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P-TRANE
Understanding Service Changes of Transit
Agencies for Modelling the Bus Transit Network
EvolutionAmr M. Mohammed1
Amer Shalaby2
Eric J Miller 3
1: Morrison Hershfield Ltd.2,3: Dept. of Civil Engineering, University of
Toronto
Paper presented at the 2011 GIS in Public Transportation Conference
URISA – The Association of GIS Professionals
September 15th 2011
Mohammed, Shalaby and Miller
P-TRANE
Overview Introduction Bus Network Evolution Model Phase I: Mississauga Transit Types of Changes Phase II: P-TRANE Results Conclusions and Recommendations Current Developments(City of Ottawa: Hospital Link, LRT)
Mohammed, Shalaby and Miller
P-TRANE
Introduction Bus operators/agencies Constantly
Adjust networks (changes)
Behaviour
Response to: Dynamic of Ridership Changes & Budget
Mohammed, Shalaby and Miller
P-TRANE
Introduction Model captures changes over time:
Predict network shape & functionality Future Time steps Aid for developers, Transit & urban planners Component in urban-transportation
demand simulation frameworks (e.g. ILUTE)
Mohammed, Shalaby and Miller
P-TRANE
IntroductionP-TRANE
“Changes triggered by socioeconomic & Urban factors (Land-Use) & dictated/Guided by service standards practices”
Two-phaseEmpirical Study (Multiple regression & simultaneous equations) – USING ArcGISService Standards (P-TRANE) - USING ArcGIS
Mohammed, Shalaby and Miller
P-TRANE
IntroductionFits into ILUTE? Transportation Planning Models Bring transportation-related components of
urban systems into integrated modelling framework
Urban Form Transportation
Model 2-way interactions
Integration
Mohammed, Shalaby and Miller
P-TRANE
Introduction: ILUTEIntegrated Land-Use, Transportation, Environment
(Salvini and Miller, 2005)
Travel Demand
Household/Person decisionsActivity of Objects (Persons/hh/firms/job market)
Travel TimesEnergy UseEmissions
Exogenous
Mohammed, Shalaby and Miller
P-TRANE
Bus Evolution ModelPhase I: Empirical analysis
Supply historical trends Macroscopic Mutual effect between Demand & Supply ‘Demand and supply are recursive & simultaneous’ (e.g.
Taylor & Miller 2003, Peng 1997)
Phase II: P-TRANE GIS of bus network growth Microscopic (bus line /period/branch detail) Service Standards
Mohammed, Shalaby and Miller
P-TRANE
PHASE I Analysing historical trends of transit supply
Purpose: Understand causes and triggers of growth
Statistical and econometric models
Phase I: Mississauga Transit
Demand?
Population?
Income?
Demographics?
Mohammed, Shalaby and Miller
P-TRANE
Phase I: Mississauga Transit
Mississauga
Downtown Toronto – Business Centre
City of Mississauga
Subway Stations (Western Terminus)Kipling/Islington
Busy
Explosive Growth
Employment Centre
Connectivity
Mohammed, Shalaby and Miller
PREMOTRANE
Phase I: Mississauga Transit• Demographic
and socioeconomic variables• ArcGIS (1/4 mile buffer zone) (Local & City wide)
• Multiple time steps (1986-2001) (Census Tract + TTS zones data)
• Multiple Regression & simultaneous equations
• Transit supply → bus frequencies (Dependant variable)
Phase I: Results
Bus Frequency = -2.623 + 0.00316 (Demand {ridership}) + 0.604 (Transfers) + 4.916 (Dummy variable representing the connection to TTC subway) + 0.001613 (Population Density) – 0.001 (No of Children) + 7.166 * 10-5 (City-wide No of Children)
Cross-sectional models using Multiple Regression
Phase I: Results
Simultaneous Regression Equations
Bus Frequency = -12.854 + 0.01394 (Demand) + 0.1661 (Labour Force) + 0.3 (No of Transfers) + 2.093 (Dummy variable representing the connection to TTC subway) --------------------------------------------------------------------------Demand = -130.83 + 0.1003 (Density) + 0.0147 (Employment) + 0.127 (Change in City-wide Density) – 0.05635 (Number of Children) + 48.214 (Bus Frequency {last period})
Phase I: ResultsSimultaneous Regression Equations: Model Testing
• 2001 data(Observed vs. Estimated)
• 2-tailed matched pairs, difference not significantly different from zero
• Previous model show strong predictive power
0
2
4
6
8
10
12
14
16
Fre
q. (
bu
s/h
ou
r)
Corridors
(6-a) Observed and Predicted Frequencies
0
100
200
300
400
500
600
700
Dem
and
. (P
ass/
ho
ur)
Corridors
(6-b) Observed and Predicted Demand
Mohammed, Shalaby and Miller
P-TRANE
Transit Evolution Model
Phase II
“Prediction Model of Transit Network Evolution”
Previously PREMOTRANE
P-TRANE
Mohammed, Shalaby and Miller
P-TRANE
P-TRANE
1- Capital-intensive changes Political – Large Scale (subway, LRT) –
Effects 2 Exogenous
Phase II: Types of Changes
Types of Bus Network Changes
2- Periodic Service changes Regular, (medium to small) Reaction to Changing demands Budget is exogenous
Extra subsidy? = No new buses into P-TRANE
Modeled in P-TRANE
New bus route
Route removal
Frequency change
Re-routing & Extending
Mohammed, Shalaby and Miller
PREMOTRANE
P-TRANE
2- Medium to Small Periodic Service changes
Phase II: Types of Changes
Mohammed, Shalaby and Miller
P-TRANE
Service Standards:
• Guidelines• Service quality & financial performance
• Proposals for new service
• Rules into P-TRANE
• Expert Systems (KB: Service Standards Rules + Priority Rules)
Frequency, Financial Performance
P-TRANE
Phase II: TTC Service Standards
Phase II: P-TRANE , Minor changes: Bus Crowding RuleP-TRANE
IF:
Ridership during the busiest hour period is greater than loading standard (peak or off peak)
THEN:
this route is flagged and will have service increase in the next time step
Financial Performance ruleFinancial Performance = Ridership / Operating cost
IF: Financial Performance < 0.23
THEN:
List route as financially poor, Order the list, Flag for reduction and use if needed for
other service improvements
This route is flagged and could be reduced (decrease frequency) or eliminated at the next time
step
P-TRANE
Implementation: Minor Changes
Phase II: P-TRANE
Algorithm (1): Frequency increase and decrease in P-TRANE for minor changes.
Calculating frequencies from headways:F = 60 / H
1. Frequencies are increased or decreased by one bus/hour in each service iterationFN = F + 1 (Service increase: overcrowding)
FN = F – 1 (Service reduction: poor financial performance)2. Unit change in frequency is equivalent to an increase in number of buses by one (approximation).
NBN = NB + 1 (Service increase: overcrowding)NBN = NB – 1 (Service reduction: poor financial performance)
3. Run crowding check for lines that experienced frequency increase to check if demand was satisfied, if not, then go to step (2) (if more than one bus is required for any line at a time step). Iterate until demand is satisfied.
For each line requiring frequency reduction:IF NB = 1 (Only one bus operating, assumed service frequency).
Then Flag this line for removal for the next time period, if the area is only covered by this line, it is assumed to be re-built using Module 2 of P-TRANE.
For each line requiring frequency increase:NBN = NB + nHN = 60 / FN
IF HN < 1.5 minutes (Maximum frequency for buses).Then Do nothing and stop
(No service increase in this process, keep service frequency and do not update.)
Where:F : Bus frequency (bus/hour)H : Bus headway (in minutes)HN : New (updated) bus headway (in minutes)FN : New (updated) bus frequency (bus/hour)NB : Number of buses per line per time periodNBN : New (updated) number of buses per line per time periodn : Number of successful iterations (number of added buses)
Phase II: P-TRANE,GISMedium Changes
RulesP-TRANE
Serve people beyond 300 m of current service Maximise interconnection with rapid transit
stations Result an overall benefit for customers. Proposal presented by customers or
councillors in the area Service Gaps + Land-use (input) +
Developments (more likelihood)
P-TRANE,GIS : build new routes
Mohammed, Shalaby and Miller
PREMOTRANE
Modelling Procedure (Proposals)
PREMOTRANE
new line/extension/adjustment in routing
Mohammed, Shalaby and Miller
P-TRANE
Transit Evolution ModelP-TRANE & ILUTE exchange demand, land-
use and supply data for future time steps and are simulated together.
P-TRANE
Demand (Ridership)
Land-use
P-TRANE
ILUTE
Socio-Economic Data
Transit Network (Supply)
Mohammed, Shalaby and Miller
PREMOTRANE
Results (Minor Changes)
P-TRANE
Frequencies and Financial performance
Test 2005 network (TTC) Output: A number of lists of changes in
frequency & financially poor lines for 2006.
97% of actual # changes modelled by P-TRANE actually changed
Results
P-TRANE
Route ChangesSheppard Subway stations - 2002 TTC made 11 changes - 3 new lines
Don Mills to Scarborough town Centre Finch East to Don Mills Victoria Park via consumers road (To Don Mills station)
P-TRANE output: Don Mills to Ellsemere station (one station south of
actual) Bayview to Finch (but through local streets) Bessarion to Finch (through local streets)
Spatial comparison Functionality / Spatially equivalent
Conclusions
P-TRANE
P-TRANE For the first time, Transit network
evolution Two phase project (empirical & DSS) P-TRANE – a (GIS) framework Simulates changes spatially and
temporally Predictive model, describes service
standards Component in ILUTE, GIS, User-friendly, Promising Results
Mohammed, Shalaby and Miller
P-TRANE
Conclusions
P-TRANE
P-TRANE DSS For Transit Agencies (e.g. TTC,
OCTranspo) Test future policies, alternatives,
standards Simulates Periodic Changes of
Service/Feeder Bus Routes Prior to (Capital Intensive) Projects
E.g., New LRT BRT, Subway Stations,
Mohammed, Shalaby and Miller
P-TRANE
Current Developments
P-TRANE
Ottawa Transit: (LRT & Hospital Link BRT) Effects on feeder bus network;
Testing, validating and updating;
Next Steps Effects of Labour Unions Budget Model