To improve the accuracy of the charge figure, our ponder employments a choice tree classifier strategy and a demonstrate choice change. Within the case of preparing information, both strategies are utilized. To spare our information in a genuine dataset, we to begin with connected a show choice prepare. Moment, we utilized the choice tree classifier approach to resolve the issues with our dataset. At long last, based on the discoveries, we made a expectation in agreement with the time, date, number of travelers, and separate between both the pick-up and drop-off areas evaluated utilizing longitude and scope information by utilizing Python Libraries.

Dataset

This dataset was created by Tushar Tailor

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Dataset

This dataset was created by DanB

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BigQuery table with the training and test datasets for the New York City Taxi Fare Prediction Competition

Subset of training and test datasets for the New York City Taxi Fare Prediction

In this notebook, we analyze data on taxi trips in New York City to gain insights into how different factors affect trip fares. We start by visualizing the spatial distribution of trip origins and the relationship between fare and distance. Then, we fit a regression tree and a random forest to predict trip fares based on variables such as pickup location, time of day, day of the week, and month. We compare the performance of the two methods and highlight the most important predictors. Finally, we visualize the predicted fares and explore how they vary across the city.

An accurate dataset describing trajectories performed by all the 442 taxis running in the city of Porto, in Portugal.

We have provided an accurate dataset describing a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, in Portugal (i.e. one CSV file named "train.csv"). These taxis operate through a taxi dispatch central, using mobile data terminals installed in the vehicles. We categorize each ride into three categories: A) taxi central based, B) stand-based or C) non-taxi central based. For the first, we provide an anonymized id, when such information is available from the telephone call. The last two categories refer to services that were demanded directly to the taxi drivers on a B) taxi stand or on a C) random street.

Each data sample corresponds to one completed trip. It contains a total of 9 (nine) features, described as follows:

TRIP_ID: (String) It contains an unique identifier for each trip;

CALL_TYPE: (char) It identifies the way used to demand this service. It may contain one of three possible values: ‘A’ if this trip was dispatched from the central; ‘B’ if this trip was demanded directly to a taxi driver on a specific stand; ‘C’ otherwise (i.e. a trip demanded on a random street).

ORIGIN_CALL: (integer) It contains an unique identifier for each phone number which was used to demand, at least, one service. It identifies the trip’s customer if CALL_TYPE=’A’. Otherwise, it assumes a NULL value;

ORIGIN_STAND: (integer): It contains an unique identifier for the taxi stand. It identifies the starting point of the trip if CALL_TYPE=’B’. Otherwise, it assumes a NULL value;

TAXI_ID: (integer): It contains an unique identifier for the taxi driver that performed each trip;

TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;

DAYTYPE: (char) It identifies the daytype of the trip’s start. It assumes one of three possible values: ‘B’ if this trip started on a holiday or any other special day (i.e. extending holidays, floating holidays, etc.); ‘C’ if the trip started on a day before a type-B day; ‘A’ otherwise (i.e. a normal day, workday or weekend).

MISSING_DATA: (Boolean) It is FALSE when the GPS data stream is complete and TRUE whenever one (or more) locations are missing

POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;

The total travel time of the trip (the prediction target of this competition) is defined as the (number of points-1) x 15 seconds. For example, a trip with 101 data points in POLYLINE has a length of (101-1) * 15 = 1500 seconds. Some trips have missing data points in POLYLINE, indicated by MISSING_DATA column, and it is part of the challenge how you utilize this knowledge. Acknowledgements

Data from ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition Inspiration

Added this dataset because competition datasets do not appear in the dataset search and this dataset could help learn basic methods in the area of geo-spatial analysis and trajectory handling

Dataset

This dataset was created by Rahul Singh Maures

Contents

A CSV dataset containing 1710671 taxi trajectories recorded over one year (from 2013/07/01 to 2014/06/30) in the city of Porto, in Portugal.The dataset is derived from the "Taxi Service Trajectory - Prediction Challenge, ECML PKDD 2015 Data Set".Each CSV row contains:- taxi_id: numeric value identifying an involved taxi;- trajectory_id: numeric value identifying a trajectory in the original dataset;- timestamp: a timestamp corresponding to the starting time of the taxi ride;- source_point: GPS point representing the origin of the taxi ride;- target_point: GPS point representing the destination of the taxi ride; Coordinates are given in POINT(longitude latitude) format using the EPSG:4326 Geodetic coordinate system.

Web Taxi-Sharing Platforms Market Outlook

The global web taxi-sharing platforms market is experiencing significant growth, with an estimated market size of $23.5 billion in 2023 and projected to reach approximately $56.4 billion by 2032, reflecting an impressive compound annual growth rate (CAGR) of 10.2% during the forecast period. This growth trajectory is driven by several factors, including the increasing urbanization, the surge in smartphone penetration, and the rising need for cost-effective and efficient transportation solutions in congested city environments.

One of the primary growth drivers of the web taxi-sharing platforms market is the rapid urbanization observed across various regions worldwide. As urban areas continue to expand, the demand for efficient and flexible transportation solutions has become paramount. Traditional modes of transport are often insufficient to meet the needs of the growing urban population, leading to traffic congestion and increased pollution levels. Web taxi-sharing platforms offer an attractive alternative by optimizing the use of vehicles, thereby reducing the number of cars on the road, alleviating congestion, and minimizing environmental impact. This trend is particularly evident in developing countries, where urban migration is most pronounced, prompting widespread adoption of these platforms.

Furthermore, the proliferation of smartphones and mobile internet has significantly bolstered the growth of web taxi-sharing platforms. With the increasing affordability and accessibility of smartphones, a greater number of consumers can easily access taxi-sharing services through dedicated apps. This digital accessibility has facilitated real-time ride booking, improved user experience, and fostered trust in these platforms. Additionally, enhanced data analytics and machine learning algorithms have enabled platforms to provide personalized and efficient services, predicting demand, optimizing ride routes, and improving overall operational efficiency. This technological advancement continues to attract users and contribute to market growth.

Another critical factor driving market growth is the shifting consumer preference towards shared mobility services. As environmental concerns gain prominence and the cost of private vehicle ownership rises, many individuals are opting for taxi-sharing platforms as a practical and economical alternative. This shift is particularly noticeable among younger generations, who prioritize access over ownership and value sustainability. The convenience and flexibility offered by web taxi-sharing platforms are encouraging more users to adopt this mode of transportation for daily commutes and short-distance travel, fueling market expansion.

In recent years, IT Spending by Cab Aggregators has become a crucial factor in the evolution of the web taxi-sharing platforms market. As these platforms strive to enhance user experience and operational efficiency, significant investments are being made in advanced technologies such as artificial intelligence, machine learning, and data analytics. These technologies enable cab aggregators to optimize ride routes, predict demand, and offer personalized services to users. Furthermore, IT spending is directed towards developing robust cybersecurity measures to protect user data and ensure secure transactions. This focus on technology not only enhances service quality but also strengthens the competitive position of cab aggregators in a rapidly evolving market.

Regionally, the web taxi-sharing platforms market is witnessing diverse growth patterns. North America, led by the United States, holds a significant market share due to the early adoption of technology and the presence of major market players. Europe follows closely, with countries like the UK, Germany, and France driving growth through government initiatives promoting shared mobility. The Asia Pacific region is expected to exhibit the fastest growth during the forecast period, attributed to rapid urbanization, a burgeoning middle class, and increasing digital literacy. Latin America and the Middle East & Africa regions also present lucrative opportunities as they continue to develop their digital infrastructure and embrace shared mobility solutions.

Service Type Analysis

The web taxi-sharing platforms market is segmented by service type into ride-hailing, carpooling, and station-based mobility, each of which plays a crucial role in enhancing urban mobility. Ride-hailing se

AI-powered price forecasts for CAB stock across different timeframes including weekly, monthly, yearly, and multi-year predictions.

Dataset

This dataset was created by Koteeswaran Nagarajan

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Dataset

This dataset was created by Shubham Varshney

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It contains the following files: