PredictHQ's Intelligent | Event Data | Traffic Data | Ride-Sharing, Transportation & Footfall Data | Global | Predict demand

Traffic data for ride-sharing & transportation is critical as event times can alter, how many people attending an event can alter depending on event type regardless of venue capacity (not all events will fill venues), and road closures from events can cause network delays. PredictHQ helps alleviate these business concerns to better mobilize driver partners and transport planners. We collate and aggregate local, national and international scheduled and unscheduled events into one handy place, so you can see what events are happening and when, make more cabs and drivers available, and send them to the right places. Uber has been a long-standing customer of ours, using the event data and traffic to decrease rider pickup times, increase rider conversion, and surfacing events to customers where they can book a ride there or learn more about the event. Data also includes Predicted Event Spend, a dollar figure that reflects the predicted amount of retail, accommodation, and transportation spending in a specific area as a result of a major event. At the core of this data sits our market leading global event coverage, predicted event attendance, local accommodation demand, third party data and more to give you greater geographical context of the scale of the economic impact an event will have. Sample Location: Mexico City, Mexico Visibility Window: 1-Year Historical Categories: Sports, Festivals, Expos, Conferences, Concerts, Performing Arts, and Community. Fields Included: - Title - Category - Labels - Description - Start date and time - End date and time - Predicted end time - Country - Lat / Lon - Venue Name - Venue Address - Rank (PHQ Rank, Local Rank) - PHQ Attendance - Event status - Place Hierarchy - Created/updated timestamps - Predicted event spend total - Predicted event spend accommodation - Predicted event spend hospitality - Predicted event spend transportation Polygon information: PredictHQ's polygons enable you to see the full area impacted by an event represented as a shape, because many types of events don't occur neatly at a point on a map. That means you will get a much more accurate picture of impact. Data samples including polygons are available upon request. Data quality: PredictHQ's data quality is one of its key strengths: 1) We have developed a set of Quality Standards for Processing Demand Causal Factors (QSPD), which are used to define the criteria for high-quality event data. By following these standards, PredictHQ ensures that their data meets the highest levels of quality. 2) We use more than 450 data sources to collect event data, including public records, social media, and ticketing websites. 3) We have built thousands of machine learning models that standardize, verify, enrich, and rank every single event. 4) On average we process 28 million events and 422,000 entities every day 5) We track the quality of our data over time and make improvements as needed. About PredictHQ: PredictHQ is the world’s first and only company that provides the missing context for the biggest external factor that impacts businesses demand – events. PredictHQ’s intelligent data of verified global events enables businesses to forecast shifts in demand from events to be able to adjust their inventory, make changes to labor, dynamically price and operate more efficiently. Think conferences, sports games, college graduations, floods, and more. PredictHQ brings all events into one place, combines it with world-first tools and intelligence to allow organizations to better predict and respond to changing customer demand created by events in an easy, reliable, and scalable way. We meet customers exactly where they are, ensuring they can access our data the way that suits them best. Learn more about PredictHQ's real-world event data by visiting our Developer and Data Science Documentation: https://docs.predicthq.com/ Keywords: attended events, attendance, sports, festivals, expos, conferences, concerts, performing arts, community, polygon, consumer spending, predicted spend, location information, demand intelligence, financial data, venue location, accommodation, transportation, restaurant, demand intelligence, event intelligence, event categorisation, business insights, event tracking, historical event data, even impact analysis, event-driven decisions, predictive analytics, ride-sharing, transportation, mobility, footfall,

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背景与挑战

背景概述

该数据集提供全球事件数据，包括体育、节日、会议等多种类别，结合交通和拼车信息，用于预测需求并优化运输规划。它涵盖事件标题、时间、地点、预测支出等字段，通过高质量数据源和机器学习模型确保准确性，帮助如Uber等客户减少接送时间并提升转化率。