California Partners for Advanced Transportation Technology

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.

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.

Dynamic toll pricing based on demand can increase transportation revenue while also incentivizing travelers to avoid peak traffic periods. However, given the unpredictable nature of traffic, travelers lack the information necessary to accurately predict congestion, so dynamic pricing has minimal effect on demand. Dynamic toll pricing also poses equity concerns for those who lack other travel options. This research explores a potential remedy to these concerns by using a simple “futures market” pricing mechanism in which travelers can lock in a toll price for expected trips by prepaying for future tolls, with the future price increasing as more travelers book an overlapping time slot. This approach encourages travelers to avoid driving during the peak periods when pricing increases toward capacity or to purchase trips in advance when the price remains low or discounted, thus using infrastructure capacity more efficiently. Travelers that do not prepurchase their trip are subject to the real-time market price, which is determined by dynamic congestion pricing. This futures-market mechanism can augment existing toll collection technologies and provide travelers with sufficient pricing information and purchasing options to preplan their travel and avoid excessive prices.

Most studies of the economic benefits of Advanced Traveler Information Systems (ATIS) have focused on the passenger transportation market. Few analyses have addressed the applications of ATIS to freight operations even though using ATIS to route or divert commercial vehicles can make a significant improvement in overall traffic flow and system performance. In this study, multivariate demand models were estimated based on large-scale surveys of commercial vehicle operators in California to determine the current use and perceptions of advanced information technologies, especially advanced traveler information systems (ATIS), among these firms. Data were used to identify organizational and operational characteristics that made these technologies more or less attractive, and to predict potential adoption of the technologies by carrier type. Many characteristics proved influential including company size, type and location of operation, length of load moves, provision of intermodal service and private versus for-hire status. A secondary goal was to explore the extent to which new logistics intermediaries,especially "infomediaries" are likely to develop advanced information technologies for the freight industry. Private sector providers of ATIS have not lived up to earlier expectations. While there still may be a significant future role for private sector involvement in providing this type of information, for now the burden appears to fall primarily on state and local transportation agencies.

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.

Cybersecurity has become a critical issue in today’s world. In the past, security of our cyberspace was an important issue for some sectors of the economy, especially those dealing with financial information, personal identification related information, corporate systems and trade secrets, government classified information, and other types of data considered valuable targets for hackers. For other sectors, there was much less attention and resources dedicated to protection of our information and control systems. These sectors were often considered less likely to be targeted and a less valuable target.

Complete streets movement is a national effort to return to traditional streets in our cities to enhance livability, safely, accommodate all modes of travel, provide travel choices, ease traffic congestion, and promote healthier communities. The California Department of Transportation (Caltrans) and several local agencies in the State have developed implementation plans for complete streets. In this project, we developed and tested improved strategies and analysis methodologies for complete streets, taking into consideration the emerging advances in technology on control devices and data availability from multiple sources. The proposed improvements to the Highway Capacity Manual (HCM) methodology for bicycle LOS, accounts for protected bicycle lanes, traffic exposure, bicycle delay and pavement quality index. A survey was also used to calibrate the proposed bikeway evaluation models. Signal control strategies for complete streets were developed and tested, including signal optimization for pedestrians, bicycles and Transit Signal Priority (TSP) along major travel corridors in San Francisco.

This document is the final report for Task ID 3710 (65A0759), a project titled “Potential Erroneous Degradation of High Occupancy Vehicle (HOV) Facilities”. This report contains a compilation of three previous technical memorandums titled “Survey of Data-Mining Methods”, “Performance of Methods”, and “Magnitude of HOV Degradation”. HOV lane sensors in Caltrans’ Performance Management System (PeMS), are sometimes misconfigured as general-purpose lanes. In this situation, HOV lane data is mistakenly aggregated with general-purpose lane data and vice versa. The purpose of this project was to understand how widespread this problem might be and the extent to which it impacts performance reporting on the degradation of HOV lanes.

Freeway on-ramp metering (RM) has been extensively used as a traffic control strategy to regulate the entry of the on-ramp vehicles to prevent congestion at the freeway merging areas and preserve the freeway capacity. Benefits of RM include improved freeway travel times, improved travel time reliability, and accident reductions. Fixed-rate ramp metering strategies are based on historical data and implemented by time of day. Traffic responsive RM strategies are based on real time freeway traffic data provided by loop detectors at the vicinity of the on-ramp. Coordinated RM determine the metering rates at the ramps along a freeway corridor to minimize the delays or maximize the freeway throughput. The objective of this research was to evaluate the traffic performance of coordinated traffic responsive systems (CRM) currently implemented by Caltrans based on field data.