Improving emergency evacuation planning with mobile phone location data

Timely responses to emergencies are critical for urban disaster and emergency management, particularly in densely populated mega-cities. Researchers and personnel involved in urban emergency management nowadays rely on computers to carry out complex evacuation planning. Agent-based modeling, which supports the representation of interactions among individuals and between individuals and their environments, has become a major approach to simulating evacuations wherein spatial–temporal dynamics and individual conditions need attention, such as congestion in urban areas. However, the development of optimal evacuation plans based upon agent-based evacuation simulations can be very time-consuming. In this study, to shorten the computation time to provide a timely response in an efficient way, we develop a knowledge database to store evacuation plans for typical population distributions generated by mobile phone location data. Subsequently, we use the prepared knowledge database (offline) to accelerate real-time (online) processes in searching for near-optimal evacuation plans. Our experimental result demonstrates that the evacuation plans generated with a knowledge database always outperform those that are generated without a knowledge database. Specifically, the knowledge database can reduce the computation time by an average of 96.76%, with an average fitness value improvement of 21.86%. This result confirms the effectiveness of our proposed approach in improving agent-based evacuation planning. With the rapid development of human sensor data collection and analysis, the estimation of a more accurate population distribution will become easier in future. Thus, we believe that the proposed approach of developing a knowledge database based on population distribution patterns will provide a more feasible alternative solution for evacuation planning in the practice of urban emergency management.

[1]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  Gang Ren,et al.  Evacuation Route Optimization based on Tabu Search Algorithm and Hill-climbing Algorithm☆ , 2013 .

[4]  Yu Liu,et al.  Towards Estimating Urban Population Distributions from Mobile Call Data , 2012 .

[5]  Aizhu Ren,et al.  Agent-based evacuation model of large public buildings under fire conditions , 2009 .

[6]  L. Bengtsson,et al.  Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti , 2011, PLoS medicine.

[7]  Umi Kalsom Yusof,et al.  Dynamic crowd evacuation approach for the emergency route planning problem: Application to case studies , 2018 .

[8]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[9]  Michael E. Bratman,et al.  Intention, Plans, and Practical Reason , 1991 .

[10]  Xiaoping Zheng,et al.  Modeling crowd evacuation of a building based on seven methodological approaches , 2009 .

[11]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[12]  Gunnar G. Løvås,et al.  On performance measures for evacuation systems , 1995 .

[13]  A. Tatem,et al.  Dynamic population mapping using mobile phone data , 2014, Proceedings of the National Academy of Sciences.

[14]  Wang Bing-Hong,et al.  Evacuation behaviors at exit in CA model with force essentials: A comparison with social force model , 2006 .

[15]  Helbing,et al.  Social force model for pedestrian dynamics. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[16]  Hendrik Vermuyten,et al.  A review of optimisation models for pedestrian evacuation and design problems , 2016 .

[17]  Qiuping Li,et al.  A space–time efficiency model for optimizing intra-intersection vehicle–pedestrian evacuation movements , 2013 .

[18]  Petter Holme,et al.  Predictability of population displacement after the 2010 Haiti earthquake , 2012, Proceedings of the National Academy of Sciences.

[19]  Yuan Cheng,et al.  Modeling cooperative and competitive behaviors in emergency evacuation: A game-theoretical approach , 2011, Comput. Math. Appl..

[20]  Soraia Raupp Musse,et al.  Simulating virtual crowds in emergency situations , 2005, VRST '05.

[21]  Xuan Song,et al.  Prediction of human emergency behavior and their mobility following large-scale disaster , 2014, KDD.

[22]  Masahiro Sasabe,et al.  Road network risk analysis considering people flow under ordinary and evacuation situations , 2018, Environment and Planning B: Urban Analytics and City Science.

[23]  Kusum Deep,et al.  A real coded genetic algorithm for solving integer and mixed integer optimization problems , 2009, Appl. Math. Comput..

[24]  Ling Yin,et al.  A framework of integrating GIS and parallel computing for spatial control problems – a case study of wildfire control , 2012, Int. J. Geogr. Inf. Sci..

[25]  Tian Lan,et al.  Zooming into individuals to understand the collective: A review of trajectory-based travel behaviour studies , 2014 .

[26]  Chenghu Zhou,et al.  Estimation of urban crowd flux based on mobile phone location data: A case study of Beijing, China , 2018, Comput. Environ. Urban Syst..

[27]  Dong Zhang,et al.  Study on evacuation behaviors at a T-shaped intersection by a force-driving cellular automata model , 2012 .

[28]  Tao Pei,et al.  Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records , 2018, Trans. GIS.

[29]  C. Dorso,et al.  Microscopic dynamics of pedestrian evacuation , 2005 .

[30]  Nathan Eagle,et al.  Spatiotemporal Detection of Unusual Human Population Behavior Using Mobile Phone Data , 2014, PloS one.

[31]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[32]  Yuan Yuan,et al.  Multi-Objective Path Selection Model and Algorithm for Emergency Evacuation , 2007, 2007 IEEE International Conference on Automation and Logistics.

[33]  Qingquan Li,et al.  Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data , 2016, Geographies of Mobility.

[34]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[35]  R. Ahas,et al.  Daily rhythms of suburban commuters' movements in the Tallinn metropolitan area: Case study with mobile positioning data , 2010 .

[36]  Elvezia Maria Cepolina,et al.  A methodology for defining building evacuation routes , 2005 .

[37]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[38]  T. Nagatani,et al.  Scaling behavior of crowd flow outside a hall , 2001 .

[39]  Chaogui Kang,et al.  Social Sensing: A New Approach to Understanding Our Socioeconomic Environments , 2015 .

[40]  T. Nagatani,et al.  Jamming transition in pedestrian counter flow , 1999 .

[41]  Tao Pei,et al.  Fine-grained prediction of urban population using mobile phone location data , 2018, Int. J. Geogr. Inf. Sci..

[42]  Kincho H. Law,et al.  A multi-agent based framework for the simulation of human and social behaviors during emergency evacuations , 2007, AI & SOCIETY.

[43]  Suzana Dragicevic,et al.  A spatially explicit network science model for emergency evacuations in an urban context , 2014, Comput. Environ. Urban Syst..

[44]  Andrew J. Tatem,et al.  Can Mobile Phone Data Improve Emergency Response to Natural Disasters? , 2011, PLoS medicine.

[45]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[46]  Dinesh Manocha,et al.  Reciprocal Velocity Obstacles for real-time multi-agent navigation , 2008, 2008 IEEE International Conference on Robotics and Automation.

[47]  Xinyue Ye,et al.  Editorial: human dynamics in the mobile and big data era , 2016, Int. J. Geogr. Inf. Sci..

[48]  Marco Dorigo,et al.  An Investigation of some Properties of an "Ant Algorithm" , 1992, PPSN.

[49]  S. Travis Waller,et al.  A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies , 2010 .

[50]  Shengwu Xiong,et al.  Multi-objective ant colony optimization model for emergency evacuation , 2010, 2010 Sixth International Conference on Natural Computation.

[51]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[52]  Shaowen Wang,et al.  HPABM: A Hierarchical Parallel Simulation Framework for Spatially‐explicit Agent‐based Models , 2009, Trans. GIS.

[53]  R. Hughes.  The flow of human crowds , 2003 .

[54]  Michael Batty,et al.  Ucl Centre for Advanced Spatial Analysis Working Papers Series Key Challenges in Agent-based Modelling for Geo-spatial Simulation Paper 121 -sept 07 Key Challenges in Agent-based Modelling for Geo-spatial Simulation , 2022 .

[55]  David A. Bennett,et al.  Parallel agent-based simulation of individual-level spatial interactions within a multicore computing environment , 2013, Int. J. Geogr. Inf. Sci..

[56]  F. Benjamin Zhan,et al.  Agent-based modelling and simulation of urban evacuation: relative effectiveness of simultaneous and staged evacuation strategies , 2008, J. Oper. Res. Soc..