Project Summary
The Institute of Transportation Studies at the University of California, Irvine, recently developed the California Vehicle Activity Database (CalVAD) for ARB. CalVAD merges Caltrans’ raw vehicle detection system (VDS) volume and occupancy data with Caltrans’ weigh in motion (WIM) data. The VDS data are collected approximately every half mile on urban California highways every 30 seconds, and the WIM data provide weights and measures for every truck at just over 100 detector stations scattered throughout the state on major truck routes.
A line layer is required to properly merge these data sets together. In the absence of official GIS network available from Caltrans, CalVAD incorporates a simple highway network using the open, freely available OpenStreetMap data, leveraging the route relation tags in those data. In this way vehicle measurements from the two data sources were merged with a highway network layer to form a picture of roadway vehicle activity.
Missing in the current version of CalVAD is information on non-highway vehicle movements. The original development of CalVAD incorporated a rudimentary form of the HPMS data set into CalVAD. Although the 2007 HPMS data are loaded into the CalVAD database, and can be queried along with other data, these data do not have any geographic referencing associated with each link. This means that straightforward bounding box and area queries that rely on the PostGIS extension to PostgreSQL can’t be performed. (It is noted that Caltrans once had a linear referencing system for these data, but their license was subsequently revoked by the commercial vendor.).
The first task in this proposed effort will wire up the HPMS records such that geographic queries can grab the right link-level data. This task is described as the first task of the project, and will be performed without requiring or referring to any GIS or network layer, but rather will use the information in each HPMS record combined with known information about geometries (cities, counties, air basins, and so on). This will provide a coarse geocoding of the HPMS data, but probably not good enough to produce something like gridded estimates of vehicle activity.
The second task, and the bulk of the project hours, will be devoted to assembling street level geographic data, and then linking this with the HPMS records. We propose two different options for this task for ARB to consider: 1) to use OpenStreetMap data once again, or 2) to turn instead to regional planning models.
The final task of this project will be to identify new and different sources of information for arterial vehicle flows that may be incorporated into the CalVAD system. The HPMS data may be the best currently available, but estimates of average annual daily traffic on a link is a few steps removed from storing the raw measurements that went into those estimates. The goal of CalVAD is to produce a portrait of vehicle activities that is reasonably accurate in time and space, but that is firmly grounded in observed data. This final task will advance the goal of entering lower level data on arterial travel, and will also identify commercial sources of information, such as Google, and provide ARB with analysis on the potential of these information sources going forward. By anticipating new kinds of data and new ways of collecting data, we can keep CalVAD relevant and perhaps even grow it into a player that shapes these new and rapidly evolving technologies.