## 4 Conclusions  
In this research, we have constructed prediction models and successfully linked them with a web application to establish a comprehensive platform for viewing real-time taxi information and easing interactions between passengers and drivers in NYC. This taxi service system has an intuitive and interactive user interface, which has substantial consequences for three important areas.  
Firstly, the platform offers drivers useful insights to maximize their profit and cut down on journey time. Drivers can utilize our system to gain access to vital data such as passenger demand trends (which pinpoints locations with strong demand at any given moment), proximity to potential passengers, estimated travel times to each customer, and projected profitability for each trip. With this information at hand, drivers are better equipped to choose passenger pickups wisely, maximizing their revenue effectively. Secondly, the platform also helps passengers by giving them access to crucial data that influences their choice of taxi service. They can locate platforms with reduced rates, decide  
Figure 6: The demand prediction interface and use cases. The demand areas and income ratings to support the decision making for taxi drivers are stated on the left-hand side of the figure. (**A**) general information at a specific zone. (**B**) the pick and drop trend of a zone. (**C**) payment analysis of the top 5 zones.  
on the best pickup locations to reduce waiting periods (finding places where it is most likely that a taxi would arrive promptly), and obtain rough estimations of the cost of the trip. With this knowledge at hand, customers can decide which taxi to take with confidence, improving their overall experience and convenience. Furthermore, the platform gives government officials helpful insights on a larger scale. The government can learn more about a variety of facets of the city's taxi sector, including profit margins, current traffic conditions, and regions prone to regular traffic jams, by utilizing the system's richness of data. These revelations can help in the decision-making process for upcoming upgrades to city road infrastructure, resulting in more effective transportation systems and better urban planning.  
In summary, our study has effectively created an interactive platform that is user-friendly and includes forecasting models for taxi demand and fare calculation. With the use of this platform, drivers, passengers, and government agencies can all make educated decisions, allocate resources more effectively, and improve overall efficiency in NYC's taxi ecosystem.  
Conceptualization, K.S.C.; demand and fare model, K.S.C. and K.L.W.; web application, K.S.C.; taxi data analysis, K.L.W.; formal analysis, K.S.C. and K.L.W.; system and model investigation, K.S.C. and K.L.W.; resource, R.T., S.-K.T. and G.P.; writing--original draft preparation, K.S.C., K.L.W. and B.Z.; writing--review and editing, K.S.C., K.L.W., B.Z., D.A., S.K.I., C.T.L., R.T., S.-K.T. and G.P.; supervision, S.K.I., C.T.L., R.T., S.-K.T. and G.P.; project administration, C.T.L., S.K.I., R.T., S.-K.T. and G.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported in part by the Macao Polytechnic University--Edge Sensing and Computing: Enabling Human-centric (Sustainable) Smart Cities (RP/ESCA-01/2020) and by the H2020 project titled \"European Bus Rapid Transit of 2030: Electrified, Automated, Connected\" EBRT--Grant Agreement No. 101095882.  
The authors declare no conflict of interest.