ZibblerTrip lets you use the awesome power of iPhone to help you during trips and afterwards.
ZibblerTrip uses your iPhone’s GPS to track trip routes and speed; these trips are stored in a Core Data database for subsequent trip analysis.
During trips, ZibblerTrip offers a GPS-based speedometer. The display is optimized towards a very simple, easy-to-read speedometer. The speed is displayed in miles per hour (which you can change to kilometers per hour if you choose ZibblerTrip’s “metric” option in Settings).
In portrait mode, ZibblerTrip will also include a trip odometer underneath the speedometer.
Throughout ZibblerTrip, you will find color-coded speeds.1 One of these places is the speedometer, which has a border whose color changes according to the current speed.
When you are logging a trip, you can leave the ZibblerTrip app to play music, record video, or use other apps on your iPhone.
Automobiles (colloquiolly known as “horseless carriages” or “cars”) are well-suited to analysis by ZibblerTrip. I typically log my daily commute as I drive in to work. Subsequent analysis of these trips has led me to alter my route in order to use routes that might “feel” slower but are actually faster!
I have found to my surprise that ZibblerTrip makes a fine svelte bike speedometer, and its route-tracking makes subsequent route analysis very easy.
Hikes in the Cascade Mountains of Washington State have a lot of ascending (and descending). I really like being able to track the elevation provile of hikes when I email the raw trip data to myself for some spreadsheet analysis.
You can view the route of a trip on a map after you finish. You can even replay it at 60X speed. (As you replay a trip, one minute of the trip takes only one second to replay.) It’s really cool to watch a trip being replayed and (re)seeing the delays at stop signs, or the faster speed hiking or biking downhill.
ZibblerTrip lets you email the raw data of a trip. This comma-separated-variable (CSV) formatted text file can be imported into a spreadsheet or other analysis tool.
Once you’ve got your trip data into a spreadsheet, why there’s no limit to the analyses you can run! And it’s fun!
I took 16-mile bicycle trip data and ran a few Numbers spreadsheet operations and graphs on it. This is just an example of what can be done if you have a humongous pile of data from a trip.
This elevation profile shows the measured elevation for each point along the route.
This track is the same trip, with elevation on the X axis and speed on the Y axis. You can see where the bicycle went downhill quickly (where the elevation changes at high speed) or where the bicycle was walked uphill (the elevation changes at low speed).
This is getting more complicated. The spreadsheet figured out the difference in elevation (negative X axis numbers indicate when the bicycle was moving downhill) between points, and compared speed against this delta elevation.
This graph answers the question “do bicycles tend to go faster as they go downhill?” The high-speed loops in negative delta-elevation territory give us a solid answer: Yes!
You can’t find this kind of analysis just anywhere, folks. Thanks to ZibblerTrip we just discovered something profound!
All the trips that you have logged with ZibblerTrip can be viewed in the trip list.
You can manage each trip in the list by tapping on the trip you’re interested in; a long press on a trip will show even more options.
1 The color range progresses according to speed as follows:
Continued use of GPS running in the background can dramatically decrease battery life.
This product uses some Creative Commons content. Thanks Creative Commons and Jan Weigand!