Mercurial Hosting > traffic-intelligence
annotate python/traffic_engineering.py @ 251:4978b5baf8f1
Win32 version for traffic intelligence. Use the README-Win32.txt to know how to
fetch the dependencies. A batch file is included to automate most of the process
author | Jeep-Tour@Jeep-Tour-PC |
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date | Mon, 23 Jul 2012 12:27:50 -0400 |
parents | 82b4101d9a2f |
children | f65b828e5521 |
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 |
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6 __metaclass__ = type |
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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 class Vehicle: |
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14 'Generic vehicle class' |
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15 def __init__(self, position = 0, speed = 0, acceleration = 0, prt = 2.5, leader = None, log=True): |
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16 self.position = position |
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17 self.speed = speed |
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18 self.acceleration = acceleration |
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19 self.prt = prt |
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20 self.leader = leader |
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21 self.log = log |
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22 if log: |
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23 self.positions = [position] |
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24 self.speeds = [speed] |
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25 self.accelerations = [acceleration] |
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26 # todo add microModel |
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27 |
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28 def updatePosition(self, dt): |
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29 speed = self.speed |
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30 self.speed += self.acceleration*dt |
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31 self.position += speed*dt |
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32 if self.log: |
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33 self.positions.append(self.position) |
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34 self.speeds.append(self.speed) |
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35 self.accelerations.append(self.acceleration) |
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36 |
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37 def updateAcceleration(self, dt): |
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38 '''Updates acceleration and speed as a function of leader |
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39 and other factors''' |
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40 pass |
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41 |
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42 def update(self, dt): |
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43 self.updatePosition(dt) |
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44 self.updateAcceleration(dt) # function of leader |
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45 |
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46 def printStats(self): |
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47 print('{0} {1} {2}'.format(self.position, self.speed, self.acceleration)) |
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48 |
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49 ######################### |
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50 # fundamental diagram |
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51 ######################### |
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52 |
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53 class FundamentalDiagram: |
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54 ''' ''' |
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55 def __init__(self, name): |
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56 self.name = name |
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57 |
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58 def q(self, k): |
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59 return k*self.v(k) |
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60 |
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61 @staticmethod |
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62 def meanHeadway(k): |
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63 return 1/k |
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64 |
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65 @staticmethod |
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66 def meanSpacing(q): |
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67 return 1/q |
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68 |
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69 def plotVK(self, language='fr', units={}): |
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70 from numpy import arange |
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71 from matplotlib.pyplot import figure,plot,xlabel,ylabel |
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72 densities = [k for k in arange(1, self.kj+1)] |
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73 figure() |
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74 plot(densities, [self.v(k) for k in densities]) |
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75 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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76 ylabel('Vitesse (km/h)') |
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77 |
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78 def plotQK(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.q(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('Debit (km/h)') |
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86 |
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87 class GreenbergFD(FundamentalDiagram): |
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88 '''Speed is the logarithm of density''' |
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89 def __init__(self, vc, kj): |
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90 FundamentalDiagram.__init__(self,'Greenberg') |
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91 self.vc=vc |
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92 self.kj=kj |
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93 |
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94 def v(self,k): |
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95 from numpy import log |
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96 return self.vc*log(self.kj/k) |
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97 |
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98 def criticalDensity(self): |
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99 from numpy import e |
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100 self.kc = self.kj/e |
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101 return self.kc |
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102 |
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103 def capacity(self): |
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104 self.qmax = self.kc*self.vc |
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105 return self.qmax |
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106 |
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107 ######################### |
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108 # intersection |
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109 ######################### |
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110 |
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111 class FourWayIntersection: |
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112 '''Simple class for simple intersection outline''' |
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113 def __init__(self, dimension, coordX, coordY): |
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114 self.dimension = dimension |
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115 self.coordX = coordX |
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116 self.coordY = coordY |
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117 |
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118 def plot(self, options = 'k'): |
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119 from matplotlib.pyplot import plot, axis |
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120 |
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121 minX = min(self.dimension[0]) |
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122 maxX = max(self.dimension[0]) |
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123 minY = min(self.dimension[1]) |
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124 maxY = max(self.dimension[1]) |
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125 |
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126 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[0], self.coordY[0], minY],options) |
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127 plot([self.coordX[1], self.coordX[1], maxX], [minY, self.coordY[0], self.coordY[0]],options) |
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128 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[1], self.coordY[1], maxY],options) |
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129 plot([self.coordX[1], self.coordX[1], maxX], [maxY, self.coordY[1], self.coordY[1]],options) |
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130 axis('equal') |
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131 |
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132 ######################### |
33
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133 # traffic signals |
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134 ######################### |
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135 |
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136 class Volume: |
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137 '''Class to represent volumes with varied vehicule types ''' |
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138 def __init__(self, volume, types = ['pc'], proportions = [1], equivalents = [1], nLanes = 1): |
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139 '''mvtEquivalent is the equivalent if the movement is right of left turn''' |
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140 |
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141 # check the sizes of the lists |
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142 if sum(proportions) == 1: |
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143 self.volume = volume |
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144 self.types = types |
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145 self.proportions = proportions |
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146 self.equivalents = equivalents |
206
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147 self.nLanes = nLanes # unused |
33
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148 else: |
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149 pass |
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150 |
205
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151 def getPCUVolume(self): |
33
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152 '''Returns the passenger-car equivalent for the input volume''' |
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153 v = 0 |
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154 for p, e in zip(self.proportions, self.equivalents): |
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155 v += p*e |
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156 return v*self.volume |
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157 |
34 | 158 class IntersectionMovement: |
33
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159 '''Represents an intersection movement |
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160 with a volume, a type (through, left or right) |
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161 and an equivalent for movement type''' |
205
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162 def __init__(self, volume, mvtEquivalent = 1): |
33
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163 self.volume = volume |
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164 self.mvtEquivalent = mvtEquivalent |
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165 |
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166 def getTVUVolume(self): |
205
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167 return self.mvtEquivalent*self.volume.getPCUVolume() |
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168 |
206
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169 class IntersectionApproach: # should probably not be used |
33
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170 def __init__(self, leftTurnVolume, throughVolume, rightTurnVolume): |
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171 self.leftTurnVolume = leftTurnVolume |
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172 self.throughVolume = throughVolume |
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173 self.rightTurnVolume = rightTurnVolume |
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174 |
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175 def getTVUVolume(self, leftTurnEquivalent = 1, throughEquivalent = 1, rightTurnEquivalent = 1): |
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176 return self.leftTurnVolume.getPCUVolume()*leftTurnEquivalent+self.throughVolume.getPCUVolume()*throughEquivalent+self.rightTurnVolume.getPCUVolume()*rightTurnEquivalent |
33
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177 |
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178 class LaneGroup: |
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179 '''Class that represents a group of mouvements''' |
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180 |
34 | 181 def __init__(self, movements, nLanes): |
182 self.movements = movements | |
183 self.nLanes = nLanes | |
33
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184 |
34 | 185 def getTVUVolume(self): |
186 return sum([mvt.getTVUVolume() for mvt in self.movements]) | |
33
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187 |
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188 def getCharge(self, saturationVolume): |
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189 return self.getTVUVolume()/(self.nLanes*saturationVolume) |
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190 |
33
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191 def checkProtectedLeftTurn(leftMvt, opposedThroughMvt): |
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192 '''Checks if one of the main two conditions on left turn is verified |
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193 The lane groups should contain left and through movement''' |
34 | 194 return leftMvt.volume >= 200 or leftMvt.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 |
195 | |
206
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196 def optimalCycle(lostTime, criticalCharge): |
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197 return (1.5*lostTime+5)/(1-criticalCharge) |
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198 |
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199 def minimumCycle(lostTime, criticalCharge, degreeSaturation=1.): |
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200 'degree of saturation can be used as the peak hour factor too' |
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201 return lostTime/(1-criticalCharge/degreeSaturation) |
34 | 202 |
203 class Cycle: | |
204 '''Class to compute optimal cycle and the split of effective green times''' | |
205 def __init__(self, phases, lostTime, saturationVolume): | |
206 '''phases is a list of phases | |
207 a phase is a list of lanegroups''' | |
208 self.phases = phases | |
209 self.lostTime = lostTime | |
210 self.saturationVolume = saturationVolume | |
33
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211 |
206
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212 def computeCriticalCharges(self): |
34 | 213 self.criticalCharges = [] |
214 for phase in self.phases: | |
206
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215 self.criticalCharges.append(max([lg.getCharge(self.saturationVolume) for lg in phase])) |
34 | 216 self.criticalCharge = sum(self.criticalCharges) |
206
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217 |
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218 def computeOptimalCycle(self): |
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219 self.computeCriticalCharges() |
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220 self.C = optimalCycle(self.lostTime, self.criticalCharge) |
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221 return self.C |
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222 |
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223 def computeMinimumCycle(self, degreeSaturation=1.): |
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224 self.computeCriticalCharges() |
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225 self.C = minimumCycle(self.lostTime, self.criticalCharge, degreeSaturation) |
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226 return self.C |
34 | 227 |
228 def computeEffectiveGreen(self): | |
229 from numpy import round | |
206
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230 #self.computeCycle() # in case it was not done before |
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231 effectiveGreenTime = self.C-self.lostTime |
34 | 232 self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges] |
233 return self.effectiveGreens | |
234 | |
33
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235 |
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236 def computeInterGreen(perceptionReactionTime, initialSpeed, intersectionLength, vehicleAverageLength = 6, deceleration = 3): |
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237 '''Computes the intergreen time (yellow/amber plus all red time) |
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238 Deceleration is positive |
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239 All variables should be in the same units''' |
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240 if deceleration > 0: |
36 | 241 return [perceptionReactionTime+float(initialSpeed)/(2*deceleration), float(intersectionLength+vehicleAverageLength)/initialSpeed] |
33
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242 else: |
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243 print 'Issue deceleration should be strictly positive' |
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244 return None |
37
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245 |
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246 def uniformDelay(cycleLength, effectiveGreen, saturationDegree): |
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247 '''Computes the uniform delay''' |
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248 return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)/(1-float(effectiveGreen*saturationDegree)/cycleLength) |
87
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249 |
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parents:
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250 ######################### |
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parents:
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251 # misc |
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parents:
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252 ######################### |
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parents:
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253 |
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254 def timeChangingSpeed(v0, vf, a, TPR): |
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255 return TPR+(vf-v0)/a |
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256 |
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257 def distanceChangingSpeed(v0, vf, a, TPR): |
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258 return TPR*v0+(vf*vf-v0*v0)/(2*a) |