Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

About this Dataset

Data retrieved from Uber Movement, (c) 2017 Uber Technologies, Inc.,https://movement.uber.com

Objectives

Over the past six and a half years, Uber has learned a lot about the future of urban mobility and what it means for cities and the people who live in them. Uber has gotten consistent feedback from cities they partner with that access to their aggregated data will inform decisions about how to adapt existing infrastructure and invest in future solutions to make our cities more efficient. Uber hopes Uber Movement can play a role in helping cities grow in a way that works for everyone.

Background

https://d3i4yxtzktqr9n.cloudfront.net/web-movement/static/pdfs/Movement-TravelTimesMethodology-76002ded22.pdf

Uber rides🚗🚗 🚗🚗

Travel Time - Uber Movement 🚗🚗🚗🚗

DESCRIPTION❤️❤️

Over the past six and a half years, Uber has learned a lot about the future of urban mobility and what it means for cities and the people who live in them. Uber has gotten consistent feedback from cities they partner with that access to their aggregated data will inform decisions about how to adapt existing infrastructure and invest in future solutions to make our cities more efficient. Uber hopes Uber Movement can play a role in helping cities grow in a way that works for everyone.

Note😃😃😃😃

• This data is for training how using data analysis 🤝🎉

• Please appreciate the effort with an upvote 👍 😃😃

Thank You ❤️❤️❤️

Context

Uber Movement provides anonymized data from over two billion trips to help urban planning around the world. See more at Uber Movement.

Content

This data set includes the aggregated mean and range for all Uber rides starting from the the San Francisco Caltrain Station, serves dozens of stations between San Francisco and the South Bay.

The CSV file contains data for all rides between October 2017 and December 2017, inclusive.

Acknowledgements

This data was downloaded from Uber Movement. From the organization:

Over the past six and a half years, we’ve learned a lot about the future of urban mobility and what it means for cities and the people who live in them. We’ve gotten consistent feedback from cities we partner with that access to our aggregated data will inform decisions about how to adapt existing infrastructure and invest in future solutions to make our cities more efficient. We hope Uber Movement can play a role in helping cities grow in a way that works for everyone.

Inspiration

What are traffic patterns like in San Francisco?

Mit der Uber Movement Plattform lassen sich Fahrtzeiten auf den Straßen in Wien digital auswerten und visualisieren. Das ermöglicht Stadtplanern, Behörden und Forschungsinstitutionen fundierte Einsichten in den Mobilitätsbedarf der Stadt. Zudem können Effekte von Infrastrukturmaßnahmen gezielt analysiert und ausgewertet werden. Uber stellt die Daten frei zugänglich auf dem Uber Movement Portal bereit.

Context

Well,the data is taken form the machine hack site.It leads us to the problem of finding the traffic problems in the metro cities. It is also about how to regulate the movement of the cabs so as to get control over the traffic problems.

Content

Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. We invite data scientists, analysts and people from all technical interests to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

Acknowledgements

1. Machine Hack

Inspiration

1. How can we manage day to day traffic ? 2 .How the moments of cabs to be regulated ?
2. Awareness about the Use of public transport.

Modellierte stündliche Verkehrsgeschwindigkeit (km/h) auf Straßenebene an einem durchschnittlichen Tag basierend auf Twitter, OpenStreetMap und Uber Movement Daten. Der Datensatz beeinhaltet die Städte Barcelona, Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo und Seattle. Methodik: Basierend auf Twitter und OpenStreetMap (OSM) Daten wurde mit Hilfe von maschinellem Lernen mehrere Modelle trainiert, welche die Verkehrsgeschwindigkiet innerhalb der Städte vorhersagt. Als Referenzdaten wurden öffentlich bereitgestellte Daten von UBER verwendet (https://movement.uber.com). Als Indikatoren im Modell wurden die OSM tags highway und maxspeed, die Stunde des Tages und die Anzahl an Tweets in der Nähe der jeweiligen Straße verwendet. Zudem wurden Autofahrten mit Hilfe des openrouteservice basierend auf der räumlichen Verteilung der Bevölkerung und relevanter POIs simuliert und im Modell berücksichtigt. Für Modellierung der Verkehrgeschwindigkeit wurden Daten von Uber Movement, (c) 2022 Uber Technologies, Inc., (https://movement.uber.com), OpenStreetMap (ohsome API und Geofabrik) und der Twitter API (https://developer.twitter.com/en/docs/twitter-api) verwendet.

Modeled congestion probability for selected cities based on Twitter and OpenStreetMap data based on grid cells in 100 meters resolution. The dataset includes the cities of Barcelona, Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ranges from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, machine learning was used to train a model that predicts the likelihood of congestion within cities. Publicly provided data from UBER were used as reference data (https://movement.uber.com). As indicators in the model, the number of tweets and the number of points of interest from OSM near roads were used. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.

Modeled probability of congestion for selected cities based on Twitter and OpenStreetMap data on a grid cell basis with a resolution of 100 meters. The data set includes the cities of Barcelona, ​​Berlin, Cincinnati, Kiev, London, Madrid, Nairobi, New York City, San Francisco, Sao Paulo and Seattle. The range of values ​​is from 0 (probably normal traffic flow) to 1 (high probability of traffic flow delay). Methodology: Based on Twitter and OpenStreetMap (OSM) data, a model was trained with the help of machine learning, which predicts the probability of traffic jams within the cities. Publicly provided data from UBER was used as reference data (https://movement.uber.com). The number of tweets and the number of points of interest from OSM near roads were used as indicators in the model. In addition, car journeys were simulated with the help of the openrouteservice based on the spatial distribution of the population and relevant POIs and taken into account in the model.

Our main question is to find out what San Francisco's road safety problems are and what the city is doing to fix them. Our first approach is to see if there is any correlation between specific populations by census tract and the collision rates. If the approach fails, the alternative is to look at how the collision rates are correlated with the public safety projects. By looking at how the projects have impacted road safety, we can assess whether the city is on the right track with the projects, or if the projects are a waste of time and money. Our original proposal was to analyze traffic in San Francisco. That was when we assumed we were able to use the data from Uber Movement. Due to certain constraints that will be mentioned in the Data Sources section, we were unable to perform such analysis. Hence, we switched to analyzing road safety instead.Notable Modules Used: Python: pandas, geopandas, shapely, matplotlib, scipy ArcGIS: aggregate_points

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Uber (UBER) Stock Forecast: Positive Outlook

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

With the taxi sector booming exponentially in the country, the ride hailing industry has been the source of employment for a number of people across India. The market is dominated by two players, Uber and Ola. The number of employees in OlaCabs was over *** thousand as of July 2016. This snowballing growth of the cab industry has been creating problems for local rickshaw and auto drivers with people opting to take a ride in an online taxi as opposed to an auto-rickshaw.

Battle of the Giants

Even after the arrival of the San-Francisco based Uber, it is the native company doing the heavy lifting in the market. Ola held the highest share of taxi apps installed across the country in 2017, whereas Uber suffered more de-installations in the same time frame.

A cab wherever you are

High penetration is presumably one of the major factors for the success of the native company. As opposed to its main competitor, OlaCabs had a reach of an additional ** percent among smartphone users in tier * cities in 2017. The firm operates in more than 100 cities, twice more than its counterpart, leading to this development. Despite the differences in their services and revenue streams, both companies still seem to thrive for greater success with new developments in the now fast-moving economy of India. With the announcement of an outpost in Australia, the home-grown startup from India does not seem willing to stop at just *** destination.

This is a series of single channel videos depicting a series of moving paintings that present a philosophical enquiry and overcome barriers for visually understanding the complex nature of connections and ruptures between memory and place. The works show time in reverse while they reappropriate various character-based portraits derived from medieval religious paintings at St. Michael's church, Schwaebisch Hall, Germany. Wilson's work has strongly influenced gothic sensibility and has had high cultural impact, helping to establish a genre identity internationally. Images were re-created through performative video as a way of repositioning memories of the original images into locations that themselves hold memories. Wilson's videoart influences include Bill Viola and Eija-Liisa Ahtila. "Wilson's hangover durationals best illustrate how cinema can affect another medium and comment on it. He steals memories from filmic locations, ones that carry the loaded weight of time and uses film techniques to burden us with their significance." "Only by using video could Wilson temporally hold us in his Altered States-like hyperbaric chamber. To use film would cost Wilson a fortune and slow him down (and it's the work he wants to slow down not the process." "It's within this militarised zone that video art can forge a universal appeal and communicate to more than a couple of art students." Lee, B (07). Artlink, Vol.27, No.3, pp28-31. Also shown at venues in Aust., Europe and the USA; eg U-Turn, Glendale College Gallery, LA, curated by Larissa Hjorth and Kate Shaw (supported by Arts Vic. International Fund). Expert Malcom Bywaters used the series for an enquiry into important contemporary Australian artworks which focus on issues of memory and place. In 2006 Wilson was visiting Prof. at the Hochshule der Medien Stuttgart and founded the International Conf. on Film and Memorialisation series, with the first at the Uni. of Applied Sciences, Schwaebsich Hall.

At The Good Seat, we believe that to create the world of tomorrow, more inclusive and sustainable, we have to change the way people move first. Our data comes from our white-label solution that makes it easy for experience platforms to add ride-hailing & taxi offers to their product.We provide these platforms - in several industries (mobility, travel, hospitality, corporates and other) - with a comparator supporting booking and payment, and favoring better offers for everyone (electric vehicles, accessibility preference and other). We acces in real time data from Uber, Bolt, Freenow, local taxis and local ride-hailing players. Our data is exhaustive and will inform you both on ride-hailing providers data and ride-hailing users' habits.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

UBER Stock Price Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Taxi and Ride-hailing Services Market Outlook

As of 2023, the global taxi and ride-hailing services market size is estimated to be approximately USD 150 billion, with a projected compound annual growth rate (CAGR) of 10.5% from 2024 to 2032, which would take the market size to around USD 374 billion by 2032. The growth of this market is primarily driven by urbanization, increasing smartphone penetration, and the convenience offered by ride-hailing services.

One of the major growth factors for the taxi and ride-hailing services market is the rapid pace of urbanization. As more people move to urban areas, the demand for efficient, cost-effective, and convenient transportation solutions has skyrocketed. Urban dwellers often prefer ride-hailing services over owning private vehicles due to the high cost of vehicle maintenance, fuel, and parking. Additionally, urban congestion has made ride-hailing services an attractive alternative, helping to reduce traffic and environmental impact.

The increasing penetration of smartphones and internet connectivity has also been a key driver for this market. With the proliferation of affordable smartphones and widespread internet access, more people can easily book rides through various ride-hailing apps. Companies like Uber, Lyft, and Didi Chuxing have capitalized on this trend by offering user-friendly interfaces and seamless booking experiences. This technological advancement has made it easier for consumers to access ride-hailing services, thereby contributing to market growth.

The convenience and flexibility offered by ride-hailing services are another significant factor contributing to market growth. Unlike traditional taxi services, ride-hailing platforms provide users with the ability to book rides on-demand, schedule rides in advance, and even choose the type of vehicle they prefer. This level of convenience has attracted a wide range of users, from daily commuters to occasional travelers. The availability of multiple payment options, including cashless transactions, further adds to the convenience, making ride-hailing services a preferred choice for many.

The role of Transportation Aggregators has become increasingly significant in the evolution of the taxi and ride-hailing services market. These aggregators act as intermediaries, connecting passengers with a network of drivers through digital platforms. By leveraging technology, transportation aggregators streamline the process of booking rides, ensuring that users can access transportation services quickly and efficiently. This model not only enhances user convenience but also provides drivers with a steady stream of passengers, optimizing their earnings potential. As the market continues to grow, transportation aggregators are likely to play a pivotal role in shaping the future of urban mobility, offering innovative solutions to meet the diverse needs of modern commuters.

Regionally, the Asia Pacific market is expected to dominate the taxi and ride-hailing services market during the forecast period. This growth can be attributed to the large population base, rapid urbanization, and increasing disposable incomes in countries like China and India. Moreover, the presence of major market players such as Didi Chuxing in China and Ola in India significantly contributes to the region's market growth. North America and Europe are also notable markets, driven by high smartphone penetration and the early adoption of ride-hailing services. Meanwhile, Latin America and the Middle East & Africa are emerging markets with significant potential for growth due to improving economic conditions and increasing urbanization.

Service Type Analysis

The taxi and ride-hailing services market is broadly segmented by service type into E-hailing, Car Rental, Car Sharing, and Station-based Mobility. E-hailing, which involves booking rides through digital platforms, is the most dominant segment and is expected to continue its growth trajectory over the forecast period. The convenience, speed, and efficiency offered by e-hailing services have made them immensely popular among urban dwellers. Companies like Uber and Lyft have played a significant role in popularizing e-hailing, and continuous technological advancements are expected to further boost this segment.

Car rental services, which allow users to rent vehicles for a specific period, have also seen a surge in demand. This segment is particularly popular among touris

As the workforce ages, how will the work lives of older people evolve? One way to ease into retirement is to move to the gig economy where workers choose hours and intensity of work that fit their needs and capabilities. However, older workers are often reaping the benefits of the latter end of an implicit contract while gig economy workers are paid their marginal product. We show that age/earnings profiles in the traditional labor market are different than for Uber drivers. While the move to the gig economy generates flexibility, older workers are paid less than their younger coworkers.

The global ridesharing market, valued at $47.57 billion in 2025, is projected to experience robust growth, driven by increasing urbanization, rising fuel prices, and the growing adoption of convenient and cost-effective transportation solutions. The market's compound annual growth rate (CAGR) of 11.45% from 2025 to 2033 indicates a significant expansion, with the market expected to surpass $150 billion by 2033. Several factors contribute to this growth. The increasing preference for app-based ridesharing services, offering real-time tracking and ease of booking, is a key driver. Furthermore, the emergence of corporate ridesharing programs, aimed at optimizing employee commutes and reducing transportation costs for businesses, is significantly boosting market expansion. Technological advancements, such as the integration of AI and machine learning for optimized routing and pricing, are further enhancing the efficiency and appeal of ridesharing services. The rise of electric vehicles and sustainable transportation initiatives also contributes positively to the sector's growth. Market segmentation reveals a dynamic landscape. App-based services dominate, reflecting the widespread smartphone penetration and preference for digital convenience. However, web-based and hybrid (web and app-based) services also maintain a significant presence, catering to diverse user preferences and technological accessibility. Geographic variations are also apparent, with North America and Europe currently holding the largest market shares due to high adoption rates and established infrastructure. However, rapidly developing economies in Asia and Latin America present significant growth opportunities, fueled by increasing disposable incomes and a burgeoning middle class seeking affordable and efficient transportation options. Competition within the market is intense, with established players such as Via Transportation and BlaBlaCar vying for market share alongside newer entrants offering innovative services and features. Regulatory frameworks and evolving consumer preferences will continue to shape the competitive dynamics of this expansive market. Recent developments include: July 2024: Google made a strategic investment in Moving Tech, the parent company of Namma Yatri, an innovative open-source ridesharing app hailing from India. The Bengaluru-based startup raised USD 11 million in a pre-Series A funding round, coinciding with Google's monumental pledge of USD 10 billion commitment to India. Namma Yatri, operating under the government-endorsed Open Network for Digital Commerce (ONDC) initiative, sets itself apart by waiving commission fees. Unlike competitors Uber and Ola, who typically charge a 25%-30% commission, Namma Yatri merely connects customers with auto-rickshaws and cab drivers, levying only a nominal monthly fee from its driver partners. While Uber and Ola are active players in the ridesharing arena, they have yet to integrate into the ONDC network., March 2024: In a collaborative effort, the Mobile Area Chamber of Commerce Foundation, alongside Via, launched "MoGo Rideshare," a cutting-edge, app-based transit pilot initiative. The core mission of MoGo is to offer Mobile residents a cost-effective and convenient transportation solution, facilitating easier access to employment, career training, and other vital workforce opportunities.. Key drivers for this market are: Cost Advantage and Increasing Availability of Carpooling/Corporate Pooling Services, Incentives and Rebates Provided by Governments in Major Markets; Increasing Cost of Vehicle Ownership and Environmental Benefits. Potential restraints include: Cost Advantage and Increasing Availability of Carpooling/Corporate Pooling Services, Incentives and Rebates Provided by Governments in Major Markets; Increasing Cost of Vehicle Ownership and Environmental Benefits. Notable trends are: App-based Services Hold Major Market Share.

Modelovaná pravděpodobnost přetížení pro vybraná města na základě dat z Twitteru a OpenStreetMap založených na buňkách mřížky v rozlišení 100 metrů. Datový soubor zahrnuje města Barcelona, Berlín, Cincinnati, Kyjev, Londýn, Madrid, Nairobi, New York City, San Francisco, Sao Paulo a Seattle. Rozsah hodnot se pohybuje od 0 (pravděpodobně normální dopravní tok) do 1 (vysoká pravděpodobnost zpoždění dopravního toku).

Metodika: Na základě dat z Twitteru a OpenStreetMap (OSM) bylo strojové učení použito k trénování modelu, který předpovídá pravděpodobnost přetížení ve městech. Veřejně poskytnuté údaje z UBER byly použity jako referenční údaje (https://movement.uber.com). Jako indikátory v modelu byl použit počet tweetů a počet bodů zájmu z OSM v blízkosti silnic. Kromě toho byly cesty automobilem simulovány pomocí služby openrouteservice na základě prostorového rozložení obyvatelstva a příslušných bodů zájmu a zohledněny v modelu.

Taxi and limousine transport operators have faced intensifying external competition and reductions in demand in recent years. The pandemic significantly reduced demand for taxi and limousine services, which heavily relies on international, domestic and business travellers moving to and from airports, all of which were heavily restricted during the pandemic. Furthermore, prolonged citywide lockdowns in Melbourne and Sydney in the 2021 calendar year required hospitality, arts and entertainment venues to close, which consumers frequently attend using industry transport services. Remote work arrangements have also reduced the need for businesses to use taxi services to move to and from meetings. Overall, taxi and limousine operators' revenue is expected to decline at an annualised 6.7% over the five years through 2023-24, to $2.8 billion. However, this performance includes an anticipated increase of 1.4% in the current year, as the industry continues to recover from pandemic lows. This has included profitability still hovering below pre-pandemic levels. The market has faced competitive pressures from alternative technologies and modes of transport. While taxi and limousine services are well accepted across most demographics, they're often considered discretionary and are easily substituted. Tight economic conditions have led price-conscious consumers to increasingly choose inexpensive transport options, like ridesharing services and public transport. Rideshare services reduce revenue attributable to the industry, as companies like Uber are not included in the industry and take a large share of the revenue generated from the trips booked on their platforms. Various state governments have sought to accommodate ridesharing services, which has led to major reforms of the point-to-point transport system in most states. As rideshare services have built greater market share, they’ve steadily increased their fees. Rising fees and additional gig worker regulations are likely to flow through to increased revenue generated by contractor drivers in the industry. Overall, revenue is projected to rise at an annualised 3.2% through the end of 2028-29, to $3.2 billion.

In-Taxi Digital Advertising Services Market was valued at 166.88 Million in 2023 and is projected to reach USD 250.55 Million by 2031, growing at a CAGR of 5.25% from 2024 to 2031.

Global In-Taxi Digital Advertising Services Market Overview

The increase in urbanization and the rapid adoption of ride-sharing services are reshaping transportation dynamics and urban lifestyles globally. Urbanization has led to more people moving to cities, intensifying the demand for efficient and sustainable mobility solutions. Simultaneously, ride-sharing services like Uber, Lyft, and Didi have emerged as convenient, cost-effective alternatives to private car ownership.