Snapshot img { margin: 10px !important; } The self-driving taxi market has the potential to grow by 56170.00 units during 2021-2025, and the market’s growth momentum will accelerate at a CAGR of 58.54%.

This self-driving taxi market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by level of autonomy (SAE level 3 and SAE level 4 and 5) and geography (North America, Europe, APAC, South America, and MEA). The self-driving taxi market report also offers information on several market vendors, including Alphabet Inc., Aurora Operations Inc., Ford Motor Co., General Motors Co., Renault SA, Stellantis NV, Tesla Inc., Toyota Motor Corp., Volkswagen AG, and Volvo Car Corp. among others.

What will the Self-driving Taxi Market Size be in 2021?

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Self-driving Taxi Market: Key Drivers and Trends

Based on our research output, there has been a negative impact on the market growth during and post COVID-19 era. The increased focus of OEMs toward the development of self-driving vehicles is notably driving the self-driving taxi market growth, although factors such as issues with system reliability may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the self-driving taxi industry. The holistic analysis of the drivers will help in deducing end goals and refining marketing strategies to gain a competitive edge.

      One of the key factors driving growth in the self-driving taxi market is the increased focus of OEMs toward the development of self-driving vehicles.
      The major OEMs and tier-1 suppliers have already initiated projects to commercialize this concept of self-driving vehicles.
      Non-automotive companies such as Google and Apple are investing in self-driving vehicles by leveraging their expertise in communications.
      The development of the autonomous vehicle concept is expected to commercialize in the coming decade as most of the major players from the automotive, electronics, and communications industries are putting in the efforts.




      The maturing autonomous vehicles concept is another major factor supporting the self-driving taxi market share growth.
      Companies such as Delphi, Continental, Bosch, Daimler, Scania, and Volvo are working on expanding the autonomous driving concept.
      The growth rate of the automotive electronics market has been boosted owing to the range of consumer needs and OEM offerings having widened to include safety, performance, stability, and comfort.
      Government regulations are forcing the deployment of minimum levels of ADAS and the presence of a conducive technology environment or platform to enable the viability of autonomous vehicles is an inherent driver of this market.

This self-driving taxi market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on the market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Self-driving Taxi Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Alphabet Inc.
Aurora Operations Inc. 
Ford Motor Co.
General Motors Co.
Renault SA
Stellantis NV
Tesla Inc.
Toyota Motor Corp.
Volkswagen AG
Volvo Car Corp.

This statistical study of the self-driving taxi market encompasses successful business strategies deployed by the key vendors. The self-driving taxi market is fragmented and the vendors are deploying growth strategies such as mergers and acquisitions, as well as strategic partnerships, to drive developments in the field of self-driving taxis.to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

The self-driving taxi market forecast report offers in-depth insights into key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Self-driving Taxi Market?

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30% of the market’s growth will originate from North America during the forecast period. The US and Canada are the key ma

This repository contains four datasets about the number of active users of selected mobile apps purchased from Selectivv company (https://selectivv.com/). Details regarding the data may be found below:

How data was collected: Selectivv uses programmatic advertisements systems that collect information on about 24 mln smartphone users in Poland

Apps:

Transportation: Uber, Bolt Driver, FREE NOW, iTaxi,

Delivery: Glover, Takeaway, Bolt Courier, Wolt;

Unit: an active user of a given app. Active = used given app at least 1 minute in a given period (e.g. 1 unit during whole month, half-year).

Period: 2018-2018; monthly and half-year data

Spatial aggregation: country level, city level, functional area level, voivodeship level. Functional area is defined as here https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

Activity time: measured by activity time of given app (in hours; average and standard deviation)

Datasets:

gig-table1-monthly-counts-stats.csv -- the monthly number of active users;

gig-table2-halfyear-demo-stats.csv -- the half-year number of active users by socio-demographic variables;

gig-table3-halfyear-region-stats.csv -- the half-year number of active users by spatial aggregation;

gig-table4-halfyear-activity-stats.csv -- the half-year activity time by working week, weekend, day (8-18) and night (18-8).

Detailed description:

1. gig-table1-monthly-counts-stats.csv

Structure:

month - YYYY-MM-DD -- we set all dates to 15th of given month but actually the data is about the whole month (active users in whole period); 2018-01-15 to 2021-12-15

app -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Deliver

1. gig-table2-halfyear-demo-stats.csv

Structure:

gender -- men, women

age -- 18-30, 31-50, 51-64

country -- Poland, Ukraine, Other

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

students -- the share of students within a given row

parents_of_children_0_4_years -- the share of parents of 0-4 years children in a given row

parents_of_children_5_10_years -- the share of parents of 5-10 years children in a given row

women_planning_a_baby -- the share of women planing a baby in a given row

standard -- the share of standard smartphones in a given row

premium_i_phone -- the share of iPhone smartphones in a given row

other_premium -- the share of other premium smartphones in a given row

category -- Transportation, Delivery

1. gig-table3-halfyear-region-stats.csv

Structure:

group -- Voivodeship, Functional Area, Cities

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

region_name:

Cities -- Białystok, Bydgoszcz, Gdańsk, Gdynia, Gorzów Wielkopolski, Katowice, Kielce, Kraków, Łódź, Lublin, Olsztyn, Opole, Poznań, Rzeszów, Sopot, Szczecin, Toruń, Warszawa, Wrocław, Zielona Góra

Functional Area -- Functional area - Białystok, Functional area - Bydgoszcz, Functional area - Gorzów Wielkopolski, Functional area - GZM, Functional area - GZM2, Functional area - Kielce, Functional area - Kraków, Functional area - Łódź, Functional area - Lublin, Functional area - Olsztyn, Functional area - Opole, Functional area - Poznań, Functional area - Rzeszów, Functional area - Szczecin, Functional area - Toruń, Functional area - Trójmiasto, Functional area - Warszawa, Functional area - Wrocław, Functional area - Zielona Góra

Voivodeship -- dolnośląskie, kujawsko-pomorskie, łódzkie, lubelskie, lubuskie, małopolskie, mazowieckie, opolskie, podkarpackie, podlaskie, pomorskie, śląskie, świętokrzyskie, warmińsko-mazurskie, wielkopolskie, zachodniopomorskie

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

number_of_users -- the number of active users

category -- Transportation, Delivery

Please note that:

the number of active users in a given functional area = number of active users in a city and a functional area of this city

the number of active users in voivodeship = number of active users in a city, its functional area and the rest of the voivodeship where this city and functional area is located

More details here: https://stat.gov.pl/en/regional-statistics/regional-surveys/urban-audit/larger-urban-zones-luz/

1. gig-table4-halfyear-activity-stats.csv

Structure:

period -- 2018.1, 2018.2, 2019.1, 2019.2, 2020.1, 2021.2

apps -- app name (Uber, Bolt Driver, FREE NOW, iTaxi, Glover, Takeaway, Bolt Courier, Wolt)

day -- Mondays-Thursdays, Fridays-Sundays

hour -- day (8-18), night (18-8)

activity_time -- in hours

statistic -- Average, Std.Dev. (standard deviation)

category -- Transportation, Delivery

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Ride Sharing Market Size 2025-2029

The ride sharing market size is forecast to increase by USD 132.4 billion, at a CAGR of 18.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing cost of vehicle ownership and the emergence of autonomous ride sharing services. The high cost of maintaining and operating personal vehicles has led consumers to opt for more cost-effective transportation alternatives. Simultaneously, the development and implementation of autonomous ride sharing technology are revolutionizing the transportation industry, offering convenience, efficiency, and cost savings. However, this market is not without challenges. The risks of theft and the need for frequent maintenance pose significant obstacles for ride sharing companies.
Ensuring the security of vehicles and passenger safety while minimizing downtime for maintenance are critical issues that must be addressed to capitalize on the market's potential. Companies that can effectively manage these challenges and leverage the opportunities presented by the increasing demand for cost-effective and convenient transportation solutions will thrive in this dynamic market.

What will be the Size of the Ride Sharing Market during the forecast period?

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The ride-sharing market continues to evolve, with dynamic interplays between various components shaping its landscape. Ride-hailing insurance policies adapt to accommodate the unique risks associated with this sector, while ride-sharing apps optimize efficiency through real-time route planning and dynamic pricing. Sustainability is a growing concern, with electric vehicle integration and emissions reduction initiatives becoming increasingly prevalent. Passenger safety remains a priority, with ongoing advancements in ride-sharing regulations and safety features. Business models evolve to cater to diverse consumer needs, from mobility-as-a-service (MaaS) offerings to fleet management solutions. Accessibility is a key focus, with partnerships between ride-sharing platforms and public transportation systems enhancing overall mobility options.

Ride-sharing revenue streams are diversifying, with network effects, cost optimization, and shared mobility models driving growth. Autonomous vehicle integration and urban planning initiatives are reshaping the ride-sharing landscape, offering potential for increased efficiency and reduced congestion. Regulations and infrastructure adapt to accommodate these changes, while customer experience is enhanced through mobile payment integration and ride-hailing analytics. The social impact of ride-sharing is under scrutiny, with ongoing discussions surrounding ride-sharing's role in community development and economic growth. Ride-sharing partnerships extend beyond transportation, with companies exploring opportunities in logistics, delivery services, and even tourism. The future of ride-sharing is characterized by continuous innovation and adaptation, with ongoing advancements in technology, business models, and regulations shaping its trajectory.

How is this Ride Sharing Industry segmented?

The ride sharing industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Individual
  Business


Type

  E-hailing
  Rental
  Station-based
  Car sharing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

.

By End-user Insights

The individual segment is estimated to witness significant growth during the forecast period.

The market is characterized by various entities that have significantly influenced its dynamics and trends. Ride sharing business models, such as Uber and Lyft, have disrupted traditional taxi services by enabling individuals to share rides in privately-owned vehicles. This collaborative approach has led to increased accessibility and affordability, making it a popular choice for commuters. Ride sharing apps have streamlined the booking process, allowing passengers to request rides at their convenience. These apps also facilitate real-time route optimization and dynamic pricing, ensuring efficient and cost-effective travel. Ride-hailing insurance and partnerships with ride-hailing platforms have addressed concerns around passenger safety and driver incentives.

Regulations and infrastructure development have also played a crucial role in the market's growth. Sustainability initiatives, such as electric vehicle integration and emissions reduction, have become essential c

These records are generated from the trip record submissions made by yellow taxi Technology Service Providers (TSPs). Each row represents a single trip in a yellow taxi. The trip records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off taxi zone locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts.

Taxi Market Outlook

According to our latest research conducted in early 2025, the global taxi market size reached USD 244.3 billion in 2024, demonstrating robust expansion across both developed and emerging economies. The industry is projected to grow at a CAGR of 7.8% from 2025 to 2033, with the market expected to reach approximately USD 482.6 billion by the end of the forecast period. This significant growth is primarily propelled by increasing urbanization, evolving consumer preferences toward convenient mobility solutions, and the widespread adoption of digital platforms for transportation services.

One of the most influential growth factors for the taxi market is the rapid expansion and penetration of ride-hailing and ride-sharing platforms. Companies like Uber, Lyft, Didi Chuxing, Ola, and Grab have transformed the traditional taxi landscape by offering seamless, app-based booking experiences, real-time tracking, and transparent pricing models. These innovations have not only enhanced user convenience but also increased trust and reliability in taxi services, attracting a broader customer base. Furthermore, the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics has enabled service providers to optimize fleet management, reduce wait times, and personalize offerings, thereby significantly improving operational efficiency and customer satisfaction.

Another crucial driver is the shifting demographic and socio-economic trends, particularly the rise in disposable incomes and the growing middle-class population in emerging markets. As urban populations swell, traffic congestion and limited parking availability have made personal vehicle ownership less attractive, fueling demand for alternative mobility solutions like taxis. The proliferation of smartphones and internet connectivity has further facilitated the adoption of app-based taxi services, especially among millennials and Gen Z consumers who prioritize convenience and flexibility. Additionally, the increasing emphasis on sustainability and environmental concerns has prompted many taxi operators to incorporate electric and hybrid vehicles into their fleets, aligning with global efforts to reduce carbon emissions and promote eco-friendly transportation.

The taxi market is also benefiting from supportive regulatory frameworks and government initiatives aimed at modernizing urban transportation infrastructure. Many cities worldwide are implementing policies to encourage shared mobility, reduce traffic congestion, and enhance public safety, such as dedicated pick-up and drop-off zones, cashless payment mandates, and stricter vehicle emission standards. These measures not only create a conducive environment for taxi operators but also foster healthy competition and innovation within the industry. Moreover, partnerships between public transit authorities and private taxi companies are emerging as a strategic approach to address last-mile connectivity challenges, further boosting market growth.

From a regional perspective, Asia Pacific continues to dominate the global taxi market, driven by its large urban population, rapid economic development, and the presence of leading ride-hailing giants. North America and Europe also represent significant markets, characterized by high adoption rates of digital taxi services and a strong focus on regulatory compliance and sustainability. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by improving transportation infrastructure and increasing smartphone penetration. Overall, the regional outlook for the taxi market remains highly positive, with each region contributing uniquely to the industry's evolution and expansion.

Service Type Analysis

The service type segment of the taxi market is broadly categorized into ride-hailing, ride-sharing, radio taxis, and others. Ride-hailing services, led by globally recognized brands such as Uber, Didi, and Ola, have become the dominant force in urban mobility, accounting for a su

This dataset is collected by the NYC Taxi and Limousine Commission (TLC) and includes trip records from all trips completed in Yellow and Green taxis in NYC, and all trips in for-hire vehicles (FHV) in the last 5 years. Records include fields capturing pick-up and drop-off dates/times, pick-up and drop-off locations, trip distances, itemized fares, rate types, payment types, and driver-reported passenger counts. For detailed information about this dataset, go to TOC Trip Record Data This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

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Moto Taxi Service Market Size 2025-2029

The moto taxi service market size is forecast to increase by USD 14.45 billion at a CAGR of 10% between 2024 and 2029.

The market is witnessing significant growth, driven by increasing investments in moto taxi startups. This trend reflects the market's potential and the investors' confidence in the business model. Furthermore, the use of social media and analytics is transforming the way moto taxi services are marketed and operated, providing valuable insights into customer preferences and behavior. However, the market faces challenges, including regulatory restrictions and bans on moto taxis in various countries. Data security and privacy policies are crucial for protecting user information. These obstacles necessitate a strategic approach to navigating regulatory environments and addressing safety concerns to ensure the sustainable growth of moto taxi services.
Companies seeking to capitalize on market opportunities must stay informed of regulatory changes and invest in technology solutions that enhance safety and customer experience. Additionally, collaborating with local authorities and stakeholders can help build trust and support for the moto taxi service industry. To maintain operational efficiency, motor taxi services employ fleet management and dispatch systems.

What will be the Size of the Moto Taxi Service Market during the forecast period?

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In the dynamic market, various strategies are shaping the competitive landscape. Referral programs are increasingly popular for customer acquisition, offering existing riders incentives to invite new users. Risk management is a critical aspect, with companies implementing comprehensive strategies to mitigate potential hazards. Public relations plays a significant role in maintaining a positive brand image, especially during crises. Market penetration is driven by API integrations, allowing seamless connectivity with various platforms. Fraud prevention is a priority, with machine learning algorithms and data encryption ensuring secure transactions. Loyalty programs and in-app messaging foster customer engagement, while big data analytics provide valuable insights for competitive advantage.

Third-party integrations, including social media and legal consulting, expand service offerings and ensure regulatory compliance. Crisis management plans are essential for handling unexpected incidents, while automated dispatch and smart routing enhance operational efficiency. Open-source technologies and artificial intelligence further optimize services, ensuring a superior user experience. Motorcycle maintenance and data anonymization are crucial for maintaining a reliable and secure fleet. Cloud computing enables scalability and flexibility, ensuring businesses remain agile in the ever-evolving market. Additionally, the use of social media and analytics is becoming increasingly prevalent in the industry, enabling providers to better understand customer preferences and tailor their services accordingly.

How is this Moto Taxi Service Industry segmented?

The moto taxi service industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Service

  E-hailing
  Ride sharing


Application

  Passenger
  Load


Vehicle Type

  Motorcycle
  Scooters


Propulsion

  ICE
  Electric


Geography

  North America

    US


  Europe

    France
    UK


  Middle East and Africa

    UAE


  APAC

    India
    Indonesia
    Thailand
    Philippines
    Vietnam


  South America

    Brazil


  Rest of World (ROW)

By Service Insights

The E-hailing segment is estimated to witness significant growth during the forecast period. E-hailing services for motor taxis have gained significant popularity due to their convenience and affordability, particularly in regions with high population density and heavy traffic congestion. These services enable passengers to easily book motor taxis through mobile applications, streamlining the process and reducing waiting times. The growing preference for cost-effective transportation solutions, coupled with the convenience offered by e-hailing platforms, is driving the market's expansion. Safety is a top priority for motor taxi services. Background checks and driver verification ensure the safety of passengers. Motorcycles are equipped with safety gear, and insurance policies cover both drivers and riders. Insurance claims processing is handled efficiently to minimize disruptions.

Rider safety features, such as helmets, are provided to ensure the safety of passengers. Real-time

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