Using Archived Stop-Level Transit Geo-Location Data for Improved Operations and Performance Monitoring
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1 Using Archived Stop-Level Transit Geo-Location Data for Improved Operations and Performance Monitoring Robert L. Bertini Portland State University September 26, 2003
2 Objectives Trends in transit technology AVL and APC BDS system and data archiving Preliminary route level analysis Other examples Conclusions and future research
3 Automatic vehicle location (AVL) Determination of vehicle location Global positioning systems (GPS) Signposts Ground-based radio Dead-reckoning U.S. transit agencies 128 operational 172 planned
4 Automatic passenger counters (APC) Count boarding and alighting passengers Infrared beams U.S. transit agencies 60 operational 124 planned
5 AVL/APC in the Bay Area AC Transit Central Contra Costa Livermore/Amador Napa County Transit Santa Clara VTA Santa Cruz Sonoma County Transit Western Contra Costa San Francisco MUNI SamTrans TOTAL AVL 800 in [131 by 2005] [600] 80 [86] 54 [62] 0 [37] [362] 1804 [2518] APC [100] [135] 0 [36] 196 [467] Source: APTS Deployment in the United States Year 2002 Update, USDOT, 2003.
6 Archiving AVL/APC Data AVL not designed with archived data in mind. Most AVL systems do not deliver data for offline planning analysis. Transit agencies didn t insist on it. APCs designed with archiving in mind. APCs often only in 10-15% of fleet. TriMet system driven by APC pays for itself in saved reporting costs Source: TCRP H-28, Uses of Archived AVL-APC Data to Improve Transit Performance and Management.
7 About TriMet Serves 1.2 M population 575 mi M annual bus trips 206,600 daily bus boardings 95 bus routes 655 buses 8100 bus stops Also LRT, Paratransit
8 Performance measurement Measuring system performance is the first step toward efficient and proactive management. Increasing attention to transit performance Transit Capacity and Quality of Service Manual Quantitative/qualitative Passenger point of view Linked to agency operating decisions NCHRP Performance Based Planning Manual Accessibility Mobility Economic Development Improve reliability Reduce variability of system performance Delay Travel time Attract more riders Reduce operations costs Increase productivity Link to service standards
9 In the past Data collection more difficult Low temporal and spatial resolution Many people to collect little data Focus on limited, general, aggregate measures for external reporting Natural air conditioning
10 Today Unlimited coverage and continuous duration Design, extract and test specific measures Actual system performance Data management/processing challenges Need for generating relevant measures
11 TriMet s Bus Dispatch System Navstar GPS Satellites Radio System Doors Lift APC (Automatic Passenger Counter) Overhead Signs Odometer Signal Priority Emitters GPS Antenna On- Board Computer Radio Radio Antenna Control Head Memory Card Garage PC s
12 TriMet s Bus Dispatch System PCMIA Card Control Head Schedule deviation
13 Real Time Elements Navstar GPS Satellites Radio System Doors Lift APC (Automatic Passenger Counter) Overhead Signs Odometer Signal Priority Emitters GPS Antenna On- Board Computer Radio Radio Antenna Control Head Memory Card Garage PC s
14 Real Time Elements Schedule Data On-board Computer GPS Location Radio/Cellular Communications 90 sec Updates Dispatch and Control Arrival Prediction
15 Archived Elements Navstar GPS Satellites Radio System Doors Lift APC (Automatic Passenger Counter) Overhead Signs Odometer Signal Priority Emitters GPS Antenna On- Board Computer Radio Radio Antenna Control Head Memory Card Garage PC s
16 Archived Elements Schedule Data On-board Computer GPS Location APC/ Lift PCMIA Card Event Data [Operator] Pass up Overload Traffic Delay Train/Bridge Delay Fare Evasion Graffiti/Vandalism Stop Data [Automatic]
17 Event Data: Fare Evasion
18 Archived Elements Schedule Data On-board Computer GPS Location APC/ Lift PCMIA Card Event Data [Operator] Stop Data [Automatic] Scheduled Unscheduled
19 Stop Data REWRITTEN ARRIVE TIME (IF DOOR OPENS) ARRIVE TIME 15 METERS 30 METERS TIME LINE LEAVE TIME DOOR OPEN DWELL TIME DOOR CLOSE STOP LOCATION
20 Stop Data Route No. Service Date Leave Time Stop Time Arrive Time Badge Direction Trip No. Location ID Dwell Door Lift Ons Offs Est. Load Max Speed Pattern Distance 14 01NOV2001 8:53:32 8:49:15 8:53: NOV2001 8:55:00 8:51:41 8:54: NOV2001 8:56:22 8:52:00 8:55: X Coor. Y Coor. Route Number Vehicle Number Service Date Actual Leave Time Scheduled Stop Time Actual Arrive Time Operator ID Direction Trip Number Bus Stop Location Dwell Time Door Opened Lift Usage Ons & Offs (APCs) Passenger Load Maximum Speed on Previous Link Distance Longitude Latitude
21 TriMet s Bus Dispatch System
22 Stop Data: Passenger Movement
23 Route 14 Case Study
24 Route 14 Case Study 7.9 miles long 105 scheduled trips per weekday 64 scheduled stops min scheduled trip time (mean 43.3, SD 2.7 min) 3-55 min headways (mean 11.4 min) Focus on two weeks April 1-12, 2002 [>1000 runs] Morning inbound from SE 94th/Foster to NW 4th/Hoyt Crossing Hawthorne Bridge
25 April 1 A.M. Inbound Trips 7 6 Distance (miles) :00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:0
26 April 1 A.M. Inbound Trips 7 6 Distance (miles) :00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:0
27 April 1 A.M. Inbound Trips 7 6 Distance (miles) :00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:0
28 April 1 A.M. Inbound Trips 7 6 Distance (miles) :00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:0
29 April 1: Two Inbound Trips Distance (miles) Time 0.0 8:30 8:45 9:00 9:15 9:30
30 April 1: Two Inbound Trips 7.0 One late, one early arrival 6.0 Distance (miles) Trip 1290 Badge 996 (28 yrs exp) Median 1.2 min late Trip 1295 Badge 2606 (4 yrs exp) Median 0.9 min late Two late departures 0.0 Time 8:30 8:45 9:00 9:15 9:30
31 April 1: Impact of Pax Load Distance (miles) Trip 1290 Load Passenger Load Time 0.0 8:30 8:45 9:00 9:15 9:30 5 0
32 On-time Performance 5,000 4,500 4,000 3,500 All "stops" n = 66,012 Median = 0.6 min Late 100% 90% 80% 70% Frequency 3,000 2,500 2,000 60% 50% 40% 1,500 1, % Early 30% 20% 10% % Late/Early (minutes)
33 On-time Performance 5,000 4,500 4,000 3,500 3,000 2,500 2,000 1,500 1, Frequency TriMet Service Standard [-1 min +5 min] 85% On-time 11% Early 4% Late 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Late/Early (minutes)
34 OTP vs. Time of Day 30 Early/Late (minutes) :00 7:00 9:00 11:00 13:00 15:00 Time 17:00 19:00 21:00 23:00
35 TriMet Operator Experience n = 3,249 Mean = 9.8 years 100% 90% 80% Frequency % 60% 50% 40% 30% % 10% %
36 TriMet Operator OTP April 1-12, 2002 Route 14 Inbound 94 Operators 65,848 stops Median Late/Early Operator -3
37 TriMet Operator OTP April 1-12, 2002 Route 14 Inbound 94 Operators 65,848 stops Median Early/Late Late Early -2-3 Years Experience
38 Stop Level Performance 0:30 0:25 April 1, 2002 Stop No Scheduled Headway 0:20 0:15 0:10 Hawthorne/ 22nd Ave Mean Headway 11:02 min St. Dev. 7:10 0:05 0:00 0:00 0:05 0:10 0:15 0:20 0:25 0:30 Actual Headway
39 Stop Level Performance 50 Cumulative Bus Arrival Number Scheduled Actual April 1, 2002 Stop No Hawthorne/SE 22nd Ave Mean Headway 11:02 min St. Dev. 7:10 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 Time
40 Route 14: Trip Time Model T = T T 0 N N N d a b 0 + an d + bn = average nonstop trip time = number of dwells = passengers alighting = passengers boarding a + cn b
41 Dwell Time 7, % 6,000 5,000 4,000 3,000 2,000 1, Frequency Nonzero Dwells n = 34,456 Mean = 13.3 s SD = 20.3 s Lift use = 232 times 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Dwell (sec)
42 Dwell Time vs. Passenger Movement On + Off 4% None 7% Dwell Time (sec) Off 28% On 61% Passenger Movement
43 Boardings Frequency 12,000 10,000 8,000 6,000 4,000 n = 37,441 Mean = 1.33 SD = 2.45 Max = % 90% 80% 70% 60% 50% 40% 30% 2, % 10% 0% Boardings
44 Alightings 16,000 14,000 12,000 n = 36,978 Mean = 1.31 SD = 2.62 Max = % 90% 80% 70% 10,000 8,000 6,000 4,000 2, Frequency 60% 50% 40% 30% 20% 10% 0% Alightings
45 Dwell Time Model Dwell Time = N n = 24,995 [non - zero, no lift, APC = "G", no layover] Boarding Only : Dwell = n = 15, N 4.27N Alighting Only : Dwell = N n = 7,021 b a b a
46 Nonstop Trip Time 2,000 1,800 1,600 n = 30,036 links NSTT= x (sec) 1,400 Travel Time (sec) 1,200 1, Link Distance (miles)
47 Trip Time Model T = N N N d a b 21.2N N = number of dwells = passengers alighting = passengers boarding d a N b
48 Run Times: April 1-12, 1 12, Run Time (min) n=856 trips Mean on=40/trip Mean off=40/trip Mean no. of dwells=35/trip 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 Arrive Time
49 Run Times: April 1-12, 1 12, n=856 trips Mean=41.5 min SD=5.1 min 100% 90% 80% 70% Frequency 60% 50% 40% 30% 20% 10% 0% Run Time (minutes)
50 Run Time vs. Operator Experience Mean Run Time n=856 trips 83 operators Run time/experience relationship (95% confidence) Hire Date
51 Actual vs. Predicted Run Time n=856 Actual Predicted Predicted Run Time Mean SD Veh-hrs 41.5 min 5.1 min min 7.4 min Actual Run Time
52 Stop Consolidation Analysis Route 14 length feet, 64 stops Mean stop spacing 670 feet Consolidate stops to 1000 foot spacing eliminate 10 stops Reduce trip time 21.2 sec per stop, save 3.5 min per inbound run 105 inbound trips per day 6.1 hours savings ($60/hr +/-) Add ~9 trips using existing resources Improve mean headway min Does it affect demand?
53 Boarding Area Improvement Analysis Streamlining program to reduce dwell time Curb extensions Nearside farside conversion Smart cards Mean boarding time estimated 2.87 sec Reduce boarding time by 1 sec at top ten locations Total of 1800 passengers boarded at these locations Save 30 min/day
54 Other Applications
55 Other Applications 1 census tract 0.25-mi buffers 38% of population
56 Transit Signal Priority
57 Transit Signal Priority Reduce run time and schedule variability 180 intersections complete, 100 more in 2003 Emitter turned on if bus is >90 sec late Remains on until <30 sec late Green extension/red truncation
58 Transit Signal Priority
59 Express Buses as Freeway Probes
60 Buses as Probes on Arterials
61 Buses as Probes on Arterials
62 Buses as Probes on Arterials
63 Buses as Probes on Arterials
64 Buses as Probes on Arterials
65 Buses as Probes on Arterials
66 Buses as Probes on Arterials tt veh = 1.23 tt pseudo V veh = 0.84 V pseudo
67 Buses as Probes on Arterials Buses as Probes on Arterials Eastman Pkwy. Birdsdale Ave. Towle Ave. Walters Rd. Main Ave. Hood Ave. Cleveland Ave. Hogan Ave. TriMet Route 9 Probe Vehicle Run Signalized Intersection Bus Stop Legend
68 Buses as Probes on Arterials EASTBOUND WESTBOUND -6% Savings 9% Savings 2% Savings 22% Savings 27% Savings 14% Savings EB Probe EB Bus EB Hypothetical WB Probe WB Bus WB Hypothetical Before (sec) After (sec)
69 Discussion Other uses for the BDS data? Conclusions? Save all of your data! Ask for archiving capabilities
70 Acknowledgements Steve Callas, TriMet Ahmed El-Geneidy, James Strathman, Thomas Kimpel, Kenneth Dueker, Center for Urban Studies Sutti Tantiyanugulchai, Civil & Environmental Engineering National Science Foundation Oregon Department of Transportation City of Portland Federal Transit Administration
71 More information Web: TriMet data readily available.
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