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Evaluating the Effects of Transit Crowding - Transbay Ridership Forecasting Model. Transportation leadership you can trust. presented to TRB Planning Applications Conference presented by Vamsee Modugula Cambridge Systematics, Inc. May 9, 2007. Why Model Transit Crowding?. - PowerPoint PPT Presentation
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Transportation leadership you can trust.
presented to
TRB Planning Applications Conference
presented by
Vamsee ModugulaCambridge Systematics, Inc.
May 9, 2007
Evaluating the Effects of Transit Crowding - Transbay Ridership Forecasting Model
2
Why Model Transit Crowding?
Models forecast travel demand (irrespective of capacity?)
Highway models account for congestion by increasing travel time
Transit models account for parking capacity by increasing drive time to station
No direct modeling of increased wait time due to crowding
Crowding affects ridership on competing transit modes
3
Transbay Ridership Study - Overview
Determine future transit ridership at Transbay Terminal
• AC Transit (Bus bay requirements)
Analyze the impact of capacity constraints on Transit
More accurate ridership estimates with improved travel forecasting tools
Provide analysis needed for the TIFIA loan application
Project study team included TJPA, AC Transit, BART, MTC and WTA
4
Innovative Features of This Project
New Mode choice model with detailed transit modes
New capability to model Transit crowding
• Model passenger perception that travel time is more onerous when they have to stand or when the vehicle is crowded
• Increased wait times when passengers are unable to board a crowded vehicle
Apply a range of capacity assumptions for BART
Analyse ridership and traffic volumes for Peak Hours
5
Traditional Method: “best path”
between home neighborhood &
work
Better analysis of competing transit modes
New Method: multiple paths” + detailed treatment
of each transit option in mode choice models
Full treatment of access/egress & transfers
6
Mode ChoiceMode
Choice
MotorizedMotorized BicycleBicycle WalkWalk
Drive AloneDrive Alone
Shared Ride 2
Shared Ride 2
Shared Ride 3+Shared Ride 3+
Walk-AccessWalk-
AccessDrive-
AccessDrive-
Access
Local Bus
Local Bus
ExpressBus
ExpressBus LRTLRT Commuter
RailCommuter
Rail BARTBART FerryFerry Local Bus
Local Bus
ExpressBus
ExpressBus LRTLRT Commuter
RailCommuter
Rail BARTBART FerryFerry
TransitTransit
Mode Choice Structure for the Trans Bay Ridership Model
7
Base year model validation - Daily Validation for the Bay Bridge
Corridor 2005 Observed
2005 Modeled
Difference % Diff
Target
Auto 267,944 276,344 8,400 3% +/- 15% or +/- 500
Bart 288,480 291,484 3,004 1% +/- 15% or +/- 500
Express Bus 11,841 13,151 1,311 11% +/- 15% or +/- 500
Ferry 3,302 3,712 410 12% +/- 15% or +/- 500
Total 571,567 584,692 13,125 2% +/- 15% or +/- 500
8
Base year model validation – Freeway Volumes
Bridge Dir AM Peak Period
PM Peak Period
Daily Volume
AM Peak Hour PM Peak Hour
2005 OBSERVED AUTO VOLUMES BY TIMEPERIOD
Bay Bridge EB 25,665 36,537 136,131 7,251 9,685
Bay Bridge WB 34,519 30,138 131,813 9,350 8,165
2005 MODEL ESTIMATED AUTO VOLUMES BY TIMEPERIOD
Bay Bridge EB 25,975 39,310 140,828 7,579 11,964
Bay Bridge WB 43,627 27,680 135,516 11,206 8,447
DIFFERENCE BETWEEN OBSERVED AND MODEL VOLUMES
Bay Bridge EB 310 2,773 4,697 328 2,279
Bay Bridge WB 9,108 -2,459 3,703 1,857 282
9
Base year model validation – Peak Hour BART
LINE 2005 Observed
2005 Modeled
Difference % Diff Target
AM Peak Hour
Dublin-SFO 8,904 10,105 1,201 13% +/- 15%
Fremont-Daly City 6,280 6,306 26 0% +/- 15%
Richmond - Daly City 8,413 9,158 745 9% +/- 15%
Pittsburg - Daly City 11,157 11,922 765 7% +/- 15%
PM Peak Hour
Dublin-SFO 8,680 9,739 1,059 12% +/- 15%
Fremont-Daly City 5,839 5,845 6 0% +/- 15%
Richmond - Daly City 8,422 8,917 495 6% +/- 15%
Pittsburg - Daly City 10,966 10,609 (356) -3% +/- 15%
10
Transit crowding model
When trains are too crowded, riders can:
• Wait for next train
• Switch to bus or ferry
• Switch to auto
Includes feedback to mode choice models
• Longer travel times for riders who would experience over-crowding
11
Transit Crowd Modeling
Balance transit demand against capacity by applying:
• In-vehicle Travel Time Adjutment
Travel Time Adjustment due to Crowding
0
0.5
1
1.5
2
0 25 50 75 100
Load
Tra
vel T
ime
Fac
tor FACTOR
12
Transit Crowd Modeling
Wait Time Adjustment
• Based on probability to board a transit line
Stochastic Assignment to reallocate ridership based on capacity
Trip Tables with excess demand
Trip tables with perceived travel times and actual wait times
13
Projected Growth in Travel in the Corridor
Growth in jobs faster than population in San Francisco County
Huge increases in commuter and total trips in the corridor
Increased Transbay Travel demand – Can BART meet demand for service
Type Increase from 2005 - 2030
Population in SF 16%
Employment in SF 44%
Commuter Trips to SF 51%
Total Trips to SF 43%
14
2030 MODEL – CAPACITY ASSUMPTIONS (Peak Hour Peak Direction)
2005 2015 (Low - High)
2030 (Low - High)
BART CAPACITY
Trains per hour 22 24-28 28-32
Train Frequency 2.7 min 2.5 – 2.15 min 2.15 – 1.9 min
Seats per car 68 56 56
Consist 9 10 10
Passengers per car 90 100 100
Passengers per train 810 1000 1000
Potential passengers/Hour 18,000 24,000 – 28,000 28,000 – 32,000
15
2030 MODEL –CAPACITY ASSUMPTIONS (Peak Hour Peak Direction)
2005 2015 (Low - high)
2030 (Low-High)
AC TRANSIT CAPACITY
Buses per hour 96 120 120-175
Average seats per bus 50 65 65
Passengers per bus 50 65 65
Potential passengers/Hour 4,800 7800 7800 – 11,375
FERRY CAPACITY
Boats per hour 5 9 9
Passengers per Boat 150 - 320 150 - 320 150 - 320
16
Year 2030 Transit Ridership – PM Peak Hour(Low Bart Capacity Alternative)
Capacity
Baseline Scenario Change
Daly City - Fremont 5,000 4,300 4,300 0
SF Airport-Dublin 5,000 6,500 5,500 -1,000
Concord - Daly City 13,000 11,200 11,200 0
Colma – Richmond 5,000 6,100 5,700 -400
TOTAL BART 28,000 28,100 26,800 -1,400
Diversion from Bart to Other Modes
TOTAL AC Transit 7,800 6,900 7,800 900
TOTAL Ferry 2,000 1,900 2,000 100
Total (All Modes) 39,600 36,800 36,400 -400
17
Year 2030 Transit Ridership – PM Peak Hour(High Bart Capacity Alternative)
Capacity
Baseline Scenario Change
Daly City - Fremont 5,750 4,300 4,300 0
SF Airport-Dublin 5,750 6,500 6,100 -400
Concord - Daly City 14,750 11,200 11,200 0
Colma – Richmond 5,750 6,100 5,800 -400
TOTAL BART 32,000 28,100 27,400 -800
Diversion from Bart to Other Modes
TOTAL AC Transit 7,800 6,900 7,100 200
TOTAL Ferry 2,000 1,900 2,000 100
Total (All Modes) 47,200 36,800 26,700 -500
18
Year 2015 Transit Ridership – PM Peak Hour(Low Bart Capacity Alternative)
Capacity
Baseline Scenario Change
Daly City - Fremont 4,300 3,400 3,400 0
SF Airport-Dublin 4,300 4,700 4,300 -400
Concord - Daly City 11,100 7,500 7,500 0
Colma – Richmond 4,300 4,000 4,000 0
TOTAL BART 24,000 19,600 19,200 -400
Diversion from Bart to Other Modes
TOTAL AC Transit 7,800 5,800 6,000 300
TOTAL Ferry 2,000 1,400 1,500 100
Total (All Modes) 33,600 26,800 26,700 -0
19
Year 2015 Transit Ridership – PM Peak Hour(High Bart Capacity Alternative)
Capacity
Baseline Scenario Change
Daly City - Fremont 5,000 3,400 3,40 0
SF Airport-Dublin 5,000 4,700 4,700 0
Concord - Daly City 13,000 7,500 7,500 0
Colma – Richmond 5,000 4,000 4,000 0
TOTAL BART 28,000 19,600 19,600 0
Diversion from Bart to Other Modes
TOTAL AC Transit 7,800 5,800 5,800 0
TOTAL Ferry 2,000 1,400 1,400 0
Total (All Modes) 39,600 26,700 26,700 0
20
Conclusions
This analysis did not include contraints on station platform capacity
Transit Crowd Modeling is a good way of forecasting realistic transit ridership
Assists transit operators in preparing service plans for future years.
