Environmental-Sensitive Generation of Street Networks for Traffic Simulations

Lindorfer, Manuel; Backfrieder, Christian; Kieslich, Christoph; Krösche, Jens; Ostermayer, Gerald

doi:10.1109/ems.2013.77

Cited by 6 publications

(1 citation statement)

References 12 publications

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“…The research on road network generation has evolved from procedural modeling to generative AI models. Traditional procedural modeling methods [2][3] typically rely on setting rules and parameters to semi-automatically generate road networks. These methods require a significant amount of manual operation and parameter tuning, and the generated road network models lack diversity, making it difficult to produce enough road networks for subsequent tasks.…”

Section: Introductionmentioning

confidence: 99%

Autonomous driving road network generation and evaluation based on generative artificial intelligence

Ye,

Li,

Yang

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The increasing number of vehicles has led to issues such as traffic congestion, air pollution, and energy consumption. Intelligent connected autonomous driving (AD) offers a solution to these transportation challenges, but it requires cooperation among dedicated AD road networks, connected cloud-based control and dispatch systems, and intelligent vehicle sensing. To harness the potential of AD, it is crucial to consider the requirements of autonomous vehicles (AVs) when planning road networks. Well-designed road networks can enhance travel experiences, reduce energy consumption, and promote urban sustainability. In order to meet the needs of AD while ensuring the operational efficiency of urban transportation networks, this paper introduces a road network evaluation framework that encompasses both static and dynamic assessment criteria, following a detailed exploration and analysis of various road network generation methods. This evaluation framework addresses the deficiency in existing road network generation methods, which lack dynamic assessment of traffic networks. To validate the proposed approach, we have chosen a road network generation method based on conditional generative adversarial network (CGAN) as an application example. Through carefully designed experiments, we confirm the feasibility and effectiveness of the proposed method for generating and evaluating road networks tailored for AD scenarios.

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Section: Introductionmentioning

confidence: 99%

Autonomous driving road network generation and evaluation based on generative artificial intelligence

Ye,

Li,

Yang

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

View full text Add to dashboard Cite

show abstract

Robustness of Intelligent Vehicular Rerouting Towards Non-ideal Communication Delay

Backfrieder

Lindorfer

Mecklenbräuker

et al. 2018

Advances in Intelligent Systems and Computing

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Incorporating Planning Intelligence into Deep Learning: A Planning Support Tool for Street Network Design

Fang

Jin

Yang

2021

Journal of Urban Technology

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Deep learning applications in shaping ad hoc planning proposals are limited by the difficulty in integrating professional knowledge about cities with artificial intelligence. We propose a novel, complementary use of deep neural networks and planning guidance to automate street network generation that can be contextaware, example-based and user-guided. The model tests suggest that the incorporation of planning knowledge (e.g., road junctions and neighborhood types) in the model training leads to a more realistic prediction of street configurations. Furthermore, the new tool provides both professional and lay users an opportunity to systematically and intuitively explore benchmark proposals for comparisons and further evaluations.

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Environmental-Sensitive Generation of Street Networks for Traffic Simulations

Cited by 6 publications

References 12 publications

Autonomous driving road network generation and evaluation based on generative artificial intelligence

Autonomous driving road network generation and evaluation based on generative artificial intelligence

Robustness of Intelligent Vehicular Rerouting Towards Non-ideal Communication Delay

Incorporating Planning Intelligence into Deep Learning: A Planning Support Tool for Street Network Design

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