import math import numpy as np def Width_factor(w): # w取3.2 if 3.0 <= w <= 3.5: fw = 1 elif 2.7 <= w < 3.0: fw = 0.4 * (w - 0.5) elif w > 3.5: fw = 0.05 * (w + 16.5) else: raise ValueError("车道宽度 w 不在定义的范围内") return fw def Cart_factor(channel, hv, q, G): channel_hv = [0 for x in range(len(channel))] if 'SL' in channel: sum_q = [q[1]] if 'S' in channel: sum_q.append(q[0]) if 'SR' in channel: sum_q.append(q[2]) mean_q = np.mean(sum_q) l_q = mean_q-q[1] hv_q = l_q*hv sl_hv = hv_q/mean_q index = channel.index('SL') channel_hv[index] = sl_hv if 'SR' in channel: sum_q = [q[2]] if 'S' in channel: sum_q.append(q[0]) if 'SL' in channel: sum_q.append(q[1]) mean_q = np.mean(sum_q) r_q = mean_q - q[2] hv_q = r_q * hv sr_hv = hv_q / mean_q index = channel.index('SR') channel_hv[index] = sr_hv if 'S' in channel: index = extract(channel, 'S') for i in index: channel_hv[i] = hv channel_fg = [1 - (G + x) for x in channel_hv] print("(2)坡度及大车校正系数fg: ", channel_fg) return channel_fg def extract(a, target): # 提取列表中某个元素的全部索引 b = [] for index, nums in enumerate(a): if nums == target: b.append(index) return b def Bicycle_factor(B, betal, C, geL, phase, direction): import math bL = B*betal*(C-geL)/C import_road = ['west', 'east', 'north', 'south'] index = import_road.index(direction) for i in range(len(phase)): direction_phase = phase[i][index] if direction_phase==[0,1,0]: fb = 1 break else: fb = 1-(1+math.sqrt(bL))/geL print("(3)自行车影响校正系数fb: ", fb) print(" B: ", B) print(" β: ", betal) print(" ge: ", geL) print(" bL: ", bL) return fb def TurnLeft_factor(channel, verse_channel, qt0, lambal, k): # # 检测当前进口道相位是否存在左转专用相位, 有的话, 对向直行车辆就不会对车流存在影响, 于是直接返回fL=1 # 当前进口道没有左转专用道 index_verse_S = verse_channel.index('S') # 对向车道数 S_lane_sum = 0 if 'S' in verse_channel: S_lane_sum += verse_channel.count('S') if 'SL' in verse_channel: S_lane_sum += verse_channel.count('SL') if 'SR' in verse_channel: S_lane_sum += verse_channel.count('SR') # 确定ξ(xi) if S_lane_sum == 1: xi = 1 elif S_lane_sum == 2: xi = 0.625 elif S_lane_sum == 3: xi = 0.51 elif S_lane_sum == 4: xi = 0.44 if 'L' in channel: fL = 1 else: fL = math.exp(-0.001 * xi * (qt0 / lambal[k])) - 0.1 print("(4)左转校正系数fL: ", fL) print(" ξ: ", xi) print(" qt0: ", qt0) print(" λ: ", lambal[k]) return fL def TurnRight_factor(channel, person, geR, lamdar, C, Ls, bT): # # 检测是否存在右转专用道, 有的话就不会受到其他因素影响, 于是直接返回1 # 判断是否有右转专用相位 # 没有右转专用道 # 确定行人影响校正系数 fp_matrix_less_than_20 = { 60: {0.4: 0.88, 0.5: 0.88, 0.6: 0.87}, 90: {0.4: 0.87, 0.5: 0.87, 0.6: 0.86}, 120: {0.4: 0.87, 0.5: 0.86, 0.6: 0.86} } fp_matrix_more_than_20 = { 60: {0.4: 0.45, 0.5: 0.42, 0.6: 0.40}, 90: {0.4: 0.40, 0.5: 0.38, 0.6: 0.36}, 120: {0.4: 0.37, 0.5: 0.36, 0.6: 0.35} } # 四舍五入 geR / C 的值到 0.4, 0.5 或 0.6 if geR/C < 0.45: rounded_geR_C = 0.4 elif 0.45 <= geR/C < 0.55: rounded_geR_C = 0.5 else: rounded_geR_C = 0.6 # 检查周期 C 是否有效 if C not in [60, 90, 120]: raise ValueError("周期 C 必须为 60, 90 或 120") # 根据 person 判断取哪个表格 if person/(3600/C) < 20: fp = fp_matrix_less_than_20[C][rounded_geR_C] else: fp = fp_matrix_more_than_20[C][rounded_geR_C] # 计算直行自行车绿初驶出停车线所占用的时间 ''' 红灯期 到达排队自行车绿初驶出停车线的饱和流量,建议取 3600辆(m * h), 自行车道宽度取 wb = 5m ''' S_TS = 3600 wb = 5 tT = (3600 * (1 - lamdar) * bT) / (S_TS * wb) # 计算自行车影响校正系数fb_ gj = geR - A + Ls fb_ = 1 - (tT / gj) # 计算fpb # 在fp和fb_中选最小的 if 'R' in channel: fpb = 1 else: fpb = min(fp, fb_) print("(5)右转校正系数fpb: ", fpb) print(" fp: ", fp) print(" λ: ", lamdar) print(" tT: ", tT) print(" fb: ", fb_) return fpb def Saturation_flow(channel,q,fw,fg,fb,fL,fpb): SbT=1650 SbL=1550 SbR=1450 channel_S=[0 for x in range(len(channel))] if 'S' in channel: index = extract(channel, 'S') ST = SbT * fw * fg[index[0]] * fb for i in index: channel_S[i] = ST if 'L' in channel: index = channel.index('L') SL = SbL * fw * fg[index] * fL channel_S[index] = SL if 'R' in channel: index = channel.index('R') SR = SbR * fw * fg[index] * fpb channel_S[index] = SR if 'SL' in channel: index = channel.index('SL') ST = SbT * fw * fg[index] * fb SL = SbL * fw * fg[index] * fL KL = ST / SL sum_q = [q[1]] if 'S' in channel: sum_q.append(q[0]) if 'SR' in channel: sum_q.append(q[2]) mean_q = np.mean(sum_q) qT = mean_q - q[1] qT1 = KL * q[1] + qT fTL = (qT + q[1]) / qT1 STL = ST * fTL channel_S[index] = STL print("(6)直左车道校正系数fTL: ", fTL) print(" qT: ", qT) print(" qL: ", q[1]) print(" SL': ", SL) print(" KL: ", KL) print(" qT': ", qT1) if 'SR' in channel: index = channel.index('SR') ST = SbT * fw * fg[index] * fb SR = SbR * fw * fg[index] * fpb kr=ST/SR sum_q = [q[2]] if 'S' in channel: sum_q.append(q[0]) if 'SL' in channel: sum_q.append(q[1]) mean_q = np.mean(sum_q) qT = mean_q - q[2] qT2 = kr * q[2] + qT fTR = (qT + q[2]) / qT2 STR = ST * fTR channel_S[index] = STR print("(7)直右车道校正系数 fTR: ", fTR) print(" qT: ", qT) print(" qR: ", q[2]) print(" SR': ", SR) print(" KR: ", kr) print(" qT': ", qT2) d = 0 if channel.count('S') > 1: index = extract(channel, 'S') to = index[1] end = index[-1]+1 del channel_S[to:end] channel_S = [round(x) for x in channel_S] print('\n饱和流量S: ', channel_S) return channel_S def Diver_volume(channel,q): if channel.count('S')==1: if 'SR' in channel and 'SL' in channel: mean = np.mean(q) qd = [mean, mean, mean] elif 'SR' in channel and 'SL' not in channel: mean = (q[0] + q[2]) / 2 qd = [mean, q[1], mean] elif 'SL' in channel and 'SR' not in channel: mean = (q[0] + q[1]) / 2 qd = [mean, mean, q[2]] else: qd = q if channel.count('S')==2: if 'SR' in channel and 'SL' in channel: mean = np.sum(q)/4 qd = [mean, mean, mean] elif 'SR' in channel and 'SL' not in channel: mean = (q[0] + q[2]) / 3 qd = [mean, q[1], mean] elif 'SL' in channel and 'SR' not in channel: mean = (q[0] + q[1]) / 3 qd = [mean, mean, q[2]] else: qd = q qd = [round(x) for x in qd] print("各车道的设计交通量qdmn: ", qd) return qd def FindchannelY(scheme, channel, Y): ylist = [] if 1 in scheme: index1 = extract(scheme, 1) if 0 in index1: if 'S' in channel: index2 = extract(channel, 'S') ylist.append(Y[index2[0]]) if 1 in index1: if 'L' in channel: index3 = channel.index('L') ylist.append(Y[index3]) if 'SL' in channel: index4 = channel.index('SL') if index4>2: index4_adj = 2 else: index4_adj = index4 ylist.append(Y[index4_adj]) if 2 in index1: if 'R' in channel: index5 = channel.index('R') ylist.append(Y[index5]) if 'SR' in channel: index6 = channel.index('SR') if index6>2: index6_adj = 2 else: index6_adj = index6 ylist.append(Y[index6_adj]) return ylist def Cal_factor(channel, verse_channel, hv, q, verse_q, B, betal, betas, phase, C, Ls, geL, geR, lamdaL, lamdaR, direction): # 1.车道宽度校正系数 fw = Width_factor(direction) # 2.坡度及大车校正系数 fg = Cart_factor(channel, hv, q, direction) # 3.直行车道的自行车影响校正系数 fb = Bicycle_factor(B, betal, C, geL, phase, direction) # 4.左转校正系数 fL = TurnLeft_factor(verse_channel, verse_q[0], lamdaL, phase, direction) # 5.右转校正系数 person = 600 # 估计各向行人流量，人/h # 注意：教材案例北进口道右转校正系数取值为 fb，为勘误，实际上右转校正系数应取 min(fp, fb) fpb = TurnRight_factor(channel, person, geR, lamdaR, C, Ls, phase, betas*B, direction) # 饱和流量计算 # 注意：教材案例 SbR 取值为 1550，为勘误。根据表 11-2，右转车道的基本饱和流量应取 1450 S = Saturation_flow(channel, q, fw, fg, fb, fL, fpb, direction) # 计算各车道分流向的设计交通量 qd qd = Diver_volume(channel, q, direction) # 计算各车道分流向的流量比 y y = list(map(lambda x,y : x/y, qd, S)) return S, qd, y def Uniform_delay(x, lamda, C): d1 = 0.5 * C * ((1 - lamda) ** 2) / (1 - min(1, x) * lamda) return d1 def Random_delay(x, cap): import math T = 0.25 # Duration of the analysis phase, typically 0.25h e = 0.5 # Adjustment factor for intersection traffic signal control, typically 0.5 d2 = 900 * T * ((x - 1) + math.sqrt((x - 1) ** 2 + 8 * e * x / (cap * T))) return d2 # 信号配时计算（主程序-迭代） if __name__ == '__main__': # 输入各进口道的大车率 west_hv = [0.04,0,0] east_hv = [0.04,0,0] north_hv = [0.02,0,0] south_hv = [0.02,0,0] # 输入各进口道的自行车平均流率，单位: 辆/min west_b = 28 east_b = 30 north_b = 20 south_b = 27 # 输入各进口道的自行车左转率 west_betal_left = 0.1 east_betal_left = 0.1 north_betal_left = 0.25 south_betal_left = 0.1 # 输入各进口道的自行车直行率； ### ???算错：左转0.1/o.25，右转统一0.25，直行不应该是0.65/0.5吗 west_betal_str = 0.65 east_betal_str = 0.65 north_betal_str = 0.5 south_betal_str = 0.65 # 输入交叉口各流向流量 q，按照直左右的顺序输入，例如 west_q = [513, 104, 113] west_q=[513,104,113] east_q = [416,101,80] north_q = [660,64,113] south_q = [757,73,120] # 输入渠化方案(S:直行 SL:直左 SR:直右 L:左转专用 R:右转专用) west_channel = ['S', 'L', 'SR'] east_channel = ['S', 'L', 'SR'] north_channel = ['S', 'SL', 'SR'] south_channel = ['S', 'L', 'SR'] #输入交叉口基本参数信息 C=60 I=3 A=3 num=3 Ls = 3+I-A gel = (C - Ls * num) / num gel = [gel, gel, gel, gel] lamdal = (C - Ls * num) / (num * C) lamdal_list = [lamdal, lamdal, lamdal, lamdal] ger = (C - Ls * num) / num lamdar = (C - Ls * num)/(num * C) lamdar_list = [lamdar, lamdar, lamdar, lamdar] ger = [ger, ger, ger, ger] phase= [[[0, 1, 0], [0, 1, 0], [0, 0, 0], [0, 0, 0]], [[1, 0, 1], [1, 0, 1], [0, 0, 0], [0, 0, 0]], [[0, 0, 0], [0, 0, 0], [1, 1, 1], [1, 1, 1]]] # 人行道 Lp_west_east = 17 # 东西向半幅人行横道长度 Lp_north_south = 14 # 南北向半幅人行横道长度 vp = 1 # 行人过街速度 # 输出，循环试算 w = 3.2 Y = 0 iteration = 6 gmin_flag = False for z in range(iteration): print("\n\n#################### 第", z+1, "次试算 ####################") print("\n******************************* west *******************************") fw = Width_factor(w) print("(1)车道宽度校正系数fw：", fw) G = 0 fg = Cart_factor(west_channel, west_hv[0], west_q, G) ### fb = Bicycle_factor(west_b, west_betal_left, C, gel[0], phase, 'west') ### fL = TurnLeft_factor(west_channel, east_channel, east_q[0], lamdal_list, 0) ### person = 600 fpb = TurnRight_factor(west_channel, person, ger[0], lamdar_list[0], C, Ls, (west_betal_str * west_b)) ### west_S = Saturation_flow(west_channel, west_q, fw, fg, fb, fL, fpb) ### west_qd = Diver_volume(west_channel, west_q) ### west_y = list(map(lambda x, y: x / y, west_qd, west_S)) print("\n******************************* east *******************************") fw = Width_factor(w) print("(1)车道宽度校正系数fw：", fw) G = 0 fg = Cart_factor(east_channel, east_hv[0], east_q, G) ### fb = Bicycle_factor(east_b, east_betal_left, C, gel[1], phase, 'east') ### fL = TurnLeft_factor(east_channel, west_channel, west_q[0], lamdal_list, 1) ### person = 600 fpb = TurnRight_factor(east_channel, person, ger[1], lamdar_list[1], C, Ls, (east_betal_str * east_b)) ### east_S = Saturation_flow(east_channel, east_q, fw, fg, fb, fL, fpb) ### east_qd = Diver_volume(east_channel, east_q) ### east_y = list(map(lambda x, y: x / y, east_qd, east_S)) print("\n******************************* north *******************************") fw = Width_factor(w) print("(1)车道宽度校正系数fw：", fw) G = 0 fg = Cart_factor(north_channel, north_hv[0], north_q, G) ### fb = Bicycle_factor(north_b, north_betal_left, C, gel[2], phase, 'north') ### fL = TurnLeft_factor(north_channel, south_channel, south_q[0], lamdal_list, 2) ### person = 600 fpb = TurnRight_factor(north_channel, person, ger[2], lamdar_list[2], C, Ls, (north_betal_str * north_b)) ### north_S = Saturation_flow(north_channel, north_q, fw, fg, fb, fL, fpb) ### north_qd = Diver_volume(north_channel, north_q) ### north_y = list(map(lambda x, y: x / y, north_qd, north_S)) print("\n******************************* south *******************************") fw = Width_factor(w) print("(1)车道宽度校正系数fw：", fw) G = 0 fg = Cart_factor(south_channel, south_hv[0], south_q, G) ### fb = Bicycle_factor(south_b, south_betal_left, C, gel[3], phase, 'south') ### fL = TurnLeft_factor(south_channel, north_channel, north_q[0], lamdal_list, 3) ### person = 600 fpb = TurnRight_factor(south_channel, person, ger[3], lamdar_list[3], C, Ls, (south_betal_str * south_b)) ### south_S = Saturation_flow(south_channel, south_q, fw, fg, fb, fL, fpb) ### south_qd = Diver_volume(south_channel, south_q) ### south_y = list(map(lambda x, y: x / y, south_qd, south_S)) Y_max = [] for scheme in phase: west_ylist = FindchannelY(scheme[0], west_channel, west_y) east_ylist = FindchannelY(scheme[1], east_channel, east_y) north_ylist = FindchannelY(scheme[2], north_channel,north_y) south_ylist = FindchannelY(scheme[3], south_channel, south_y) # print("west_ylist:",west_ylist) ymax = max(west_ylist + east_ylist + north_ylist + south_ylist) Y_max.append(ymax) print("相位最大流量比Ymax:", Y_max) Y = sum(Y_max) print("最大流量比总和Y:", Y) if Y > 0.9: print("\nError: Y> 0.9，进口车道划分不合理或周期时间太短需重新设计！") # 输入新的进口道渠化方案 west_channel = ['S', 'L', 'SR'] east_channel = ['S', 'L', 'SR'] north_channel = ['S', 'S', 'L', 'SR'] south_channel = ['S', 'S', 'L', 'SR'] continue else: print("\n******************************* 配时参数 *******************************") # 计算总损失时间 L = Ls * num print("总损失时间\n","L: ", L) # 计算信号周期时长 if gmin_flag == False: C = L / (1 - Y) else: C = 60 print("验算/选取周期\n", "C: ", C) # 计算总有效绿灯时间 Ge = C - L print("总有效绿灯时间\n", "Ge: ", Ge) # 计算相位有效绿灯时间 ge_j = [Ge * x / Y for x in Y_max] print("有效绿灯时间\n","ge_j: ", ge_j) # 计算各相位的绿信比 lamda_j = [x / C for x in ge_j] print("绿信比\n","λj: ", lamda_j) # 计算各相位显示绿灯时间 g_j = [x - A + Ls for x in ge_j] print("显示绿灯时间\n","gj: ", g_j) # 计算最短绿灯时间 Lp_we = 17 # 东西向半幅人行横道长度取17m，采用路中央设置行人过街安全岛的方法 Lp_ns = 14 # 南北向半幅人行横道长度取14m vp = 1 # 行人过街速度取1m/s gmin_phase2 = 7 + Lp_we / vp - I gmin_phase3 = 7 + Lp_ns / vp - I gmin = [None, gmin_phase2, gmin_phase3] print("最短绿灯时间\n","gmin: ", gmin) if gmin[1] > g_j[1] or gmin[2] > g_j[2]: print("\nError: 配时方案不满足最短绿灯时间，应该扩大周期时长重新计算！") # 按照最短绿灯时间要求，重新计算信号配时参数 C = 60 Ge = C - L ge_j = [Ge * x / Y for x in Y_max] lamda_j = [x / C for x in ge_j] g_j = [x - A + Ls for x in ge_j] gel = [g_j[0], g_j[0], g_j[2], g_j[2]] # 计算各进口道的左转相位有效绿灯时长 ger = [g_j[1], g_j[1], g_j[2], g_j[2]] # 计算各进口道的右转相位有效绿灯时长 lamdal = [lamda_j[0], lamda_j[0], lamda_j[2], lamda_j[2]] # 左转车道的绿信比 lamdar = [lamda_j[1], lamda_j[1], lamda_j[2], lamda_j[2]] # 右转车道的绿信比 gmin_flag = True continue else: print("\n******************************* 通行能力与饱和度 *******************************") # 通行能力 print("各车道通行能力") west_cap = [west_S[0] * lamda_j[1], west_S[1] * lamda_j[0], west_S[2] * lamda_j[1]] west_cap = [round(x) for x in west_cap] east_cap = [east_S[0] * lamda_j[1], east_S[1] * lamda_j[0], east_S[2] * lamda_j[1]] east_cap = [round(x) for x in east_cap] north_cap = [north_S[0] * lamda_j[2], north_S[1] * lamda_j[0], north_S[2] * lamda_j[2]] north_cap = [round(x) for x in north_cap] south_cap = [south_S[0] * lamda_j[2], south_S[1] * lamda_j[2], south_S[2] * lamda_j[2]] south_cap = [round(x) for x in south_cap] print("west cap: ", west_cap) print("east cap: ", east_cap) print("north cap: ", north_cap) print("south cap: ", south_cap) # Calculate saturation for each lane print("各车道饱和度") west_x = list(map(lambda x, y: x / y, west_qd, west_cap)) east_x = list(map(lambda x, y: x / y, east_qd, east_cap)) north_x = list(map(lambda x, y: x / y, north_qd, north_cap)) south_x = list(map(lambda x, y: x / y, south_qd, south_cap)) print("west x: ", west_x) print("east x: ", east_x) print("north x: ", north_x) print("south x: ", south_x) print("\n******************************* 服务水平评估 *******************************") # Average delay print("(1) 车道均匀延误") west_d1 = [ Uniform_delay(west_x[0], lamda_j[1], C), Uniform_delay(west_x[1], lamda_j[0], C), Uniform_delay(west_x[2], lamda_j[1], C), ] east_d1 = [ Uniform_delay(east_x[0], lamda_j[1], C), Uniform_delay(east_x[1], lamda_j[0], C), Uniform_delay(east_x[2], lamda_j[1], C), ] north_d1 = [ Uniform_delay(north_x[0], lamda_j[2], C), Uniform_delay(north_x[1], lamda_j[2], C), Uniform_delay(north_x[2], lamda_j[2], C), ] south_d1 = [ Uniform_delay(south_x[0], lamda_j[2], C), Uniform_delay(south_x[1], lamda_j[2], C), Uniform_delay(south_x[2], lamda_j[2], C), ] print("west d1: ", west_d1) print("east d1: ", east_d1) print("north d1: ", north_d1) print("south d1: ", south_d1) # 随机延误 print("(2) 车道随机延误") west_d2 = [ Random_delay(west_x[0], west_cap[0]), Random_delay(west_x[1], west_cap[1]), Random_delay(west_x[2], west_cap[2]), ] east_d2 = [ Random_delay(east_x[0], east_cap[0]), Random_delay(east_x[1], east_cap[1]), Random_delay(east_x[2], east_cap[2]), ] north_d2 = [ Random_delay(north_x[0], north_cap[0]), Random_delay(north_x[1], north_cap[1]), Random_delay(north_x[2], north_cap[2]), ] south_d2 = [ Random_delay(south_x[0], south_cap[0]), Random_delay(south_x[1], south_cap[1]), Random_delay(south_x[2], south_cap[2]), ] print("west d2: ", west_d2) print("east d2: ", east_d2) print("north d2: ", north_d2) print("south d2: ", south_d2) # 车道信号延误 print("(3) 车道信号延误") west_d = list(map(lambda x, y: x + y, west_d1, west_d2)) east_d = list(map(lambda x, y: x + y, east_d1, east_d2)) north_d = list(map(lambda x, y: x + y, north_d1, north_d2)) south_d = list(map(lambda x, y: x + y, south_d1, south_d2)) print("west d: ", west_d) print("east d: ", east_d) print("north d: ", north_d) print("south d: ", south_d) # 进口道平均信号延误 print("(4) 进口道平均信号延误") west_da = sum(list(map(lambda x, y: x * y / 4, west_d, west_qd))) / (sum(west_qd) / 4) east_da = sum(list(map(lambda x, y: x * y / 4, east_d, east_qd))) / (sum(east_qd) / 4) north_da = sum(list(map(lambda x, y: x * y / 4, north_d, north_qd))) / (sum(north_qd) / 4) south_da = sum(list(map(lambda x, y: x * y / 4, south_d, south_qd))) / (sum(south_qd) / 4) print("west dA: ", west_da) print("east dA: ", east_da) print("north dA: ", north_da) print("south dA: ", south_da) # 交叉口平均信号延误 print("(5) 交叉口平均信号延误") # 每个方向的平均延误列表 dA = [west_da, east_da, north_da, south_da] # 每个方向的车道流量总和 qA = [sum(west_q), sum(east_q), sum(north_q), sum(south_q)] # 计算交叉口的平均信号延误 dl = sum(list(map(lambda x, y: x * y / 4, dA, qA))) / (sum(qA) / 4) print("dl: ", dl) break