model1-version1-complete

AHPMethod.py

@@ -0,0 +1,13 @@
+from AHP import AHP
+import numpy as np
+
+criteria=np.array([
+    [],
+    [],
+    []
+])
+
+max_eigen,CR,criteria_eigen=AHP(criteria,np.array([[0]])).cal_weights(criteria)
+
+print()
+print(max_eigen,CR,criteria_eigen)

get-nonrestrict-tourists.py

@@ -0,0 +1,25 @@
+import csv
+import numpy as np
+
+data={}
+with open('data/passenger.csv', 'r', encoding='utf-8') as f:
+    reader = csv.reader(f)
+    next(reader)  # skip header row
+    for row in reader:
+        data[int(row[0])] = np.sum(np.array(row[1:4], dtype=int))
+
+dataList = []
+for i in range(2015,2020):
+    # print(data[i]/data[i-1])
+    dataList.append(data[i]/data[i-1])
+# print(data[2023]/data[2022])
+dataList.append(data[2023]/data[2022])
+
+dataList = np.array(dataList)
+avgGrowth = np.mean(dataList)
+
+prediction = data[2023]*((avgGrowth)**(2025-2023))
+print("predicting 2025 total passengers when maintaining current taxation rate:",prediction)
+
+def predictTotalPassengers(taxationRate):
+    return (prediction/(0.946**(1.09/0.1)))*(0.946**(taxationRate/0.1))

optimizer1.py

@@ -12,7 +12,7 @@ p2=25
 p3=25
 cp=500
 c=15
-cmax=8000/0.2
+cmax=400
 cb=200
 m=0.01
 r=0.85
@@ -50,4 +50,10 @@ plt.xlabel('x')
 plt.ylabel('f(x)')
 plt.legend()
 plt.savefig('result/optimized1.png')
-plt.show()
+plt.show()
+
+from scipy.optimize import minimize
+
+res = minimize(lambda x: -f(x), 0.2)
+print(res)
+print(res.x,f(res.x))

optimizerIeco.py

@@ -0,0 +1,64 @@
+import csv
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+data={}
+with open('data/passenger.csv', 'r', encoding='utf-8') as f:
+    reader = csv.reader(f)
+    next(reader)  # skip header row
+    for row in reader:
+        data[int(row[0])] = np.sum(np.array(row[1:4], dtype=int))
+
+dataList = []
+for i in range(2015,2020):
+    # print(data[i]/data[i-1])
+    dataList.append(data[i]/data[i-1])
+# print(data[2023]/data[2022])
+dataList.append(data[2023]/data[2022])
+
+dataList = np.array(dataList)
+avgGrowth = np.mean(dataList)
+
+prediction = data[2023]*((avgGrowth)**(2025-2023))
+print("predicting 2025 total passengers when maintaining current taxation rate:",prediction)
+
+taxShift=0.03
+torShift=1-0.054
+curTaxationRate=1.0
+
+def predictTotalPassengers(taxationRate):
+    return (prediction/(torShift**(curTaxationRate/taxShift)))*(torShift**(taxationRate/taxShift))
+
+temp1 = np.log(prediction/(torShift**(curTaxationRate/taxShift)))
+temp2 = np.log(torShift)
+
+def tax(x):
+    return taxShift*(np.log(x)-temp1)/temp2
+
+RNGk = 37.648854
+RNGb = 59397421.185785
+
+temp3=(curTaxationRate*(RNGk*(predictTotalPassengers(curTaxationRate))+RNGb))
+def f1(x):
+    return 5*((tax(x))*(RNGk*x+RNGb) / temp3 -1)+1
+
+psgRange = predictTotalPassengers(np.arange(0.6,1.4,0.01))
+
+
+from scipy.optimize import minimize_scalar
+
+result = minimize_scalar(lambda x: -f1(x),bounds=(np.min(psgRange),np.max(psgRange)),method='bounded')
+print(result)
+
+plt.plot(psgRange,f1(psgRange),label='Ieco',color=colorList[0])
+plt.plot(psgRange,tax(psgRange),label='tax(x)',color=colorList[1])
+plt.xlabel('total passengers')
+plt.ylabel('Ieco / taxation rate')
+plt.plot(predictTotalPassengers(1),1,'o',label='maintain taxation rate',color=colorList[2])
+plt.plot(result.x,f1(result.x),'o',label='optimal for Ieco',color=colorList[3])
+plt.legend()
+plt.savefig('result/taxation-and-f1.png')
+plt.show()

optimizerIenv.py

@@ -0,0 +1,19 @@
+C=1
+Cb=1e7
+
+def f2(x):
+    return 1 - C*x/Cb
+
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+plt.plot(np.arange(1e6,5e6,1e5),f2(np.arange(1e6,5e6,1e5)),label='Ienv',color=colorList[0])
+plt.xlabel('total passengers')
+plt.ylabel('Ienv')
+plt.legend()
+plt.show()
+
+

optimizerIsoc.py

@@ -0,0 +1,19 @@
+C=1
+Cb=1e7
+
+def f3(x):
+    return 1 - C*x/Cb
+
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+plt.plot(np.arange(1e6,5e6,1e5),f3(np.arange(1e6,5e6,1e5)),label='Isoc',color=colorList[0])
+plt.xlabel('total passengers')
+plt.ylabel('Isoc')
+plt.legend()
+plt.show()
+
+

optimizerO.py

@@ -0,0 +1,59 @@
+import numpy as np
+import json
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+psgRange=np.arange(1e6,5e6,1e5)
+
+prediction = 2433827
+taxShift=0.03
+torShift=1-0.054
+curTaxationRate=1.0
+temp1 = np.log(prediction/(torShift**(curTaxationRate/taxShift)))
+temp2 = np.log(torShift)
+def tax(x):
+    return taxShift*(np.log(x)-temp1)/temp2
+def predictTotalPassengers(taxationRate):
+    return (prediction/(torShift**(curTaxationRate/taxShift)))*(torShift**(taxationRate/taxShift))
+RNGk = 37.648854
+RNGb = 59397421.185785
+temp3=(curTaxationRate*(RNGk*(predictTotalPassengers(curTaxationRate))+RNGb))
+def f1(x):
+    return 5*((tax(x))*(RNGk*x+RNGb) / temp3 -1)+1
+
+C2=1
+Cb2=1e8
+def f2(x):
+    return 1 - C2*x/Cb2 - 0.2
+
+C3=1
+Cb3=4e8
+def f3(x):
+    return 1 - C3*x/Cb3
+
+influenceFactor = np.array([0.21061,0.54848,0.24091])
+def f(x):
+    return f1(x)*influenceFactor[0] + f2(x)*influenceFactor[1] + f3(x)*influenceFactor[2]
+
+
+from scipy.optimize import minimize_scalar
+
+result = minimize_scalar(lambda x: -f(x),bounds=(np.min(psgRange),np.max(psgRange)),method='bounded')
+print(result)
+
+plt.plot(psgRange,f(psgRange),label='result',color=colorList[0])
+plt.plot(psgRange,f1(psgRange),label='Ieco',color=colorList[1])
+plt.plot(psgRange,f2(psgRange),label='Ienv',color=colorList[2])
+plt.plot(psgRange,f3(psgRange),label='Isoc',color=colorList[3])
+plt.plot(result.x,f(result.x),'o',label='optimal',color=colorList[4])
+
+plt.xlabel('total passengers')
+plt.ylabel('Optimized objective function')
+plt.legend()
+plt.savefig('result/O.png')
+plt.show()
+
+print("Optimal total passengers:",result.x)
+print("Optimal taxation rate:",tax(result.x))
+print("Score",f(result.x))

passenger-and-locals-relation.py

@@ -0,0 +1,47 @@
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+colorList = json.load(open('color/config.json','r'))["color"]
+
+import csv
+
+data_pasg = {}
+data_temp = {}
+
+with open('data/passenger.csv', 'r') as f:
+    reader = csv.reader(f)
+    header = next(reader)
+    data = [row for row in reader]
+
+data = np.array(data).astype(float).T
+data[0]=data[0].astype(int)
+
+xList=data[1]+data[2]+data[3]
+yList=np.full(np.shape(data[6])[0],0)
+for i in range(1,np.shape(data[6])[0]):
+    yList[i]=data[6][i]-data[6][i-1]
+
+
+print(yList)
+
+plt.scatter(xList[1:],yList[1:],color=colorList[0])
+
+from scipy.optimize import curve_fit
+def linear(x,k,b):
+    return k*x+b
+valk,valb = curve_fit(linear,xList,yList)[0]
+residuals = yList - linear(xList,valk,valb)
+ss_res = np.sum(residuals**2)
+ss_tot = np.sum((yList-np.mean(yList))**2)
+r_squared = 1 - (ss_res / ss_tot)
+print("R-squared:", r_squared)
+
+plt.plot(np.arange(0,2.5e6,1e5),linear(np.arange(0,2.5e6,1e5),valk,valb),color=colorList[1])
+
+plt.xlabel('Passenger')
+plt.ylabel('Local Population Decline')
+plt.title('Relation between Passenger and Local Population Decline')
+plt.text( 60000, 110, "k = %f\nb = %f\nR^2 = %f"%(valk,valb,r_squared),color=colorList[1])
+plt.savefig('result/passenger-and-locals-relation.png')
+plt.show()

passenger-and-revenue-relation.py

@@ -42,5 +42,5 @@ plt.xlabel('Total Passengers')
 plt.ylabel('Total Revenue')
 plt.legend()
 plt.title('Passenger-Revenue Relation')
-plt.savefig('result/passenger-and-revenue-relation.png')
+plt.savefig('result/passenger-and-revenue-relation.png',dpi=1024)
 plt.show()

passenger-and-revenue-simple-plot.py

@@ -30,5 +30,5 @@ ax2.plot(data[0],data[5], label=header[5], color=colorList[4])
 ax2.set_ylabel('Revenue')
 plt.legend(loc='upper right')
 plt.title('Passenger and Revenue Yearly')
-plt.savefig('result/passenger-and-revenue.png')
+plt.savefig('result/passenger-and-revenue.png',dpi=1024)
 plt.show()