train.csv.zip

We have provided an accurate dataset describing a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, in Portugal (i.e. one CSV file named "train.csv"). These taxis operate through a taxi dispatch central, using mobile data terminals installed in the vehicles. We categorize each ride into three categories: A) taxi central based, B) stand-based or C) non-taxi central based. For the first, we provide an anonymized id, when such information is available from the telephone call. The last two categories refer to services that were demanded directly to the taxi drivers on a B) taxi stand or on a C) random street.Each data sample corresponds to one completed trip. It contains a total of<br>9 (nine) features, described as follows:TRIP_ID: (String) It contains an unique identifier for each trip;CALL_TYPE: (char) It identifies the way used to demand this service. It may contain one of three possible values:<br>‘A’ if this trip was dispatched from the central;<br>‘B’ if this trip was demanded directly to a taxi driver on a specific stand;<br>‘C’ otherwise (i.e. a trip demanded on a random street).ORIGIN_CALL: (integer) It contains an unique identifier for each phone number which was used to demand, at least, one service. It identifies the trip’s customer if CALL_TYPE=’A’. Otherwise, it assumes a NULL value;ORIGIN_STAND: (integer): It contains an unique identifier for the taxi stand. It identifies the starting point of the trip if CALL_TYPE=’B’. Otherwise, it assumes a NULL value;TAXI_ID: (integer): It contains an unique identifier for the taxi driver that performed each trip;TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;DAYTYPE: (char) It identifies the daytype of the trip’s start. It assumes one of three possible values:<br>‘B’ if this trip started on a holiday or any other special day (i.e. extending holidays, floating holidays, etc.);<br>‘C’ if the trip started on a day before a type-B day;<br>‘A’ otherwise (i.e. a normal day, workday or weekend).MISSING_DATA: (Boolean) It is FALSE when the GPS data stream is complete and TRUE whenever one (or more) locations are missingPOLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;The total travel time of the trip (the prediction target of this competition) is defined as the (number of points-1) x 15 seconds. For example, a trip with 101 data points in POLYLINE has a length of (101-1) * 15 = 1500 seconds. Some trips have missing data points in POLYLINE, indicated by MISSING_DATA column, and it is part of the challenge how you utilize this knowledge.AcknowledgementsData from ECML/PKDD 15: Taxi Trip Time Prediction (II) CompetitionInspirationAdded this dataset because competition datasets do not appear in the dataset search and this dataset could help learn basic<br>methods in the area of geo-spatial analysis and trajectory handling

本数据集完整记录了葡萄牙波尔图市442台出租车在2013年7月1日至2014年6月30日一整年的行驶轨迹，对应单个名为"train.csv"的CSV文件。这些出租车均通过车载移动数据终端，由出租车调度中心完成调度运营。
我们将每一次乘车服务划分为三类：A类为调度中心派单服务；B类为固定出租车站点召车服务；C类为随机路边召车服务。对于A类服务，若可从通话记录中获取相关用户信息，我们将提供匿名化的用户标识。后两类服务则分别对应于在指定出租车站点直接向出租车司机发起的召车需求，以及在随机街道上发起的召车需求。
每条数据对应一次已完成的行程，共包含9项特征，具体说明如下：
1. TRIP_ID（字符串型）：每条行程的唯一标识符；
2. CALL_TYPE（字符型）：标识本次服务的发起方式，仅可取以下三种值：
- ‘A’：本次行程由调度中心派单；
- ‘B’：本次行程由乘客在指定出租车站点直接向出租车司机发起；
- ‘C’：本次行程由乘客在随机路边发起。
3. ORIGIN_CALL（整数型）：每条用于发起至少一次召车服务的电话号码的唯一标识符。当CALL_TYPE取值为‘A’时，该字段可用于标识本次行程的乘客；其余情况下该字段取值为NULL。
4. ORIGIN_STAND（整数型）：出租车站点的唯一标识符。当CALL_TYPE取值为‘B’时，该字段用于标识本次行程的起点；其余情况下该字段取值为NULL。
5. TAXI_ID（整数型）：执行本次行程的出租车司机的唯一标识符；
6. TIMESTAMP（整数型）：Unix时间戳（Unix Timestamp），单位为秒，用于标识本次行程的开始时间；
7. DAYTYPE（字符型）：标识行程开始当日的日期类型，仅可取以下三种值：
- ‘B’：行程开始当日为节假日或其他特殊日期（含延长假期、浮动节假日等）；
- ‘C’：行程开始当日为B类日期的前一日；
- ‘A’：其余情况（即普通工作日或周末）。
8. MISSING_DATA（布尔型）：当GPS数据流完整时该字段取值为FALSE，当存在一处或多处位置信息缺失时该字段取值为TRUE；
9. POLYLINE（字符串型）：以字符串形式存储的GPS坐标列表，采用WGS84坐标系（World Geodetic System 1984）。字符串以方括号（[ 和 ]）分别标记首尾，每一组坐标同样以方括号包裹，格式为[经度, 纬度]。该列表中每15秒记录一组坐标，列表的最后一项对应行程的目的地，第一项则对应行程的起点；
本次行程的总行驶时长（本竞赛的预测目标）计算公式为：（坐标点数量 - 1）×15秒。例如，若POLYLINE字段包含101个数据点，则行程时长为(101-1)×15=1500秒。部分行程的POLYLINE字段存在缺失数据点，由MISSING_DATA字段标记，如何合理利用此类缺失数据是本次任务的挑战之一。

致谢
本数据集源自ECML/PKDD 15：出租车行程时长预测（II）竞赛。

收录说明
本数据集的收录缘由在于竞赛数据集通常无法在通用数据集检索平台中获取，而该数据集可用于学习地理空间分析与轨迹处理领域的基础方法。

搜集汇总

背景与挑战

背景概述

该数据集包含葡萄牙波尔图市442辆出租车在2013年7月1日至2014年6月30日一整年的完整轨迹数据，以每次行程为样本，共9个特征，包括行程ID、呼叫类型、GPS轨迹等，适用于地理空间分析和轨迹处理方法的学习。