## 1 Introduction  
Taxi service systems within large cities exhibit complexity due to the intricate interplay and self-organization between taxi drivers and passengers. Two significant inefficiencies in this complex system stand out excessive numbers of vacant trips and prolonged passenger wait times [1]. These challenges primarily arise from an imbalance between supply and demand, which could be mitigated through the establishment of a reliable mechanism for forecasting taxi demand. Central to this challenge is the fare structure. It directly influences the economic viability of taxi trips with varying distances and destinations [2]. In densely populated areas, there is often an oversupply of taxis, while remote areas suffer from insufficient availability [3]. Employing real-time point-to-point taxi pricing data alongside route information enhances the accuracy of demand prediction models, offering valuable insights into taxi demand [4]. Likewise, by providing fare projections, it is possible to create a closer approximation of pricing between taxis and third-party service providers, thereby reducing wait times and increasing overall efficiency. Giving taxi drivers access to a predicted taxi demand system is the main way to handle this issue [5]. By forecasting future taxi demand and proactively positioning their vehicles in various places in advance, they can balance operational efficiency and profitability [6]. Urban traffic forecasting poses unique geographical and temporal challenges due to the intricate interplay between these variables. The characteristics of nodes (locations) and edges (relationship between locations) subtly influence these correlations [7]. Traditional approaches fall short when relying solely on basic Global Positioning System (GPS) coordinates to describe location information [8]. Leveraging a fusion of deep learning models with varying levels of complexity and adaptiveness shows promise based on available data characteristics [9; 10; 11; 12]. However, certain factors, such as time of day, day of the week, weather conditions, and special events, introduce regularities and patterns in taxi demand, making the widely used classic models, such as autoregressive integrated moving average (ARIMA) model, less accurate [13; 14]. A more convincing model comes in the form of an MDN and recurrent neural network (RNN) fusion instead of offering a broad city-wide average. In this setting, the RNN model predicts temporal demand fluctuations, whereas the MDN model prognosticates pickup and dropoff demand distribution in each area [15]. Significantly improving the accuracy of traditional taxi fare models, such as the conventional LR model, holds substantial potential [16]. Taxi fares depend on various factors beyond distance, including drop charges and duration charges. While duration costs can be predicted, drop charges remain constant. [PERSON] et al. (2005) found that variable taxi fares linearly increase with trip length due to traffic [17]. However, the accuracy of the non-linear results is still unknown in spite of this [18]. In order to learn non-linear patterns, researchers have investigated ensemble models for improved precision, including stacking classifiers and utilizing location-based social networks [19; 20]. Among these, the ensemble model with a voting mechanism emerges as a particularly compelling approach [15]. This project aims to build a complete system that uses deep learning methods and historical taxi trip data to forecast demand trends and taxi fare estimates for the benefit of both passengers and taxi drivers. Accompanying this system is a user-friendly web application bridging the gap between theoretical research and practical execution. Visualizations, such as heatmaps and route maps, are anticipated to aid end users in comprehending the prediction results [21]. The platform also enables in-depth data analysis for research purposes, potentially facilitating future comparisons of fare structures among different taxi service providers.