如何根据最近的日期时间更新坐标？

Question

如何根据记录的日期更新坐标。例如，没有 6/27 的观察，但如果我可以假设两辆车都以与上次观察相同的方向和速率行驶，我将如何更新以使用最近记录的 lat/lng 计算距离？

import pandas as pd

# Car 1
car1_data = {
    'id': ['id0120169', 'id0386349', 'id1962532', 'id0335207', 'id0273508'],
    'date_recorded': ['7/1/2016 9:05', '6/29/2016 22:26', '6/29/2016 2:47', '6/26/2016 22:29', '6/25/2016 19:19'],
    'longitude': [-73.99017334, -73.97213745, -73.91963959, -73.98487091, -73.99116516],
    'latitude': [40.75667953, 40.75746155, 40.80377579, 40.76160812, 40.75000763],
    'rate': [50, 67, 43, 35, 46],
    'direction': ['N', 'E', 'E', 'S', 'W']}
car1_df = pd.DataFrame(car1_data)

# Car 2
car2_data = {
    'id': ['id2681896', 'id3308448', 'id2108525', 'id3952220', 'id2771348'],
    'date_recorded': ['6/30/2016 23:59', '6/29/2016 19:33', '6/28/2016 20:50', '6/26/2016 21:07', '6/25/2016 18:46'],
    'longitude': [-73.9881286621093, -73.9656295776367, -73.9716567993164, -73.9820938110351, -73.9761657714843],
    'latitude': [40.7320289611816, 40.7527198791503, 40.7945442199707, 40.7694053649902, 40.7602424621582],
    'rate': [81, 76, 59, 48, 55],
    'direction': ['S', 'E', 'N', 'W', 'W']}
car2_df = pd.DataFrame(car2_data)

Answer 1

您需要做的是根据最后观察到的每辆车的方向和速率来计算其运动。如果我们假设两辆车在 6 月 27 日之前最后一次观察到的 24 小时内都沿着最后一次观察到的方向移动。

鉴于您的数据：

import pandas as pd

car1_data = {
    'id': ['id0120169', 'id0386349', 'id1962532', 'id0335207', 'id0273508'],
    'date_recorded': ['7/1/2016 9:05', '6/29/2016 22:26', '6/29/2016 2:47', '6/26/2016 22:29', '6/25/2016 19:19'],
    'longitude': [-73.99017334, -73.97213745, -73.91963959, -73.98487091, -73.99116516],
    'latitude': [40.75667953, 40.75746155, 40.80377579, 40.76160812, 40.75000763],
    'rate': [50, 67, 43, 35, 46],
    'direction': ['N', 'E', 'E', 'S', 'W']}
car1_df = pd.DataFrame(car1_data)

car2_data = {
    'id': ['id2681896', 'id3308448', 'id2108525', 'id3952220', 'id2771348'],
    'date_recorded': ['6/30/2016 23:59', '6/29/2016 19:33', '6/28/2016 20:50', '6/26/2016 21:07', '6/25/2016 18:46'],
    'longitude': [-73.9881286621093, -73.9656295776367, -73.9716567993164, -73.9820938110351, -73.9761657714843],
    'latitude': [40.7320289611816, 40.7527198791503, 40.7945442199707, 40.7694053649902, 40.7602424621582],
    'rate': [81, 76, 59, 48, 55],
    'direction': ['S', 'E', 'N', 'W', 'W']}
car2_df = pd.DataFrame(car2_data)

您应该定义以下函数来更新位置并预测相对于前一个位置的位置：

def update_position(row, hours=24):
    delta = row['rate'] * 0.001 * hours
    if row['direction'] == 'N':
        return row['latitude'] + delta, row['longitude']
    elif row['direction'] == 'S':
        return row['latitude'] - delta, row['longitude']
    elif row['direction'] == 'E':
        return row['latitude'], row['longitude'] + delta
    elif row['direction'] == 'W':
        return row['latitude'], row['longitude'] - delta

def preprocess_df(df):
    df['date_recorded'] = pd.to_datetime(df['date_recorded'])
    df.sort_values('date_recorded', inplace=True)

def predict_positions(df, missing_date):
    """
    Predict positions for a given date assuming constant movement from the last recorded position.
    """
    preprocess_df(df)
    df_before_missing = df[df['date_recorded'] < pd.to_datetime(missing_date)].copy()
    if df_before_missing.empty:
        return df
    last_observation = df_before_missing.iloc[-1]
    new_lat, new_long = update_position(last_observation)
    new_row = pd.DataFrame([{
        'id': last_observation['id'],
        'date_recorded': pd.to_datetime(missing_date),
        'longitude': new_long,
        'latitude': new_lat,
        'rate': last_observation['rate'],
        'direction': last_observation['direction']
    }])
    df_updated = pd.concat([df, new_row], ignore_index=True)
    return df_updated

因此，对于所需的日期：

missing_date = '6/27/2016'
car1_df_updated = predict_positions(car1_df, missing_date)
car2_df_updated = predict_positions(car2_df, missing_date)

您将获得汽车1

          id       date_recorded  longitude   latitude  rate direction
0  id0273508 2016-06-25 19:19:00 -73.991165  40.750008    46         W
1  id0335207 2016-06-26 22:29:00 -73.984871  40.761608    35         S
2  id1962532 2016-06-29 02:47:00 -73.919640  40.803776    43         E
3  id0386349 2016-06-29 22:26:00 -73.972137  40.757462    67         E
4  id0120169 2016-07-01 09:05:00 -73.990173  40.756680    50         N
5  id0335207 2016-06-27 00:00:00 -73.984871  39.921608    35         S

2号车

          id       date_recorded  longitude   latitude  rate direction
0  id2771348 2016-06-25 18:46:00 -73.976166  40.760242    55         W
1  id3952220 2016-06-26 21:07:00 -73.982094  40.769405    48         W
2  id2108525 2016-06-28 20:50:00 -73.971657  40.794544    59         N
3  id3308448 2016-06-29 19:33:00 -73.965630  40.752720    76         E
4  id2681896 2016-06-30 23:59:00 -73.988129  40.732029    81         S
5  id3952220 2016-06-27 00:00:00 -75.134094  40.769405    48         W