AUTOMOTIVE & TRANSPORTATION
Global Artificial Intelligence (AI) in Transportation Market - Industry Trends and Forecast to 2032
REPORT OVERVIEW
Global Artificial Intelligence (AI) in Transportation Market, By Offering (Hardware and Software), Process (Signal Recognition, Object Recognition, and Data Mining), Machine Learning (Deep Learning, Computer Vision, Context Awareness, and Natural Language Processing), IoT Communication (Cellular, LPWAN, LoRaWAN, Z-Wave, Zigbee, NFC, Bluetooth, and Others), Application (Autonomous Trucks, HMI In Trucks, Semi-Autonomous Trucks, Truck Platooning, Precision and Mapping, Predictive Maintenance, and Others), Region (North America, Europe, Asia-Pacific, South America, Middle East and Africa) – Industry Trends and Forecast to 2032.
Market Insights
The global Artificial Intelligence (AI) in transportation market size is valued to be USD xx million in 2023 and is expected to reach USD xx million by 2032, and it is expected to register a CAGR of xx% over the forecast period 2024-2032.
Artificial intelligence (AI) in transportation refers to the application of various AI techniques and technologies to improve the efficiency, safety, and sustainability of transportation systems. This market has a diverse set of stakeholders, including technology providers, transportation corporations, government organizations, and end users.
The respective global report analyses market trends, consumer behaviour and industry dynamics to guide towards entry into new markets with ease. Also, it assists in tailoring market specific and related products and services to meet the needs, preferences, and expectations of target audience by delving into their psychology. The report also specializes with comprehensive and extensive competitive analysis which offers useful insights into competitor strengths, weaknesses, opportunities, and threats. The respective report offers exclusive insights into the potential impact of disruptive developments and technologies that are expected to completely transform corporate operations. The context includes tailor-made research solutions to create a stronger footprint in their particular industries thereby offering dedicated customized solutions according to the client needs which helps in addressing unique business challenges with more simplified and efficient decision-making solutions.
Market Dynamics
DRIVERS
- Demand for transportation efficiency
- Shift towards sustainable transportation
RESTRAINTS
- High implementation costs
- Data privacy and security concerns
OPPORTUNITIES
- Technological advancements
- Government initiatives and regulations
CHALLENGES
- Lack of skilled workforce
- Interoperability issues
SEGMENTATION
- Offering
- Hardware
- Neuromorphic
- Von Neumann
- Software
- Platforms
- Solutions
- Process
- Signal Recognition
- Object Recognition
- Data Mining
- Machine Learning
- Deep Learning
- Computer Vision
- Context Awareness
- Natural Language Processing
- IoT Communication
- Cellular
- LPWAN
- LoRaWAN
- Z-Wave
- Zigbee
- NFC
- Bluetooth
- Others
- Application
- Autonomous Trucks
- HMI In Trucks
- Semi-Autonomous Trucks
- Truck Platooning
- Precision and Mapping
- Predictive Maintenance
- Others
The respective global report is completely customizable specific to regions (North America, Europe, Asia-Pacific, South America, Middle East and Africa), countries, segments, and key players as per the client requirements.
REGIONAL SEGMENTATION
- North America
- U.S.
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- U.K.
- France
- Italy
- Spain
- Russia
- The Netherlands
- Belgium
- Turkey
- Rest of Europe
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Singapore
- Malaysia
- Australia
- Thailand
- Philippines
- Rest of Asia-Pacific
- South America
- Brazil
- Argentina
- Chile
- Colombia
- Rest of South America
- Middle East and Africa
- Kingdom of Saudi Arabia
- South Africa
- U.A.E.
- Egypt
- Rest of Middle East and Africa
KEY MARKET PLAYERS
- Volvo
- Daimler
- Scania
- Paccar
- Valeo
- Xevo
- ZF
- Zonar
- NVIDIA
- Intel
Table OF CONTENTS
- SECTION 1 - INTRODUCTION
- 1.1 Taxonomy
- 1.2 Market Overview
- 1.3 Currency and Limitations
- 1.3.1 Currency
- 1.3.2 Limitations
- 1.4 Key Competitors
- SECTION 2 - RESEARCH METHODOLOGY
- 2.1 Research Approach
- 2.2 Data Collection and Validation
- 2.2.1 Secondary Research
- 2.2.2 Primary Research
- 2.3 Market Assessment
- 2.3.1 Market Size Estimation
- 2.3.2 Bottom-up Approach
- 2.3.3 Top-down Approach
- 2.3.4 Growth Forecast
- 2.4 Market Study Assumptions
- 2.5 Data Sources
- SECTION 3 - EXECUTIVE SUMMARY
- 3.1 Global Artificial Intelligence (AI) in Transportation Market, by Offering
- 3.2 Global Artificial Intelligence (AI) in Transportation Market, by Process
- 3.3 Global Artificial Intelligence (AI) in Transportation Market, by Machine Learning
- 3.4 Global Artificial Intelligence (AI) in Transportation Market, by IoT Communication
- 3.5 Global Artificial Intelligence (AI) in Transportation Market, by Application
- 3.6 Global Artificial Intelligence (AI) in Transportation Market, by Geography
- 3.7 Market Position Grid
- SECTION 4 - PREMIUM INSIGHTS
- 4.1 Regulatory Framework
- 4.1.1 Standards
- 4.1.2 Regulatory Landscape
- 4.2 Value Chain Analysis
- 4.3 Supply Chain Analysis
- 4.4 COVID-19 Impact
- 4.5 Russia-Ukraine War Impact
- 4.6 PORTER's Five Force Analysis
- 4.7 PESTLE Analysis
- 4.8 SWOT Analysis
- 4.9 Go to Market Strategy
- 4.10 Opportunity Orbit
- 4.11 Multivariate Modelling
- 4.12 Pricing Analysis
- SECTION 5 - MARKET DYNAMICS
- 5.1 Trends
- 5.1.1 Autonomous vehicle advancements
- 5.1.2 Growing urbanization
- 5.1.3 Trend 3
- 5.2 Drivers
- 5.2.1 Demand for transportation efficiency
- 5.2.2 Shift towards sustainable transportation
- 5.2.3 Driver 3
- 5.2.4 Driver 4
- 5.3 Restraints
- 5.3.1 High implementation costs
- 5.3.2 Data privacy and security concerns
- 5.3.3 Restraint 3
- 5.4 Opportunities
- 5.4.1 Technological advancements
- 5.4.2 Government initiatives and regulations
- 5.4.3 Opportunity 3
- 5.4.4 Opportunity 4
- 5.5 Challenges
- 5.5.1 Lack of skilled workforce
- 5.5.2 Interoperability issues
- 5.5.3 Challenge 3
- SECTION 6 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY OFFERING
- 6.1 Offering Summary
- 6.2 Market Attractive Index
- 6.3 Global Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- SECTION 7 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY PROCESS
- 7.1 Process Summary
- 7.2 Market Attractive Index
- 7.3 Global Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- SECTION 8 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY MACHINE LEARNING
- 8.1 Machine Learning Summary
- 8.2 Market Attractive Index
- 8.3 Global Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- SECTION 9 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY IOT COMMUNICATION
- 9.1 IoT Communication Summary
- 9.2 Market Attractive Index
- 9.3 Global Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- SECTION 10 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY APPLICATION
- 10.1 Application Summary
- 10.2 Market Attractive Index
- 10.3 Global Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- SECTION 11 - GLOBAL ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET, BY GEOGRAPHY
- 11.1 Regional Summary
- 11.2 Market Attractive Index
- 11.3 Global Artificial Intelligence (AI) in Transportation Market, by Geography (2019-2032)
- SECTION 12 - NORTH AMERICA ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET
- 12.1 North America Summary
- 12.2 Market Attractive Index
- 12.3 North America Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- 12.4 North America Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- 12.5 North America Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- 12.6 North America Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- 12.7 North America Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- 12.8 North America Artificial Intelligence (AI) in Transportation Market, by Country (2019-2032)
- 12.8.1 U.S.
- 12.8.2 Canada
- 12.8.3 Mexico
- 12.8.4 Rest of North America
- SECTION 13 - EUROPE ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET
- 13.1 Europe Summary
- 13.2 Market Attractive Index
- 13.3 Europe Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- 13.4 Europe Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- 13.5 Europe Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- 13.6 Europe Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- 13.7 Europe Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- 13.8 Europe Artificial Intelligence (AI) in Transportation Market, by Country (2019-2032)
- 13.8.1 Germany
- 13.8.2 U.K.
- 13.8.3 France
- 13.8.4 Italy
- 13.8.5 Spain
- 13.8.6 Russia
- 13.8.7 The Netherlands
- 13.8.8 Belgium
- 13.8.9 Turkey
- 13.8.10 Rest of Europe
- SECTION 14 - ASIA-PACIFIC ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET
- 14.1 Asia-Pacific Summary
- 14.2 Market Attractive Index
- 14.3 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- 14.4 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- 14.5 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- 14.6 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- 14.7 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- 14.8 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, by Country (2019-2032)
- 14.8.1 China
- 14.8.2 India
- 14.8.3 Japan
- 14.8.4 South Korea
- 14.8.5 Singapore
- 14.8.6 Malaysia
- 14.8.7 Australia
- 14.8.8 Thailand
- 14.8.9 Philippines
- 14.8.10 Rest of Asia-Pacific
- SECTION 15 - SOUTH AMERICA ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET
- 15.1 South America Summary
- 15.2 Market Attractive Index
- 15.3 South America Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- 15.4 South America Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- 15.5 South America Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- 15.6 South America Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- 15.7 South America Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- 15.8 South America Artificial Intelligence (AI) in Transportation Market, by Country (2019-2032)
- 15.8.1 Brazil
- 15.8.2 Argentina
- 15.8.3 Chile
- 15.8.4 Colombia
- 15.8.5 Rest of South America
- SECTION 16 - MIDDLE EAST AND AFRICA ARTIFICIAL INTELLIGENCE (AI) IN TRANSPORTATION MARKET
- 16.1 Middle East and Africa Summary
- 16.2 Market Attractive Index
- 16.3 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by Offering (2019-2032)
- 16.4 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by Process (2019-2032)
- 16.5 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by Machine Learning (2019-2032)
- 16.6 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by IoT Communication (2019-2032)
- 16.7 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by Application (2019-2032)
- 16.8 Middle East and Africa Artificial Intelligence (AI) in Transportation Market, by Country (2019-2032)
- 16.8.1 Kingdom of Saudi Arabia
- 16.8.2 South Africa
- 16.8.3 U.A.E.
- 16.8.4 Egypt
- 16.8.5 Rest of Middle East and Africa
- SECTION 17 - COMPANY SHARE ANALYSIS
- 17.1 Global Artificial Intelligence (AI) in Transportation Market, Company Share Analysis
- 17.2 North America Artificial Intelligence (AI) in Transportation Market, Company Share Analysis
- 17.3 Europe Artificial Intelligence (AI) in Transportation Market, Company Share Analysis
- 17.4 Asia-Pacific Artificial Intelligence (AI) in Transportation Market, Company Share Analysis
- SECTION 18 - COMPANY PROFILES
- 18.1 Volvo
- 18.1.1 Company Snapshot
- 18.1.2 Financial Overview
- 18.1.3 Product Portfolio
- 18.1.4 Recent Developments
- 18.2 Daimler
- 18.2.1 Company Snapshot
- 18.2.2 Financial Overview
- 18.2.3 Product Portfolio
- 18.2.4 Recent Developments
- 18.3 Scania
- 18.3.1 Company Snapshot
- 18.3.2 Financial Overview
- 18.3.3 Product Portfolio
- 18.3.4 Recent Developments
- 18.4 Paccar
- 18.4.1 Company Snapshot
- 18.4.2 Financial Overview
- 18.4.3 Product Portfolio
- 18.4.4 Recent Developments
- 18.5 Valeo
- 18.5.1 Company Snapshot
- 18.5.2 Financial Overview
- 18.5.3 Product Portfolio
- 18.5.4 Recent Developments
- 18.6 Xevo
- 18.6.1 Company Snapshot
- 18.6.2 Financial Overview
- 18.6.3 Product Portfolio
- 18.6.4 Recent Developments
- 18.7 ZF
- 18.7.1 Company Snapshot
- 18.7.2 Financial Overview
- 18.7.3 Product Portfolio
- 18.7.4 Recent Developments
- 18.8 Zonar
- 18.8.1 Company Snapshot
- 18.8.2 Financial Overview
- 18.8.3 Product Portfolio
- 18.8.4 Recent Developments
- 18.9 NVIDIA
- 18.9.1 Company Snapshot
- 18.9.2 Financial Overview
- 18.9.3 Product Portfolio
- 18.9.4 Recent Developments
- 18.10 Intel
- 18.10.1 Company Snapshot
- 18.10.2 Financial Overview
- 18.10.3 Product Portfolio
- 18.10.4 Recent Developments
- SECTION 19 - RELATED REPORTS
- SECTION 20 - DISCLAIMER
RESEARCH METHODOLOGY
The research methodology employed in Uniprism Market Research involves four basic steps namely research and data collection, data pre-processing, modeling and forecasting, quality assurance and output.
RESEARCH AND DATA COLLECTION
A tripod model research technique is followed for research and data collection in which various approaches such as primary research, secondary research, and product mapping are considered.
Primary research basically involves the process of conducting personalized interviews with market related professionals of major market players, investors, distributors, vendors and many more.
The secondary research include data published by government, annual reports, press releases, investor presentations of companies, white papers, certified publications, annual manufacturing limit of the respective industries related to the market, production consumption analysis of certain products respective to the market and many more.
Below mention are few of the sources which we have considered while estimating the market size:
For instance,
- Research articles published on Technium
- Science and MDPI
- Research publications by government approved associations and societies
Product mapping means the process of mapping the list of products that a key player contributes to the market as well as estimating the revenue of those products in order to define the Global Company share analysis of the respective Global Company in global, regional, and country level markets.
DATA PRE-PROCESSING
The term "data pre-processing" refers to the collection of procedures and methods used to clean, modify, and make ready for analysis the raw data gathered during research and data collection. The completion of this phase is necessary to guarantee that the data are reliable, consistent, and appropriate for statistical analysis and other data-driven tasks. The data pre-processing ensures that the information gathered from research and data collection is comparable and expressed in standard units, by the integration of missing data pointers and algorithmic approaches.
MODELING AND FORECASTING
The process of developing mathematical, statistical, or computational representations of real-world occurrences or relationships is known as modelling. These models are intended to replicate and explain market interactions, interdependence, and dynamics. These models are used by Uniprism Market Research to acquire a better knowledge of numerous market characteristics such as customer preferences, pricing elasticity, competition dynamics, and more. Depending on the individual study aims, many types of models are utilized, such as regression models, econometric models, decision tree models, and machine learning models.
Forecasting is the process of predicting future market conditions, trends, and occurrences using past data and models. Forecasting is used by Uniprism Market Research to estimate future sales, demand for products or services, market growth, and other important performance metrics. Forecasting accurately can assist organizations in making educated decisions about resource allocation, pricing, inventory management, and marketing tactics.
We create standardized bottom-up or top-down models that scale by leveraging data science and machine learning technology. All our market models consider the unique market characteristics of each country. Forecasting is based on major market indicators and a combination of traditional methodologies, such as exponential smoothing, time series analysis, regression analysis, and more modern techniques such as machine learning algorithms are all forecasting methodologies. The method chosen is determined on the nature of the data and the specific forecasting aims.
QUALITY ASSURANCE AND OUTPUT
Quality assurance and output involves the process of validation, adjustments, further publications of key market indicators. Extensive plausibility and consistency tests are performed on derived time series to ensure the high degree of quality of our market analysis. This quality assurance procedure also includes rigorous inspection, validation, and editing by an experienced management team to assure the dependability of the published data.