## 3 Result And Discussion
### Interface and Use Cases  
The demand interface is developed for NYC taxi drivers with three key features (i.e., identify the most demanded areas, highest payment zones, customer pick up and drop off trends). First, using the heatmap provided by the map, taxi drivers can pinpoint the areas with the most demand. The most popular zones were predicted historically in direct relationship to the number of pickups from travel data. The zone information at the top of the page on the right side of Figure 6A gives further summaries of the taxi ride for the zones. Figure 6B's line graph, which uses the minute as its unit of measurement, depicts the total demand for the following timestep as determined by users. Finally, Figure 6C shows a list of the top five demanded zones and a payment analysis to help taxi drivers determine the zones with the highest revenue levels. Taxi drivers can choose which zone to travel in using the interfaces in the following use cases:  
1. Simply using a heatmap, point to the neighboring zone with the highest demand in the upcoming timestep. By moving in the direction of greater need, this situation is regarded as one of the quickest methods to gain a new client.
2. When neighboring zones have comparable demand, the top five demand zones are listed along with a payment analysis for the higher-income zone. Extending from above, a taxi driver could choose to travel to the area with the highest revenue while the other regions have a comparable demand. Consequently, integrating the heatmap, top zones list, and payment analysis can result in superior decision support with historical income for taxi drivers.
3. With a thorough evaluation of the demand heatmap, payment analysis, and trip summary for the maximum zone. Suppose there was a demand scenario on a Sunday between 8 and 8:30 p.m. Penn Station/Madison Sq West is the highest demand zone, and it has the information (designated in Figure 6A) of the average journey distance, speed, and duration. On Monday, the zone's average speed was around 10 miles per hour higher than typical. Combining the demand line chart (labeled as B in Figure 6), which displays the decreasing trend in the number of pickups and the higher trend in the number of dropoffs, provides proof that the taxi service is over-served. Label C denotes the highest earning zone, suggesting that drivers could be able to earn more money during the over-served hour.
4. By comparing the trip information in various timesteps, it is possible to estimate traffic jams using historical data (label A in Figure 6). The area's typical speed is revealed by the high average speed at night. There is presumed traffic congestion when the average speed of the trips in a particular zone is low and the average length is high.  
In order to provide protection for passengers by providing a straightforward fare for all types of taxi services, including yellow taxis, green taxis, and Uber, a fare prediction interface has been created. After the user picks the destinations and the precise time, the fare forecast result is shown in Figure 4d. To choose the origin and destination, the user can enter them manually or click on the map. The nearest day with a 30 min interval in the provided DateTime in the history data or by the ML fare prediction is used to obtain the fare. The query also considers the duration and distance generated in real-time by the Google Maps API. Additionally, the uploading of taxi datasets is taken into account for the ease of data analysis for machine learning models and potential expansions. The dataset upload interface, which supports the format of CSV files with cleaned data, is also shown in Figure 4d. The CSV file is also placed into the database for real-time access to facilitate taxi demand and fare searching.