Travel time data for modelers the do s don ts and maybes sam granato ohio dot
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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.

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Why do we need data like this?

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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

  • CMS / CMAQ project effectiveness

  • Used 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)


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

  • “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


New Sources of Speed Data

  • “Archive” data from vehicle fleets & cell probes

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


How we ended up with GPS vendor data:


GPS Data availability :

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


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

  • 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”: Second, compare to statewide floating car surveys

  • Differences exist in route segmentation

  • Very small sample sizes in the floating car surveys

  • Overall, in close agreement statewide on average speeds including by time of day


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


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

  • Volume offset by driver and vehicle characteristics

  • Signal timing, parking management


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


Questions?

  • Sam.granato@dot.state.oh.us

  • 614-644-6796


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