South San Francisco Count Database by Billy Ray Johnson

South San Francisco, CA

December 2015

Alta Planning + Design 100 Webster Street, Ste 300 Oakland, CA 94607 Ph. (510) 540-5008 Fax. (510) 540-5039 www.altaplanning.com

Prepared by

Hugh Louch

Project Manager

Hugh Louch

Project Title

Bicycle and Pedestrian Database

Client

Patrick Caylao City of South San Francisco

Client reference Alta reference

2014-286

Pedestrian and Bicycle Study Rev.

Description

Date

Reviewed by

Draft report for client review and approval

12/14/15

Brett Hondorp

City of South San Francisco Bicycle and Pedestrian Database Final Report

Introduction ................................................................................................................... 1 1.1 1.2

Bicycle Facility Audit ....................................................................................................... 1 2.1 2.2

Updated Facility Map ..........................................................................................................1 Other Findings.....................................................................................................................4

User Survey .......................................................................... Error! Bookmark not defined. 3.1 3.2 3.3

Project Objectives ...............................................................................................................1 Approach ............................................................................................................................1

Desired Destinations .................................................................Error! Bookmark not defined. System Condition ......................................................................Error! Bookmark not defined. Distribution of Bicyclists ............................................................Error! Bookmark not defined.

Count Data ........................................................................... Error! Bookmark not defined. 4.1 Approach and Methodology ......................................................Error! Bookmark not defined. Count Locations .................................................................................................................................... 2 4.2 Count Data ..........................................................................................................................3 Automated Bicycle Count Data ............................................................................................................. 3 Automated Pedestrian (Infrared) Count Data ...................................................................................... 4 Manual Counts ...................................................................................................................................... 4 4.3 Average Daily Counts by Location ........................................................................................6 4.4 Impact of Weather ..............................................................................................................8 4.5 Extrapolation ............................................................................Error! Bookmark not defined. Extrapolating from 2-Hour to Day......................................................................................................... 1 Extrapolating from Day to Week........................................................................................................... 3 Extrapolating from Week to Month...................................................................................................... 4 Extrapolating from Month to ANnual ................................................................................................... 4 4.6 Bicycle Miles of Travel ....................................................................................................... 10

Recommendations ........................................................................................................ 12

City of South San Francisco Bicycle and Pedestrian Database Final Report

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City of South San Francisco Bicycle and Pedestrian Database Final Report

iii

The City of South San Francisco is designing and implementing a Bicycle Database (Database) to document current bicycle volumes, behaviors, and patterns. The Database was identified as a goal in the City’s 2011 Bicycle Master Plan. The Database will help establish trends over time, indicate the effectiveness of various interventions (such as the addition of a bicycle lane), optimize investments, and provide information to quantify air quality and other benefits for funding applications. By developing a more robust estimate of the number of bicycle trips, the City will be able to establish network performance measures including estimated bicycle collision rates rather than simply the number of collisions. Alta Planning + Design was hired by the City to develop a database and methodology, including piloting the use of a combination of manual and automated counters to understand bicycle and pedestrian travel in South San Francisco.

The Bicycle Database project include collection and organization of several data items to support long term bicycle planning in South San Francisco. Table 1 lists the data items collected for this study:

Network implementation and facility audit

 Table of proposed routes and progress  Table listing conditions of bicycle facilities

Collision data

 GIS data based on SWITRS collisions data  Summary of existing collisions in the City

Bicyclist survey

 Summary responses and analysis of survey of bicyclist preferences and issues

Bicycle and pedestrian counts

 Manual counts at 6 locations in Spring and Fall

 Photos of typical conditions of each facility

 Automatic bicycle counts at 8 locations in Spring and Fall  Automatic pedestrian counts at 6 locations in Fall  Estimates of bicycle and pedestrian volumes in the City

This document describes the process to select count locations and counts. In addition, Appendix A provides the findings from the bicycle facility audit. The collision analysis and survey findings are documented separately.

City of South San Francisco Bicycle and Pedestrian Database Final Report

The primary purpose of this project was to develop a database of counts to help the City of South San Francisco monitor use of the active transportation system in the City over time. The primary focus of these counts was on bicyclists, but some pedestrian counts were also taken for context. Counts were taken in two ways: 

Automated – using tube counters to capture bicyclists and active infrared counters to capture pedestrians. Because the infrared counter does not distinguish between bicyclists and pedestrians, bicyclists are captured on shared facilities or where bicyclists use sidewalks.



Manual – using trained counters to calibrate the automatic counts and capture additional counts, following the methodology established in the National Bicycle Pedestrian Documentation (NBPD) project.

Counts were selected following available national research and guidance. There are two known methods for estimating the number of count sites required: the population-based method and the factor group method. 



The population method1 suggests counters depending on the population of the study area as listed in Table 5.1. The shaded row indicates the population range that applies for the City of South San Francisco.

< 25,000 2-3* 25,000 – 50,000 3-4 50,000 – 100,000 4-6 100,000 – 150,000 6-9 150,000 – 200,000 8-12 200,000 – 250,000 11-15 250,000 – 300,000 14-17 300,000 – 400,000 17-20 Statistically robust only where there is a high level of bicycling

The factor group method is somewhat more sophisticated, using categories of bicycle users such as ‘recreational riders’, ‘commuters’, or ‘parents and children going to school’. The Federal Highway Administration Traffic Monitoring Guide (TMG) suggests this approach. FHWA recommends installing at least three counters per factor group, geographically dispersing the counters in areas near schools and adult employment areas.

Given available budget for this project, a total of eight count sites were selected for automatic counts. An additional 7 manual counts were conducted, providing the ability to consider a range of types. The locations of these counts are shown in Figure 2.1.

Strong, Mark (2006), “Practical Monitoring of Cycling. Transport Practitioners’ Meeting 2006”, slide 15.

City of South San Francisco Bicycle and Pedestrian Database Final Report

Table 2.2 describes the locations of the bicycle counts conducted by the technology and the percent of the system represented by the counts. The ‘Corridor Length’ captures the length of the bicycle facility in miles. For example, the length of the Bay Trail within South San Francisco. The Sample Length captures the length of the segment in miles on which data were collected (i.e., between the nearest two intersections). The ‘Sample Percent’ is the portion of the corridor (or the overall system) that is captured via the data collection effort. Finally the ‘Factor Group’ represents the type of users primarily captured via the particular count site, which will be useful for future extrapolation. A few segments are ‘off system’, meaning that there are no markings, signage or specific facilities. These were necessary for context, to capture travel to schools, and because some of the intersections of manual counts were not on bicycle facilities themselves.

Note: Segment lengths are measured between nearest intersections on either side of the count location and are approximate.

City of South San Francisco Bicycle and Pedestrian Database Final Report

Automated bicycle counts were collected for at least two weeks, though in some cases data collection issues (i.e., tubes removed by users) reduced the total number of days available. Manual counts were collected at each location in spring and fall for up to 3 times for 2-hour periods. Those periods were stratified by expected trip purposes. Commuter-oriented sites were counted in the morning peak (7 to 9 AM), evening peak (5 to 7 PM), and weekend mid-day (10 AM to 12 PM). For sites near schools, data were collected during the after school period (2:30 to 4:30 PM) instead of the evening peak.

Count data were provided separately in a set of excel documents, one for each type of count – automatic bicycle, automatic pedestrian, and manual. This section describes how the counts are collected and stored.

The automated bicycle counts were collected using EcoCounter pneumatic tubes. These counters have two tubes, allowing for counts in both directions. On bike lanes, these help provide an understanding of the share of users traveling the wrong direction, a common safety concern. The data from the counters are provided in the attached file: 'SSF_AutoBikeCounts.xlsx'. The data are provided at three different levels of aggregation (organized by the 'tab' in the Microsoft Excel Workbook):    

15min. Raw data were available from the counters in 15 minute increments. These data have been processed and cleaned and detailed count data are available in 15 Hour. The 15 minute data were aggregated into hourly data. Only complete hours were retained. Day. The 15 minute data were also aggregated into daily data. Only complete days were retained for this tab. These are likely the easiest data to use. Aggregated. This tab provides data aggregated for average, weekday/weekend average, rainy vs dry day averages, etc. This tab was used to generate the charts in this report.

The automated bicycle counts include the following data field.     

Location of the count Date and time Day of week Hour Counts (each direction and total). Counts in the primary direction are marked 'In' and in the secondary direction 'Out'. [[Create readme for each file that shows which direction = in

and out]] 

 

Rain. Data from the weather station at San Francisco Airport were linked to the data and the total rain in inches by day was captured. The data were also qualitatively marked as occurring on rainy or dry days if there was more than nominal (half inch) rain Tmax. The maximum temperature for the day, linked in the same fashion as the rain data. Tmin. The minimum temperature.

City of South San Francisco Bicycle and Pedestrian Database Final Report

While not directly part of the bicycle database, a set of automated pedestrian counts were collected using TrailMaster 1550 active infrared counters. These counters create a beam of infrared light between sending and receiving devices and record when that beam is broken. These counters cannot distinguish direction and are sensitive to people that linger near the counters. In general, these counts may over count pedestrians, especially near intersections. Placement is import for these counters. If they are placed such that only some pedestrians will break the infrared beam, then they will likely miss pedestrians. Similarly, if they are cover part of a travel lane, they may pick up bicyclists and people passing in cars. The data from the counters are provided in the attached file: 'SSF_AutoPedCounts.xlsx'. The data are provided only aggregated to the hour of collection. TrailMaster data are provide as a simple column of the date and time stamp (hour:minute) that the infrared beam was broken. These have been summarized into the hour of collection. The TrailMaster has limited memory, making it difficult to obtain more than a small number of days of data before the memory is full, especially in high pedestrian locations like recreational trails). The automated pedestrian counts include the following data field.      

 

Location of count Date and time Day of week Hour Counts Rain. Data from the weather station at San Francisco Airport were linked to the data and the total rain in inches by day was captured. The data were also qualitatively marked as occurring on rainy or dry days if there was more than nominal (half inch) rain Tmax. The maximum temperature for the day, linked in the same fashion as the rain data. Tmin. The minimum temperature.

Manual counts were collected using the method established for the NBPD Project. These counts are collected on a standardized form that captures bicyclists and pedestrians as they approach an intersection. This means that, for each location, there are eight total counts - bicycle and pedestrian for each of four directions. Counts were collected in 15 minute increments. Additional information collected included:    

Weather – qualitative assessment of weather Number of bicyclists under 12 (using judgement) Number of bicyclists who are female (using judgement) Number of bicyclists going the wrong way or on the sidewalk

Note that not all counters accurately recorded age and gender for bicyclists. These data are only provided where they had basic face validity (i.e., excluding data with more female or young cyclists than total cyclists). City of South San Francisco Bicycle and Pedestrian Database Final Report

Manual data are organized by the site. All times (both fall and spring) are provided on each sites sheet. For example, the Centennial Path at Spruce Avenue tab in the Excel Workbook has the one spring count and three fall counts conducted at that location, each one listed after the previous. In addition, the data have been aggregated by total for each 2-hour count period, showing both the overall bicycle, pedestrian, and combined counts and the bicycle and pedestrian counts in each direction.

City of South San Francisco Bicycle and Pedestrian Database Final Report

This section summarizes the findings from the counts conducted for this project.

Figure 3.1 shows the average numbers of bicyclists counted by automated counters at each site in both the spring and fall periods. Locations with a recreational emphasis (Bay Trail and Centennial) had the highest counts, though as the remainder of this section demonstrates, these facilities are appear to receive significant commuter use in addition to recreational use.

Figure 3.2 shows the variation in bicycle counts from weekday to weekend. At almost all sites, there were more bicyclists during the week than on the weekend, even at the recreational sites.

City of South San Francisco Bicycle and Pedestrian Database Final Report

Table 3.1 presents the manual counts taken by location, period, and season. Each of these counts was taken for a 2 hour period. The busiest locations from the manual counts were primarily along Grand Ave, though the Del Monte Avenue and Romney location saw significant pedestrian traffic at the end of the school day in the fall. The busiest locations for bicyclists were at E. Grand Avenue and Gateway, which had over 70 bicyclists during both the fall and the spring counts, and the Centennial Path, which averaged over 50 riders in the peak period and over 80 in one individual count period. Other locations on Grand Avenue also saw substantial bicycle traffic (at least 20 riders), as did Orange Avenue and Canal. Only the Del Monte school-based site and the Baden and Spruce site (the intersection of a Class III route and a relatively quiet residential street) had 10 or fewer riders. Overall, bicycling was more prevalent during the week than on the weekend, with some sites experiencing double or more weekday riders than weekend riders. Only Baden and Spruce had a higher number of bicyclists on a weekend day than on a weekday.

City of South San Francisco Bicycle and Pedestrian Database Final Report

AM Peak Del Monte & Romney

Baden & Spruce

Magnolia & Grand

Centennial & Spruce

Orange & Canal Linden

Spring

Bike 10

Total 155

Weather Cloudy

413

416

Drizzle

Light Drizzle

Afternoon

Fall

Weekend

Spring

AM Peak

Fall

120

122

Cloudy

Fall

100

Clear

Spring

Windy

PM Peak

Clear

Weekend

Fall

307

316

Warm

AM Peak

Fall

196

213

Cloudy

Afternoon

Fall

261

266

Clear

Fall

253

267

Clear

Spring

206

227

Foggy

Fall

145

217

Cloudy

Spring

128

Foggy

PM Peak

Fall

106

Clear

AM Peak

Fall

216

259

Cloudy

Fall

191

276

Clear

Spring

146

175

Cloudy

PM Peak

Grand & Gateway

Fall

Ped 145

AM Peak

PM Peak Weekend

Fall

111

Warm

PM Peak

Fall

147

173

Cloudy

Fall

137

158

Clear

115

Cloudy

264

290

Cool

Weekend AM Peak

Spring Fall

The charts in this section have provided a high level look at the number of bicycle and pedestrian counts. When using the data for analysis purposes, using the weather data will provide additional useful information. Figure 3.3 shows the impact of rain on the automatic count location data. The chart shows the difference in the average count for days that have some rain and days that have none. Not all locations experienced rain, so not all locations are included. Rainy days have between 17 and 69 percent fewer bicyclists

City of South San Francisco Bicycle and Pedestrian Database Final Report

A similar examination was made of temperature, looking at both high and lows (Figure 3.4). Because of the generally mild weather of the region, counts were primarily collected during what could be considered mild weather. The few counts that occurred in more cold (highs and lows under 60) or hot (highs over 90 degrees) had lower average counts than those that occurred in more mild weather.

City of South San Francisco Bicycle and Pedestrian Database Final Report

Low Temperature

Note: Each bar represents a 10 degree band of the high temperature (from 40s to 90s). The size of the bars indicate how much higher than average (blue bars) or below average (red bars) the counts were at that temperature combination than at more moderate temperature.

An estimate of bicycle miles of travel in South San Francisco was generated using local extrapolation factors drawn from the automated data collectors and month-to-month variation in bicycle travel in San Francisco (Table 3.2). This estimate is preliminary and should be used cautiously until additional years of data are collected. The estimate assumes that the unmeasured portion of the network is roughly similar to the locations where data were collected. The estimates were generated by type of facility, and are only valid for areas with a defined bicycle network. This necessarily means that actual bicycle travel is significantly undercounted as many bicyclists use facilities that are not specifically signed or striped for bicycle use. Three of the manual counts and one automated count captured users off of the existing bicycle network, providing some context, but this is necessarily a partial estimate. Overall, on Class I, II, and III, the counts suggest over 700,000 bicycle miles of travel. The existing network sees about 31,000 users on average, though there are more average users of Class I facilities (47,000) than Class II (34,000) or Class III (8,000). City of South San Francisco Bicycle and Pedestrian Database Final Report

City of South San Francisco Bicycle and Pedestrian Database Final Report

Counts can serve several purposes. Among the most important are to (1) track active transportation use over time and (2) to conduct before and after studies to help understand how new investments impact bicycling and walking. Generally, national guidance recommends conducting counts annually. However, the cost of an annual count may be cost prohibitive for South San Francisco. Instead, we recommend the following approach to continue to build the count database started by this project: 1. Conduct bi-annual automated bicycle counts at a core set of sites. The recommended sites are listed in Table 5.1. 2. For each major count cycle, rotate in at least two sites not conducted as part of the core set. Additional Class II facilities along Junipero Sera Blvd and Sister Cities in the Blvd would be appropriate to include in the rotation. As the system of bicycle lanes are built out in the eastern side of the City, data collection would help demonstrate use over time. Finally, adding Class III sites and other schools off the primary bicycle network will help provide a more complete picture of bicycle travel in the City. The City's BPAC can provide advice on locations that may have significant use by bicyclists or pedestrians and would contribute significantly to the count. 3. Require before and after bicycle and pedestrian counts for proposed developments and transportation projects, conducted in a fashion that is consistent with and can contribute to the count databased begun for this project. Ideally, automatic counts of at least two weeks would be conducted in the spring or fall before a project. However, smaller projects may acceptably provide only 3-day counts (ideally on Tuesday to Thursday) or manual 2-hour counts. 4. Attempt to organize a volunteer manual count annually at a rotating set of sites, leveraging the City's BPAC to regularly conduct counts. Ideally this would be organized with the annual count organized by the NBPD Project in the spring and/or fall. With enough advance time, the Cityâ&#x20AC;&#x2122;s BPAC may be helpful for tracking down volunteers for an ongoing effort. 5. Examine requiring bicycle counts to be included in traffic counts for traffic analysis projects, again in a format consistent with the database developed for this project. Many tube counters, including those by EcoCounter, can collect both automobile and bicycle travel. Because traffic counts are typically required for proposed developments, there may be an opportunity to leverage the data collection to support the active transportation program.

City of South San Francisco Bicycle and Pedestrian Database Final Report

A comprehensive audit was conducted of the bike facilities in South San Francisco over the course of several days in the spring of 2015. The audit included:  

Bicycling every mile of bikeways in the City using a GIS-based data collector application Updating the existing bicycle facility map to capture the findings of the audit

Approximately 5.3 miles of the network were updated based on the audit (Figure 2.1 and Table 2.1). Several new bike facilities were completed or extended since the completion of the City’s Bicycle Mater Plan, including: 

Completion of the Centennial Trail (Class I) to the South San Francisco BART station.



Development of Class II bike lanes on Grand Avenue between Chestnut and Spruce



Completion of a segment of Class II bike lanes on East Grand, several additional segments remain proposed



Extension of Class II bike lanes on the eastern end of Oyster Point Blvd



Development of Class II bike lanes south of the South San Francisco BART station on McLellan St.

The audit also revealed several gaps in the network that were in the City’s previous list of facilities, specifically:   

South Airport Avenue Bike lanes do not exist in several locations East Grand Avenue Bike lanes are not clearly marked in the US 101 underpass Class III facilities not marked or signed (or both) on Victory Ave, Arroyo Drive Newman Drive, and Alta Loma Drive

A complete list of reviewed facilities is provided in Table 2.1.

City of South San Francisco Bicycle and Pedestrian Database Final Report

A-1

City of South San Francisco Bicycle and Pedestrian Database Final Report

A-2

Route

Class

From

Was Miles Proposed Notes

Alta Loma Park

Maintenance Romney Ave road

0.12

Clay overbridge

Newman Drive

Longford Drive

0.09

Centennial Trail

Antoinette Ln

SSF Bart Station

0.53

Bay Trail

West end of E/W Bay Trail 0.06 Marina Connection

Airport Blvd

Butler Ave

Airport Blvd

Chestnut Ave San Mateo Ave 0.44

Grandview Dr.

Parking Lot Ped Xing

0.06

Grand Ave

Orange Ave Spruce Ave

0.40

Yes

Grand Ave

Chestnut Ave Orange Ave

0.40

Yes

Oyster Point Blvd

Dominic's Restaurant

Cul-de-Sac

0.18

Yes

McLellan Dr

El Camino

Alta Loma Park

0.21

Yes

Needs curb ramp improvement at Alta Loma park and way finding

E Grand Ave

Allerton Ave Bay Trail

0.20

Yes

Split at Haskins, existing bike through lane west of Haskins, then East to Genentech

E Grand Ave

101 FWY Underpass

0.20

Slight Directional Discontinuity at Eastern terminus (EB start has 80 Bikeway sign)

Victory Ave

III

S Linden Ave S Spruce Ave

0.34

No signs or markings, 25mph, low traffic

Arroyo Dr

III

W Orange Ave

Junipero Serra 0.85 Blvd

Sign present no markings

Newman Dr

III

Clay Ave

Alta Loma Dr

No signs or markings. Parking on both sides.

Alta Loma Dr

III

Newman Dr Del Monte Ave 0.18

Sign present, no markings

Hickey Blvd

III

State Longford Dr Highway 82

6' bike lane on uphill desirable and feasible

Tower Place

Grand Ave

City of South San Francisco Bicycle and Pedestrian Database Final Report

Yes

0.49

0.07

0.47

Southbound Lane Not Present

Bike Lanes end through this section (sharrows recommended)

A-3

In addition to the segment by segment review of the South San Francisco bike network, several other more general issues were noted in the audit of the bicycle network. Specifically, 

Sharrow placement was noted as an issue on several Class III routes. Sharrows should be placed to suggest bicycle travel that keeps clear of driver side doors, and per the California MUTCD should be placed at least 11’ from the curb. Some of South San Francisco’s sharrows have been placed too far on the right side of the travel lane, placing cyclists in the door zone.



Operations and maintenance issues were noted on several routes, both in terms of pavement quality and missing bike lanes or sharrows that appear to be associated with repaving projects. Providing contractor training on proper pavement marking and quality checks for pavement condition on pothole filling and patches.

City of South San Francisco Bicycle and Pedestrian Database Final Report

A-4

A good sample of counts allows South San Francisco to extrapolate the level of bicycle use across the City. Over the last 10 years, there has been increasing attention at the national level to developing methods for bicycle and pedestrian counts and extrapolation that are more similar to those conducted for automobile traffic. Two primary resources are available to help South San Francisco extrapolate counts: 

The National Bicycle/Pedestrian Documentation Project (NBPDP) provides both a method for counts and a national repository of counts.



The FHWA Traffic Monitoring Guide (TMG) includes a new chapter in its latest edition on collecting bicycle and pedestrian counts and a general method for extrapolation drawn from similar approaches

The basic approach to extrapolating raw counts works in steps. From the 2-hour period to the day to the week to the month and finally to the year. Making each jump requires either data or an assumption about the distribution of counts at the next more aggregate period. The NBPD project provides data that can be used to help extrapolate 2-hour counts to daily, weekly, monthly, and annual dates. The automated data collected in this project and other data sources were used to help refine those extrapolation factors. Appendix B provides a comparison of the data from South San Francisco to these national sources. This section primarily compares local and NBPD Project extrapolation options at each step of the chain.

Figure B.1 compares the distribution of counts by hour of day between the NBDPP data and the automated bicycle count data for paths on weekdays. The NBDPP separated sites into two general types – multi-use paths and pedestrian-oriented urban districts. The figure presents the weekday distribution for paths and pedestrian districts. Only the Bay Trail and Centennial Trail data were used for the path comparison. For paths, South San Francisco weekday data look quite different than the NBPDP distributions. There are strong AM and PM peaks consistent with travel to work. The location of trails within South San Francisco (near BART and employment centers on the east side of the City), suggesting use of more work-oriented travel distributions for extrapolation of path data.

City of South San Francisco Bicycle and Pedestrian Database Final Report

B-1

By comparison, the distributions for paths on weekends show few differences between the local data and the NBPD Project (Figure B.2). For pedestrian district locations, there were similar differences between the NBPD Project and the South San Francisco data.

City of South San Francisco Bicycle and Pedestrian Database Final Report

B-2

For extrapolation purposes, a combination of the automated bike and pedestrian counter data and the NBPD Project data can be used to extrapolate manual 2-hour counts to daily counts. Automatic counter data would be used as they are collected.

The next step is to extrapolate from daily counts to weekly counts. Most of the automatic bicycle counts took place over two weeks allowing for a comparison of day-to-day variation in South San Francisco to the NBPD Project (Figure B.3). Overall, the NBPD Project shows higher average counts on the weekends than is experienced in South San Francisco. This is especially true at the Ponderosa count site, which is directly adjacent to an elementary school. Overall, however, there still were fewer counts on the weekend than was expected from the NBPD Project. For the purposes of extrapolation, it may be simplest to simply assume almost equal distribution of counts across the week, with somewhat lower levels on the weekend. Across all sites, the five weekdays had roughly similar distributions of counts.

Note: the black hash marks at the bottom of the chart indicate what an equal distribution by day of the week would look like.

City of South San Francisco Bicycle and Pedestrian Database Final Report

B-3

The next step in extrapolation is to grow week counts to monthly counts. This is typically done by simply multiplying an average weekly count for a month times the number of weeks in the given month, taking into account in which month the counts were taken.

The final step in extrapolation is to grow the month-specific estimate into an annual estimate. Generating annual estimates of bicycle and pedestrian travel requires permanent count locations that track counts over the course of an entire year, and ideal overall several. South San Francisco has no permanent count data, but other nearby cities do, especially the City of San Francisco. Figure B.4 compares the distribution by month from the NBPD Project to all of the automated data collected by the City of San Francisco. Ideally, a permanent counters in the same factor group would be used, but for current purposes only the overall distribution was available. The NBPD Project shows a strong summer peak for counts; San Francisco shows a much more even distribution of counts across the year and significantly higher volumes in October, when San Francisco often experiences clear, temperate days. With relatively similar weather, it is probably appropriate for South San Francisco to use these City of San Francisco monthly extrapolation factors to generate annual totals.

City of South San Francisco Bicycle and Pedestrian Database Final Report

B-4