绍兴市市民卡出行数据

利用公交、地铁交易数据、自行车租借数据，通过出行分析模型，提供不同年龄段、不同时间段、不同路段的出行综合优化分析，应用于出行推荐、交通建设规划、建设投资分析等。通过数据采集，数据库实时采集每日市民出行数据，从而根据智能用户行为引导模型实时推荐出行方案。
出行方式推荐指数= 年龄影响因素*时间影响因素*路段影响因素
式中：年龄影响因素：该年龄出行人数占所有年龄总人数的比例。
时间影响因素：该时间出行人数占全天出行总人数的比例。
路段影响因素：该路段出行人数占所有路段总人数的比例。
例如，工作日上午8点，用户a输入目的地xxx商场，系统会根据用户a市民卡号自动测算用户年龄假设为28岁，模型会计算出年龄段22-30岁人群当天公交车出行人数占全年龄段当天公交车出行人数的比值，该值为公交车出行的年龄影响因素值，同理可得公交车出行的时间影响因素和路段影响因素，最终计算出公交车出行推荐指数=公交车出行的年龄影响因素*公交车出行的时间影响因素*公交车出行的路段影响因素，同时计算出自行车出行推荐指数和地铁出行推荐指数。最终会对比三个指数大小，给出最推荐的出行方式。

This dataset leverages public transit, subway transaction data and bike-sharing rental data, and adopts travel analysis models to conduct comprehensive optimization analysis of travel behaviors across different age groups, time periods and road sections. It is applicable to scenarios such as travel recommendation, transportation infrastructure planning and construction investment analysis.

Through real-time data collection, the database captures daily citizen travel data, and generates real-time travel recommendations based on the intelligent user behavior guidance model.

The Travel Mode Recommendation Index is calculated as: Age Impact Factor * Time Impact Factor * Road Section Impact Factor

Where:
Age Impact Factor: The ratio of the number of travelers in the corresponding age group to the total number of travelers across all age groups.
Time Impact Factor: The ratio of the number of travelers in the corresponding time period to the total number of travelers throughout the day.
Road Section Impact Factor: The ratio of the number of travelers on the corresponding road section to the total number of travelers across all road sections.

For example, at 8:00 AM on a weekday, when user A inputs the destination "XXX Mall", the system will automatically estimate the user's age (hypothetically set to 28 years old) based on their citizen ID card number. The model will calculate the ratio of the number of bus travelers in the 22-30 age group on that day to the total number of bus travelers across all age groups on that day, which serves as the Age Impact Factor for bus travel. Similarly, the Time Impact Factor and Road Section Impact Factor for bus travel can be obtained. The bus travel recommendation index is then calculated as Bus Travel Age Impact Factor * Bus Travel Time Impact Factor * Bus Travel Road Section Impact Factor. The recommendation indices for bike travel and metro travel are also calculated in the same way. Finally, the three indices are compared to recommend the optimal travel mode.