Fast identification of critical roads by neural networks using system optimum assignment information

Ivanchev, Jordan; Zehe, Daniel; Nair, Suraj; Knoll, Alois

doi:10.1109/itsc.2017.8317626

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…They provide valuable insights into ranking critical road segments and their contributions to network flow, supporting decision-making in transportation planning and management. Despite the significant advantages of the betweenness centrality metric, computing it to rank road segments in a large network of hundreds or thousands of nodes and edges remains challenging [49].…”

Section: Literature Reviewmentioning

confidence: 99%

Edge-based graph neural network for ranking critical road segments in a network

Jana,

Malama,

Narasimhan

et al. 2023

PLoS ONE

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Transportation networks play a crucial role in society by enabling the smooth movement of people and goods during regular times and acting as arteries for evacuations during catastrophes and natural disasters. Identifying the critical road segments in a large and complex network is essential for planners and emergency managers to enhance the network’s efficiency, robustness, and resilience to such stressors. We propose a novel approach to rapidly identify critical and vital network components (road segments in a transportation network) for resilience improvement or post-disaster recovery. We pose the transportation network as a graph with roads as edges and intersections as nodes and deploy a Graph Neural Network (GNN) trained on a broad range of network parameter changes and disruption events to rank the importance of road segments. The trained GNN model can rapidly estimate the criticality rank of individual road segments in the modified network resulting from an interruption. We address two main limitations in the existing literature that can arise in capital planning or during emergencies: ranking a complete network after changes to components and addressing situations in post-disaster recovery sequencing where some critical segments cannot be recovered. Importantly, our approach overcomes the computational overhead associated with the repeated calculation of network performance metrics, which can limit its use in large networks. To highlight scenarios where our method can prove beneficial, we present examples of synthetic graphs and two real-world transportation networks. Through these examples, we show how our method can support planners and emergency managers in undertaking rapid decisions for planning infrastructure hardening measures in large networks or during emergencies, which otherwise would require repeated ranking calculations for the entire network.

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Section: Literature Reviewmentioning

confidence: 99%

Edge-based graph neural network for ranking critical road segments in a network

Jana,

Malama,

Narasimhan

et al. 2023

PLoS ONE

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“…In [32] it is shown, however, that the construction of Braess paradox free networks is NP hard and state that the paradox cannot be detected efficiently. In [33] a machine learning-inspired identification methodology for critical roads, which also include Braess roads, is described.…”

Section: B Braess Paradoxmentioning

confidence: 99%

Hard and Soft Closing of Roads Towards Socially Optimal Routing

Ivanchev

Litescu²,

Zehe

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Recent advances in Intelligent Transportation Systems, navigation tools and personal smart devices enable the development of effective mechanisms for improvement of traffic conditions. We present an information dissemination technique, which provides minimal but the right context to a population and steers the traffic system into a more efficient operational state. Selfish routing in large cities leads to a small group of roads being congested, while the rest of the road network remains underutilized [1], [2]. A routing steering mechanism is suggested, where we homogenize the traffic distribution by selectively disseminating information about the unavailability of certain roads, based on simulated outcomes of their closing. We demonstrate that the full removal of some road segments from the network can redistribute traffic in a socially beneficial way. We identify the most harmful roads and quantify their negative effect on the system. Furthermore, we introduce the concept of soft closing. Instead of informing the whole population to avoid a certain road, we inform only a portion of the drivers, further improving the network utilization. We use the city of Singapore as a case study for our traffic assignment model which we calibrate and validate using both survey and GPS tracking devices data.

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“…Blocking certain lanes for CVs, and thereby limiting the overall road capacity for human drivers is certainly a step that can have considerable ramifications, depending on the portion of AVs in the system. While at the early stages, where only few AVs are on the road, it would constitute an incentive to obtain an AV, it could also possibly generate traffic congestion and increase travel times for other vehicles [4]. As the level of AV penetration in the road transportation system is increased, the total congestion level would likely drop, however, the advantage of using AVs over CVs would gradually be diminished as well.…”

Section: Introductionmentioning

confidence: 99%

A Macroscopic Study on Dedicated Highway Lanes for Autonomous Vehicles

Ivanchev¹,

Knoll

Zehe³

et al. 2019

Lecture Notes in Computer Science

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The introduction of Autonomous Vehicles (AVs) will have far-reaching effects on road traffic in cities and on highways.The implementation of Automated Highway Systems (AHS), possibly with a dedicated lane only for AVs, is believed to be a requirement to maximise the benefit from the advantages of AVs. We study the ramifications of an increasing percentage of AVs on the traffic system with and without the introduction of a dedicated AV lane on highways. We conduct an analytical evaluation of a simplified scenario and a macroscopic simulation of the city of Singapore under user equilibrium conditions with a realistic traffic demand. We present findings regarding average travel time, fuel consumption, throughput and road usage. Instead of only considering the highways, we also focus on the effects on the remaining road network. Our results show a reduction of average travel time and fuel consumption as a result of increasing the portion of AVs in the system. We show that the introduction of an AV lane is not beneficial in terms of average commute time. Examining the effects of the AV population only, however, the AV lane provides a considerable reduction of travel time (≈ 25%) at the price of delaying conventional vehicles (≈ 7%). Furthermore a notable shift of travel demand away from the highways towards major and small roads is noticed in early stages of AV penetration of the system. Finally, our findings show that after a certain threshold percentage of AVs the differences between AV and no AV lane scenarios become negligible.

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Fast identification of critical roads by neural networks using system optimum assignment information

Cited by 3 publications

References 23 publications

Edge-based graph neural network for ranking critical road segments in a network

Edge-based graph neural network for ranking critical road segments in a network

Hard and Soft Closing of Roads Towards Socially Optimal Routing

A Macroscopic Study on Dedicated Highway Lanes for Autonomous Vehicles

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