Caltrans QuickMap to Add Waze Data


Caltrans recently announced changes to their popular QuickMap travel app. The app is available online at 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!

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 But they are often wrong or out of date.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.

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.