Updates

  • Mar 15, 2014
    Thanks for bearing with us. Here is a final listing of all prediction contestants by email username.
    yxyang
    rmxu
    matted
    garthee
    mgymrek
    saclarke
    tcleung
    mfeng
    ncvc
    qys
    zhaozl
    jerrysun
    sangwen
    wpli
    antonw
    lyons66
    bolei
    francky
    kimci
    mingot
    theja
    raminm
    ayblonski
    camarait.edu
    eugenewu
  • Mar 12, 2014
    The following is a link to the ground truth for the final prediction challenge. Download ground truth
  • Mar 5, 2014 11:40AM
    We have now posted the final visualization and prediction challenge winners. Thank you to everyone who participated!
  • Feb 28 3:04pm
    Several teams did not see themselves on the final prediction challenge rankings. We are sorry about this oversight and are looking into putting up a complete list
  • Feb 27 6:30pm
    Now that the final ceremony has ended, here is the final ranking of the prediction challenge contestants!
    We are currently updating the final list. Stay tuned!

Congratulations to the winners!

Prediction Challenge

  • HUMNET
    Lauren Alexander, Serdar Colak, Suma Desu, Jameson Toole, Yingxiang Yang
    MIT, Civil and Environmental Engineering
  • PIGGY XU
    Runmin Xu, MIT, Civil and Environmental Engineering and
    Yihan Xu, NEU
  • MATTED
    Matthew Edwards, MIT EECS
  • GARTHEE
    Gartheeban Ganeshapillai, MIT CSAIL
  • TAXIOMICS
    Jenny Chen, Melissa Gymrek, Thomas Willems, MIT
  • SACLARKE
    Sean Clarke, MIT and
    Christoph Hafemeister, NYU

Visualization Challenge

Introduction

What can we learn from 2.3 Million Taxi Rides?

The MIT Big Data Initiative at CSAIL working in partnership with the City of Boston is hosting a Big Data Challenge seeking to develop innovative prediction algorithms and compelling visualizations of transportation in the Boston area.

Launch
Nov 12, 2013
End Date
Jan 20, 2014
$10,000 in prize money!

Leaderboard

  • User
    Time
    Score
  • qys 1st
    Jan 18 4:28 p.m.
    1.0000000
  • zhaozl
    Jan 18 4:36 p.m.
    1.0000000
  • sangwen
    Jan 18 4:38 p.m.
    1.0000000
  • raminm@mit.edu
    Jan 18 11:04 a.m.
    0.3660254
  • garthee
    Jan 15 8:58 p.m.
    0.3660254
  • matted
    Jan 18 4:15 p.m.
    0.3660254
  • yangjy
    Jan 19 7:10 p.m.
    0.3660254
  • smartians
    Jan 20 8:23 p.m.
    0.3660254
  • cypresspoint
    Jan 20 3:47 a.m.
    0.3660254

Brought to you by

In collaboration with

Motivation

The City of Boston is interested in gaining new insights into how people use all modes of transportation travel in and around the downtown Boston area. A critical imperative of Boston's Complete Streets Policy is to move all modes of transportation more efficiently and to use real-time data to facilitate better trip-planning between modes of transportation. With urban congestion on the rise, city planners are looking for ways to improve transportation such as providing people with more options to get from one place to another (walking, biking, driving, or using public transit) and by reducing and more efficiently routing vehicles in the city.

This MIT Big Data Challenge will focus primarily on one mode of public transportation: Taxi Cabs. By better understanding patterns in taxi ridership, we hope to provide new insights for city planners, such as:

  • How to get more cabs where they are needed, when they are needed?
  • What are the ideal locations for cab stands?
  • When and where should the City add or remove cab stands?
  • How many cabs should be waiting around a specific location at a specific time of day?
  • Are there viable alternatives to taking a cab?
  • Are there easy ways to 'link trips' between cabs and other forms of transportation?
  • How does taxi ridership patterns differ on weekdays vs. weekends? Seasonally? During different types of events?
  • Where should you go at 1am to catch a cab downtown?
  • Do Bruins fans take more cabs than Celtics fans? Does the result of the game impact transportation patterns?

Prediction Challenge

Here the goal is to predict the number of taxi trips originating at different times of day from different locations around city. A total of $5000 will be awarded (a $4000 winner and a $1000 runner-up).

Visualization Challenge

Visualization Challenge: Here the goal is to produce the most compelling visualization (static, animated or interactive) of taxi activity in Boston. A total of $5000 will be awarded (a $4000 winner and a $1000 runner-up).

Submissions will be judged by a distinguished panel of visualization experts!