Short Talks, Big Ideas: Recap and video

Last night’s Video of last night’s excellent Short Talks, Big Ideas session is now up:
Short Talks, Big Ideas

Thanks to the 100 or so attendees, and in particular, to all of our excellent presenters:
David Mahfouda, Weeels, brought to light the concept of taxis as public transit
Taylor Reiss, NYC Dept. of Transportation, showcased exciting plans for Select Bus Service
Jesse Friedman, Google, proposed new ideas to make bus ridership more appealing
Brian Langel, Dash, presented his new app Dash for personalized car data
Susi Wunsch, Velojoy, discussed the importance of women in bicycling efforts
- Raz Schwartz, Rutgers, showed the compelling urban data that can be gleaned from social media and neighborhood connectivity
Matt Healy, Foursquare, demonstrated the movements of New Yorkers shown through FourSquare checkins

We’ll see you in the Spring with more exciting events. If you have speaker suggestions for our next Short Talks, Big Ideas event, please get in touch!

Event Recap: Social Media and Hurricane Sandy

This morning’s panel, Social Media and Hurricane Sandy, showcased the importance of various channels of information from official, unofficial and media-based information sources during and after the storm. The panel included Robin Lester Kenton of NYC Department of Transportation; Aaron Donovan and JP Chan of the Metropolitan Transportation Authority; Ben Kabak of Second Avenue Sagas; and Tyson Evans of The New York Times.

Several themes emerged during the discussion:

Speed Overrides Risk: It’s often better to get information out quickly and risk its incorrectness than to wait, since customers will get (potentially incorrect) information from elsewhere. While it seems NYC DOT was more risk-averse during the hurricane, MTA posted two tweets that later had to be retracted, but, as Aaron noted, “the world didn’t stop revolving,” and the overall information sharing process was overwhelmingly positive.

Photos and Videos are Essential: Illustrations of storm damage and workers in the field are vital in public understanding, patience and support of recovery efforts. MTA posted prolifically on Flickr and YouTube, NYC DOT posted sporadically on Instagram (but will now add more posts during the next event), and those images were used widely, including on Second Avenue Sagas. Panelists agreed that “timeliness was more important than quality,” as Aaron said, since people were focused on the newsworthiness.

Behind the scenes, it’s resource-intensive: All information-dissemination efforts required extensive research, collaboration and coordination. Tyson demonstrated the New York Times’ internal working spreadsheet used to populate the website’s transportation guide, explaining that a large team simultaneously updated the document from a plethora of sources. Robin reported that with power out at DOT’s office, major efforts across teams spread across the City were needed to update the website, while Ben recalled updating SAS while conducting his day job from home.

All panelists agreed that greater transparency in the public sector leads to greater trust of the information provided. They all plan to take the lessons learned from Hurricane Sandy to the next major event to provide open, image-intensive information.

Finally, the panelists were asked to name their transportation (or not) social media role models. The list included:

- Washington State DOT

- Steve Vance

- BARTtv

- Boris Johnson

- Dana Rubinstein, Ted Mann and Matt Flegenheimer as complementary Twitter accounts

- NY Times Metro

Thanks to all who attended and participated, and we hope to see you at our next event, Short Talks, Big Ideas: Innovations in Transportation.

Photo Credit: Susi Wunsch of Velojoy

Commuting After Hurricane Sandy: Survey Results

Sarah Kaufman and Carson Qing

As part of the NYU Rudin Center’s recent report on transportation impacts from Hurricane Sandy, we conducted a survey of commuters to learn about their experiences of getting to work after the storm.

The survey was conducted online, on the site, and was publicized via email blasts and social media. Three hundred-fifteen people in 98 zip codes responded anonymously between October 31 and November 6th, answering questions about their typical and post-Sandy commutes.

Key findings from the survey included:

Many people in the region worked after the storm, either physically reporting to an office or by telecommuting. New Jersey had the lowest rate of people who continued to work, at 56%, while 85% of Brooklyn respondents worked, at the highest percentage.

With limited transit options after the storm, New York commuters significantly altered their commute patterns. Bus ridership rose in Brooklyn (5% of respondents normally used buses, but 12% reported using buses November 1-2) after shuttle buses were put in place of subway routes disrupted due to flooding. Bike commuting rose significantly in Manhattan (15% normal to 24% Nov 1/2) and Queens (17% to 30%).

Post-hurricane commute lengths varied significantly by home region, as shown in the table below. The largest differences were in Staten Island, where commute times almost tripled, and Brooklyn, where they doubled. Variations among home locations are due to the wide range of transportation options available to each set of commuters, and the lower number of survey respondents who reported physically to work, rather than telecommuting or not working.

Post-hurricane commutes were twice or three times as long, varying by mode, as shown in the chart below.Average post-Sandy commute lengths ranged from 43 minutes (walked on Nov 1/2) to 115 minutes (drove, or took subway and bus). Frustration levels ranged from 2.3 on the lower end (walked) to 5.7 on the higher end (drove). Commuters who drove, or took a subway and bus combination, had the greatest difference, with travel times at nearly triple their typical lengths. As expected, they were also among the most frustrated commuters.

Walking and biking commuters were, on average, the least frustrated. Commuters who biked to work Nov 1/2 had the fewest delays in their commutes, as they were only 9 minutes longer than their usual commute. Telecommuters ranked their level of frustration on a similar level as transit commuters, 3.7 to 3.8, perhaps due to communications difficulties of connecting to work.

Commuters used a variety of communications channels to learn about transportation resources, as shown in the chart below. They most commonly referred to official websites and social media, and least from smartphone apps and community groups. The lack of smartphone app connectivity was likely due to the lack of schedule and outage data used for programming the apps.

These figures show the need for increased storm preparation and ever-present public information in times of crisis to ensure residents’ mobility. However, the survey results also demonstrate the resilience of New Yorkers and their workplaces; even in the face of detrimental circumstances, New Yorkers’ businesses maintained operations, showcasing the extreme adaptability of their operations, finances and creativity. The adaptations to new, longer commutes are uniquely New York, in that the population quickly adapted to alternate and substitute transportation modes, new norms of local business practices, flexible, temporary workplaces, and continuous communications.


Survey respondents’ home and workplace locations, by zip code:



Average commute times and frustration levels by home region, November 1-2, 2012

Home Region Pre-Sandy Typical Commute Time (min) Post-Sandy Commute Time (min) Percent Reporting Physically to Work* Self-Reported Frustration Level, 1 (min) – 10 (max)
Manhattan 29 52 56% 2.97
Brooklyn 42 86 58% 3.93
Queens 45 47 65% 3.00
Bronx 41 63 100% 2.14
Staten Island 84 240 25% 7.00
New Jersey 52 69 27% 5.67
Northern Suburbs 73 61 33% 2.40
Long Island 85 85 33% 2.00

* Excludes telecommuters



Commuters’ travel time by mode and self-reported frustration level:

NOV 1/2 MODE Pre-Sandytravel time (min) Post-Sandy travel time (min) Avg frustration index (1-10)
Walk only 21.1 43.3 2.3
Bike only 43.6 52.0 2.7
Drive only 47.3 114.7 5.7
Taxi only 30.0 65.0 5.5
Subway only 35.0 51.4 2.9
Bus only 42.3 100.8 4.2
Rail only 80.0 85.0 2.0
Subway + bus 46.5 115.1 4.9
Subway + bus + rail 60.0 75.0 2.0
Any transit* 41.7 86.3 3.8
Telecommuting 40.1 0.0 3.7
Did not work 42.3 0.0 5.6

*includes PATH, private buses, ferries and other miscellaneous transit options


Sources of Transportation Information

Respondents were asked to select all that apply.

Event Recap: The Wisdom of Transportation Crowds

Last night’s panel, The Wisdom of Transportation Crowds, showed us the power of large groups in improving transportation through participation, advocacy, and funding. Our esteemed panelists taught us a few lessons:

Robin Lester Kenton, of NYC Department of Transportation, showed us that the crowds don’t always come up with the best solution; but with 10,000 bike share location requests on the web portal, plus nearly 400 community meetings, numerous key and popular locations emerged for New York’s forthcoming landmark system.

Jeff Maki, of OpenPlans, discussed the role of the “third sector” – between public and private – to create solutions, particularly their forthcoming Kickstarter-funded iPhone app, JoyRide, which uses combinations of official data and user input to create trip planners across modes.

John Raskin, of Riders Alliance, posed the notion that an alternate sector exists for communities interested in making incremental transit improvements, even when reforming the entire transit system is overwhelming.

All panelists agreed that when people were shown their direct benefit from crowdsourcing their efforts and funds, they were more likely to participate. And it seems that the third sector is emerging as the best place for innovation and collaborative wisdom for transportation improvements.

Thanks to all who attended and participated, and we hope to see you at our two upcoming events, Social Media, Transportation and Hurricane Sandy and Short Talks, Big Ideas: Innovations in Transportation.

New Post: The Importance of Information in Transportation

NYU Rudin Center Research Associate Sarah Kaufman has posted a new piece on The City Fix blog, about how information moves cities, and the rise of the third sector. Here’s an excerpt:

Information and transportation are so intricately intertwined that smartphones and other technologies have reshaped how urban dwellers get around in cities all over the world. In fact, two of the most important transportation innovations of the last five years have been the opening of data and the use of social media tools for service updates. Open transportation data, now provided by more than 500 US cities, has led to a large, powerful sub-economy of third-party applications (an estimated half-million app downloads have come from the NY MTA’s data alone), while social media and third-party websites have become the primary means of communicating with transit customers (JetBlue has 20 Twitter followers per weekday passenger, according to forthcoming NYU Rudin Center research).

See the entire post here:

New Report: How Social Media Moves New York

We’re thrilled to release a new report, “How Social Media Moves New York,” focused on how social tools, particularly Twitter, are used for transportation in New York City. From the abstract:

The goals of social media in transportation are to inform (alert riders of a situation), motivate (to opt for an alternate route), and engage (amplify the message to their friends and neighbors). Ideally, these actions would occur within minutes of an incident.

This report analyzes the use of social media tools by the New York region’s major transportation providers. It is focused on the effectiveness of their Twitter feeds, which were chosen for their immediacy and simplicity in messaging, and provided a common denominator for comparison between the various transportation providers considered, both public and private. Based on this analysis, recommendations are outlined for improving social media outreach.

Download the full report here, and leave your comments below.


Animation: 3 Days of Geotagged Tweets in NYC

What’s in a tweet?  A lot, when there’s a set of latitude and longitude coordinates attached to it.  Using the twitter streaming API, Rudin research assistant Chris Whong was able to compile three full days worth of geotagged tweets from around the New York City region, totaling more than 74,000 data points.  Instead of simply visualizing the location and time of individual tweets, we can “connect the dots” through time and space for a given user, showing a movement vector across the map.

Played back at one minute per frame, the video clearly shows the ebbs and flows of activity throughout the day.  The mass movement of people during rush hours is visible, as well as movement to and from several hotspots in the region.  (Keep an eye on Metlife Stadium in New Jersey during the first 20 seconds of the video – you’ll many people who tweeted during a Monday night football game moving back to their homes – JFK airport also stands out as a key destination)

The Importance of Twitter to Transportation

NYU Rudin Center researcher Sarah M. Kaufman gives an early look at her forthcoming research on social media use and transportation today on Google’s Policy By The Numbers Blog. Here’s a snippet from the piece; read it in its entirety on the blog:

Social media tools, such as Twitter, allow transportation providers to  communicate directly with users: alert customers about  service changes, suggest alternative routes, and amplify the message to friends and neighbors. Ideally, these actions would occur within moments of a delay’ Twitter is superb platform, since it is free, fast and packed with dynamic features.


But our research at NYU’s Rudin Center indicates that transportation providers in the New York Metropolitan region have yet to use Twitter to its fullest potential. Our research, based on all tweets from May 1 to June 30, 2012, offers policy recommendations for using Twitter in a transportation setting.


How do you use social media for transportation? Let us know in the comments.

A Day in the Life: How the Sept. 11 TweetMap Was Created

Yesterday we showed you Chris Whong’s tweet map from September 11th, 2012. Here’s how he did it:
A Day in the Life is a dump of 15,000 geocoded tweets, all from a single day, all from the five boroughs of New York City.  Created by NYU Urban Planning Student and civic techie Chris Whong, the map is labeled a social media experiment, a visualization of social media interactions that allows a user to freely explore the city and see who was tweeting what, and most interestingly, where they tweet from.  Our online social networks tend to mirror our real world networks, and A Day in the Life offers a peek into thousands of other networks that share the Urban Landscape, even if their many nodes and linkages don’t cross paths often (online or in real life).
The addition of latitude and longitude coordinates to the normal tweet data has some powerful implications, and adds a spatial element to the typical analysis of tweets by keyword or hashtag, and even see the movement of individual tweeters around the city over the course of the day (provided they tweet regularly of course).  A Day in the Life is meant more for exploration, but other static maps and visualizations of links and specific keywords can be produced from the same types of data sets. (Eric Fischer released a series of maps highlighting movement corridors through cities using geocoded tweets earlier this year)  The New York map is based on a similar one for Baltimore ( that also features layers for Census data and Baltimore’s Vacant properties, giving the user some context for the location of the tweeter.
Interesting? Yes.  Entertaining? Of course!  Alarming? Sometimes (tweets about violence, drug use, truancy, etc can be seen here and there), but is this data really useful for drawing real conclusions about a city and effecting change?  Maybe.  It should be noted that this collection represents only a small sample of all tweets, 2-4% by some estimates.  While there is certainly a broad geographic representation, with no corner of the city left out, the only people on these maps are those who had location services on, and the picture might be very different if all tweets were considered.  Those who tweet their location, for whatever reason, may not be a representative sample of all tweeters.
The data source for these maps is Twitter’s streaming API, which allows a user to specify a bounding box.  Any geocoded tweets that occur within the box are sent in real-time, and can be stored in a database for future use.  The Baltimore Map was a result of impromptu civic hacktivism born on a Facebook group called Baltimore tech.  Dave Troy, a local tech entrepreneur and urbanist wrote a script to pull Baltimore tweets from the API, and then published a link to the data for any who could find something useful to do with it.  The results included animations of user movement overs time, aggregate tweet trail maps that highlight frequently traveled routes, word clouds that attempt to highlight themes, A Day in the Life, and more.  So, we used Facebook connections to do twitter data analysis.  Social Media begets Social Media.