The last paragraph in an otherwise normal article on Montreal’s upcoming passenger information system:
The iBus system will be rolled out as quickly as drivers can be trained. Each driver must be trained for seven hours on how to operate the new equipment.
Seven hours? I take this as a combination of the following:
- The equipment is complex.
- Training can only be accomplished in a shift.
- A win/giveaway for labor relations.
Talking buses, realtime location data coming to STM next year, Montreal Gazzette
Special trains for GHQ personnel appeared one after another in 1946. The main trains were the Allied Limited and Dixie Limited connecting Tokyo and Kyushu, and the Yankee Limited between Tokyo and Hokkaido.
–Yoshiki Soga, “The Story of Foreign Language Timetables in Japan” Japan Railway & Transport Review 53, 2009
I’ve gotten similar unsoliscited emails (below the fold) from a group three times recently. Accepting such an offer is against the law of my ultimate employer, as well as backed up by additional policies by my direct employer. That said, this behavior is still legal in many states, which is why it still occurs.
Continue reading “Blow the Whistle: Pay to Play”
In my recent post on Swyft , I briefly mentioned the factors that go into public transit service reliability, alluding to another post in the making. It turns out that part of my work has been done for me by a couple of papers in the latest issue of Public Transport.
In an open access paper, “Data driven improvements in public transport: the Dutch example,” the introduction is pretty much what I was going to say. It then goes on into more detail on the state of the practice of data-driven process improvement.
Another paper “Quantifying the Joint Impacts of Stop Locations, Signalized Intersections, and Traffic Conditions on Bus Travel Time” (paywalled but available here) takes a deep dive into the impacts of physical characteristics of service on buses in mixed traffic and their impact on travel time. Want even more detail? This paper was distilled from a PhD dissertation.
TLDR: Swyft shows their capacity for the three kinds of lies: lies, damned lies, and statistics.
Earlier this week, startup Swyft hit hard at NextBus, the arrival prediction service for San Francisco’s NextMuni:
NextBus predictions can be inaccurate 40 percent of the time if a bus is 20 minutes or more away, according to a Swyft study, released Thursday. “NextBus accuracy plummets as it tries to predict arrivals further out in time,” Swyft wrote in a summary of its study findings.
Below the fold, I will argue that conclusions reached in the aforementioned ‘study’ are easily debunked. I’m going to discuss why Swyft’s criteria for ‘incorrect’ are wrong from both a rider’s standpoint, as well that as the math behind predictions in laymen’s terms and why their analysis is innumerate. Finally, their post, while focusing on objectivity for their competitors, says nothing about the actual performance of their product.
Continue reading “Don’t believe the hype: Swyft”
Science literacy is not so much knowing how your microwave oven works … It’s knowing how to think… When someone says something to you, do you have the capacity to assess that it is true?
Neil deGrasse Tyson
This was a draft for quite some time, as I was uncertain about my divinations. I’ve had conversations in the past year lead me to believe that my suspicions were correct.
Google Transit is a great product. There has been some criticism that while data goes into Google in the form of GTFS, the results of user interaction with Google Maps– useful for planning service– do not.
Well, Big G may now be capitalizing information. I recevied the following message:
Learn how to track and coordinate staff and resources more effectively, respond to unanticipated situations more quickly, and ensure effective management of transit assets—while getting the most out of your budget. With Google Enterprise solutions, you can…
Transportation agencies nationwide are improving efficiency with Google Enterprise solutions. Find out what Google can do for your organization.
While short on actual details, it leads me to believe that the data that Google has collected is now a commodity. Specifically, this might include where current customers are and what they are searching for. But most important are those searches that do not result in a trip taken by transit as the options are too onerous or nonexistant.
Ideally, there would be a feedback loop between the creators of data (e.g. the public sector) and the end results (e.g. what has been ‘enriched’ by Google’s products), but alas, there is no business or moral obligation to provide. Welcome to the 21st century!