Resilience of Traffic Networks with Partially Controlled Routing

Bianchin, Gianluca; Pasqualetti, Fabio; Kundu, Soumya

doi:10.23919/acc.2019.8815259

Cited by 7 publications

(3 citation statements)

References 19 publications

(39 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some researchers integrated evolutionary algorithms and a novelty search to improve the performance and resilience of autonomous systems and have developed tools for this purpose [13]. The use of infrastructure-to-vehicle (I2V) communication to generate routing suggestions for drivers in transportation systems, with the goal of optimizing a measure of overall network congestion, was addressed in [14].…”

Section: Related Workmentioning

confidence: 99%

N-Versions-Based Resilient Traffic Control Systems

et al. 2022

View full text Add to dashboard Cite

Increasing the resilience of traffic control systems is a priority for many important cities worldwide. This is due to the ever-increasing problems leading to different failures in such systems. We are witnessing the intensive introduction of new technologies that automatically manage traffic but are exposed to different kinds of attacks. There are also unpredictable increases in climatic changes and the number of cars in many cities. These factors will surely enhance the failure risks of such systems and consequently increase the damage caused by traffic jams and road accidents. In this paper, we introduce a resilient traffic control system that consists of three levels: sensor control, display, and light control. Each level has three (or more) versions and a dynamic voter. Hence, the introduced system is based on diversity and redundancy (replication), called N-versions. We propose two techniques for the introduced resilient traffic control system. The first technique uses N-versions and dynamic voters to vote between the outcomes in each level. The second technique uses N-versions, dynamic voters, and acceptance testing units. The overhead in the second technique is evidently greater than that of the first technique, but its resilience is better. A fine analytical study is conducted and shows that the first technique requires only three versions to reach the optimal results, bounded by 1/15 probability of having a faulty system. The second technique leads to better results, which can determine small probabilities.

show abstract

Section: Related Workmentioning

confidence: 99%

N-Versions-Based Resilient Traffic Control Systems

et al. 2022

View full text Add to dashboard Cite

show abstract

“…To model the traffic dynamics, consider the following conservation law for the traffic density [16],…”

Section: Density Estimation Modelmentioning

confidence: 99%

Flow and Density Estimation in Grenoble Using Real Data

Rodriguez-Vega

Canudas‐de‐Wit

Fourati

2021

The 7th International Conference on Time Series and Forecasting

View full text Add to dashboard Cite

This work deals with the Traffic State Estimation (TSE) problem for urban networks, using heterogeneous sources of data such as stationary flow sensors, Floating Car Data (FCD), and Automatic Vehicle Identifiers (AVI). A data-based flow and density estimation method is presented and tested using real traffic data. This work presents a study case applied to the downtown of the city of Grenoble in France, using the Grenoble Traffic Lab for urban networks (GTL-Ville), which is an experimental platform for real-time collection and analysis of traffic data.

show abstract

“…The traffic dynamics for a given graph defines the trajectories of the density ρ(t), the incoming (or upstream) flow ϕ in (t), and the outgoing (or downstream) flow ϕ out (t), for each road at every time t. The dynamics are obtained as in Bianchin et al (2019) from the conservation law…”

Section: Traffic Modelmentioning

confidence: 99%

Average density estimation for urban traffic networks: Application to the Grenoble network

Rodriguez-Vega

Canudas‐de‐Wit

Fourati

2021

Transportation Research Part B: Methodological

View full text Add to dashboard Cite

This paper deals with the problem of the average density estimation in largescale traffic networks, without requiring to know the density of each individual road. This is achieved by the design of a reduced-order open-loop observer. In general, this type of observers require some specific graph properties, but we show that it is possible to find a virtual representation of the network that satisfies these conditions, by diving each road into a number of cells of specific length. The virtual network is shown to provide close approximations to the average density of the real system for large enough networks. Algorithms to efficiently calculate the observer parameters are proposed. This approach is based on the assumption that traffic dynamics are linear, and is applied at first in free-flow regime. Using microscopic simulations and real data we evaluate the observer performance even when congestion is present in the network.

show abstract

Resilience of Traffic Networks with Partially Controlled Routing

Cited by 7 publications

References 19 publications

N-Versions-Based Resilient Traffic Control Systems

N-Versions-Based Resilient Traffic Control Systems

Flow and Density Estimation in Grenoble Using Real Data

Average density estimation for urban traffic networks: Application to the Grenoble network

Contact Info

Product

Resources

About