基于Geolife GPS轨迹数据集的用户指定时段出行时长计算方法

数据集说明 #

Geolife是记录用户移动过程的GPS轨迹数据集。本次分析基于Sina Dabiri提供的预处理版本，处理后的数据覆盖69名可用用户的GPS日志，构建为标准pandas DataFrame结构。

样例数据说明 #

本次选取3名用户的少量数据片段用于逻辑演示：

import pandas as pd

data = {'user': [10,10,10,10,10,10,10,10,21,21,21,54,54,54,54,54,54,54,54,54],
 'lat': [39.921683,39.921583,39.92156,39.13622,39.136233,39.136241,39.136246,39.136251,42.171678,42.172055,
         42.172243,39.16008333,39.15823333,39.1569,39.156,39.15403333,39.15346667,39.15273333,39.14811667,39.14753333],
 'lon': [116.472342,116.472315,116.47229,117.218033,117.218046,117.218066,117.218166,117.218186,123.676778,123.677365,
         123.677657,117.1994167,117.2002333,117.2007667,117.2012167,117.202,117.20225,117.20255,117.2043167,117.2045833],
 'date': ['2009-03-21 13:30:35','2009-03-21 13:33:38','2009-03-21 13:34:40','2009-03-21 15:30:12','2009-03-21 15:32:35',
          '2009-03-21 15:38:36','2009-03-21 15:44:42','2009-03-21 15:48:43','2007-04-30 16:00:20', '2007-04-30 16:05:22',
          '2007-04-30 16:08:23','2007-04-30 11:47:38','2007-04-30 11:48:07','2007-04-30 11:48:27','2007-04-30 12:04:39',
          '2007-04-30 12:04:07','2007-04-30 12:04:32','2007-04-30 12:19:41','2007-04-30 12:20:08','2007-04-30 12:20:21']
 }

对应生成的DataFrame结构如下：

df = pd.DataFrame(data)
df

输出结果：

user    lat        lon            date
0   10  39.921683   116.472342  2009-03-21 13:30:35
1   10  39.921583   116.472315  2009-03-21 13:33:38
2   10  39.921560   116.472290  2009-03-21 13:34:40
3   10  39.136220   117.218033  2009-03-21 15:30:12
4   10  39.136233   117.218046  2009-03-21 15:32:35
5   10  39.136241   117.218066  2009-03-21 15:38:36
6   10  39.136246   117.218166  2009-03-21 15:44:42
7   10  39.136251   117.218186  2009-03-21 15:48:43
8   21  42.171678   123.676778  2007-04-30 16:00:20
9   21  42.172055   123.677365  2007-04-30 16:05:22
10  21  42.172243   123.677657  2007-04-30 16:08:23
11  54  39.160083   117.199417  2007-04-30 11:47:38
12  54  39.158233   117.200233  2007-04-30 11:48:07
13  54  39.156900   117.200767  2007-04-30 11:48:27
14  54  39.156000   117.201217  2007-04-30 12:04:39
15  54  39.154033   117.202000  2007-04-30 12:04:07
16  54  39.153467   117.202250  2007-04-30 12:04:32
17  54  39.152733   117.202550  2007-04-30 12:19:41
18  54  39.148117   117.204317  2007-04-30 12:20:08
19  54  39.147533   117.204583  2007-04-30 12:20:21

统计规则说明 #

• 按用户维度对GPS记录按时间升序排序
• 同一用户相邻两条GPS记录时间间隔超过30分钟时，判定间隔前后为两段独立行程
• 单段行程时长 = 该段最后一条记录时间 - 该段首条记录时间
• 指定时间范围内所有用户所有行程的时长总和，即为该时段总出行时长

统计示例 #

• 2009年3月总出行时长：该月仅用户10有出行记录，2009-03-21的记录中13:34:40与下一条15:30:12间隔超过30分钟，判定为2段独立行程，第一段时长约5分钟，第二段约19分钟，总时长为24分钟。
• 2007年4月总出行时长：该月用户21、54同日有出行记录，用户21行程时长约8分钟；用户54的记录中相邻最大间隔不足30分钟，判定为单段行程，时长约33分钟，总时长为41分钟。
• 需支持任意自定义时间范围（如2008年2月至2009年3月）的出行时长统计。

具体实现方案 #

实现思路 #

1. 数据预处理：将时间字段转为datetime格式，方便时间差计算
2. 范围过滤：按传入的起止时间，只保留统计区间内的GPS记录
3. 排序：按用户分组，组内记录按时间升序排列
4. 行程切分：计算同一用户相邻记录的时间差，时间差超过30分钟的位置标记为新行程起点
5. 时长计算：为每条记录分配所属行程ID，按「用户+行程ID」分组计算每段行程的时长
6. 结果汇总：累加所有符合条件的行程时长，得到指定时间段的总出行时长

完整实现代码 #

import pandas as pd

def calc_total_travel_time(df: pd.DataFrame, start_time: str, end_time: str, gap_threshold: int = 30) -> float:
    """
    统计指定时间范围内的用户总出行时长
    :param df: 原始GPS轨迹DataFrame，需包含user、date字段
    :param start_time: 统计开始时间，支持'YYYY-MM-DD'或'YYYY-MM-DD HH:MM:SS'格式
    :param end_time: 统计结束时间，格式同上
    :param gap_threshold: 行程切分的时间间隔阈值，单位分钟，默认30分钟
    :return: 总出行时长，单位分钟，保留2位小数
    """
    # 避免修改原始数据
    df = df.copy()
    # 时间格式转换
    df['date'] = pd.to_datetime(df['date'])
    start = pd.to_datetime(start_time)
    end = pd.to_datetime(end_time)
    # 过滤统计时间范围内的数据
    df = df[(df['date'] >= start) & (df['date'] <= end)].reset_index(drop=True)
    if df.empty:
        return 0.0

    # 按用户、时间排序
    df = df.sort_values(by=['user', 'date']).reset_index(drop=True)

    # 计算同用户相邻记录的时间差，单位分钟
    df['time_gap'] = df.groupby('user')['date'].diff().dt.total_seconds() / 60
    # 标记新行程起点：用户第一条记录、或与上一条记录间隔超过阈值
    df['is_new_trip'] = (df['time_gap'].isna()) | (df['time_gap'] > gap_threshold)
    # 生成每个用户内部的行程唯一ID
    df['trip_id'] = df.groupby('user')['is_new_trip'].cumsum()
    # 生成全局唯一行程ID
    df['global_trip_id'] = df['user'].astype(str) + '_' + df['trip_id'].astype(str)

    # 计算每段行程的起止时间和时长
    trip_info = df.groupby('global_trip_id').agg(
        trip_start=('date', 'min'),
        trip_end=('date', 'max')
    ).reset_index()
    trip_info['duration_min'] = (trip_info['trip_end'] - trip_info['trip_start']).dt.total_seconds() / 60

    # 返回总时长
    return round(trip_info['duration_min'].sum(), 2)

# 样例测试
if __name__ == '__main__':
    # 加载样例数据
    data = {'user': [10,10,10,10,10,10,10,10,21,21,21,54,54,54,54,54,54,54,54,54],
     'lat': [39.921683,39.921583,39.92156,39.13622,39.136233,39.136241,39.136246,39.136251,42.171678,42.172055,
             42.172243,39.16008333,39.15823333,39.1569,39.156,39.15403333,39.15346667,39.15273333,39.14811667,39.14753333],
     'lon': [116.472342,116.472315,116.47229,117.218033,117.218046,117.218066,117.218166,117.218186,123.676778,123.677365,
             123.677657,117.1994167,117.2002333,117.2007667,117.2012167,117.202,117.20225,117.20255,117.2043167,117.2045833],
     'date': ['2009-03-21 13:30:35','2009-03-21 13:33:38','2009-03-21 13:34:40','2009-03-21 15:30:12','2009-03-21 15:32:35',
              '2009-03-21 15:38:36','2009-03-21 15:44:42','2009-03-21 15:48:43','2007-04-30 16:00:20', '2007-04-30 16:05:22',
              '2007-04-30 16:08:23','2007-04-30 11:47:38','2007-04-30 11:48:07','2007-04-30 11:48:27','2007-04-30 12:04:39',
              '2007-04-30 12:04:07','2007-04-30 12:04:32','2007-04-30 12:19:41','2007-04-30 12:20:08','2007-04-30 12:20:21']
     }
    df = pd.DataFrame(data)
    
    # 2009年3月统计
    print(f"2009年3月总出行时长：{calc_total_travel_time(df