Using ArcGIS to Propose an On-Street Bicycle Network

Bryan Townley

Geography 5221 GIS Simulation & Modeling

12/6/13

Introduction

Background

Across the United States, transportation alternatives to the automobile are being

considered. This stems from a multitude of reasons including, but not limited to: the rising cost

of gasoline, concern for the degradation of the environment, and safety issues arising from the

dominance of cars in America’s cities. Of the 56 largest cities in the United States, 39 saw

bicycle use increases between 2000 and 2009 (Byrnes 2011). As more cyclists use the roadways,

it is obvious that modes of transportation (automobiles, cyclists, pedestrians) come into contact

with one another, creating a great concern for the safety of all involved. Bicycles are seen as a

nuisance to cars (and vice versa) as they impede the automobile’s speed. Bicycles can also be

seen as nuisance to pedestrians (and vice versa) as inexperienced cyclists often travel on

sidewalks instead of roadways.

To amend the conflict, transportation planners are looking to the implementation of

bike-specific infrastructure to ease some of the tensions between transportation uses. This

infrastructure can come in the form of “sharrows,” or arrows painted onto a roadway’s traffic

lane indicating that bicycles are able to use the roadway, bike lanes, which demarcate an area

of roadway that can only be used by cyclists, or protected lanes, which while still part of the

roadway, include some type of barrier which physically separates bike space from automobile

and pedestrian space. However, it is often difficult for transportation planners and

municipalities alike to decide where to place such infrastructure as safety is tough to measure

quantitatively. Most municipalities only have finite resources allocated for such bike

infrastructure implementation, so prioritizing what roadways need bike infrastructure is

imperative. In addition to these problems, cycling is seen by many as a solely recreational

venture, not one in which people use to commute to work, travel to the store, or use on a daily

basis. Because of this, existing bicycle infrastructure, such as greenway trails, often only

connects recreational areas, such as parks. This infrastructure is also usually completely

disconnected from roadways. Major residential and commercial nodes thus remain

disconnected by bikeway infrastructure. Municipalities across the country are scrambling to

find a way to implement bicycle infrastructure efficiently in a car-dominated environment.

Objectives

With this project I hope to propose on-street bicycle networks for two Central Ohio

cities: Reynoldsburg and Westerville. I chose to look at these cities based on three reasons: 1)

they are large enough to warrant a bicycle network, 2) they have little to no existing bicycle

infrastructure, and 3) I am very familiar with both cities. The infrastructure that I propose will

be built on existing primary and secondary streets in both cities. Building the infrastructure on

these main streets will ensure that major commercial and residential nodes are connected.

Lastly, I hope to give examples of “what-if” scenarios where small changes can be made to the

existing street network in order to see what effects they have on the safety of the network

overall (example: lower speed limits). These “what-if” scenarios can help demonstrate to

municipal leaders how small changes to a road network can improve safety for automobile

drivers, cyclists, and pedestrians alike.

Data

Data and Sources

I used the following data and their sources in parentheses:

- Franklin County All Roads County-based shapefile (US Census Bureau TIGER/Line 2011

Shapefiles http://data.geocomm.com/catalog/US/61070/988/group226.html)

- Ohio Place State-based shapefile (US Census Bureau TIGER/Line 2010 Shapefiles

http://data.geocomm.com/catalog/US/61070/group225.html)

- Central Ohio Traffic Counts (Mid-Ohio Regional Planning Commission

http://www.morpc.org/info_center/dataport/transportation_traffic.asp)

- Street network speed limits (taken from personal observations in each location)

- Number of travel lanes (taken from personal observations in each location)

- Usable bike space (measured using Google Earth)

From the Franklin County All Roads shapefile I selected the streets that corresponded with

Westerville and Reynoldsburg. From the Ohio Place shapefile I selected the municipal

boundaries of both cities. The traffic count, speed limit, number of travel lanes, and usable bike

space data were entered as fields under the attribute table of the street network shapefile

created from the All Roads shapefile. I created a shapefile containing the primary and

secondary streets for each city based on a selection of the streets for which traffic count data

was available.

Data Quality

I feel like the data I used during this project was fairly sound. The All Roads and Place

shapefiles I used both came from a third party website, but were originally created by the US

Census Bureau, probably one of the most trustworthy sources. Traffic count data always opens

the door for human error when actually counting the traffic. However, this data was taken

directly from the MORPC website, so I also believe this is the most trustworthy source for the

data I was looking for. Next, I believe that the speed limit, number of lanes, and usable bike

space data that I collected myself is also reputable since it is coming from a primary source.

Finally, the usable bike space data was probably the least sound out of the data used simply

because it was compiled using the ruler tool on Google Earth. There is the distinct possibility

that some measurements are not exactly correct since I measured an image of the streets, not

the physical streets themselves. The only issue I had when working with the data itself was that

several streets in the All Roads shapefile contained multiple names for the same street

segment. To fix this I simply did not select these duplicates when creating my new primary

streets shapefile.

Methods

Note: The methods described here were repeated for both Reynoldsburg and Westerville.

Please see the Appendix for Python code.

Data Preparation

1. Obtain municipal boundary layer and select Reynoldsburg (Ohio Places shapefile)

2. Export selected data as a new shapefile

3. Obtain street network data (All Roads shapefile)

4. Create a one mile buffer around Reynoldsburg’s municipal boundary

5. Intersect the street network data with the municipal boundary

6. Export selected data as a new shapefile

7. Obtain traffic count and add it as a field in the street network shapefile’s attribute table

8. Select streets based on the availability of traffic count data

9. Export the selected data as a new shapefile

10. Obtain lane number, speed limit, and usable bike space data

11. Add the data from step 10 as fields in the street network shapefile’s attribute table

Clark Index and Impedance Calculation

A main component of this project was the use of the Clark Index, an index created by

transportation planners in order to assess a street segment’s bicycle safety in a quantitative

measure. The index is as follows:

Clark Index = [(Average daily traffic flows/50)*(Speed limit- Cyclist’s speed)^2]/

[10*(Number of travel lanes) + 4*(Usable bike space)^2]

1. I used the field calculator within the primary and secondary street shapefile’s attribute

table to calculate the Clark Index and add it as a field.

2. I then calculated a new field, labeled impedance, by using the field calculator to multiply

the Clark Index by the street segment’s length. This field was used during the creation of

the network dataset (described below).

Network Dataset

1. Create a network dataset in order to perform network analysis procedures (such as

route analysis)

2. The network dataset is based on the primary and secondary street shapefile

3. The network dataset uses the newly created impedance field as the “Field Evaluator,” or

the measure of cost of traversing the street segments

4. Build and import the network dataset

Network Analysis

1. The creation of the network dataset now allows for route analysis procedures to occur

2. Create a new point feature class

3. Add a point to the feature class which demarcates a high density residential area

4. Obtain a point shapefile containing city landmark locations (schools, libraries, parks)

5. Choose the New Route tool from the network analyst toolbar

6. Select the high density shapefile as the first “stop”

7. Select the landmark shapefile as the next stops

8. Choose to solve the route

9. The Route Analysis displays the new route on the street network based on the

impedance evaluator used during the creation of the network dataset

10. Record the total impedance of the entire network

What-If Scenario

1. Duplicate the primary and secondary streets shapefile

2. Open attribute table and change all speed limits that were above 35 miles per hour to

be 35

3. Create a new network dataset (same steps as the section above) based on this new

under 35mph street network shapefile

4. Run a route analysis (same steps as the section above) between high density residential

and landmark shapefiles

5. Note changes

6. Record the new total impedance value and compare to previous

Results

Reynoldsburg Impedance Levels

When viewing the impedance levels (Clark Index*Street segment length) for

Reynoldsburg, it can be seen that with the existing street network would be fairly safe for

cyclists (Figure 1). The majority of the streets tended to be safer (lower levels of impedance),

with a few very unsafe streets. The streets that were unsafe tended to be located along the

periphery of the city’s boundaries, which corresponds to their higher speed limits than the

more central, older parts of town.

Data Preparation

Clark Index and Impedance Calculation

Network Dataset

Network Analysis (Routes)

What-If Scenario (speed limit

change)

Figure 1

Safety Level Impedance Range Number of Streets

Safe 0-5 14 (73.7%)

Unsafe 6-13 3 (15.8%)

Very Unsafe 14+ 2 (10.5%)

Total Network Impedance: 241

These findings show that Reynoldsburg’s street network would already be great for the

implementation of bicycle infrastructure due to its high level of safety based on the Clark Index.

When looking at the new impedance levels created by the speed limit reduction (to under

35mph), it is obvious that the network overall is safer (Figure 2).

Figure 2

Safety Level Impedance Range Number of Streets

Safe 0-5 17 (89.5%)

Unsafe 6-13 2 (10.5%)

Very Unsafe 14+ 0 (0%)

Total Network Impedance: 134 (44.4% reduction)

While the table above does not display much difference in trends, it is worthwhile to note that

after the speed limit reduction, no street segments were “very unsafe.” Also, the total network

impedance was almost halved (44.4% reduction), showing a substantial increase in bicycle

safety.

Reynoldsburg Route Analysis

Figure 3

As described in the methods section, I created a route from the area of Reynoldsburg

with the highest residential density to the city’s landmarks (Figure 3). After viewing the

impedance map created before network analysis, the results of the route analysis were

somewhat obvious. The route traversed street segments that all had impedances of less than

13, avoiding the streets labeled “very unsafe” and most listed as “unsafe.” After the speed limit

reduction and the creation of the new network dataset, the route did change (Figure 4). The

route now traversed a portion of roadway that was originally listed as “very unsafe,” but after

the speed limit reduction was simply “unsafe.”

Figure 4

Westerville Impedance Levels

When viewing the impedance levels for Westerville, it can be seen that it is less safe

overall for cyclists than Reynoldsburg (Figure 5). Fewer of the streets were deemed “safe,” with

more tending to be “unsafe” or “very unsafe.” Also, unlike Reynoldsburg, the streets that were

“very unsafe” were not only found along the periphery of the city, they were found throughout.

(The Westerville Impedance Range values differ from those in Reynoldsburg because in

Reynoldsburg the street segment length was measured in decimal degrees, while in Westerville

it was measured in feet.)

Figure 5

Safety Level Impedance Range Number of Streets

Safe 0-186895.530599 11 (52.4%)

Unsafe 186895.530600-536288.852494 4 (19%)

Very Unsafe 536288.852495+ 6 (28.6%)

Total Network Impedance: 19912251.231008

These findings show that while some streets in Westerville would be great for the

implementation of bicycle infrastructure, many of the city’s streets are still too unsafe for such

a project. Again, when the speed limit reduction occurs, the network overall becomes safer,

however not at the same rate as Reynoldsburg (Figure 6).

Figure 6

Safety Level Impedance Range Number of Streets

Safe 0-186895.530599 11 (52.4%)

Unsafe 186895.530600-536288.852494 7 (33.3%)

Very Unsafe 536288.852495+ 3 (14.3%)

Total Network Impedance: 14150012.700232 (28.9% reduction)

While the number of safe streets did not increase, the number of very unsafe streets was

halved, showing definite improvement. Also, the total network impedance was reduced by

28.9%. Even though this is not as great as Reynoldsburg’s reduction, it is still a substantial

improvement to the safety of the network overall.

Westerville Route Analysis

Figure 7

Like with Reynoldsburg, I created a route in Westerville from its area of highest

population density to the city’s landmarks (Figure 7). Much like with Reynoldsburg, the route

favored traversing street segments that had lower levels of impedance, only traveling along

“unsafe” and “very unsafe” segments when the landmark destination was located along such a

street. Unlike Reynoldsburg, however, after the speed limit reduction the route did not change;

the route traversed the exact same path as before (Figure 8). These results suggest that: 1) A

speed limit change has less of an effect on Westerville’s street network and this caused the

route to be the same before and after the change, and 2) Other factors are contributing to the

unsafe conditions on Westerville’s streets, such as usable bike space, traffic count, or number

of lanes.

Figure 8

Discussion

Problems and Future Work

Originally I wanted to create a tool in ArcGIS for this process since it is a fairly replicable

procedure, however the need to create a network dataset in order to run the route analysis

impeded my progress. As far as I could observe during this project, there is no way to build a

network dataset for the purpose of running route analysis using Python or Model Builder. If this

project was to be completed again, I would do more of a manual process, such as implementing

the NetworkX package, as other presenters in the class suggested. This would allow me to

completely bypass the need to create a network dataset. I also ran into problems with how the

street segment lengths were measured: with Reynoldsburg they were measured with decimal

degrees and Westerville’s were in feet. I could not figure out a simple way to change these

without completely re-doing my network datasets. Finally, for the sake of simplicity in the short

time frame of this project, I only used the streets in each city that had traffic count data readily

available. For a municipality to completely gauge street network safety, however, city planners

would have to look at every street within the city’s boundaries. To do so would require a more

in-depth traffic study requiring much more time.

Overview

I believe that utilizing ArcGIS in the process of planning for the implementation of

bicycle infrastructure can be very useful for municipalities. Its main advantage is that it can

allow for a visualization of how small changes to a network can create large impacts on the

network as a whole. In this example, a speed limit reduction was simulated which created

substantial safety improvements for both Reynoldsburg and Westerville. The use of ArcGIS in

the planning process also demonstrates the notion that no two cities are the same; what works

in one city will not necessarily work in another. This project suggests that while a reduction in

speed in Reynoldsburg had a large impact on its street network, changes to other factors that

were taken into account in the impedance calculation may create a greater improvement to the

safety of Westerville’s network. This project also shows how municipalities can prioritize their

bicycle infrastructure implementation. Streets that are traversed as part of the shortest path

between major nodes such as residential areas and city landmarks (schools, libraries, parks) can

be the focus of municipalities during beginning phases of implementation. This form of

evidence and the quantitative nature of the Clark Index will help city leaders to better

demonstrate the need to build bicycle infrastructure on certain streets.

Appendix

References

Byrnes, Mark. “Is Bicycle Commuting Really Catching On? And if So, Where?” Atlantic Cities. The

Atlantic Monthly Group, 21 Sep. 2011. Web. 6 Dec. 2013.

Sample Python Code

Calculating Clark Index

>>> arcpy.AddField_management("primstreet", "Clark1", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Clark1", "[Traffic_Co]/50")<Result 'primstreet'>

>>> arcpy.AddField_management("primstreet", "Clark2", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Clark2", "([Speed_lim]-[Cyclist_sp])*([Speed_lim]-[Cyclist_sp])*[Clark1]")<Result 'primstreet'>

>>> arcpy.AddField_management("primstreet", "Clark3", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Clark3", "10*[Lane_num]")<Result 'primstreet'>

>>> arcpy.AddField_management("primstreet", "Clark4", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Clark4", "(([Bike_sp]*[Bike_sp])*4)+[Clark3]")<Result 'primstreet'>

>>> arcpy.AddField_management("primstreet", "Clark", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Clark", "[Clark2]/[Clark4]")<Result 'primstreet'>

Adding Impedance Field

>>> arcpy.AddField_management("primstreet", "Impedance1", "DOUBLE")<Result 'primstreet'>

>>> arcpy.CalculateField_management("primstreet", "Impedance1", "[Shape_Leng]*[Clark]")<Result 'primstreet'>

Creating route from residential area to landmarks

>>> arcpy.na.MakeRouteLayer("Bike", "densitytolandm", "Impedance")

>>> arcpy.na.AddLocations("densitytolandm", "Stops", "high density", "", 1000)

>>> arcpy.na.AddLocations("densitytolandm", "Stops", "landmarks", "", 1000)

>>> arcpy.Solve_na("densitytolandm")

>>> arcpy.SaveToLayerFile_management("densitytolandm", "densitytolandm")

Creating route from residential area to landmarks after speed limit reduction

>>> arcpy.na.MakeRouteLayer("under35_ND", "densitytolandm2", "Impedance")

<Result 'densitytolandm2'>

>>> arcpy.na.AddLocations("densitytolandm2", "Stops", "high density", "", 1000)

<Result 'densitytolandm2'>

>>> arcpy.na.AddLocations("densitytolandm2", "Stops", "landmarks", "", 1000)

<Result 'densitytolandm2'>

>>> arcpy.Solve_na("densitytolandm2")

<Result 'densitytolandm2'>

>>> arcpy.SaveToLayerFile_management("densitytolandm2", "densitytolandm2")