The Science of Bike-sharing

TAU develops a high-tech tool to improve two-wheeled public transportation

The new environmentally-friendly concept of municipal “bike-sharing is taking over European cities like Paris, and American cities like New York are also looking into the idea. It allows a subscriber to “borrow” a bike from one of hundreds of locations in the city, use it, and return it to another location at the end of the journey. It’s good for commuters and for running short errands.

While the idea is gaining speed and subscribers at the 400 locations around the world where it has been implemented, there have been growing pains — partly because the projects have been so successful. About seven percent of the time, users aren’t able to return a bike because the station at their journey’s destination is full. And sometimes stations experience bike shortages, causing frustration with the system.

To solve the problem, Dr. Tal Raviv and Prof. Michal Tzur of Tel Aviv University‘s Department of Industrial Engineering are developing a mathematical model to lead to a software solution. “These stations are managed imperfectly, based on what the station managers see. They use their best guesses to move bikes to different locations around the city using trucks,” explains Dr. Raviv. “There is no system for more scientifically managing the availability of bikes, creating dissatisfaction among users in popular parts of the city.”

Their research was presented in November 2010 at the INFORMS 2010 annual meeting in Austin, Texas.

Biking with computers

An environmentalist, Dr. Raviv wants to see more cities in America adopt the bike-sharing system. In Paris alone, there are 1,700 pick-up and drop-off stations. In New York, there soon might be double or triple that amount, making the management of bike availability an extremely daunting task.

Dr. Raviv, Prof. Tzur and their students have created a mathematical model to predict which bike stations should be refilled or emptied — and when that needs to happen. In small towns with 100 stations, mere manpower can suffice, they say. But anything more and it’s really just a guessing game. A computer program will be more effective.

The researchers are the first to tackle bike-sharing system management using mathematical models and are currently developing a practical algorithmic solution. “Our research involves devising methods and algorithms to solve the routing and scheduling problems of the trucks that move fleets, as well as other operational and design challenges within this system,” says Dr. Raviv.

For the built environment

The benefits of bike-sharing programs in any city are plentiful. They cut down traffic congestion and alleviate parking shortages; reduce air pollution and health effects such as asthma and bronchitis; promote fitness; and enable good complementary public transportation by allowing commuters to ride from and to train or bus stations.

Because of the low cost of implementing bike-sharing programs, cities can benefit without significant financial outlay. And in some cities today, bicycles are also the fastest form of transport during rush hour.

The city of Tel Aviv is now in the process of deploying a bike sharing system to ease transport around the city, and improve the quality of life for its residents. Tel Aviv University research is contributing to this plan, and the results will be used in a pilot site in Israel

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Reference Link
http://www.aftau.org/site/News2/1039511600?page=NewsArticle&id=13823&news_iv_ctrl=-1

Courtesy
American Friends of Tel Aviv University

 

Challenging the Limits of Learning

TAU measures the human mind against the yardstick of a machine

Although we’re convinced that baby is brilliant when she mutters her first words, cognitive scientists have been conducting a decades-long debate about whether or not human beings actually “learn” language.

Most theoretical linguists, including the noted researcher Noam Chomsky, argue that people have little more than a “language organ” — an inherent capacity for language that’s activated during early childhood. On the other hand, researchers like Dr. Roni Katzir of Tel Aviv University‘s Department of Linguistics insist that what humans can actually learn is still an open question — and he has built a computer program to try and find an answer.

“I have built a computer program that learns basic grammar using only the bare minimum of cognitive machinery — the bare minimum that children might have — to test the hypothesis that language can indeed be learned,” says Dr. Katzir, a graduate of the Massachusetts Institute of Technology (where he took classes taught by Chomsky) and a former faculty member at Cornell University. His early results suggest that the process of language acquisition might be much more active than the majority of linguists have assumed up until now.

Dr. Katzir’s work was recently presented at a Cornell University workshop, where researchers from fields in linguistics, psychology, and computer science gathered to discuss learning processes.

A math model in mind

Able to learn basic grammar, the computer program relies on no preconceived assumptions about language or how it might be learned. Still in its early stages of development, the program helps Dr. Katzir explore the limits of learning — what kinds of information can a complex cognitive system like the human mind acquire and then store at the unconscious level? Do people “learn” language, and if so, can a computer be made to learn the same way?

Using a type of machine learning known as “unsupervised learning,” Dr. Katzir has programmed his computer to “learn” simple grammar on its own. The program sees raw data and conducts a random search to find the best way to characterize what it sees.

The computer looks for the simplest description of the data using a criterion known as Minimum Description Length. “The process of human learning is similar to the way computers compress files: it searches for recognizable patterns in the data. Let’s say, for instance, that you want to describe a string of 1,000 letters. You can be very naïve and list all the letters in order, or you can start to notice patterns — maybe every other character is a vowel — and use that information to give a more compact description. Once you understand something better, you can describe it more efficiently,” he says.

Artificial intelligence for answering machines

His early results point to the conclusion that the computer, modeling the human mind, is indeed able to “learn” — that language acquisition need not be limited to choosing from a finite series of possibilities.

While it’s primarily theoretical, Dr. Katzir’s research may have applications in technologies such as voice dialogue systems: a computer that, on its own, can better understand what callers are looking for. A more advanced version of Dr. Katzir’s program might learn natural language grammar and be able to process data received in a realistic setting, reflecting the manner in which humans actually talk.

The results of the research might also be applied to study how we learn to “read” visual images, and may be able to teach a robot how to reconstruct a three-dimensional space from a two-dimensional image and describe what it sees. Dr. Katzir plans to pursue this line of research with engineering colleagues at Tel Aviv University and abroad.

“Many linguists today assume that there are severe limits on what is learnable,” Dr. Katzir says. “I take a much more optimistic view about those limitations and the capacity of humans to learn.”

Reference Link
http://www.aftau.org/site/News2?page=NewsArticle&id=13753

Courtesy
American Friends of Tel Aviv University