Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/traffic_engineering.py @ 1192:606817bc31e8
bug correction
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 29 Jun 2022 23:02:47 +0200 |
parents | 5874ece33637 |
children | fa07a78b29f6 |
rev | line source |
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1 #! /usr/bin/env python |
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2 ''' Traffic Engineering Tools and Examples''' |
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3 |
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4 from math import ceil |
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5 from numpy import e, log, arange |
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6 |
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7 from matplotlib.pyplot import figure,plot,xlabel,ylabel, xlim, ylim |
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8 |
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9 from trafficintelligence import prediction |
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10 |
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11 ######################### |
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12 # Simulation |
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13 ######################### |
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14 |
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15 def generateTimeHeadways(meanTimeHeadway, simulationTime): |
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16 '''Generates the time headways between arrivals |
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17 given the meanTimeHeadway and the negative exponential distribution |
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18 over a time interval of length simulationTime (assumed to be in same time unit as headway''' |
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19 from random import expovariate |
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20 headways = [] |
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21 totalTime = 0 |
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22 flow = 1/meanTimeHeadway |
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23 while totalTime < simulationTime: |
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24 h = expovariate(flow) |
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25 headways.append(h) |
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26 totalTime += h |
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27 return headways |
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28 |
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29 class RoadUser(object): |
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30 '''Simple example of inheritance to plot different road users ''' |
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31 def __init__(self, position, velocity): |
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32 'Both fields are 2D numpy arrays' |
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33 self.position = position.astype(float) |
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34 self.velocity = velocity.astype(float) |
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35 |
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36 def move(self, deltaT): |
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37 self.position += deltaT*self.velocity |
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38 |
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39 def draw(self, init = False): |
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40 from matplotlib.pyplot import plot |
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41 if init: |
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42 self.plotLine = plot(self.position[0], self.position[1], self.getDescriptor())[0] |
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43 else: |
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44 self.plotLine.set_data(self.position[0], self.position[1]) |
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45 |
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46 |
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47 class PassengerVehicle(RoadUser): |
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48 def getDescriptor(self): |
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49 return 'dr' |
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50 |
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51 class Pedestrian(RoadUser): |
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52 def getDescriptor(self): |
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53 return 'xb' |
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54 |
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55 class Cyclist(RoadUser): |
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56 def getDescriptor(self): |
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57 return 'og' |
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58 |
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59 ######################### |
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60 # queueing models |
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61 ######################### |
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62 |
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63 class CapacityReduction(object): |
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64 def __init__(self, beta, reductionDuration, demandCapacityRatio = None, demand = None, capacity = None): |
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65 '''reduction duration should be positive |
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66 demandCapacityRatio is demand/capacity (q/s)''' |
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67 if demandCapacityRatio is None and demand is None and capacity is None: |
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68 print('Missing too much information (demand, capacity and ratio)') |
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69 import sys |
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70 sys.exit() |
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71 if 0 <= beta < 1: |
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72 self.beta = beta |
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73 self.reductionDuration = reductionDuration |
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74 |
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75 if demandCapacityRatio is not None: |
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76 self.demandCapacityRatio = demandCapacityRatio |
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77 if demand is not None: |
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78 self.demand = demand |
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79 if capacity is not None: |
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80 self.capacity = capacity |
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81 if capacity is not None and demand is not None: |
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82 self.demandCapacityRatio = float(self.demand)/self.capacity |
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83 if demand <= beta*capacity: |
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84 print('There is no queueing as the demand {} is inferior to the reduced capacity {}'.format(demand, beta*capacity)) |
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85 else: |
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86 print('reduction coefficient (beta={}) is not in [0, 1['.format(beta)) |
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87 |
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88 def queueingDuration(self): |
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89 return self.reductionDuration*(1-self.beta)/(1-self.demandCapacityRatio) |
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90 |
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91 def nArrived(self, t): |
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92 if self.demand is None: |
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93 print('Missing demand field') |
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94 return None |
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95 return self.demand*t |
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96 |
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97 def nServed(self, t): |
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98 if self.capacity is None: |
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99 print('Missing capacity field') |
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100 return None |
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101 if 0<=t<=self.reductionDuration: |
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102 return self.beta*self.capacity*t |
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103 elif self.reductionDuration < t <= self.queueingDuration(): |
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104 return self.beta*self.capacity*self.reductionDuration+self.capacity*(t-self.reductionDuration) |
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105 |
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106 def nQueued(self, t): |
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107 return self.nArrived(t)-self.nServed(t) |
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108 |
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109 def maxNQueued(self): |
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110 return self.nQueued(self.reductionDuration) |
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111 |
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112 def totalDelay(self): |
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113 if self.capacity is None: |
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114 print('Missing capacity field') |
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115 return None |
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116 return self.capacity*self.reductionDuration**2*(1-self.beta)*(self.demandCapacityRatio-self.beta)/(2*(1-self.demandCapacityRatio)) |
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117 |
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118 def averageDelay(self): |
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119 return self.reductionDuration*(self.demandCapacityRatio-self.beta)/(2*self.demandCapacityRatio) |
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120 |
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121 def averageNQueued(self): |
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122 return self.totalDelay()/self.queueingDuration() |
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123 |
198
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124 |
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125 ######################### |
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126 # fundamental diagrams |
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127 ######################### |
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128 |
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129 class FundamentalDiagram(object): |
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130 ''' ''' |
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131 def __init__(self, name): |
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132 self.name = name |
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133 self.kj = None |
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134 self.kc = None |
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135 self.vf = None |
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136 self.qmax = None |
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137 |
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138 def getJamDensity(self): |
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139 return self.kj |
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140 |
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141 def getCriticalDensity(self): |
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142 return self.kc |
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143 |
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144 def getCapacity(self): |
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145 return self.qmax |
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146 |
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147 def getFreeFlowSpeed(self): |
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148 return self.vf |
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149 |
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150 def q(self, k): |
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151 return k*self.v(k) |
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152 |
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153 @staticmethod |
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154 def meanHeadway(k): |
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155 return 1/k |
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156 |
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157 @staticmethod |
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158 def meanSpacing(q): |
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159 return 1/q |
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160 |
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161 def plotVK(self, language='fr', units={}): |
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162 densities = [k for k in arange(1, self.kj+1)] |
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163 figure() |
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164 plot(densities, [self.v(k) for k in densities]) |
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165 xlim(xmin=0) |
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166 ylim(ymin=0) |
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167 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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168 ylabel('Vitesse (km/h)') |
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169 |
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170 def plotQK(self, language='fr', units={}): |
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171 densities = [k for k in arange(1, self.kj+1)] |
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172 figure() |
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173 plot(densities, [self.q(k) for k in densities]) |
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174 xlim(xmin=0) |
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175 ylim(ymin=0) |
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176 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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177 ylabel('Debit (km/h)') |
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178 |
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179 class GreenshieldsFD(FundamentalDiagram): |
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180 '''Speed is a linear function of density''' |
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181 def __init__(self, vf, kj): |
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182 FundamentalDiagram.__init__(self,'Greenshields') |
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183 self.vf=vf |
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184 self.kj=kj |
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185 self.kc=kj/2 |
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186 self.qmax=vf*kj/4 |
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187 |
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188 def v(self,k): |
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189 from numpy import log |
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190 return self.vmax*(1-k/self.kj) |
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191 |
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192 |
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193 class GreenbergFD(FundamentalDiagram): |
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194 '''Speed is the logarithm of density''' |
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195 def __init__(self, vc, kj): |
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196 FundamentalDiagram.__init__(self,'Greenberg') |
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197 self.vc=vc |
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198 self.kj=kj |
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199 self.qmax = self.kc*self.vc |
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200 self.kc = self.kj/e |
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201 |
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202 def v(self,k): |
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203 return self.vc*log(self.kj/k) |
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204 |
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205 class TriangularFD(FundamentalDiagram): |
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206 def __init__(self, vf = None, kc = None, kj = None, qmax = None, w = None): |
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207 FundamentalDiagram.__init__(self,'Triangular') |
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208 if vf is not None and qmax is not None and kj is not None: |
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209 self.vf=vf |
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210 self.qmax = qmax |
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211 self.kj = kj |
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212 self.kc = qmax/vf |
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213 self.w = qmax/(self.kc-kj) |
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214 |
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215 def v(self, k): |
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216 if k<self.kc: |
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217 return self.vf |
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218 else: |
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219 return self.vf*self.kc*(self.kj/k-1)/(self.kj-self.kc) |
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220 |
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221 ######################### |
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222 # intersection |
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223 ######################### |
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224 |
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225 class FourWayIntersection(object): |
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226 '''Simple class for simple intersection outline''' |
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227 def __init__(self, dimension, coordX, coordY): |
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228 self.dimension = dimension |
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229 self.coordX = coordX |
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230 self.coordY = coordY |
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231 |
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232 def plot(self, options = 'k'): |
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233 from matplotlib.pyplot import plot, axis |
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234 |
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235 minX = min(self.dimension[0]) |
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236 maxX = max(self.dimension[0]) |
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237 minY = min(self.dimension[1]) |
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238 maxY = max(self.dimension[1]) |
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239 |
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240 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[0], self.coordY[0], minY],options) |
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241 plot([self.coordX[1], self.coordX[1], maxX], [minY, self.coordY[0], self.coordY[0]],options) |
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242 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[1], self.coordY[1], maxY],options) |
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243 plot([self.coordX[1], self.coordX[1], maxX], [maxY, self.coordY[1], self.coordY[1]],options) |
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244 axis('equal') |
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245 |
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246 ######################### |
33
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247 # traffic signals |
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248 ######################### |
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249 |
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250 class Volume(object): |
33
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251 '''Class to represent volumes with varied vehicule types ''' |
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252 def __init__(self, volume, types = ['pc'], proportions = [1], equivalents = [1], nLanes = 1): |
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253 '''mvtEquivalent is the equivalent if the movement is right of left turn''' |
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254 |
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255 # check the sizes of the lists |
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256 if sum(proportions) == 1: |
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257 self.volume = volume |
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258 self.types = types |
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259 self.proportions = proportions |
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260 self.equivalents = equivalents |
314
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261 self.nLanes = nLanes |
33
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262 else: |
302
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263 print('Proportions do not sum to 1') |
33
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264 pass |
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265 |
314
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266 def checkProtected(self, opposedThroughMvt): |
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267 '''Checks if this left movement should be protected, |
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268 ie if one of the main two conditions on left turn is verified''' |
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269 return self.volume >= 200 or self.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 |
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270 |
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271 def getPCUVolume(self): |
33
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272 '''Returns the passenger-car equivalent for the input volume''' |
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273 v = 0 |
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274 for p, e in zip(self.proportions, self.equivalents): |
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275 v += p*e |
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276 return v*self.volume |
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277 |
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278 class IntersectionMovement(object): |
33
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279 '''Represents an intersection movement |
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280 with a volume, a type (through, left or right) |
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281 and an equivalent for movement type''' |
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282 def __init__(self, volume, mvtEquivalent = 1): |
33
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283 self.volume = volume |
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284 self.mvtEquivalent = mvtEquivalent |
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285 |
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286 def getTVUVolume(self): |
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287 return self.mvtEquivalent*self.volume.getPCUVolume() |
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288 |
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289 class LaneGroup(object): |
33
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290 '''Class that represents a group of mouvements''' |
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291 |
34 | 292 def __init__(self, movements, nLanes): |
293 self.movements = movements | |
294 self.nLanes = nLanes | |
33
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295 |
34 | 296 def getTVUVolume(self): |
297 return sum([mvt.getTVUVolume() for mvt in self.movements]) | |
33
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298 |
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299 def getCharge(self, saturationVolume): |
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300 return self.getTVUVolume()/(self.nLanes*saturationVolume) |
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301 |
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302 def optimalCycle(lostTime, criticalCharge): |
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303 return (1.5*lostTime+5)/(1-criticalCharge) |
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304 |
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305 def minimumCycle(lostTime, criticalCharge, degreeSaturation=1.): |
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306 'degree of saturation can be used as the peak hour factor too' |
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307 return lostTime/(1-criticalCharge/degreeSaturation) |
34 | 308 |
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309 class Cycle(object): |
34 | 310 '''Class to compute optimal cycle and the split of effective green times''' |
311 def __init__(self, phases, lostTime, saturationVolume): | |
312 '''phases is a list of phases | |
313 a phase is a list of lanegroups''' | |
314 self.phases = phases | |
315 self.lostTime = lostTime | |
316 self.saturationVolume = saturationVolume | |
33
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317 |
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318 def computeCriticalCharges(self): |
314
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319 self.criticalCharges = [max([lg.getCharge(self.saturationVolume) for lg in phase]) for phase in self.phases] |
34 | 320 self.criticalCharge = sum(self.criticalCharges) |
206
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321 |
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322 def computeOptimalCycle(self): |
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323 self.computeCriticalCharges() |
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324 self.C = optimalCycle(self.lostTime, self.criticalCharge) |
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325 return self.C |
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326 |
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327 def computeMinimumCycle(self, degreeSaturation=1.): |
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328 self.computeCriticalCharges() |
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329 self.C = minimumCycle(self.lostTime, self.criticalCharge, degreeSaturation) |
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330 return self.C |
34 | 331 |
332 def computeEffectiveGreen(self): | |
314
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333 #from numpy import round |
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334 #self.computeCycle() # in case it was not done before |
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335 effectiveGreenTime = self.C-self.lostTime |
34 | 336 self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges] |
337 return self.effectiveGreens | |
338 | |
33
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339 |
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340 def computeInterGreen(perceptionReactionTime, initialSpeed, intersectionLength, vehicleAverageLength = 6, deceleration = 3): |
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341 '''Computes the intergreen time (yellow/amber plus all red time) |
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342 Deceleration is positive |
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343 All variables should be in the same units''' |
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344 if deceleration > 0: |
36 | 345 return [perceptionReactionTime+float(initialSpeed)/(2*deceleration), float(intersectionLength+vehicleAverageLength)/initialSpeed] |
33
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346 else: |
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347 print('Issue deceleration should be strictly positive') |
33
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348 return None |
37
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349 |
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350 def uniformDelay(cycleLength, effectiveGreen, saturationDegree): |
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351 '''Computes the uniform delay''' |
796 | 352 return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)**2/(1-float(effectiveGreen*saturationDegree)/cycleLength) |
353 | |
354 def randomDelay(volume, saturationDegree): | |
355 '''Computes the random delay = queueing time for M/D/1''' | |
356 return saturationDegree**2/(2*volume*(1-saturationDegree)) | |
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357 |
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358 def incrementalDelay(T, X, c, k=0.5, I=1): |
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359 '''Computes the incremental delay (HCM) |
493
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360 T in hours |
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361 c capacity of the lane group |
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362 k default for fixed time signal |
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363 I=1 for isolated intersection (Poisson arrival)''' |
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364 from math import sqrt |
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365 return 900*T*(X - 1 + sqrt((X - 1)**2 + 8*k*I*X/(c*T))) |
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366 |
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367 ######################### |
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368 # misc |
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distance and time to change speed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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369 ######################### |
f234154207d4
distance and time to change speed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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370 |
f234154207d4
distance and time to change speed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
73
diff
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371 def timeChangingSpeed(v0, vf, a, TPR): |
789 | 372 'for decelerations, a < 0' |
373 return TPR-(vf-v0)/a | |
87
f234154207d4
distance and time to change speed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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374 |
f234154207d4
distance and time to change speed
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
73
diff
changeset
|
375 def distanceChangingSpeed(v0, vf, a, TPR): |
789 | 376 'for decelerations, a < 0' |
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c5863e04d302
corrected bug
Nicolas Saunier <nicolas.saunier@polymtl.ca>
parents:
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377 return TPR*v0+(vf**2-v0**2)/(2*a) |