Mobile Communica.ons and Transporta.on in Metropolitan Regions

Transcription

1 Mobile Communica.ons and Transporta.on in Metropolitan Regions Presenta.on at BitCity 2011 Conference at Columbia University Mitchell L. Moss, Josh Mandell, and Carson Qing New York University Robert F. Wagner School of Public Service November 2011

2 RESEARCH GOALS The role of mobile communicajons in urban transportajon systems and idenjfies American metropolitan regions well posijoned to capitalize on the growth of mobile technologies. Key quesjons explored: data accessibility, mobile network strength, and mobile tech user/developer demographics, using data from several public resources in an analysis of major Metropolitan StaJsJcal Areas (MSAs).

3 Why mobile communica8ons & transporta8on?

4 GROWTH OF SMARTPHONE USAGE According to Cisco, Global mobile data traffic grew 2.6- fold in 2010, nearly tripling for the third year in a row s mobile data traffic was three Jmes the size of the en.re global Internet in Average smartphone usage doubled in According to the comscore 2010 Mobile Year in Review, over 60 million Americans accessed maps from their mobile device in December 2010, up 46% from December Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, Mobile Year in Review, comscore, February

5 SHIFT TO INFORMATION- BASED SOLUTIONS As public funding for transportajon becomes more difficult to obtain, there will be increasing use of mobile communicajons by commuters and passengers seeking to plan their trips to minimize delays, waijng Jme, and uncertainty. Simply put, informajon will play a larger role in the transportajon, especially as consumers face a world of transportajon characterized by limited new public investment. Within this context, local agencies have the opjon to be an acjve player, or a spectator.

6 Which ci8es are best posi8oned to capitalize on the shi^ to informa8on- based solu8ons?

7 MEASURES Data Accessibility: Providing open data is a major step towards truly networked cijes. Public agencies should release data and build relajonships with the developer and user communijes to address key transportajon and other urban challenges. Network Strength: Wireless connecjvity is crijcal when trying to connect to the Internet or use a web- based applicajon. ApplicaJons that address real- Jme issues such as transit, traffic, and parking, demand conjnuous connecjvity. Most smart city programs that rely on sensors and real- Jme data are relayed by mobile broadband. User & Developer Demographics: Metropolitan regions vary in their share and number of smartphone users and so]ware developers, meaning stronger probability of adopjon.

8 Types of Data 1. Open Data: publicly available for developers - Google Transit Feed SpecificaJon (GTFS) DATA ACCESSIBILITY 2.Real- Time ApplicaJon Programming Interfaces (APIs): live data on vehicle locajon - I.e., NextBus Evalua8ng Data Accessibility Focus on three transit modes - Bus - Subway/Light Rail - Commuter Rail Points were assigned for - Transit providers with open/real- Jme data - Real- Jme parking and traffic info Weighted based on modal split in region and ridership by transit provider

9 Top 10 Regional Transporta.on Networks for Data Accessibility RANK AND METROPOLITAN AREA SCORE 1. Boston- Cambridge- Quincy, MA- NH Poughkeepsie- Newburgh- Middletown, NY Portland- Vancouver- Beaverton, OR- WA Washington- Arlington- Alexandria, DC- VA- MD- WV San Diego- Carlsbad- San Marcos, CA Salt Lake City, UT San Francisco- Oakland- Fremont, CA Philadelphia- Camden- Wilmington, PA- NJ- DE- MD BalJmore- Towson, MD San Jose- Sunnyvale- Santa Clara, CA 3.9

10 DATA ACCESSIBILITY

11 NETWORK STRENGTH Metrics 1.Mobile broadband coverage across regions 2.WiFi coverage across regions 3.Median mobile broadband download speed (mbps.) 4.WiFi availability in underground rapid transit systems Weigh8ng - 50%: Mobile broadband coverage - 20%: WiFi coverage across regions - 20%: WiFi availability in underground rapid transit systems - 10%: Median mobile broadband download speed

12 Top 10 Metropolitan Regions for Network Strength RANK AND METROPOLITAN AREA SCORE 1. San Jose- Sunnyvale- Santa Clara, CA Philadelphia- Camden- Wilmington, PA- NJ- DE- MD Milwaukee- Waukesha- West Allis, WI Salt Lake City, UT Phoenix- Mesa- Scomsdale, AZ San Francisco- Oakland- Fremont, CA BalJmore- Towson, MD Minneapolis- St. Paul- Bloomington, MN- WI New York- Northern N.J.- Long Island, NY- NJ- PA Houston- Sugar Land- Baytown, TX 2.4 Source: NaJonal Broadband Map (2011)

13 SMARTPHONE USERS AND PRODUCERS Demographics of Smartphone Adop8on Income Level EducaJonal Amainment Age Racial Diversity Producers of Smartphone Technology ConcentraJon and total number of so]ware developers in region Weigh8ng - 15% Income - 15% Age - 10% EducaJonal Amainment - 10% Racial Diversity - 33% ConcentraJon of Developers - 17% Total Number of Developers

14 Top 10 Metropolitan Regions for Smartphone Users and Producers RANK AND METROPOLITAN AREA SCORE 1. San Jose- Sunnyvale- Santa Clara, CA Washington- Arlington- Alexandria, DC- VA- MD- WV San Francisco- Oakland- Fremont, CA San Diego- Carlsbad- San Marcos, CA Atlanta- Sandy Springs- Mariema, GA Seamle- Tacoma- Bellevue, WA 6.0 T7. Denver- Aurora- Broomfield, CO 5.7 T7. Los Angeles- Long Beach- Santa Ana, CA New York- Northern N.J.- Long Island, NY- NJ- PA BalJmore- Towson, MD 3.0 Source: 2009 US Census American Community Survey, Bureau of Labor StaJsJcs OccupaJon Employment StaJsJcs (2011)

15 Rank Metropolitan Sta8s8cal Area COMPOSITE RANKINGS Data Rank Network Rank People Rank Overall Score* 1 San Jose- Sunnyvale- Santa Clara, CA San Francisco- Oakland- Fremont, CA Boston- Cambridge- Quincy, MA- NH 1 T Philadelphia- Camden- Wilmington, PA- NJ- DE- MD 8 2 T Salt Lake City, UT T- 6 Bal.more- Towson, MD T- 6 Washington- Arlington- Alexandria, DC- VA- MD- WV San Diego- Carlsbad- San Marcos, CA Poughkeepsie- Newburgh- Middletown, NY 2 14 T New York City- Northern NJ- Long Island, NY- NJ- PA Portland- Vancouver- Beaverton, OR- WA Chicago- Naperville- Joliet, IL- IN- WI T- 13 Seacle- Tacoma- Bellevue, WA T- 13 Denver- Aurora- Broomfield, CO T- 7 2 T- 13 Los Angeles- Long Beach- Santa Ana, CA T- 7 2 T- 16 Sacramento- Arden- Arcade- Roseville, CA T T- 16 Minneapolis- St. Paul- Bloomington, MN- WI Milwaukee- Waukesha- West Allis, WI T- 19 Houston- Sugar Land- Baytown, TX T- 19 Phoenix- Mesa- Scocsdale, AZ T- 21 Picsburgh, PA 16 T T- 21 Atlanta- Sandy Springs- Marieca, GA St. Louis, MO- IL T New Orleans- Metairie- Kenner, LA

16 FINDINGS San Francisco and San Jose s user and industry demographics combined with strong connecjvity and bemer than average data accessibility make them clear leaders in the potenjal for mobile technology adopjon and creajon. Washington D.C. (2 nd composite ranking) and San Diego (4 th composite ranking) share comparable traits in that both were top four for data accessibility and demographics, but were the worst two regions for wireless network strength. Regions such as Boston (composite ranking 3 rd ) Portland (composite ranking 11 th ), and Poughkeepsie (composite ranking 9 th ) were among the best in data accessibility, but did not place in the top ten for either of the other categories.

17 FINDINGS CONT. The most populous regions New York, Los Angeles, and Chicago were in the middle of the rankings in each category. These metropolitan regions, especially New York in recent months, have embarked on high profile open data and smart city programs. The challenge for New York is that there is massive demand for public transit, leading to coordinajon problems on the metropolitan level with the considerable number of both public and private transit operators. The rankings reflect that not enough data is provided with respect to the number of agencies and riders on a regional level. In Los Angeles, the coordinajon challenge derives from the enormous number of municipalijes that offer bus services. Chicago is more consolidated at the regional level, but suffers from lack of open data on its heavily used commuter rails.

18 IMPLICATIONS The diversity in ranking across the three variables illustrates the need for greater coordinajon and amenjon on this topic. Metropolitan regions with a high number of transportajon agencies need to create a synchronized data policy, while regions with more consolidated systems need to release data for the modes with greatest ridership. All metropolitan regions need to reassess mobile broadband and Wi- Fi networks to ensure that sufficient infrastructure exists to accommodate the coming surge in mobile traffic. Public agencies should also engage with local communijes to get users and developers more involved. Specifically, developers responsible for creajng these programs must be mindful of user demographic transportajon pamerns.

19 FINAL THOUGHT Mobile communicajons systems offer the greatest opportunity to improve the capacity of users to make decisions that lead to more producjve responsive transportajon services. Although it is not government s role to dictate smartphone adopjon and usage, public transportajon agencies can play an acjve role by releasing high quality data, thoughsully engaging with users and developers, and advocajng for high quality, affordable mobile service opjons.