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Showing 401 - 420 of 636 results

Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-06-14 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Hawaii, and ...

10/05/2026

Title : Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-06-14 URL : https://rosap.ntl.bts.gov/view/dot/93765
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93765

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93765.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93765.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Hawaii, and between Hawaii and other States. Flow does not include imports and exports that use Hawaii as the gateway port and go out of the state. AN - dot:93765 DA - 2022-06-14 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2050 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93765 ER -
Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-03-29 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through H ...

10/05/2026

Title : Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-03-29 URL : https://rosap.ntl.bts.gov/view/dot/93764
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93764

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93764.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93764.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through Hawaii including imports and exports that use Hawaii as the gateway port. AN - dot:93764 DA - 2022-03-29 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Passing Through Hawaii on National Highway System 2017 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93764 ER -
Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-03-29 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Hawaii, and ...

10/05/2026

Title : Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-03-29 URL : https://rosap.ntl.bts.gov/view/dot/93763
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93763

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93763.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93763.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Hawaii, and between Hawaii and other States. Flow does not include imports and exports that use Hawaii as the gateway port and go out of the state. AN - dot:93763 DA - 2022-03-29 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Going To, From, and Within Hawaii on National Highway System 2017 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93763 ER -
Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-05-04 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through G ...

10/05/2026

Title : Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-05-04 URL : https://rosap.ntl.bts.gov/view/dot/93762
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93762

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93762.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93762.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through Georgia including imports and exports that use Georgia as the gateway port. AN - dot:93762 DA - 2022-05-04 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2050 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93762 ER -
Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-05-04 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Georgia, and ...

10/05/2026

Title : Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-05-04 URL : https://rosap.ntl.bts.gov/view/dot/93761
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93761

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93761.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93761.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Georgia, and between Georgia and other States. Flow does not include imports and exports that use Georgia as the gateway port and go out of the state. AN - dot:93761 DA - 2022-05-04 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2050 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93761 ER -
Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-03-29 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through G ...

10/05/2026

Title : Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-03-29 URL : https://rosap.ntl.bts.gov/view/dot/93760
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93760

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93760.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93760.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks that pass through Georgia including imports and exports that use Georgia as the gateway port. AN - dot:93760 DA - 2022-03-29 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Passing Through Georgia on National Highway System 2017 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93760 ER -
Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations

2022-03-29 | PDF

Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Georgia, and ...

10/05/2026

Title : Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017 Corporate Creator(s) : United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Published Date : 2022-03-29 URL : https://rosap.ntl.bts.gov/view/dot/93759
United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations (2022). Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. https://rosap.ntl.bts.gov/view/dot/93759

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations. Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022. https://rosap.ntl.bts.gov/view/dot/93759.

United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017. United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations, 2022, ROSA P. https://rosap.ntl.bts.gov/view/dot/93759.
Export RIS Citation Information. TY - Booklet/Pamphlet A2 - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations AB - Major flows include domestic and international freight moving by truck on highway segments with more than twenty-five FAF trucks per day and between places typically more than fifty miles apart. Flows include 42 different commodities represented in FAF. State to State flows represent annual KTons of freight transported by trucks within Georgia, and between Georgia and other States. Flow does not include imports and exports that use Georgia as the gateway port and go out of the state. AN - dot:93759 DA - 2022-03-29 KW - Freight transportation KW - Commodity flow KW - Traffic flow KW - Truck routes LA - English PB - United States. Department of Transportation. Federal Highway Administration. Office of Freight Management and Operations PY - 2022 TI - Estimated FAF Flow for Trucks Going To, From, and Within Georgia on National Highway System 2017 M3 - Booklet/Pamphlet N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93759 ER -
Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions

Dong, Hongwei

2026-09-01 | PDF

In response to California's ongoing housing affordability crisis, state and local governments have adopted a range of prohousing policies aimed at reducing barriers to housing production and promoting the integration of housing with transportation. This study examines two distinct but related dimensions of prohousing policies in California. One con ...

10/05/2026

Title : Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions Creator(s) : Dong, Hongwei Corporate Creator(s) : San Jose State University. College of Business. Mineta Transportation Institute Corporate Contributor(s) : State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration;United States. Department of Transportation. University Transportation Centers (UTC) Program;United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology; Published Date : 2026-09-01 Report Number : 26-32 URL : https://rosap.ntl.bts.gov/view/dot/93758
Dong, H. (2026). Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions (Report No. 26-32). San Jose State University. College of Business. Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/93758

Dong, Hongwei. Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions. Report no. 26-32. San Jose State University. College of Business. Mineta Transportation Institute, 2026. https://rosap.ntl.bts.gov/view/dot/93758.

Dong, Hongwei Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions. San Jose State University. College of Business. Mineta Transportation Institute, 2026, Report no. 26-32, ROSA P. https://rosap.ntl.bts.gov/view/dot/93758.
Export RIS Citation Information. TY - Tech Report AU - Dong, Hongwei A2 - San Jose State University. College of Business. Mineta Transportation Institute A3 - State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration A3 - United States. Department of Transportation. University Transportation Centers (UTC) Program A3 - United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology AB - In response to California's ongoing housing affordability crisis, state and local governments have adopted a range of prohousing policies aimed at reducing barriers to housing production and promoting the integration of housing with transportation. This study examines two distinct but related dimensions of prohousing policies in California. One concerns the Prohousing Designation Program (PDP) (established by the 2019-2020 Budget Act), analyzing the adoption and implementation of prohousing policies in 56 California cities that received Prohousing Designation. The other examines recent state and local policy shifts aimed at promoting affordable housing in transit-rich areas. The study uses a mixed-methods research design, combining policy and document review with semi-structured interviews with urban planners, municipal housing professionals, transit-oriented development (TOD) managers in large transit agencies, and real estate developers. The study finds that prohousing designations skew toward large urban cities. Interviewed planners identified a few especially effective prohousing tools in practice: entitlement and permitting streamlining, specific plans, inclusionary zoning, and local measures that facilitate and incentivize ADUs. They also identified several major barriers to housing production, including high development cost and financing constraints, land cost and constrained sites, understaffing and the administrative burden of keeping pace with rapidly evolving state housing laws, and community opposition. In addition, interview participants emphasized several factors beyond the purview of municipal planning and housing authorities that significantly influence housing production. These include housing funding and subsidies, the availability and timely expansion of infrastructure and utilities needed to support new development, and building and fire codes that can increase development costs and timelines. The analysis of recent TOD policy changes suggests that, following a series of state laws facilitating housing near transit, major coastal cities, metropolitan planning organizations (MPOs), and transit agencies have reformed local policies governing affordability requirements, density regulations, and parking standards in transit-rich areas. Despite these reforms, TOD implementation remains complex and faces multiple obstacles, including community opposition to density increases and parking reductions, high development costs and financing constraints, fragmented funding sources, insufficient interagency coordination, and institutional and technical challenges. The findings provide practical insights for state and local policymakers seeking to strengthen prohousing initiatives and accelerate the production of affordable, transit-oriented housing throughout California. AN - dot:93758 DA - 2026-09-01 KW - Housing KW - Land use planning KW - Public transit KW - Interviewing KW - Policy analysis KW - Investments KW - Metropolitan planning organizations KW - Policy making KW - Urban areas KW - Cities KW - Infrastructure KW - transit oriented development LA - English PB - San Jose State University. College of Business. Mineta Transportation Institute PY - 2026 SN - 26-32 TI - Assessing the Co-Benefits of Housing and Transportation Investments in Prohousing Designated Jurisdictions M3 - Tech Report N1 - United States N1 - SB1-SJAUX_2023-26 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93758 ER -
Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief]

Kwon, Jaymin

;

Ahn, Yushin

2026-09-01 | PDF

This study built a comprehensive geospatial database integrating air quality, transportation emissions, roadway infrastructure, and land cover data from 1985 through projected 2032 trends.

10/05/2026

Title : Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief] Creator(s) : Kwon, Jaymin;Ahn, Yushin;Chung, Steve; Corporate Creator(s) : San Jose State University. College of Business. Mineta Transportation Institute Corporate Contributor(s) : State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration;United States. Department of Transportation. University Transportation Centers (UTC) Program;United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology; Published Date : 2026-09-01 Report Number : 2444 URL : https://rosap.ntl.bts.gov/view/dot/93757
Kwon, J., Ahn, Y., & Chung, S. (2026). Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief] (Report No. 2444). San Jose State University. College of Business. Mineta Transportation Institute. https://rosap.ntl.bts.gov/view/dot/93757

Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief]. Report no. 2444. San Jose State University. College of Business. Mineta Transportation Institute, 2026. https://rosap.ntl.bts.gov/view/dot/93757.

Kwon, Jaymin, et al. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief]. San Jose State University. College of Business. Mineta Transportation Institute, 2026, Report no. 2444, ROSA P. https://rosap.ntl.bts.gov/view/dot/93757.
Export RIS Citation Information. TY - Brief AU - Kwon, Jaymin AU - Ahn, Yushin AU - Chung, Steve A2 - San Jose State University. College of Business. Mineta Transportation Institute A3 - State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration A3 - United States. Department of Transportation. University Transportation Centers (UTC) Program A3 - United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology AB - This study built a comprehensive geospatial database integrating air quality, transportation emissions, roadway infrastructure, and land cover data from 1985 through projected 2032 trends. AN - dot:93757 DA - 2026-09-01 ET - Research Brief KW - Transportation KW - Air pollution KW - Geospatial data KW - Public health KW - Air quality management KW - Carbon dioxide KW - Passenger vehicles KW - Urban areas KW - Equity KW - Land use KW - Infrastructure KW - Vehicle miles of travel KW - Transportation-related air pollutant KW - ambient exposure to emissions KW - public health impact KW - air quality trends LA - English PB - San Jose State University. College of Business. Mineta Transportation Institute PY - 2026 SN - 2444 TI - Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis [Research Brief] M3 - Brief N1 - United States DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93757 ER -
Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis

Kwon, Jaymin

;

Ahn, Yushin

2026-09-01 | PDF

Transportation-related air pollution remains a significant environmental and public health challenge in California's San Joaquin Valley. This report presents a spatio-temporal analysis of roadside transportation-related air quality in Fresno County, California, from 1985 to 2023 with projections from 2024 through 2032. Using historical emissions in ...

10/05/2026

Title : Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis Creator(s) : Kwon, Jaymin;Ahn, Yushin;Chung, Steve; Corporate Creator(s) : San Jose State University. College of Business. Mineta Transportation Institute Corporate Contributor(s) : State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration;United States. Department of Transportation. University Transportation Centers (UTC) Program;United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology; Published Date : 2026-09-01 Report Number : 26-15 DOI : https://doi.org/10.31979/mti.2026.2444
Kwon, J., Ahn, Y., & Chung, S. (2026). Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis (Report No. 26-15). San Jose State University. College of Business. Mineta Transportation Institute. https://doi.org/10.31979/mti.2026.2444

Kwon, Jaymin, Yushin Ahn, and Steve Chung. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis. Report no. 26-15. San Jose State University. College of Business. Mineta Transportation Institute, 2026. https://doi.org/10.31979/mti.2026.2444.

Kwon, Jaymin, et al. Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis. San Jose State University. College of Business. Mineta Transportation Institute, 2026, Report no. 26-15, ROSA P. https://doi.org/10.31979/mti.2026.2444.
Export RIS Citation Information. TY - Tech Report AU - Kwon, Jaymin AU - Ahn, Yushin AU - Chung, Steve A2 - San Jose State University. College of Business. Mineta Transportation Institute A3 - State of California SB1 2017/2018, Trustees of the California State University Sponsored Programs Administration A3 - United States. Department of Transportation. University Transportation Centers (UTC) Program A3 - United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology AB - Transportation-related air pollution remains a significant environmental and public health challenge in California's San Joaquin Valley. This report presents a spatio-temporal analysis of roadside transportation-related air quality in Fresno County, California, from 1985 to 2023 with projections from 2024 through 2032. Using historical emissions inventories, projected vehicle miles traveled (VMT), infrastructure data, and land cover analysis, the study examines how transportation activity and urban expansion have influenced air quality across the region over time. Findings reveal that while PM10, PM2.5, NOx, SOₓ, CO, and CO₂ emissions from on-road mobile sources have declined significantly due to regulatory programs and cleaner technologies, the contribution of passenger vehicles has become proportionally more significant in recent years. Additionally, the study found that Fresno County experienced substantial land-use changes. These include the conversion of more than 130 km² of agricultural land into urban development and major losses in evergreen forest cover in the Sierra Nevada region, correlated with drought and wildfires, and tree mortality. At the same time, continued low-density residential growth has contributed to increased vehicle travel and ongoing air quality challenges. The findings highlight the interconnected relationship between transportation systems, land-use change, environmental conditions, and public health outcomes. This report calls for coordinated land-use planning, sustainable transportation investments, freight and emissions regulation, and environmental equity strategies to support healthier and more resilient communities throughout Fresno County and the broader San Joaquin Valley. AN - dot:93756 DA - 2026-09-01 DO - https://doi.org/10.31979/mti.2026.2444 KW - Transportation KW - Air pollution KW - Geospatial data KW - Public health KW - Air quality management KW - Carbon dioxide KW - Passenger vehicles KW - Urban areas KW - Equity KW - Land use KW - Vehicle miles of travel KW - Transportation-related air pollutant KW - ambient exposure to emissions KW - public health impact KW - air quality trends LA - English PB - San Jose State University. College of Business. Mineta Transportation Institute PY - 2026 SN - 26-15 TI - Spatio-Temporal Analysis of the Roadside Transportation-Related Air Quality (StarTraq 2024): Time-Series Analysis M3 - Tech Report N1 - United States N1 - SB1-SJAUX_2023-26 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93756 ER -
The Deep Patch Technique for Landslide Repair

Helwany, M. Bassam

1994-10-01 | PDF

This report describes the laboratory testing of the "USFS deep patch" technique and a Colorado Transportation Institute (CTI) modification of this technique for repairing landslides with geosynthetic reinforcement. The technique involves replacing sections of roadway lost due to landslides on top of a geosynthetically-reinforced embankment. The CTI ...

10/05/2026

Title : The Deep Patch Technique for Landslide Repair Creator(s) : Helwany, M. Bassam Corporate Creator(s) : Colorado. Department of Transportation Corporate Contributor(s) : United States. Department of Transportation. Federal Highway Administration Published Date : 1994-10-01 Report Number : CTI-UCD-2-94 URL : https://rosap.ntl.bts.gov/view/dot/93755
Helwany, M. B. (1994). The Deep Patch Technique for Landslide Repair (Report No. CTI-UCD-2-94). Colorado. Department of Transportation. https://rosap.ntl.bts.gov/view/dot/93755

Helwany, M. Bassam. The Deep Patch Technique for Landslide Repair. Report no. CTI-UCD-2-94. Colorado. Department of Transportation, 1994. https://rosap.ntl.bts.gov/view/dot/93755.

Helwany, M. Bassam The Deep Patch Technique for Landslide Repair. Colorado. Department of Transportation, 1994, Report no. CTI-UCD-2-94, ROSA P. https://rosap.ntl.bts.gov/view/dot/93755.
Export RIS Citation Information. TY - Tech Report AU - Helwany, M. Bassam A2 - Colorado. Department of Transportation A3 - United States. Department of Transportation. Federal Highway Administration AB - This report describes the laboratory testing of the "USFS deep patch" technique and a Colorado Transportation Institute (CTI) modification of this technique for repairing landslides with geosynthetic reinforcement. The technique involves replacing sections of roadway lost due to landslides on top of a geosynthetically-reinforced embankment. The CTI modification involves replacing the reinforced slope with a geosynthetically-reinforced retaining wall with a truncated base. Both techniques rely on the cantilevering ability of the reinforced mass to limit the load on the foundation with a high slide potential. The testing was done in a plane-strain device, [8 ft high, 16 ft long, and 3 ft wide (2.44 m high, 4.88 m long, and 0.9 m wide)] with a floor that can be lowered down to simulate the loss of support due to additional sliding of the foundation. Both road base fill and shredded tire fill were tested. The tests with road base showed that (1) both the USFS and CTI repair reduced effectively the adverse effects of local landsliding on the highway pavement by preventing crack propagation; (2) the USFS repair increased the stability of the repaired slope, which was in progressive failure, by reducing the stresses exerted on it; and (3) the CTI repair produced substantially greater stresses on its foundation due to the truncated base of the reinforced mass. These higher stresses of the CTI method will probably aggravate the stability of the slope which was in the state of progressive failure before repair. The CTI method, however, provides a wider base for pavement reconstruction. Tests with shredded tires found that excessive deformation makes them unsuitable for either repair technique. AN - dot:93755 DA - 1994-10-01 ET - Final Report KW - Geosynthetics KW - Landslides KW - Tires KW - Material reinforcement KW - Repairing KW - Slope failure KW - Testing KW - shredded tires LA - English PB - Colorado. Department of Transportation PY - 1994 SN - CTI-UCD-2-94 TI - The Deep Patch Technique for Landslide Repair M3 - Tech Report N1 - United States N1 - 93-482 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93755 ER -
Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact

Walsh, Edward

;

Hardison, Mathew A.

2026-09-30 | PDF

The Railroad Information Sharing Environment (RISE) is a voluntary, non-regulatory, non-punitive, data-driven safety partnership consisting of railroad stakeholders, including the Federal Railroad Administration (FRA), to advance railroad safety. Through a data trust, railroad stakeholders voluntarily share data and work collaboratively to address ...

10/05/2026

Title : Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact Creator(s) : Walsh, Edward;Hardison, Mathew A.; Corporate Creator(s) : MITRE Corporation Corporate Contributor(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Published Date : 2026-09-30 Report Number : DOT/FRA/ORD-26/26 URL : https://rosap.ntl.bts.gov/view/dot/93754
Walsh, E., & Hardison, M. A. (2026). Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact (Report No. DOT/FRA/ORD-26/26). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/93754

Walsh, Edward and Mathew A. Hardison. Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact. Report no. DOT/FRA/ORD-26/26. United States. Department of Transportation. Federal Railroad Administration, 2026. https://rosap.ntl.bts.gov/view/dot/93754.

Walsh, Edward, and Mathew A. Hardison Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact. United States. Department of Transportation. Federal Railroad Administration, 2026, Report no. DOT/FRA/ORD-26/26, ROSA P. https://rosap.ntl.bts.gov/view/dot/93754.
Export RIS Citation Information. TY - Tech Report AU - Walsh, Edward AU - Hardison, Mathew A. A2 - MITRE Corporation A3 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology A4 - Wornoff, Jason; Blue, Shala AB - The Railroad Information Sharing Environment (RISE) is a voluntary, non-regulatory, non-punitive, data-driven safety partnership consisting of railroad stakeholders, including the Federal Railroad Administration (FRA), to advance railroad safety. Through a data trust, railroad stakeholders voluntarily share data and work collaboratively to address complex railroad safety challenges. Between October 2024 and March 2025, FRA contracted MITRE Corporation (MITRE) to review the RISE program to assess and document lessons learned and identify opportunities to expand the data trust and the impact of RISE. The research team reviewed RISE program documentation, interviewed RISE stakeholders, and assessed the functionality of RISE in relation to similar programs operating a data trust. AN - dot:93754 DA - 2026-09-30 ET - October 2024 - March 2025 KW - Railroad safety KW - Data KW - Public private partnerships KW - Railroads KW - Data sharing LA - English PB - United States. Department of Transportation. Federal Railroad Administration PY - 2026 SN - DOT/FRA/ORD-26/26 TI - Railroad Information Sharing Environment (RISE): Program Attributes, Lessons Learned, and Opportunities for Expanded Future Impact M3 - Tech Report N1 - United States N1 - 693JJ624C000049 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93754 ER -
Railroad Information Sharing Environment Data Steward: Phase II

Franz, Mark L.

;

Emtenan, Tahsin

2026-09-30 | PDF

Building on the success of Phase I, Phase II of the FRA sponsored Railroad Information Sharing Environment (RISE) established collaborative relationships among rail stakeholders in class I freight, passenger and short line railroads, in addition to labor groups, state DOTs and railroad safety special interests groups with support from the Volpe Tra ...

10/05/2026

Title : Railroad Information Sharing Environment Data Steward: Phase II Creator(s) : Franz, Mark L.;Emtenan, Tahsin;Aras, Rohan;Zahedian, Sara;Sekula, Przemyslaw;Maharjan, Ateet; Corporate Creator(s) : University of Maryland (College Park, Md.);University of Maryland (College Park, Md.). Center for Advanced Transportation Technology; Corporate Contributor(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Published Date : 2026-09-30 Report Number : DOT/FRA/ORD-26/27 URL : https://rosap.ntl.bts.gov/view/dot/93753
Franz, M. L., Emtenan, T., Aras, R., Zahedian, S., Sekula, P., & Maharjan, A. (2026). Railroad Information Sharing Environment Data Steward: Phase II (Report No. DOT/FRA/ORD-26/27). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/93753

Franz, Mark L., Tahsin Emtenan, Rohan Aras, Sara Zahedian, Przemyslaw Sekula, and Ateet Maharjan. Railroad Information Sharing Environment Data Steward: Phase II. Report no. DOT/FRA/ORD-26/27. United States. Department of Transportation. Federal Railroad Administration, 2026. https://rosap.ntl.bts.gov/view/dot/93753.

Franz, Mark L., et al. Railroad Information Sharing Environment Data Steward: Phase II. United States. Department of Transportation. Federal Railroad Administration, 2026, Report no. DOT/FRA/ORD-26/27, ROSA P. https://rosap.ntl.bts.gov/view/dot/93753.
Export RIS Citation Information. TY - Tech Report AU - Franz, Mark L. AU - Emtenan, Tahsin AU - Aras, Rohan AU - Zahedian, Sara AU - Sekula, Przemyslaw AU - Maharjan, Ateet A2 - University of Maryland (College Park, Md.) A2 - University of Maryland (College Park, Md.). Center for Advanced Transportation Technology A3 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology AB - Building on the success of Phase I, Phase II of the FRA sponsored Railroad Information Sharing Environment (RISE) established collaborative relationships among rail stakeholders in class I freight, passenger and short line railroads, in addition to labor groups, state DOTs and railroad safety special interests groups with support from the Volpe Transportation Systems Center and RISE third-party data steward, the University of Maryland - Center for Advanced Transportation Technology Laboratory (CATT Lab). This Phase, conducted between September 2021 and February 2025, developed Issue Analysis Teams (IAT) to pursue railroad safety topics identified by FRA, Volpe, and railroad stakeholders, with the goal of improving railroad safety. Two IATs were established around the topics of rail property trespassing and vertical profiles of highway-rail grade crossings. This report will discuss the development of these IATs and three RISE products, a Grade Crossing Planar Deviation Dashboard, Grade Crossing Vehicle Simulation Model, and a Non- Injury Railroad Property Trespassing Collection Form that were developed with the direction of RISE stakeholders. AN - dot:93753 DA - 2026-09-30 ET - Sept 2021- Feb 2025 KW - Data sharing KW - Railroad safety KW - Grade crossing protection systems KW - Simulation LA - English PB - United States. Department of Transportation. Federal Railroad Administration PY - 2026 SN - DOT/FRA/ORD-26/27 TI - Railroad Information Sharing Environment Data Steward: Phase II M3 - Tech Report N1 - United States N1 - 69A36521403060RRDMD DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93753 ER -
A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards

Sohrabi, Soheil

;

Tajik, Amir

2026-09-30 | PDF

Researchers from Michigan Technological University, in collaboration with Argonne National Laboratory, conducted this study from May 2022 to July 2025. They aimed to identify and mitigate risks of current and future tropical storms impacting the railroad infrastructure along the Gulf of America. The study focused on two critical track segments owne ...

10/05/2026

Title : A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards Creator(s) : Sohrabi, Soheil;Tajik, Amir;Darestani, Yousef;Pringle, William;Lautala, Pasi;Xue, Pengfei; Corporate Creator(s) : Michigan Technological University Corporate Contributor(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Published Date : 2026-09-30 Report Number : DOT/FRA/ORD-26/29 URL : https://rosap.ntl.bts.gov/view/dot/93752
Sohrabi, S., Tajik, A., Darestani, Y., Pringle, W., Lautala, P., & Xue, P. (2026). A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards (Report No. DOT/FRA/ORD-26/29). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/93752

Sohrabi, Soheil, Amir Tajik, Yousef Darestani, William Pringle, Pasi Lautala, and Pengfei Xue. A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards. Report no. DOT/FRA/ORD-26/29. United States. Department of Transportation. Federal Railroad Administration, 2026. https://rosap.ntl.bts.gov/view/dot/93752.

Sohrabi, Soheil, et al. A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards. United States. Department of Transportation. Federal Railroad Administration, 2026, Report no. DOT/FRA/ORD-26/29, ROSA P. https://rosap.ntl.bts.gov/view/dot/93752.
Export RIS Citation Information. TY - Tech Report AU - Sohrabi, Soheil AU - Tajik, Amir AU - Darestani, Yousef AU - Pringle, William AU - Lautala, Pasi AU - Xue, Pengfei A2 - Michigan Technological University A3 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology AB - Researchers from Michigan Technological University, in collaboration with Argonne National Laboratory, conducted this study from May 2022 to July 2025. They aimed to identify and mitigate risks of current and future tropical storms impacting the railroad infrastructure along the Gulf of America. The study focused on two critical track segments owned and operated by CSX Transportation and CN railway in the Northern Gulf of America (NGOA). AB - The research team developed a comprehensive, risk-based framework to estimate the impact of storm-induced surge and wave hazards on coastal railroads along the NGOA coastline. The research team integrated a physics-based high-fidelity hydrodynamic model to simulate the storm surge and wave generated by tropical storms. The simulations projected future storms and data adjustments for the year 2050. The research team integrated the probabilistic hazard model with a statistical model that assessed the failure probability of railroad components to determine the probability of failure of tracks and bridges due to ballast and embankment scour and the uplift of ballasted bridge decks. The failure probabilities were used within a statistical analysis to determine expected life of tracks and bridges and to devise mitigation strategies that can increase the expected life of coastal tracks and bridges. AN - dot:93752 DA - 2026-09-30 ET - 05/01/2022 - 08/01/2025 KW - Coasts KW - Railroad tracks KW - Bridge waterways KW - Embankments KW - Floods KW - Storms LA - English PB - United States. Department of Transportation. Federal Railroad Administration PY - 2026 SN - DOT/FRA/ORD-26/29 TI - A Risk-Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards M3 - Tech Report N1 - United States N1 - 693JJ622C000006 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93752 ER -
FRA DOTX-216 Passenger Vehicle Characterization and Modeling

Klopp, Adam

;

Gauvain, Ben

2026-09-01 | PDF

Transportation Technology Center, Inc. (TTCI), now MxV Rail, performed work funded by the Federal Railroad Administration to characterize and model the high-speed passenger vehicle DOTX-216 car. The objectives of this project were to develop a list of parameters necessary to complete a dynamic model of the passenger vehicle, perform tests to measur ...

10/05/2026

Title : FRA DOTX-216 Passenger Vehicle Characterization and Modeling Creator(s) : Klopp, Adam;Gauvain, Ben;Walker, Russell; Corporate Creator(s) : Transportation Technology Center, Inc. (now MxV Rail) Corporate Contributor(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Published Date : 2026-09-01 Report Number : DOT/FRA/ORD-26/30 URL : https://rosap.ntl.bts.gov/view/dot/93751
Klopp, A., Gauvain, B., & Walker, R. (2026). FRA DOTX-216 Passenger Vehicle Characterization and Modeling (Report No. DOT/FRA/ORD-26/30). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/93751

Klopp, Adam, Ben Gauvain, and Russell Walker. FRA DOTX-216 Passenger Vehicle Characterization and Modeling. Report no. DOT/FRA/ORD-26/30. United States. Department of Transportation. Federal Railroad Administration, 2026. https://rosap.ntl.bts.gov/view/dot/93751.

Klopp, Adam, et al. FRA DOTX-216 Passenger Vehicle Characterization and Modeling. United States. Department of Transportation. Federal Railroad Administration, 2026, Report no. DOT/FRA/ORD-26/30, ROSA P. https://rosap.ntl.bts.gov/view/dot/93751.
Export RIS Citation Information. TY - Tech Report AU - Klopp, Adam AU - Gauvain, Ben AU - Walker, Russell A2 - Transportation Technology Center, Inc. (now MxV Rail) A3 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology A4 - Meddah, Abe AB - Transportation Technology Center, Inc. (TTCI), now MxV Rail, performed work funded by the Federal Railroad Administration to characterize and model the high-speed passenger vehicle DOTX-216 car. The objectives of this project were to develop a list of parameters necessary to complete a dynamic model of the passenger vehicle, perform tests to measure the parameters, develop a mathematical model using the measured parameters, and perform on-track tests to validate the model. TTCI performed these tasks and validated the model of the DOTX-216 car using a combination of validation methods. This report describes the procedures, results, and model validation analysis for the DOTX-216 car. AN - dot:93751 DA - 2026-09-01 KW - Railroad cars KW - Predictive control KW - Test procedures KW - Motor vehicle dynamics KW - Validation KW - Rail vehicle parameters KW - Dynamic model LA - English PB - United States. Department of Transportation. Federal Railroad Administration PY - 2026 SN - DOT/FRA/ORD-26/30 TI - FRA DOTX-216 Passenger Vehicle Characterization and Modeling M3 - Tech Report N1 - United States N1 - 326 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93751 ER -
Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results]

Baillargeon, Jay P. ;

Salazar, Juan Carlos Valdes

2026-09-01 | PDF

The team evaluated adding a complementary fixture to the VTU to allow assessment of TGMS accuracy under dynamic scenarios in a controlled laboratory environment. The fixture would dynamically move short railhead samples, providing the same rail deviation input to the optical sensors of the tested TGMS that the VTU is inputting to the rail vehicle. ...

10/05/2026

Title : Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results] Creator(s) : Baillargeon, Jay P.;Salazar, Juan Carlos Valdes;Bruzek, Radim;Bloom, Jeff; Corporate Creator(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology;ENSCO, Inc; Published Date : 2026-09-01 Report Number : RR 26-08 URL : https://rosap.ntl.bts.gov/view/dot/93750
Baillargeon, J. P., Salazar, J. C. V., Bruzek, R., & Bloom, J. (2026). Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results] (Report No. RR 26-08). United States. Department of Transportation. Federal Railroad Administration. https://rosap.ntl.bts.gov/view/dot/93750

Baillargeon, Jay P., Juan Carlos Valdes Salazar, Radim Bruzek, and Jeff Bloom. Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results]. Report no. RR 26-08. United States. Department of Transportation. Federal Railroad Administration, 2026. https://rosap.ntl.bts.gov/view/dot/93750.

Baillargeon, Jay P., et al. Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results]. United States. Department of Transportation. Federal Railroad Administration, 2026, Report no. RR 26-08, ROSA P. https://rosap.ntl.bts.gov/view/dot/93750.
Export RIS Citation Information. TY - Brief AU - Baillargeon, Jay P. AU - Salazar, Juan Carlos Valdes AU - Bruzek, Radim AU - Bloom, Jeff A2 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology A2 - ENSCO, Inc AB - The team evaluated adding a complementary fixture to the VTU to allow assessment of TGMS accuracy under dynamic scenarios in a controlled laboratory environment. The fixture would dynamically move short railhead samples, providing the same rail deviation input to the optical sensors of the tested TGMS that the VTU is inputting to the rail vehicle. Researchers explored two potential concepts for the fixture, one based on a Stewart platform and the other using a set of linear motor actuators. The team found that the VTU fitted with the additional fixture can be used to validate TGMS systems in tangents with reasonable fidelity, despite the inability to input gage deviations. This concept also would not be able to evaluate the performance of TGMS systems in curves due to VTU's inability to replicate centripetal accelerations and forces. AN - dot:93750 DA - 2026-09-01 ET - Research Results KW - Railroad tracks KW - Measurement KW - Vibration tests KW - Validation KW - Feasibility analysis LA - English PB - United States. Department of Transportation. Federal Railroad Administration PY - 2026 SN - RR 26-08 TI - Feasibility of Vibration Test Unit Modifications for TGMS Validations [Research Results] M3 - Brief N1 - United States N1 - 693JJ624F00039N DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93750 ER -
Machine Learning-based Analysis for Railway Track Maintenance

Goodarzi, Saeed

;

Ho, Carlton L.

2026-09-30 | PDF

Understanding and mitigating track degradation rates (TDRs) is crucial for ensuring the safety and efficiency of railway systems, as well as for controlling maintenance costs and preserving ride quality. The Federal Railroad Administration (FRA) sponsored the University of Massachusetts Amherst and Loram Technologies, Inc. to extensively analyze TD ...

10/05/2026

Title : Machine Learning-based Analysis for Railway Track Maintenance Creator(s) : Goodarzi, Saeed;Ho, Carlton L.;Kashani, Kevin;Hyslip, James P.; Corporate Creator(s) : University of Massachusetts at Amherst;Loram Technologies, Inc; Corporate Contributor(s) : United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology Published Date : 2026-09-30 Report Number : DOT/FRA/ORD-26/28 URL : https://rosap.ntl.bts.gov/view/dot/93749
Goodarzi, S., Ho, C. L., Kashani, K., & Hyslip, J. P. (2026). Machine Learning-based Analysis for Railway Track Maintenance (Report No. DOT/FRA/ORD-26/28). United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology. https://rosap.ntl.bts.gov/view/dot/93749

Goodarzi, Saeed, Carlton L. Ho, Kevin Kashani, and James P. Hyslip. Machine Learning-based Analysis for Railway Track Maintenance. Report no. DOT/FRA/ORD-26/28. United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology, 2026. https://rosap.ntl.bts.gov/view/dot/93749.

Goodarzi, Saeed, et al. Machine Learning-based Analysis for Railway Track Maintenance. United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology, 2026, Report no. DOT/FRA/ORD-26/28, ROSA P. https://rosap.ntl.bts.gov/view/dot/93749.
Export RIS Citation Information. TY - Tech Report AU - Goodarzi, Saeed AU - Ho, Carlton L. AU - Kashani, Kevin AU - Hyslip, James P. A2 - University of Massachusetts at Amherst A2 - Loram Technologies, Inc A3 - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development and Technology A4 - Thompson, Hugh AB - Understanding and mitigating track degradation rates (TDRs) is crucial for ensuring the safety and efficiency of railway systems, as well as for controlling maintenance costs and preserving ride quality. The Federal Railroad Administration (FRA) sponsored the University of Massachusetts Amherst and Loram Technologies, Inc. to extensively analyze TDR over 280 miles of passenger revenue track using data from 2011 to 2021. Using a large dataset was pivotal, as it enhanced the robustness of the analysis, allowing for more accurate and reliable conclusions. The researchers used ground penetrating radar (GPR) and light detection and ranging (LiDAR) along with geometry data to examine subsurface and drainage conditions of the tracks. The project employed two distinct methods to estimate TDR, each offering unique insights and applicability based on available historical data and desired precision. These complementary approaches provide the industry with versatile tools for estimating TDR and improving maintenance scheduling, emphasizing their reliability and adaptability. AN - dot:93749 DA - 2026-09-30 ET - July 2022 - July 2024 KW - Railroads KW - Machine learning KW - Data KW - Drainage KW - Laser radar KW - Ground penetrating radar LA - English PB - United States. Department of Transportation. Federal Railroad Administration. Office of Research, Development, and Technology PY - 2026 SN - DOT/FRA/ORD-26/28 TI - Machine Learning-based Analysis for Railway Track Maintenance M3 - Tech Report N1 - United States N1 - FR22RPD31000000010 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93749 ER -
Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System

Wang, Yinhai

;

Shi, Yan

2025-12-01 | PDF

This project developed and evaluated a cost-effective, camera-based system for FHWA 13-bin vehicle classification using roadside video, edge computing, and deep learning. Traditional vehicle classification methods, such as inductive loops, pneumatic tubes, and manual counts, are often expensive to deploy and maintain and provide limited classificat ...

10/05/2026

Title : Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System Creator(s) : Wang, Yinhai;Shi, Yan;Liu, Chenxi;Chen, Kehua; Corporate Creator(s) : University of Washington. Washington State Transportation Center (TRAC);University of Washington. Department of Civil and Environmental Engineering; Corporate Contributor(s) : Washington State Department of Transportation Contributor(s) : Jahangirnejad, Shervin Published Date : 2025-12-01 Report Number : WA-RD 961.1 URL : https://rosap.ntl.bts.gov/view/dot/93748
Wang, Y., Shi, Y., Liu, C., & Chen, K. (2025). Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System (Report No. WA-RD 961.1). Washington State Department of Transportation. https://rosap.ntl.bts.gov/view/dot/93748

Wang, Yinhai, Yan Shi, Chenxi Liu, and Kehua Chen. Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System. Report no. WA-RD 961.1. Washington State Department of Transportation, 2025. https://rosap.ntl.bts.gov/view/dot/93748.

Wang, Yinhai, et al. Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System. Washington State Department of Transportation, 2025, Report no. WA-RD 961.1, ROSA P. https://rosap.ntl.bts.gov/view/dot/93748.
Export RIS Citation Information. TY - Tech Report AU - Wang, Yinhai AU - Shi, Yan AU - Liu, Chenxi AU - Chen, Kehua A2 - University of Washington. Washington State Transportation Center (TRAC) A2 - University of Washington. Department of Civil and Environmental Engineering A3 - Washington State Department of Transportation AB - This project developed and evaluated a cost-effective, camera-based system for FHWA 13-bin vehicle classification using roadside video, edge computing, and deep learning. Traditional vehicle classification methods, such as inductive loops, pneumatic tubes, and manual counts, are often expensive to deploy and maintain and provide limited classification detail, particularly under challenging traffic, weather, and lighting conditions. To address these limitations, a portable roadside sensing platform was designed that integrates video cameras with edge computing hardware for on-site, near-real-time vehicle detection and classification. The system operates effectively in daytime, nighttime, and rainy conditions while minimizing data transfer requirements. At the core of the platform is an enhanced YOLOv11 deep learning model incorporating a mixture-of-experts (MoE) architecture. The model uses a shared backbone with multiple expert heads tailored to different environmental conditions, supported by a weather encoder and feature-wise linear modulation(FiLM) to adapt to variations in illumination, glare, and wet pavement. Model training and evaluation were conducted using a curated dataset of more than 20,000 vehicle instances annotated into the 13 FHWA vehicle classes and collected at WSDOT-relevant sites under diverse conditions. The training pipeline addressed class imbalance and visibility challenges through data augmentation, nighttime image enhancement, and class-weighted loss functions. The proposed system achieved an overall classification accuracy of up to 97.9 percent, with strong performance for heavy-duty vehicles and rare FHWA classes, even under adverse weather and lighting conditions. AN - dot:93748 DA - 2025-12-01 KW - Vehicle classification KW - Vehicle detectors KW - Deep learning KW - Expert systems KW - Data collection KW - Remote sensing KW - Cost effectiveness KW - Video cameras KW - multi-expert learning LA - English PB - Washington State Department of Transportation PY - 2025 SN - WA-RD 961.1 TI - Cost-Effective Traffic and Roadway Data Collection Using an Edge-Based Comprehensive Sensing System M3 - Tech Report N1 - United States N1 - T1461-AI DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93748 ER -
Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data

Caspi, Anat ;

Mehta, Kunal

2026-02-01 | PDF

This report synthesizes insights from eighteen WSDOT staff interviews to identify the roles, workflows, and steps needed to maintain a complete, reliable statewide sidewalk dataset that supports daily project delivery. Interviews pointed to recurring breakdowns in governance, approval, and update processes, especially closeout updates, provenance d ...

10/05/2026

Title : Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data Creator(s) : Caspi, Anat;Mehta, Kunal;Sepulveda, Tanisha; Corporate Creator(s) : University of Washington. Washington State Transportation Center (TRAC);University of Washington. Taskar Center for Accessible Technology; Corporate Contributor(s) : Washington State Department of Transportation Contributor(s) : Craig, Thomas Published Date : 2026-02-01 Report Number : WA-RD 957.1 URL : https://rosap.ntl.bts.gov/view/dot/93747
Caspi, A., Mehta, K., & Sepulveda, T. (2026). Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data (Report No. WA-RD 957.1). Washington State Department of Transportation. https://rosap.ntl.bts.gov/view/dot/93747

Caspi, Anat, Kunal Mehta, and Tanisha Sepulveda. Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data. Report no. WA-RD 957.1. Washington State Department of Transportation, 2026. https://rosap.ntl.bts.gov/view/dot/93747.

Caspi, Anat, et al. Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data. Washington State Department of Transportation, 2026, Report no. WA-RD 957.1, ROSA P. https://rosap.ntl.bts.gov/view/dot/93747.
Export RIS Citation Information. TY - Tech Report AU - Caspi, Anat AU - Mehta, Kunal AU - Sepulveda, Tanisha A2 - University of Washington. Washington State Transportation Center (TRAC) A2 - University of Washington. Taskar Center for Accessible Technology A3 - Washington State Department of Transportation AB - This report synthesizes insights from eighteen WSDOT staff interviews to identify the roles, workflows, and steps needed to maintain a complete, reliable statewide sidewalk dataset that supports daily project delivery. Interviews pointed to recurring breakdowns in governance, approval, and update processes, especially closeout updates, provenance documentation, and unclear data ownership, alongside practical constraints in collection, validation, and integration. The outcome of the project is a recommendation for a pilot implementation providing the path forward for high-quality, up-to-date sidewalk data usable by all WSDOT staff and the public. WSDOT would benefit from this new data primarily through three new opportunities available: 1) improve project planning and engineering, 2) prioritize active transportation projects, and 3) support ADA coordination and transition planning. AN - dot:93747 DA - 2026-02-01 KW - Sidewalks KW - Data collection KW - Transportation planning KW - Implementation KW - Compliance KW - Plan implementation LA - English PB - Washington State Department of Transportation PY - 2026 SN - WA-RD 957.1 TI - Identification of the Optimal Approach to Maintain Complete Statewide Sidewalk Data M3 - Tech Report N1 - United States N1 - T1461, Task AT DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93747 ER -
Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction

Al-Qadi, Imad L.

;

Golparvar-Fard, Mani

2026-09-01 | PDF

This study developed a contactless and automated technique to estimate hot-mix asphalt pavement surface friction by combining computer vision image-based 3D reconstruction with machine learning. Existing continuous friction estimation equipment, including high-speed laser scanners, are costly to purchase and operate-limiting routine, frequent monit ...

10/05/2026

Title : Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction Creator(s) : Al-Qadi, Imad L.;Golparvar-Fard, Mani;Fakhreddine, Mohammad;Lang, Hong;Villamil, William D.; Corporate Creator(s) : Illinois Center for Transportation;University of Illinois at Urbana-Champaign; Corporate Contributor(s) : Illinois. Department of Transportation;United States. Department of Transportation. Federal Highway Administration; Published Date : 2026-09-01 Report Number : FHWA-ICT-26-017;UILU-2026-2019; DOI : https://doi.org/10.36501/0197-9191/26-019
Al-Qadi, I. L., Golparvar-Fard, M., Fakhreddine, M., Lang, H., & Villamil, W. D. (2026). Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction (Report No. FHWA-ICT-26-017). Illinois Center for Transportation. https://doi.org/10.36501/0197-9191/26-019

Al-Qadi, Imad L., Mani Golparvar-Fard, Mohammad Fakhreddine, Hong Lang, and William D. Villamil. Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction. Report no. FHWA-ICT-26-017. Illinois Center for Transportation, 2026. https://doi.org/10.36501/0197-9191/26-019.

Al-Qadi, Imad L., et al. Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction. Illinois Center for Transportation, 2026, Report no. FHWA-ICT-26-017, ROSA P. https://doi.org/10.36501/0197-9191/26-019.
Export RIS Citation Information. TY - Tech Report AU - Al-Qadi, Imad L. AU - Golparvar-Fard, Mani AU - Fakhreddine, Mohammad AU - Lang, Hong AU - Villamil, William D. A2 - Illinois Center for Transportation A2 - University of Illinois at Urbana-Champaign A3 - Illinois. Department of Transportation A3 - United States. Department of Transportation. Federal Highway Administration AB - This study developed a contactless and automated technique to estimate hot-mix asphalt pavement surface friction by combining computer vision image-based 3D reconstruction with machine learning. Existing continuous friction estimation equipment, including high-speed laser scanners, are costly to purchase and operate-limiting routine, frequent monitoring. This study addressed this limitation through the development and utilization of cost-effective and scalable equipment. A Structure-from-Motion computer vision framework was developed to reconstruct pavement macrotexture in three dimensions and compute mean profile depth (MPD). Ground-control-based measurements agreed with a reference laser scanner within 15% across all test locations. A high-speed prototype using industrial cameras and a real-time kinematic positioning system was built. It was able to collect data up to 35 mi/hr and perform 3D reconstruction on cloud environments, increasing processing speed. The prototype was validated against a laser scanner at a controlled test track. The results demonstrated that contactless texture measurement and friction prediction are feasible and provide a foundation for more frequent, lower-cost monitoring of pavement surface characteristics, supporting proactive pavement management, safety screening, and more cost-effective maintenance decisions. The developed device measures texture and estimates friction for $61/mile, about 65% less than the $140/mile needed to collect both with current methods, and it costs only $73,500 to buy, compared with about $1,100,000 for a combined laser scanner and a transverse profiler. In addition, a data-driven model was developed to predict sideways friction measured by a Sideway-force Coefficient Routine Investigation Machine using pavement texture, traffic, roadway geometry, operating speed, and surface temperature. Three datasets collected in Florida and Illinois were used to evaluate the influence of dataset size, available features, and data distribution. Among the tree-based ensemble models evaluated, the Extra Trees model performed best, explaining approximately 85% of the variance, with MPD, traffic, operating speed, and surface temperature identified as the most influential variables. Finally, a finite element framework coupling a 3D tire model, texture-based friction model, and water film was developed to evaluate hydroplaning. The framework demonstrated that coarser macrotextures resulted in relatively better tire-pavement contact at higher speeds because of their improved surface drainage capacity. AN - dot:93746 DA - 2026-09-01 DO - https://doi.org/10.36501/0197-9191/26-019 ET - Final Report 7/1/22-9/30/26 KW - Pavement maintenance KW - Friction KW - Asphalt pavements KW - Photogrammetry KW - Machine learning KW - Hydroplaning KW - Transportation safety KW - Estimates KW - Microtexture KW - Hot mix asphalt KW - Datasets KW - Computer vision KW - Pavement Friction KW - Pavement Macrotexture LA - English PB - Illinois Center for Transportation PY - 2026 SN - FHWA-ICT-26-017 TI - Computer Vision-Based Contactless Estimation of Asphalt Pavement Surface Friction M3 - Tech Report N1 - United States N1 - R27-247 DB - ROSA P UR - https://rosap.ntl.bts.gov/view/dot/93746 ER -

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