traffic flow to analyze it. The results show that as spatial scale increases, the congestion propagation decreases sharply and spatial adjacency within the fourth order can account for more than 90% of the propagation; the frontage road to the entrance and exit is the place where the congestion first happens, and the congestion gradually attenuates as it propagates to the inner lane and the upstream of the road segments; the lateral congestion propagation attenuates faster, so the area affected by congestion is mainly distributed in the outermost lane. This paper can provide theoretical guidance for alleviating traffic congestion in the entrance and exit areas of parking facilities and has theoretical and empirical significance.

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“…Probe vehicles or Lagrangian sensors (Herrera and Bayen (2010)) can be considered as tracking-device equipped vehicles that can report critical information such as direct travel time, speed (Ramezani and Geroliminis (2012); Koutsopoulos (2013, 2015); Hans et al (2015); Zheng et al (2018)) flow (Duret and Yuan (2017); Seo et al (2019)) or inferred delay (Florin and Olariu (2020)) and queue lengths (Bae et al (2019); Wang et al (2020)) as they traverse transportation networks. Commercial taxis, volunteers, transit buses, maintenance vehicles, commercial trucks, etc., can report their location and timestamps through cellphones and GPS devices for improved traffic operations or better planning.…”

Section: Introductionmentioning

confidence: 99%

A Combinatorial Approach for Nonparametric Short-Term Estimation of Queue Lengths using Probe Vehicles

Comert¹,

Amdeberhan²,

Begashaw³

et al. 2021

Preprint

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Traffic state estimation plays an important role in facilitating effective traffic management. This study develops a combinatorial approach for nonparametric short-term queue length estimation in terms of cycle-by-cycle partially observed queues from probe vehicles. The method does not assume random arrivals and does not assume any primary parameters or estimation of any parameters but uses simple algebraic expressions that only depend on signal timing. For an approach lane at a traffic intersection, the conditional queue lengths given probe vehicle location, count, time, and analysis interval (e.g., at the end of red signal phase) are represented by a Negative Hypergeometric distribution. The estimators obtained are compared with parametric methods and simple highway capacity manual methods using field test data involving probe vehicles. The analysis indicates that the nonparametric methods presented in this paper match the accuracy of parametric methods used in the field test data for estimating queue lengths.

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Spatio-temporal traffic queue detection for uninterrupted flows

Cited by 17 publications

References 28 publications

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Automatic Traffic Queue-End Identification using Location-Based Waze User Reports

Research on the Evolution Mechanism of Congestion in the Entrances and Exits of Parking Facilities Based on the Improved Spatial Autoregressive Model

A Combinatorial Approach for Nonparametric Short-Term Estimation of Queue Lengths using Probe Vehicles

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