TABLE II. Summary of Works for Crowd Evacuation (Acronyms Used in the Table- : Cellular IoT, : Common Objects in Context, : Long-Short-Term-Memory, : Recurrent CNN, : Structural Similarity Index).

Category	Reference	Target issue	Technology used	Hardware/API used	ML involved	Case studies
IoT	[96], 2018	Crowd Evacuation	CLOTHO (APA and APA-RF)	None	No	Chemical plant in Nanjing, China
	[103], 2018	CIoT	UAVs	None	No	Snow Avalanches
Android based	[104], 2012	Evacuation program	OpenStreetMap	DMS	No	Bangladesh, India
	[105], 2014	Rescue team tracking	Algorithm based on location	GPS-Receiver	No	Indonesia
	[106], 2016	Finding a safezone route	OpenStreetMap	PostGIS database	No	Makati, Phillipines (Earthquakes)
UAV	[107], 2019	Priority based route selection	Clustering (Bird flocking)	FANETS	No	None
	[72], 2019		Graph theory, Euler cycle and integer programming	None	No	Hospital Pavia Arecibo
ML	[108], 2015	Activating Contraflows	Decision trees	Weka (Data mining software)	Yes	Hurricanes
	[102], 2013	Prevent crowd disasters	SVM, Linear regression and Gaussian model	GPS Sensors	Yes	San Francisco
	[81], 2016	Determine an evacuation route	BB-SVM, Dijkstra’s algorithm and Depth first search (DFS)	ERESS	Yes	Kansai university
	[36], 2019		Deep Neural networks	Raspberry Pi-3 and spectrum analyzer	Yes	Ritsumeikan university
	[48], 2019		K-medoids and Reinforcement learning	None	Yes	Office scenario
	[109], 2017		LSTM model	Spatio and temporal features	Yes	Kumamoto earthquakes
	[110], 2014		Naive Bayes	None	Yes	Fire hazard
	[19], 2018		SVM and Fuzzy logic	None	Yes	Hajj (A Muslim pilgrimage event)
	[45], 2018		K-means and hierarchial clustering	Alarms	Yes	An office building
	[111], 2016	Classification of crowd situation	Deep CNN and Random Forest	None	Yes	UMN, UCSD and Pets2009
	[46], 2019		CNN classifier and K-means	None	Yes	Video data
	[50], 2018	Planning evacuation route	Reinforcement learning	None	Yes	Hong Kong fire outbreak