Integrate Cooperative Automated Transportation Data with Traditional Sensors for Effective Traffic Management During Low Connected and Automated Vehicle Market Penetration.
The Ramp-Metering Simulation Case Study on I-210 in Los Angeles, California, Suggested Incorporating Cooperative Automated Transportation Data to Improve Mobility, Efficiency, Productivity, Reliability, and Safety.
Los Angeles, California, United States
Using Cooperative Automated Transportation Data for Freeway Operational Strategies
Summary Information
Cooperative Automated Transportation (CAT) involves the collaboration of all transportation system stakeholders to enhance safety, mobility, and efficiency through interconnected vehicle, infrastructure, and systems automation, enabled by data exchange. CAT data, generated and shared among connected and automated vehicles (CAVs), mobile devices, and connected infrastructure, is utilized by transportation management systems (TMS) to improve traffic flow and safety. This study aimed to improve freeway operational strategies by transmitting data between a TMS and the broader CAT system, either directly or via a third party. It developed use cases for 10 key strategies with high potential for improvement using CAT data: 1) Queue warning, 2) Ramp metering, 3) Dynamic route guidance, 4) Speed harmonization and lane control for a single lane, 5) Traffic incident management, 6) Integrated decision support and demand management, 7) Speed harmonization for an entire roadway, 8) Performance monitoring, 9) Variable pricing for a single lane, and 10) Variable pricing for an entire roadway. Using the I-210 corridor in Los Angeles, California, as a case study, the study developed, tested, and evaluated the effectiveness of incorporating CAT data to enhance ramp-metering strategies with data from 2019 in a traffic simulation model.
Key takeaways from the study are listed below.
- Integrate CAT data with traditional sensors to provide comprehensive traffic management, particularly during the transition period when CAV market penetration is still low. CAT data offer advantages over conventional sensors but only provide traffic information from a small subset of vehicles. Combining CAV data with existing sensors is necessary for accurate system estimations, considering reduced costs under high market penetration rate.
- Use clustering methods and improved data selection to optimize traffic condition recognition. The choice of clustering data, time of day, and techniques can influence the recognition of traffic patterns or conditions, thus requiring multiple scenarios to determine the optimal clustering structure.
- Select a suitable step length in simulations to balance accuracy and computational efficiency when using a traffic simulation approach. Shorter step lengths enable frequent updates, more accurate simulation results, and may be necessary for higher-accuracy scenarios like safety analysis using surrogate safety measurements.
- Fulfill agency goals and performance measures by tailoring data collection and granularity. The data collected should align with the agency's objectives and performance measures, with high-resolution datasets for safety evaluations and lower-granularity assessments for mobility, efficiency, and reliability assessments.
- Deploy a sufficient number of roadside units (RSUs) in high-congestion areas. Increasing RSU deployment near long queues and high counts of traffic incidents is crucial for accurate traffic estimation and efficient management strategies, prioritizing areas with severe congestion.
- Conduct thorough evaluations of freeway operational strategies using CAT data based on multiple goal areas, such as mobility, safety, and productivity. Comprehensive assessment of CAT data integration is crucial, considering national strategic goals, ITS benefits, safety, infrastructure, congestion reduction, customer satisfaction, freight movement, economic vitality, and project delivery delays.
- Balance and optimize testing scenario space for productive evaluations. Optimizing the size of the testing scenario space is crucial for a comprehensive evaluation. A small scenario space, as used in this study, could lead to oversimplification, and missed factors, underscoring the importance of balancing scenario size for effective strategy assessment.
