Data Latency and Work Zone ITS

We met recently with a large local agency to discuss the idea of connected work zones and the concept of reporting work zones in real time to the digital maps we all use to get from Point A to Point B. She was excited about the idea but had concerns about delays that are sometimes experienced between the time when an incident occurs and the time when it is reported to you by your navigation app.

According to Waze, 65 million drivers regularly use their navigation service to get home as quickly and efficiently as possible. Drivers want to know about problems along their routes before they reach them and in time to take another faster route if it makes sense to do so. Richard Russell, a former sales engineer with Google, said five years ago that, “we actually want negative latency, and will perceive anything less as latency.”

That was about the time that Google purchased Waze. Waze works because users report problems in real time thus helping to reduce latency. HERE has found another way to reduce latency. They look at in-vehicle sensors such as hard braking sensors to identify and locate traffic issues the moment they begin. HERE also plans to begin including user reports to get as close to real-time reporting as possible.

Today, work zones are the single largest cause of non-recurring congestion. So, if we could report work zones in real time (see Work Zone Reporting to Autonomous Vehicles – posted 9/25/18) it will make these services even more valuable. Imagine arrow boards equipped with a device to report location and display status every time it is turned on or off!

Yet how will these services process an unimaginable amount of data including location, date & time, type of incident, and some form of verification and get it to the user without at least some delay? That is a problem only Waze or HERE can answer. We can tell you they are working on it.

In the meantime, some small amount of latency (a few seconds to as much as a minute) is going to exist. But the service is still valuable. In today’s worst-case scenario Driver A leaves home and asks for the fastest route to work. The app recommends the best one based on conditions at that time. Perhaps moments earlier an arrow board was turned on when a contractor closed a lane along that route for maintenance work. A short time later the app reports that roadwork and reroutes Driver A along a now preferable route. The app still saves him time, just not quite as much time as it might have with instant knowledge of all work zones.

Zero latency is the goal. But let’s not allow the perfect to be the enemy of good.

USDOT Roundtable on Data for Automated Vehicle Safety

On December 7th of 2017 the USDOT convened an interesting group of stakeholders to discuss automated vehicle data needs. The goal was simply to better understand what will be needed, so we can all work in that same direction. Attendees included automakers, regulators, local agencies, privacy advocates, data aggregators including Waze and HERE, universities, and industry.

They have published a short document detailing their findings. Download “roundtable-data-automated-vehicle-safety-report[3585]” here.

A set of four principles was discussed and supported by the group. Those included

  • Promote best practices for data security and privacy.
  • Act as a facilitator to promote voluntary data exchanges.
  • Start out small to find what works and then build on that.
  • Coordinate across modes to save time and money.

Number 2 is perhaps the most problematic. Vehicle and component manufacturers are still playing their cards very close to their vests. They will continue to protect whatever competitive advantage they feel they have. They don’t mind sharing what everyone else is sharing but don’t want to go beyond that point for obvious reasons. So, what will be shared will start with basics such as crash data, AV hours driven, etc. and will grow from there.

The good news, for our purposes here, is the discussion of high priority use cases. #1 on the list is “Monitoring Planned and Unplanned Work Zones”. The data they felt was of the highest value included, “Work zone locations, planned duration of project, updates, planned lane closures, changes in signing, directions, or parking.”

Other encouraging use cases include #2 “Providing Real-Time Road Conditions”. There they discuss the need for data on detours and missing or deficient signs and pavement markings.

Under testing discussions, there was an emphasis on safety-critical scenarios which would have to include work zones. Clearly manufacturers must test not just in ideal conditions, but in all conditions including bad weather, poorly delineated work zones, and in and around major and minor incidents.

They coined the term “Edge Cases” which refer to a “problem or situation that occurs only at the extreme operating parameter.” Certainly, most testing today will continue at or below 35 MPH on a sunny day and under controlled conditions. But once we are all satisfied that AVs can drive safety in ideal conditions, it will be time for the worst-case scenarios. Again, work zones will surely be a part of that.

The last use case of interest was improving roadway inventories. The group felt high-value data for this effort included,””edge-to-edge”, high-definition map elements (e.g., signs and signals, curbs, pavement markings, tolls, express lanes, bridge heights and weight capacities, highway dividers, overpasses, pedestrian areas, bicycle lanes, taxi drop-off zones, (and) quality metrics.”

Under “proposed federal roles” they talk about the USDOT acting as a facilitator of sharing and discussions between the various stakeholders. It’s good to know work zones are now a part of that discussion. Thank you to USDOT for helping make that happen. Our greatest fear just a few short years ago was that the automotive industry would get too far down the road with their development to accommodate special circumstances including work zones, special events and incident response. It’s great to see that won’t be the case.

Are Autonomous Vehicles Safe?

On February 6th we sat in on a FHWA T3 webinar entitled “Are Autonomous Vehicles Safe?” It was moderated by Dr. Francesca Favaro of San Jose State’s Mineta Transportation Institute. She runs a program known as RISA2S – “Risk & Safety Assessment of Autonomous Systems” and recently examined California DMV data from 2014 through 2016 on crashes and disengagements of automated vehicles.

Because their data is from automated vehicles on the road today, their focus has been on SAE Level 3 automation. That is, by and large, what is being tested now. Most manufacturers are not planning to offer Level 3 vehicles to the general public. But as a result their findings point out the strengths and weaknesses of Level 3 automation. So for that reason most of their presentation came to focus on disengagements – when the vehicle gives control back to the driver – and driver reaction time to those disengagements.

This is an issue of critical importance to work zones. At some point in the future autonomous vehicles will negotiate work zones without need of human input. But that is many, many years from now. In the meantime, a mixed fleet of cars and trucks with varying levels of automation will be passing through our work zones. As Paul Carlson said at the recent ATSSA Innovation Council meeting, “We have had a mixed fleet for some time now. Any discussions of a mixed fleet now are just the next iteration of that.”

So the two related issues of when AVs disengage control, and how drivers react to those disengagements will be an important point of discussion for the foreseeable future.

Their data on disengagements was very interesting. The frequency of disengagements is declining. In fact in 2016 they were one-third of what they were per mile travelled in 2014. So the technology is improving rapidly. Machine learning will continue that trend.

11% of all disengagements were due to external conditions. 49% were due to system failures of some kind. 33% were due to human factors. And 7% to “other”.

Work zones fall within external conditions. 2.22% of all disengagements were due to construction zones and 4.63% were due to poorly marked lanes. Now, we don’t know much about the conditions when and where this testing took place. Speeds were all at or below 30 MPH. But we don’t know if it was dry and sunny, or raining at night. And we don’t know if the time spent in work zones was typical of your average driver.

Chances are they were not. In fact, chances are current testing avoids work zones most of the time so disengagements in real-world work zones would likely be many times greater.

30.12% of disengagements are due to human factors – usually driver discomfort. Some of that is a trust issue with the technology. But a large part is a desire to stay safe in fast changing situations such as near incidents, work zones, or other higher volume conditions. So I think we need to include some of that data as well.

Next let’s consider driver reaction to these disengagements. This is really two issues: what the driver does, and how fast he or she does it. But the study looked only at driver reaction time. The California DMV has not yet defined what is meant by “reaction time” yet manufacturers are required to measure it. So the data presented is inconsistent in its terminology. With that in mind it is still interesting and helps point the way for future studies.

This testing was done in driving simulators. Subjects reaction times varied from 0.87 seconds to as much as 3.17 seconds. Again, that is in a simulator when the test subject was told to expect something. Other studies have shown mean reaction times of 2 seconds or more.

Let’s use the 2 second number, though it may be much higher. At 65 MPH a vehicle would travel nearly 200 feet in that time. A lot can happen in 200 feet when approaching or adjacent to a work zone. So clearly disengagements should occur in advance of the work zone giving drivers time to acclimate themselves to the situation around them. For the next 20+ years many of the vehicles passing through work zones will turn control over to the driver. So rather than try to deal with those within the confines of the work zone, lets’ just automatically turn control over to drivers before they enter it. That will provide a more predictable hand-off in a safer environment.

This study was just their first attempt. As more data is collected and as terminology becomes better defined and as testing ventures into more “real world” scenarios, we will learn much more. We look forward to future reports from RISA2S and other AV research organizations.

5G Cell Service and Opportunities for Our Industry

By now, most of you are aware that 5G phone service will be here soon. But you may not understand what that means for our industry. An article written by Hongtao Zhan of SureCall and published recently in VB talks about its potential:

https://venturebeat.com/2017/09/30/5g-isnt-just-faster-it-will-open-up-a-whole-new-world/

As the article points out, download speeds could be as much as 100 times faster than we currently experience with 4G service. This service will be expensive at first but once everyone has switched to 5G devices those faster download speeds could result in greater use of video as data rates eventually decline.

But the most important aspect of 5G for our purposes is latency. Latency is a measure of how quickly critical data is transmitted. 5G offers near zero latency. This will enable an incredible array of new technologies affecting every part of our lives. That is why “Qualcomm is calling 5G the “platform for invention.””

Mr. Zhan describes ways 5G could be used for things like haptic controls in vehicles for purposes such as lane keeping, collision avoidance and more. For our world haptic controls mean we could deploy “virtual rumble strips” in advance of work areas to wake up drivers and perhaps even to return control of autonomous vehicles over to drivers.

Zero latency means workers could be removed from the work area and could perform many dangerous operations remotely using a virtual reality head set and controls. For example, they could “drive” TMA trucks remotely. We might also create a remote control cone setting machine. Striping trucks and RPM installation might also be automated.

How about a phone app to warn workers? With near zero latency, we could create an intrusion warning system that works fast enough to save lives, while requiring very little in additional equipment – just the smart phones everyone is already carrying around. The work area could be delineated on a digital map and any vehicle crossing those lines would trigger warnings to anyone in the area who has downloaded the app.

The possibilities are endless and this new communications protocol is right around the corner. It is time for us to begin thinking about how we might use it to improve safety and save lives.

Google Maps are Wrong!

At any given time, perhaps 5% of Google Maps data is wrong. And the reason is simple. Construction traffic control requires contractors to close lanes, redirect traffic into oncoming lanes, or close roads altogether until the work has been completed. Those closures are reported to state and local agencies. And those reports are picked up by Google and other traffic data aggregators. But they are often wrong or out of date.

In most states, contractors are required to request permission to close a lane. That request must be made well in advance of the date on which they wish to close the lane, 7 to 10 days on average. By the time that day comes long, construction delays, weather, and other issues often postpone the work and the lane closure does not take place.

Contractors also often make several requests so they will have a multi-day window in which they can perform the work. The days they don’t work are called ghost closures. Some states have moved to eliminate ghost closures by requiring contractors to call the local traffic management center when the lane is taken and again when it is opened back up. This certainly helps, but it does not eliminate the problem altogether.

To make matters worse, many closures are never reported at all. Utility companies are notorious for closing lanes without permission. They reason that they are only there for a short time and so won’t affect traffic all that much. But as traffic becomes more dependent on accurate travel time and route information, any disruption causes problems, and may even be dangerous.

Incident response closes lanes; school crossing guards stop traffic; special events close roads and reroute traffic; flooding, fires and other environmental events also result in route closures and restrictions.

This is an important point of discussion in the automated/autonomous vehicle world, too. If autonomous vehicles depend on historic GPS data to plan and drive a route, they will run into unexpected construction. So they must decide how they will adapt to changes in geometry, in the number and location of lanes, and much more. And delays resulting from these closed lanes and detours should be measured and included in any travel time algorithms.

It is worth noting that the folks in the traffic data companies know of the problem but they can’t solve it on their own. Industry is beginning to fill this need. Arrow boards and flagger stop/slow paddles are being reinvented to become “smart devices”. They report in automatically when work begins and ends. And they also report their precise location. As the work moves, that is reported as well, so map data for work zones can now be reported in real time.

Much work remains to be done. But the solution to this problem is clear. The closures must be reported in real time from the field. And that includes any changes in geometry when lanes are temporarily shifted in one direction or another. Highway construction, incident response and special events all experience unexpected changes on a daily and often hourly basis. Maps must reflect those changes if our system is to be as safe and efficient as possible.

Machine Learning and Work Zones

Last July we talked about the phenomenon of Poke’mon Go and augmented reality and considered ways in which that new technology might be applied to work zones. Today let’s consider another new technology. This one is not really new but is enjoying renewed interest thanks to Big Data and an abundance of inexpensive computing power. Using machine learning we can now process our data in ways we could not before.

In its simplest form, machine learning is an algorithm or model that goes through data looking for patterns. Then using those patterns it can make predictions for another, similar set of data. Machine learning works well with different types of data. So, for example, it could crunch through traffic speed data and compare it to social media sites focused on local traffic problems. In this way it can find trends or commonalities that don’t come through in conventional data analysis.

Machine learning is a faster and more accurate way of adapting to human factors issues related to work zones. We set up a system before the project starts and configure it for conditions at that time. But conditions change in work zones. So what worked early may not work as well later. Now don’t misunderstand. The system still works. It still warns of queuing ahead. But if the timing of the messages varied depending on changing conditions throughout the day, coming on sooner, or turning off faster, it could improve driver compliance. And the only way to learn that is through machine learning.

Now there is one problem with machine learning, and it is a big problem. It acts as a black box taking in data and spitting out recommendations. But the process is not something you can review after the fact to understand what influenced the outcome. So agencies could not tell their governor why they have made changes. But if you are more interested in being able to adapt to changing conditions quickly and accurately than in being able to explain it, machine learning is the tool for you.

Let’s consider a few possible applications:

  • Algorithms used to trigger messages could learn to de-emphasize input at certain sensors at certain times of the day when that sensors data is not in line with conditions elsewhere.
  • Models could make better recommendations about the best times and days of the week for lane closures while still meeting goals for minimal level of service.
  • In time, machine learning could establish baselines for crashes, fatalities, etc. resulting from a project both with and without work zone ITS systems in place. The system would be justifying itself! Of course, the predictions would have to prove true over time.
  • Predictive analytics reviewing probe and spot speed data may see locations of higher crash frequency or recommend a different geometry or staging in that location.

Is this complicated? Absolutely. But tools are already available to make it easy to get started, to choose the best algorithm, and to scale up and down in size as the situation demands. And Iowa DOT has already doing it at their Center for Transportation Research and Education.

Until now we could only design a system for a project to get the best overall performance. Now we can save even more lives through machine learning. Once our systems begin to learn from their own data, they will perform even better than they are today.

Work Zone ITS and Data Sharing Services

wazeOn January 2nd iCone announced a new data-sharing partnership with Waze.  Waze, as you may know, collects travel time data from its users and then shares it with them. Users may also note special problems like work zones or crashes as they travel. They even note the prices at individual gas stations.

Waze already shows the work zones reported by state DOTs. But now any work zone equipped with iCones will also show up. After all, not all work zones are reported. And not all that are reported actually take place. Better yet, they will appear as soon as the work begins, and they will disappear as soon as the work ends, making this truly real-time!

This is important for one simple reason. As work zone data is generated by our systems, it quickly overwhelms the DOTs. Most systems offer a live feed to the DOT but only a very few DOTs have incorporated that data into their DOT travel time systems. Portable work zone system data stands alone and apart from permanent systems. So it is not reaching Waze or Inrix or any travel time system. And that means it is not reaching the end user.

This new partnership skips the intermediate step and supplies the data directly to Waze. Users will begin seeing the benefits right away, rather than years from now. Drivers will become more aware of work zones and many will bypass them altogether. In both cases work zones become safer, and our roads more efficient.

Read the press release HERE.

Learn more about Waze or download the app HERE.