Mercurial Hosting > traffic-intelligence
annotate trafficintelligence/traffic_engineering.py @ 1256:56d0195d043e
cleaning indicators (no more time interval) and runtimeerror with arccos
author | Nicolas Saunier <nicolas.saunier@polymtl.ca> |
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date | Wed, 03 Apr 2024 14:41:20 -0400 |
parents | 770306fef827 |
children | 3bfdb2ffd29d |
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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 from scipy import stats |
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7 |
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8 from matplotlib.pyplot import figure,plot,xlabel,ylabel, xlim, ylim |
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9 |
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10 from trafficintelligence import prediction |
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11 |
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12 ######################### |
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13 # Simulation |
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14 ######################### |
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15 |
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16 def generateTimeHeadways(meanTimeHeadway, simulationTime): |
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17 '''Generates the time headways between arrivals |
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18 given the meanTimeHeadway and the negative exponential distribution |
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19 over a time interval of length simulationTime (assumed to be in same time unit as headway''' |
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20 from random import expovariate |
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21 headways = [] |
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22 totalTime = 0 |
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23 flow = 1/meanTimeHeadway |
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24 while totalTime < simulationTime: |
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25 h = expovariate(flow) |
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26 headways.append(h) |
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27 totalTime += h |
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28 return headways |
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29 |
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30 class RoadUser(object): |
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31 '''Simple example of inheritance to plot different road users ''' |
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32 def __init__(self, position, velocity): |
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33 'Both fields are 2D numpy arrays' |
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34 self.position = position.astype(float) |
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35 self.velocity = velocity.astype(float) |
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36 |
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37 def move(self, deltaT): |
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38 self.position += deltaT*self.velocity |
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39 |
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40 def draw(self, init = False): |
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41 from matplotlib.pyplot import plot |
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42 if init: |
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43 self.plotLine = plot(self.position[0], self.position[1], self.getDescriptor())[0] |
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44 else: |
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45 self.plotLine.set_data(self.position[0], self.position[1]) |
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46 |
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47 |
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48 class PassengerVehicle(RoadUser): |
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49 def getDescriptor(self): |
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50 return 'dr' |
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51 |
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52 class Pedestrian(RoadUser): |
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53 def getDescriptor(self): |
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54 return 'xb' |
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55 |
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56 class Cyclist(RoadUser): |
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57 def getDescriptor(self): |
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58 return 'og' |
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59 |
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60 ######################### |
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61 # queueing models |
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62 ######################### |
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63 |
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64 class CapacityReduction(object): |
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65 def __init__(self, beta, reductionDuration, demandCapacityRatio = None, demand = None, capacity = None): |
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66 '''reduction duration should be positive |
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67 demandCapacityRatio is demand/capacity (q/s)''' |
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68 if demandCapacityRatio is None and demand is None and capacity is None: |
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69 print('Missing too much information (demand, capacity and ratio)') |
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70 import sys |
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71 sys.exit() |
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72 if 0 <= beta < 1: |
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73 self.beta = beta |
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74 self.reductionDuration = reductionDuration |
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75 |
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76 if demandCapacityRatio is not None: |
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77 self.demandCapacityRatio = demandCapacityRatio |
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78 if demand is not None: |
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79 self.demand = demand |
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80 if capacity is not None: |
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81 self.capacity = capacity |
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82 if capacity is not None and demand is not None: |
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83 self.demandCapacityRatio = float(self.demand)/self.capacity |
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84 if demand <= beta*capacity: |
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85 print('There is no queueing as the demand {} is inferior to the reduced capacity {}'.format(demand, beta*capacity)) |
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86 else: |
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87 print('reduction coefficient (beta={}) is not in [0, 1['.format(beta)) |
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88 |
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89 def queueingDuration(self): |
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90 return self.reductionDuration*(1-self.beta)/(1-self.demandCapacityRatio) |
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91 |
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92 def nArrived(self, t): |
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93 if self.demand is None: |
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94 print('Missing demand field') |
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95 return None |
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96 return self.demand*t |
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97 |
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98 def nServed(self, t): |
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99 if self.capacity is None: |
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100 print('Missing capacity field') |
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101 return None |
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102 if 0<=t<=self.reductionDuration: |
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103 return self.beta*self.capacity*t |
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104 elif self.reductionDuration < t <= self.queueingDuration(): |
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105 return self.beta*self.capacity*self.reductionDuration+self.capacity*(t-self.reductionDuration) |
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106 |
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107 def nQueued(self, t): |
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108 return self.nArrived(t)-self.nServed(t) |
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109 |
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110 def maxNQueued(self): |
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111 return self.nQueued(self.reductionDuration) |
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112 |
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113 def totalDelay(self): |
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114 if self.capacity is None: |
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115 print('Missing capacity field') |
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116 return None |
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117 return self.capacity*self.reductionDuration**2*(1-self.beta)*(self.demandCapacityRatio-self.beta)/(2*(1-self.demandCapacityRatio)) |
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118 |
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119 def averageDelay(self): |
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120 return self.reductionDuration*(self.demandCapacityRatio-self.beta)/(2*self.demandCapacityRatio) |
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121 |
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122 def averageNQueued(self): |
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123 return self.totalDelay()/self.queueingDuration() |
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124 |
198
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125 |
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126 ######################### |
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127 # fundamental diagrams |
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128 ######################### |
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129 |
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130 class FundamentalDiagram(object): |
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131 ''' ''' |
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132 def __init__(self, name): |
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133 self.name = name |
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134 self.kj = None |
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135 self.kc = None |
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136 self.vf = None |
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137 self.qmax = None |
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138 |
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139 def getJamDensity(self): |
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140 return self.kj |
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141 |
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142 def getCriticalDensity(self): |
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143 return self.kc |
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144 |
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145 def getCapacity(self): |
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146 return self.qmax |
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147 |
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148 def getFreeFlowSpeed(self): |
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149 return self.vf |
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150 |
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151 def q(self, k): |
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152 return k*self.v(k) |
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153 |
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154 @staticmethod |
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155 def meanHeadway(k): |
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156 return 1/k |
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157 |
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158 @staticmethod |
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159 def meanSpacing(q): |
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160 return 1/q |
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161 |
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162 def plotVK(self, language='fr', units={}): |
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163 densities = [k for k in arange(1, self.kj+1)] |
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164 figure() |
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165 plot(densities, [self.v(k) for k in densities]) |
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166 xlim(xmin=0) |
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167 ylim(ymin=0) |
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168 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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169 ylabel('Vitesse (km/h)') |
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170 |
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171 def plotQK(self, language='fr', units={}): |
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172 densities = [k for k in arange(1, self.kj+1)] |
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173 figure() |
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174 plot(densities, [self.q(k) for k in densities]) |
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175 xlim(xmin=0) |
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176 ylim(ymin=0) |
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177 xlabel('Densite (veh/km)') # todo other languages and adapt to units |
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178 ylabel('Debit (km/h)') |
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179 |
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180 class GreenshieldsFD(FundamentalDiagram): |
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181 '''Speed is a linear function of density''' |
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182 def __init__(self, vf, kj): |
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183 FundamentalDiagram.__init__(self,'Greenshields') |
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184 self.vf=vf |
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185 self.kj=kj |
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186 self.kc=kj/2 |
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187 self.qmax=vf*kj/4 |
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188 |
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189 def v(self,k): |
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190 from numpy import log |
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191 return self.vf*(1-k/self.kj) |
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192 |
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193 |
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194 class GreenbergFD(FundamentalDiagram): |
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195 '''Speed is the logarithm of density''' |
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196 def __init__(self, vc, kj): |
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197 FundamentalDiagram.__init__(self,'Greenberg') |
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198 self.vc=vc |
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199 self.kj=kj |
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200 self.qmax = self.kc*self.vc |
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201 self.kc = self.kj/e |
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202 |
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203 def v(self,k): |
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204 return self.vc*log(self.kj/k) |
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205 |
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206 class TriangularFD(FundamentalDiagram): |
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207 def __init__(self, vf = None, kc = None, kj = None, qmax = None, w = None): |
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208 FundamentalDiagram.__init__(self,'Triangular') |
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209 if vf is not None and qmax is not None and kj is not None: |
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210 self.vf=vf |
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211 self.qmax = qmax |
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212 self.kj = kj |
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213 self.kc = qmax/vf |
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214 self.w = qmax/(self.kc-kj) |
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215 |
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216 def v(self, k): |
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217 if k<self.kc: |
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218 return self.vf |
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219 else: |
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220 return self.vf*self.kc*(self.kj/k-1)/(self.kj-self.kc) |
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221 |
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222 def generateDensities(n, maxDensity): |
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223 return stats.uniform.rvs(size=n)*maxDensity |
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224 |
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225 def generateSpeedVolumes(fd, n, maxDensity, maxHGVProportion = 0, etrucks = 2.5): |
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226 densities = generateDensities(n, maxDensity) |
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227 speeds = [fd.v(k) for k in densities] |
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228 volumes = [fd.q(k) for k in densities] |
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229 if maxHGVProportion > 0: |
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230 hgvProportions = stats.uniform.rvs(size=n)*maxHGVProportion # en pourcent |
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231 volumes = [v/(1+(etrucks-1)*p/100) for v,p in zip(volumes, hgvProportions)] |
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232 else: |
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233 hgvProportions = None |
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234 return speeds, volumes, hgvProportions |
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235 |
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236 def highwayLOS(k): |
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237 'returns the highway level of service for density k in veh/km' |
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238 if k>=28: return 'F' |
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239 elif k>=22: return 'E' |
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240 elif k>=16: return 'D' |
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241 elif k>=11: return 'C' |
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242 elif k>=7: return 'B' |
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243 else: return 'A' |
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244 |
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245 ######################### |
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246 # intersection |
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247 ######################### |
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248 |
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249 class FourWayIntersection(object): |
116
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250 '''Simple class for simple intersection outline''' |
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251 def __init__(self, dimension, coordX, coordY): |
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252 self.dimension = dimension |
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253 self.coordX = coordX |
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254 self.coordY = coordY |
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255 |
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256 def plot(self, options = 'k'): |
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257 from matplotlib.pyplot import plot, axis |
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258 |
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259 minX = min(self.dimension[0]) |
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260 maxX = max(self.dimension[0]) |
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261 minY = min(self.dimension[1]) |
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262 maxY = max(self.dimension[1]) |
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263 |
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264 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[0], self.coordY[0], minY],options) |
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265 plot([self.coordX[1], self.coordX[1], maxX], [minY, self.coordY[0], self.coordY[0]],options) |
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266 plot([minX, self.coordX[0], self.coordX[0]], [self.coordY[1], self.coordY[1], maxY],options) |
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267 plot([self.coordX[1], self.coordX[1], maxX], [maxY, self.coordY[1], self.coordY[1]],options) |
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268 axis('equal') |
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269 |
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270 ######################### |
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271 # traffic signals |
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272 ######################### |
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273 |
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274 class Volume(object): |
33
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275 '''Class to represent volumes with varied vehicule types ''' |
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276 def __init__(self, volume, types = ['pc'], proportions = [1], equivalents = [1], nLanes = 1): |
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277 '''mvtEquivalent is the equivalent if the movement is right of left turn''' |
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278 |
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279 # check the sizes of the lists |
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280 if sum(proportions) == 1: |
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281 self.volume = volume |
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282 self.types = types |
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283 self.proportions = proportions |
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284 self.equivalents = equivalents |
314
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285 self.nLanes = nLanes |
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286 else: |
302
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287 print('Proportions do not sum to 1') |
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288 pass |
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289 |
314
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290 def checkProtected(self, opposedThroughMvt): |
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291 '''Checks if this left movement should be protected, |
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292 ie if one of the main two conditions on left turn is verified''' |
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293 return self.volume >= 200 or self.volume*opposedThroughMvt.volume/opposedThroughMvt.nLanes > 50000 |
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294 |
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295 def getPCUVolume(self): |
33
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296 '''Returns the passenger-car equivalent for the input volume''' |
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297 v = 0 |
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298 for p, e in zip(self.proportions, self.equivalents): |
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299 v += p*e |
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300 return v*self.volume |
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301 |
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302 class IntersectionMovement(object): |
33
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303 '''Represents an intersection movement |
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304 with a volume, a type (through, left or right) |
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305 and an equivalent for movement type''' |
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306 def __init__(self, volume, mvtEquivalent = 1): |
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307 self.volume = volume |
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308 self.mvtEquivalent = mvtEquivalent |
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309 |
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310 def getTVUVolume(self): |
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311 return self.mvtEquivalent*self.volume.getPCUVolume() |
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312 |
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313 class LaneGroup(object): |
33
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314 '''Class that represents a group of mouvements''' |
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315 |
34 | 316 def __init__(self, movements, nLanes): |
317 self.movements = movements | |
318 self.nLanes = nLanes | |
33
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319 |
34 | 320 def getTVUVolume(self): |
321 return sum([mvt.getTVUVolume() for mvt in self.movements]) | |
33
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322 |
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323 def getCharge(self, saturationVolume): |
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324 return self.getTVUVolume()/(self.nLanes*saturationVolume) |
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325 |
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326 def optimalCycle(lostTime, criticalCharge): |
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327 return (1.5*lostTime+5)/(1-criticalCharge) |
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328 |
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329 def minimumCycle(lostTime, criticalCharge, degreeSaturation=1.): |
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330 'degree of saturation can be used as the peak hour factor too' |
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331 return lostTime/(1-criticalCharge/degreeSaturation) |
34 | 332 |
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333 class Cycle(object): |
34 | 334 '''Class to compute optimal cycle and the split of effective green times''' |
335 def __init__(self, phases, lostTime, saturationVolume): | |
336 '''phases is a list of phases | |
337 a phase is a list of lanegroups''' | |
338 self.phases = phases | |
339 self.lostTime = lostTime | |
340 self.saturationVolume = saturationVolume | |
33
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341 |
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342 def computeCriticalCharges(self): |
314
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343 self.criticalCharges = [max([lg.getCharge(self.saturationVolume) for lg in phase]) for phase in self.phases] |
34 | 344 self.criticalCharge = sum(self.criticalCharges) |
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345 |
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346 def computeOptimalCycle(self): |
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347 self.computeCriticalCharges() |
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348 self.C = optimalCycle(self.lostTime, self.criticalCharge) |
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349 return self.C |
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350 |
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351 def computeMinimumCycle(self, degreeSaturation=1.): |
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352 self.computeCriticalCharges() |
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353 self.C = minimumCycle(self.lostTime, self.criticalCharge, degreeSaturation) |
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354 return self.C |
34 | 355 |
356 def computeEffectiveGreen(self): | |
314
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357 #from numpy import round |
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358 #self.computeCycle() # in case it was not done before |
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359 effectiveGreenTime = self.C-self.lostTime |
34 | 360 self.effectiveGreens = [round(c*effectiveGreenTime/self.criticalCharge,1) for c in self.criticalCharges] |
361 return self.effectiveGreens | |
362 | |
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363 |
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364 def computeInterGreen(perceptionReactionTime, initialSpeed, intersectionLength, vehicleAverageLength = 6, deceleration = 3): |
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365 '''Computes the intergreen time (yellow/amber plus all red time) |
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366 Deceleration is positive |
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367 All variables should be in the same units''' |
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368 if deceleration > 0: |
36 | 369 return [perceptionReactionTime+float(initialSpeed)/(2*deceleration), float(intersectionLength+vehicleAverageLength)/initialSpeed] |
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370 else: |
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371 print('Issue deceleration should be strictly positive') |
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372 return None |
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373 |
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374 def uniformDelay(cycleLength, effectiveGreen, saturationDegree): |
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375 '''Computes the uniform delay''' |
796 | 376 return 0.5*cycleLength*(1-float(effectiveGreen)/cycleLength)**2/(1-float(effectiveGreen*saturationDegree)/cycleLength) |
377 | |
378 def randomDelay(volume, saturationDegree): | |
379 '''Computes the random delay = queueing time for M/D/1''' | |
380 return saturationDegree**2/(2*volume*(1-saturationDegree)) | |
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381 |
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382 def incrementalDelay(T, X, c, k=0.5, I=1): |
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383 '''Computes the incremental delay (HCM) |
493
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384 T in hours |
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385 c capacity of the lane group |
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386 k default for fixed time signal |
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387 I=1 for isolated intersection (Poisson arrival)''' |
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388 from math import sqrt |
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389 return 900*T*(X - 1 + sqrt((X - 1)**2 + 8*k*I*X/(c*T))) |
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390 |
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391 ######################### |
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392 # misc |
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393 ######################### |
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394 |
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395 def timeChangingSpeed(v0, vf, a, TPR): |
789 | 396 'for decelerations, a < 0' |
397 return TPR-(vf-v0)/a | |
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398 |
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399 def distanceChangingSpeed(v0, vf, a, TPR): |
789 | 400 'for decelerations, a < 0' |
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401 return TPR*v0+(vf**2-v0**2)/(2*a) |