Mapping Bike Share Trips A spatial approach to evaluating supply and demand for bike facilities in New York City

Katie O’Sullivan

Road Map 1.

Introduction

2.

Existing Bike Share Research

3.

Methods

4.

Results

5.

Discussion

6.

Conclusion

N

E

W S

Intro

Background • Spatial

information gap

How is bicycle travel distributed within cities?

Travel data is an important foundation for planning capital projects and investments Bike share system data

Intro

Background Origin

Desire Line

Destination

Low

High Density

Intro

Implications for Planning • Prioritize

investments in the facilities that will have the biggest impact on ridership and safety

• Increase

modal share of bicycling Graphs by NYC DOT In-Season Cycling Indicator (2013) Cycling Risk Indicator (2013)

Intro

Personal Activity Tracking Bicycling in NYC: Human Smartphone App

Unprecedented route-level detail

Poor sample quality

http://cities.human.co/static/images/cities/original/newyorkcity-cycling.png

Existing Research

Spatial Information and Design Lab Rebalancing Study: Mapping origin and destination hotspots • Activity hotspots relatively constant 10am – 12am • Most of the activity centered around Union Square. • Both Grand Central and Penn Station become strong hotspots during peak hours. • 5am shift: hotspots switch from East Village/Lower East Side area, to Grand Central and Penn Station. This is probably due to the fact that during most of the night, compared to other areas, the stations in the East Village/Lower East Side continue to have high activity, but during most of the day, and specially during peak hours, they are not as active as the stations around Union Square or Grand Central and Penn Station.

http://www.spatialinformationdesignlab.org/projects/citibike-rebalancing-study

Existing Research

Existing Research

Map from the blog I Quant NY “In Search of the Safest Citi Bike Stations Using Open Data”

Methods

Research Question

• How are bike share trips distributed across NYC relative to the network of onstreet bike facilities? • Where should improvements/investments be prioritized?

Methods

Data Sources • On-street

bicycle facilities

 New York City Bike Route Network (May 2010) From NYC DOT Data Feed

Bike Share Trips  Origins  Destinations From Citi Bike Systems Data, sample of three months: July 2013, December 2013, and May 2014.

Methods

Methods In SPSS: •

For each month, create trip table (cross-tabulation) of the number of trips that occurred on all possible routes (origin-destination combinations)

•

Restructure into a desire line frequency table

In ArcGIS: •

Geocode desire lines (“XY to Line”) and visualize according to number of trips taken.

•

Create heat map for the spatial density of trips (“Line Density”) with a search radius of 500 ft.

•

Compare “hot spots” to existing network of bicycle facilities to identify strengths and areas for improvement.

•

Examine street characteristics to recommend potential improvements.

Methods

Daily Citi Bike Trips May 2013 – July 2014 50,000

45,000

40,000

35,000

30,000

25,000

20,000

15,000

10,000

5,000

0 5/1/13

6/1/13

7/1/13

8/1/13

9/1/13

10/1/13

11/1/13

12/1/13

1/1/14

2/1/14

3/1/14

4/1/14

5/1/14

6/1/14

Results

Summary of Trips Most number of trips on one Routes route

Month

Active Stations

Total Trips

May 2013

328

835,921

76,964

December 2013

332

439,955

54,960

May 2014

327

866,117

70,484

2,099 (Central Park S & 6 Ave) 369 (Central Park S & 6 Ave) 1,433 (Central Park S & 6 Ave)

Trips with same O-D 27,871 (3.33%) 7,635 (1.74%)

24,994 (2.89%)

• The most popular routes begin and end at the same station just below Central Park

• “Leisure” trips make up a small percentage of total bike share use

Discussion

Findings •

Prioritize maintenance of facilities along Broadway and 8th Ave – they are heavily-biked corridors.

•

Underserviced “hot spots” around transit stations:  Union Station  Designated corridors for cyclists needed around perimeter of square with better network connections.

 Grand Central Station  Facility needed for North-South connections to Central Park and Union Station

Discussion

Recommendation 1: Madison Ave Between Union Square and Central Park Google Maps Madison Ave south of E 40th St

Madison Ave Currently 3 lanes (north-bound) One lane parking on left side

Replace one lane of through-traffic with Two-way bike lane and buffer, protected by parking. http://nacto.org/wp-content/gallery/twoway_cycletrack_3d/two-way-cycle-track-street-level-plan.jpg

Recommendation 2: Enhance connections to bike route network in and around Union Square

Discussion Google Maps E 14th St below Union Square

Demarcation of cyclist corridor around Union Square pedestrian area Google Maps University Pl below E 14th St

Sharrows or signed route on University Pl from Union Square to E 10th Street

Conclusion

Future Research •

Bike share / transit interface  Improved multi-modal connections involve more than on-street bike facilities. Transit stations, bus stops, transit vehicle design, cross-walks, curb-cuts, and bike parking.

Image from clker.com

With expansion of Citi Bike into uptown Manhattan, Queens, and more of Brooklyn, mapping bike share trips may help guide expansion of bike route network in these areas with less extensive coverage.

•

 Route-tracking opportunities with non-station bike Share systems. •

Trip density by gender, age, time of day, day of week.

Image from Boisebikeshare.org

Conclusion

Conclusions •

Bike share systems offer unprecedented volumes of geographic trip data that can be used to generate desire lines.

•

In Manhattan, bike share traffic “hot spots” occurred along corridors with existing on-street facilities (Broadway and 8th Avenue).

•

Areas around transit hubs (Union Square and Grand Central Terminal) had the highest densities, as well as gaps in the onstreet bike facility network.

•

With the spread of GPS-enabled bikes and individual tracking via smart phones, the quality and quantity of trip data will improve greatly.

Thank you!