本文主要讲解：使用地铁客流业务逻辑来计算重庆地铁断面客流量
主要思路：
重庆各站点间的区间长度，18年的数据，共8条线142个站点
3、相关技术
1、panda读取文件后的数据为Dataframe，关于Dataframe的遍历方法itertuples
2、Json数据的key，value格式，适合存储与计算断面客流量
代码输出如下：
from itertools import permutationsimport pandas as pdfrom my_utils.read_write import writeOneCsv, writeOneJsonsubway_dict = []transfer_dict = []src = r'D:\项目\重庆地铁断面客流量计算\\'try: yearStop = pd.read_excel(src + '区间长度.xlsx', en@R_301_5563@='gbk')except Exception as e: yearStop = pd.read_excel(src + '区间长度.xlsx', en@R_301_5563@='utf-8')def getRoute(): for feature in yearStop.itertuples(index=False): line = str(getattr(feature, '线路名称')) sstation = str(getattr(feature, 'sstation')) EStation = str(getattr(feature, 'EStation')) Length_Dow = getattr(feature, 'Length_Dow') start = line + '-' + sstation end = line + '-' + EStation subway_dict.append([start, end, Length_Dow])transfer = {}def getTransfer(): '''sstation EStation Length_Dow 线路名称''' features = yearStop.groupby('sstation') for name, feature in features: if feature.shape[0] > 0: for one in feature.itertuples(): linename = getattr(one, '线路名称') line_station = str(linename) + '-' + str(name) if name not in transfer.keys(): transfer[name] = [] transfer[name].append(line_station) # 列举排列结果[(1, 2), (1, 3), (2, 1), (2, 3), (3, 1), (3, 2)] data4 = List(permutations([i for i in range(0, 4)], 2)) for key in transfer.keys(): data = transfer[key] if len(data) == 2: transfer_dict.append([data[1], data[0], 120]) transfer_dict.append([data[0], data[1], 120]) elif len(data) == 3: transfer_dict.append([data[1], data[0], 180]) transfer_dict.append([data[0], data[1], 180]) transfer_dict.append([data[0], data[2], 180]) transfer_dict.append([data[2], data[0], 180]) transfer_dict.append([data[1], data[2], 180]) transfer_dict.append([data[2], data[1], 180]) elif len(data) == 4: for one in data4: transfer_dict.append([data[one[0]], data[one[1]], 240])# getTransfer()'''弗洛伊德算法（Floyd算法）'''class Vertex: def __init__(self, key): self.ID = key self.connectedTo = {} def addNeighbor(self, nbr, weight=0): self.connectedTo[nbr] = weight def __str__(self): return str(self.ID) + ' connectedTo: ' + str([x.ID for x in self.connectedTo]) def getConnections(self): return self.connectedTo.keys() def getID(self): return self.ID def getWeight(self, nbr): return self.connectedTo[nbr]class Graph: def __init__(self): self.vertList = {} self.numVertices = 0 def addVertex(self, key): self.numVertices = self.numVertices + 1 newVertex = Vertex(key) self.vertList[key] = newVertex return newVertex def getVertex(self, n): if n in self.vertList: return self.vertList[n] else: return None def __contains__(self, n): return n in self.vertList def addEdge(self, f, t, cost=0): if f not in self.vertList: nv = self.addVertex(f) if t not in self.vertList: nv = self.addVertex(t) self.vertList[f].addNeighbor(self.vertList[t], cost) def getVertices(self): return self.vertList.keys() def __iter__(self): return iter(self.vertList.values())def Floyd(Graph, ShortestPath): global path NodeNum = len(Graph) lastShortestdistance = [[0 for i in range(NodeNum)] for j in range(NodeNum)] currentShortestdistance = [[0 for i in range(NodeNum)] for j in range(NodeNum)] path = [[0 for i in range(NodeNum)] for j in range(NodeNum)] for i in range(NodeNum): for j in range(NodeNum): path[i][j] = j for i in range(NodeNum): for j in range(NodeNum): lastShortestdistance[i][j] = Graph[i][j] currentShortestdistance[i][j] = lastShortestdistance[i][j] for k in range(NodeNum): for i in range(NodeNum): for j in range(NodeNum): if lastShortestdistance[i][j] > lastShortestdistance[i][k] + lastShortestdistance[k][j]: currentShortestdistance[i][j] = lastShortestdistance[i][k] + lastShortestdistance[k][j] path[i][j] = path[i][k] lastShortestdistance = currentShortestdistance for i in range(NodeNum): for j in range(NodeNum): # if i==j: # ShortestPath[i][j]=0 # else: ShortestPath[i][j] = currentShortestdistance[i][j] print("path is: ", path) print("shortestPath is: ", ShortestPath) # print(currentShortestdistance) return Nonedef printPath(i, j, shortestPath, path): print(zhandian[i] + "----" + zhandian[j] + "\n最短时间:" + str(shortestPath[i][j]) + "s\n路径:\n" + zhandian[i]) temp = path[i][j] odname = zhandian[i] + "_" + zhandian[j] JsonData = {} routeSplitData = [zhandian[i]] while (temp != j): routeSplitData.append(zhandian[temp]) print("-->" + zhandian[temp]) temp = path[temp][j] routeSplitData.append(zhandian[j]) JsonData[odname] = routeSplitData mainjson.append(JsonData) print("-->" + zhandian[j])'''sstation EStation Length_Dow 线路名称'''def splitTime(i, j, shortestPath, path): temp = path[i][j] routeSplitData = [zhandian[i]] time = shortestPath[i][j] while (temp != j): routeSplitData.append(zhandian[temp]) temp = path[temp][j] routeSplitData.append(zhandian[j]) start = zhandian[i] sstation = routeSplitData[len(routeSplitData) - 2].split('-')[1] EStation = zhandian[j].split('-')[1] if sstation != EStation: save = [start, sstation, EStation, time] print(save) writeOneCsv(save, src+r'all.csv')def writeOneCsv(relate_record, src): with open(src, 'a', newline='\n') as csvfile: writer = csv.writer(csvfile) writer.writerow(relate_record)# 写Json文件,一个数据一个文件def writeOneJson(relate_record, src): Json_str = Json.dumps(relate_record, ensure_ascii=False) with open(src, 'w', en@R_301_5563@='utf-8') as Json_file: Json_file.write(Json_str) Json_file.close()if __name__ == "__main__": mainjson = [] getRoute() getTransfer() g = Graph() # vel = 10 # 地铁的速度 m/s vel = 10 # 地铁的速度 m/s for x in subway_dict: g.addEdge(x[0], x[1], int(float(x[2]) / vel)) # 由距离得到时间 g.addEdge(x[1], x[0], int(float(x[2]) / vel)) # 双向都有 for x in transfer_dict: g.addEdge(x[0], x[1], int(float(x[2]))) # 换站给的直接就是时间 print(len(g.getVertices())) # 打印图中顶点数 print(g.getVertices()) # 打印图中所有的顶()点 zhandian = [] for i in g.getVertices(): zhandian.append(i) zhandian = tuple(List(zhandian)) x = float('inf') matrix = [[x for i in range(338)] for i in range(338)] for i in range(338): matrix[i][i] = 0 for v in g: # 对图中的每一个顶点，获取与它有连接关系的顶点 for w in v.getConnections(): time = v.getWeight(w) matrix[zhandian.index(v.getID())][zhandian.index(w.getID())] = time graph = matrix nodeNum = len(graph) shortestPath = [[0 for i in range(nodeNum)] for j in range(nodeNum)] print("计算时间比较长，请耐心等待几分钟！") Floyd(graph, shortestPath) allStation = [] '''sstation EStation Length_Dow 线路名称''' for one_s in yearStop.itertuples(): line = str(getattr(one_s, '线路名称')) start = line + '-' + str(getattr(one_s, 'sstation')) end = line + '-' + str(getattr(one_s, 'EStation')) if start not in allStation: allStation.append(start) if end not in allStation: allStation.append(end) for start in allStation: for end in allStation: if start != end: s = zhandian.index(start) e = zhandian.index(end) printPath(s, e, shortestPath, path) # splitTime(s, e, shortestPath, path) writeOneJson(mainjson, src + '最短路径.Json')主运行程序入口
import pandas as pdfrom my_utils.read_write import writeOneJson, readJson, writeOneCsv'''此文件用于计算重庆地铁线路断面客流量计算步骤：1、找到最短路径；2、计算每个段面的客流量（算0这个时间段所有深圳所有断面的客流量）'''src = r'D:\项目\重庆地铁断面客流量计算\\'try: od_people = pd.read_excel(src + 'OD客流量.xlsx', en@R_301_5563@='gbk')except Exception as e: od_people = pd.read_excel(src + 'OD客流量.xlsx', en@R_301_5563@='utf-8')try: yearStop = pd.read_excel(src + '区间长度.xlsx', en@R_301_5563@='gbk')except Exception as e: yearStop = pd.read_excel(src + '区间长度.xlsx', en@R_301_5563@='utf-8')all_split = []for one_s in yearStop.itertuples(): line = str(getattr(one_s, '线路名称')) start = line + '-' + str(getattr(one_s, 'sstation')) end = line + '-' + str(getattr(one_s, 'EStation')) all_split.append(start + '_' + end) all_split.append(end + '_' + start)def add_count(split, od_count, routeSplitJson): if split not in routeSplitJson.keys(): routeSplitJson[split] = 0 # 某个断面某个时间加入od的数量 routeSplitJson[split] = routeSplitJson[split] + od_countdef readJson(filepath): try: with open(filepath, 'r', en@R_301_5563@='GBK') as file_open: data = Json.load(file_open) file_open.close() return data except: try: with open(filepath, 'r', en@R_301_5563@='utf-8') as file_open: data = Json.load(file_open) file_open.close() return data except: with open(filepath, 'r', en@R_301_5563@="unicode_escape") as file_open: data = Json.load(file_open) file_open.close() return datadef routeSplit(df, routeSplitJson): minWays = readJson(src + '最短路径.Json') min_ways = {} for one in minWays: keys = one.keys() for i in keys: if i not in min_ways.keys(): min_ways[i] = {} # 某个断面某个时间加入od的数量 min_ways[i] = one[i] allStation = [] for one_s in yearStop.itertuples(): line = str(getattr(one_s, '线路名称')) start = line + '-' + str(getattr(one_s, 'sstation')) end = line + '-' + str(getattr(one_s, 'EStation')) if start not in allStation: allStation.append(start) if end not in allStation: allStation.append(end) # o_station,d_station,tw,od for row in df.itertuples(index=False): line = str(getattr(row, '线路')) start = line + '-' + str(getattr(row, '车站编号')) for n in range(len(allStation)): if start != allStation[n]: od_count = row[n + 2] od_name = start + '_' + allStation[n] # 获取到最短路径的数据 minStations = min_ways[od_name] for m in range(0, len(minStations)): if m + 1 < len(minStations): split = minStations[m] + '_' + minStations[m + 1] if split in all_split: # 把断面客流量加到Json里面去 add_count(split, od_count, routeSplitJson)def final(): routeSplitJson = {} routeSplit(od_people, routeSplitJson) writeOneJson(routeSplitJson, src + '重庆断面客流量.Json')def deal_Json(): data = readJson(src + '重庆断面客流量.Json') # 上行客流量,断面,下行客流量 alr = [] one = [] for key in data.keys(): if key not in alr: split_data = key.split('_') add = split_data[0].split('-')[1] + '-' + split_data[1].split('-')[1] down_key = split_data[1] + '_' + split_data[0] alr.append(down_key) alr.append(key) if down_key in data.keys(): writeOneCsv([data[key], add, data[down_key]], src + '重庆地铁断面客流量.csv') else: writeOneCsv([data[key], add, 0], src + '重庆地铁断面客流量.csv')if __name__ == '__main__': deal_Json() final()read_write文件在我的下载里有，请自行搜索
如需数据示例请私聊我，有偿
60行python代码_计算深圳地铁断面客流量
python_pandas_计算深圳地铁线路断面客流量
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