Coronavirus (COVID-19) has proven a formidable adversary worldwide, with tremendous impact on the United States. The transportation system is an essential element of our response to this pandemic but has also been significantly impacted across all modes, both passenger and goods movement. This environment is where research is needed most. This report details meetings with high-level participants who developed a research agenda that can assist research funding agencies in prioritizing research activities and funding. On April 29, 2020, TRB convened an online meeting of high-level participants to help identify potential issues that could be addressed in a research agenda for COVID-19–related transportation topics. The broad range of research questions identified are organized by: Operations, Resilience, and Disaster Recovery;Supply Chain and Goods Movement;Changes in Demand, Transportation Planning, and Data;Social Justice, Access, and Mobility Equity;Effects on Economics, Revenues, and Costs (Including Stimulus);Governance and Roles During a Pandemic, and Public Health.
In the last few months, the COVID-19 pandemic has impacted transportation agencies across the United States in a variety of ways. On July 15, 2020, the Transportation Asset Management (TAM) Committee of the Transportation Research Board (TRB) hosted a webinar that featured executives and CEOs from state, regional, and transit agencies. The panelists in this webinar discussed how the pandemic has impacted their agencies and their planned investments in the transportation systems they manage. These agency leaders manage a collective transportation system that includes roads, bridges, passenger and freight rail, buses, and more.
Granular-surfaced roads in seasonally cold regions regularly experience damage and degradation due to freeze–thaw cycles and steadily increasing traffic loads. Repair and maintenance of such roads can consume significant portions of budgets from counties and secondary roads departments. Disciplines Civil and Environmental Engineering | Geotechnical Engineering Comments This proceeding is published as Ashlock, Jeramy C., Yijun Wu, Bora Cetin, Halil Ceylan, and Cheng Li. “Construction of Chemically and Mechanically Stabilized Test Sections to Reduce Freeze–Thaw Damage of Granular Roads.” 12th International Conference on Low-Volume Roads. Transportation Research Circular E-C248 (2019): 58-63. Posted with permission. This conference proceeding is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/ ccee_conf/107
his session focused on recent advances in measuring e-commerce and residential delivery activity. Presenters discussed new data sources, data integration, and machine learning methods for demand analysis. n this session, presenters discussed freight data needs, sources, and analysis approaches for highway planning applications. The following presentations addressed performance measurement, resiliency planning, truck routing, and lane prioritization. n this session, experts from leading transportation establishments discussed how their organizations use freight data for strategic and operational decision-making. n this session, state and regional planning agencies discussed experiences, best practices, and challenges for procuring new and innovative freight data. his session highlighted innovative data and analytical approaches to inform freight planning and investment decisions. The following presentations focused on statewide, urban, and port applications. 29 methods for local, state, and national demand estimation, as well as an innovative data platform approach for improving operational efficiency through information sharing. n this session, presenters discussed innovative data sources and data integration approaches for measuring freight activity for ports, inland waterways, and border crossings. his speed session highlighted recent applications of truck probe data to measure and assess long-haul truck parking challenges. The following presentations were followed by a roundtable discussion with presenters. n this speed session, presenters discussed innovative artificial intelligence and machine learning approaches for addressing common freight data gaps. The following presentations of applications were followed by a panel discussion. his session brought together community leaders and multidisciplinary researchers to discuss performance measures and data, as well as research needs for measuring the health, safety, and quality of life impacts of freight activity and infrastructure on local communities. his is the third Innovations in Freight Data Workshop that has taken place, and each time it has become a more comprehensive and enlightening event. I am going to start by talking a little bit about what we have learned as a group, and then I will provide some comments on applications that we have seen. I will then close with a few comments on the future to segue into a closing discussion, which will help shape the research agenda going forward. The Truck Congestion Analysis Tool (TCAT) is a web-based planning tool for visualizing, analyzing, and monitoring truck mobility in Texas. The underlying data were developed as part of the Texas Top 100 Congested Roadways effort between the TTI and the Texas DOT. Through TCAT, practitioners have access to many of the traditional mobility performance measures, including annual delay, delay per mile, congestion cost, travel time index, planning time index, and others. TCAT also provides practitioners context information regarding truck congestion. These performance measures are based on traffic volumes from the Texas DOT Roadway-Highway Inventory (RHiNo) road network and proprietary traffic speed data (INRIX). Practitioners also have access to the Top 100 Congested Roadways information and the more comprehensive RHiNo-based roadway segment dataset that allows for evaluation at different geographic scales including individual roadway segment, custom/user generated corridors, or regional summaries based on the selected region. Significant geographical regions are pre-defined in TCAT, including Texas DOT districts, Texas counties, MPOs, major regions of interest, and major corridors–roadways of interest. TCAT has two distinct analysis levels: road segment and regional. The road segment analysis level provides access to the performance measures associated with each road segment. Custom corridors of multiple road segments can also be created using from here/to here selections similar to adding pushpins to a map. The regional level provides practitioners quick access to summaries of the mobility performance at the much broader area level. TCAT has summaries at pre-defined aggregation levels (i.e., Texas DOT district, county, roadway classification, rural–urban) and a custom summary that provides the ability to aggregate performance measures by multiple categories. As an extension of the region summaries, annual truck congestion report cards are available for the Top 100 Truck Congested Roadways, as well as each of the pre-defined regions. These report cards were designed to quickly examine performance trends. This research consisted of an innovative data mining process conducted by the TTI and sponsored by the Texas DOT on existing oversize/overweight (OS/OW) permit applications data to improve statewide understanding and planning for OS/OW truck movements. The Motor Carrier Division of the Texas Department of Motor Vehicles (DMV) administers the OS/OW truck permitting system in Texas through the online Texas Permitting and Routing Optimization System (TxPROS). It is used for request, approval, issuing of permits, and designation of assigned routing for OS/OW loads. For this project, Texas DMV provided data on all permits issued from CY2015 through CY2020. Texas DMV provided two files: a permit report describing the applying company, type of load, dimensions, and GVW, and a GIS shapefile of the proscribed, permitted route. The raw permit data required extensive processing to remove extraneous inputs and common misspellings by applicants to identify common industry loads and commodity types. Statistics about the number of OS/OW permits issued, types of permits, load types, and time series data were generated from the processed data. The permit data were then joined to the route shapefiles to expand analyses on OS/OW movements for the top four industry types (construction, manufacturing, manufactured housing, and oil and gas) and specific load characteristics (loads over 250,000 pounds GVW and over 16.5 ft in height). This was then spatially joined to the Texas roadway network files to obtain total GVW and number of permits issued for each segment. This process was done for each month and then summed into annual summaries for each roadway segment level. The outcome of this process was a modified roadway network layer that contained roadway attributes and the number of OS/OW permits and total GVW for each month of the analysis period for each road segment. Roadway attributes include AADT, annual average daily truck traffic, crash data information, and congestion measures which allowed spatial comparisons with the OS/OW permits and GVW. OD trends for OS/OW permitted loads were analyzed to understand load This the the Integrated Traffic Analysis Network, an incident database available from the of to model high-level system performance as well as specific roadway segment performance over time. The study identified truck accident clusters using network kernel density estimates and then used Bayesian estimation to assess factors such as travel time, average vehicle speed, and traffic volume trajectories, determining the spatial and temporal impacts of truck accidents on subsequent traffic flows. on Wilmington Avenue resulted in travel time savings. These results demonstrated Eco-Drive’s potential to significantly improve travel and energy efficiencies of freight movement by medium-duty and heavy-duty vehicles during the first and last miles of their trips. The Minnesota DOT’s Urban Freight Perspectives Study is focused on enhancing freight mobility in the Metro District by improving communication with freight stakeholders and integrating freight considerations into the district’s project development process. The study focuses on how to integrate key takeaways from past freight planning work into Metro District’s project development process. A key tool being developed for this project is a “living” database with segment-level and census-block-group-level freight information to help district project managers develop projects that integrate freight issues into the design process and address freight impacts during construction. The database is being developed as an ArcGIS Online web map series consisting of multiple StoryMaps, interactive maps, and embedded Tableau dashboards. The data used in this tool includes StreetLight Insight location-based services data, FAF data, Census County Business Pattern Data, Longitudinal Employer-Household Dynamics data, BTS T-100 air cargo data, Data Axle Business Information, and location-specific information collected during a series of stakeholder interviews. Many aspects of the data analysis process were expedited by the use of APIs to access StreetLight and Census data, and the use of software, such as RStudio to efficiently process the data. Currently completed sections of the freight database include: A major hurdle in freight demand modeling and freight mobility studies has been a lack of adequate data on freight movements. The FAF integrates data from a variety of sources to create a comprehensive picture of freight movement among states and major metropolitan areas by all modes of transportation. This study focuses on the estimation of crude oil movement in the latest version of FAF. The movement between FAF zones was estimated by assembling crude oil production, attraction, and movement information from several diverse sources. Compared to previous versions of FAF, several major data sources with higher geographic resolution and coverage were identified and fused to improve the comprehensiveness of the crude oil movement estimation. These data contain production, attraction, and movement data at various geographical levels for different modes and include: generated by the Port Transportation Analysis Model (PortTAM)); and 2) improved performance of the internal HDT model by focusing on intra-regional trade trends and supply chains associated w
