MobBlog

Following on from some recent research about how individuals move about town, we are calling out to all Londoners to participate in a survey that we recently put together. The survey asks about two things: your travel habits and how you fund those travel habits. But a good place to start is: why should anyone care about these things?

At face value, topping up your Oyster card with credit or buying a travel card seems simple and mundane. However, we all know that the cost of travel in London is not only always growing – it also depends on who you are (which determines which discounts you are eligible for) where you travel to and from (i.e. what zones), when you travel (e.g., rush-hour or day time) and how frequently you tend to move between places over time periods that span from single days to an entire year (anyone out there ever bought an annual travel card? Not me!).

In other words, there isn’t really a transparent link between how you travel and what the cheapest fare for you to be paying is. Yes, I know about the daily capping on pay as you go – but if you are going to be travelling every day for seven days in a row, there is no “cap” on your weekly spend- so maybe you should have bought that 7-day pass! How do Londoners make these decisions?

There are some fascinating numbers relating to money and the tube. Over £40,000 was refunded to travellers between January and August 2010 as a result of complaints regarding overcharging. TfL itself estimates that over £300,000 is wasted per day by passengers buying paper tickets instead of opting for the electronic equivalent (see here), and other investigations have revealed that approximately £30 million of travel credit is sitting in the system, idle and unused. These vast sums of wasted money all point to the fact that making the correct decision at the point of purchase is not only uninformed and lacking in transparency, but also incredibly difficult for travellers to reason about in order to purchase the cheapest fare for themselves.

The survey has three parts:

1. Questions about your travel habits! Where do you start/end your days? How often do you travel? What times do you travel? How consistent are your commutes?
2. Questions about your topping up/travel card purchase habits! How much do you top-up by? Why and when do you use pay as you go? What travel cards do you buy? Why do you buy them?
3. An opportunity for you to really help our research and enter a prize draw for a new Apple iPad! All you have to do is give us your Oyster card number and allow us to get your 8 week travel history from Transport for London. How will we use this? Your travel history will give us a direct insight into how groups of Londoners navigate our city. Keep in mind that we don’t want or ask for your name, telephone number, age, gender, or occupation. You are, to that extent, very anonymous (we ask for your email address for the prize draw). We just care about your Oyster card number and what kind of Oyster card it is- your travel history data will be stored safely and anonymously and will only be used for this research project. If you have any concerns or need clarification, get in touch with me (email or twitter).

So, have I linked to the survey enough already? Please help us and fill it out!

I’m just on my way back from beautiful Sydney, where I presented a paper called “Mining Public Transport Usage for Personalised Intelligent Transport Systems” (by me, Jon Froehlich, and Licia Capra) at the IEEE 2010 International Conference on Data Mining. The abstract of the paper reads as follows:

Traveller information, route planning, and service updates have become essential components of public transport systems: they help people navigate built environments by providing access to information regarding delays and service disruptions. However, one aspect that these systems invariably lack is a way of tailoring the information they offer in order to provide personalised trip time estimates and relevant notifications to each traveller. Mining each user’s travel history, collected by automated ticketing systems, has the potential to address this gap. In this work, we analyse one such dataset of travel history on the London underground. We then propose and evaluate methods to (a) predict personalised trip times for the system users and (b) rank stations based on future mobility patterns, in order to identify the subset of stations that are of greatest interest to each other and thus provide useful travel updates.

This roughly translates to:

Public transport in a large city like London can be chaotic; the information services that were built to support it do not take into consideration who you are when they spit out updates. At the same time, most Londoners now use Oyster cards, that record detailed traces of each person’s movements around the city. The research question we address in the paper is: can Oyster card records be leveraged to build personalised travel info services? Much like the way Amazon says “recommended especially for you” – can we do similar things with travel data? Short answer: yes. Long answer: read the paper. Medium answer: look at slides below.

salvo told me that  foursquare had one of its databases overloaded with check-ins and consequently experienced a downtime of 11 hours! so central services are unscalable (unreliable) and they need to be decentralized. partial decentralization is the solution in the industry, and full decentralization still remains in the academic circles ($$ reasons). few years ago, i asked the question: what if mobile social-networking services were to be decentralized. it was an academic exercise that resulted in:

• Problem statement: Tapping the Mobile Digital Tapestry: Can mobile 2.0 companies make money without being greedy for personal data?
• Preliminary solutions: Selecting trustworthy content using tags (from Sect 2.3 onwards)

following this post, notes for day 2. featured:  luis von ahn, jonathan zittrain, nourish contractor, and manuel castells.

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Here’s my pick for day 2: “Hapori: Context-based Local Search for Mobile Phones using Community Behavioral Modeling and Similarity“, from Dartmouth/Microsoft. This is a very neat example of context-aware recommender system, where POIs are being recommended to people based on a rich variety of information, encompassing temporal information (day of the week, time of the day, etc), weather, and the activity of the user. As the authors summarise: “The goal of Hapori is to meet the diverse needs of different people (such as the teenager and senior) taking into account their context, behavioral profile and behavioral similarities with others in the broader community of local search users“.

notes on the first day of the royal society event web science. featured: lazlo barabasi, lord may, jon kleinberg, and david karger

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My pick from the first day at Ubicomp: “Rethinking Location Sharing: Exploring the Implications of Social-Driven vs. Purpose-Driven Location Sharing” (from CMU). Key idea: the rationale behind sharing location has a big impact on people willingness to share and what they share. The main distinction is between sharing for a need (purpose sharing, for example, to get phone calls redirected to where I am – like in Active Badge), versus sharing because I want (social sharing, for example to stay more connected with my friends). Their study reveals that location sharing varies a lot between the two cases (need vs want), with different labels being picked by people to describe places, and with different privacy concerns being attached to them. Another more privacy-focused paper from the same people was also interesting (“Empirical Models of Privacy in Location Sharing“).