GPS Insight Blog

A big part of why GPS is so popular with customers is the ability to report on speeding vehicles. We offer real-time speed alerts with thresholds you can define, along with boundaries (e.g. > 50 mph in this construction area, > 75 mph everywhere else).

Here is an example of one which a customer wanted us to verify for them yesterday [they claimed the vehicle was done moving at 2:30 PM and couldn't have been going that fast anyway in rush hour traffic].

I pulled that vehicle’s history up, and saw it had been moving, and probably just accelerated quickly up, then back down, from an average of 63 mph to 82, and then back down to 41 mph when the point was actually sent (we send instantaneous, average speed, and max speed every two minutes).

Here is a snapshot of that information, along with the 2.38 miles I show the vehicle moved in that 2 minutes since the prior point:

Doing the math, 2.38 miles in 120 seconds equals 71.4 mph average. Taking into account the fact the vehicle had slowed to 41 mph when it reported, and the fact that the average speed was 63mph, intuitively it makes sense that the vehicle had to have sped up at one point in order to cover that distance in only 2 minutes.

But is that enough to PROVE the vehicle was speeding? Since we get our speeds right off the engine’s computer, they’re highly accurate. We do get an occasional bad speed reading from some vehicles’ computers [literally 1 in 200,000 by my estimates], however, so we still try to sanity check speed alerts. Rather than break out my statistics books, however, let’s look at this vehicle’s recent history:

We can do that by only dialing in the last half hour or so of activity before the supposed speeding infraction of 82mph at 5:10 PM

This shows our map with ONLY the points from 4:40 to 5:14 visible, but the day’s path is still shown in light-to-dark, thin-to-thick blue (to make it intuitive by itself):

It helps to know that LIGHT GREEN dots (vs. regular green) indicate speeding > 66 mph (adjustable), and this vehicle’s recent history shows a lot of them. Clicking on one for more detail shows a max speed of 81 mph between 4:56 PM and 4:58, with an instantaneous speed of 75. Clicking on a few more show the speeding activity is consistent for that period of time, which would refute the “it’s 5:00 PM rush hour, I couldn’t possibly have been going that fast” argument:

Depending on circumstances, a max speed of 82 mph isn’t really horrible. Many freeways have 75mph speed limits, and accelerating to avoid an accident or to safely merge or change lanes is not something to discipline a driver over. But with a GPS system, 99.99% bullet-proof speed/location reporting, AND the tools to quickly react (alerts) and validate (maps/reports) this data, you’ll be able to know exactly what you want about your drivers’ speeding habits without too much difficulty. And trends definitely start to emerge for a number of our customers’ drivers.

The next article I think I will analyze this using some statistics from my all-but-forgotten 9 statistics classes I took in college. A friend’s new book How to Measure Anything got me thinking about how we can quantify whether or not single-point samples such as this are able to be used to prove speeding behavior. Doug Hubbard, the author, and I knew each other in the Army and have talked frequently about these types of measurements. I’ll see if I can enlist his expert help to better provide our customers with statistical mechanisms for identifying problem behavior in their drivers.

Rob.

Tags: gps fleet tracking, GPS Tracking, gps vehicle tracking

Over 3 years ago, when I founded GPS Insight, I named it that because I wanted to help customers get through the mountains of GPS data which were collected using GPS hardware.

I had heard about failed GPS implementations because no one had time to sift through the data. GPS Insight seemed a fitting name, plus GPSI sounds like “Gypsy” which is about the only interesting “mascot” company name you can make with the letters g p s other than Galapagos, Egyptians, Poughkeepsie and glimpse.

So, getting to the point of this article, collecting GPS data isn’t enough. It needs to be “boiled down” to quantifiable results. Those could be graphs, exception alerts, or summary reports.

But most GPS products’ summary reports aren’t always enough. Knowing how many hours/miles/stops doesn’t necessarily help you streamline your company.

Here is a screenshot of a large trash hauling operation in Denver, and the tremendous amount of data which they collect each day using GPS Insight:

What exactly is someone supposed to do with this? (of course the answer is to ask GPS Insight to help with standard or custom reports):

Even with custom colors for each vehicle, that is just too much to make sense of for mere mortals. But GPS Insight’s reports make VERY quick work of it.

Using landmarks and a Route Report from GPS Insight, you can define a route between two points and crunch numbers for data such as:

• How many trips per truck
• Average stopped time
• Average to & from times
• Exceptions

We define a “route” between two landmarks (in this case, a “transfer station” and a landfill — a transfer station is where the large trash-hauling trucks get loaded with trash in order to bring it to the landfill):

Then we run the report, and it completes within seconds, after crunching through tens of thousands of data points:

Here is part of the output, which shows each vehicle which made a “round trip” between those two landmarks (DenverSouth and Dads_Landfill), along with trip and stop times, and averages, by vehicle.

This helps to very quickly show averages and differences between certain vehicles.

BUT, what if you need even more help with a report that gives you exactly the information you require? In this case, the trucks work two shifts, and the day-time shifts experience worse “cycle times” than the night due to traffic, transfer-station and landfill wait times, etc.

So we have yet another automated, nightly report which we send to this and other customers to help crunch through all of this data. We have many options, and this one is able to handle multiple routes per day, per shift, per vehicle, so that if a vehicle goes to any combination of transfer-station and landfill, it will show up. This comes through to the end-customer as an email with a spreadsheet attached.

Here is a sample, in Shift/Transfer Station/Average “Loop” Time order. This report helps easily identify the trucks/drivers, per shift, per route, which are slower than the others:

So, we can now look at ONLY the “problem” vehicles. They may have been broken down, or it could be a driver simply not doing the best job possible. Let’s pick one and “drill down.”

Looking at only the 7197 and 7188 vehicles, one in green and one in blue, we see that they took different routes and 7197 only went that East Highway 30 route once. Perhaps it was to beat traffic, or to take longer than necessary (overtime). The combination of our exception reports and powerful mapping helps you to compare vehicles, drivers, routes, and overall efficiency between shifts/routes.

Here is a movie of the day’s activity, which sometimes helps to give an intuitive idea of what went on with a large number of vehicles for the day. Off-Hours, out of bounds, and suboptimal routes/driving behavior are often determinable by watching something in a movie form:

http://www.gpsinsight.com/content/trucks/trucks.html

Please ask us how our mapping and reporting tools can help your company get to the bottom of its efficiency (or lack thereof…).

Thanks,
Rob.

Tags: gps fleet tracking, GPS Tracking, gps vehicle tracking