paper-with-me

홈 › Papers

Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements through Case Studies

2020-02-14 · Yu Chen, S. Yusef Shafi, Yi-fan Chen

Traffic evacuation plays a critical role in saving lives in devastating disasters such as hurricanes, wildfires, floods, earthquakes, etc. An ability to evaluate evacuation plans in advance for these rare events, including identifying traffic flow bottlenecks, improving traffic management policies, and understanding the robustness of the traffic management policy are critical for emergency management. Given the rareness of such events and the corresponding lack of real data, traffic simulation provides a flexible and versatile approach for such scenarios, and furthermore allows dynamic interaction with the simulated evacuation. In this paper, we build a traffic simulation pipeline to explore the above problems, covering many aspects of evacuation, including map creation, demand generation, vehicle behavior, bottleneck identification, traffic management policy improvement, and results analysis. We apply the pipeline to two case studies in California. The first is Paradise, which was destroyed by a large wildfire in 2018 and experienced catastrophic traffic jams during the evacuation. The second is Mill Valley, which has high risk of wildfire and potential traffic issues since the city is situated in a narrow valley.

📄 PDF Abstract BibTeX arXiv:2002.06198

Code (1)

google-research/google-research/tree/master/simulation_research/traffic 공식 구현 tf

Tasks

Management

Similar Papers 제목 키워드 기반

Crowdsourced-based Deep Convolutional Networks for Urban Flood Depth Mapping

2022-09-06 · Bahareh Alizadeh, Amir H. Behzadan

Successful flood recovery and evacuation require access to reliable flood depth information. Most existing flood mapping tools do not provide real-time flood maps of inundated streets in and around residential areas. In …

A Deep Learning Approach for Network-wide Dynamic Traffic Prediction during Hurricane Evacuation

2022-02-25 · Rezaur Rahman, Samiul Hasan

Proactive evacuation traffic management largely depends on real-time monitoring and prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic prediction is challenging due to the uncerta…

ManagementTraffic PredictionTransfer Learning

Network Wide Evacuation Traffic Prediction in a Rapidly Intensifying Hurricane from Traffic Detectors and Facebook Movement Data: A Deep Learning Approach

2023-11-16 · Md Mobasshir Rashid, Rezaur Rahman, Samiul Hasan

Traffic prediction during hurricane evacuation is essential for optimizing the use of transportation infrastructures. It can reduce evacuation time by providing information on future congestion in advance. However, evacu…

PredictionTraffic PredictionTransfer Learning

GREAT-EER: Graph Edge Attention Network for Emergency Evacuation Responses

2026-02-16 · Attila Lischka, Balázs Kulcsár arxiv

Emergency situations that require the evacuation of urban areas can arise from man-made causes (e.g., terrorist attacks or industrial accidents) or natural disasters, the latter becoming more frequent due to climate chan…

Reinforcement LearningGraph Learning

Simulating the Integration of Urban Air Mobility into Existing Transportation Systems: A Survey

2023-01-25 · Xuan Jiang, Yuhan Tang, Junzhe Cao, Vishwanath Bulusu 외

Urban air mobility (UAM) has the potential to revolutionize transportation in metropolitan areas, providing a new mode of transportation that could alleviate congestion and improve accessibility. However, the integration…

Survey