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.

Improving the Effectiveness of Smart Work Zone Technologies, Part 1

illinoisstudyA brilliant study was published in November by the Illinois Center for Transportation Studies entitled “Improving the Effectiveness of Smart Work Zone Technologies.” The principal researchers were Yanning Li, Juan Carlos Martinez Mori and Daniel Work. Download it here.

It is brilliant for a couple of reasons. It moves past the studies we conduct over and over again that look at the effectiveness of smart work zone systems. Those have been done, and there really isn’t much more to be learned from additional studies. We know they work. It has been proven. Enough said.

Instead, this study looks at ways of making something good, even better. There is so much new information is this study that we will discuss it in two separate blog posts. This first one will look at their conclusions regarding sensor types, sensor spacing, and missing data. The second will consider travel time estimation and, in particular, their recommendation of the Kalmar filter algorithm.

Let’s begin with sensor types. The study focuses primarily on doplar and side-fire (RTMS) sensors.  They compared the advantages and disadvantages of each and concluded that for most work zones systems there is no advantage to RTMS type sensors. The RTMS provide more accurate flow measurements than radar due to fewer occlusion issues. But they do cost more and are more difficult and time-consuming to set-up and configure. They wrote the, “analysis indicates the types of sensors do not have significant influence of the performance of existing smart work zone systems. It is suggested the choice of sensor types should depend on the specific requirements and constraints in each work zone.”

They went on to say that given the extra cost of RTMS, you are normally better off spending that money on additional doplar sensors, as more sensors improve the data quality far more than the sensor type. Of course, if lane by lane counts and classifications are needed to meet the deployment goals, RTMS is the only practical solution.

Next they discussed sensor spacing. Most of us in the work zone ITS industry suggest sensor spacing of between 1 mile and one-half mile apart. This study confirms what we learned through experience. They said the closer you place your sensors to each other, the better the data quality. “When more sensors are deployed, the systems provide faster notification of changes of the traffic conditions and increase the estimation accuracy of the traffic conditions.”

However, anything closer than a half mile apart provides negligible additional benefits. “When the sensor spacing is smaller than 0.5 mile, the benefit of additional sensors … is marginal.”

A third topic they covered was missing data records. This has not been covered in any detailed way in previous studies. But it is important. They studied only two projects but for those projects found they were missing as much as 10% of the data records. They did point out that the system messages continued to be sent to the message signs. But what if the interruption was sufficient to delay those messages?

One vendor suggested the data records were missed due to the cellular carrier dropping them in favor of voice transmissions. If true, this underlines the need for redundant communications. Satellite backup or better cell service is a must. If data is dropped for more than a few seconds it could affect the timeliness of warnings to traffic upstream. And if that data will also be used for work zone performance measurement, it causes additional problems. A measure of system data transmission performance should be included in the evaluation of every work zone ITS deployment.

So, in short, simple systems with more sensors are better than more complicated ones.

Spacing of a half to one mile is best. Anything greater quickly loses accuracy and anything less is not cost-effective.

And agencies should require a data transmission reliability report to be sure that most of it is getting through, even during major incidents.

In a future post we will return to this study to examine algorithms, especially for travel time systems. In the meantime, download this important study!

What Do Automated and Connected Vehicles Need to Know About Work Zones?

AUVS

On July 20th, Ross Sheckler of iCone made a presentation to the Autonomous Vehicles Symposium in San Francisco. The title of his presentation was “What Do Automated and Connected Vehicles Need to Know About Work Zones?” His message was very important. It was well-received by those in attendance, but the group that needs to hear this is many times larger than the 100 or so people in the room that day. So we will try to make his main points in today’s post.

Remember, most of the attendees were not work zone people, though a few of us were there that day. Most work for automotive manufacturers or component manufacturers. They produce navigation systems – some in use today and some that will guide autonomous vehicles in the future. Those cars will drive through our work zones, yet the folks who produce them know very little about temporary traffic control. So Ross began by pointing out that the map changes 1,000 times per day due to work zones. 1,000 times per day workers change the law, and 10,000 times per day warnings are posted. His point being, of course, that we must find a way to inform these systems.

Mr. Sheckler also explained that most closures are never reported. And of those that are reported, most don’t occur on the dates and times they are scheduled. He went on to say that the most dangerous closures are probably those unreported ones. He used the example of a short term utility closure on a rural road with bad line of sight.  The people doing that type of work often do not worry much about traffic control. They might place a 10 foot taper of cones and a ROAD WORK AHEAD sign, but even that is somewhat rare. Automotive systems must be able to recognize these work areas and react appropriately.

And when traffic control is reported, it only shows up in navigation apps as “roadwork”. It does not say it is a lane shift, or multiple lane closure sure to cause queuing. It does not say the entire geometry has changed by moving traffic over into the oncoming lanes separated by concrete barrier. And it does not tell you if the work is causing traffic to slow or stop. A shoulder closure is reported the same way as a full roadway closure with detour. Yet one does not affect traffic at all while the other may affect travelers’ choice of routes.

His point is that by reporting these changes as they occur it gives drivers the opportunity to avoid the area altogether. But the information must be posted as the changes occur and it must be accurate. If it is, drivers will learn to depend on it and change their routes. But if they get erroneous or inaccurate information, they will continue to drive along their intended path.

Ross finished by listing the details that are important to navigation apps, and this applies to current apps as well as future autonomous driving systems.

  1. Work zone status: scheduled versus equipment on sight and ready to work versus workers present.
  2. Map changes including lane shifts, capacity reductions of any kind, or roads closed.
  3. Queue details including slow or stopped traffic, delay times, early or late merge systems, and location of merge point.
  4. Presence of active flagging operations including location.
  5. Presence and location of attenuator trucks, especially when the attenuator is in the down or active position.

These are all details a system will require to make informed routing recommendations. And if the work does cause significant impacts, we prefer they avoid the area altogether. It is safer and more efficient for everyone involved: travelers, contractors, and for the owner/agency.

Our industry can supply this information today. So please encourage system designers to engineer with that in mind. We can all avoid a future full of expensive, time consuming, and even dangerous problems by getting the word out now.

Adapting Existing Technology to Unusual Traffic Problems

The work zone ITS industry has produced many creative ways to help mitigate the impacts to traffic from work zones and to protect workers from that same traffic. But often the problems we solve aren’t the same ones we set out to address. This is true for most industries when they encounter new technology.

According to author H. W. Brand it was true for the movie industry as well. When the first “talkies” were released, “Sam Warner (of Warner Brothers’ fame) convinced his brothers to purchase a technology that allowed the attachment of sound to recording film.” “The initial appeal was that sound would permit theaters to dispense with the orchestras that played accompaniment to otherwise silent films.” Today we can’t imagine movies without the sounds of explosions, gun fire, and, of course, dialogue.  But they were focused on the economic benefits of the technology and so missed what we all see as the obvious artistic advantages.

The same is often true in our industry. Our technologies are more mature now, though new ideas are introduced every day. But too often we miss good opportunities to improve the safety or efficiency of our roads because we don’t have a prepackaged system ready to deploy.

In fact, we do have them ready. We just don’t think it through far enough. Most of our systems use sensors to measure traffic flow, then compare that data to a set of rules, which then trigger outputs like messages to message signs, or alarms at a traffic management center. So it does not matter what your traffic concern is, a system can probably be created to address it. And while such a system could be called “custom”, it won’t normally be saddled with the costs and lead times normally associated with custom systems.

Redding Map

A good example was a demo project done for Caltrans a few years ago. They were closing one of their busiest ramps in Redding for reconstruction. The plan called for them to send traffic to alternate ramps. But no one of those was capable of handling the volumes at the closed ramp. Road-Tech proposed a simple solution. A sensor was placed on each of the alternate ramps. And portable changeable message signs directed traffic to the best alternate. As traffic backed up on the first alternate ramp the sensor detected the stopped traffic. That caused the system to change the message signs to recommend the second alternate ramp. If that ramp backed up traffic was sent to a third alternate ramp.

It was simple, inexpensive, and worked very well. The only problem encountered was public outreach efforts scared everyone away. So the volumes were never as high as expected. But this does show what can be done with the tools we already have. No one talks about alternate ramp systems. But it turns out we had one ready to go. We just didn’t know it.

Next time you are faced with a traffic problem, try to imagine a rule. That rule would say, “If traffic does X, make Y happen.” So if traffic slows I want to change the message signs to warn of STOPPED TRAFFIC AHEAD. Or if average traffic speeds exceed 75 MPH, I want to send an alarm to the police department. If you can come up with a rule, a solution is probably already available. Keep that in mind and you’ll be surprised what can be done!