Traffic state estimation using stochastic Lagrangian dynamics

Zheng, Fangfang; Jabari, Saif Eddin; Liu, Henry; Lin, Dianchao

doi:10.1016/j.trb.2018.07.004

Cited by 46 publications

(18 citation statements)

References 42 publications

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“…is one where the parameters v a , w a , and ρ a , denoting free-flow speed, backward wave speed, and jammed traffic density, respectively, are random variables. We refer to [18,47] for the properties of the stochastic dynamics that arise as a result of a parametric treatment.…”

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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“…We utilize this conversion in our paper as a simplification and for purposes of testing the performance of BP in situations where there is incomplete information. The inversion can be improved by utilizing FIGURE 3 Density-speed relationship probabilistic models [42,43], statistical/learning methods for parameter estimation [35,[44][45][46][47][48], and online update techniques [49][50][51].…”

Section: Speed To Densitymentioning

confidence: 99%

Backpressure control with estimated queue lengths for urban network traffic

Okoth²,

Jabari

2021

IET Intelligent Trans Sys

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Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, a fully connected environment in the real world is still far. This paper tests the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. BP control is combined with a speed/density field estimation module suitable for a partially connected environment. The proposed system is referred to as a BP with estimated queue lengths (BP-EQ). The robustness of BP-EQ is tested to varying levels of connected vehicle penetration, and BP-EQ is compared with the original BP (i.e. assuming accurate knowledge of traffic conditions), a real-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. These results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.

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“…Unlike the loop detector data, which is more convenient to use a Eulerian expression, the CV data can be directly used in the Lagrangian coordinates ( 15 ). Zheng et al proposed a stochastic traffic model in Lagrangian coordinates and used the Kalman filter to construct the complete trajectories using a fusion of detector and CV data ( 16 ). To deal with the nonlinear dynamic model, Xie et al proposed a generic trajectory reconstruction framework at a signalized intersection using a particle filter ( 17 ).…”

Section: Potential Applicationsmentioning

confidence: 99%

Data Infrastructure for Connected Vehicle Applications

Wang

Shen

Bezzina

et al. 2020

Transportation Research Record: Journal of the Transportation R

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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.

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Traffic state estimation using stochastic Lagrangian dynamics

Cited by 46 publications

References 42 publications

Position weighted backpressure intersection control for urban networks

Position weighted backpressure intersection control for urban networks

Backpressure control with estimated queue lengths for urban network traffic

Data Infrastructure for Connected Vehicle Applications

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