赛题说明

共享单车，延伸了城市公共交通脉络，解决了市民出行“最后一公里”问题。然而，随着共享经济模式被越来越多市民接受，成为出行习惯，潮汐现象也随之出现。白天工作、晚上休息的人类活动规律的客观存在，加之上下班时间段的集中，导致早晚高峰“一车难寻”、“无地可停”的供需矛盾。本题希望通过对车辆数据的综合分析，对厦门岛内早高峰阶段潮汐点进行有效定位，进一步设计高峰期群智优化方案，缓解潮汐点供需问题，以期为城市管理部门和共享单车运营方研究制定下一步优化措施提供数据支撑。

赛题任务

任务一：为更好地掌握早高峰潮汐现象的变化规律与趋势，参赛者需基于主办方提供的数据进行数据分析和计算模型构建等工作，识别出工作日早高峰07:00-09:00潮汐现象最突出的40个区域，列出各区域所包含的共享单车停车点位编号名称，并提供计算方法说明及计算模型，为下一步优化措施提供辅助支撑。 任务二：参赛者根据任务一Top40区域计算结果进一步设计高峰期共享单车潮汐点优化方案，通过主动引导停车用户到邻近停车点位停车，进行削峰填谷，缓解潮汐点停车位（如地铁口）的拥堵问题。允许参赛者自带训练数据，但需在参赛作品中说明所自带数据的来源及使用方式，并保证其合法合规。（城市公共自行车从业者将发生在早晚高峰时段共享单车“借不到、还不进”的问题称之为“潮汐”现象。本题涉及的“潮汐现象”聚焦“还不进”的问题，识别出早高峰共享单车最淤积的40个区域）

代码

#任务一：为更好地掌握早高峰潮汐现象的变化规律与趋势，参赛者需基于主办方提供的数据进行数据分析和计算模型构建等工作，识别出工作日早高峰07:00-09:00潮汐现象最突出的40个区域，列出各区域所包含的共享单车停车点位编号名称，并提供计算方法说明及计算模型，为下一步优化措施提供辅助支撑。

#共享单车潮汐点分析：[https://coggle.club/learn/dcic2021/task2](https://coggle.club/learn/dcic2021/task2) 共享单车潮汐点优化：[https://coggle.club/learn/dcic2021/task3](https://coggle.club/learn/dcic2021/task3)

import os, codecs
import pandas as pd
import numpy as np

%pylab inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('svg')

from matplotlib import font_manager as fm, rcParams
import matplotlib.pyplot as plt
PATH = './input/'

def bike_fence_format(s):
    s = s.replace('[', '').replace(']', '').split(',')
    s = np.array(s).astype(float).reshape(5, -1)
    return s


# 共享单车停车点位（电子围栏）数据
bike_fence = pd.read_csv(PATH + 'gxdc_tcd.csv')
bike_fence['FENCE_LOC'] = bike_fence['FENCE_LOC'].apply(bike_fence_format)

# 共享单车订单数据
bike_order = pd.read_csv(PATH + 'gxdc_dd.csv')
bike_order = bike_order.sort_values(['BICYCLE_ID', 'UPDATE_TIME'])


import geohash
bike_order['geohash'] = bike_order.apply(lambda x: 
                        geohash.encode(x['LATITUDE'], x['LONGITUDE'], precision=9), axis=1)

from geopy.distance import geodesic

bike_fence['MIN_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 1]))
bike_fence['MAX_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 1]))

bike_fence['MIN_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 0]))
bike_fence['MAX_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 0]))

bike_fence['FENCE_AREA'] = bike_fence.apply(lambda x: geodesic(
    (x['MIN_LATITUDE'], x['MIN_LONGITUDE']), (x['MAX_LATITUDE'], x['MAX_LONGITUDE'])
).meters, axis=1)

bike_fence['FENCE_CENTER'] = bike_fence['FENCE_LOC'].apply(
    lambda x: np.mean(x[:-1, ::-1], 0)
)

bike_order['geohash'] = bike_order.apply(
    lambda x: geohash.encode(x['LATITUDE'], x['LONGITUDE'], precision=6), 
axis=1)

bike_fence['geohash'] = bike_fence['FENCE_CENTER'].apply(
    lambda x: geohash.encode(x[0], x[1], precision=6))
geohash.encode(24.521156, 118.140385, precision=6), \
geohash.encode(24.521156, 118.140325, precision=6)
bike_order['UPDATE_TIME'] = pd.to_datetime(bike_order['UPDATE_TIME'])
bike_order['DAY'] = bike_order['UPDATE_TIME'].dt.day.astype(object)
bike_order['DAY'] = bike_order['DAY'].apply(str)

bike_order['HOUR'] = bike_order['UPDATE_TIME'].dt.hour.astype(object)
bike_order['HOUR'] = bike_order['HOUR'].apply(str)
bike_order['HOUR'] = bike_order['HOUR'].str.pad(width=2,side='left',fillchar='0')

bike_order['DAY_HOUR'] = bike_order['DAY'] + bike_order['HOUR']
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY_HOUR'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY_HOUR'], aggfunc='count', fill_value=0
)
bike_inflow.loc['wsk52r'].plot()
bike_outflow.loc['wsk52r'].plot()
plt.xticks(list(range(bike_inflow.shape[1])), bike_inflow.columns, rotation=40)
plt.legend(['Inflow', 'OutFlow'])
bike_inflow.loc['wsk596'].plot()
bike_outflow.loc['wsk596'].plot()
plt.xticks(list(range(bike_inflow.shape[1])), bike_inflow.columns, rotation=40)
plt.legend(['Inflow', 'OutFlow'])
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)
bike_remain = (bike_inflow - bike_outflow).fillna(0)
bike_remain[bike_remain < 0] = 0  
bike_remain = bike_remain.sum(1)
bike_fence['DENSITY'] = bike_fence['geohash'].map(bike_remain).fillna(0)
import hnswlib
import numpy as np

p = hnswlib.Index(space='l2', dim=2)
p.init_index(max_elements=300000, ef_construction=1000, M=32)
p.set_ef(1024)
p.set_num_threads(14)

p.add_items(np.stack(bike_fence['FENCE_CENTER'].values))

index, dist = p.knn_query(bike_order[['LATITUDE','LONGITUDE']].values[:], k=1)
bike_order['fence'] = bike_fence.iloc[index.flatten()]['FENCE_ID'].values
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_remain = (bike_inflow - bike_outflow).fillna(0)
bike_remain[bike_remain < 0] = 0  
bike_remain = bike_remain.sum(1)
# bike_fence = bike_fence.set_index('FENCE_ID')
bike_density = bike_remain / bike_fence.set_index('FENCE_ID')['FENCE_AREA']

bike_density = bike_density.sort_values(ascending=False).reset_index()
bike_density = bike_density.fillna(0)
bike_density['label'] = '0'
bike_density.iloc[:100, -1] = '1'

bike_density['BELONG_AREA'] ='厦门'
bike_density = bike_density.drop(0, axis=1)
bike_density.columns = ['FENCE_ID', 'FENCE_TYPE', 'BELONG_AREA']
bike_density.to_csv('result.txt', index=None, sep='|')

结果