Learning Traffic Flow Dynamics Using Random Fields

Jabari, Saif Eddin; Dilip, Deepthi Mary; Lin, Dianchao; Thodi, Bilal Thonnam

doi:10.1109/access.2019.2941088

Cited by 20 publications

(13 citation statements)

References 40 publications

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“…3). This partially explains why the spatial distribution of probe vehicles along a road section can affect state estimation accuracy [34], [35]. The output of the down-sampling Sparse input trajectory Before sub-sampling After sub-sampling…”

Section: B Representation Of Speed Reconstruction Modelmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

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We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels.

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Section: B Representation Of Speed Reconstruction Modelmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

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“…Hence, when the CV penetration rate is low, we have a higher probability of having no CVs right before the stop line. This is why we see more sudden changes in Figure 2 It is worth mentioning that there also exist many other sophisticated queue length/size estimation and traffic state estimation methods in the literature [31][32][33][34][35][36][37]. We chose [30] here because of its simplicity and its ability to estimate the traffic state along the entire length of the road (not just the segment with queued vehicles near the stop line) under both light and heavy traffic conditions.…”

Section: Estimate Speedmentioning

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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“…To incorporate the flow conservation constraints in the super-graph, we first introduce non-negative slack variables, denoted by s t i for each cell i, which represents the number of vehicles in cell i that do not advance to neighboring cell j during t. Hence, they are bounded from above by the maximum occupancy of cell i: (17) and the flow restrictions (7) becomes an equality:…”

Section: B Super Graph: a Static Representation Of Traffic Dynamicsmentioning

confidence: 99%

“…In summary the network dynamics can be represented by a system of mass balance equations along with capacity constraints (8), (17), and (21).…”

Section: B Super Graph: a Static Representation Of Traffic Dynamicsmentioning

confidence: 99%

“…Transportation networks utilizing computing and wireless/ wired networking are known as Intelligent Transportation Systems (ITS). Such systems implement smart algorithms to efficiently coordinate and monitor traffic [13]- [17]. The use of computers and computer networks in the traffic infrastructure introduced several benefits, including intelligent traffic…”

Section: Introductionmentioning

confidence: 99%

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Noticeability Versus Impact in Traffic Signal Tampering

Thodi¹,

Mulumba²,

Jabari

2020

IEEE Access

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This paper investigates the vulnerability of urban traffic networks to cyber attacks on traffic lights. We model traffic signal tampering as a bi-objective optimization problem that simultaneously seeks to reduce vehicular throughput in the network over time (maximize impact) while introducing minimal changes to network signal timings (minimize noticeability). We represent the Spatio-temporal traffic dynamics as a static network flow problem on a time-expanded graph. This allows us to reduce the (non-convex) attack problem to a tractable form, which can be solved using traditional techniques used to solve linear network programming problems. We show that minor but objective adjustments in the signal timings over time can severely impact traffic conditions at the network level. We investigate network vulnerability by examining the concavity of the Pareto-optimal frontier obtained by solving the bi-objective attack problem. Numerical experiments are carried to illustrate the types of insights that can be extracted from the Pareto-optimal frontier. For instance, our experiments suggest that the vulnerability of a traffic network to signal tampering is independent of the demand levels. INDEX TERMS Network vulnerability, signal tampering, traffic flow dynamics, urban traffic networks, adversarial attacks, cybersecurity.

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Learning Traffic Flow Dynamics Using Random Fields

Cited by 20 publications

References 40 publications

Traffic Data Imputation Using Deep Convolutional Neural Networks

Traffic Data Imputation Using Deep Convolutional Neural Networks

Backpressure control with estimated queue lengths for urban network traffic

Noticeability Versus Impact in Traffic Signal Tampering

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