Mercurial Hosting > traffic-intelligence
annotate python/traffic_engineering.py @ 677:ae07c7b4cf87
update to utils for pavement results
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Sat, 30 May 2015 23:30:21 +0200 |
parents | 15e244d2a1b5 |
children | 3666342dabe2 |
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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 import prediction |
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5 |
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6 from math import ceil |
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7 |
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8 |
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9 ######################### |
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10 # Simulation |
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11 ######################### |
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12 |
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13 def generateTimeHeadways(meanTimeHeadway, simulationTime): |
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14 '''Generates the time headways between arrivals |
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15 given the meanTimeHeadway and the negative exponential distribution |
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16 over a time interval of length simulationTime (assumed to be in same time unit as headway''' |
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17 from random import expovariate |
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18 headways = [] |
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19 totalTime = 0 |
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20 flow = 1/meanTimeHeadway |
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21 while totalTime < simulationTime: |
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22 h = expovariate(flow) |
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23 headways.append(h) |
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24 totalTime += h |
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25 return headways |
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26 |
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27 class RoadUser(object): |
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28 '''Simple example of inheritance to plot different road users ''' |
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29 def __init__(self, position, velocity): |
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30 'Both fields are 2D numpy arrays' |
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31 self.position = position.astype(float) |
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32 self.velocity = velocity.astype(float) |
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33 |
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34 def move(self, deltaT): |
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35 self.position += deltaT*self.velocity |
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36 |
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37 def draw(self, init = False): |
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38 from matplotlib.pyplot import plot |
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39 if init: |
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40 self.plotLine = plot(self.position[0], self.position[1], self.getDescriptor())[0] |
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41 else: |
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42 self.plotLine.set_data(self.position[0], self.position[1]) |
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43 |
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44 |
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45 class PassengerVehicle(RoadUser): |
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46 def getDescriptor(self): |
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47 return 'dr' |
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48 |
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49 class Pedestrian(RoadUser): |
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50 def getDescriptor(self): |
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51 return 'xb' |
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52 |
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53 class Cyclist(RoadUser): |
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54 def getDescriptor(self): |
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55 return 'og' |
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56 |
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57 |
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58 ######################### |
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59 # fundamental diagram |
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60 ######################### |
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61 |
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62 class FundamentalDiagram(object): |
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63 ''' ''' |
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64 def __init__(self, name): |
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65 self.name = name |
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66 |
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67 def q(self, k): |
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68 return k*self.v(k) |
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69 |
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70 @staticmethod |
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71 def meanHeadway(k): |
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72 return 1/k |
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73 |
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74 @staticmethod |
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75 def meanSpacing(q): |
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76 return 1/q |
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77 |
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78 def plotVK(self, language='fr', units={}): |
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79 from numpy import arange |
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80 from matplotlib.pyplot import figure,plot,xlabel,ylabel |
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81 densities = [k for k in arange(1, self.kj+1)] |
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82 figure() |
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83 plot(densities, [self.v(k) for k in densities]) |
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84 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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85 ylabel('Vitesse (km/h)') |
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86 |
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87 def plotQK(self, language='fr', units={}): |
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88 from numpy import arange |
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89 from matplotlib.pyplot import figure,plot,xlabel,ylabel |
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90 densities = [k for k in arange(1, self.kj+1)] |
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91 figure() |
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92 plot(densities, [self.q(k) for k in densities]) |
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93 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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94 ylabel('Debit (km/h)') |
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95 |
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96 class GreenbergFD(FundamentalDiagram): |
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97 '''Speed is the logarithm of density''' |
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98 def __init__(self, vc, kj): |
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99 FundamentalDiagram.__init__(self,'Greenberg') |
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100 self.vc=vc |
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101 self.kj=kj |
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102 |
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103 def v(self,k): |
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104 from numpy import log |
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105 return self.vc*log(self.kj/k) |
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106 |
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107 def criticalDensity(self): |
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108 from numpy import e |
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109 self.kc = self.kj/e |
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110 return self.kc |
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111 |
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112 def capacity(self): |
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113 self.qmax = self.kc*self.vc |
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114 return self.qmax |
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115 |
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116 ######################### |
116
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117 # intersection |
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118 ######################### |
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119 |
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120 class FourWayIntersection(object): |
116
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121 '''Simple class for simple intersection outline''' |
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122 def __init__(self, dimension, coordX, coordY): |
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123 self.dimension = dimension |
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124 self.coordX = coordX |
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125 self.coordY = coordY |
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126 |
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127 def plot(self, options = 'k'): |
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128 from matplotlib.pyplot import plot, axis |
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129 |
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130 minX = min(self.dimension[0]) |
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131 maxX = max(self.dimension[0]) |
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132 minY = min(self.dimension[1]) |
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133 maxY = max(self.dimension[1]) |
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134 |
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135 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[0], self.coordY[0], minY],options) |
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136 plot([self.coordX[1], self.coordX[1], maxX], [minY, self.coordY[0], self.coordY[0]],options) |
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137 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[1], self.coordY[1], maxY],options) |
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138 plot([self.coordX[1], self.coordX[1], maxX], [maxY, self.coordY[1], self.coordY[1]],options) |
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139 axis('equal') |
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140 |
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141 ######################### |
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142 # traffic signals |
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143 ######################### |
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144 |
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145 class Volume(object): |
33
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146 '''Class to represent volumes with varied vehicule types ''' |
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147 def __init__(self, volume, types = ['pc'], proportions = [1], equivalents = [1], nLanes = 1): |
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148 '''mvtEquivalent is the equivalent if the movement is right of left turn''' |
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149 |
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150 # check the sizes of the lists |
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151 if sum(proportions) == 1: |
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152 self.volume = volume |
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153 self.types = types |
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154 self.proportions = proportions |
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155 self.equivalents = equivalents |
314
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156 self.nLanes = nLanes |
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157 else: |
302
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158 print('Proportions do not sum to 1') |
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159 pass |
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160 |
314
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161 def checkProtected(self, opposedThroughMvt): |
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162 '''Checks if this left movement should be protected, |
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163 ie if one of the main two conditions on left turn is verified''' |
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164 return self.volume >= 200 or self.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 |
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165 |
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166 def getPCUVolume(self): |
33
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167 '''Returns the passenger-car equivalent for the input volume''' |
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168 v = 0 |
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169 for p, e in zip(self.proportions, self.equivalents): |
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170 v += p*e |
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171 return v*self.volume |
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172 |
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173 class IntersectionMovement(object): |
33
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174 '''Represents an intersection movement |
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175 with a volume, a type (through, left or right) |
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176 and an equivalent for movement type''' |
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177 def __init__(self, volume, mvtEquivalent = 1): |
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178 self.volume = volume |
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179 self.mvtEquivalent = mvtEquivalent |
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180 |
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181 def getTVUVolume(self): |
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182 return self.mvtEquivalent*self.volume.getPCUVolume() |
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183 |
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184 class LaneGroup(object): |
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185 '''Class that represents a group of mouvements''' |
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186 |
34 | 187 def __init__(self, movements, nLanes): |
188 self.movements = movements | |
189 self.nLanes = nLanes | |
33
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190 |
34 | 191 def getTVUVolume(self): |
192 return sum([mvt.getTVUVolume() for mvt in self.movements]) | |
33
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193 |
206
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194 def getCharge(self, saturationVolume): |
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195 return self.getTVUVolume()/(self.nLanes*saturationVolume) |
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196 |
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197 def optimalCycle(lostTime, criticalCharge): |
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198 return (1.5*lostTime+5)/(1-criticalCharge) |
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199 |
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200 def minimumCycle(lostTime, criticalCharge, degreeSaturation=1.): |
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201 'degree of saturation can be used as the peak hour factor too' |
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202 return lostTime/(1-criticalCharge/degreeSaturation) |
34 | 203 |
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204 class Cycle(object): |
34 | 205 '''Class to compute optimal cycle and the split of effective green times''' |
206 def __init__(self, phases, lostTime, saturationVolume): | |
207 '''phases is a list of phases | |
208 a phase is a list of lanegroups''' | |
209 self.phases = phases | |
210 self.lostTime = lostTime | |
211 self.saturationVolume = saturationVolume | |
33
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212 |
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213 def computeCriticalCharges(self): |
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214 self.criticalCharges = [max([lg.getCharge(self.saturationVolume) for lg in phase]) for phase in self.phases] |
34 | 215 self.criticalCharge = sum(self.criticalCharges) |
206
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216 |
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217 def computeOptimalCycle(self): |
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218 self.computeCriticalCharges() |
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219 self.C = optimalCycle(self.lostTime, self.criticalCharge) |
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220 return self.C |
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221 |
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222 def computeMinimumCycle(self, degreeSaturation=1.): |
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223 self.computeCriticalCharges() |
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224 self.C = minimumCycle(self.lostTime, self.criticalCharge, degreeSaturation) |
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225 return self.C |
34 | 226 |
227 def computeEffectiveGreen(self): | |
314
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228 #from numpy import round |
206
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229 #self.computeCycle() # in case it was not done before |
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230 effectiveGreenTime = self.C-self.lostTime |
34 | 231 self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges] |
232 return self.effectiveGreens | |
233 | |
33
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234 |
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235 def computeInterGreen(perceptionReactionTime, initialSpeed, intersectionLength, vehicleAverageLength = 6, deceleration = 3): |
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236 '''Computes the intergreen time (yellow/amber plus all red time) |
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237 Deceleration is positive |
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238 All variables should be in the same units''' |
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239 if deceleration > 0: |
36 | 240 return [perceptionReactionTime+float(initialSpeed)/(2*deceleration), float(intersectionLength+vehicleAverageLength)/initialSpeed] |
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241 else: |
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242 print 'Issue deceleration should be strictly positive' |
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243 return None |
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244 |
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245 def uniformDelay(cycleLength, effectiveGreen, saturationDegree): |
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246 '''Computes the uniform delay''' |
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247 return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)/(1-float(effectiveGreen*saturationDegree)/cycleLength) |
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248 |
493
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249 def overflowDelay(T, X, c, k=0.5, I=1): |
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250 '''Computes the overflow delay (HCM) |
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251 T in hours |
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252 c capacity of the lane group |
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253 k default for fixed time signal |
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254 I=1 for isolated intersection (Poisson arrival)''' |
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255 from math import sqrt |
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256 return 900*T*(X - 1 + sqrt((X - 1)**2 + 8*k*I*X/(c*T))) |
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257 |
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258 ######################### |
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259 # misc |
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260 ######################### |
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261 |
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262 def timeChangingSpeed(v0, vf, a, TPR): |
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263 return TPR+(vf-v0)/a |
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264 |
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265 def distanceChangingSpeed(v0, vf, a, TPR): |
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266 return TPR*v0+(vf*vf-v0*v0)/(2*a) |