Travel time data for modelers -

the Do’s, Don’ts, and Maybes:

Sam Granato, Ohio DOT

Because our customers don’t care about volume to “capacity” ratios, instead they want to know:

Why do we need data like this?

CMS / CMAQ project effectivenessUsed for MPO travel model validation since

1990’s to better model congestion & Level of Service

Statewide, developed for “speed table” by type of road – both average and running speeds (to start up some “junction-based” model networks in Ohio)

In the beginning – floating car surveys and spot speed sensors

“high sample size” floating car (arterials in Parkersburg/Marietta and freeways in Cleveland)

Can use to measure variability in travel time as well as more confident average, and how the variability changes as function of distance/# segments (i.e. from link-level to travel-path level

Then, the same but more (and more things to use them for)

“Archive” data from vehicle fleets & cell probes

Extensive road network coverage, could replace or reduce/redeploy need for “floating car” surveys

New Sources of Speed Data

How we ended up with GPS vendor data:

About 33,000 directional miles of TMC roadway statewide (including five miles into adjacent states)

GPS Data availability :

Differences exists in how these are measured (spot vs space mean speeds)

Statewide, average speeds higher on the ATR’s (about 7%)

Check for vehicle class based on WIM station locations on I-70 (Licking county) and I-77 (Noble county).

Quality checks for any “biases”: First, compare to ATR sites (mostly rural

freeways):

Differences exist in route segmentationVery small sample sizes in the floating car

surveysOverall, in close agreement statewide on

average speeds including by time of day

Quality checks for any “biases”: Second, compare to statewide floating car

surveys

GPS data (vs floating car)– uses & limitations

Far higher sample sizes, more versatility on hour of day / day of week / season of year

Good for overall speed validation of model on average values, not necessarily for variability/reliability

Depending on level of access, might not have ability to see the impact of distance on reliability / journey time

“Buffer index” measures found to measure system-level, not user-level reliability

Local sample speed data provided us both (expected) sample sizes by corridor/HOD AND percentile values

Sample finding #1: V/C ratio does not predict congestion (and LOS) very well

Volume offset by driver and vehicle characteristics

Signal timing, parking management

Sample finding #1-A: Speed does not vary that much by time of day

Sample finding #2: Curves and Railroad crossings don’t seem to slow us down that

much

Sam.granato@dot.state.oh.us 614-644-6796

Questions?