Find Taxi Stands
Welcome to "Find Taxi Stands" mobile app! Our APP is designed for taxi drivers, aiming to help you quickly locate the optimal nearby taxi stands, improve pickup efficiency, and increase your income.
Finding taxi stands in the city can be inconvenient, especially in unfamiliar areas.
Extended Empty Running Time
Research shows that taxis spend 40-60% of their time running empty while searching for passengers, resulting in significant waste of taxi resources.
Supply-Demand Imbalance
Some areas have high passenger demand but insufficient taxi supply, while other areas may have excess taxis but few passengers, affecting overall operational efficiency.
Our application offers multiple features to help taxi drivers improve work efficiency and income.
Optimal Stand Recommendations
Based on real-time data analysis, intelligently recommend the best nearby taxi stands to reduce empty running time and improve passenger pickup efficiency.
Best Cruising Routes
Plan optimal cruising routes for drivers, covering high-demand areas to increase the probability of finding passengers and reduce fuel consumption.
Real-time Hotspot Map
Display real-time passenger demand hotspot maps in the city, helping drivers make smarter decisions by heading to areas with higher demand.
Find Taxi Stands app brings multiple benefits to both taxi drivers and passengers.
Increase Income
Reduce empty running time by 40%, significantly improving drivers' daily income and operational efficiency.
Save Fuel
Optimize cruising routes, reduce unnecessary mileage, and lower fuel consumption and operating costs.
Balance Supply and Demand
Guide taxis to high-demand areas, alleviating urban traffic supply-demand imbalances and improving overall service levels.
Our application uses advanced reinforcement learning algorithms to provide precise recommendations for taxi drivers.
Reinforcement Learning Algorithm
The application employs customized reinforcement learning algorithms that learn from historical data and continuously optimize recommendation strategies to improve accuracy.
Big Data Analysis
Combine Hong Kong taxi industry big data and urban traffic data for real-time analysis and prediction, providing scientific decision support for drivers.
User Experience Optimization
Clean and intuitive interface design, suitable for drivers to quickly browse and operate while driving, ensuring safe and efficient use.
Reduced Empty Running Time (%)
Supported Languages
Number of Taxi Stands
App Launch Year
Hu Jianping
Chief Executive Officer of Haylion TechnologiesFounder of eMaaS, Founder and Chairman of Haylion Technologies, Chief Expert of the Urban Passenger Transport Expert Committee of China Road Transport Association, Ph.D. in Road and Traffic Engineering from Tongji University. Previously served as General Manager and Chairman of Shenzhen Bus Group Co., Ltd., Executive Deputy General Manager of Shenzhen Metro Group Co., Ltd., Assistant to Director and Head of Personnel Division of Shenzhen Government Transport Bureau, Vice Chairman of China Road Transport Association, and Senior Vice President of US Microvast Power Systems.
Qiu Jiandong
Senior Engineer of SUTPCDirector of Transportation Information and Modeling Institute, High-level Leading Talent of Shenzhen. 17 years of experience in traffic modeling and big data. Led over 30 urban modeling and big data platform projects and 10 national and provincial research projects. Authorized 30 patents, co-authored 5 monographs and industry standards, published 35 papers, and received 24 national and provincial awards.
Ke Jintao
Assistant Professor of HKUServes as Youth Editor of TRC and Editor of TRE, leading international journals in transportation. Leads multiple research projects, including National Natural Science Foundation, Hong Kong Transport Department Smart Transport Fund, Hong Kong Research Grants Council Fund, and Hong Kong Environmental Protection Department Green Fund, with total funding exceeding HKD 10 million. Published over 50 SCI/SSC papers with over 4,900 citations, ranked as World Top 2% Scholar in Smart Transportation by Stanford in 2023.