In order to improve corridor network operations, the vision of integrated corridor management (ICM) is to identify corridor managers who serve as experts for individual corridors, and to enable these managers to oversee corridor operations, to coordinate with partner agencies, and to improve collaborative, multiagency planning. While it makes sense to manage freeways, arterials, and transit in a coordinated way within a corridor, it is less clear how multiple corridors interact with each other, and how incidents and response plans along one corridor impacts a nearby corridor or multiple corridors. This project formulates recommendations and strategies for large scale traffic management and enabling multiple corridor management efforts and/or ICMs to work together. In addition, it identifies situations where conditions on one corridor influences management decisions on another corridor. To accomplish this, both probe data and traditional sensor data are analyzed to answer questions about aggregate traffic patterns on a multi-corridor scale.

The California Department of Transportation (Caltrans) collects megabytes of data every day using a dedicated traffic sensing infrastructure. The collected data provide support for traffic management and system performance monitoring activities that are crucial for supporting the agency’s mission, vision, and strategic goals to strengthen stewardship and drive efficiency. Operating this vast detection system requires extensive resources in the form of engineering and maintenance support, along with millions in capital funds to keep the system running. Within the above context, alternate hybrid data collection models utilizing purchased or third-party data to augment existing data collection system capabilities may enable a reduction in the number of physical detection stations required while maintaining suitable accuracy for Caltrans’ purposes. In addition to the potential for cost savings, the reliance on fewer physical sensors also offers the potential to reduce the exposure of Caltrans employees to the occupational hazard of maintaining roadside detection stations, in alignment with the agency’s “safety first” strategic goal.

This report describes how current methods of estimating truck traffic volumes from existing fixed roadway sensors could be improved by using tracking data collected from commercial truck fleets and other connected technology sources (e.g., onboard GPS-enabled navigation systems and smartphones supplied by third-party vendors). Using Caltrans District 1 in Northern California as an example, the study first reviews existing fixed-location data collection capabilities and highlights gaps in the ability to monitor truck movements. It then reviews emerging data sources and analyzes the analytical capabilities of StreetLight 2021, a commercial software package. The study then looks at the Sample Trip Count and uncalibrated Index values obtained from three weigh-in-motion (WIM) and twelve Traffic Census stations operated by Caltrans in District 1. The study suggests improvements to StreetLight’s “single-factor” calibration process which limits its ability to convert raw truck count data into accurate traffic volume estimates across an area, and suggests how improved truck-related calibration data can be extracted from the truck classification counts obtained from Caltrans’ WIM and Traffic Census stations. The report compares uncalibrated StreetLight Index values to observed truck counts to assess data quality and evaluates the impacts of considering alternate calibration data sets and analysis periods. Two test cases are presented to highlight issues with the single-factor calibration process. The report concludes that probe data analytical platforms such as StreetLight can be used to obtain rough estimates of truck volumes on roadway segments or to analyze routing patterns. The results further indicate that the accuracy of volume estimates depends heavily on the availability of sufficiently large samples of tracking data and stable and representative month-by-month calibration data across multiple reference locations.

This report explores opportunities for employing autonomous driving technology to dampen stop-and-go waves on freeways. If successful, it could reduce fuel consumption and emissions. This technology was tested in an on-road experiment with 100 vehicles over one week. Public stakeholders were engaged to assess the planning effort and feasibility of taking the technology to the next level: a pilot involving 1000+ vehicles over several months. Considerations included the possible geographical boundaries, target fleets of vehicles, and suitable facilities such as bridges or managed lanes. Flow smoothing technology may improve the user experience and operations of managed lanes or bridges, however it may require external incentives such as reduced tolls to entice the traveling public to use it. This must be matched with other goals such as verifying vehicle occupancy. It might be possible for some hybrid solution that addresses both challenges to provide a way forward. A concept of operations needs to be developed specifically for a target road geometry and a California partner. This concept should benefit from lessons learned from previous pilot projects and will need to be defined so as to achieve both (1) a penetration rate sufficient to achieve measurable effects; and (2) sufficient quality and quantity of data to confirm benefits.

Connected and automated vehicles (CAVs) willrevolutionize the way we travel; however, what impact this revolution will have on advancing broader societal goals is uncertain. To date, the private sector technology rollout has emphasized the automation side of CAVs and neglected the potentially transformative possibilities brought by a more collaborative notion of connectivity. This may have significant downsides from a broader societal perspective. For example, CAVs (including those on the road today) collect a vast amount of data gathered through onboard systems (e.g., radar, lidar, camera), however, this data is not typically shared with other vehicles, roadside infrastructure, or public transportation agencies. This lack of collaboration will likely make traffic worse and forfeit the opportunity to manage traffic at the systems-level, which is where significant gains can be made in terms of improving traffic flow and safety, reducing greenhouse gas emissions and vehicle energy use, and more.