why do we need data like this?
DESCRIPTION
Travel time data for modelers - the Do’s, Don’ts, and Maybes: Sam Granato, Ohio DOT. Why do we need data like this?. Because our customers don’t care about volume to “capacity” ratios, instead they want to know:. In the beginning – floating car surveys and spot speed sensors. - PowerPoint PPT PresentationTRANSCRIPT
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Travel time data for modelers -
the Do’s, Don’ts, and Maybes:
Sam Granato, Ohio DOT
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Because our customers don’t care about volume to “capacity” ratios, instead they want to know:
Why do we need data like this?
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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
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“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)
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“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
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How we ended up with GPS vendor data:
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About 33,000 directional miles of TMC roadway statewide (including five miles into adjacent states)
GPS Data availability :
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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):
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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
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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
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Local sample speed data provided us both (expected) sample sizes by corridor/HOD AND percentile values
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Sample finding #1: V/C ratio does not predict congestion (and LOS) very well
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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
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Sample finding #2: Curves and Railroad crossings don’t seem to slow us down that
much