mstops pms abc30 20150113v3-annotated - GLRTOC · 2020-04-22 · I love heatmaps, and so should...
Transcript of mstops pms abc30 20150113v3-annotated - GLRTOC · 2020-04-22 · I love heatmaps, and so should...
This specific presentation is the last of several rapid (~7 min each) presentations on mobility PMs presented at the beginning of the January 2015 ABC30 (Performance Measures/Management Committee) meeting. As the last, it strives not to repeat what was already presented.
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Two unique (relative to preceding presenters) key points are emphasized about mobility performance measures, and are done so in a Pecha Kecha [pe‐CHA ke‐CHA) format (see www.pechakucha.org), including this slide. It totals 6 minutes and 40 seconds, with no bullets and no time for rambling. The first point is that probe data affords unprecedented opportunity for anybody (almost) to examine multistate mobility, including for freight.
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Does anybody still love wire loops? Maintaining them? Calibrating them? Dealing with their data? Today, agencies have lots of really great options, as was presented before this today (presenters included HERE, UMD CATT Lab RITIS, INRIX, and Iteris). But my second point is anybody (almost) can do cool and meaningful stuff with probe data, and especially the free NPMRDS. Learn how yourself, as I did, or toss it to your computer geek, they love this stuff and will thank you for it.
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Moving ahead for progress! Performance measures aren’t anything new, but SHRP2 and MAP‐21
have sure lit a fire under them. The mobility PMs rule making is still coming… For reliability, what geographic extents? What time frames? What measures? For delay, same questions, but it also requires volume data.
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There are tons of business applications for probe data, apart from real‐time info, even just archive data. Multistate operations information.
Performance reporting (delay, user cost, reliability), like we now do for WisDOT usingNPMRDS. Planning processes, engineering studies, operational needs assessment, reliability valuation, bottleneck identification, etc.
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Then in comes the free NPMRDS ‐ wow, what an opportunity! Travel times for the wholeNational Highway System, by passenger and freight, and including GIS with 1.8 million features (i.e., can make nice maps and do spatial analysis out of the box). Clean file structure and data integrity, although you have to crack open the shipping container and get your bearings.
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It’s nothing compared to what others (e.g., Michael Pack (CATT/RITIS), Monali Shah (HERE), Scott Perley (Iteris), Ted or Pete (INRIX)) deal with, but… you do need a bit of database and scripting resources. I used Excel here to illustrate why you can’t use Excel for this. Access won’t work either. If mapping, requires GIS expertise. We’ve been using SQL Server and Stata a lot, but we’re digging PostgreSQL and PostGIS.
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And being a research data set, you’ve got wrangling/munging with (a) missing observations ‐ see the rural Wyoming interstates, and (b) outliers ‐ that’s six sigma blackbelt territory there, veeeeerylooog tails on the distrubtions, hours‐long travel times on short segments. Just stuff to watch out for so you don’t get burned, especially for delay. “Confusing conflation and fusion leads to inflated confusion.”
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There are lots of statistical data comparisons available, which I will spare you here. These are comparisons to Bluetooth travel times. Some familiar quirks like at higher speeds or with stopped traffic, full closures, or arterials, but pretty well validated, and definitely no issues with usability.
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Comparing freight versus passenger data, we see that (a) freight travel times are systematically slower than passenger at free flow, as expected, and (b) tere’s a LOT more passenger data than freight, which is very minimal off of interstates.
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Okay, here’s a whopper of a graph based on five billion data points. Two things to note: (a) freight observation counts vary, including an unexplained dip August 2013, make note of that depending on how you’re aggregating, and (b) tey are increasing, with a big jump eleven months ago.
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Enough about data. Returning to my point about the value of probe data for multistate corridor performance, here’s a cool map of megaregions. Most of my work is with the Great Lakes megaregion, with some things elsewhere, and we have a project going on now using NPMRDS for mobility measures.
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This snapshot shows a measure of reliability over a year for all interstates nationwide. This type of view has been really valuable for agency coalitions, but it hadn’t been freely available prior to NPMRDS. When it came out 15 months ago, this was among the first things we put together.
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I love heatmaps, and so should you. I’ll end with a handful of them. This one shows 600 miles of I‐94 across North Dakota and Minnesota, left to right, and the first few weeks of February 2013, bottom to top. Due to weather, hundreds of miles of interstate were closed 12‐18 hours. This shows data availability, and the horizontal banding are days/nights.
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…and this one shows average speed. You can really see the effects of winter storms. The gray area shows where data aren’t available due to the closure – a reminder to watch for that when you’re doing analysis, but also an illustration of the value of this free research data set.
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Prompted by disruptive work zones, this one shows a year of delay – which includes volume fusion – for a 40‐mile section of I‐94 between Milwaukee and Chicago. Northbound into downtown Milwaukee you can clearly see when the work zone ended. Later that summer, you can see the southbound Sunday traffic returning to Chicago being delayed by a different lane closure.
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Zooming out, here’s 1200 miles of Interstate 70 across five states over a year of data. This example illustrates a few metrics for every TMC. Some key cities stand out, as does something unusual between St. Louis and Indianapolis.
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Here’s the corresponding heatmap (shows reliability failure measure). 1200 miles left to right, one year bottom to top. You can see the horizontal bands from bad winter weather. The work zones west of Indy really jump out.
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This is the other direction. The problem here is that work zones in IL and IN were not well publicized to neighboring agencies nor to truckers, with very real economic impact. From a performance management perspective, this is how we can do better.
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Last pair. Back to I‐94. About 240 miles between Milwaukee, through Chicago (the big red vertical blob as expected) and Indiana, into Michigan. Similar horizontal bands from winter weather. Note the impact from work zones – same one in WI mentioned earlier, and prominent ones in IN.
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The other direction. IN bridge work had substantial impacts, even backing up traffic into MI at some points. Again, by illustrating this we strive to do better for system performance.
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Please be in touch with questions.
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