Simulation Study Using Real-World Road Network Data in NY and NJ Reveals Lessons Learned for Successfully Converting Basic Safety Messages into Mobility and Safety Traffic Performance Measures.
New York, United States
Seattle, Washington, United States
New Jersey, United States
Algorithms to Convert Basic Safety Messages into Traffic Measures
Summary Information
The utilization of Basic Safety Message (BSM) data within connected vehicles (CVs) serves as a means of sharing safety and travel information with nearby vehicles, personal communication devices, and transportation infrastructure equipment. Although BSMs have the primary function of supporting safety applications, there is an important opportunity for state and local agencies to utilize BSMs for managing traffic on roads and in work zones, as well as system-wide management of the surface transportation system, as BSM includes attributes such as vehicle size, brake system status, and event trigger flags that cannot be measured using traditional surveillance technology. Given all this, the objective of this study was to develop and validate algorithms that use BSMs to estimate mobility and safety traffic measures that could be used for performance monitoring, traffic management, and traveler information. These measures were route-specific travel time, space mean speed, queue length/count, mean time to detect and verify incidents, hard braking, deceleration rate to avoid a collision, and time to collision with disturbance. The study used simulated data to develop, test, and validate the algorithms, applied to the New York/New Jersey metropolitan area freeway and arterial network, as well as an urban freeway in Seattle, Washington.
- Ensure data from multiple sources are transformed into a consistent frame of geographical and temporal resolution. Data fusion of BSMs and other data sets will be a necessity in situations when CV market penetration is low. To successfully fuse data from multiple files, their formats and units of measurement need to be consistent. This requires a careful review of metadata, which can help determine any disparities in data types and units across datasets and make the data consistent across types, formats, and units before fusing or integrating disparate data sets.
- Fuse BSMs with the help of supporting data is necessary to contextualize BSMs. BSMs becomes more meaningful when it is connected to roadway network data. This network data, such as geometry, number of lanes, and signal timing plans, helps contextualize the detailed vehicle position and status information contained within BSMs.
- Aggregate BSMs spatially and temporally to obtain a comprehensive view of traffic conditions for mobility measure estimation. BSMs were designed for high granularity to detect potential crashes in real-/near real-time, but their amount and granularity can be overwhelming for estimation of mobility measures without removing redundancies, managing any outliers, and smoothing the data. Therefore, it is imperative to aggregate BSMs spatially and temporally to obtain a more comprehensive view of traffic conditions for mobility measure estimation.
- Rely on multiple independent sources of data rather than any one source to represent the ground truth. Real-world BSMs often do not fall neatly into roadway links and lanes, due to Global Positioning System (GPS) positional errors which make it difficult to develop lane-specific traffic measures using BSM data. Therefore, to fix the GPS drift and other positional errors is likely best addressed using multiple independent sources of data rather than relying on any one source to represent “ground truth.”
- Extract only what is needed from the BSMs to put less burden on communication networks. Agencies should develop algorithms using high-value BSMs that meet current transportation system management needs to reduce the burden on communications networks, especially for real-time operations. For instance, capture rates could be dynamically altered based on information regarding incidents or adverse weather.
