Is There an Ideal Sensor Location?

Are there “perfect” sensor locations? For example, when we deploy a queue warning system, are there sensor locations that will get us better data? Could that data inform us of slowing traffic sooner? Or could it be a better indication of traffic conditions than data from another location would be?

For end-of-queue warning systems we submit that the ideal sensor location is just upstream of where queuing is most likely to begin and, therefore where average speeds vary most.

There are locations like that throughout the work zone. Narrowing of lanes, lane shifts, temporary concrete barrier, bridge falsework and other construction activities affect drivers sense of safety. Anything that negatively affects that feeling of comfort will reduce the 85th percentile speed.

Short on-ramps with reduced merge distance have the same affect. However, if traffic always quickly accommodates those merges and returns to the previous 85th percentile speed, then that is not a perfect location. Only when the geometry in combination with traffic volume results in dynamic queuing does that become a good sensor location for queue warning systems.

The power source is our greatest limiting factor today.  Batteries, solar systems, etc. take up space. They must be located where we can reach them easily for maintenance. For this reason, many sensors are located on message signs and arrow boards where they can draw power from them and even share communications devices.

Arrow boards are placed at the taper. Queuing begins there, of course. But we will only catch speed variance due to conflicts at that merge point. We won’t see if that variance continues upstream.

Message signs are placed in advance of the work to warn of slowing downstream. We should always place one sensor at a point that queuing would reach as a result of a worst-case scenario. And a message sign location may be able to serve both purposes. But we normally want the sensors located where queuing begins and we want the message signs located upstream to warn of that slowing – not located together. If sensors and message signs share the same locations they are likely either too close to the work zone or too far from the source of the queuing to warn traffic before they reach the problem area.

We generally space sensors out every half mile to a mile apart with the understanding that we will learn about any queuing quickly. And that is a good approach. After all, we can’t predict all causes of queuing. But couldn’t we adjust those locations a little one way or the other to catch these obvious causes of slowing a little earlier?

It would be helpful to see research into sensor location. But in the meantime, let’s evaluate our work zones and adjust our sensor locations to monitor the more obvious sources of slowing. Our systems will perform better and improve work zone safety even more than they do today.

Work Zone Traffic Control “Down-Under”

We just returned from a wonderful trip to Australia where we spoke to the Traffic Management Association of Australia (TMAA) about work zone ITS. Their members were all excited and focused on finding safer, more efficient ways to manage their work zones.

The program was packed full of interesting speakers and a variety of timely topics. They also gave us all just the right amount of time to discuss those topics between sessions. It was very well run.

The attendees seemed to enjoy talking to Americans and all asked what we thought of the meeting. My first answer was always the same: traffic control companies in both countries share the exact same set of problems:

1) Speeding in work zones.

2) End-of-queue crashes.

3) Hiring, training and retaining good employees.

4) A perception by the driving public that we are there to make their lives miserable.

5) Insufficient funding for maintenance and construction.

6) Changing standards and levels of enforcement from one state to the next.

7) Varying commitment and funding levels from one state to the next.

Just like ATSSA, the TMAA brings contractors, manufacturers, academia and government agencies together to discuss these problems and identify solutions. The TMAA does an especially good job of this. We look forward to learning more from them in the years to come!

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.

Combining Queue Warning with Dynamic Late Merge

In our last post we talked about the ATSSA “Tuesday Topics” webinar held June 27th. Joe Jeffrey began the webinar with a discussion of work zone ITS basics. Chris Brookes of Michigan DOT shared some of his lessons learned. The final speaker that day was Ross Sheckler of iCone there to talk about coming trends in work zone ITS. Ross declared that the next big thing will be queue warning combined with dynamic late merge.

Mr. Sheckler began by looking at the state of our industry. He said that nationally there are nearly 1,000 deployments per year now. Costs of these systems are dramatically lower than they once were. And the economy and simplicity of these systems have not affected their flexibility. In fact, because applications vary, flexibility always has been and always will be an important feature of work zone ITS.

And for that reason it is very easy to add features, including dynamic late merge. As Ross pointed out, queue warning systems have their limitations. When volumes increase and queue lengths extend beyond the limits of a queue warning system additional steps should be taken. By instructing drivers to stay in their lanes and take turns at the merge point, it reduces the overall queue length, makes the best use of limited capacity, reduces road rage, and sometimes can even improve throughout.

In his drawings of typical system configurations he listed 4 sensors and 1 portable changeable message sign (PCMS) for queue warning. For queue warning with dynamic late merge he added a second PCMS at the merge point to tell drivers to take turns and a fifth sensor to narrow the gap between sensors midway through the affected area. So, in total, just 1 more sensor and 1 more sign. This is a minimal added cost and significantly increases the capabilities of the system.

The message here is that we can often solve multiple problems with one system. It just takes a slightly different logic in the controlling software. In this case you can solve problems with end of queue crashes and conflicts at the merge point with one inexpensive, easy to use system. So please remember this the next time you specify a work zone ITS system. Consider all of the challenges you face on that project, and think about ways work zone ITS may mitigate one, two or perhaps even many of them.

This webinar covered a lot of ground in a very short time.  It was recorded and can be viewed by ATSSA members anytime at: http://www.atssa.com/TuesdayTopics/Recorded. Or watch for possible future webinars on this same topic.

 

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.

Medical Wearable’s and Autonomous Vehicles (and Work Zone ITS)

screenshot-www-linkedin-com-2017-01-10-10-12-32Marty Weed, who recently retired from WsDOT, is a good friend and still very much involved in work zone ITS. He ran across a talk on LinkedIn comparing medical wearable technology with autonomous vehicle technology. In the video Randy Hamlin, a VP and engineer at Phillips, said that both technologies are further ahead than policy or behavior. The technology won’t slow down adoption. Instead it will be policies or behaviors. I believe we can add Work Zone ITS to this comparison as well.

You can view his presentation HERE.

Mr. Hamlin stated that both industries have the opportunity to meet a very large and growing need. For medical wearable’s it is the opportunity to reduce chronic disease. For the autonomous vehicle industry it is the opportunity to reduce the chronic roadway fatalities of 30,000+ per year. But before either industry can make an impact, each must first achieve three key factors:

  1. Access
  2. Integration
  3. Adoption

Under access he said that for a technology to achieve widespread adoption it must be accessible. One easy way to make that happen today is to take advantage of the personal cell phone market. These devices have become very powerful, very inexpensive, and everyone owns one. So instead of expensive, stand alone systems, create hardware that interfaces with your phone using an app. This is already taking place in all three industries.

His second factor, integration, revolves around data. Medical wearable’s generate a great deal of data. So do autonomous vehicles. And so do work zone ITS systems. The data quickly overwhelms the practitioners. They quickly begin to ignore it and go back to doing what they did before. Because they just don’t know where to start in making use of that data. As he pointed out, data must be relevant.

Autonomous vehicle manufacturers are addressing this by keeping most of the data to themselves. They use it for product improvement and verification. They only release data to other users that is relevant. Data packages are customized for each group of users. This requires a good understanding of each user’s needs and habits, but results in faster and broader adoption.

Work zone ITS can learn from this. We must understand what our customers need from data. Traffic operations will want one package. Construction may want something else. And systems operations folks may want yet another set.

This will vary from state to state and even from one district office to another. A more urban district may watch volumes while a more rural district may be more interested in speeds or queue lengths.

As Mr. Hamlin said, for our systems to impact roadway work zone fatalities we must first achieve access and then integration. Only by packaging our data so that it is relevant can we hope to achieve the third factor, adoption. Once users become accustomed to receiving timely and useful data, they will come to depend on it. And once they depend on it, our systems will see far more common use.

Improving the Effectiveness of Smart Work Zone Technologies, Part 2

illinoisstudyIn our last post we discussed a brilliant new paper published in November by the Illinois Center for Transportation Studies. Today let’s look at their conclusions regarding work zone travel time systems. The writers point out that, “Two critical components for the success of a smart work zone deployment are the quality of the traffic data collected by sensor networks and the algorithms used for data processing.” We examined sensor types last time. Today we look at algorithms.

They conclude that, “The travel time estimation is consistently poor for all algorithms and sensor networks investigated in this study. The main reason is that the instantaneous travel time calculation is a poor estimator of the true travel time in a dynamic traffic environment. In addition, the use of Bluetooth sensors can only provide the travel time of vehicles that just exited the work zone. Consequently, the travel time estimation even using Bluetooth sensors is not likely to improve the accuracy of the travel time estimates when the traffic conditions are quickly changing.”

This makes perfect sense. In a work zone you are more likely to see frequent and dynamic queuing. And that is the kryptonite for every algorithm superman. It’s too bad, because we would all like to see accurate travel time estimates, especially for work zones with significant impacts. But, ironically, it is those impacts that make estimation so difficult.

They also discussed the potential use of more advanced algorithms. This is a subject for which I have only a very limited understanding. So I am not able to examine the relative advantages and disadvantages of popular methods. But for work zones, they really aren’t practical anyway. Unless it is a very long term project, one lasting several years, the work required ahead of time to test and adjust the algorithms is expensive and still won’t make much of a difference in the travel time accuracy.

As an industry, we have worked for years to make our systems faster and easier to set up. This, to my mind, would be moving backwards. Instead, let’s work to make our travel time estimates more useful to travelers. Perhaps it makes more sense to talk about delay times. Drivers seem to expect predicted travel times to match their experience perfectly. But when it comes to delay times, they are more likely to be relieved when the delay they encounter is slightly less than predicted.