Success.ai’s Transport and Logistics Data for Transportation, Trucking & Railroad Industry Leaders Globally provides a robust and reliable dataset designed to connect businesses with decision-makers and professionals across the transportation and logistics sectors. Covering leaders in trucking, railroads, and supply chain management, this dataset offers verified contact details, firmographic insights, and actionable business data.

With access to over 700 million verified global profiles and insights from key logistics companies, Success.ai ensures your marketing, sales, and operational strategies are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is ideal for navigating the ever-evolving transport and logistics industries.

Why Choose Success.ai’s Transport and Logistics Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, fleet managers, and supply chain leaders in transportation, trucking, and railroads.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and optimizing engagement.

2. Comprehensive Global Coverage

   • Includes profiles of professionals from major transportation hubs and logistics companies worldwide, including North America, Europe, and Asia-Pacific.
   • Gain insights into global transportation trends, logistics strategies, and supply chain innovations.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership, market expansions, and operational strategies.
   • Stay ahead of industry developments and identify emerging opportunities effectively.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and lawful use of data.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with professionals and decision-makers in transportation, trucking, and railroads worldwide.
• Leadership and Professional Insights: Access profiles of CEOs, fleet managers, and logistics experts driving the transport sector.
• Verified Contact Details: Gain phone numbers, work emails, and LinkedIn profiles for precise targeting.
• Firmographic Data: Understand company structures, operational capacities, and regional market footprints.

Key Features of the Dataset:

1. Decision-Maker Profiles in Transport and Logistics

   • Identify and connect with professionals managing trucking fleets, railroad operations, and supply chain logistics.
   • Target individuals responsible for fleet optimization, operational efficiency, and vendor selection.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by industry focus (transportation, trucking, railroads), geographic location, or job function.
   • Tailor campaigns to address specific challenges, such as fleet electrification, route optimization, or regulatory compliance.

3. Industry and Regional Insights

   • Leverage data on global transportation trends, freight logistics, and emerging technologies in trucking and railroads.
   • Refine strategies to align with regional needs and market conditions.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Sales and Lead Generation

   • Promote logistics software, fleet management tools, or operational solutions to transportation and trucking professionals.
   • Build relationships with railroads and supply chain companies seeking innovative strategies for efficiency and cost management.

2. Market Research and Competitive Analysis

   • Analyze global trends in transportation and logistics to guide product development, marketing strategies, and business expansion plans.
   • Benchmark against competitors to identify growth opportunities and emerging market demands.

3. Partnership Development and Collaboration

   • Engage with logistics leaders, transportation companies, and railroads exploring partnerships for supply chain optimization or technology adoption.
   • Foster alliances that expand market reach and enhance operational capabilities.

4. Recruitment and Talent Acquisition

   • Target HR professionals and hiring managers in transportation and logistics sectors recruiting for roles in fleet management, supply chain operations, or rail transport.
   • Provide workforce optimization platforms or training solutions tailored to the industry.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality transport and logistics data at competitive prices, ensuring strong ROI for your outreach, marketing, and operational initiatives.

2. Seamless Integration

   • Integrate verified data into CRM systems, analytics tools, or marketing platforms via APIs o...

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.

The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap.

Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.

Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.

Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.

Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 When using this dataset, please cite also the above-mentioned paper.

Performance of public transport services that have been provided by Approved Organisations, in each region and in NZ over the last 10 years.

This dataset is a list of Department of Transportation (DOT) Artificial Intelligence (AI) use cases. Artificial intelligence (AI) promises to drive the growth of the United States economy and improve the quality of life of all Americans. Pursuant to Section 5 of Executive Order (EO) 13960, "Promoting the Use of Trustworthy Artificial Intelligence in the Federal Government," Federal agencies are required to inventory their AI use cases and share their inventories with other government agencies and the public. In accordance with the requirements of EO 13960, this spreadsheet provides the mechanism for federal agencies to create their inaugural AI use case inventories. https://www.federalregister.gov/documents/2020/12/08/2020-27065/promoting-the-use-of-trustworthy-artificial-intelligence-in-the-federal-government

Usecase/Applications possible with the data:

Container and vessel tracking datasets have various use cases, particularly in the logistics and shipping industry. Here are different use cases for container and vessel tracking datasets:

1. Supply Chain Visibility: Container and vessel tracking data provide insights into the entire supply chain. Companies can track the movement of goods from manufacturers to distribution centers and ultimately to customers, ensuring transparency and efficiency.

2. Market Research: Researchers and analysts can use tracking data to gain insights into global trade patterns, shipping trends, and market dynamics.

3. Real-time Container Tracking: Container tracking datasets allow businesses to monitor the real-time location and status of their shipping containers. This is crucial for supply chain optimization and ensuring the timely delivery of goods.

4. Inventory Management: By knowing the exact location and status of containers, businesses can better manage their inventory. They can plan for restocking and distribution more effectively.

5. Route Optimization: These datasets can be used to analyze historical routes and optimize future shipping routes. This can lead to cost savings and reduced transit times.

6. Environmental Impact Analysis: Tracking data can be used to assess the environmental impact of shipping activities. This includes monitoring emissions and fuel consumption, helping companies adopt more sustainable practices.

7. Insurance Claims: In case of accidents or damages during transit, tracking data can serve as evidence for insurance claims, simplifying the claims process.

These use cases demonstrate the versatility and importance of container and vessel tracking datasets in the modern shipping and logistics industry, contributing to operational efficiency, security, and overall business success.

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This dataset contains Official Light Rail Utilisation figures. Opal tap-on/tap-off data (representing an individual entering and exiting a Light Rail station), is aggregated to a total monthly figure representing the estimated number of trips. Starting July 1, 2024, the methodology for calculating trip numbers for individual lines and operators will change to more accurately reflect the services our passengers use within the transport network. This new approach will apply to trains, metros, light rail, and ferries, and will soon be extended to buses. Aggregations between line, agency, and mode levels will no longer be valid, as a passenger may use multiple lines on a single trip. Trip numbers at the line, operator, or mode level should be used as reported, without further combinations. The dataset includes reports based on both the new and old methodologies, with a transition to the new method taking place over the coming months. As a result of this change, caution should be exercised when analysing longer trends that utilise both datasets. More information on NRT ROAM can be accessed here Caution School Student travel using concessional Opal cards is included. However this may be underrepresented, due to inconsistent tap-on/tap-off behaviour by students at light rail stations Magnetic Stripe Ticketing (MST – paper tickets) data was also available in July 2016. MST patronage data for July is available here Opal data may be subject to minor revision for the two months following upload Data is static at a point in time, and may not match other reports that are real time All non-Opal travel is excluded, for example transport concession entitlement cards, integrated ticketing for major events, and fare non-compliance An Opal Trip is defined as a tap-on/tap-off pair (including where only a single tap-on or tap-off is recorded) A significant portion of the Light Rail line was closed during the months of January 2017 and 2018, resulting in lower number of trips in both months Please note: the data includes Newcastle Light Rail

This 20MB download is a zip file containing 1 docx document and 2 xlsx spreadsheets.Waka Kotahi has been running an ongoing study across New Zealand for the effects of COVID-19 on transport choices. The study started on 3 April 2020 and runs for 28 waves, with the final wave scheduled to take place in late 2021. This market research analysis was conducted by Ipsos, with the results data kept in the Harmoni application by Infotools, an external vendor. We have created summarised tables from this data, in the form of an Excel spreadsheet, for release as open data. The data records how New Zealanders felt, behaved and travelled under the different COVID-19 alert levels.The data tables from the study, to allow you to do your own analysis. We have already made analysed data from this study available as reports on the Waka Kotahi website.Read 'covid-19 impacts on transport' reportsComplete open dataset: click on the download button for a .zip file containing this item. Watch our video about the impacts of COVID-19 on New Zealanders' transport choices Data reuse caveats: as per license. Additionally, this data is from research currently being undertaken by Ipsos on behalf of Waka Kotahi NZ Transport Agency. While Waka Kotahi provided investment, the research was undertaken independently, and the resulting findings should not be regarded as being the opinion, responsibility or policy of Waka Kotahi or indeed of any NZ Government agency. We have removed the data for sample sizes of fewer than 60 people, to protect privacy. Data quality statement: high level of confidence. Data quality caveats: none known. Other metadata: technical report - click on the download button for a .zip file containing this itemquestionnaire changes tracking log - click on the download button for a .zip file containing this item.

The dataset shows, from the year 2015, the number of buses used for local public transport by emission class (Euro 4 or less, Euro 5, Euro 6) in the municipality of Milan.

Dataset extrapolated from table 6.2 Urban mobility Istat.

Success.ai’s Transport and Logistics Data provides comprehensive, verified B2B contact and company information tailored for the global logistics sector. Drawing from a database of over 170 million verified professional profiles and 30 million company profiles, this dataset delivers accurate contact details, firmographic insights, and operational data on logistics service providers, freight forwarders, trucking companies, 3PLs, and supply chain management firms worldwide. Whether you’re targeting key decision-makers for partnerships, offering freight optimization technology, or conducting market research, Success.ai ensures your outreach and strategic planning are anchored in reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Transport and Logistics Data?

1. Comprehensive Contact Information

   • Access verified work emails, direct phone numbers, and LinkedIn profiles of logistics leaders, operations managers, procurement officers, and supply chain directors.
   • AI-driven validation ensures 99% accuracy, allowing confident communication and reducing wasted outreach efforts.

2. Global Reach Across the Logistics Sector

   • Includes profiles from freight carriers, warehousing solutions, customs brokers, freight forwarders, and last-mile delivery companies.
   • Covers regions including North America, Europe, Asia-Pacific, South America, and the Middle East, giving you a panoramic view of logistics networks worldwide.

3. Continuously Updated Datasets

   • Real-time updates ensure your data remains current, reflecting changes in leadership, service offerings, and operational expansions in the rapidly evolving logistics industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, guaranteeing that your outreach and data usage align with legal and ethical standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with logistics professionals, key decision-makers, and operational leaders.
• 50M Work Emails: AI-validated for seamless communication with minimized bounce rates.
• 30M Company Profiles: In-depth firmographic and operational insights on logistics companies worldwide.
• 700M Global Professional Profiles: Enriched datasets to support competitive analysis, market entry strategies, and global expansion.

Key Features of the Dataset:

1. Logistics Decision-Maker Profiles

   • Identify and connect with CEOs, COOs, logistics managers, route planners, and procurement specialists influencing transportation strategies, warehousing decisions, and supply chain optimization.

2. Operational Firmographics and Insights

   • Access data on fleet size, service areas, warehouse locations, technology adoption, and shipping volumes to refine targeting and tailor your value propositions.
   • Utilize detailed operational insights to understand capacity constraints, specialization areas, and strategic priorities.

3. Advanced Filters for Precision Targeting

   • Filter contacts by mode of transportation (road, rail, air, sea), geographic coverage, company size, or industry specialization.
   • Align campaigns with unique market conditions, regulatory environments, and customer demands.

4. AI-Driven Enrichment

   • Profiles are enriched with actionable data, enabling personalized messaging, highlighting value-add solutions, and improving engagement outcomes with logistics stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Engage with freight forwarders, trucking companies, and 3PL executives to present transportation management systems, visibility tools, or cost-reduction strategies.
   • Build relationships with leaders who control capacity planning, vendor selection, and route optimization.

2. Market Research and Competitive Analysis

   • Gain insights into emerging logistics hubs, evolving supply chain models, and technology adoption rates.
   • Benchmark against industry leaders to inform product innovation, pricing strategies, and market entry plans.

3. Partnership and Network Building

   • Identify potential partners for intermodal solutions, joint ventures, or collaborative warehousing arrangements.
   • Target logistics professionals interested in sustainability initiatives, e-commerce integrations, and cross-border trade solutions.

4. Recruitment and Talent Acquisition

   • Find HR professionals and operations managers seeking qualified drivers, dispatchers, warehouse staff, and logistics analysts.
   • Offer recruitment services or training programs to companies aiming to enhance operational efficiency and workforce quality.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access top-quality verified data at competitive prices, ensuring maximum ROI for your outreach and expansion efforts in the logistics sector.

2. Seamless Integration

   • Integrate verified contact an...

The funding and transport dashboard shows funding and performance data for the following transport-related activities. Expenditure on major transport-related activities by Approved Organisations. Performance of public transport services provided by Approved Organisations. Performance of total mobility services provided by Approved Organisations. Vehicle use (VKT, or ‘vehicle kilometres travelled’) within Road Controlling Authority areas, in each region and in New Zealand. The condition of the surface and pavement of local roads and state highways, as well as the quality of ride people experience by Road Controlling Authority areas, each region and New Zealand as a whole. The number and length of infrastructure in Road Controlling Authority areas, each region and New Zealand as a whole. The number and length of roads built, reconstructed or ‘seal extended’ in Road Controlling Authority areas, each region and New Zealand as a whole. You can find further information underneath the dashboard, including caveats, a glossary and a user guide.

About Transportation Services Index

The Transportation Services Index (TSI), created by the U.S. Department of Transportation (DOT), Bureau of Transportation Statistics (BTS), measures the movement of freight and passengers. The index, which is seasonally adjusted, combines available data on freight traffic, as well as passenger travel, that have been weighted to yield a monthly measure of transportation services output.

For charts and discussion on the relationship of the TSI to the economy, see our Transportation as an Economic Indicator: Transportation Services Index page (https://data.bts.gov/stories/s/TET-indicator-1/9czv-tjte)

For release schedule see: https://www.bts.gov/newsroom/transportation-services-index-release-schedule

About seasonally-adjusted data

Statisticians use the process of seasonal-adjustment to uncover trends in data. Monthly data, for instance, are influenced by the number of days and the number of weekends in a month as well as by the timing of holidays and seasonal activity. These influences make it difficult to see underlying changes in the data. Statisticians use seasonal adjustment to control for these influences.

Controlling of seasonal influences allows measurement of real monthly changes; short and long term patterns of growth or decline; and turning points. Data for one month can be compared to data for any other month in the series and the data series can be ranked to find high and low points. Any observed differences are “real” differences; that is, they are differences brought about by changes in the data and not brought about by a change in the number of days or weekends in the month, the occurrence or non-occurrence of a holiday, or seasonal activity.

This layer shows total trips by mode and their corresponding emissions across different neighborhoods in Seattle. The data is mapped to census tracts. The data in this layer has been populated using an output from the Puget Sound Regional Council's (PSRC's) regional travel demand model. This model is updated only once every few years and is therefore not ideal for frequent data updates. The City is working on procuring more frequent measured travel data from alternate sources. For more information please visit the One Seattle Climate Portal item description page.

The dataset shows, from 2014, the low emission buses used for local public transport in the Municipality of Milan (indicator for 100 buses used and absolute value).

Electric buses (hybrids or full-wheel drive buses, including those powered by hydrogen with fuel cell technology) and gas-powered buses (with bi-fuel petrol/methane or petrol/LPG engines) are considered to be low emissions.

Dataset extrapolated from tables 7.1-7.2 Urban Mobility Istat.

This dataset is a highly versatile and precisely annotated large-scale dataset of smartphone sensor data for multimodal locomotion and transportation analytics of mobile users.The dataset comprises 7 months of measurements, collected from all sensors of 4 smartphones carried at typical body locations, including the images of a body-worn camera, while 3 participants used 8 different modes of transportation in the southeast of the United Kingdom, including in London.In total 28 context labels were annotated, including transportation mode, participant’s posture, inside/outside location, road conditions, traffic conditions, presence in tunnels, social interactions, and having meals.The total amount of collected data exceed 950 GB of sensor data, which corresponds to 2812 hours of labelled data and 17562 km of traveled distance. The potential applications arising from this dataset include:Machine-learning systems to automatically recognize modes of transportations from mobile phone dataRoad condition analysis and recognitionTraffic conditions analysis and recognition.Assessment of Google’s activity and transportation recognition API in comparison to custom algorithmsProbabilistic mobility modellingActivity recognition (e.g. automatic detection of eating and drinking)Novel localization techniques using dynamic fusion of sensorsRadio signal propagation analsisImage-based activity and transportation mode recognition The current recommended publication regarding the dataset is [1]. The current recommended publication regarding the application which was used to collect the dataset is [2].[1] H. Gjoreski, M. Ciliberto, L. Wang, F. J. Ordoñez Morales, S.Mekki, S.Valentin, D. Roggen, “The University of Sussex-Huawei Locomotion and Transportation Dataset for Multimodal Analytics with Mobile Devices”, In IEEE Access, 2018[2] M. Ciliberto, F. J. Ordoñez Morales, H. Gjoreski, D. Roggen, S.Mekki, S.Valentin. “High reliability Android application for multidevice multimodal mobile data acquisition and annotation.” In ACM Conference on Embedded Networked Sensor Systems. ACM, 2017.We recommend to refer to the dataset as follows in your publications:Use at least once the complete name: “The University of Sussex-Huawei Locomotion and Transportation Dataset” or “The Sussex-Huawei Locomotion and Transportation Dataset“. You may introduce the acronym of the dataset as well: “The University of Sussex-Huawei Locomotion and Transportation (SHL) Dataset“.Subsequently, you may refer to the dataset with its acronym: “The SHL Dataset“.

Use of transportation services by industry

Daily domestic transport use by mode. Daily usage of selected domestic transport by mode for Great Britain.

This dataset provides information related to access and transportation-related claims. It contains information about the total number of patients, total number of claims, and total dollar amount, grouped by recipient race, gender and age group. Restricted to claims with service date between 01/2012 to 12/2017. Transportation claims identified as billing provider type 26 and related category of service type. This data is for research purposes and is not intended to be used for reporting. Due to differences in geographic aggregation, time period considerations, and units of analysis, these numbers may differ from those reported by FSSA.

Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.

Operator specific Fares data is available in the NeTEx format. NeTEx is a CEN standard that can be used to represent many aspects of a multi-modal transport network. The UK profile includes the elements related to fares for buses, for more information on the schema and profile use the following links:

http://netex.uk/farexchange/ http://www.transmodel-cen.eu/standards/netex/