Use of Passenger Count Data to provide Cost-Benefit Analysis of Fixed-Route Cluster Operations Helping Agencies Do More with Less

Transcription

1 Use of Passenger Count Data to provide Cost-Benefit Analysis of Fixed-Route Cluster Operations Helping Agencies Do More with Less Dennis Fletcher M.E.S., director, transit solutions Gera Taubkin, M.S.,senior transit planner GENIVAR, Markham, ON

2 PASSENGER GLOBAL SOLUTIONS IN ENGINEERING COUNTING ANALYSIS Buildings Municipal Infrastructure Transportation Industrial Energy Environment

3 3 PC DATA. MAIN PURPOSES to check and clean How PC Data Quality to maintain and keep Readiness for PC Data use PC Data to use and analyze Transit Operation Service and Efficiency

4 4 PRESENTATION HIGHLIGHTS -Developed set of parameters for diagnostic analysis of transit operations and identification of appropriate potential solutions without manual drill-down into stop level (passenger density, crowding factor, weight focuses of passenger s route activity, load characteristics, route pattern similarity) - Procedure for Route Correspondence Matrix creation to provide IF-WHAT comparative Cost-Benefit Analysis for proposed solutions Matrix Parameters - Computer EXCEL-based tools incorporating these methodologies and presentation of examples Tools Process

5 5 PLACE OF PC DATA USAGE IN TRANSIT PLANNING PROCESS PC Analysis

6 6 TRANSIT ANALYTICAL PROCESS Auto - Diagnostic Computer procedure analyzing PC Data to classify found GAPs Manual-Diagnostic PC Data Analysis by planner. Drill-down methodology Operational GAPs Identification Auto - Proposing Computer procedure proposing appropriate variants of solutions according to GAP type Proposing required solutions by result of PC Analysis and professional knowledge of planner Proposing of directions/solutions to fix GAPs Auto-Manual Choice Manual - Proposing Interactive computer application to choice preferable solution in IF-WHAT mode Choice of preferable solution Key Meanings Similarity

7 7 AUTO DIAGNOSTIC AND PROPOSING / SELECTED ROUTE 3 4 Back

8 8 AUTO DIAGNOSTIC AND PROPOSING / SELECTED ROUTE/ SELECTED PERIOD stops? Back

9 COMPARATIVE COST-BENEFIT ANALYSIS/ SELECTED ROUTE/ SELECTED PERIOD LOAD spare No branching, frequency increase Branching, frequency variation = * Base Short-1 Short-2 Short-3 Back Next

10 10 COMPARATIVE COST-BENEFIT ANALYSIS/ IF-WHAT ANALYSIS AND CRITERIAS PC usage Options comparison Cost assessment Buses, Hours/KM, Riders per Cost unit $! LOS assessment Classification and Quantification of existing riders by groups according to impact from proposed solutions : - No significant travel pattern change -Service Improvement (frequency, travel time, transfer ) - Service worsening (frequency, travel time, transfer ) - Service cancelation Back Similarity

11 COMPARATIVE COST-BENEFIT ANALYSIS/ COST LOAD spare Hours Buses = * Base Short-1 Short-2 Back LOS

12 COMPARATIVE COST-BENEFIT ANALYSIS/ LOS LOAD spare Crowding Factor Load Density, pass/sq m Acceptable level = * Base Short-1 Short-2 Cost Similarity

13 13 AUTO DIAGNOSTIC AND PROPOSING / SELECTED ROUTE / SIMILARITY Riders number, Max Load Is it enough to recognize ridership pattern? Process NEXT

14 14 AUTO DIAGNOSTIC AND PROPOSING / SELECTED ROUTE / SIMILARITY Two Trips/Day Periods may be considered as SIMILAR if their pattern accommodate the same planning solution, based on 3 ingredients : - Branch structure - Frequency - Vehicle Type Headway Bus Type minutes Branch 1 Base 10 Regular Branch 2 Short 10 Regular Branch 3 Express 20 Small Departure Headway betwee bus departure Branch1 7:00 Branch2 7:05 0:05 0:05 Branch 1 Branch1 7:10 0:05 0:05 0:10 0:10 Branch 2 Branch2 7:15 0:05 0:05 Branch3 7:18 0:03 0:08 Branch 3 Branch1 7:20 0:02 0:05 0:02 0:10 Branch2 7:25 0:05 0:05 Branch1 7:30 0:05 0:05 0:10 0:10 Branch2 7:35 0:05 0:05 Branch3 7:38 0:03 0:08 Branch1 7:40 0:02 0:05 0:02 0:10 Branch2 7:45 0:05 0:05 Branch1 7:50 0:05 0:05 0:10 0:10 Branch3 7:53 0:03 0:08 Branch1 7:55 0:02 0:05 0:02 0:10 Base Short Express Back Key Meanings

15 COMPARATIVE COST-BENEFIT ANALYSIS/ COST LOAD spare Hours Buses = * Base Short-1 Short-2 Back Key Meanings

16 16 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS Passenger Density on Bus Crowding factor Load - Characteristics Weight Centers of riders Correspondence Matrix BRT DESIGN Agency Macro Analysis HIGHLIGHTS START PROCESS

17 17 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / DENSITY Key Meanings Next

18 18 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / DENSITY Level 1 : Just Seats Level 2 : Low density Level 3 : Medium density Dencity Levels pass/sq.m Seats Standing Area low medium Level 4 : High density Level 5: Very High density high very high Bus Seats Square Capacity based on 4 pass/sq.m Dencity in case of ON-BOARD=35 Dencity in case of ON-BOARD=55 Dencity in case of ON-BOARD= Small Regular Articulated Back

19 Small Bus Regular Bus Articulated Bus = * = * = * = * = * = * Density 19 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CROWDING FACTOR LOAD LOAD spare spare # # # # # # # # # # # LOAD LOAD spare spare # # # # # # # # # # # LOAD LOAD spare spare # # # # # # # # # # # Back Next

20 Articulated Bus Regular Bus Small Bus 20 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CROWDING FACTOR 7.8% 13.8% Density Distribution 17.2% 33.6% 27.6% * * * * % Density Distribution 9.5% 1.7% 5.2% 7.8% * * * *1.0 Seats Low Mid High Very High 0.17*5.0 Seats Low Mid High Very High 0.76* % Density Distribution 11.2% 2.6% 0.0% 82.8% Seats Low Mid High Very High * * * * *5.0 Density Distribution 0.0% 19.8% 24.1% 18.1% 37.9% Seats Low Mid High Very High * * * * *5.0 Density Distribution 0.0% 0.0% 6.0% 94.0% * * * * % 0.0% Density Distribution 0.0% 49.1% 46.6% * * * *1.0 Seats Low Mid High Very High 0.00*5.0 Seats Low Mid High Very High 0.00*5.0 Back

21 21 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS /LOAD CHARACTERISTICS = * Max ON-BOARD Square of : -Total Polygon : PKM (Passenger-KM) -Polygon of black color : Load factor closed to MAX(>80% from MAX) -Polygon of dark gray color : Load factor in range (50%-80% from MAX) -Polygon of light gray color : Load factor in range (20%-50% from MAX) -Polygon of very light gray color : Load factor closed to MIN (<20% from MAX) Average ON-BOARD LOAD # Max Load Stop Key Meanings Next

22 22 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS /LOAD CHARACTERISTICS = * LOAD # Max Load 56.8 Max Segment 5 distance: % 14.7% Average Load PassKM Variation 64.3% Variation Factor 2.31 Average Rider Distance km 2.49 from Distance % 21.5% Chanchebility 4.7 = * Max Load 60.0 Max Segment 12 distance: % 44.0% Average Load 47 PassKM Variation 34.6% Variation Factor 1.29 Average Rider Distance km 4.73 from Distance % 40.8% Chanchebility 2.5 LOAD # Back Focuses

23 = * = * 23 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS LOAD # LOAD # Key Meanings Back

24 24 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CORRESPONDENCE MATRIX Boarding Alighting Matrix Algorithm Correspondence between each pair of stops Key Meanings Examples

25 25 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CORRESPONDENCE MATRIX Stops Distance ON OFF Example Correspondence Matrix Boarding Matrix Total Alighting Matrix Boarding Key Meanings Back Next

26 new 26 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CORRESPONDENCE MATRIX 77% % 17% 77% 17% 2% 2% 2% 2% 2% Key Meanings Back Next

27 27 AUTO DIAGNOSTIC AND PROPOSING / KEY MEANINGS / CORRESPONDENCE MATRIX 100% LOS change by riders percentage 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Key Meanings Back

28 28 PC DATA USAGE-EXAMPLES / BRT-ZUM / VEHICLE FLEET STRUCTURE Run Key Meanings

29 29 PC DATA USAGE-EXAMPLES / BRT-ZUM / VEHICLE FLEET STRUCTURE Graph Key Meanings Back

30 30 PC DATA USAGE-EXAMPLES / BRT-ZUM / VEHICLE FLEET STRUCTURE Key Meanings Back Fleet

31 31 PC DATA USAGE-EXAMPLES / BRT-ZUM / VEHICLE FLEET STRUCTURE Headway, minutes Queen Corridor. Option1.1 Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Regular Artic Key Meanings Back

32 32 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS / TRIPS Key Meanings Bus Type Route Efficiency

33 33 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /TRIPS / CROWDING Artic Regular Short Trips

34 34 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /TRIPS / CROWDING Artic Regular Short Trips

35 35 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /TRIPS / CROWDING Artic Regular Short Trips

36 36 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /ROUTES/ CROWDING Artic Regular Short Trips Load

37 37 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /ROUTES/ CROWDING Artic Regular Short Trips Load

38 38 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /ROUTES/ CROWDING Artic Regular Short Trips Load

39 39 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /ROUTES/ PREDICT Trips Routes

40 40 PC DATA USAGE-EXAMPLES / MACRO ANALYSIS /ROUTES/ EFFICIENCY Trips Routes