NVTA CTP Appendix

NAPA VALLEY COUNTYWIDE TRANSPORTATION PLAN MAY 2021

Appendix

APPENDIX CONTENTS PUBLIC OUTREACH PERFORMANCE METRICS TRANSIT ACCESSIBILITY SAFETY DELAY INDEX ON-TIME BUS PERFORMANCE TRUCK TRAVEL TIME RELIABILITY JOB ACCESSIBILITY PAVEMENT CONDITION INDEX

FREIGHT FLOWS GREENHOUSE GAS CALCULATIONS TRAVEL DEMAND FORECASTING – MODE SHARES FUNDING PROGRAMS

Public Outreach

NVTA Countywide Transportation Plan 2045

Community Engagement Phase 1 Summary Report Round 1 engagement activities were conducted from August 2019 - January, 2020.

February 10, 2020

Table of Contents Community Engagement Overview

3

Project Timeline

4

Engagement Process

5

In-Person

5

Online

5

Promotional Campaign

7

Results

8

Participation

8

Transportation Needs Assessment Survey

9

Survey Response Data

9

Survey Response Data

11

Interactive Map (online & in-person)

19

Transportation Mode Poll

21

Discussion

23

Findings by Focus Area

23

Appendix

25

1. Pop-up Event Posters

25

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Community Engagement Phase I Overview From August 2019 to January 2020, we gathered input from residents across the County via an interactive website and a series of pop-up events. The same input mechanisms were available online and offline. Input was used to inform the draft Transportation Plan.

Online engagement opportunities: ● ● ●

Survey to understand community transportation needs Interactive Map to gather ideas for transportation improvements Poll to gather data on current transportation usage

All online engagement opportunities were also available in analogue form at community events.

Community engagement events:

1 Copia, 500 First Street, Napa, CA, 94559

September 18th, 2019 4:00pm-6:00pm

2 195 Gasser Dr, Napa, CA 94559

October 16th, 2019 9:00am - 1:00pm

3 2185 Elliott Dr, American Canyon, CA 94503

December 11th, 2019 9:00am - 10:30am

4 500 Cedar St, Calistoga, CA 94515

December 19th, 2019 12:30pm - 1:30pm

5 1500 Jefferson St, Napa, CA 94559 City of Napa Senior Center

January 13th, 2020 10:00am-12:00pm

6 1360 Oak Ave, St Helena, CA 94574

January 16th, 2020 4:30pm - 6:30pm

NVTA Transportation Summit Napa Farmers Market

American Canyon Senior Center Up Valley Family Center

Carnegie Building, St Helena

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Project Timeline

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4

Phase I Community Engagement Process To gather input on the NVTA Countywide Transportation Plan 2045 (“Transportation Plan”) from Napa County’s community members, NVTA worked with contractors DKS and CivicMakers (NVTA team) to conduct a series of in-person and online engagement activities from August 2019 to January 2020.

In-Person The NVTA team hosted a series of pop-up events during popular community events in Napa, St. Helena, American Canyon and Calistoga. At each event, the public was presented with a set of engagement posters to share project information and solicit input on focus areas and measures for the Transportation Plan. The posters were translated to Spanish for targeted events.

Online In addition, a digital engagement platform was launched to amplify reach. The platform was also designed to make it easier, more convenient and accessible to share input on the Transportation Plan. The website (https://www.nvtatransportationplan.org/), available in English, Spanish and Filipino, was set up as a hub for sharing project information, as well as for gathering input via interactive maps and survey components.

Text messaging integration was included in order to better reach those people who may not have computers or smartphones at home. All online engagement opportunities were also available in analogue form at community pop-up events.

Below is a screen capture of the webpage.

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Promotional Campaign To promote the pop-up events and online engagement activities, the NVTA team developed a promotional campaign that included emails, social media, press releases, and printed poster ads on buses, in bus shelters, and in other public places. All materials were translated into Spanish.

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Results Overall Participation Combined Participation (In-Person + Online) In total we estimate that nearly 900 people shared input on the Transportation Plan.

Activity

In-Person

Online

SMS

SUM

11

374

-

385

Interactive Map

189

73

19

281

Transportation Mode Poll

72

9

-

81

Focus Area Prioritization

152

-

-

152

424

456

19

899

Transportation Needs Assessment Survey

SUM

Participation at Community Events (In-Person) While we did not do a head count at events, we estimate 100+ in-person participants based on the number of comments received. Many more learned about the project without sharing input.

Event

Map Comments

% of Total

Total Votes1 Collected

% of Total

1

Transportation Summit (Napa; 9/18/19)

53

28.04%

91

40.63%

2

Napa Farmers Market (Napa; 10/19/19)

67

35.45%

42

18.75%

3

Boys and Girls Club (American Canyon; 12/11/19)

37

19.58%

27

12.05%

4

Up Valley Family Center (Calistoga; 12/19/19)

14

7.41%

0

0.00%

5

Napa Senior Center (Napa; 1/13/20)

6

3.17%

28

12.50%

6

Pop-up at Carnegie Building (St Helena; 1/16/20)

12

6.35%

36

16.07%

TOTAL_

189

224

Total Votes includes the number of pompoms collected to indicate primary travel mode (for which, participants could cast only 1 vote) and the number of dot votes placed on important focus areas (for which, participants could cast 1-3 votes).

1

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Participation Online In total, we saw 500+ online participants and many more learned about the effort by visiting the project website.

Types of Online Visitors

Online

Total Site Views

3,621

Unique Participants (contributors)

460

Comments Received (survey + map + poll)

1,007

Survey: ● ●

883 comments2 (a single survey contained 13 opportunities to provide a comment) 290 unique commenters ○ Range 1-10 comments ○ Average per respondent: 2.16 comments ○ Average per commentator : 2.88 comments

Map + Poll ● ●

124 comments 79 unique commenters

Data from Transportation Needs Assessment Survey Purpose.

Understand the transportation needs, concerns and ideas of Napa residents and workers.

Submission Source: ● Online (372) ● Community Events (11)

385 Surveys Received 2

‘Comments’ for the survey include both open-answer responses and boxes where respondents

could clarify their quantitative responses.

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About the survey. ●

13 question

●

Available online and as a printed survey during community events

1. Survey Participants by Age Group: (247 question responses; 64.2% response rate3)

The response rate is the number of people who responded to this question compared to the total number of surveys submitted.

3

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2. School Age Children at Home: (250 question responses; 64.9% response rate)

3. Number of Household Vehicles: (250 question responses; 64.9% response rate)

4. Annual Household Income: (220 question responses; 57.1% response rate)

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5. What is your main form of transportation?

(331 question responses; 86% response rate)

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6. If you're employed, how do you usually travel to work?

(370 question responses; 96.1% response rate)

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7. What zip code do you live in?

(236 question responses; 61.3% response rate) Within Napa County

209

Napa

94558, 94559

135

57.20% *

American Canyon

94503

25

10.59%

Saint Helena

94574

20

8.47%

Calistoga

94515

12

5.08%

Pope Valley

94567

6

2.54%

Yountville

94599

6

2.54%

Angwin

94508

4

1.69%

Rutherford

94573

1

0.42%

Outside Napa County

27 14

4.66%

Sonoma County (Sonoma, Windsor, Santa 95476, 95492, 95401, 95403 Rosa)

5

2.11%

Marin County (Novato, Belvedere Tiburon) 94949, 94920

2

0.85%

Lake County (Middletown)

95461

1

0.42%

Yolo County (West Sacramento)

95691

1

0.42%

Sacramento County (Sacramento)

95818

1

0.42%

Contra Costa County (Walnut Creek)

94596

1

0.42%

San Mateo (Belmont)

94002

1

0.42%

Solano County (Suisun City, Benicia, Vallejo, Fairfield, Vacaville)

94585, 94510, 94590, 94534, 95687

*percentage of total zip codes (within Napa County and Outside the County, combined)

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8. What zip code do you travel to most often?

(199 question responses; 51.7% response rate) Within Napa County

145

Napa

94558, 94559

101

50.75% *

Saint Helena

94574

22

11.06%

American Canyon

94503

9

4.52%

Calistoga

94515

5

2.51%

Angwin

94508

4

2.01%

Yountville

94599

3

1.51%

Rutherford

94573

1

.50%

Outside Napa County

54

Solano County (Fairfield,Vallejo, Vacaville, 94534, 94533, 94589, 94590, Suisun City) 94592, 95688, 94585

12

6.03%

Alameda County (Hayward, Oakland, Berkeley, Livermore)

94541, 94612, 94611, 94601, 94610, 94707, 94703, 94550

10

5.03%

Sonoma County (Santa Rosa, Sonoma)

95407, 95401, 95409, 95476

8

4.02%

San Francisco County (San Francisco)

94103, 94116, 94104, 94114, 94111

8

4.02%

Contra Costa County (San Pablo, Moraga, San Ramon, Byron)

94806, 94575, 94583, 94514

4

2.01%

San Mateo (San Carlos, Pacifica, Brisbane)

94070, 94044, 94005

4

2.01%

Marin County (Novato, San Rafael)

94949, 94948, 94903

3

1.51%

Monterey County (Monterey)

93940

1

0.50%

Amador County (Pioneer)

95666

1

0.50%

*percentage of total zip codes (within Napa County and Outside the County, combined)

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9. How does each of the following conditions compare to five years ago?

(332 responses; 86.2% response rate)

Much Somewhat About the better now better now same

Somewhat Much No worse now worse now opinion

Traffic on local highways (Routes 12, 29, 121, 128)

1

14

25

67

155

19

281

Pavement conditions on the local highways (Routes 12, 29, 121, 128)

15

48

73

67

60

22

285

Traffic on local streets and roads

2

11

43

113

104

10

283

Pavement conditions on local streets and roads

4

50

51

72

96

12

285

Reliability of Vine Transit

4

29

44

15

12

168

272

Frequency of Vine Transit

6

27

40

15

12

166

266

Ability to walk places to meet everyday needs

6

12

149

29

28

49

273

Safety and ease of biking

8

44

54

43

34

87

270

46

235

479

421

501

533

2215

2.08%

10.61%

21.63%

19.01%

22.62%

24.06%

SUM %

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10. What do you find most challenging/frustrating as you travel around Napa County? (302 responses; 78.4% response rate) [See chart below] 11. What is the best aspect of Napa County's transportation system?

(187 responses; 48.6% response rate) Most challenging/frustrating part of traveling in the County

Best aspect of the County transportation system

Congestion (or lack thereof)

215

71.19%

17

9.09%

Transit Options

25

8.28%

52

27.81%

Pedestrian Facilities

3

0.99%

17

9.09%

Travel Information

0

0.00%

25

13.37%

Pavement Conditions

47

15.56%

31

16.58%

Bicycle Facilities

12

3.97%

39

20.86%

Carpool/ vanpool options

0

0.00%

6

3.21%

Other

0

0.00%

0

0.00%

SUM

302

187

12. If you had unlimited resources and no limits, what is the one thing (or things) that you would do to make transportation in Napa County easier, safer or more pleasant?

(248 question responses; 64.75% response rate)

Number of Open Comments

231

73.10%

Number of Map pins

85

26.90%

TOTAL COMMENTS

316

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13. Below are some ways in which Napa County's transportation system could change in the future. In general, how much do you support each change?

(252 question responses; 65.5% response rate) Strongly

Somewhat

Not at all

Not sure or Indifferent

Congestion relief on local highways (Routes 12, 29, 121, 128)

182

29

2

3

216

Improving local streets and roads

146

61

2

7

216

Increase the number of park-and-ride lots

63

79

31

45

218

Faster or more frequent Vine Transit service

95

54

13

54

216

Faster or more frequent Vine Paratransit service

64

44

16

90

214

More walkable neighborhoods

112

60

14

29

215

More walkable central business districts

121

56

18

22

217

Safe bikeways

138

43

13

22

216

Carpool / vanpool options

76

67

19

51

213

Safer routes to school

118

45

11

38

212

921

426

109

272

1728

41.58%

19.23%

4.92%

12.28%

SUM

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Data from Interactive Map (online & in-person) Purpose.

Identify where Napa residents and workers would like to see improvements to the County transportation system.

Comment Source: ● In-Person (189) ● Online (73) ● SMS (19)

281 Comments Collected About the Map Activity ●

●

Online, an interactive map allowed participants to pin comments throughout the County At Community Events, larga maps were printed of the County and each jurisdiction. Attendees were invited to place a post-it comment where they would like to see improvements

Online Mapping Activity

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In-Person Mapping Activity Where would you like to see improvement?

(189 post-it comments placed) In-Person Mapping Data: Comments per jurisdiction

Napa

59

38.82%

American Canyon

48

31.58%

County-wide

46

30.26%

Calistoga

18

11.84%

St Helena

13

8.55%

Yountville

5

3.29%

SUM

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189

20

Data from Transportation Mode Poll Purpose.

Understand how participants typically move through the transportation system, and begin the conversation around mode options and practicality.

Source: ● In-Person (72) ● Online (9)

81 Votes Cast About the poll ●

Online, participants voted in a poll

●

At Community Events, attendees were invited to place a pom pom in the jar of the mode they use most often

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Data from County-wide Plan Focus Areas Only conducted at in-person events.

What is most important to improve transportation in Napa County?

(152 dot votes placed4)

Participants were invited to place up to three dots across the focus areas. The number of votes cast is not an accurate depiction of the number of participants.

4

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Discussion Findings by Focus Area Community sentiment from comments received via digital engagement and pop-up events is summarized below according to the goals of the Countywide Transportation Plan 2045.

Equity Goal: Serve the transportation needs of the entire community regardless of age, income or ability.

Most comments related to equity emphasized the need to provide and maintain safe, reliable, and comfortable transportation options to schools for youth (e.g., increase bus service, expand bike routes, and maintained sidewalks). Some comments expressed a desire for better or alternative transportation options for seniors and persons with disabilities, such as on-demand rideshare services because existing transportation options are not considered accessible or comfortable.

Safety Goal: Improve system safety in order to support all modes and serve all users.

Most comments expressed a desire to see more safety improvements for bikers and pedestrians (e.g. designated bike lanes, pedestrian signal crossings, etc.). Many comments raised concerns over dangerous vehicular traffic, identifying specific unsafe intersections or crossings and calling for increased traffic calming measures and road enforcement. A few comments asked about planned evacuation routes in the case of an emergency or natural disaster.

Reduce Congestion Goal: Use taxpayer dollars efficiently and reduce traffic congestion.

The majority of comments (over 200) were concerned with traffic congestion. Over half of the comments advocated for more frequent, reliable public transportation services that better connect Napa County cities to each other as well as to San Francisco, East Bay, and Sonoma County (at least to the Vallejo Ferry or Richmond Bart). The other comments want traffic congestion alleviation via roadway improvements, such as via synchronized traffic signals, roundabouts, road widening, designated turning and passing lanes, and over/underpasses.

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Economic Vitality Goal: Support Napa County’s economic vitality.

Most comments related to economic vitality want to see better coordination with the wine and tourist industry to manage transportation demand. A few comments (also related to equity) want to see better public transportation options for existing residents/workers and more reliable service to connect residents to economic opportunities inside and outside of Napa Valley.

Sustainability Goal: Minimize the energy and other resources required to move people and goods.

Overall, there were few comments explicitly about sustainability. Of those related, some comments advocated for electric vehicles while others discouraged driving in favor of promoting alternative modes of transportation, such as public transit, e-scooters, biking, and walking, through improving efficiency and safety or providing incentives (e.g. free transit passes, helmets, etc.) to protect the environment.

Maintenance & Preservation Goal: Prioritize the maintenance and rehabilitation of the existing system.

Many commenters want to see roads maintained and repaved, biking and pedestrian networks continue to be expanded and maintained, and public transportation frequency improved to encourage ridership. Additionally, some commenters felt public transportation could be improved through easier, seamless payment methods and better real-time trip planning tools.

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Appendix 1. Pop-up Event Posters

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2. Website Pages Banner

Introduction to the Site (1) English

(2) Spanish

(3) Tagalog

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About the Project

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‘Take the Survey!’ Page

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Performance Metrics TRANSIT ACCESSIBILITY SAFETY DELAY INDEX ON-TIME BUS PERFORMANCE TRUCK TRAVEL TIME RELIABILITY JOB ACCESSIBILITY PAVEMENT CONDITION INDEX

TRANSIT ACCESSIBILITY

TRANSIT BUFFER ANALYSIS MEMORANDUM DATE:

August 19, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for estimating the number of households with lower than median income that are served by Napa’s Vine Transit stops. The accessibility of Vine Transit service is determined by the walkable distance to these transit stops, which is assumed to be one quarter of a mile.

DATA

The following data was utilized for this analysis: 1. Number of households in Napa County – obtained from the American Community Survey (ACS) 2014-2018 2. Census Block Groups shapefile – obtained from TIGER (Topologically Integrated Geographic Encoding and Referencing 3. Vine Transit Stops shapefile – obtained from the NVTA web site.

METHODOLOGY

A combination of Microsoft Excel and ESRI’s ArcGIS was used for this analysis.

1. Load the Napa County Basemap ArcMap file.

2. Add the Vine Transit Stops shapefile to the map.

3. Use the Geoprocessing  Buffer tool in ArcMap to create a 1/4th mile buffer around Vine stops. Select the dissolve option to merge overlapping buffers. Save this buffer polygon feature class in the desired geodatabase.

4. Load the Block Groups shapefile. Using the Geoprocessing  Clip tool, clip the Block Groups to the existing Napa County boundary. This will create a subset of the Block Groups that only lie within Napa County’s boundary.

5. Certain block groups have houses build only over a small portion of their entire area. This is especially evident in block groups outside the dense city center, where built-up area does not cover the entire block group. To obtain the number of households within such transit buffers that lie along the periphery of cities, we calculate the ratio of area covered by transit buffers to the area of residential build-up in the block group, and multiply by the total number of households in that block group. Polygons around built-up residential area are created in Google Maps and imported into ArcMap as shown below.

FI GURE 1. EX AMPLE OF BLOCK GROUPS WI TH S MALL BUILT-UP AREA

6. Load the residential built-up area polygons onto ArcMap. 7. Using the Geoprocessing  Clip tool, join the Block Groups and Residential Polygons. Save as shapefile. Let’s call this “BlockGroup_Poly” for purposes of referencing. The Clip tool will attach the Block Group’s GeoID field to the Polygons shapefile, for easy cross-referencing later.

8. Using the Geoprocessing  Intersect tool, create an intersect between the Block Groups and Transit Buffers. This will split the buffers and divide them across block groups as highlighted in teal below. Save as shapefile and call this “BlockGroup_Buffer_Intersect”

FIGURE 2. CREATE AN INTERSECT OF TRANSIT BUFFER AND BLOCK GROUP SHAPEFILES

9. Using the Geoprocessing  Clip tool, join the BlockGroup_Poly and BlockGroup_Buffer_Intersect obtained in Step 8. Save this as a shapefile and name it as “BlockGroup_Poly_Buffer”. This gives the residential area that is intersected by transit buffers. Area of these polygons can be found under the field “Shape_Area”. Refer Figure 5 to view how each of the layers from Step 7 to 9 look on the map.

10. Export the table data of shapefiles BlockGroup_Poly and BlockGroup_Poly_Buffer.

11. In an Excel Worksheet, import the data from BlockGroup_Poly, BlockGroup_Poly_Buffer and the Demographic data obtained from ACS. Note that GeoID in ACS data is in a slightly different format than that in Block Groups. Edit the ACS GeoID field to match the Block Groups.

FIGURE 3. GEOID FIELD IN ACS 2014-2018

F I G U R E 4 . G E O I D I N B L OC K G R O U P S

12. Using VLOOKUP function in Excel, for every block group obtain the built-up area i.e.

Shape_Area of BlockGroup_Poly, and the built-up area intersecting the transit buffers, i.e.

Shape_Area of BlockGroup_Poly_Buffer.

13. Calculate the ratio of “Built-up area intersecting transit buffer” to “Built-up area”. Let’s call this “Area Ratio”.

14. Again using the VLOOKUP function obtain the total number of households for every block group. Multiply these by the Area Ratio to obtain an estimate of the number of households served by Vine Transit in that block group.

15. Summarize the number of households within 1\4th mile of Vine transit stops by income category, as shown in Table 1.

16. Obtain the number of households that lie below the median income of Napa, as shown in Table 2. Note that Napa’s median income is $84,753, however ACS income bins are broad, therefore $75,000 has been considered as the threshold income.

T ABLE 1 . HOUSEHOLDS WITHI N ACS -DEFINED IN COME CAT EGORIES INCOME CATEGORY

NUMBER OF HOUSEHOLDS WITHIN 1/4 MILE OF VINE TRANSIT STOP

< $10,000

1,131

$10,000 TO $14,999

1,141

$15,000 TO $19,999

1,098

$20,000 TO $24,999

1,385

$25,000 TO $29,999

1,286

$30,000 TO $34,999

1,187

$35,000 TO $39,999

1,310

$40,000 TO $44,999

1,332

$45,000 TO $49,999

1,282

$50,000 TO $59,999

2,447

$60,000 TO $74,999

3,271

$75,000 TO $99,999

4,756

$100,000 TO $124,999

3,876

$125,000 TO $149,999

2,732

$150,000 TO $199,999

3,380

$200,000 OR MORE

3,746

FI GURE 5. CLIPPI NG AN D IN TERSECTI NG BLOCK GROUPS , BUI LT-UP POLYGONS AN D TRAN SIT BUFF ER LAYERS

RESULTS

The number of households with income less than Napa’s median income and lying within onefourth mile of any Vine Transit stop are presented below.

TABLE 2 . NUMBER OF HOUS EHOLDS SERV ED BY VI NE T RAN SIT IN NAP A COUN TY

NUMBER OF HOUSEHOLDS IN NAPA COUNTY

NUMBER OF HOUSEHOLDS IN NAPA COUNTY BELOW MEDIAN INCOME*

% OF HOUSEHOLDS IN NAPA COUNTY BELOW MEDIAN INCOME* ($75,000)

NUMBER OF HOUSEHOLDS UNDER MEDIAN INCOME WITHIN ¼ MILE FROM TRANSIT STOP

% OF HOUSEHOLDS UNDER MEDIAN INCOME* THAT ARE WITHIN ¼ MILE FROM TRANSIT STOP

47%

16,869

85%

($75,000)

42,747

19,951

*Napa County median income is $84,753; however ACS provides data in income brackets like [$75,000 to $99,999].

Therefore households with an income less than $75,000 have been counted.

SAFETY

SAFETY ANALYSIS MEMORANDUM DATE:

August 19, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Erin Vaca | DKS Associates Aditi Meshram | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum discusses the methodology used in obtaining the number of traffic collisions in Napa County and depicting them on a map.

DATA

Traffic collision data for the state of California is available through the Statewide Integrated Traffic Records System (SWITRS). This data has been geocoded for ease of mapping, and has been made available on the Transportation Injury Mapping System (TIMS) maintained by the University of California, Berkeley Safe Transportation Research and Education Center. Note that the TIMS system includes only injury collision data and does not include property-damage-only (PDO) records. Collision data within the County of Napa was acquired for the years 2015 to 2018.

METHODOLOGY

The SWITRS collision data is mapped in ArcMap of ESRI’s ArcGIS Suite through the following steps: 1. Download the collision data from TIMS and save it as Comma Separated Value (csv) file.

2. Load the Napa County Basemap ArcMap file.

3. Using the Add Data feature, add the collision data csv file to ArcMap. 4. In the Table of Contents, right click on the collision csv layer and select ‘Display XY Data’.

Using the dialogue box, set the X Field as LONGITUDE, and Y Field LATITUDE as shown

below. Click OK.

5. A Point feature layer will be added to the map. Right click on this layer, select Data then Export Data. Set the file path for where you wish to save the output feature class, rename the shapefile and hit save. Add the exported data to the map as a layer.

6. Go to Layer Properties of the layer added in Step 5. Under Symbology  Categories  Unique Values, select the value field COLLISIO_1. Use the Add Values button to add collision severity values of 1 and 2, which correspond to Fatal and Severe Injury collisions, respectively.

7. Change the Symbol properties as desired and edit legend to display the feature counts.

8. The raw collision data obtained from TIMS was summarized in Excel using a Pivot Table.

The traffic collisions map of Napa County between 2015 and 2018 is shown in Figure 1. Traffic collisions involving pedestrian and bicycle fatalities are shown in Figure 2.

Table 1 summarizes the collisions by city and mode involved.

T ABLE 1 . FAT AL AN D SEVERE I NJURY COLLISIONS BY CITY 20 15- 2018

CITY

TOTAL FATAL

SEVERE INJURY

TOTAL INJURY

ALCOHOL INVOLVED

PEDESTRIAN INVOLVED

AMERICAN CANYON

1

12

324

18

15

CALISTOGA

1

13

57

5

4

NAPA

5

59

1,467

155

96

2

105

SAINT HELENA

2

3

133

10

10

1

4

1

3

UNINCORPORATED

39

202

2,198

190

14

1

47

2

180

9

YOUNTVILLE

-

2

16

2

1

-

-

-

291

4195

380

140

3

261

9

TOTAL IN NAPA COUNTY

48

PEDESTRIAN FATALITIES

-

-

-

4

BICYCLE INVOLVED

7

10

3

176

BICYCLE FATALITIES

-

-

-

MOTORCYCLE INVOLVED

17

2

59

MOTORCYCLE FATALITIES

-

-

-

-

FI GURE 1. F AT AL AN D S EV ERE TRAFF I C COLLI SI ONS IN N AP A COUNT Y (2 015 -20 18 ) SO UR CE: STATEWIDE INTEGRATED TR AFFIC R ECO RDS SYSTEM (SW ITR S), 2015 -201 8

FI GURE 2. F ATAL P EDEST RI AN AN D BI CYCLE COLLISI ON S SO UR CE: STATEWIDE INTEGRATED TR AFFIC R ECO RDS SYSTEM (SW ITR S), 2015 -201 8

RESULTS

Between the years 2015 and 2018, a total of 48 fatal and 291 severe collisions have occurred within the boundary of Napa County. The proposed target for CTP is zero fatal and severe injury collisions.

DELAY INDEX

DELAY INDEX MEMORANDUM DATE:

August 24, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for calculating the delay index of study roadway segments in Napa county.

Segments of the following roadways were studied: •

Trancas Street

•

Imola Avenue

•

SR 12 (Sonoma Hwy)

•

Silverado Trail

•

SR 221 (Napa-Vallejo Highway)

•

SR 29

•

SR 128/SR 29 (St Helena Highway)

•

Soscol Avenue

DATA

Travel demand model outputs: • •

Free-flow travel time Congested travel time

METHODOLOGY AND RESULTS

1. Obtain the free-flow and congested travel time outputs from the travel demand model for desired study corridors.

2. For every segment or extent of a corridor, sum the free-flow travel time.

3. Similarly, for each segment or extent of the corridor, sum the congested travel time.

4. Divide the congested travel time by free-flow travel time.

5. Repeat the process for every study route and time period (AM and PM).

Delay index results are summarized for study routes and their extents below. Segments with delay index greater than 2.0 are bolded.

The proposed target is a peak period delay index less than or equal to 2.0.

T ABLE 1 . DELAY I N DEX - EASTBOUN D AN D WESTBOUN D DI RECTI ONS

DIRECTION

Eastbound

Westbound

ROUTE

EXTENTS

PEAK PERIOD DELAY INDICES AM

PM

Trancas St

SR 29 (St Helena Hwy)-Silverado Trail

1.00

1.00

Imola Ave

SR 29-SR 221 (Napa Vallejo Hwy)

1.00

1.00

Old Sonoma Rd - SR 12/29/121 Jnct.

1.04

1.04

Trancas St

Silverado Trail - SR 29 (St Helena Hwy)

1.00

1.00

Imola Ave

NapaValley-Jefferson

1.00

1.00

SR 12/29/121 Jnct-Old Sonoma Rd

1.04

1.04

SR 12 (Sonoma Hwy)

SR 12 (Sonoma Hwy)

T ABLE 2 . DELAY I N DEX - S OUTHBOUN D DIRECTION

ROUTE

Silverado Trail SR-221 (NapaVallejo Hwy) SR-29

SR 128/29 (St Helena Hwy) Soscol Ave

EXTENTS

PEAK PERIOD DELAY INDICES AM

PM

Deer Park Rd-Trancas St.

1.00

1.00

Trancas St - Lincoln Ave

1.01

1.01

Lincoln Ave -Imola Ave

1.01

1.01

Imola Ave - SR 12

1.01

1.05

Soscol Jnct-SR 12 (Lincoln Hwy)

1.88

3.14

SR 12-Donaldson Way

1.14

1.16

Donaldson Way - American Canyon Rd

1.01

1.04

Pope St -Trancas St

1.00

1.02

Trancas St -Lincoln Ave

1.20

1.22

Lincoln Ave -Imola Ave

1.09

1.13

Imola Ave-SR 12 (Sonoma Hwy)

1.01

1.01

Sonoma Hwy-Soscol Jnct.

1.02

1.03

Trancas St -Imola Ave

1.00

1.01

T ABLE 3 . DELAY I N DEX - N ORTHBOUN D DIRECT ION

ROUTE

EXTENTS American Canyon Rd -Donaldson Wy

SR-29 SR-221 (NapaVallejo Hwy) Silverado Trail

SR 128/29 (St Helena Hwy) Soscol Ave

PEAK PERIOD DELAY INDICES AM

PM

1.03

1.02

Donaldson Way-SR 12

1.09

1.07

SR 12 (Lincoln Hwy) -Soscol Jnct.

3.71

2.40

Soscol Jnct.-Imola Ave

1.01

1.01

Imola Ave-Lincoln Ave

1.00

1.01

Lincoln Ave -Trancas St

1.01

1.01

Trancas St -Deer Park Rd

1.00

1.00

Soscol Jnct-SR 12 (Sonoma Hwy)

1.00

1.00

SR 12 (Sonoma Hwy) - Imola Ave

1.00

1.00

Imola Ave-Lincoln Ave

1.11

1.10

Lincoln Ave -Trancas St

1.08

1.08

Trancas St -Pope St

1.03

1.01

Imola Ave -Trancas St

1.03

1.02

ON-TIME BUS PERFORMANCE

TRANSIT PERFORMANCE MEMORANDUM DATE:

April 26, 2021

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for calculating on-time performance and ridership of Vine Transit in Napa county.

DATA

Transit Ridership: •

Vine Transit ridership data from NVTA (FY 2018-2019)

On-Time Performance: • • •

Vine Transit on-time performance data by route for year FY 2018 (routes changed December 2019) Vine Transit ridership data by route for year FY 2018 Routes categorized by o City (Routes 1 to 8) o Intercity (Routes 10 and 11) o Regional routes (Routes 21 and 29)

METHODOLOGY AND RESULTS TRANSIT RIDERSHIP

The Vine transit ridership data obtained from NVTA has ridership data for FY 2018-19 broken down by routes. This data was simply summarized for Commuter Service (routes 10X, 11X, 21 and 29), Regional Service (routes 10, 11, 10X, 11X, 21 and 29) and Local Routes (routes 1 to 8). These are

shown in Table 1. It is recommended that transit ridership be maintained or improved from current levels.

T ABLE 1 . VIN E TRANSIT RI DERS HI P BY SERVI CE TYP E SERVICE TYPE

RIDERSHIP

COMMUTER SERVICE

87,737

REGIONAL SERVICES

570,066

LOCAL ROUTES

382,023

ON-TIME PERFORMANCE

On-time performance and ridership data for year 2018 is available individually for routes 1 to 8, routes 10, 10x, 11, 11x, 21 and 29. Out of these, routes 1 to 8 are considered City Routes, 10 and 11 are Intercity Routes, and 21 and 29 are Regional Routes. This data is depicted in Figure 1 and Figure 2.

Since the level of ridership varies across routes, performance was measured to capture how many riders are impacted when a bus is late. Therefore, on-time performance is weighted by ridership for each route category - City, Regional and Intercity.

For this, weights are first calculated for each route within a route category based on ridership.

These ridership weights are then applied to on-time performance to obtain a weighted average.

Calculations are shown in Table 2. On-time performance for City Routes is 79%, for Intercity 63% and for Regional Routes is 65%.

90.00%

100,000

85.00%

80,000 60,000

80.00%

40,000

75.00% 70.00%

20,000 Route 1 Route 2 Route 3 Route 4 Route 5 Route 6 Route 7 Route 8

0

Axis Title On-Time Performance

Ridership

FI GURE 1. ON -TI ME PERFORMANCE AN D RI DERS HIP OF CI TY ROUTES

Ridership

On-Time Performance

City Routes, FY 2018

300,000

70.00%

250,000

65.00%

200,000

60.00%

150,000 100,000

55.00% 50.00%

Ridership

On Time Performance

Intercity and Regional Routes, FY 2018

50,000 Route 10

Route 11

Route 21

Route 29

0

Axis Title On-Time Performance

Ridership

FI GURE 2. ON -TI ME PERFORMANCE AN D RI DERS HIP OF I NT ERCIT Y AN D REGI ON AL ROUTES

T ABLE 2 . CALCUL ATI ON OF RI DERS HIP WEIGHTS AN D WEIGHTED ON -T IME P ERF ORMAN CE ROUTE CATEGORY

CITY ROUTES

INTERCITY ROUTES REGIONAL ROUTES

ROUTE

RIDERSHIP

ON-TIME PERFORMANCE (2018)

WEIGHTAGE

Route 1

18,533

84.82%

5%

Route 2

51,810

80.09%

14%

Route 3

60,592

79.16%

16%

Route 4

50,853

84.72%

13%

Route 5

51,219

81.08%

13%

Route 6

38,632

75.42%

10%

Route 7

16,689

76.59%

4%

Route 8

93,695

75.70%

25%

Route 10

230,578

58.25%

48%

Route 11

251,751

67.57%

52%

Route 21

21,140

68.21%

25%

Route 29

62,922

64.10%

75%

WEIGHTED ON-TIME PERFORMANCE

79.22%

63.12%

65.13%

It is recommended that weighted on-time performance be increased to 90% for all route categories.

TRUCK TRAVEL TIME RELIABILITY

FREIGHT RELIABILITY MEMORANDUM DATE:

August 16, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for calculating the Truck Travel Time Reliability (TTTR) Index for a given set of corridors within Napa County. This index will serve as the freight performance measure for the Napa countywide transportation plan. Results from this analysis are also discussed in the final section of the memo.

TRAVEL TIME RELIABILITY

The TTTR is calculated using data from the National Performance Management Research Data Set (NPMRDS), which provides travel time data for trucks on a given system of roads, mainly Interstate and State highways. 1 The TTTR compares days with extremely high delay to days with average delay, which gives an estimate of unexpected or unplanned delays or reliability. Travel time reliability measures the extent of this unexpected delay. As per FHWA, a formal definition for travel time reliability is the consistency or dependability in travel times, as measured from day-today and/or across different times of the day. 2 Truck Travel Time Reliability (TTTR) is simply the travel time reliability calculated for truck traffic.

TTTR is the ratio of the longer travel times (95th percentile) to a “normal” travel time (50th percentile). The TTTRs of a highway corridor’s segments are then used to create the TTTR Index for

1

https://ops.fhwa.dot.gov/publications/tt_reliability/brochure/ttr_brochure.pdf

2

https://ops.fhwa.dot.gov/publications/tt_reliability/TTR_Report.htm

the entire corridor using a weighted aggregate calculation for the worst performing times of each segment. 3 A higher TTTR Index denotes a less reliable highway. A TTTR Index of 1 represents freeflow conditions. For Interstate highways, a TTTR Index less than 1.69 is considered desirable. 4 For non-interstate highways studied within smaller regions (e.g. Counties or Cities), the TTTR Index can be of much higher value due to the constricted nature of traffic flow in these regions.

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (95𝑡𝑡ℎ) = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 (50𝑡𝑡ℎ) DATA

National Performance Management Research Data Set (NPMRDS) obtained from RITIS at https://npmrds.ritis.org/analytics/. Login with your RITIS account and using the Massive Data Downloader, download the truck-only data for Napa County with the following attributes as shown in Figure 1.

FI GURE 1. N PMRDS DATA DOWNLOAD S ETTIN GS

3

https://crcog.org/wp-content/uploads/2017/12/FINAL-2018_09-Freight-Reliability-TTTR.pdf

4

https://www.fhwa.dot.gov/tpm/reporting/state/reliability.cfm?state=California

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2

The download will contain segment performance data for trucks as well as a segment identification file named “TMC_Identification.csv”. The segment performance data contains speed and travel time information for each segment and for each time stamp. In this case we have obtained data averaged over fifteen minutes.

As shown in Figure 2, the data has a column named “tmc_code”. TMC stands for Traffic Message Channel, the purpose of which is to deliver information about traffic. As discussed earlier, each highway is divided into numerous segments and data is collected for each of these segments by TMCs which are identified by unique TMC codes.

FI GURE 2. TRUCK DATA DOWN LOADED F ROM N P MRDS

The TMC Identification file also contains a column for TMC codes as well as other information, used to relate any TMC in our data file to the road name (column: road) and the length of the segment (column: miles).

FI GURE 3. T MC I DENTIF I CATI ON FI LE ACCOMP AN YI N G THE TRUCK DAT A

N V T A C O U N T Y W I D E T RA N S P O RT A T I O N P L A N • F R E I G H T RE L I A B I L I T Y M E M O • M A RC H 2 4, 2 02 0

3

METHODOLOGY

The methodology for calculating Travel Time Reliability Index is obtained from a FHWA presentation on “National Travel Time Data Processing and Utilization” by the Office of Highway Policy Information. 5 The freight reliability measure, TTTR, is the sum of maximum TTTR for each reporting segment, divided by the total study corridor miles. The higher the TTTR Index, less reliable is the highway.

The method to calculate TTTR for each segment of a given study corridor is detailed in this section.

1. Join the obtained truck data with the TMC Identification information. Filter out the data based on road name of desired highway and create a sub-data set for each highway. For this analysis, four highway corridors have been studied: • • • •

CA-12 CA-29 CA-121 Napa-Vallejo Highway

2. Group the sub-data into 5 time periods for each TMC: • • • • •

Weekday 6:00 - 10:00 am Weekday 10:00 am - 4:00 pm Weekday 4:00 - 8:00 pm Everyday Overnight 8:00 pm - 6:00 am Weekend 6:00 am - 8:00 pm

3. Sort and rank the travel times in each group to obtain 95th and 50th percentile travel times for each TMC segment as shown below. Calculate their ratio to get Truck Travel Time Reliability (TTTR).

TTTR = 95th percentile travel time / 50th percentile travel time for each TMC. Find the maximum TTTR out of these. A maximum TTTR will be obtained in this manner for every TMC segment.

5

https://www.fhwa.dot.gov/tpm/guidance/hif18040.pdf N V T A C O U N T Y W I D E T RA N S P O RT A T I O N P L A N • F R E I G H T RE L I A B I L I T Y M E M O • M A RC H 2 4, 2 02 0

4

FI GURE 4. CALCULATIN G TTT R F OR EACH TI ME WIN DOW AN D OBTAIN IN G MAXIMUM TTTR

4. To obtain the TTTR Index for the entire study corridor, we need length of the TMC segments, obtained from the ‘TMC Identification’ data. Multiply the length of each TMC segment by the Maximum TTTR of that segment. Add all the length-weighted TTTRs for this study corridor and divide by total length of the corridor. This gives the TTTR Index, as illustrated in Figure 5.

FI GURE 5. CALCULATIN G LENGT H-WEI GHTED TTT R AN D T TT R I N DEX

5. An overall TTTR Index can be calculated for all four study corridors combined in a similar way by summing the length-weighted TTTR obtained in Step 4 for all corridors and dividing by the sum of lengths of all corridors.

𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿𝐿ℎ 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤ℎ𝑡𝑡𝑡𝑡𝑡𝑡 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 = 𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂𝑂 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑆𝑆𝑆𝑆𝑆𝑆 𝑜𝑜𝑜𝑜 𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙ℎ𝑠𝑠 𝑜𝑜𝑜𝑜 𝑎𝑎𝑎𝑎𝑎𝑎 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 N V T A C O U N T Y W I D E T RA N S P O RT A T I O N P L A N • F R E I G H T RE L I A B I L I T Y M E M O • M A RC H 2 4, 2 02 0

5

This process (Steps 1 to 4) needs to be repeated for all study corridors. The task of grouping data by time window and sorting to obtain 95th and 50th percentile travel time can become cumbersome when dealing with an entire year’s data containing more than 740,000 records. To make this process easily reproducible, a Python code has been written that calculates the TTTR Index for chosen study corridors. The same inputs are used by this code – NPMRDS Truck Data and TMC Identification file. The code is attached in the Appendix.

In order to run the code it is essential to download and install Python 3 (Python 3.7.4 was used for this analysis) 6. An interactive python development environment (IDE) ‘Spyder’ was used to edit and run the code 7. Spyder can be installed using Anaconda Navigator. Python packages NumPy and Pandas have been utilized in the code. Since Spyder is a Python environment, there is usually no need to set up the mentioned Python packages separately. However, in case Pandas and NumPy need to be installed for Spyder, detailed instructions can be found here.

6

https://www.python.org/downloads/

7

https://www.spyder-ide.org/

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6

RESULTS

The Truck Travel Time Reliability Index for specified highway corridors within Napa County are tabulated for the years 2016, 2017 and 2019.

T ABLE 1 . TT TR IN DEX F OR S TUDY CORRI DORS IN N AP A COUNTY 2017 S. No.

HIGHWAY/ROAD

1

CA-12

2

CA-121

3

CA-29

4

Napa-Vallejo Hwy

2018

2019

DIRECTION

LENGTH (miles)

TTTR Index

LENGTH (miles)

TTTR Index

LENGTH (miles)

TTTR Index

Eastbound

13.02

2.79

13.02

2.83

9.48

2.74

Westbound

13.04

2.43

13.04

2.41

8.68

2.88

Northbound

26.89

2.13

26.89

1.98

26.87

1.74

Southbound

27.08

2.20

27.08

2.19

27.06

2.15

Northbound

53.74

1.99

53.74

2.12

50.36

1.96

Southbound

53.73

2.05

53.73

1.95

53.75

2.06

Northbound

2.75

2.82

2.72

2.62

2.73

2.46

Southbound

2.75

3.18

2.70

3.78

2.73

3.10

These numbers denote how much longer it takes to traverse a roadway in extremely congested situation (e.g. during an accident) compared to normal conditions. For Westbound CA-12, thirteen miles of corridor length has been analyzed for 2019 giving a TTTR Index of 2.88. With a posted speed limit of 60 mph, under normal conditions the truck travel time along this corridor is thirteen minutes. During an extreme incident this travel time might increase to about thirty seven minutes, obtained by multiplying the normal truck travel time (13 minutes) to the TTTR Index (2.88). Thus, lower TTTR Index implies higher reliability of highway and therefore better on-time performance of freight.

The numbers here look slightly amplified because the lengths of study corridors considered are very small compared to statewide Interstate corridor lengths, which have long segments with near free-flow conditions for freight traffic. On the other hand, segments considered here pass through busy regions of Napa County for their most lengths, thereby showing a higher TTTR Index.

However, these numbers are useful for defining a baseline at present conditions and setting targets for the Countywide Transportation Plan.

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7

TTTR Index over three years 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00

2017

2018

CA-12

CA-121

CA-29

2019

Napa-Vallejo Hwy

FI GURE 6. T REN D OF TTTR IN DEX OV ER F OUR YEARS

PROPOSED TARGETS

As seen in Figure 7, the Overall Truck Travel Time Reliability Index has seen a decrease from 2017 to 2019. With increasing traffic over time, this decline can be very slow in the future. For the Napa Countywide Transportation Plan (CTP), it is recommended to maintain, or reduce by planned highway improvements, the current overall TTTR Index of 2.09.

2.18

2.17

2.16

Overall TTTR

2.16 2.14 2.12

2.09

2.10 2.08

2.09

2.06 2.04

2017

2018

Year

Overall TTTR

2019 Target

FI GURE 7. OV ERALL TTT R IN DEX F OR ALL S TUDY CORRIDORS

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8

APPENDIX

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

# -*- coding: utf-8 -*""" Created on Fri Feb 21 12:12:04 2020 @author: Aditi.Meshram """ import pandas as pd import numpy as np import tkinter as tk from tkinter.filedialog import askopenfilename print("Select truck data file") csv_file_path = askopenfilename() truck_data = pd.read_csv(csv_file_path)

#split measurement stamp into date and time and store in new columns in truck_data truck_data_new = truck_data["measurement_tstamp"].str.split(" ", n=1, expand = True) truck_data["date"] = truck_data_new[0] truck_data["time"] = truck_data_new[1]

#Obtain day of week truck_data['date'] = pd.to_datetime(truck_data['date']) truck_data['day_of_week'] = truck_data['date'].dt.day_name()

#Convert 24-hour time to integer truck_data["time_int"] = truck_data['time'].str.replace(':','') truck_data['time_int'] = truck_data['time_int'].astype(int)

weekday_list = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday"] weekend_list = ["Saturday", "Sunday"] truck_data["time_window"] = "" window

#create an empty column in dataframe to assign time

print("Assigning time windows to truck data rows...") #Assign time window categories to rows for index, row in truck_data.iterrows(): dayWeek = row['day_of_week'] timeInt = row['time_int'] if dayWeek in weekday_list: if timeInt >= 60000 and timeInt < 100000: #between 6am and 10am row['time_window'] = 1 elif timeInt >= 100000 and timeInt < 160000: #between 10am and 4pm row['time_window'] = 2 elif timeInt >= 160000 and timeInt < 200000: #between 4pm and 8pm row['time_window'] = 3 else: row['time_window'] = 4 #between 8pm and 6am if dayWeek in weekend_list: if timeInt >= 60000 and timeInt < 200000: #between 6am and 8pm row['time_window'] = 5 else: row['time_window'] = 4 #between 8pm and 6am #print("...") print("Time windows assignment complete") #-------------------------------------------------------------#

66 67 68 69

70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111

#define function to calculate TTTR def calculate_TTTR(time_win_sub): # TTTR = 95th percentile/50th percentile travel time tttr_time = np.percentile(time_win_sub['travel_time_seconds'],95)/np.percentile(time_win_sub['tra vel_time_seconds'],50) return tttr_time #-------------------------------------------------------------# #Read TMC Identification file print("Reading TMC Identification file") print("Select TMC Identification file") tmc_file_path = askopenfilename() tmc_data = pd.read_csv(tmc_file_path) tmc_data = tmc_data.sort_values('active_start_date') tmc_data = tmc_data.drop_duplicates(subset ='tmc', keep = 'last', inplace = False) #List of roads from TMC file R = tmc_data['road'].unique() R.sort() #Merge Truck Data and TMC Identification data based on tmc codes in Truck Data dataMerge = pd.merge(truck_data, tmc_data, how='left', left_on='tmc_code', right_on='tmc') dataMerge = dataMerge.dropna(subset = ['travel_time_seconds']) #drop rows with missing travel time values #-------------------------------------------------------------# #Process to calculate TTTR Index print("Creating sub-dataframe for road") for i in R: tmc_sub = dataMerge[dataMerge['road'] == i] #create sub-dataframe with data of each roadway D = tmc_sub['direction'].unique() #get list of directions for the roadway D.sort() for direction in D: tmc_sub2 = tmc_sub[tmc_sub['direction'] == direction] #create sub-dataframe for each direction of the roadway Z = [] #empty list to store TTTR M = [] #empty list to store Max TTTR LWT = [] #empty list to store length-weighted TTTR road_len = [] #empty list to store lenghts of all TMCs for direction of roadway TMC_list = tmc_sub2['tmc'].unique() for tmc in TMC_list: tmc_sub3 = tmc_sub2[tmc_sub2['tmc'] == tmc] for each tmc tmc_avgLen = round(tmc_sub3['miles'].mean(), 6) tmc segment.

112

113 114 115 116 117

time_window = tmc_sub3['time_window'].unique() windows [1,2,3,4,5] time_window.sort()

#create sub-dataframe #Find length of each #Unique TMCs should have the same lengths but the numbers might differ by 0.000001, so find mean. #Get list of time

for n in time_window: tmc_sub4 = tmc_sub3[tmc_sub3['time_window'] == n] #create sub-dataframe for time window "n"

118 119 120 121 122 123

124 125 126 127 128 129 130 131 132 133 134

TTTR = calculate_TTTR(tmc_sub4) #calculate TTTR for time window Z.append(TTTR) #append TTTR to list Z. TTTRs of all time windows for a TMC will be appended to Z. max_TTTR = max(Z) #find maximum TTTR amongst all time windows of a TMC LW_TTTR = max_TTTR*tmc_avgLen #find length-weighted TTTR LWT.append(LW_TTTR) #append length-weighted TTTR to list LWT. Length-weighted TTTRs of all TMCs of a road direction will be appended to LWT. road_len.append(tmc_avgLen) #append length of TMC to list road_len.

Lengths of all TMCs of a road direction will be appended to road_len.

Z = [] max_TTTR = 0

#empty the list Z for next TMC #reset max_TTTR for next TMC

TTTR_index = sum(LWT)/sum(road_len) #calculate TTTR Index print(i,direction,":","Road segment length =",round(sum(road_len),2),"miles,","TTTR Index =",TTTR_index)

#-------------------------------------------------------------# end

#print results

JOB ACCESSIBILITY

JOB ACCESSIBILITY BY TRANSIT MEMO DATE:

March 19, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for estimating the number of jobs accessible by transit within sixty minutes of travel time.

DATA AND TOOLS

Isochrones of transit coverage were obtained from www.remix.com, centered at five jurisdictions with Napa County and at three different start times per bus schedules: 6:40 AM, 7:00 AM and 7:40 AM. Jobs data was obtained from Census Transportation Planning Product (CTPP) at TAZ level. Data was exported as shapefiles to obtain the area of TAZs. The shapefile also contains job information.

Open the shapefile in ArcMap, export the attribute table and save as a CSV file.

ArcMap by Esri was used to map the transit coverage and relate the jobs data to the area of coverage.

METHODOLOGY OBTAINING ISOCHRONES FROM REMIX

Go to www.remix.com. Add the Vine Transit lines using Add Transit Line option.

Place the isochrone marker “Jane” at the desired location (city center or City Hall). Select the desired start time, wait times based on ‘Timetables’, travel time of 60 minutes and coverage option for jobs.

0

FI GURE 1. T RANSIT COV ERAGE IS OCHRON ES ON REMIX PLATF ORM

Using the export option, download the shapefile. Repeat this for all desired start times and then for each jurisdiction.

MERGING SHAPEFILES

Import isochrones for all desired start times into ArcMap, one jurisdiction at a time. Merge isochrones pertaining to all start times of a given jurisdiction using the Geoprocessing  Merge tool.

FI GURE 2. MERGI NG IS OCHRON ES FROM DIFF ERENT S TART TI MES I N ARCMAP

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

1

Select the polygons for “60 minutes” travel time and merge them using the Edit layer option. Save this merged transit coverage shapefile.

JOINING JOBS DATA TO SHAPEFILE

Import jobs data into ArcMap. Using the Geoprocessing tool in ArcMap, Clip the jobs data to the transit coverage shapefile. The clipped polygons are shown below.

In the attribute table, add a field and calculate the shape area of the clipped polygons. Export the attribute table of the obtained Clip file. This should contain the area of clipped polygons which will be used to calculate the ratio of area of polygons to the total area of TAZs to estimate the number of jobs accessed.

FI GURE 3. JOBS BY TAZ CLIPP ED TO T RAN SIT COV ERAGE I SOCHRON E

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

2

OBTAINING NUMBER OF JOBS ACCESSIBLE

In an excel workbook, import the attribute tables exported in the previous steps as well as the jobs

by TAZ data obtained from CTPP. For each field, calculate the ratio of the area of polygon to the area of TAZ. Multiply this ratio by the number of jobs corresponding to that field. This gives an estimate of the number of jobs within the coverage area reachable by transit under sixty minutes from the given start point and within the given time period. In this case, start times of 6:40 AM, 7:00 AM and 7:40 AM were considered to observe maximum transit coverage and number of jobs accessible during the morning commute hours.

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

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RESULTS TRANSIT COVERAGE FROM EACH JURISDICTION

FI GURE 4. T RANSIT COV ERAGE FROM CALI STOGA WI T HI N 60 MIN UT ES

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

4

FI GURE 5. T RANSIT COV ERAGE FROM ST . HELEN A WITHI N 60 MIN UTES

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

5

FI GURE 6. T RANSIT COV ERAGE FROM YOUNTVILLE WITHI N 60 MINUT ES

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

6

FI GURE 7. T RANSIT COV ERAGE FROM N AP A WI T HIN 60 MI NUTES

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

7

FI GURE 8. T RANSIT COV ERAGE FROM AMERICAN CAN YON WIT HIN 6 0 MIN UTES

N V T A C T P • J O B S A C C E S S I B I L I T Y M E M O RA N D U M • M A RC H 1 9, 2 02 0

8

NUMBER OF JOBS ACCESSIBLE BY VINE TRANSIT UNDER 60 MINUTES

NUMBER OF JOBS ACCESSIBLE BY TRANSIT FROM CITY CENTERS BETWEEN 6:30 AM AND 8:00 AM JURISDICTION NUMBER OF JOBS

AMERICAN CANYON

CALISTOGA

NAPA

ST. HELENA

YOUNTVILLE

37,725

8,831

40,241

8,475

29,521

9

PAVEMENT CONDITION INDEX

PAVEMENT CONDITION MAPPING MEMORANDUM DATE:

April 24, 2021 18, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for mapping the pavement condition index for Napa county.

DATA

Pavement Condition Index (PCI) at street level and at county level from MTC Vital Signs (2018)

METHODOLOGY

The overall score for pavement condition of Napa County and all jurisdictions within the county are reported at https://www.vitalsigns.mtc.ca.gov/street-pavement-condition under Regional Distribution.

The following steps were applied to obtain street level PCI data from MTC: 1. Go to https://www.vitalsigns.mtc.ca.gov/street-pavement-condition

2. Scroll down to the Local Focus section that displays PCI by street segment.

3. Click on “Download Indicator Data From This Page.

4. Select the Street and Roads [Shapefile] data

5. Select Export  Shapefile.

6. Map the PCI for street segments using ArcGIS or other mapping software.

RESULTS

The overall pavement condition index for Napa county is 58, as reported by MTC for 2018. PCI score by jurisdiction is presented in Table 1. The pavement condition of street segments is mapped in Figure 1 and summarized in Table 2. Currently 64% of the streets have Good/Fair or Excellent/Very Good pavement condition.

The proposed target is to achieve a countywide PCI score of 80.

T ABLE 1 . COUNTY AN D JURI S DI CTI ON P CI S CORE Pavement Condition Index by MTC (2018) Jurisdiction

PCI score

American Canyon

64

Calistoga

56

Napa

70

St. Helena

56

Yountville

74

Unincorporated

50

Napa County

58

T ABLE 2 . PERCEN T OF S TREETS BY P AV EMENT CON DIT ION

Pavement Condition

% of streets

Excellent/Very Good

42%

Good/Fair

22%

At Risk

7%

Poor/Failed

29%

FI GURE 1. P AVEMENT CON DITI ON OF S TREETS IN N AP A COUNT Y

Freight Flows

MEMORANDUM - GOODS MOVEMENT ANALYSIS DATE:

November 6, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Aditi Meshram | DKS Associates Erin Vaca | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

MEMO OBJECTIVE

This memorandum describes the methodology for estimating freight flows of Napa County.

Freight flow data is described by its associated travel patterns, which include inbound to the County, outbound from the County, and internal trips that start and end within the County. In addition, freight flows were assessed for two basic categories: •

Flow by weight of shipment

•

Flow by value of shipment

DATA

•

Freight Analysis Framework version 4.5.1 (FAF4) estimated 2018 flows Note that the FAF data is aggregated at the San Jose-San Francisco-Oakland, CA area (FAF zone 64). This includes the nine counties of Bay Area: Alameda, Contra Costa, Marin, Napa, San Francisco, San Mateo, Santa Clara, Solano and Sonoma county.

•

Employment by industry data for the nine-county Bay Area (FAF zone 64) and for Napa County, from Employment Development Department (EDD) 1

•

Household income and number of households by county from American Community Survey (ACS) 2018 5-year estimates 2

1

https://www.labormarketinfo.edd.ca.gov/data/employment-by-industry.html

2

Table S1901 for the Bay Area counties.

METHODOLOGY

For disaggregating the regional FAF goods flow data to obtain county-level flow, proportional weighting allocation method has been used. This method allocates values from a source unit to target units proportional to a proxy variable. A proxy variable is a variable that can be easily measured in lieu of another variable that is difficult to measure.

We applied employment data as a proxy variable to represent “goods production”, since this data is readily available for Napa County based on industry type. Outbound and inbound goods flow has been assumed to be proportional to the level of relevant industry-specific employment of the County relative to other Bay Area counties.

Similarly, median income is the proxy variable for “goods consumption”. For goods flow within Bay Area counties, consumption factor has been calculated using household median income weighted by number of households.

The desired FAF flow data was disaggregated to the county level based on these production and consumption factors.

The steps to obtain the goods flow estimate for Napa County are detailed below. A screenshot of the resulting calculating is shown in Figure 1.

1. Identify the appropriate proxy variable. Employment by industry and median income weighted by number of households have been chosen as proxy variables for this disaggregation process.

2. Obtain the employment data of the relevant industries mentioned in Step 4 for the entire San Francisco-San Jose-Oakland area (FAF zone number 64), as well as for Napa County alone.

3. Calculate the percent proportion of median income weighted by number of households for each county relative to the parent FAF zone 64. Call this the consumption factor.

4. Calculate the percent proportion of industry employment of each county to the parent FAF region using EDD employment data. This will be referred to as production factor. This should be calculated for the following industry sector groups: a. Total Farm b. Mining, Logging, and Construction c. Manufacturing d. Wholesale Trade e. Retail Trade f. Transportation, Warehousing & Utilities 5. For each of the above industries, identify relevant commodities from FAF data. The complete list of FAF commodity codes is included as an attachment.

Industry

FAF Commodity Code

Farming

1 to 9

Mining, Logging, and Construction

10 to 19, 25

Manufacturing

20 to 40

Wholesale Trade

20 to 40

Retail Trade

20 to 40

Transportation, Warehousing & Utilities

37, 41,43,99

6. From FAF data of each mentioned industry (or corresponding commodity codes), find the total shipment weight and shipment value for: a. Outbound flow (origin as zone 64 and destination as other zones) b. Inbound flow (other zones to zone 64) c. Internal flow (zone 64 to zone 64) These totals were also checked against the FAF4 data tabulation tool. 3 INDUSTRY

FARMING MINING, LOGGING, AND CONSTRUCTION MANUFACTURING WHOLESALE TRADE RETAIL TRADE TRANSPORTATION, WAREHOUSING & UTILITIES

SHIPMENT WEIGHT (THOUSAND TONS) Outbound Inbound Internal

SHIPMENT VALUE (MILLION DOLLARS) Outbound Inbound Internal

31,576

21,493

14,160

41,325

40,736

21,834

29,134

46,503

104,308

18,703

21,475

42,436

32,441

35,563

37,587

190,459

194,879

81,555

32,441

35,563

37,587

190,459

194,879

81,555

32,441

35,563

37,587

190,459

194,879

81,555

6,197

4,484

24,450

26,404

14,472

22,400

7. For internal goods flow within zone 64 (i.e. the intra-Bay Area flow), multiply the internal flow totals by production factor of the origin county and by consumption factor of the destination county.

8. For outbound goods flow, multiply the total outbound flow from zone 64 with production factor of the origin county.

3

https://faf.ornl.gov/fafweb/Extraction1.aspx

FI GURE 1. EX AMPLE CALCULATI ON S OF GOODS FLOW FOR F ARMING IN DUST RY

9. For inbound goods flow, multiply the total inbound flow to zone 64 with production factor of the destination county.

10. Repeat the steps for each industry.

Note: As the FAF commodity codes for Manufacturing, Wholesale Trade, and Retail Trade coincide, the estimated goods flow for these industries have been combined as an average of the three.

RESULTS

Out of total 26,798 thousand tons of shipment to, from, and within Napa County, 62% is outbound and about 37% is inbound. As expected, farm products are the largest exports from the county, in terms of both shipment weight and value.

Goods flow share by shipment weight 0.4%

37.4% 62.2%

Intra

Inbound

Outbound

Goods flow share by shipment value 0.6%

36.4%

63.0%

Intra

Inbound

Outbound

Total weight of goods in thousands of tons 30000 25000 20000 15000 10000 5000 0 Total Farm

Mining, Logging, and Construction

Manufacturing, Wholesale Trade, Retail Trade

Outbound

11493

3016

1604

544

16657

Inbound

5588

2616

1381

446

10030

54

36

13

7

111

Intra

Intra

Inbound

Outbound

Transportation, Warehousing & Utilities

Total

Total value of goods in million dollars 25000 20000 15000 10000 5000 0 Total Farm

Mining, Logging, and Construction

Manufacturing, Wholesale Trade, Retail Trade

Outbound

6374

1384

6255

871

14883

Inbound

1180

1123

5715

595

8613

84

15

29

6

134

Intra

Intra

Inbound

Outbound

Transportation, Warehousing & Utilities

Total

ATTACHMENT – FAF COMMODITY CODES

Greenhouse Gas Calculations

GHG EMISSION SAVINGS MEMORANDUM DATE:

October 13, 2020

TO:

Alberto Esqueda | Napa Valley Transportation Authority

FROM:

Erin Vaca | DKS Associates Aditi Meshram | DKS Associates Jim Damkowitch | DKS Associates

SUBJECT: NVTA Countywide Transportation Plan

Project #19118-000

OBJECTIVE

This memorandum describes the methodology for estimating the reduction (or savings) in on-road mobile source greenhouse gas (GHG) emissions for the following scenarios: 1. Potential GHG savings if passenger loading on existing express commuter route services provided by NVTA increase by 80%.

2. Potential GHG savings if a 10% mode shift to transit were to occur for the top five divertible commuter markets as identified in NVTA’s Travel Behavior Study (TBS).

3. Potential GHG savings if a 10% mode shift 1 to active transportation were to occur for all trips under 3 miles in length (one-way trip end) based on the TBS. The 10% mode shift is consistent with a goal from the Bike and Ped Plan goal).

DATA AND TOOLS

The following data sources were utilized to inform the mode shift analyses and resulting GHG emission benefits: 1. Streetlight OD trip data and zones for Fall 2018 (August-November) 2. Vine Transit commuter route load factors 3. Vine transit stops and routes

1

A ten percent mode shift is part of the vision statement for the Napa Countywide Bicycle Plan (NVTA, 2019).

4. Google distance API - a service that provides travel distance and time for a matrix of origins and destinations. The API returns information based on the recommended route between start and end points, as calculated by the Google Maps API. 5. QGIS mapping tool - QGIS is an Open Source Geographic Information System (GIS) 6. SB1 emissions calculator – developed by the California Transportation Commission (CTC) to estimate on-road mobile source emissions changes for transportation projects. The tool applies vehicle speed, vehicle type and vehicle technology group corrected on-road mobile source base emission rates consistent from EMFAC emissions model developed by the California Air Resources Board (CARB) for application in California. The methodology is similar to CARB’s California Freight Investment Program.

METHODOLOGY

The following steps were undertaken to estimate GHG savings for each scenario.

SCENARIO 1

1. Using QGIS, map the Streetlight OD zones.

2. Create ½ mile buffers around commuter stops and extract zones that intersect with these buffers.

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

2

3. To these zones, add zones in San Francisco and Alameda that can be accessed via transfers from El Cerrito BART and Vallejo ferry. This would be the set of zones that can be accessible by transit. Let’s call these “divertible zones”.

4. Using QGIS, find the centroids of each of these zones.

5. Using Google Distance API, find the travel distance array for divertible zones.

6. For these zones, find the trips from Streetlight OD data for the following combinations: a. Trips from zones in Napa county to zones outside Napa b. Trips from zones outside Napa county to zones within Napa

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

2

7. Multiply obtained trip matrices to corresponding distance arrays to yield the following VMT estimates:

Napa to Outside Outside to Napa Total

VMT of total divertible trips 118,654 126,145 244,799

8. From transit load factor data provided by NVTA, determine the increase in daily ridership assuming load factors increase by 80%. Correspondingly, determine the number of daily vehicle trips that would be reduced by applying an Average Vehicle Occupancy (AVO) of 1.1 2

Route 29 21 11 11X 10 10X

Number of runs in peak AM and PM periods 13+11 6+8 8+8 7+9

Number of runs in AM

Number of runs in PM

13 6 8

11 8 8

7

9

What is the maximum load capacity 33 33 33 33 33 33

Average % of load for a given run

Load Passengers Vehicles factor increased reduced increased by by by (AM+PM) (AM+PM)

21% 14% 39%

59% 66% 41%

31%

49% Total

468 304 214 0 260 0 1246

425 277 194 0 236 0 1133

9. Based on Steps 1-8, an estimated 1,133 vehicle trips will be reduced in the AM and PM combined if load factors of commuter routes are increased to 80% 10. Determine the weighted average trip length of all divertible trips to and from Napa County and multiply by 1,133 vehicle trips to yield the VMT reduction/savings. This results in a daily VMT reduction of 227,302.

Napa to Outside Outside to Napa Total

VMT of total divertible trips (No Build) 118,654 126,145 244,799

VMT if 1,133 vehicle trips shift to bus (Build)

227,302

Average trip length for vehicles 15.35 15.54 15.44

11. Based on the estimated VMT reduction, apply the SB1 Emissions Calculator to estimate the GHG reduction. No operational benefits are assumed as part of this analysis (i.e., average vehicle speeds are considered constant between the No Build (no mode shift) and Build

2

This AVO is a conservatively rounded estimate consistent with that reported in the American Community

Survey 2018 5-year estimates for Napa County (Table S0801).

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

2

(with mode shift) condition (i.e., set constant at 35 mph). An immediate mode shift was assumed, so a project construction phase of 0 years was assumed.

12. The emissions calculator calculates the on-road mobile source emissions saved in tons per year for both criteria (health-based) pollutants and climate change pollutants. For purposes of this analysis, only CO2 emissions are counted as GHG emissions.

TONS EMISSIONS SAVED (tons/yr) CO 6.57

CO2 1448.55

NOX 0.52

PM10 0.01

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

SOX 0.01

VOC 0 0.27

PM2.5 0.01

2

SCENARIO 2

1. Identify the top five divertible markets from TBS that have access to Vine transit routes.

These were identified as Napa County, Solano County, Contra Costa County, San Francisco County and Alameda County. Though Sonoma County is a significant market, it was not accounted for given that it does not have access to Vine routes.

2. Similar to Scenario 1, find the VMT between zones of the identified divertible markets. The following trips were analyzed: a. Napa County to external counties b. External counties to Napa County c. Trips within Napa county that are accessible by transit 3. Calculate the VMT if 10% of above trips shift to transit. All new transit service needed to accommodate this influx of new riders is assumed to be by electric vehicle technology buses and therefore have zero emissions (point source electric generation is not accounted for).

4. Find the average trip length of trips reduced.

Napa to External Counties External Counties to Napa Subtotal Within Napa

VMT of total divertible trips

VMT if 10% vehicle trips shift to bus

Average trip length for vehicles

236,092

23,609

15.61

243,051 479,143 802,895

24,305 431,228 722,606

15.91 15.76 4.75

5. Using the SB1 Emissions Calculator, estimate the on-road mobile source emissions saved in tons per year for both criteria (health-based) pollutants and climate change pollutants for the two sub-scenarios: a) Napa to external, external to Napa, and b) Within Napa. For purposes of this analysis, only CO2 emissions are counted as GHG emissions.

Scenario 2.1 Scenario 2.2 Scenario 2 (total)

Emissions Saved (tons/yr)

CO

TONS EMISSIONS SAVED (tons/yr) CO2 NOX PM10 SOX VOC

PM2.5

3,986

17.94

3965.52

1.43

0.02

0.04

0.74

0.02

6,907

39.12

6862.30

3.34

0.05

0.07

2.49

0.04

10,893

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

2

SCENARIO 3

1. For all zones within Napa county, calculate the VMT for trips that are less than 3 miles.

2. Calculate the VMT if 10% of trips move to active transportation.

Napa trips within 3 mi

VMT of total divertible trips 96,127

VMT if 10% vehicle trips shift to bus 80,971

Average trip length for vehicles 1.58

3. Using the Emissions Calculator, estimate the on-road mobile source emissions saved in tons per year for both criteria (health-based) pollutants and climate change pollutants. For purposes of this analysis, only CO2 emissions are counted as GHG emissions.

TONS EMISSIONS SAVED (tons/yr) Scenario 3

NOX

PM10

SOX

VOC

PM2.5

1.15

0.01

0.01

1.15

0.01

CO

TONS EMISSIONS SAVED (tons/yr) CO2 NOX PM10 SOX VOC

PM2.5

1,456

6.57

1448.55

0.52

0.01

0.01

0.27

0.01

3,986

17.94

3965.52

1.43

0.02

0.04

0.74

0.02

6,907

39.12

6862.30

3.34

0.05

0.07

2.49

0.04

12.30

1413.36

1.15

0.01

0.01

1.15

0.01

Emissions Saved (tons/yr) 1,428

CO 12.30

CO2 1413.36

RESULTS SUMMARY

Scenario 1 Scenario 2.1 Scenario 2.2 Scenario 2 (total) Scenario 3

Emissions Saved (tons/yr)

10,893 1,428

Scenario 1: 1,456 tons of GHG (CO2) emissions will be saved per year if load factors on express commuter routes are increased to 80%.

Scenario 2: A total of 10,893 tons of GHG emissions will be saved in a year if 10% vehicle trips from top five divertible markets shift to transit.

Scenario 3: 1,428 tons of GHG emissions will be saved in a year if 10% of all trips within 3 miles in Napa County shift to active transportation.

N V T A C O U N T Y W I D E P L A N • G H G C A L C U L A T I O N S • O C T O B E R 20 2 0

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Travel Demand Forecasting – Mode Shares

NVTA Advancing Mobility 2045

Metric

Drive Alone Mode Share Shared Ride Mode Share Transit Mode Share Bike Mode Share Ped Mode Share TNC (Uber, Lyft, etc) Total VMT Delay

2015 Conditions Baseline

Scenario 1 Basic Plan

Scenario 2 - Proposed Plan Difference from (1)

Scenario 3 - Transit+ (3)

Difference from (1)

Scenario 4 - SR-29 Lanes Scenario 4 Lanes+

Difference from (1)

(1)

(2)

57.03%

56.76%

56.63%

-0.13%

56.29%

-0.47%

56.76%

0.00%

31.68%

29.82%

29.89%

0.07%

29.78%

-0.03%

29.82%

0.00%

1.00% 1.49% 7.21% 1.58% 2,914,618 5,468

0.90% 3.99% 6.95% 1.59% 3,976,098 22,811

1.01% 3.99% 6.90% 1.58% 3,962,930 22,170

0.12% 0.00% -0.05% -0.01% -0.3% -2.8%

1.46% 3.99% 6.95% 1.53% 3,957,253 22,076

0.56% 0.00% 0.00% -0.06% -0.5% -3.2%

0.90% 3.99% 6.95% 1.59% 3,977,227 22,601

0.00% 0.00% 0.00% 0.00% 0.0% -0.9%

Notes: Mode shares based on average daily person trips with origin and/or destination in Napa County. Total VMT is daily and occurring on Napa County roadways. Delay is total daily person hours of delay for trips beginning or ending in Napa County. Bike mode share adjusted outside travel demand model.

4/22/2021

Funding Programs

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Current FTA Grant Programs Program Accelerating Innovative Mobility Access and Mobility Partnership Grants

Better Utilizing Investments to Leverage Development (BUILD) Transportation Grants Program (formerly TIGER) Capital Investment Grants - 5309

Description Accelerating Innovative Mobility (AIM) will highlight FTA’s commitment to support and advance innovation in the transit industry.

This program provides competitive funding to support innovative capital projects for the transportation disadvantaged that will improve the coordination of transportation services and non-emergency medical transportation services.

US DOT’s Better Utilizing Investments to Leverage Development (BUILD) Transportation Discretionary Grants program funds investments in transportation infrastructure, including transit.

Type Competitive Competitive

Competitive

Provides funding through a multi-year competitive process for transit capital Competitive investments, including heavy rail, commuter rail, light rail, streetcars, and bus rapid transit. Federal transit law requires transit agencies seeking CIG funding to complete a series of steps over several years to be eligible for funding.

Enhanced Mobility of Seniors & Formula funding to states for the purpose of assisting private nonprofit Formula Individuals with Disabilities groups in meeting transportation needs of the elderly and persons with Section 5310 disabilities.

Expedited Project Delivery Pilot The EPD Pilot Program, authorized by Section 3005(b) of the Fixing Competitive Program - Section 3005(b) America’s Surface Transportation Act (FAST Act), is aimed at expediting delivery of new fixed guideway capital projects, small starts projects, or core capacity improvement projects. These projects must utilize public-private partnerships, be operated and maintained by employees of an existing public transportation provider, and have a Federal share not exceeding 25 percent of the project cost. FTA will notify applicants in writing within 120 days after the receipt of a complete application whether the application has been...

Flexible Funding Programs CMAQ provides funding to areas in nonattainment or maintenance for Formula Congestion Mitigation and Air ozone, carbon monoxide, and/or particulate matter. States that have no Quality Program - 23 USC 149 nonattainment or maintenance areas still receive a minimum apportionment of CMAQ funding for either air quality projects or other elements of flexible spending. Funds may be used for any transit capital expenditures otherwise eligible for FTA funding as long as they have an air quality benefit.

Flexible Funding Programs - Surface Provides funding that may be used by states and localities for a wide range Formula Transportation Block Grant of projects to preserve and improve the conditions and performance of Program - 23 USC 133 surface transportation, including highway, transit, intercity bus, bicycle and pedestrian projects.

Formula Grants for Rural Areas Provides capital, planning, and operating assistance to states to support Formula 5311 public transportation in rural areas with populations less than 50,000, where many residents often rely on public transit to reach their destinations.

Grants for Buses and Bus Facilities Provides funding to states and transit agencies through a statutory formula Formula Formula Program - 5339(a) to replace, rehabilitate and purchase buses and related equipment and to construct bus-related facilities. In addition to the formula allocation, this program includes two discretionary components: The Bus and Bus Facilities Discretionary Program and the Low or No Emissions Bus Discretionary Program.

Grants for Buses and Bus Facilities Provides funding through a competitive allocation process to states and Competitive Program transit agencies to replace, rehabilitate and purchase buses and related equipment and to construct bus-related facilities. The competitive allocation

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Program

Description

Type

provides funding for major improvements to bus transit systems that would not be achievable through formula allocations.

Helping Obtain Prosperity for In keeping with the U.S. Department of Transportation’s focus on addressing Competitive Everyone Program the deteriorating conditions and disproportionately high fatality rates on our rural transportation infrastructure, FTA’s Helping Obtain Prosperity for Everyone (HOPE) Program supports projects that will address the transportation challenges faced by areas of persistent poverty.

Integrated Mobility Innovation FTA's Integrated Mobility Innovation (IMI) Program funds projects that Competitive demonstrate innovative and effective practices, partnerships and technologies to enhance public transportation effectiveness, increase efficiency, expand quality, promote safety and improve the traveler experience.

Low and No-Emission Component On September 29, 2016, FTA announced the opportunity for eligible Competitive Assessment Program (LoNo-CAP) institutions of higher education to apply for funding to conduct testing, evaluation, and analysis of low or no emission (LoNo) components intended for use in LoNo transit buses used to provide public transportation. The deadline for applications is November 28, 2016.

Low or No Emission Vehicle Provides funding through a competitive process to states and transit Competitive Program - 5339(c) agencies to purchase or lease low or no emission transit buses and related equipment, or to lease, construct, or rehabilitate facilities to support low or no emission transit buses. The program provides funding to support the wider deployment of advanced propulsion technologies within the nation’s transit fleet.

Metropolitan & Statewide Planning Provides funding and procedural requirements for multimodal Formula and Non-Metropolitan transportation planning in metropolitan areas and states. Planning needs to Transportation Planning - 5303, be cooperative, continuous, and comprehensive, resulting in long-range 5304, 5305 plans and short-range programs reflecting transportation investment priorities.

Mobility for All Pilot Program This funding opportunity seeks to improve mobility options through Competitive Grants employing innovative coordination of transportation strategies and building partnerships to enhance mobility and access to vital community services for older adults, individuals with disabilities, and people of low income.

Mobility on Demand (MOD) Funds projects that promote innovative business models to deliver high Competitive Sandbox Demonstration Program - quality, seamless and equitable mobility options for all travelers.

5312 Passenger Ferry Grant Program Provides competitive funding to public ferry systems in urbanized areas.

Competitive Section 5307 Pilot Program for Transit-Oriented Provides funding to local communities to integrate land use and Competitive Development Planning – Section transportation planning with a transit capital investment that will seek 20005(b) funding through the Capital Investment Grant (CIG) Program.

Public Transportation COVID-19 This program will fund grants through public transit agencies to develop, Competitive Research Demonstration Grant deploy, and demonstrate innovative solutions that address COVID-19 Program related concerns to increase operating efficiencies and improve mobility.

Public Transportation Emergency Helps states and public transportation systems pay for protecting, repairing, Formula Relief Program - 5324 and/or replacing equipment and facilities that may suffer or have suffered serious damage as a result of an emergency, including natural disasters such as floods, hurricanes, and tornadoes. It provides authorization for Section 5307 and 5311 funds to be used for disaster relief in response to a declared disaster.

Public Transportation Innovation - Provides funding to develop innovative products and services assisting Competitive 5312 transit agencies in better meeting the needs of their customers.

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Program Public Transportation on Indian Reservations Program; Tribal Transit Program

Real-Time Transit Infrastructure and Rolling Stock Condition Assessment Research and Demonstration Program

Redesign of Transit Bus Operator Compartment to Improve Safety, Operational Efficiency, and Passenger Accessibility (Bus Operator Compartment) Program Rural Transportation Assistance Program - 5311(b)(3)

Description

Type

The Tribal Transit Program is a set-aside from the Formula Grants for Rural Competitive Areas program consisting of a $30 million formula program and a $5 million discretionary grant program subject to the availability of appropriations. A 10-percent local match is required under the discretionary program, however, there is no local match required under the formula program.

FTA’s Public Transportation Innovation Program (49 U.S.C. § 5312), Competitive authorizes FTA to fund research, development, demonstrations, and deployment projects to improve public transportation. The Real-Time Transit Infrastructure and Rolling Stock Condition Assessment Demonstration Program is a competitive demonstration opportunity under FTA's research emphasis area of infrastructure. This priority area supports the U.S. Department of Transportation's Infrastructure strategic goal, as well as the strategic objective of life cycle and preventive maintenance for asset management planning and...

This program supports research projects to develop transit bus operator Competitive compartment designs that improve bus operator and public safety as well as bus operator access to vehicle instruments and controls without hindering the accessibility of passengers.

Provides funding to states for developing training, technical assistance, Formula research, and related support services in rural areas. The program also includes a national program that provides information and materials for use by local operators and state administering agencies and supports research and technical assistance projects of national interest.

Safety Research and Demonstration The Safety Research and Demonstration (SRD) Program is part of a larger Competitive Program safety research effort at the U.S. Department of Transportation that provides technical and financial support for transit agencies to pursue innovative approaches to eliminate or mitigate safety hazards. The SRD program focuses on demonstration of technologies and safer designs.

State of Good Repair Grants - 5337 Provides capital assistance for maintenance, replacement, and rehabilitation Formula projects of existing high-intensity fixed guideway and high-intensity motorbus systems to maintain a state of good repair. Additionally, SGR grants are eligible for developing and implementing Transit Asset Management plans.

Technical Assistance & Standards Provides funding for technical assistance programs and activities that Formula Development - 5314(a) improve the management and delivery of public transportation and development of the transit industry workforce.

Transit Cooperative Research Research program that develops near-term, practical solutions such as best Competitive Program - 5312(i) practices, transit security guidelines, testing prototypes, and new planning and management tools.

Urbanized Area Formula Grants - Provides funding to public transit systems in Urbanized Areas (UZA) for Formula 5307 public transportation capital, planning, job access and reverse commute projects, as well as operating expenses in certain circumstances.

Zero Emission Research On November 22, 2016, FTA announced the opportunity for nonprofit Competitive Opportunity (ZERO) organizations to apply for funding to conduct research, demonstrations, testing, and evaluation of zero emission and related technology for public transportation applications.

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SB 1 Funding SB 1 discretionary funding programs that are available to NVTA and local jurisdictions are as follows:

Transit and Intercity Rail Capital Program Guidelines (TIRCP) The TIRCP was created to fund transformative capital improvements that modernize California’s intercity rail, bus (including feeder buses to intercity rail services, as well as vanpool services that are eligible to report as public transit to the Federal Transit Administration), ferry, and rail transit systems (collectively referred to as transit services or systems) to achieve all of the following policy objectives, as codified in Section 75220(a) of the PRC: 1. Reduce emissions of greenhouse gases 2. Expand and improve transit service to increase ridership 3. Integrate the rail service of the state’s various rail operations, including integration with the high‐ speed rail system 4. Improve transit safety Eligible applicants must be public agencies, including joint powers agencies, that operate or have planning responsibility for existing or planned regularly scheduled intercity or commuter passenger rail service (and associated feeder bus service to intercity rail services), urban rail transit service, or bus or ferry transit service (including commuter bus services and vanpool services). Public agencies include construction authorities, transportation authorities, and other similar public entities created by statute.

Active Transportation Program (ATP) – ATP is a competitive statewide program created to encourage increased use of active modes of transportation, such as biking and walking. The goals of the ATP are to: •

Increase the proportion of trips accomplished by biking and walking.

•

Increase the safety and mobility for non-motorized users.

• Advance the active transportation efforts of regional agencies to achieve greenhouse gas reduction goals as established pursuant to Senate Bill 375 (Chapter 728, Statutes of 2008) and Senate Bill 391 (Chapter 585, Statutes of 2009).

• Enhance public health, including reduction of childhood obesity through the use of programs including, but not limited to, projects eligible for Safe Routes to School Program funding.

•

Ensure that disadvantaged communities fully share in the benefits of the program.

•

Provide a broad spectrum of projects to benefit many types of active transportation users.

Eligible applicants include Local, Regional or State Agencies which include city, county, and Regional Transportation Planning Agencies, transit agencies, public schools and school districts, tribal governments, Natural Resource and Public Land Agencies, and private non-profit tax-exempt organizations (eligible only for recreational trail programs).

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Trade Corridor Enhancement Program (TCEP) – The purpose of the Trade Corridor Enhancement Program is to provide funding for infrastructure improvements on federally designated Trade Corridors of National and Regional Significance, on California's portion of the National Highway Freight Network, as identified in California Freight Mobility Plan, and along other corridors that have a high volume of freight movement. The Trade Corridor Enhancement Program also supports the goals of the National Highway Freight Program, the California Freight Mobility Plan, and the guiding principles in the California Sustainable Freight Action Plan.

A freight project is a project that significantly contributes to the freight system’s economic activity or vitality; relieves congestion on the freight system; improves the safety, security, or resilience of the freight system; improves or preserves the freight system infrastructure; implements technology or innovation to improve the freight system; or reduces or avoids adverse community and/or environmental impacts of the freight system; or improves system connectivity.

Eligible applicants include local, regional, and public agencies such as cities, counties, Metropolitan Planning Organizations, Regional Transportation Planning Agencies, port authorities, public construction authorities, and Caltrans. Project proposals from private entities must be submitted by a public agency sponsor.

This statewide, competitive program will provide approximately $300 million per year in state funding and approximately $515 million in National Highway Freight Program funds, if the federal program continues under the next federal transportation act.

Local Partnership Program (LPP) – SB 1 created the Local Partnership Program and continuously appropriates $200 million annually from the Road Maintenance and Rehabilitation Account to local and regional transportation agencies that have sought and received voter approval of taxes or that have imposed fees, which taxes or fees are dedicated solely for transportation improvements.

The primary objective of this program is to provide funding for entities who have previously approved fees/taxes dedicated solely to transportation improvements (as defined by Government Code Section 8879.67(b)).

The program funds are distributed through a 40% statewide competitive component and a 60% formulaic component.

The program provides funding to local and regional agencies to improve: •

Aging infrastructure

•

Road conditions

•

Active Transportation

•

Transit and rail

•

Health and safety benefits

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Solutions for Congested Corridor Program (SCCP) The purpose of the Solutions for Congested Corridors Program is to provide funding to achieve a balanced set of transportation, environmental, and community access improvements to reduce congestion throughout the state.

This statewide, competitive program makes $250 million available annually for projects that implement specific transportation performance improvements and are part of a comprehensive multimodal corridor plan by providing more transportation choices while preserving the character of local communities and creating opportunities for neighborhood enhancement.

Regional transportation planning agencies, county transportation commissions and Caltrans are eligible to apply for program funds through the nomination of projects. All projects nominated must be identified in a currently adopted regional transportation plan and an existing comprehensive multimodal corridor plan.

NVTA was successful in securing $25 million in SCCP funds for the Soscol Junction project in Cycle 2. The funding is for FY 2021-22 for the construction phase of the project. The project is currently at 65% design with construction on schedule for summer 2022.