金华市公交车站点人流热度分析数据

通过金华市公交集团的指挥调度监控系统，收集公交车辆的载客相关信息，实时掌握公交车路线的各个站点人流情况，有助于更加精准地评估各站点的热度和使用频率，从而调整和改进公交线路，确保公交服务的高效性和便捷性，满足金华市民日益增长的出行需求，推动城市公共交通系统向更高质量、更人性化的方向发展。1、数据采集：从金华市公交集团的指挥调度监控系统实时采集公交车的站点上行载客数量、站点上行客流总量、下行载客数量和下行客流总量；
2、数据处理：对采集到的数据按公交站点进行汇总分类、排列清洗，并按照时间维度进行排列；
3、数据加工：计算某一路线的站点流量拥挤指数=（上行载客数量+下行载客数量）/（上行载客总数+下行载客总数），判断站点流量指数的大小，大于500为高流量，在100至500之间为中等流量，在100以内为低流量。
4、数据应用：通过以上数据的统计加工分析，获得公交车的站点流量拥挤数据，有助于根据客流量信息优化扩大站点设立的密度及临时的排班调度。

This dataset collects passenger-related data of public transit vehicles through the Command, Dispatch and Monitoring System of Jinhua Public Transport Group, enabling real-time monitoring of passenger flow at each stop along bus routes. This facilitates accurate assessment of the popularity and usage frequency of each bus stop, thereby adjusting and optimizing bus routes, ensuring the efficiency and convenience of public transit services, meeting the growing travel demands of Jinhua residents, and advancing the urban public transit system toward higher-quality and more people-oriented development.
1. Data Collection: Real-time collection of upbound passenger count, total upbound passenger volume, downbound passenger count, and total downbound passenger volume at each bus stop, sourced from the aforementioned Command, Dispatch and Monitoring System of Jinhua Public Transport Group.
2. Data Processing: Aggregate, classify, sort and clean the collected data by bus stop, and arrange the dataset according to the time dimension.
3. Data Enrichment: Calculate the passenger flow congestion index for stops on a specific bus route using the formula:
Congestion Index = (Upbound Passenger Count + Downbound Passenger Count) / (Total Upbound Passenger Volume + Total Downbound Passenger Volume)
Then classify the congestion index: values greater than 500 are categorized as high passenger flow, values between 100 and 500 as medium passenger flow, and values below 100 as low passenger flow.
4. Data Application: Through statistical processing and analysis of the aforementioned data, the passenger flow congestion data of bus stops can be derived. This helps optimize and expand the density of bus stop establishments and adjust temporary scheduling and dispatching based on passenger flow information.