INCORPORATING CYCLING IN OTTAWA-GATINEAU TRAVEL MODEL

INCORPORATING CYCLING IN

OTTAWA-GATINEAU TRAVEL MODEL

Surabhi Gupta & Peter Vovsha, Parsons Brinckerhoff Inc.

Ahmad Subhani, City of Ottawa

Innovations in Travel Modeling, April 27-30, 2014, Baltimore, MD

Ottawa-Gatineau Region

National Capital Region

Fourth Largest Urban area in Canada (1.2 million population; 670K jobs)

Ottawa region is 2,796 square kms (1,800 square miles)

On the banks of the Ottawa, Rideau, and Gatineau rivers

Transportation System

Good Network of Highways

Transit Options – Local Buses, Express Buses, O-Train

Transitway (BRT)

LRT (under development)

Other Modes –

Spring/Summer/Fall – Bicycling

Winter – Skating on the Canal


Why Cycling?

Targets for Cycling in Ottawa

Strong growth in cycling observed between 2005 and 2011

Regional bicycling share 2005 2011

Daily 1.25% 1.75%

Peak Periods 1.5% 2.5%


Promoting Bike-and-Ride

4

Focusing on Cycling Networks around Transit Oriented Development Zones

Providing ample parking at Transit Stations


Existing Model

Java based population synthesizer

Daily tour-based structure for travel generation and spatial

distribution

EMME based

Limited segmentation (HH variables)

Conventional trip-based mode choice and traffic/transit

simulations for AM and PM


Objective

Bike as an additional mode in mode choice model

Detailed bike routing/assignment based on LOS measures

with cross-impacts of auto traffic and bike movements

Develop LOS measures for bicycles that include link and

node-level factors

Consider effect of perceived bike LOS measures on mode

choice


Model

Traffic Stress (Mineta)

BLOS (Landis et al)

BCI (FHWA-RD-98-095)

BLOS (NCHRP-

616) SFCTA Model

BLOS (Dixon)

League of IL Bicyclists

Bike Route Preferences

(Stinson and Bhat)

NY State BCI Variables

Link-Level Type

Vehicle Flow Rate Y Y Y EN Number of Through Lanes Y Y Y EX Speed Y Y Y Y Y Y EX % Heavy Vehicles Y Y Y Y EN Pavement Condition Y Y Y Y EX Lane Width Y Y Y Y EX Shoulder (yes/no) Y Y Y Y EX Parking (Yes/No) Y Y Y Y Y EX Parking Width Y Y Y EX Bike Lane Blockage (Yes/no) Y Y EX

Bike Facility /Lane/Shoulder Y Y Y Y Y EX

Bike Lane Width Y Y EX Shoulder Width Y Y Y EX Type of Road: Residential, Arterial etc Y Y Y EX Trip Generation Intensity Y EN Driveways (Yes/No) Y EX Unrestricted Sight Distance Y EX Terrain (Flat, Hilly, Mountainous) Y EX AADT Y Y EX Median (Yes/No) Y EX Curb Lane Width Y Y Y EX

Node/Turn Level Type Signal (Yes/No) Y EX Right Turn Lane (Yes/No) Y Y Y EX Left Turn Lane (Yes/No) EX Right Turn Pocket Lane (Yes/No) Y EX Cross Street Width Y EX Turn Movements EX Right Turn Lane Length Y EX Left Turn Lane Length EX

Variables affecting Bike LOS & route choice


Summary of State of Practice

Large number of qualitative studies

First attempts to incorporate bike in mode choice (San

Francisco, Portland, LA)

No examples yet of bicycle network assignment


Bicycle LOS and Volume-Delay Function

LOS can be segmented based on User class and Bike facility

type

Types of Users

Classification A (Mineta Report, Dill et al., Geller) – Strong, Enthusiastic and

Interested

Example splits in Portland (Dill et al.): 4% Strong, 9% Enthusiastic, 56%

Interested, 31% Never

Classification B (Bhat et al.) – Experienced and Inexperienced

Types of Bike Facilities - Grade Separated, Exclusive Lane,

Mixed-Traffic


Bicycle travel time affected by:

Auto volume – high V/C ratio for autos implies a steeper bicycle

VDF as they have to navigate through high congestion for mixed-

traffic

Bicyclist type – Stronger/experienced bicyclists have higher free

flow time and lower sensitivity to congestion and auto traffic

Bike lane type – Easier to navigate through dedicated bike lane than

mixed traffic

Total effective capacity – effective capacity available to bikes

conditional on the modeled traffic volumes

Bicycle LOS and Volume-Delay Function


Bicycling Facility Types in Ottawa Region

Multi-use Stone pathway and Asphalt pathways (shared by

pedestrians and bicyclists)



Exclusive Bike Lanes (physically separated or marked)



Sharrow Lanes

Bikes allowed in mixed traffic

Paved Shoulders


Bike Travel Time Function (VDF)

Bike link

travel time

(VDF)

= Link Delay

Factor

(LDF)

× Bike Free

Flow Time

Cycling

Conditions

Auto

Congestion

Factor

(ACF)

×

bt

Reduced Speed

due to Auto

Congestion

)( bVt

Congestion

effect due

to other

Bicycles

×


Link Delay Factor (LDF)

Delay (travel time) experienced by the bicyclist:

𝐋𝐢𝐧𝐤 𝐃𝐞𝐥𝐚𝐲𝐢𝐣𝐦 = 𝐋𝐃𝐅𝐢𝐣𝐦 × 𝐁𝐢𝐤𝐞 𝐅𝐫𝐞𝐞 𝐅𝐥𝐨𝐰 𝐓𝐢𝐦𝐞 ∀ 𝐢, 𝐣 ∈ 𝐀 ∀𝐦 ∈ 𝐌

Where, the link delay factor (LDF) is defined as:

𝐋𝐃𝐅𝐢𝐣𝐦 = 𝟏 + 𝐋𝐎𝐒𝐢𝐣𝐦 ∀ 𝐢, 𝐣 ∈ 𝐀 ∀𝐦 ∈ 𝐌

In turn, 𝐋𝐎𝐒𝐢𝐣𝐦 is defined as:

𝐋𝐎𝐒𝐢𝐣𝐦 = 𝐌𝐚𝐱 𝐟 𝐀𝐢𝐣, 𝐏𝐢, 𝐏𝐣, 𝐌𝐦 × 𝐀𝐢𝐣 , 𝟎

Where:

𝐀𝐢𝐣 = Link specific variables,

𝐏𝐢 = Downstream node-specific variables,

𝐏𝐣 = Upstream node-specific variables,

𝐌𝐦 × 𝐀𝐢𝐣 = Link-user specific interaction variables.

Link Delay

Factor (LDF)

1+LOS


Link Delay Factor (LDF)- Example

Variables

Network Attribute Bicyclist

Units Value [A] Effect Multiplier

[B] [A] x [B]

Link-Level

Bicycle Lane (yes/no) N/A 0 Decrease -1.12 0

Sharrow Lanes N/A 1 Decrease -0.5 -0.5

Bike Lane Width Feet 5 Decrease -0.4 -2

Curb Lane Width Feet 10 Decrease -0.0498 -0.498

Traffic Speed kph 35 Increase 0.01375 0.481

Curb Lane Volume Vph 600 Not Good 0.002 1.2

Other Lane Volume Vph 1200 Not Good 0.0004 0.48

Parking Lane (yes/no) N/A 1 Increase 0.506 0.506

% Heavy Vehicle Volume Ratio 15 Increase 0.034 0.51

Frequency of driveways N/A 3 Increase 0.019 0.057

Pavement Condition (good/bad) 0-4 0 Increase 0.05 0

Node-Level

Signal N/A 1 Increase 0.011 0.011

LOS 0.23625

Free Flow Travel Time (FFTIME) mins 6

Delayed Travel Time (FFTIME x

(1+LOS)

mins 7.48


Auto Congestion Factor (ACF)

Auto

Congestion

Factor

(ACF)

Reduced Speed

due to Auto

Congestion

Reduction in Bike Speed due to Auto congestion:

Only affects the bicycles in mixed traffic

For bicycle lanes and multi-use pathways, ACF = 1.0


Link Volume Delay Function (Bicycle)

0

5

10

15

20

25

30

35

0 1000 2000 3000 4000 5000 6000

Tra

vel T

ime (

Min

s)

Bike Volume

High Auto V/C

Medium Auto V/C

Low Auto V/C

Parameters

α β γ θ μ ν δ LDF

5000 6 0.1 4 1 1 0.5 4 0.1 1.2

𝑡 𝑉𝑏 = 𝑡𝑏 × 1 + 𝛿𝑏 𝑉𝑏𝐶𝑒𝑓𝑓

𝐸𝑥𝑝 𝑒𝑓𝑓

Bike VDF is given by:

Congestion effect due

to other Bicycles


Link Volume Delay Function (Auto)

Impact of bicycles on auto travel times

Presence of bicycles increase auto travel

times

Bicycles take up capacity, and since they move

slower than autos, they take up more capacity

than their physical dimensions

The reduction in capacity due to bicycles

accounts for these impacts

3

3.5

4

4.5

5

5.5

0 1000 2000 3000 4000 5000

Tra

vel T

ime (

min

s)

Auto Volume

High Bike

Volume

Medium Bike

Volume

Low Bike

Volume

High Bike

Volume

Medium Bike

Volume

Low Bike

Volume

Bikes in Mixed-

Traffic

Dedicated Bike Lane

Parameters

ζ β

5000 3 0.1 3 1 1.33

PCE 0.8


Iterative Auto-Bicycle Assignment


Modeled Bike Flow Map: AM Peak Hour


Model Validation

0

200

400

Westbound Eastbound

Count Volume

Laurier at Metcalfe

0

100

200

300

Southbound Northbound

Count Volume

Protage Bridge

0

100

200


Count Volume

Alexandre Bridge

0

50

100

150


Count Volume

Colonel By Dr


Future Improvements: Turn Penalties

j

i

k

k

Turn

i—j—k

k

𝒌 k

𝒌

j

i

𝑓𝑘 𝑉𝑎 , 𝑉𝑏, …



For turn i-j-k:

𝑡 𝑉𝑎 = 𝑇𝑃𝑎𝑖𝑗𝑘 + 𝑡0 × 𝐿𝐹 × 1 + 𝜁𝑎 𝑉𝑎 + 𝑝𝑏𝑙𝑏 𝑉𝑏

𝐶

𝛽𝑎

𝑡 𝑉𝑏 = 𝑇𝑃𝑏𝑖𝑗𝑘 + 𝐴𝐶𝐹 × 1 + 𝜁𝑏 𝑉𝑏𝐶𝑒𝑓𝑓

𝐸𝑥𝑝𝑒𝑓𝑓


QUESTIONS!

Documents

INCORPORATING CYCLING IN OTTAWA-GATINEAU TRAVEL MODEL