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.

Work Zone Reporting to Autonomous Vehicles

We just returned from ATSSA’s Midyear meetings in Louisville, Kentucky. The Innovation Council meeting was well attended and included several very interesting speakers. Many topics were discussed but the real focus of these discussions, both during and after the meeting, was autonomous and automated vehicles and how our members can best prepare for them.

Speakers including Dr. Paul Carlson talked about the importance of signs and pavement markings bright enough to be seen and recognized by automated vehicles. AV manufacturers have stated that this is the most important thing we as an industry can do to prepare, at least from the autonomous vehicle perspective.

But from a stakeholders’ perspective – specifically work zone safety – many wonder how autonomous vehicles will know where work zones are located and what they will encounter as they drive through them. This blog has discussed this subject several times over the past few weeks, but given the interest in Louisville, it seemed a good time to review all of the likely ways in which this will be accomplished and consider the advantages and disadvantages of each.

There are at least 6 ways to do this. And by “this” we mean update digital maps in real time. First we must tell everyone where work zones are active. That’s the most important part. For by telling them, those autonomous vehicles can then trigger a return of control to the driver well before the vehicle enters the actual work zone. But ideally, these systems will also include information about that work zone including which lanes are closed, prevailing speeds, and geometric changes including lane shifts, narrow lanes, etc.

So, in no particular order, these are the more likely ways of getting that information out in real time:

Traffic Control Device Automated Reporting

Devices including arrow boards, traffic sensors, flashing beacons, and stop/slow paddles can be equipped to report to a traffic data service or DOT website. This is already being done today. When the device is turned on, it reports its GPS coordinates and the type of work zone. For example, an arrow board, when turned on would report a lane closure. When it is turned off, the device reports the work zone is no longer active.

The advantage of this approach is the activity is truly reported in real time without human input. Another advantage is the location will change as the equipment moves, say for a paving or crack sealing operation. The disadvantage is the need to replace older devices with newer devices that include this feature.

3M Two-Dimensional Bar Codes

This was the subject of a post on August 21st and was discussed by Chuck Bergman of Michigan DOT and Eric Hedman of 3M at the Innovation Council meeting. 3M has installed signs on I-75 in Michigan with two-dimensional bar codes embedded in their sign sheeting. A driver might see a sign saying ROAD WORK AHEAD but infrared cameras in the car would see a second embedded message telling the car to relinquish control to the driver, or to reduce speed automatically to 45 MPH, or any one of a number of other possibilities.

This approach will work well for longer term work zones and ones where the desired message is unlikely to change often. It will likely be low cost and could act as a fail-safe warning to autonomous vehicles. It does not update digital maps simply by installing the signs, but we assume that will be done manually at about the same time.

State DOT Work Zone Phone Apps

Many states require contractors to request lane closures in advance and then to report when those closures begin and end. Some now accomplish this through smart phone apps that make it quick and easy o report in real time.

This is already taking place but it does require someone to key in the closure when it begins and ends. And moving operations won’t be precisely geo-located. Still, it is inexpensive and requires very little effort.

Waze, HERE and other Crowd Sourced Traffic Apps

Users of these smart phone apps can note active work zones and other issues affecting traffic and that information is shared with all other users. This additional information is helpful but depends on users to remain current. Interestingly these apps are beginning to include data streams from work zone ITS systems. So the hybridization of these systems has already begun. And in our last post we noted that Caltrans traffic website known as QuickMap now includes Waze work zone data.

I2V (Infrastructure to Vehicle) Reporting via 4G/5G or DSRC

This was how we originally envisioned the process taking place. A radio of some sort might be installed in advance warning message signs or arrow boards where it would broadcast to approaching traffic to warn of upcoming work zones. These devices might also report slow or stopped traffic ahead. This may still happen, but advances in V2V (vehicle to vehicle) communications both 5G and DSRC make this less likely.

Automatic Reporting by Autonomous Vehicles

AV data collection will “see” and take note of variations of the real world roads from the digital map. This might include some standard deployment of devices in advance of work zones that could be recognized by algorithms to mean a work zone lies ahead.

This has not been suggested that we know of, but autonomous vehicles collect data continuously. That’s a lot of data. Machine learning and sophisticated algorithms will, in time, learn to recognize work zones. Logically those will then be reported automatically as work zones change. This may not occur for many years but it will happen automatically one day.

The change from driven to autonomous vehicles will be a very gradual one. Most experts believe it will take at least 25 years and even then older vehicles, collector cars, etc. will still be sharing the road with driverless ones. Furthermore, the choice of technology to warn of work zones will vary with location, construction activity, project duration, and more. As a result, differing combinations of technologies will likely be used in an effort to reach the greatest number of vehicles and to provide redundancy. After all, as time has proven over and over again, as cars become easier to drive, we become worse drivers. So it will be all the more important that we warn drivers and vehicles of work zones ahead.

Caltrans QuickMap to Add Waze Data

 

Caltrans recently announced changes to their popular QuickMap travel app. The app is available online at http://quickmap.dot.ca.gov/ and from your app store for both Apple and Android. In QuickMap users choose what they want to see, and the area they are interested in seeing, and a custom map shows them exactly that and no more. Travel times and factors affecting travel times such as work zones, weather and other incidents are all displayed in near real time.

They have now added an option to see Waze data. This is significant to those of us interested in work zone ITS because Waze displays iCone sensor data as work zone locations. For example, Road-Tech currently has an iCone system on SR99 in Fresno and the work zone is displayed in QuickMap with an icon of a worker in a hard hat:

Eventually these icons when clicked on will display detailed travel speeds and other pertinent information. That data will help drivers avoid delays and better plan their routes. And in Waze, drivers can add their own information on work zones, crashes and other incidents.

QuickMap is testing the next mobile version in which users can input their own Waze markers directly from QuickMap. It will be one more way to get real-time work zone information to the people that need it most!

Report from the Automated Vehicles Symposium, Part 2

In our last post we discussed the need recognized at the Automated Vehicle Symposium for varying levels of vehicle autonomy based on the road and current conditions. One of those conditions is clearly work zones. A car may be able to operate at Level 4 autonomy in freeway traffic, but may have difficulty ding the same in some work zones.

In those cases we must signal the vehicle to alert the driver to prepare to retake control. And that warning will have to be given leaving sufficient time for the driver to become cognizant of the dangers around his or her vehicle. A poster session at the AV Symposium by Chris Schwartz of the University of Iowa looked at that timing. Their study focused on large trucks and found drivers needed as much as 10 seconds to get their wits about them for normal driving. Work zones should probably allow a little more time, as drivers may have to start immediately to negotiate lane shifts, narrow lanes, or other challenges. So ideally this signal would come at about the first construction area sign (ROAD WORK AHEAD).

The conventional method would occur through the cars digital map. That will be how other hand-offs take place, such as when driving from a roadway capable of supporting level 4 automation to an older stretch only capable of supporting level 3. But those are points that rarely move or change. Work zones may only take place for a few days, or a few hours. As we have discussed in past posts, the map must be updated in real time for features like this to work correctly.

Manufacturers are working today on beacons, arrow boards, and more that will signal when lane closures begin and when they end. This is already happening today and will only become more popular as smart technology is accepted in more and more work zones.

But another option was mentioned in the same session. 3M is experimenting with a way of placing two-dimensional bar codes within their reflective sign sheeting. The bar codes are only readable by infrared cameras. Drivers would never see them. They would just see the static sign saying something like ROAD WORK AHEAD. But the car they are driving would be triggered to return control to the driver.

This technology is in the very early stages of testing. 3M has signs up on freeways in Michigan now and hopes to test more of them in the Bay Area of California soon. It is too early to say this is a solution but it does show promise. A combination of map triggers and these signs would provide some redundancy and might also be a simpler way of notifying drivers of very short term work zones such as those installed by utility companies and smaller agencies.

The good news is that both the traffic control industry and the AV industry recognize the importance of this hand-off prior to work zones and they are working to find solutions.

Variable Speed Limit Systems – Revisited

In a post on April 24th we discussed a recent webinar on variable speed limit signs. The speakers voiced their disappointment in the technology and found very little if any benefit from their use. But now a new study by the University of Missouri saw far better results for VSL signs used in work zones.

“Evaluation of Variable Advisory Speed Limits in Congested Work Zones” by Praveen Edara, Carlos Sun and Yi Hou found far better, but still mixed results when using Variable Advisory Speed Limit signs in work zones.

As we have known for some time, the VSL results depend to a great extent on the algorithm used. In the Missouri study the original algorithm used in the field resulted in shorter queue lengths and reduced speed differentials. In fact the maximum speed differential was reduced by a remarkable 10 MPH. However it also reduced throughput by 7 to 11% and travel time increased by 4 to 8%. So the results were mixed.

They then experimented through simulation. Using the same traffic data they tried two other algorithms. One smoothed over a one minute period and the other over a 5 minute period. The original field algorithm smoothed over a 30 second period.

The 5 minute smoothing algorithm still reduced throughput but only by about 1%. At the same time it reduced end of queue conflicts by 30% and lane changing conflicts by 20%. Speed variances remained low. And they saw medium to high compliance with these advisory signs.

Compliance is key, of course, and the Missouri experience was very different from previous installations in places such as Utah and Oregon. Even where VSL posted limits were enforceable, compliance only occurred when message signs explained the reason for the speed limit reduction or when law enforcement was present and actively enforcing those limits.

The reasons for this could be many. Maybe Missouri drivers are just more law abiding. It was not discussed in the study but perhaps MoDOT did a better job of explaining VSLs to the public before they were installed. Or perhaps the need for variable limits is more apparent to drivers in a congested urban work zone. But whatever the reason, it is clear we should not give up on variable speed limit systems just yet. More studies are needed, especially on the subject of the best applications for these systems and the algorithms driving them.

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.