New Stations

Network Analysis

Location Allocation

Hoboken Bike Sharing Analysis

Hannah Rogge

Dann Walters

Robbie Rose

(Proposed from Network Analysis)

Suitability Analysis

(Proposed from Network Analysis)

Use Analysis

Total Routes Traveled via

Existing Bike Infrastructure

New Proposed Bicycle Infrastructure

for City of Hoboken

New Proposed Bicycle Lanes

Introduction

Our client, Social Bicycles, is a company which uses “wireless technology to enhance personal mobility” to provide affordable bike sharing in urban areas. Social Bicycles recently conducted a pilot study in Hoboken, New Jersey, so we asked for the GPS data of stops made on social bicycles during the study. We used these pilot stop locations to determine where to best locate new Social Bicycles hubs and suggested new bike paths in areas of high bike traffic.

Purpose

The purpose of this project is to determine where to locate five new Social Bicycles Hubs and recommend streets for new bike lanes in the city of Hoboken, New Jersey.

Method: Network Analysis

To determine the best location for five Social Bicycles Hubs, we built a Network Analysis from Hudson Roads TIGER dataset and ran a Location-Allocation Model. To prepare our data, we clipped our Hudson County Roads and GPX pilot stop point data to the Hoboken Municipal Boundary. To build our Network Analysis we created a Geodatabase with a Transportation Feature Dataset. We then imported the clipped Hudson County Roads as a Feature Class and dragged it into the Transportation Dataset. We added a ‘MILES’ Field as a float-ing point and used the Field Calculator to calculate the values. When this was complete, we clicked ‘Build New Network Dataset.’ To Create a New Location-Allocation Model we used Road Intersections as our Facilities and GPX pilot stop point data as our Demand Points. We used ‘Maximize Market Share’ to choose the five best locations for bike hubs with an impedance cutoff of .5 miles. All of our 13991 GPX pilot stop point data fell within .5 miles of these five locations, thus reaching 100% of Market Share. The flow chart below describes the process in three main steps and our final map below indicates the five hub locations. We chose to incorpo-rate these hub locations as Opportunities in our Suitability Analysis.

Flowchart: Network Analysis

36.7 %Percent of Total Bicycle Traffic Utilizing Bicycle Lanes

40 %Percent of Hoboken Streets Containing Dedicated Bike Lanes

18.2 %Percent of Bicycle Traffic on Dedicated Bike Lanes (Exclud-ing Waterfront Drive)

79.1 %

Percent Increase in Dedicated Bicycle Infrastructure Proposed by Our Study

17.9 %

Percent Increase in Non-Water-front Bicycle Coverage Utilzing our Suggested Upgrades

Method: Suitability Analysis

To determine the most suitable location for new Social Bicycle Hubs we per-formed a Suitability Analysis. We gathered data from the New Jersey Geographic Information Network, the Fema Flood Resource Map, and the City of Hoboken website. To build our model we compiled opportunity factors, which include: Target Market Locations from our network analysis, current bike paths, bus stops and popular destinations. We also compiled constraint factors which in-clude: pedestrian deaths, flood areas, and areas with population less than 200 people per city block. We clipped all of our data using the Hoboken Municipal Boundary. We then buffered our constraints layers at 1000 feet and we buffered our opportunities at 1000 feet for Popular Destinations and Target Market Loca-tion and then 500 feet for Bus Stops and Bike Paths. We weighted our constraints with negative numbers (-1,-2,-3) and our opportunities with positive numbers (1,2,3,4). Next we added a field called tot_opps and a field called tot_ cons to each respective map and used the field calculator to add all the weight factors together. Then we performed a union on the opportunity layers to create an “op-portunity composite map” and a union on the constraint layers to create a “con-straints composite map. Finally we performed a union on our composite maps and created our suitability model.

Method: Bike Lane Modelling

It is important for Social Bicycles to be aware of how bike lane infrastructure affects its user experience. We were unable to spatially join the GPX pilot stop points to the Bike Lanes layer as it seemed to contain some projection error. Instead, we performed a spatial join of the GPX pilot stop points to the Roads layer, and then manually deleted all roads which did have bike lanes. Our Bike Lane Model shows that 36.79% of all pilot stop points are located on current bike lanes. Of these pilot stop points located on bike lanes, 50.5% were along the water which indicates a tourist demand. By analyzing the non-bike lane GPX pilot stop points, we determined the need for 2.3 miles of additional bike lanes along 1st, 4th, 5th, and Washington Street which account for 17.23% of all pi-lot stop locations. This information is important to Social Bicycles as it shows that a majority of bike traffic is traveling on street without proper infrastructure support resulting in safety hazards and traffic impedance for the city and its residents. We incorporated current bike lanes into our Suitability Analysis as an Opportunity and layered the proposed bike lanes over it for our Solution Map.

F I R S T

F O U R T H

F I F T H

M

O

N

T

G

O

M

E

R

Y

4

2

3

1

5

FINAL PROPOSAL

This Solution Map depicts our recommended locations for five new Social Bicycles Hubs and five new bike lanes in Hoboken, New Jersey. We com-piled a Location-Allocation Model, a Bike Lane Model, and a Suitability Model to determine the best location to site these hubs and bike lanes. The Location-Allocation Model locates our ideal hub locations within 0.5 miles from all of the pilot stop locations recorded in the GPX data. Our Bike Lane Model determines the importance of locating Social Bicycles Hubs near bike paths which account for 36.79% of all pilot stop locations. We suggest the creation of approximately 2.3 miles of bike lanes along 1st, 4th, 5th, and Washington Street which account for 17.23% of pilot stop loca-tions. The suitability analysis incorporates the ideal hub locations from the Location Allocation Model as well as current bike paths, bus stops, and popular destinations as Opportunities, and uses pedestrian deaths, flooding, and population under 200 (per block) as its Constraints. Our solution map combines all three models by layering our proposed bike lane locations over the Suitability Analysis to depict our recommended bike hubs and bike paths.

We suggest that Social Bicycles create five new hubs at the shown locations and present the city of Hoboken with our maps which show the usage of current bike lanes as well as the proposed bike lanes which will serve an additional 17.23% of all pilot stop locations and improve transportation infrastructure and safety in Hoboken.

Proposed Solution

for Hoboken

Flowchart:

Works Cited:

1. Municipalities of New Jersey. New Jersey Geographic Information Network.<https://njgin.state.nj.us/NJ_NJGINExplorer/ShowMetadata.jsp?docId={E86BD5A0318411DD8B970003BA2C919E}>.

2. NJDEP TIGER Roads in Hudson County. New Jersey Department of Environmental Protection.<http://www.state.nj.us/dep/gis/digidownload/metadata/tgr2000/hudtgr2000.htm>.3. Pedestrian Fatalities 20072009. Tri State Transportation Campaign Analysis of National Highway TrafficSafety Administration’s Fatality Analysis Reporting System.<http://www.tstc.org/reports/danger11/hudson.pdf>.

4. Social Bicycles Hoboken Pilot Program Routes GPX Data. Social Bicycles.<http://socialbicycles.com/#contact>.

5. Metadata for Hoboken Popular Destinations, Bus Stops, and Bike Paths. City of Hoboken.www.hobokennj.org.

6. Fema Flood Hazard Resource Map. FEMA.https://fema.maps.arcgis.com/home/webpage/viewer.html?webmap

7.New Jersey Census Information. NJGINE. https://njgin.state.nj.us/NJ_NJGINExplorer/DataDownloads.jsp

Flowchart: