Here are two graphs I made of accelerometer data logged by my smartphone during two different trips I took on Metro Transit lines back in June: One on light rail, and the other on a regular bus. They were just experiments and shouldn’t be relied on too much, but they do show that the two types of vehicles are substantially different in the ways they behave.
I’ve been surprised to have some difficulty finding information about ride quality on mass transit systems, particularly since I find the relative smoothness of the ride to be a compelling argument in favor of rail transit—In my view, ride quality is likely a major contributor to “rail bias.” I have to put that in quotes because the implication is that favoritism toward trains, streetcars, and everything in between is based on pure emotion rather than physically-quantifiable factors.
The smartphone revolution (including gadgets like tablets and MP3 players) means that anyone can start gathering data about how good or bad their trip is and start doing some analysis. No longer do you have to wait for a report from some engineering firm contracted out to do the work, or try to sift through dense academic papers. The tool I used to gather data just ended up spitting out raw numbers, so it took a little post-processing to make it a manageable set of data, but it wouldn’t be difficult for a smartphone app to be built or extended to directly present users with useful information and comparison points.
Data gathering alone will not end the old bus vs. streetcar vs. whatever else debate, but it’s important to bring some real-world measurements into the conversation, since they have been lacking.
For those that are interested, my first graph shows a trip made on the Blue Line (Hiawatha) between Bloomington Central station and Terminal 2–Humphrey station in Bloomington and the MSP Airport area. The second set of data is from a route 3A bus along Como Avenue between about Hamline Avenue and Raymond Avenue. I didn’t do anything special to secure my phone in place other than gently holding it down—something that would need to be changed if people try to make repeatable measurements. One document I came across suggested mounting a measuring device on top of a heavy rubber cone placed on the seat—such a cone would dampen some vibrations, but make others more measurable.
Still, with my rudimentary methods, I found it amazing in my graphs to see how much the motion on the ‘x’ axis (blue, side-to-side motion) went up between taking the train and taking the bus. The the green ‘y’-axis plot is for front-to-back motion, while the orange ‘z’-axis plot represents up-and-down motion—both of those are harder to interpret visually, though some rudimentary math suggests that y- and z-axis motion is still substantially better than the bus ride. I’ll also note that the bus I took the sample on had a very thick cushion on the seat, while the Bombardier LRV I took the light-rail sample on has a very thin seat cushion.
Here are some averages and standard deviations for samples taken along the different axes. I used absolute values when making the averages, otherwise the periodic swinging past the origin line would make the x- and y- numbers very close to zero, and the z-axis average would be very close to 1 (since the z-axis is always being pulled on by Earth’s gravity).
In these two fairly unscientific samples, the bus had average g-loading on the x-axis that was 160% higher than the bus, and it was 24% worse on front-to-back motion, and 17% worse on the z-axis. For me at 220 lbs., the aggregate motion could mean that I’d feel 20 to 40 pounds heavier on the bus than on the train, like I’m swinging around a heavy sack of salt pellets.
But the story isn’t all rosy for light rail either—there’s a big spike about halfway through my recording, probably due to the train crossing running through a switch, or it may just have been a jerk of my arm as I was holding the phone (perhaps both). Sometime soon I’ll have to try this on the Northstar train to see how that one behaves, since it has much more substantial padding on the seats—often making the ride feel smoother—but it also has to share track with heavy freight trains which do a lot of damage to the rails. Hopefully someday we will have some local streetcars to also add into the mix of comparisons.
This is a great analysis, one I think Metro Transit should do systematically. One of the key questions is how much this is due to roads vs. vehicle. A cost savings for bus is off-loading costs to the road agency. The dis-benefit is poor ride quality on older roads.
I think in either case it makes a good case for the cities, counties, and state investing more in road quality, especially on roads that carry many passengers.
A related question of course is the age of the tracks, and how recently they have been constructed or rehabilitated. Running this experiment on an older rail transit system (One imagines the New York subway, or even Twin Cities streetcars c. 1953 after years of neglect) might also show less than ideal results. Was this before or after the recent rehab of the Blue Line between the Airport and Mall of America?
I took the LRT sample on June 9th, which was before the rail grinders had gotten down to that section of track. They had done some work in downtown Minneapolis by that time, but didn’t get down to the MSP-MOA area until a few weeks later. The rail grinding caused some interesting changes too — the grinding seemed to add a mesh of small grooves to the top of the rails, making it sound as though there was a circular saw running right next to the trains. The sound has probably begun to dissipate now, though.
Also makes me think more bus bulbs would help to reduce the lateral deltas which are a huge contributor to ride quality issues.
We should do the same test with decibels.
Awesome. Have you considered taping the phone to the wall of the bus/car? If you’re holding it, your body and arm may be cushioning any motion that occurs. It would be interesting to see the ‘absolute’ data (what the vehicle is doing where passengers sit), vs what people standing vs what people sitting (on various surfaces like padded vs plastic) seats experience. This is obviously a lot to gather but even 1-2 samples on long enough rides would paint a pretty good picture on that experience.
Any thought to the notion of varying rates by ride quality/experience as opposed to just by time (peak hour rates) and speed/frequency?
Yeah, at least some of those ideas had occurred to me — I think measuring the vibration of the wall or window is useful, since many people like to lean up against it in some way or another — My eyes and brain tend to get scrambled on our local buses if I put my head against the window, which generally isn’t true for the Blue Line. Similarly, there should be a good, repeatable setup for measuring the movement induced in people’s arms — lots of folks spend their bus trip reading from books, tablets, and other devices.
Good data logging apps should allow for recording the time and date when the data was collected, the ability to add comments (which route was it? which direction? which seat? what bus/LRV type?), and it would probably be good to have the GPS unit running at the same time, which can validate some of those questions I just mentioned and help quickly narrow down the spots where the road or guideway is in bad shape.
Having just returned from a vacation to Norway and Sweden, I was struck by how fantastic their LRT, Subway, Commuter Rail and Airport HSR systems are, particularly the excellence of the “Ride Quality”: whisper quiet, super smooth ride, comfortable seats, high quality information screens, smooth door operations, clean windows, clean cars, lack of vibrations, lack of grafitti and other damage, and on-time service. In addition, the stations, ticket machines and way-finding signage were all excellent. Clearly, rail transit is a high-priority for government spending in those countries. The US would do well to emulate these and other cutting edge rail systems around the world.
This could be quite useful and powerful, especially if you could 1. simplify or better semi-automate the data massaging to get it into the useful form, 2. come up with a way to statistically rate the data in ways that are useful to people, and 3. standardize the positioning of the measurement device (smartphone). Sounds like you are aware of these issues.
Good luck in pursuing this.
Maybe I should use it to compare my driving versus my wife’s – or maybe not!