摘要: |
This project proposes a bicycle and pedestrian count campaign that will be systematically designed to describe non-motorized traffic patterns for the entire transportation network in Blacksburg, VA. The approach involves a two stage process: 1) sitting a long-term reference network of automated counters and performing short-duration counts (~1 week) to estimate annual average daily traffic (AADT) on ~10% of the street segments in Blacksburg and 2) developing regression models based on land use and characteristics of the street network to estimate AADT at locations where counts were not collected. Previous research has found that methods developed for scaling short-duration counts of motor vehicles to long-term averages can be adjusted to provide reliable estimates of AADT for bicycles and pedestrians (Hankey et al. 2014, Nordback et al., 2013, Nosal et a. 2014); a limitation of these studies is that they focused on limited networks (i.e. off-street trails) or specific transportation corridors. The proposed work would be the first to implement this method for an entire transportation network for bicycles and pedestrians. Identifying spatial and temporal trends of bicycle and pedestrian traffic is crucial for evaluating exposure to hazard and assessing the impact of investment in future infrastructure. The project has designed the count campaign to fit seamlessly into existing best practices for motor vehicles; for example, the project will calculate analogous performance measures (i.e. AADT) and structure the counts (i.e. a combination of short-duration and references sites) in ways that could easily integrate into existing state and federal Department of Transportation (DOT) databases. The proposed study would serve as a proof-of-concept for the approach in a rural, University town. The project envisions later expanding the approach to places where land use and traffic patterns may differ; for example, locations where other members of the research team are located (Charlottesville and Alexandria, VA); communities that have demonstrated interest (e.g. Roanoke, VA; see Letters of Support) or satellite locations of the institutions (e.g. Richmond, VA). The project expects that the method could be implemented in any location throughout the country and data readily assimilated into existing databases currently maintained by state DOTs. |