A probabilistic stationary speed–density relation based on Newell’s simplified car-following model

Jabari, Saif Eddin; Zheng, Jianfeng; Liu, Henry

doi:10.1016/j.trb.2014.06.006

Cited by 70 publications

(43 citation statements)

References 51 publications

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“…where A loc a are 'local' macroscopic acceleration models [37, Chapter 9] and V a is a stationary stochastic speed-density relation. The stochasticity in Q a and V a is parametric, that is, they can be described as generalizations of equilibrium fundamental relations that capture heterogeneity in the driving population as described in [19]. For example, a generalization of Newell's simplified relation [28]:…”

Section: Stochastic Arc Dynamicsmentioning

confidence: 99%

Position weighted backpressure intersection control for urban networks

Jabari

2019

Transportation Research Part B: Methodological

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Decentralized intersection control techniques have received recent attention in the literature as means to overcome scalability issues associated with networkwide intersection control. Chief among these techniques are backpressure (BP) control algorithms, which were originally developed of for large wireless networks. In addition to being light-weight computationally, they come with guarantees of performance at the network level, specifically in terms of network-wide stability. The dynamics in backpressure control are represented using networks of point queues and this also applies to all of the applications to traffic control. As such, BP in traffic fail to capture the spatial distribution of vehicles along the intersection links and, consequently, spill-back dynamics.This paper derives a position weighted backpressure (PWBP) control policy for network traffic applying continuum modeling principles of traffic dynamics and thus capture the spatial distribution of vehicles along network roads and spill-back dynamics. PWBP inherits the computational advantages of traditional BP. To prove stability of PWBP, (i) a Lyapunov functional that captures the spatial distribution of vehicles is developed; (ii) the capacity region of the network is formally defined in the context of macroscopic network traffic; and (iii) it is proved, when exogenous arrival rates are within the capacity region, that PWBP control is network stabilizing. We conduct comparisons against a real-world adaptive control implementation for an isolated intersection. Comparisons are also performed against other BP approaches in addition to optimized fixed timing control at the network level. These experiments demonstrate the superiority of PWBP over the other control policies in terms of capacity region, networkwide delay, congestion propagation speed, recoverability from heavy congestion (outside of the capacity region), and response to incidents.

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Section: Stochastic Arc Dynamicsmentioning

confidence: 99%

Position weighted backpressure intersection control for urban networks

Jabari

2019

Transportation Research Part B: Methodological

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“…The function also determines the speed of each shockwave. In particular, the speed of startup waves is a constant related to the speed limit of the road [33], and the speed has been shown to be greater than any concentration waves [34]. Therefore, the following startup wave will definitely catch up with the previous concentration waves, which means that the influence of the incident will not be infinite.…”

Section: Derivation Of Shockwave Superpositionmentioning

confidence: 99%

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Zhang

et al. 2017

IJGI

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Traffic incidents have a broad negative impact on both traffic systems and the quality of social activities; thus, analyzing and predicting the influence of traffic incidents dynamically is necessary. However, the traditional geographic information system for transportation (GIS-T) mostly presents fundamental data and static analysis, and transportation models focus predominantly on some typical road structures. Therefore, it is important to integrate transportation models with the spatiotemporal analysis techniques of GIS to address the dynamic process of traffic incidents. This paper presents a dynamic spatiotemporal analysis model to predict the influence of traffic incidents with the assistance of a GIS database and road network data. The model leverages a physical traffic shockwave model, and different superposition situations of shockwaves are proposed for both straight roads and road networks. Two typical cases were selected to verify the proposed model and were tested with the car-following model and real-world monitoring data. The results showed that the proposed model could successfully predict traffic effects with over 60% accuracy in both cases, and required less computational resources than the car-following model. Compared to other methods, the proposed model required fewer dynamic parameters and could be implemented on a wider set of road hierarchies.

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“…The distribution of v s can be established from a sample of individual vehicles in a queue. For instance, assuming Newell's car following rule (Newell 2002) (validated in Ahn et al 2004;Chiabaut et al 2009Chiabaut et al , 2010, one can empirically establish a sample of wave speeds (see for example Chiabaut et al 2010), to which a probability distribution may be fitted or an empirical distribution may be used directly (Jabari et al 2014). Furthermore, the location of the incident, x i , is also assumed to be a random variable.…”

Section: Variability In Traffic Conditionsmentioning

confidence: 99%

Sensor placement with time-to-detection guarantees

Jabari¹,

Wynter²

2016

EURO Journal on Transportation and Logistics

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We present a novel and effective method for determining the placement of sensors so as to be able to satisfy probabilistic constraints on the time-to-detection of an incident. Indeed, with the wealth of real-time traffic data available today, an important new goal of intelligent traffic management systems is incident detection with time-to-detection guarantees, in particular on expressways with large distances between sensors. This goal drives investment decisions in new sensor deployment, hence making the topic a pressing need for traffic management. The method we provide makes use of a probabilistic formulation of traffic behavior and incident localization to determine the minimum spacing of sensors needed to achieve the time-to-detection goal with a specified probability.

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A probabilistic stationary speed–density relation based on Newell’s simplified car-following model

Cited by 70 publications

References 51 publications

Position weighted backpressure intersection control for urban networks

Position weighted backpressure intersection control for urban networks

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

Sensor placement with time-to-detection guarantees

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