DS-24 · How many cars, really?

VDOT publishes traffic counts at limited locations. Can you estimate volume on unmonitored segments from road characteristics and network position — and how confident should anyone be in those estimates?

VDOT traffic volume counts (https://www.vdot.virginia.gov/doing-business/technical-guidance-and-support/traffic-operations/traffic-counts/). OpenStreetMap road network (https://www.openstreetmap.org/). Census ACS commuting data (https://www.census.gov/data/developers/data-sets/acs-5year.html).

Counters are placed on important roads, so the training sample is not representative of the segments needing prediction — the extrapolation is the whole difficulty. Annual average daily traffic hides enormous variation. Spatial autocorrelation makes random cross-validation wildly optimistic; spatial cross-validation is required.

A volume model validated with spatially blocked cross-validation, with the difference from random CV reported explicitly, and a stated road class beyond which the model should not be trusted.

CT: understanding why random CV lies here. CR: constructing network-position features. CO: peer implementation of the spatial split. CM: an uncertainty-carrying map for a planner. CZ: traffic estimates drive road spending and neighborhood impacts.

A transportation planner, traffic engineer, or GIS analyst reviews the validation design at Checkpoint 1 and the uncertainty map at Checkpoint 2. School-supervised.

R1: prediction on unmonitored segments is the stated goal. R2: counter placement bias documented. R3: a road-class-mean baseline. R4: spatial CV and random CV both reported, with the gap explained. R5: uncertainty visible on the map. R6: states where the model must not be used.

(a) Random cross-validation puts adjacent segments in train and test; R² of 0.95 collapses on genuinely new roads. (b) Estimates are extended to residential streets from a model trained entirely on arterials.