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Large-Scale Evacuation Planning and Spatial Optimization with Connected Vehicles and...
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Large-Scale Evacuation Planning and Spatial Optimization with Connected Vehicles and Big Data Sources

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 8143

Special Issue Editor

Dr. Ayberk Kocatepe

E-Mail Website
Guest Editor
Senior Modeler, Travel Modeling and Analytics, Connetics Transportation Group, FL, USA
Interests: travel demand modeling; big data analytics, machine learning; large-scale simulation and optimization of transportation networks; urban mobility and smart cities; emergency evacuation operations; traffic safety and crash analysis

Special Issue Information

Dear Colleagues,


Large-scale evacuation planning is about ensuring people’s access to critical facilities, whether they are hotels, shelters, or safe zones and havens, in times of disasters. This challenging task depends on the available transportation infrastructure as well as the overall population, travel behavior, and traffic. Such planning takes on additional complexity when multiple layers, such as warning and response time, evacuation routes, information procedures, traffic flow, logistics, and socioeconomics, are considered.


Nowadays, with the advances in technology, decision-makers are being challenged to plan and execute large-scale evacuation scenarios involving connected vehicles and big data sources such as smart phones and mobile apps. However, the volume and variety of recent traffic/socioeconomic-related data also create enormous complexities challenging decision-makers to harness these new data sources and technologies.


Connected vehicles and big data are transforming numerous sectors, but they are still in the early days of effective use in the evacuation planning. Only now are the decision-makers beginning to understand the investment in leveraging the wealth of available data that are collected/received to enhance evacuation planning, understand driver and passenger behavior, and adopt new technologies.


The main goal of this Special Issue is to bring together scholars, researchers, scientists, engineers, and administrators on a common platform to develop, design, and publish new ideas and concepts to improve large-scale evacuation planning in the jungle of connected vehicles and big data. The relevant topics include, but are not limited to, the following:

  • Integration of connected vehicles to large-scale evacuation planning;
  • Spatial optimization for evacuation modeling using big data;
  • Interoperability between connected vehicles and transportation infrastructure for evacuation planning;
  • Data analysis and machine learning for large -scale evacuation planning;
  • Interdependencies of transportation systems and new technology for better decision-making.
  • Data-driven decision support systems for evacuation planning;
  • Location-based data integration in the evacuation planning paradigm.


Dr. Ayberk Kocatepe
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • evacuation planning
  • connected vehicles
  • big data
  • spatial optimization
  • machine learning

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Published Papers (2 papers)

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Research

18 pages, 3287 KiB  
Article
Indoor Emergency Path Planning Based on the Q-Learning Optimization Algorithm
by Shenghua Xu, Yang Gu, Xiaoyan Li, Cai Chen, Yingyi Hu, Yu Sang and Wenxing Jiang
ISPRS Int. J. Geo-Inf. 2022, 11(1), 66; https://doi.org/10.3390/ijgi11010066 - 14 Jan 2022
Cited by 10 | Viewed by 3989
Abstract
The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great [...] Read more.
The internal structure of buildings is becoming increasingly complex. Providing a scientific and reasonable evacuation route for trapped persons in a complex indoor environment is important for reducing casualties and property losses. In emergency and disaster relief environments, indoor path planning has great uncertainty and higher safety requirements. Q-learning is a value-based reinforcement learning algorithm that can complete path planning tasks through autonomous learning without establishing mathematical models and environmental maps. Therefore, we propose an indoor emergency path planning method based on the Q-learning optimization algorithm. First, a grid environment model is established. The discount rate of the exploration factor is used to optimize the Q-learning algorithm, and the exploration factor in the ε-greedy strategy is dynamically adjusted before selecting random actions to accelerate the convergence of the Q-learning algorithm in a large-scale grid environment. An indoor emergency path planning experiment based on the Q-learning optimization algorithm was carried out using simulated data and real indoor environment data. The proposed Q-learning optimization algorithm basically converges after 500 iterative learning rounds, which is nearly 2000 rounds higher than the convergence rate of the Q-learning algorithm. The SASRA algorithm has no obvious convergence trend in 5000 iterations of learning. The results show that the proposed Q-learning optimization algorithm is superior to the SARSA algorithm and the classic Q-learning algorithm in terms of solving time and convergence speed when planning the shortest path in a grid environment. The convergence speed of the proposed Q- learning optimization algorithm is approximately five times faster than that of the classic Q- learning algorithm. The proposed Q-learning optimization algorithm in the grid environment can successfully plan the shortest path to avoid obstacle areas in a short time. Full article
(This article belongs to the Special Issue Large-Scale Evacuation Planning and Spatial Optimization with Connected Vehicles and Big Data Sources)
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20 pages, 52668 KiB  
Article
Integrating Evacuation and Storm Surge Modeling Considering Potential Hurricane Tracks: The Case of Hurricane Irma in Southeast Florida
by Mahyar Ghorbanzadeh, Linoj Vijayan, Jieya Yang, Eren Erman Ozguven, Wenrui Huang and Mengdi Ma
ISPRS Int. J. Geo-Inf. 2021, 10(10), 661; https://doi.org/10.3390/ijgi10100661 - 30 Sep 2021
Cited by 4 | Viewed by 3116
Abstract
Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west [...] Read more.
Hurricane Irma, in 2017, made an unusual landfall in South Florida and the unpredictability of the hurricane’s path challenged the evacuation process seriously and left many evacuees clueless. It was likely to hit Southeast Florida but suddenly shifted its path to the west coast of the peninsula, where the evacuation process had to change immediately without any time for individual decision-making. As such, this study aimed to develop a methodology to integrate evacuation and storm surge modeling with a case study analysis of Irma hitting Southeast Florida. For this purpose, a coupled storm surge and wave finite element model (ADCIRC+SWAN) was used to determine the inundation zones and roadways with higher inundation risk in Broward, Miami-Dade, and Palm Beach counties in Southeast Florida. This was fed into the evacuation modeling to estimate the regional clearance times and shelter availability in the selected counties. Findings show that it takes approximately three days to safely evacuate the populations in the study area. Modeling such integrated simulations before the hurricane hit the state could provide the information people in hurricane-prone areas need to decide to evacuate or not before the mandatory evacuation order is given. Full article
(This article belongs to the Special Issue Large-Scale Evacuation Planning and Spatial Optimization with Connected Vehicles and Big Data Sources)
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