WORKING GROUP MEETING. August 13, 2020

(1)

WORKING GROUP MEETING

(2)

Phase 2 Status Report



Scenario Planning

• _{Awaiting Working Group determination on whether adequate differentiation has been}

achieved

• Preparing to populate dashboard as model runs are completed



Travel Demand Model

• _{Fine tuning technology template}

• Ran model with and without technology for baseline and greater growth scenarios



Website

• Up to date with minutes, agendas, other documents



Schedule

(3)

Phase 2 Status Report (Cont.)



Deliverables

• _{Scenario Planning Methodology White Paper –}

_Complete

• _{Memo Summarizing Economic Trends and Opportunities –}

_Complete

• _{Memo Summarizing Travel Behavior Data Review –}

_{Late August}

• _{Memo Summarizing Travel Demand Model Evaluation –}

_{Late August}

• _{Tech Memo on Drivers, Spatial Assumptions, and Travel Parameters –}

Complete

• _{Tech Memo on Performance Measures –}

_Complete

• _{Technical Guide on Scenario Evaluation –}

_{Early-September}

(4)

Phase 3 Status Report



Task 1 – Engagement

• _{Uploading agendas, minutes, webinars, and reports to website}



Task 2 – Preliminary Alternatives

• _{No activity}



Task 3 – Determination of Candidate Alternatives

• _{No activity}



Task 4 – Scenario Planning

(5)

Phase 3 Status Report (Cont.)



Schedule

• _{September 2022}



Major Deliverables

• _{Summary of Mandated Preliminary Segments -}

_Complete

• _{Updated Cost Estimates for Mandated Preliminary Alternatives -}

_Complete

• _{Summary of Candidate Alternatives - TBD}

• _{Tech Memo on Microsimulation Analysis – TBD}

• _{Scenario Planning Report – TBD}

• _{Engagement Summary Report – TBD}

• _{Study Report - TBD}

(6)

(7)

Objectives Today



Confirm that the current Greater Growth forecasts provide adequate

differentiation between the three Greater Growth scenarios

• _{Recap the land use results shared in the spring}

• _{Review the new travel demand model results}



What are we looking for?

• _{Confirm the model results “tell the story” of the scenario narratives?}

• _{Will the scenarios provide a strong test of alternative, plausible futures for}

transportation investments?

• _{Keep in mind the study will test every}

_{candidate alternative against all}

_the

scenarios to see which proposals are effective in multiple scenarios

(8)

Exploratory Planning – Preparing for

Uncertainty

(9)

Scenario Planning Process

9

Potential futures organized into alternative Land Use Scenarios

Urban

Planning in the context of

future uncertainty Transportation Alternatives tested against each Scenario Decisions based on Testing ResultsGives the Ability to make Informed

?

Results for each Transportation

Alternative

Suburban

Water

Scenario

Narratives Control Totals ModelingLand Use

Travel Demand Modeling Economic Modeling Vision, Goals & Objectives

Where we are in the Scenario Planning Process

Testing Transportation

(10)

Exploratory Scenario Planning Framework

Drivers

Scenarios

Inputs

Outcomes

Economic, Lifestyle/Demographic, Technology, Environment

Drivers organized into three Greater Growth Scenarios with an equal amount of additional

employment and population growth in each.

Control totals, and assumptions about the

drivers, translated through “Levers” in the land

use and travel demand models.

Performance Measures, based on the study

Goals and Objectives and produced by the land use, travel demand, and economic models

(11)

Scenario Narratives

11

Greater Growth on the

Water

Growth in water-oriented activity. Port of Virginia becomes even more competitive with freight more multimodal. More dispersed housing locations. Moderate

assumptions for CAV adoption and network adaptation.

Greater Growth in Urban

Centers

Significant economic

diversification. Low space

requirements per job. Large role for “digital port.” New

professionals prefer to live/work in urban settings. High level of CV adoption and low auto ownership/high TNC mode.

Greater Suburban/Greenfield

Growth

Growth is suburban/ exurban, but growth includes walkable mixed-use centers. Port of Virginia becomes even more

competitive. “Digital port” brings additional jobs. Housing is more suburban. High level of AV

adoption and network adaptation.

NOTE: Sea Level Rise assumed as 3 ft. in all Scenarios

Test greater cross-harbor travel in

particular. Test more urban and multimodal travel patterns. Test more overall regional travel.

(12)

Greater Growth Control Totals



Agreed on 16% employment

growth from 2015-2045

• _{Additional 82,972 jobs}



HRPDC provided population

growth control total using

regional REMI model

• _{Additional 110,460 population}

2015 2045 Baseline Forecast Greater Growth Forecast Regional Population/ Employment

Baseline & Greater Growth Forecast Concept Greater Growth Allocation Growth Rates Employment Population 2015-2045 7.90% 17.29% Greater Growth 7.51% 5.48%

(13)

Objectives and Performance Measures

13

ECONOMIC VITALITY

Support regional growth and

productivity

Support efficient freight

movement Support accessibility for tourism

SUSTAINABILITY: EQUITY,

COMMUNITY & ENVIRONMENTAL

Improve the sustainability of communities through increased housing choice

and reduced auto-dependency

Ensure that mobility benefits positively affect

low income residents

Minimize the environmental impact of

future growth and transportation

CONNECTIVITY & ACCESSIBILITY

Improve connectivity and reliability between the Peninsula and Southside

Improve connectivity and

access for all improve travel efficiencyReduce delay and

SAFETY, RESILIENCY & INNOVATION

Improve safety through a more adaptive transportation network

Make investments that improve flood resiliency

Consider the impacts of technology on system

demand and performance

LAND USE & DEVELOPMENT Percent of population in multi-family housing Percent of growth near key destinations Percent of growth near transit stops Percent of growth in urban place types

Percent of growth on formerly undeveloped land (per 2016 Land Cover Data) Percent of growth near flood-prone areas

(Change in) cost of emissions

Ratio of user costs for low income travelers to all user costs (ratio of savings) ECONOMIC

(Change in) Lost productivity from delay

(Economic impact of change in) Labor market accessibility Performance on the freight network - total delay + spatial results (Change in) Percent of freight traffic on secondary streets - total + spatial TRANSPORTATION NETWORK

(Change in) Delay on cross-harbor trips [time and dollar value] (Change in) Circuity of cross-harbor trips

(Change in) Reliability for cross-harbor trips [time and dollar value] (Change in) Cross-harbor accessibility

(Change in) Regional delay [total + spatial] System reliability

(Change in) User cost Cost of forecasted crashes

(Change in) Transportation network impact from flood-prone conditions [e.g., delay, trip length, and/or circuity] ACCESSIBILITY & TRAVEL MODE

(Change in) Multimodal accessibility to jobs (Change in) Accessibility index by mode

Performance of the transit-serving roadway network [i.e., average speed] (Change in) Mode share index

(Change in) Accessibility to major tourist attractions (Change in) Transit ridership

Percent of jobs/pop within (15 min) drive time to airport or Amtrak station Low income household access to employment

TECHNOLOGY

Percent of trips by automated vehicles

(Change in) Percent of travel using facilities with adaptive technologies [e.g., V2I, ITS] Reliability enhancement from technology

Induced trip demand from technology

(14)

Land Use Indicators

Transportation Indicators

Economic Indicators

Land Use Model

Travel Demand Model

TREDIS Model DASHBOARDSTUDY

(15)

LAND USE MODELING

(16)

Virtual Present & Virtual Future

(17)

No Build Areas

17

• Water • Wetland

(18)

Land Use Modeling for Greater Growth

Scenarios

• 2045 land use map (place types and

locations) was validated by localities (based on each locality’s Comprehensive Plan)

• Differentiation in growth allocations for each scenario was achieved through:

• Using Suitability Factors to guide growth spatially

• A separate suitability map and factors were developed based on each

scenario narrative

GROWTH ALLOCATOR

CAPACITY

SUITABILITY

(19)

Land Use Modeling for Greater Growth

Scenarios

19 GROWTH ALLOCATOR

CAPACITY

SUITABILITY

Suitability acts as a magnet for growth

Amount of growth in the 2045 Baseline Additional capacity for growth

Total Capacity in the Place Type

Growth Allocator

(20)

Suitability Factors & Weighting by

Scenario

Sutability Factor Method Weight Sutability Factor Method Weight Tourism Distance Tourism Distance

Military Presence Overlap Military Presence Distance Major Roadways Distance Major Roadways (-) Distance Urbanized Waterfront Overlap Active Transportation Distance Shipbuilding Distance Shoreline OVerlap IPA Placetype Distance Utilities Overlap IPA Placetype Overlap

Utilities Overlap

A. Water Scenario

Jobs Population

Sutability Factor Method Weight Sutability Factor Method Weight Shipbuilding Distance Utility Service Overlap

Urbanized Waterfront Distance Active Transportation Distance Utility Service Overlap Employment Accessibility Distance -Active Transportation Distance Transit Proximity Distance Employment Accessibility Distance - City Center Proximity Distance Transit Proximity Distance Redevelopment Potential Distance City Center Proximity Distance Higher Education Facilities Distance Redevelopment Potential Distance MCR Placetype Distance Higher Education Facilities Distance 2045 Employment Density Distance MCR Placetype Distance 2045 Population Density Distance MCI Placetype Distance RLD PT Distance VFEMP Density Distance RHD PT Distance RMD PT Distance

B. Urban Scenario

Jobs Population

Sutability Factor Method Weight Sutability Factor Method Weight Active Transportation OVerlap Active Transportation Distance

Vacant Land Availability Distance Major Roadways (-) Distance Large Developable Sites Distance Vacant Land Availability Distance Existing Warehouse Facilities Distance MCR Placetype Distance MCR Placetype Distance Utility Service Overlap

MCI Placetype Distance CR Placetype Distance Utilities Overlap ELU IH Distance IP Distance C. Suburban Scenario Jobs Population

• The length of the blue bar indicates the relative “weight” of the suitability factor as an attractor • Red bars indicate factors that are detractors

(21)

21

Water

Suburban Urban

POPULATION EMPLOYMENT POPULATION EMPLOYMENT

SUITABILITY ALLOCATION

(22)

Results of Allocations

26,077

32,714

26,444

Water Urban Suburban Population in multifamily housing

55,839

69,011

56,088

Water Urban Suburban

Population in urban place types

29,046

36,821

29,894

Water Urban Suburban Population near key destinations

30,572

33,281

26,721

(23)

23

Results of Allocations

Note that these outputs represent only the land use modeling output portions of the Dashboard results

18,324

11,038

20,884

Population on generally undeveloped land (per 2016 Land Cover Data)

33,925

40,676

35,440

Jobs near transit stops

33,475

43,595

33,424

(24)

Land Use Modeling Conclusions



The results of the Suitability Factor calibrations yielded growth

allocation patterns that generally matched the Scenario Narratives



There was sufficient differentiation between the scenario modeling

results to provide a good platform for travel demand model testing

and for resilience testing of transportation alternatives



If additional growth was added to the models, it is hard to predict

what the impacts would be on the land use model results and the

relationship between additional growth and model results is not

linear

(25)

TRAVEL DEMAND MODELING

(26)

Validating the Effects of Technology

Daily Induced Demand – 2045 Baseline

Description

_Tech

w/o

_Tech

w/

_Change

%

Observations

Persons 7,674,155 8,125,764 5.9% Latent demand for home-based discretionary travel for households with

access to AVs.

Trucks – Internal 59,357 59,357 0.0% No induced demand specified.

Trucks – External 27,595 28,277 2.5% Input parameter specifies +30% for _{through truck traffic only.}

Passenger Vehicles

-Internal 5,699,137 6,431,416 12.8%

Latent demand for home-based

discretionary travel and introduction of zero-occupant vehicles.

Passenger Vehicles

(27)

Mode Share – 2045 Baseline

Description

_Tech

w/o

_Tech

w/

Observations

Private Conventional

Auto 74.0% 54.7% Model estimate of MaaS reflects weighted average of _{input parameters specifying 10% for work trips and}

20-30% for non-work trips.

MaaS (Conventional) 25.0% 18.8%

Private AV - 19.1% Model estimate of 25.4% AVs reflects average of _{input parameters that range from 20% - 30%. Note}

that AVs account for approximately ¼ of the total MaaS share in accordance with input parameters.

MaaS (AV) - 6.3%

Bus 0.88% 0.86% _{Transit shares decrease slightly reflecting}

competition from new AV modes.

Light Rail 0.14% 0.13%

(28)

Impacts on Regional Roadway Network (Daily)

Description

_{Base Year}

2017

Baseline

2045

w/o Tech

%

Change

2045

Baseline

w/Tech

%

Change

Vehicle-Miles Traveled 43,150,459 41,570,058 -3.7% 46,623,754 12.2% Vehicle-Hours Traveled 1,201,853 1,667,684 38.8% 1,871,093 12.2% Delay (Hours) 244,459 738,973 202.2% 824,517 11.6% Average Free-flow Speed (mph) 45.1 44.8 -0.7% 44.5 -0.7% Average Congested Speed (mph) 35.9 24.9 -30.6% 24.9 0.0%

(29)

Exploring the Differences Between

Scenarios

2045 Travel Demand (Daily)

WATER URBAN SUBURBAN

X.X% - difference from 2045 Baseline w/ Tech

(30)

2045 Travel Demand (Daily)

(31)

2045 Average Vehicle

1 Trip Length Change

2

WATERURBAN SUBURBAN

X.XX – average trip length

1 - includes zero-passenger vehicles and trucks; 2 - verses 2045 Baseline w/Tech

(32)

(33)

Impacts on Regional Roadway Network (Daily)

Description

_Water

2045

_Change

%

_Urban

2045

_Change

%

_Suburban

2045

_Change

%

Vehicle-Miles Traveled 50,179,522 7.6% 47,069,198 1.0% 49,629,880 6.4% Vehicle-Hours Traveled 2,014,533 7.7% 1,885,958 0.8% 1,989,374 6.3% Delay (Hours) 886,352 7.5% 826,051 0.2% 876,143 6.3% Average Free-flow Speed (mph) 44.5 0.0% 44.4 -0.2% 44.6 0.2% Average Congested Speed (mph) 24.9 0.0% 25.0 0.4% 24.9 0.0% 33

(34)

**Change* in Delay on Harbor Crossings**

X,XXX – daily delay in hours. * verses 2045 Baseline w/Tech

(35)

Travel Demand Modeling Conclusions

35



When comparing the 2045 Baseline scenarios, the model estimates

validate well to the input parameters that regulate the effects of

technology on induced demand and mode shares.



Without greater growth and considerations for induced demand as a

result of technology, there is a significant degradation in regional

network level-of-service moving from 2017 travel conditions to 2045

with the baseline land use. Regional delay increases by over 200%.

This delay is most likely muting some of the differences we can

measure for the greater growth scenarios.



Average vehicle trip lengths are trending shorter for the Urban and

Suburban scenarios. This trend is reinforced by the prevalence of

zero-passenger vehicles in these scenarios.

(36)

Travel Demand Modeling Conclusions

• Moderate assumptions for CAV adoption. Model estimate at 28%. • Greater demand for cross harbor travel. Model estimate for

increased delay at 14% compared with 7-8% for other scenarios.

Growth on the Water

• High level of CAV adoption. Model estimate at 38%.

• High TNC (MaaS) usage; multimodal travel. Model estimate at 52%.

• Low auto ownership. Model estimates for increased vehicle trips at 7% and is the lowest of all scenarios.

Growth in Urban

Centers

• High level of CAV adoption. Model estimate at 70%.

• More overall regional travel. Model estimates for increased person trips at 11% and is the greatest of all scenarios.

Suburban/Greenfield

Growth

(37)