Smarter Urban Mobility SystemsAround the Pacific Rim

Jerry WaltersFehr & Peers

Virtuous Cycles in City Planning and Operation

Simulate

Plan

Iterate /Implement

Approve

Evaluate Analyze

Design

Build

Operate

Monitor +

Manage

How Scale MattersUrban Forms that Reduce Traffic, Energy and Emissions

Dimensions of Urban Form and Vehicle Use

1. Density

2. Diversity

3. Design

4. Destinations5. Distance to Transit

6. Development Scale

7. Demographics

8. Demand Management

National Evidence on MXD Travel Generation

GROSS TRIP GENERATION TRIP GENERATION REDUCTIONS

64,036 5,288 6,168

SAMPLE DEVELOPMENT PROJECT PRELIMINARY TRANSPORTATION SUMMARY

Residential

Commercial

Others

Daily

Residential

Commercial

Others

AM

Residential

Commercial

Others

PM

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Daily AM PM

44,0

54

3,90

3

4,36

8

31% 26% 29%Reductions Net Trips

SUMMARY OF VMT REDUCTIONS (FROM MXD)External Vehicle Trips

Daily

AMPM

-27%

-22%-25%

External Walk Trips

External Transit Trips

Internal Trip Capture

Net External Auto Trips

SUMMARY OF TRIPS (FROM MXD)

Daily

AMPM

10,790 729 934

3,658 287 334

5,534 369 532

44,0543,9034,368

SHARED PARKING MODEL*

-

2,000

4,000

6,000

8,000

10,000

12,000

6AM

7AM

8AM

9AM

10AM

11AM

12PM

1 PM 2 PM 3 PM 4 PM 5 PM 6 PM 7 PM 8 PM 9 PM 10PM

11PM

12AM

Weekday Peak Demand Weekend Peak Demand

11,170 9,929

Weekday

Shared Parking Demand ReductionWeekend

18% 27%

Potential Cost Savings

Surface LotAbove Ground

StructureSubterranean

$6,150,000 $51,250,00 $82,000,000

GHG EmissionsCO2 Equivalent/Day

41.6 Metric Tons

TOP ISSUES/CONCERNS• Congestion at freeway ramps and intersections adjacent to the project .• Parking intrusion into the neighborhood north of the project.• Cut-through traffic through the neighborhood.

POTENTIAL TDM OPPORTUNITIES• Ride Share Programs• Parking Cash Out• Flextime/Telecommuting Opportunities

DailyAMPM

-31%Daily

AM -26%

PM -29%

Raw Net270,046 196,72726,499 20,68427,455 20,589

Vehicle Miles Travel (VMT)

DailyAMPM

64,036 44,0545,288 3,9036,168 4,368

Raw Net

GrossNet 44,054 3,903 4,368

Network Data, Analytics and Simulation

Kunming-Chenggong New Town

• 2,800 acres• 232,300 population• 243,300 jobs

Objectives Energy efficiency Emissions and GHG reduction Economic and fiscal performance Health and safety

Kunming New Town / Sustainable Objectives

… and Challenges

Macro: Pace of development Planning and performance mandates Car culture Lack of data and models

Integrated Zoning, Circulation Systems

Sesame Street Quiz

Inter-Connected Network Option

Conventional Network Plan

Kunming Urban Form / Network Form

Network Simulation

(Ignores benefits of 9% trip reduction and traffic dispersion to parallel routes)

Operational Comparison

Measure Standard Arterial Couplet

Pedestrian Crossing Distance 35.0 meters 12.7 meters

Min. Pedestrian Crossing Time 37.3 seconds 13.6 seconds

Number of Signal Phases 4 to 8 2 to 5

# of LOS E/F Intersections 4 of 4 (100%) 5 of 16 (31%)

Sustainability Indicators

Measure Standard Arterial Couplet

East-West Travel Time 8 minutes 6 minutes (-25%)

Vehicle Hours of Delay (VHD) 860 VHD 640 VHD (-25%)

Fuel Consumption 9,100 liters 7,500 liters (-18%)

California Legislated Mandates

• AB 32 – Greenhouse gas reduction targets, Cap + Trade • SB 97 – CEQA requirements for GHG assessment

• SB 375 - Linkages among: GHG targets regional transportation sustainable communities strategies

• SB 732 – Grant funding for sustainable communities

• SB226 –Approval streamlining for infill development

• Location Efficiency

• Network Management

• Multi-Modal Focus

• Speed Suitability

Caltrans Smart Mobility Performance Measures

Global Cap Road Pricing

| | |

Max Reduction Work, School:

25%/ 65%

Max Reduction (all VMT): 25%

| | | | | || | | | | |

Land Use/ Location

Neighborhood/ Site Enhancements

Parking Policy/ Pricing

Transit System Improvements

Commute Trip Reduction (CTR)

Progams(assuming mixed-use

Road Pricing/ Management

Max Reduction = 65% (urban), 30% (compact infill), 10% (suburban

center), 5% (suburban)

Max Reduction =5% (without NEV)15% (with NEV)

Max Reduction = 20% Max Reduction = 10% Max Reduction = 25% work VMT Max Reduction =25%

Density (30%) Pedestrian Network (2%)

Parking Supply Limits (12.5%)

Network Expansion (8.2%)

CTR Program<Required> (21% work VMT)

<Voluntary> (6.2% work VMT)

Cordon Pricing (22%)

Design (21.3%) Traffic Calming (1%)Unbundled

Parking Costs (13%)

Service Frequency/Speed

(2.5%)

Transit Fare Subsidy (20% work VMT)

Traffic Flow Improvements

(45% CO2)

Location Efficiency (65%)

NEV Network (14.4%)<NEV Parking>

On-Street Market Pricing (5.5%)

Bus Rapid Transit (3.2%)

Employee Parking Cash-Out (7.7% work VMT)

Required Contributions by

Project

Diversity (30%) Car Share Program (0.7%)

Residential Area Parking Permits

Access Improvements

Workplace Parking Pricing (19.7% work VMT)

Destination Accessibility (20%)

Bicycle Network <Bike Lanes> <Bike Parking>

<Land Dedication for Bike Trails>

Station Bike ParkingAlternative Work Schedules and

Telecommute Program (5.5% work VMT)

Transit Accessibility (25%)

Urban Non-Motorized Zones Local Shuttles CTR Marketing (4.0% work

VMT)

Global Max Reduction (all VMT)75% (urban), 40% (compact infill), 20% (suburban center or suburban with NEV), 15% (suburban)

Cross-Category Max Reduction (all VMT)70% (urban), 35% (compact infill), 15% (suburban center or suburban with NEV), 10% (suburban)

CAPCOA Best Management Practices

High Speed Rail Sustainability Evaluation

Micro/ Macro Analysis of 3-B-L Performance

Data / Analytics

Benefits of Sustainable Transport/ Land Use

Better-Informed Transportation Decisions ASAP

Simulate

Plan

Measure

Approve

Evaluate Analyze

Design

Build

Operate

Manage +

Monitor

Thank You!

Jerry Walters Fehr & Peers

Operationalizing Smarter Urban Mobility Systems

Jerry WaltersFehr & Peers

Goals for Smarter Mobility Systems

• Decisions ASAP and better informed

• Better infrastructure design decisions 

• Accurate impact assessments of land development

• User-oriented transit service plans and station designs

• Optimal sizing and integration of on-demand systems

• Tailored mobility services to optimize TDM effectiveness

Lab Work

• Bus reliability by route segment

• GHG, fuel use at traffic signals, roundabouts, stop signs

• Biases/inaccuracies in self-reported journey times

Field Work

• Transit ridership optimization

• Neighborhood parking management enforcement

• Bike station analytics: opportunity effectiveness assessment

Leading-Edge Work

• Express-lane bottleneck removal through GPS-calibrated simulation

• Regional traffic modeling through video and cell O/D identification

• Simulation of campus operation, expansion options

Next Gen Studies for Smart Mobility

Next Gen Studies for Smart Mobility

• Why and how people travel – Longitudinal measurement – Traveler demographics, market segmentation– O/D data vs built environment (D’s)– Models with complex AI objective functions

• Safety studies: – Road, signing and traffic conditions, driver attention

Next Gen Studies for Smart Mobility

• Operational improvements

– Traffic queues and delays, simulation models

– Cruising for parking

– Traveler-weighted transit service level

– Un-served markets • Service availability, traveler characteristics of transit non-users• Demographics of bike-share users, demographics and journey

characteristics of non-users• Comprehensive bicyclist route choice factors, including safety

and security

Data Aggregation and Synthesis Needed

A Dozen Data Desires

1. Higher fidelity traffic flow data

2. Complete traveler O/D movements by all modes

3. Operating flow, interactions and incidents among modes

4. Longitudinal data: before/after stimulus

5. Land use and employment inventories by parcel

6. Over-the-net accessibility: time, cost, reliability, uncertainty

• * calibrate/ validate

A Dozen Data Desires

7. GPS verification of household and workplace surveys

8. Geo-correlation of travel surveys with built context

9. Transaction data to discern travel purpose

10.Consistent variable definitions to allow cross-walking data

11. Consistent sample rates by region

12.Open data from synthesizers via clients and from big actor

data sources

• * calibrate/ validate

Multi-Modal Network Simulation - Silicon Valley

Multi-Modal Network Simulation – Kunming China