This data is a spatial representation of street construction projects. Street and Highway capital projects are major street reconstruction projects, ranging from general street resurfacing projects to full reconstruction of the roadbed, sidewalks, sewer and water pipes and other utilities. Capital projects are essential to keep the City's infrastructure in a state of good repair.

Analysis of the projects proposed by the seven finalists to USDOT's Smart City Challenge, including challenge addressed, proposed project category, and project description.

The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

From https://www.bts.gov/faf/county:The Freight Analysis Framework (FAF) database provides estimates of the weight and value of shipments throughout the United States for all commodity types and forms of transportation using a geographic system of 132 FAF zones. The Bureau of Transportation Statistics (BTS) developed an experimental county-to-county commodity flow product to provide the user community with more geographically granular commodity flow data to support planning, policymaking, and operational decisions at the state and local levels. Users can download state-specific files or the entire set of disaggregation factors to create customized queries. This experimental product provides flows for five commodity groups and five mode categories (see documentation for more details). BTS welcomes users to email FAF@dot.gov with feedback on this experimental product.The state FIPS code is also shown next to the state. Each zip file contains four tables with 1) county-level OD flows for the state of interest and every adjacent state, 2) county-to-FAF OD flows from the multi-state area to all other FAF zones, 3) FAF-to-county OD flows from all other FAF zones to the multi-state area, and 4) FAF-to-FAF OD flows from all other FAF zones to all other FAF zones. The files use county-level geography for the state of interest and states adjacent to this state. FAF zones represent flows outside of this area.The main Freight Analysis Framework files are loaded to Data Lumos separately here: https://www.datalumos.org/datalumos/project/231642/version/V1/view. Additional documentation is available at that link.The faf5_county_readme.txt and faf5_county_readme.xlsx were created for this upload and were not created by the DOT. The direct url to download each state-level dataset is in faf5_county_readme.xlsx.

EDTS/PAPAI is a monitoring system that tracks NEPA project progress between major milestones, and helps accurately determine the total processing time from initiation of an Environmental Impact Statement (EIS) or Environmental Assessment (EA) to the approval of the final decision document, one component of the Every Day Counts Initiative. This information is used for regular reports to the Council on Environmental Quality (CEQ) as well as FHWA and US DOT leadership. EDTS is being upgraded to add project and action tracking, with the enhanced system being renamed PAPAI.%3F

The New Jersey Department of Transportation allocates funds to projects and programs through two main capital program documents: the Transportation Capital Program and the Statewide Transportation Improvement Program. Active Construction Projects:The Division of Construction and Materials (DC&M) is responsible for the oversight of all Construction projects in the Capital Program, and several Operation Construction projects. Projects are awarded to DC&M for administration of the construction contract and enforcement of all contract provisions, materials inspection, and the quality of constructed work. The Project Data within the map contains various fields from the main Status of Construction Projects File and is updated frequently.Major construction projects may impact motorists over several seasons and alter travel patterns. You can keep abreast of the latest information at these work areas: https://www.state.nj.us/transportation/commuter/roads/Construction notices and traffic advisories can be found on the511NJ Travel Information web page.

The Principal Port file contains United States Army Corps of Engineers (USACE) port codes, geographic locations (longitude, latitude), names, and commodity tonnage summaries (total tons, domestic, foreign, imports and exports) for Principal USACE Ports. This feature set was extracted from the national data set by the Metropolitan Transportation Commission. The data source is https://data-usdot.opendata.arcgis.com/datasets/major-ports. The Major Ports data set is as of October 18, 2021 and is from the USACE, and part of the United States Department of Transportation/Bureau of Transportation Statistics’ National Transportation Atlas Database. The Major Ports are politically defined by port limits or Corps projects, excluding non-Corps projects not authorized for publication. The determination for the published Major/Principal Ports is based upon the total tonnage for the port for the particular year; therefore the top 150 list can vary from year to year. These data show Major Ports in 2018. The Principal Port data set contains USACE port codes, geographic locations (longitude, latitude), names, and commodity tonnage summaries (total tons, domestic, foreign, imports and exports) for Principal USACE Ports.

Costa Rica CR: Investment with Private Participation: Transport data was reported at 663.000 USD mn in 2015. This records an increase from the previous number of 34.000 USD mn for 2009. Costa Rica CR: Investment with Private Participation: Transport data is updated yearly, averaging 232.600 USD mn from Dec 2000 (Median) to 2015, with 6 observations. The data reached an all-time high of 663.000 USD mn in 2015 and a record low of 34.000 USD mn in 2009. Costa Rica CR: Investment with Private Participation: Transport data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Costa Rica – Table CR.World Bank.WDI: Investment Statistics. Investment in transport projects with private participation refers to commitments to infrastructure projects in transport that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility), and divestitures. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars.;World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org).;Sum;

PlanWorks is a Web resource that supports collaborative decision making in transportation planning and project development. PlanWorks is built around key decision points in long-range planning, programming, corridor planning, and environmental review. PlanWorks suggests when and how to engage cross-disciplinary partners and stakeholder groups. PlanWorks has four major components:-A troubleshooting guide describing the common decision points and opportunities for cooperation in the transportation planning and environmental review process. For each of the key decision points, PlanWorks provides policy and stakeholder questions, data needs, case studies and examples, and links to tools that can help support the decision.-Interactive assessments that enable project stakeholders to identify opportunities to work together, improve interagency cooperation, and expedite project delivery.

Brazil BR: Public Private Partnerships Investment In Transport: Current Price data was reported at 10.235 USD bn in 2022. This records an increase from the previous number of 6.527 USD bn for 2021. Brazil BR: Public Private Partnerships Investment In Transport: Current Price data is updated yearly, averaging 1.616 USD bn from Dec 1987 (Median) to 2022, with 31 observations. The data reached an all-time high of 33.685 USD bn in 2014 and a record low of 20.000 USD mn in 1987. Brazil BR: Public Private Partnerships Investment In Transport: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Investment Statistics. Public Private Partnerships in transport (current US$) refers to commitments to infrastructure projects in transport that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars.;World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org).;Sum;

"SHRP 2 initiated the L38 project to pilot test products from five of the program’s completed projects. The products support reliability estimation and use based on data analyses, analytical techniques, and decision-making framework. The L38 project has two main objectives: (1) to assist agencies in using travel time reliability as a measure in their business practices and (2) to receive feedback from the project research teams on the applicability and usefulness of the products tested, along with their suggested possible refinements. SHRP 2 selected four teams from California, Minnesota, Florida, and Washington. Project L38C tested elements from Projects L02, L05, L07, and L08. Project L02 identified methods to collect, archive, and integrate required data for reliability estimation and methods for analyzing and visualizing the causes of unreliability based on the collected data. Projects L07 and L08 produced analytical techniques and tools for estimating reliability based on developed models and allowing the estimation of reliability and the impacts on reliability of alternative mitigating strategies. Project L05 provided guidance regarding how to use reliability assessments to support the business processes of transportation agencies. The datasets in this zip file, which is 7.83 MB in size, support of SHRP 2 reliability project L38C, "Pilot testing of SHRP 2 reliability data and analytical products: Florida." The accompanying report can be accessed at the following URL: https://rosap.ntl.bts.gov/view/dot/3609 There are 12 datasets in this zip file, including 2 Microsoft Excel worksheets (XLSX) and 10 Comma Separated Values (CSV) files. The Microsoft Excel worksheets can be opened using the 2010 and 2016 versions of Microsoft Word, the CSV files can be opened using most text editors.

Colombia CO: Public Private Partnerships Investment In Transport: Current Price data was reported at 2.070 USD bn in 2022. This records an increase from the previous number of 586.220 USD mn for 2021. Colombia CO: Public Private Partnerships Investment In Transport: Current Price data is updated yearly, averaging 496.450 USD mn from Dec 1992 (Median) to 2022, with 28 observations. The data reached an all-time high of 5.999 USD bn in 2016 and a record low of 10.800 USD mn in 2002. Colombia CO: Public Private Partnerships Investment In Transport: Current Price data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Investment Statistics. Public Private Partnerships in transport (current US$) refers to commitments to infrastructure projects in transport that have reached financial closure and directly or indirectly serve the public. Movable assets and small projects are excluded. The types of projects included are management and lease contracts, operations and management contracts with major capital expenditure, and greenfield projects (in which a private entity or a public-private joint venture builds and operates a new facility). It excludes divestitures and merchant projects. Investment commitments are the sum of investments in facilities and investments in government assets. Investments in facilities are the resources the project company commits to invest during the contract period either in new facilities or in expansion and modernization of existing facilities. Investments in government assets are the resources the project company spends on acquiring government assets such as state-owned enterprises, rights to provide services in a specific area, or the use of specific radio spectrums. Data is presented based on investment year. Data are in current U.S. dollars.;World Bank, Private Participation in Infrastructure Project Database (http://ppi.worldbank.org).;Sum;

The global engineering project logistics market is experiencing robust growth, projected to reach $32.41 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 3.8% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing complexity and scale of global infrastructure projects, particularly in emerging economies, necessitates specialized logistics solutions for the efficient transportation and management of heavy machinery, specialized equipment, and oversized cargo. Secondly, the rise of renewable energy projects, such as wind farms and solar power plants, fuels demand for logistical services capable of handling large-scale, geographically dispersed components. Technological advancements, including the adoption of digital platforms and real-time tracking systems, contribute to improved efficiency, transparency, and cost optimization within the supply chain. Furthermore, a growing focus on sustainable and environmentally conscious logistics practices is driving innovation and demand for eco-friendly transportation modes. The market segmentation reveals strong demand across diverse sectors, including transportation, warehousing, and various industrial applications such as petroleum & gas, energy & electricity, construction, and manufacturing. Key players like Kuehne + Nagel, DHL, and DB Schenker are actively shaping the market landscape through strategic acquisitions, technological investments, and service diversification. Regional growth patterns reflect varying levels of infrastructure development and investment. North America and Europe are currently major markets, while Asia-Pacific is projected to exhibit significant growth in the coming years, fueled by substantial infrastructure investment in countries like China and India. However, challenges remain, including geopolitical uncertainties, fluctuating fuel prices, and potential supply chain disruptions that can impact project timelines and costs. Despite these challenges, the long-term outlook for the engineering project logistics market remains positive, driven by sustained global investment in infrastructure and energy projects, technological innovation, and the growing need for efficient and reliable logistics solutions. The market's continued expansion will be shaped by the evolving needs of diverse industries, focusing on enhanced efficiency, sustainability, and digitalization.

The Highway Capacity Manual (HCM) historically has been among the most important reference guides used by transportation professionals seeking a systematic basis for evaluating the capacity, level of service, and performance measures for elements of the surface transportation system, particularly highways but also other modes. The objective of this project was to determine how data and information on the impacts of differing causes of nonrecurrent congestion (incidents, weather, work zones, special events, etc.) in the context of highway capacity can be incorporated into the performance measure estimation procedures contained in the HCM. The methodologies contained in the HCM for predicting delay, speed, queuing, and other performance measures for alternative highway designs are not currently sensitive to traffic management techniques and other operation/design measures for reducing nonrecurrent congestion. A further objective was to develop methodologies to predict travel time reliability on selected types of facilities and within corridors. This project developed new analytical procedures and prepared chapters about freeway facilities and urban streets for potential incorporation of travel-time reliability into the HCM. The methods are embodied in two computational engines, and a final report documents the research. This zip file contains comma separated value (.csv) files of data to support SHRP 2 report S2-L08-RW-1, Incorporating travel time reliability into the Highway Capacity Manual. Zip size is 1.83 MB. Files were accessed in Microsoft Excel 2016. Data will be preserved as is. To view publication see: https://rosap.ntl.bts.gov/view/dot/3606

The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel time reliability monitoring systems will be built on top of existing traffic monitoring systems. The focus of this project was on travel time reliability. The data from the monitoring systems developed in this project – from both public and private sources –included, wherever cost-effective, information on the seven sources of non-recurring congestion. This data was used to construct performance measures or to perform various types of analyses useful for operations management as well as performance measurement, planning, and programming. The datasets in the attached ZIP file support SHRP 2 reliability project L38B, "Pilot testing of SHRP 2 reliability data and analytical products: Minnesota." This report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3608 This ZIP file package, which is 22.1 MB in size, contains 6 Microsoft Excel spreadsheet files (XLSX). This file package also contains 3 Comma Separated Value files (CSV). The XLSX and CSV files can be opened using Microsoft Excel 2010 and 2016. The CSV files can be opened using most available text editing programs.

The main dataset is a 232 MB file of trajectory data (I395-final.csv) that contains position, speed, and acceleration data for non-automated passenger cars, trucks, buses, and automated vehicles on an expressway within an urban environment. Supporting files include an aerial reference image (I395_ref_image.png) and a list of polygon boundaries (I395_boundaries.csv) and associated images (I395_lane-1, I395_lane-2, …, I395_lane-6) stored in a folder titled “Annotation on Regions.zip” to map physical roadway segments to the numerical lane IDs referenced in the trajectory dataset. In the boundary file, columns “x1” to “x5” represent the horizontal pixel values in the reference image, with “x1” being the leftmost boundary line and “x5” being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. The origin point of the reference image is located at the top left corner. The dataset defines five lanes with five boundaries. Lane -6 corresponds to the area to the left of “x1”. Lane -5 corresponds to the area between “x1” and “x2”, and so forth to the rightmost lane, which is defined by the area to the right of “x5” (Lane -2). Lane -1 refers to vehicles that go onto the shoulder of the merging lane (Lane -2), which are manually separated by watching the videos.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments.
• I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pi

The New Jersey Department of Transportation allocates funds to projects and programs through two main capital program documents: the Transportation Capital Program and the Statewide Transportation Improvement Program. Active Construction Projects:The Division of Construction and Materials (DC&M) is responsible for the oversight of all Construction projects in the Capital Program, and several Operation Construction projects. Projects are awarded to DC&M for administration of the construction contract and enforcement of all contract provisions, materials inspection, and the quality of constructed work. The Project Data within the map contains various fields from the main Status of Construction Projects File and is updated frequently.Major construction projects may impact motorists over several seasons and alter travel patterns. You can keep abreast of the latest information at these work areas: https://www.state.nj.us/transportation/commuter/roads/Construction notices and traffic advisories can be found on the511NJ Travel Information web page.

The main dataset is a 9 MB file of trajectory data (I294_L2_final.csv) that contains position, speed, and acceleration data for small and large automated (L2) and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for twelve distinct data collection “Runs” (I294_L2_Run_X_ref_image_with_lanes.png, where X equals 5, 28, 30, 36, 38, and 42 for southbound runs and 23, 29, 31, 33, 35, and 41 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L2-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L2.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L2_lane-2.png through I294_L2_lane-5.png and the northbound lanes are shown visually in I294_L2_lane2.png through I294_L2_lane5.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day.

As part of this dataset, the following files were provided:

• I294_L2_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the L2 test vehicles ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I294_L2_Run_X_ref_image_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.
• I294_L2_Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. T

The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day.

As part of this dataset, the following files were provided:

• I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.
• I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane cent

The objective of this project was to develop system designs for programs to monitor travel time reliability and to prepare a guidebook that practitioners and others can use to design, build, operate, and maintain such systems. Generally, such travel time reliability monitoring systems will be built on top of existing traffic monitoring systems. The focus of this project was on travel time reliability. The data from the monitoring systems developed in this project – from both public and private sources –included, wherever cost-effective, information on the seven sources of non-recurring congestion. This data was used to construct performance measures or to perform various types of analyses useful for operations management as well as performance measurement, planning, and programming. The datasets in this zip file, which is 338.39 MB in size, are in support of SHRP 2 reliability project L38A, "Pilot testing of SHRP 2 reliability data and analytical products: Southern California." This report can be accessed via the following URL: https://rosap.ntl.bts.gov/view/dot/3611 This zip file contains 11 files, including 8 Microsoft Excel worksheets (XLSX), 2 Comma Separated Values (CSV), and 1 Zip Package (PKZIP) files. The Microsoft Excel worksheets can be opened using the 2010 and 2016 versions of Microsoft Word, the CSV files can be opened using most text editors, and the PKZIP files can be opened using most zip file extraction programs.