Getting There On Time - All The Time

Ernest Tufuor Author

10/28/2020 Added

22 Plays

Description

Traffic congestion in many cities is common and getting worse. Commuters are used to congestion and understand how to adjust their schedules during typical peak hour travel times for on-time arrivals. However, unexpected or nonrecurrent congestion (caused by inclement weather, incidents, work zones, special events) constitute about 60% of all congestion and causes the most frustration to commuters because it results in late arrivals. Travel time reliability measures the extent of these delays. This video introduces a new methodology for estimating and predicting accurate travel time reliability metrics for on-time arrivals.

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[00:00:06.890]Thank you.
[00:00:07.723]I'm here to make you get there on time, all the time.
[00:00:12.090]As you know, plenty cities are getting congested,
[00:00:15.280]and congestion is increasing day in and day out.
[00:00:18.620]People value their time.
[00:00:20.190]They don't want to stay in traffic.
[00:00:21.610]They want to work,
[00:00:22.443]to get money to feed their families.
[00:00:24.650]Commuters and drivers normally know about,
[00:00:27.630]recurrent congestion because they're expected,
[00:00:30.230]especially during peak hours.
[00:00:31.580]So they plan accordingly,
[00:00:33.000]in order to meet schedule plans.
[00:00:36.870]But the most tent causes the frustration,
[00:00:39.640]is when you do is pay the delay,
[00:00:42.540]we call it not a care and congestion,
[00:00:44.820]which is normally caused by raised snow,
[00:00:48.690]or incidents or wet zone or even game day.
[00:00:54.560]So how do you do this?
[00:00:56.170]The mission of this tent of this delay,
[00:00:58.020]that is caused by what's expected,
[00:01:00.410]and what's unexpected,
[00:01:01.660]it's called a travel time reliability.
[00:01:04.130]And this measure basically is defined as,
[00:01:07.350]a probability of getting there on time,
[00:01:09.270]or the consistency or the dependency,
[00:01:11.760]of the travel times.
[00:01:14.590]And I must say the basic measure for this reliability,
[00:01:19.970]is getting the distribution,
[00:01:21.460]of Wiener extended period of time.
[00:01:23.460]If you know your distribution of travel time,
[00:01:25.310]you'll be able to be certain about the probability.
[00:01:28.440]So there are so many metrics that is used to measure,
[00:01:31.440]this travel time reliability metrics,
[00:01:33.650]and include buffer time in desk.
[00:01:35.530]That's additional time need to plan,
[00:01:37.510]in addition to the mean travel time,
[00:01:39.640]for you to be there or 95% of almost the time.
[00:01:44.800]High importance is very important,
[00:01:47.260]because business travel is important.
[00:01:49.620]The value of time for business travel is very important.
[00:01:53.510]Logistic companies listen,
[00:01:55.450]you know that to be competitive,
[00:01:56.750]to meet their client's needs,
[00:01:59.050]we all need it,
[00:01:59.930]to always get on time all the time.
[00:02:03.880]Then how is it measured?
[00:02:05.260]There's a methodology which is new,
[00:02:07.260]and the latest edition of the highway capacity manual.
[00:02:10.250]This manual is sometimes referred to as,
[00:02:12.660]maybe of transportation engineers bible, in quotes.
[00:02:16.070]So we use it for so many other things.
[00:02:17.800]So I use this manual,
[00:02:20.120]and I tested on six arterial corridors in Nebraska,
[00:02:23.870]and I saw that the methodology in the manual,
[00:02:26.570]underestimated the variability in travel times,
[00:02:29.020]about average of 54% for thesis corridors.
[00:02:33.420]And as you can see,
[00:02:37.040]the rate shows what the Istio methodology predicted,
[00:02:41.660]and the blue shows what is actually observed,
[00:02:44.480]after I collected the data on these corridors.
[00:02:47.050]So it's made to say that the travel time is reliable,
[00:02:51.720]well, actually it is not reliable.
[00:02:55.420]So in my research,
[00:02:56.600]what I do is that I check the HCM,
[00:02:58.810]to see what is causing those arrows.
[00:03:01.440]So I do a comprehensive performance analysis of it,
[00:03:04.380]and I develop a calibration methodology.
[00:03:06.580]The literature research shows that,
[00:03:09.280]it has no calibrated methodology or validated methodology.
[00:03:13.422]So I introduced,
[00:03:14.510]a new calibration and validation methodology,
[00:03:17.070]and then I develop a new methodology,
[00:03:19.450]that will address the limitations,
[00:03:20.970]of the travel time reliability methodology in the HCM.
[00:03:24.770]And in my new methodology,
[00:03:26.100]I introduce a simulation model,
[00:03:28.120]that will best capture,
[00:03:29.650]what is happening on that interior corridor,
[00:03:33.620]and when I calibrated the methodology,
[00:03:35.860]this is standard boss plot.
[00:03:38.580]The lower part is the distribution,
[00:03:41.200]The 25th percentile,
[00:03:42.933]the upper is the 75th percentile,
[00:03:44.960]the middle is the medium value,
[00:03:46.450]and this length is entire average,
[00:03:48.620]you see, if the STM is not calibrated,
[00:03:50.770]this is what it gives,
[00:03:52.230]and when I calibrate using my methodology,
[00:03:54.830]I made ACM now,
[00:03:56.910]look like the empirical travel time distribution.
[00:04:00.990]So using my methodology,
[00:04:02.700]you can always get there on time all the time
[00:04:06.000]And even doing snow conditions,
[00:04:07.910]I use my methodology which is in red,
[00:04:10.280]and the blue is what is observed.
[00:04:11.350]And you can see that it matches very well doing snow,
[00:04:14.960]during work zone conditions.
[00:04:17.230]It's also matches very well,
[00:04:19.480]during normal conditions,
[00:04:20.980]which is now snow wet zones.
[00:04:22.500]It's also matches well.
[00:04:24.060]I didn't get much data on incidents,
[00:04:26.090]and I load them to a methodology can also,
[00:04:29.230]be able to try it,
[00:04:30.290]which will be a future stage.
[00:04:32.040]And even when you combine all conditions,
[00:04:34.090]which include just 2% of incidents,
[00:04:36.430]is also working out very well.
[00:04:39.480]So I'd like to acknowledge,
[00:04:41.120]Dr. Rillet and Sean, my research team,
[00:04:43.550]and a city of Lincoln, Omaha,
[00:04:45.550]and HDR for the empirical data they gave,
[00:04:49.730]and also thank you all,
[00:04:51.199]and I would be happy to discuss further research offline,
[00:04:54.910]via this email.
[00:04:56.330]Thank you.

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Getting There On Time - All The Time

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