Floating carshare research we should do

I have a small code project dedicated to analyzing floating one-way carshare usage data. It’s called electric2go and is open source.

Floating carshare systems offer cars for rent by the minute or hour. Cars do not have to be returned to where they were picked up, but can be dropped off almost anywhere within a specified operating area and picked up from there by another member for the next trip. This mimics most bikeshare operations, and in a sense provides a taxi you drive yourself.

car2go and Drivenow are the major multi-city operators, with some regional systems like Enjoy in five Italian cities, Communauto Automobile in Montréal, and Evo in Vancouver.

I have previously done a bit of statistics and some availability map animations for selected cities. But there’s a lot more that can be done. Here are some of the possible questions for further study and research.

This post is equal parts roadmap, to-do list, motivation for me, inspiration for others, and a call out for the interested.

Usage statistics

There are some obvious statistics: where is the service used most – densest cores, spread-out suburbs, in-between? When are trips most frequently started? What are the differences in use in mornings vs evenings vs late nights; on weekdays vs weekdays? What is the median parking duration, how much of total parked time do parking periods over 12 hours comprise, where are the cars parked the longest, vs the shortest?

How can we quantify availability of carshare vehicles? One idea could be to calculate area (in square kilometers) of a neighbourhood/borough; for any given time, calculate area within 400 m of a carshare vehicle and divide the two to get a ratio; then repeat over time to get graphs of when the given area is has best carshare availability.

From quick analyses, in Canadian cities, floating carshare usage is correlated with population density. But I think there is more. For example, is one-way carsharing more popular in cities with a grid system of streets vs radial patterns? In bigger cities or smaller? Is there an optimal population density for this kind of carsharing? It might not work very well in Hong Kong. Can we model and predict usage of the systems, how long a car will be parked, where it will move next?

Carshare is fundamentally about transportation, so compare it with other transportation methods. Compared to a car parked outside a person’s house, how much trip time does walking to a carshare vehicle add?

Compared to a transit trip, how much time is saved, and on what kind of trips does carshare save the most time? Pick out a sample of trips and run its start and end points through a transit planner specifying time and day of week, to see how driving compared with transit. Which carshare trips – which origin-destination pairs, which times of day – save the most time, and which ones overlap with transit the most?

Are floating carshares used as “last-mile” transportation, travelling to and from rapid transit stations? Could they be – how much time would that save? (E.g.: Calgary/Montréal/Vancouver, outside of downtowns: how many trips between 7 and 10 am end within 200 m of a rapid transit station?) How does the total trip duration and cost compare with using a traditional taxi?

Systems other than car2go often have a choice of vehicles and car2go has started introducing various size 4 and 5-door vehicles as well. Compare the usage of 2-door vs 4-door car2go vehicles; is car2go used more now that they’ve introduced the option? How does usage of 4-door car2go vehicles compare with vehicles from competing systems? In cities with multiple systems (e.g. Milan has four currently!), how does each system’s usage compare and why is it different?

Looking farther into the future, try to estimate how many fewer cars would be needed if vehicles could reposition themselves. Driving back to a busy area at peak hours is an obvious application. However, it would be equally interesting to quantify if and how last-mile connectivity in less dense areas could be improved if a car can move itself slightly (say 1 km) to be closer to the hailer.

Maintenance and operations

Much has been written about plans for transportation services based on self-driving cars. I believe that much of motivation for the car manufacturers running carshare programs is hedging and research for such a possibility. Through their efforts, we can also learn what might be required.

A key of carshare operations is that there is no central base where vehicles are returned and could be maintained. More important than rare repairs or yearly maintenance are everyday tasks required for cars, particularly intensively used cars: cleaning and refueling/charging. Although users are expected to keep the cars relatively clean, over dozens of rentals dirt will inevitably accumulate, especially in rainy or snowy weather. Accidental spills and sickness happen and must also be cleaned up. In a floating carshare, vehicles are also sometimes relocated by the employees.

The maintenance requirements of a self-driving fleet would be fairly similar to that of a floating carshare. It will be useful to see when, where, and how maintenance is currently done.

For an example implementation, pick around 100 cars within a fleet at random. Over one month, chart their cleanliness ratings and fuel level as a scatter plot: x axis = time since last clean/refuel, y axis = fuel level or cleanliness rating. We’ll see the cleanliness spike and the fuel go to 100% when they are cared for. By measuring time between spikes we can determine how much maintenance a car needs.

For finding relocations of vehicles by staff, scatter-plot time spent parked vs hour of day when it was finally moved. I would expect to find a specific time of day where a lot of the long-parked cars are moved, for example 11 am as it is during work hours and should have relatively low road traffic.

There is, of course, a lot more; I also have a technically-oriented list. Contact me if you’re interested in any of the above!

Berlin, three months

I moved to Berlin in late April. I quite like it overall.

Where deciding where to move next after London, I initially had pencilled in Melbourne, though half of my motivation was to get my English accent to be very confusing by mixing Canadian, English, and Australian. But ultimately I’d decided that after Toronto, Vancouver, and London, Melbourne would be more of the very nice, very anglosaxon same, and jumped a tiny bit further out of comfort zone.

So then: Berlin is smaller, not as intense, more relaxed. No one’s in a rush. There’s fewer crowds. The subways run every 5 minutes in rush hour and aren’t packed.

It definitely helps that it’s summer and I don’t have to work yet. I am reminded of my first summer in Toronto: warm, parks, railway corridor, a TV tower.

And no, I don’t listen to techno.

Read the rest of this entry »


There is a certain romance to the legendary quietness of a crammed tube carriage in the morning peak. But getting onto Victoria line trains at Euston before 9 a.m. was one of the only times I was glad for my height.

London is interesting. It’s big and it’s busy and it’s obviously successful. It’s tough to write about without getting into nobody goes there anymore, it’s too crowded territory. I am not a Londoner, but for a while, I could almost pretend. I lived and worked in London for two years from May 2014 to April 2016 – here are some of my thoughts.

Read the rest of this entry »

No free publishing

Twitter is great. You can say anything you like, for free, and reach a huge audience. As long as Twitter likes it. Or Facebook, or Medium, or Snapchat.

In the past, you could have your books distributed by being copied by monks – as long as the church liked the book. Then the printing press came around and it turned out people had lots to say that the church didn’t like.

Or you could have gotten your message out for free by being interviewed on ad-supported TV or in a magazine. As long as the TV station, and ultimately the advertisers didn’t mind your message. People started photocopying zines and it turned out lots of people were into punk.

Not a fan of ad-supported TV? There was the free TV in authoritarian states. The government paid the bill, the government got the final say. Except in bibuła and samizdat.

Ultimately someone always pays for publishing, however little. When you’re the one paying, you control what is being published. When someone else pays, they control.

The goal should be not to make publishing free, but to make it cheaper.

Postcodes and London

Many western American and Canadian cities have addressing quadrants – NW, NE, SW, SE – to help with their addressing schemes. Some Portlanders use its five quarters as support for its quirkyness. But that’s nothing on London, which takes this to the next level with six quadrants and two parts in the centre.

The UK uses post codes for addressing and locating, in the way Canada feels like it should have. Both countries have alphanumeric codes that are short enough to say and memorize and precise enough to be useful to navigate. But I’ve never seen Canadian postal codes used for anything but mail and locate-closest-thing tools; for route-finding major crossroads are more common, but “Dundas and Spadina” is a fair bit less precise than “NW1 9LJ”. OpenStreetMap will map “V6B 2X6” but no one in Vancouver knows what V6B or V6 covers.

The first part of the postal code is used informally within London as a larger neighbourhood indicator; NW1 means the innermost northwest part. The full code narrows the address down to a few dozen buildings at most — normally a house number and postcode is sufficient to uniquely identify a building.

The usage for neighbourhoods is probably helped by use of major postcodes for disambiguation of the million High Streets and Church Streets within London “postal town.” Most street name signs in Greater London have the first part of the postcode on them to disambiguate.

The actual postcode quadrants are SW, SE, W, NW, N, E, and EC and WC. EC is east central, roughly the City of London, and WC is west central, roughly the West End of London.

The missing quadrants, S and NE, were abolished in the 19th century. The S code was split and merged into SE and SW, and S itself is now owned by Sheffield, which is very much not London. The NE code was merged into E and is now owned by Newcastle, also very much not London.

NW1, SE1, N1 and so on are the innermost districts; but after NW1, numbers are assigned based on alphabetical order of given name of the district, and so NW1 borders NW5 (Kentish Town) and NW8 (St John’s Wood). SE1 borders, clockwise, SE16 (Rotherhithe), SE15 (Peckham), SE5 (Camberwell), SE17 (Walworth), and SE11 (Kennington); SE2 (Abbey Wood) is the farthest SE postcode from SE1.

In east London, the border between E and SE follows the Thames; in west London, the border between W and SW does not. Also, SE1 reaches quite far west and includes all of London’s South Bank, all the way to area across the Thames from the Parliament, and the main station of London and South Western Railway, Waterloo Station.

The postal service reckoned, rightly or wrongly, that postal delivery didn’t have much to do with local government borders, and so postcode borders don’t follow London borders and never have:

… the London postal district, which formed a special post town, did not conform to any administrative boundaries. The postal district was created in 1858 and has periodically been revised. However, at no point has its boundary coincided with either the metropolis (later County of London) of 1855—1965, which was somewhat smaller, or the Greater London area created in 1965, which was much bigger. … Sewardstone, in the Epping Forest district of Essex, is the only locality outside Greater London to be included in the London postal district. — Wikipedia, Postal counties of the United Kingdom