This data set contains 40 instances of the Dynamic Pickup and Delivery Problem with Time Windows, each containing 1000 orders, used in the article The Value of Information Sharing for Platform-Based Collaborative Vehicle Routing by J. Los, F. Schulte, M.T.J. Spaan, and R.R. Negenborn, published in Transportation Research Part E.

The USGS Transportation downloadable data from The National Map (TNM) is based on TIGER/Line data provided through U.S. Census Bureau and supplemented with HERE road data to create tile cache base maps. Some of the TIGER/Line data includes limited corrections done by USGS. Transportation data consists of roads, railroads, trails, airports, and other features associated with the transport of people or commerce. The data include the name or route designator, classification, and location. Transportation data support general mapping and geographic information system technology analysis for applications such as traffic safety, congestion mitigation, disaster planning, and emergency response. The National Map transportation data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and structures, to produce general reference base maps. The National Map viewer allows free downloads of public domain transportation data in either Esri File Geodatabase or Shapefile formats. For additional information on the transportation data model, go to https://nationalmap.gov/transport.html.

Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is the processed integrated traffic data with work zone and incident information. Attached below are the number of lanes data and impacted work zone .pkl file.

Content

This data set contains DOT construction project information. The data is refreshed nightly from multiple data sources, therefore the data becomes stale rather quickly.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated monthly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Ricardo Gomez Angel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

The Transportation Secure Data Center TSDC at https//www.nrel.gov/tsdc provides free access to detailed transportation data from travel surveys and studies conducted across the nation. It features millions of data points including second-by-second GPS readings vehicle characteristics and demographics for all modes of travel. NREL screens the initial data for quality control translates each data set into a consistent format and interprets the data for spatial analysis. NREL processing routines add information on fuel economy and road grades and join data points to the road network. NREL maintains the TSDC in partnership with the U.S. Department of Transportation and the U.S. Department of Energy.Before viewing the data for the first time you will be required to fill out a short registration form.If you use TSDC data in a publication please notify NREL at tsdc@nrel.gov and include a citation in your publication.

The Transportation and Health Tool (THT) was developed by the U.S. Department of Transportation and the Centers for Disease Control and Prevention to provide easy access to data that practitioners can use to examine the health impacts of transportation systems. The tool provides data on a set of transportation and public health indicators for each U.S. state and metropolitan area that describe how the transportation environment affects safety, active transportation, air quality, and connectivity to destinations. You can use the tool to quickly see how your state or metropolitan area compares with others in addressing key transportation and health issues. It also provides information and resources to help agencies better understand the links between transportation and health and to identify strategies to improve public health through transportation planning and policy.

The National Transportation Library (NTL) provides national and international access to transportation information, coordinates information creation and dissemination, and provides reference services for Department of Transportation (DOT) employees and public stakeholders. Established in 1998 by the Transportation Equity Act for the 21st Century (TEA-21; P.L. 105-178), NTL’s authorized role was expanded in 2012’s Moving Ahead for Progress in the 21st Century (MAP-21; P.L. 112- 141). NTL’s primary product and service is the Repository and Open Science Access Portal (ROSA P) (https://rosap.ntl.bts.gov). NTL’s collections in ROSA P are full-text digital publications, datasets, and other resources. Legacy print materials that have been digitized are collected if they have historic, technical, or national significance. The repository is also designated as the full-text repository for USDOT-funded research under the USDOT Public Access Plan. Collections in ROSA P are available without restriction to transportation researchers, statistical organizations, the media, and the general public.

Transportation Analytics Market Outlook

The global transportation analytics market size was valued at approximately $15.3 billion in 2023 and is expected to reach around $42.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.1%. One of the primary growth factors contributing to this robust market expansion is the rising need for efficient transportation systems to cope with increasing urbanization and the resultant traffic congestion.

The proliferation of smart city initiatives across various regions is significantly driving the growth of the transportation analytics market. Governments and urban planners are increasingly adopting advanced analytics to improve traffic flow, enhance safety, and optimize route planning. These analytics provide valuable insights that help in reducing congestion, minimizing travel time, and lowering emissions, thereby contributing to sustainable urban development. Furthermore, the integration of Internet of Things (IoT) devices and sensors in transportation systems is generating a vast amount of data, which can be analyzed for real-time decision-making and predictive analysis.

Another crucial growth factor is the advancement in artificial intelligence (AI) and machine learning technologies. These technologies enable the development of sophisticated analytics solutions that can predict traffic patterns, optimize logistics, and enhance safety measures. AI-driven analytics can process large datasets with high speed and accuracy, providing actionable insights that can significantly improve the efficiency of transportation networks. Additionally, the growing adoption of autonomous and connected vehicles is creating a demand for advanced analytics solutions to manage and analyze the data generated by these vehicles.

Furthermore, the increasing focus on enhancing the customer experience in the transportation sector is propelling the market growth. Transportation service providers are leveraging analytics to understand customer preferences, improve service quality, and offer personalized experiences. For instance, airlines and railways are using analytics to optimize pricing, manage bookings, and enhance in-transit services. This customer-centric approach not only improves satisfaction but also drives revenue growth for transportation companies.

Regionally, North America and Europe are leading the market due to the early adoption of advanced technologies and well-established transportation infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Rapid urbanization, increasing investments in smart city projects, and the growing need for efficient transportation systems in emerging economies are driving the market growth in this region. Countries like China, India, and Japan are at the forefront of adopting transportation analytics solutions to tackle urbanization challenges and improve public transportation services.

Component Analysis

The transportation analytics market can be segmented into three main components: software, hardware, and services. Each component plays a crucial role in the effective deployment and operation of transportation analytics solutions. The software segment includes various analytics platforms and tools that process and analyze transportation data. These software solutions enable real-time monitoring, predictive analytics, and data visualization, helping stakeholders make informed decisions to enhance transportation efficiency and safety. The increasing adoption of cloud-based analytics solutions is further fueling the growth of this segment, as it offers scalability, flexibility, and cost-effectiveness.

The hardware segment comprises sensors, cameras, GPS devices, and other IoT-enabled devices that collect data from various transportation modes. These hardware components are essential for the accurate collection and transmission of data, which is then analyzed by software solutions. The growing adoption of connected vehicles and smart infrastructure is driving the demand for advanced hardware solutions. Moreover, advancements in sensor technologies and the decreasing cost of IoT devices are making it feasible for transportation agencies to implement comprehensive data collection systems.

The services segment includes consulting, implementation, and maintenance services provided by analytics vendors. These services are crucial for the successful deployment and operation of transportation analytics solutions. Consulting services help organizations identify their specific needs and choose t

The Integrated Transportation Information Platform (formerly data linking) is a virtual data warehouse that incorporates a variety of reporting and analytical functions pertaining to the data maintained in four core FHWA systems (FMIS, HPMS, NBI, and RADS).

This product leverages first-party and third party data sources. It provides anonymized statistics helpful to: - measure the overall transport & logistics activity in Europe and in the UK (depending on the source), potentially potentially by type of goods transported (works, goods transportation, refrigerated...) - provide insights about origins and destinations of vehicles & trucks across Europe, in the UK between cities, communities... - identify stops and standstill areas of trucks, vans and all types of vehicles

All these statistics can feed various use cases: - marketing study for mobility advisory firms (Origins, Destinations, Stops / Standstill) that try to understand how the EV trucks are going to develop - help with marketing and geomarketing use cases to identify where to build / open a new branch or site - help explain and / or predict performance of businesses across geographies

Use cases: ==> Transport & logistics analytics: Traffic consultants, road operators, municipalities and SaaS analytics platforms use our data for understanding road safety, road usage ==> Supply chain: managers trying to look after new transport & logistics solutions ==> Site-selection : Our data help companies looking to open EV charging stations, new shops and stores where the traffic is adapted to their business ==> Dynamic pricing / geomarketing : Our data help companies adjust prices across geographies

The size and share of this market is categorized based on Data Collection (Mobile Data, Sensor Data, Geospatial Data, User-Generated Data, Third-party Data) and Data Processing (Data Integration, Data Analytics, Data Visualization, Data Enrichment, Data Management) and Application (Retail and E-commerce, Transportation and Logistics, Smart Cities, Healthcare, Telecommunications) and Deployment Mode (Cloud-based, On-premises, Hybrid) and End-user Industry (Government, BFSI, Transportation and Logistics, Retail, Healthcare) and geographical regions (North America, Europe, Asia-Pacific, South America, Middle-East and Africa).

The global Transportation & Logistics Platform market is experiencing robust growth, driven by the increasing adoption of digital technologies within the logistics sector. The market's expansion is fueled by several key factors, including the need for enhanced supply chain visibility, optimization of logistics operations, and the growing demand for real-time tracking and data analytics. The shift towards cloud-based solutions offers scalability, cost-effectiveness, and accessibility, further accelerating market expansion. Freight forwarding companies, courier service providers, and network service providers are key adopters, leveraging these platforms to streamline processes, reduce operational costs, and improve customer service. While the precise market size for 2025 is not provided, considering a plausible CAGR of 15% (a conservative estimate given industry trends) and a reasonable starting point based on comparable market reports, we can assume a market size of approximately $15 billion in 2025. This robust growth is projected to continue, with a sustained CAGR potentially reaching 12% through 2033, significantly impacting the overall logistics landscape. The market segmentation reveals a strong preference for cloud-based solutions, reflecting the benefits of accessibility and scalability. Geographically, North America and Europe are currently leading the market, benefiting from early adoption and established logistics infrastructure. However, significant growth potential exists in the Asia-Pacific region, driven by rapid economic growth and expanding e-commerce activities. Despite this positive outlook, the market faces certain challenges, such as high initial investment costs, the need for robust data security measures, and the complexities associated with integrating legacy systems with new platforms. Furthermore, the competitive landscape is intensely dynamic, with established players and emerging startups vying for market share, leading to intense innovation and competition. The integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is further expected to shape market dynamics in the coming years.

The files in this data environment were produced using the Vehicle Awareness Device (VAD) installed on one test vehicle over a two month period. This data environment consists of data collected on 143 trips taken during the period from October 18, 2012 through December 19, 2012. Activities included numerous repetitive trips by one individual in and around Leesburg, Virginia and one long road trip from Ann Arbor, Michigan to Leesburg, Virginia by way of eastern Indiana.

The Synthetic Data Platform market is experiencing robust growth, driven by the increasing need for data privacy and security, coupled with the rising demand for AI and machine learning model training. The market's expansion is fueled by several key factors. Firstly, stringent data privacy regulations like GDPR and CCPA are limiting the use of real-world data, creating a surge in demand for synthetic data that mimics the characteristics of real data without compromising sensitive information. Secondly, the expanding applications of AI and ML across diverse sectors like healthcare, finance, and transportation require massive datasets for effective model training. Synthetic data provides a scalable and cost-effective solution to this challenge, enabling organizations to build and test models without the limitations imposed by real data scarcity or privacy concerns. Finally, advancements in synthetic data generation techniques, including generative adversarial networks (GANs) and variational autoencoders (VAEs), are continuously improving the quality and realism of synthetic datasets, making them increasingly viable alternatives to real data. The market is segmented by application (Government, Retail & eCommerce, Healthcare & Life Sciences, BFSI, Transportation & Logistics, Telecom & IT, Manufacturing, Others) and type (Cloud-Based, On-Premises). While the cloud-based segment currently dominates due to its scalability and accessibility, the on-premises segment is expected to witness growth driven by organizations prioritizing data security and control. Geographically, North America and Europe are currently leading the market, owing to the presence of mature technological infrastructure and a high adoption rate of AI and ML technologies. However, Asia-Pacific is anticipated to show significant growth potential in the coming years, driven by increasing digitalization and investments in AI across the region. While challenges remain in terms of ensuring the quality and fidelity of synthetic data and addressing potential biases in generated datasets, the overall outlook for the Synthetic Data Platform market remains highly positive, with substantial growth projected over the forecast period. We estimate a CAGR of 25% from 2025 to 2033.

The Bay Area Transportation Study (BATS) Commission collected data from 30,686 Bay Area households using a face-to-face, in-home survey for a regional travel study to better understand their transportation needs and preferences. These households represented about 2.1% of the 1,387,000 Bay Area households in 1965. All household members around five years old were asked to record their travel for one assigned travel day. Data from this survey were used in efforts to develop travel demand models from the mid-1960s to the late 1970s.

This data is City of Salem, Oregon information that is uploaded from City data sources into ArcGIS Online as "Hosted Feature Layers". The City is sharing this data into its Open Data platform for citizen and business uses. Please read the GIS Access and Use Constraints for more information.For data support and questions, please email gis@cityofsalem.net

The Vision Zero Safety data comes from a web-based application developed to allow the public to communicate the real and perceived dangers along the roadway from the perspective of either a pedestrian, bicyclist or motorist. The data is captured from a site visitor who can click or tap on a location to report a transportation hazard.

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Emile Seguin on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

This dataset is distributed under the following licenses: Public Domain

This data set contains the coordinates of the plots in the thesis "Solving Large-Scale Dynamic Collaborative Vehicle Routing Problems - An Auction-Based Multi-Agent Approach" by Johan Los. It represents the results of various computational experiments in collaborative vehicle routing that were conducted to investigate to what extent an auction-based multi-agent system can be applied to solve dynamic large-scale collaborative vehicle routing problems. The data set indicates, among others, the value of information sharing, the profits that can be obtained by cooperation under different circumstances, and the individual profits that can be obtained when strategic bidding is applied.

Data for Artificial Intelligence: Data-Centric AI for Transportation: Work Zone Use Case proposes a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms and introduces a novel deep learning model to predict the traffic speed and traffic collision likelihood during planned work zone events. This dataset is raw Maryland 2019 Average Annual Daily Traffic data