Tag Archives: MIT

Jim Melcher and America’s Perestroika

Jim Melcher at a Boston Area bicycle Coalition rally on Boston Common, 1979. Photo by Anita Brewer-Siljeholm

Jim Melcher at a Boston Area Bicycle Coalition rally on Boston Common, 1979. Photo by Anita Brewer-Siljeholm

Jim Melcher was one of my professors at MIT. He was also was a year-round bicycle commuter and in 1977, one of the first 25 members of the Boston Area Bicycle Coalition, In the 1980s, his activism expanded into issues of national economic and military policy.

Jim died of cancer on January 5, 1991, weeks before the outbreak of the first Iraq war. In his final months, he composed a long essay, “America’s Perestroika”, which includes stories that have a familiar ring for any bicycle commuter, a discourse on the role of academics in formulating national policy, and an uncompromisingly straightforward description of political issues as well as his disease.

Jim’s wife, Janet Melcher, gave me permission to publish “America’s Perestroika” on the Internet, and I have made it available on my Web site.

Lyon study — cyclists ride faster in rush hour?

A blog posting published by the Massachusetts Institute of Technology describes a study of cycling in Lyon, France.

News accounts of the report are making some rather strange assertions, such as that cyclists ride faster during rush hour than in the middle of the day, and faster on Wednesdays. On the other hand, the Lyon study is very interesting in that it aggregates data on millions of bicycle trips, recorded in the database of Lyon’s card-swipe bicycle-rental system.

I see two problems with this study, and more so with news items about it: not all data were collected that would be needed to describe cyclists’ trips accurately; also, there is a rush to conclusions, without looking at some rather important characteristics of cyclists’ trips.

Certainly, cycling can achieve shorter trip times than motoring when motor traffic is congested, whether by cyclists’ filtering forward on the same street or by choosing other streets, paths, riding against traffic, whatever. What I can’t grasp is how any individual cyclist would achieve a shorter travel time in rush hour than at other times.

Congested motor traffic slows bicyclists, though not as much as it slows motorists — because cyclists have a lower speed capability in the first place, and because cyclists have a greater choice of routes, and can filter forward. Even with bike lanes (of which, according to the article, Lyon doesn’t have any), congested motor traffic slows bicyclists. But also, congested bicycle traffic and pedestrian traffic slow bicyclists.

The Lyon data include only the times and locations of rental and return of the bicycles, and odometer readings. The data, then, cannot show where bicyclists went, and can record only an average speed. Importantly, the slower mid-day times may reflect rentals during which the bicycle is parked in mid-trip — shopping trips, or trips to appointments, lunch dates, classes, (Lyon is a major university city). Women’s speed capability is generally only slightly lower than men’s; to claim it as an important explanation is at least vaguely offensive. Morning and evening bicycle commuters, whether male or female, might be regular bicycle users, in better physical condition, more skillful in traffic and so capable of higher speeds. Without demographic data, there’s no way to know.

The report does include some interesting results about the shortest travel times, which it is safe to assume do not involve parking in mid-trip.

Bicycles included in the study are available at rental stations spaced around Lyon. A renter may obtain a bicycle at one station and leave it at another, making the system practical for single-direction trips. But renters must walk to the stations — the bicycles are, for example, not at their homes. Bicycles will not be used for the shortest trips unless stations happen to be very convenient to trip origins and destinations. Also, the system works only within the limited area where stations exist. These factors can be expected to affect the trip lengths recorded in the study.

I look forward with eager anticipation to a study using GPS data correlated with user data, so it is possible to categorize the cyclists, determine where they went, how fast they actually rode, and whether they parked the bicycle in mid-trip.