1. Future of BRT:
Flexible Capacity Operations
Juan Carlos Muñoz
Bus Rapid Transit Centre of Excellence
Pontificia Universidad Católica de Chile
September 20, 2013
4. What can we say about bus service?
Bus is critical to provide a good door-to-door transit alternative
for many journeys:
• Much higher network density and coverage than rail
• Greater flexibility in network structure
• Low marginal cost for service expansion
BUT as traditionally operated, it also has serious limitations:
• Low-speed
• Subject to traffic congestion
• Unreliable
• Harder to convey network to the public
• Negative public image
5. What can we say about the user?
• Perceives waiting time and walking time twice as important as
travel time inside the vehicle.
• Avoids transferring, specially if they are uncomfortable
• Needs a reliable experience
• Requests a minimum comfort experience
• Requests information
• Needs to feel safe and secure
6. What can we say about the bottlenecks?
Capacity per lane:
• “Only a fool breaks the two second rule” => 1,800 veq/hr-lane
• 1 Bus ≈ 2 veq => 900 buses/hr-lane
Capacity per lane at junctions:
• 40 – 60 % of lane capacity => 450 buses/hr-lane
Capacity at Bus Stops:
• Depends on the amount of passengers boarding and alighting
• ≈ 20 - 40 sec. per bay => 180 – 90 buses/hr-bay
7. Buses are involved in this vicious cycle
Operation cost grows
Income and Population
grows
More cars in the city
Bus Demand drops
Car becomes more
attractive
Bus frequency drops Buses cover fewer miles
per day
Bus fare increases
And we need to make buses attractive to car drivers…
More congestion
And delays
9. Can we provide Metro-like service with buses?
• Fast
• Low wait time
• Comfortable
• Reliable
• Good information
• Branding
10. Can we provide Metro-like service with buses?
Transit Leaders Roundtable MIT, June 2011
• Fast
• Low wait time
• Comfortable
• Reliable
• Good information
• Branding
11. Yes we can … We still believe
(several pieces are already there in cities worldwide)
Can we provide Metro-like service with buses?
The good news are:
COURAGE WILL BE REWARDED
12. IMPROVED
EFFICIENCY
IMPROVED
SERVICE QUALITY
Reduced bus
costs
•Less buses required
•Lower cost per km
Improved bus
productivity
•More pax/bus-day
Attracts more
passegers
Improves revenue
IMPROVED
FINANCIAL
VIABILITY
Better buses
More investment into
new buses & cleaner
technology
Lower
Subsidies
Reduced private car use
& traffic congestion
Improved energy
efficiency
Reduced emissions
Operational
benefits
•Shorter cycle time
•Reliable operations
•Higher productivity
Increase Bus speed, Frequency,
Capacity and Reliability Passenger
benefits
•Reduced travel time
•Reduced waiting
time
•Higher comfort
•Reliability
Source: Frits Olyslagers, May 2011
34. Choosing the Right Express Services for a
Bus Corridor with Capacity
Constraints
Homero Larrain, Ricardo Giesen and
Juan Carlos Muñoz
Department of Transport Engineering and Logistics
Pontificia Universidad Católica de Chile
35. Introduction
Operación “Carretera” Operación Expresa
Higher in-vehicle travel time Lower in-vehicle travel time
No transfers May force some transfers
Higher operation costs, in
terms of $/Km
Lower operation costs, in
terms of $/Km
Other aspects: capacity, comfort, accessibility, etc.
Limited stop servicesAll stop services
*Jointly operated with all stop services,
assuming a constant fleet size.
*
36. Objective
• Formulate a model that allows to choose
which combination of services to provide on a
corridor, and their optimal frequencies.
• Determine opportunities for express services
(or limited stop) on a corridor based on its
demand characteristics.
38. The Problem
• Different operation schemes.
p1 p2 pi pn
… …
… …l1, f1
… …l2, f2
… …l3, f3
… …l4, f4
The goal is to find which services to offer, and their optimal frequencies.
li: Line i
fi: frequency of line i
39. The Model
• The goal of this model is to find the set of
services that minimize social costs:
– Operator costs: will depend on what services are
provided, and their frequencies.
– User costs:
• In-vehicle travel time.
• Wait time.
• Transfers.
40. The Model: Assumptions
• Given transit corridor, with a given set of
stops.
• Fares are constant for a full trip.
• Number of trips between stops is known for a
certain time frame.
• Random arrival of passengers at constant
average rate.
• Passengers minimize their expected travel
times.
41. The Experiment
• Steps:
– Defining network topology.
– Defining demand profiles.
• Load profile shape.
• Demand scale.
• Demand unbalance.
• Average trip length.
– Build scenarios and construct an O/D matrix for each one.
– Optimize scenarios defining the optimal set of lines for
each one.
42. Express Services: Main Conclusions
• Allow increasing the capacity of the system
• Significantly reduces social costs
• Few services bring most of the benefits
• Limited stop services are more promising in these
situations:
– The longer the average trip length
– High demand
– High stop density
– Demand is mostly concentrated into a few O/D pairs
67. + - + - + - +
And so on so forth.
Our challenge is to keep an inherently unstable system: buses evenly spaced
Now, if we want to prevent bunching from occurring … when is the right time to intervene?
69. Bus bunching
Severe problem if not controlled
Most passengers wait longer than they should for crowded
buses
Reduces reliability affecting passengers and operators
Affects Cycle time and capacity
Creates frictions between buses (safety)
Put pressure in the authority for more buses
Contribution: Control Mechanism to Avoid Bus Bunching
based on real-time GPS data
70. 2. Research
Propose a headway control mechanism for a high frequency & capacity-
constrained corridor.
Consider a single control strategies: Holding
Based on real-time information (or estimations) about Bus position, Bus
loads and # of Passengers waiting at each stop
We run a rolling-horizon optimization model each time a bus reaches a
stop or every certain amount of time (e.g. 2 minutes)
The model minimizes:
Time waiting for first bus + time waiting for subsequent buses + time held
71. No control
Spontaneous evolution of the system.
Buses dispatched from terminal as soon as they arrive or until the design headway is
reached.
No other control action is taken along the route.
Threshold control
Myopic rule of regularization of headways between buses at every stop.
A bus can be held at every stop to reach a minimum headway with the previous bus.
Holding (HRT)
Solve the rolling horizon optimization model not including green extension or boarding
limits.
Estrategias de control simuladas
3. Experiment: Control strategies
72. 4. Results: Simulation Animation
Simulation includes events randomness
2 hours of bus operation. 15 minutes “warm-up” period.
76. Results: Cycle Time
25 30 35 40 45
0
50
100
150
200
250
300
350
mean =33.64
Std.Dev. =3.51
No control
Frequency
Cycle Time (Minutes)
25 30 35 40 45
0
50
100
150
200
250
300
350
mean =32.11
Std.Dev. =1.2
HRT 05
Frequency
Cycle Time (Minutes)
HRTNo Control
77. Results: Waiting time Distribution
% of passengers that have to wait between:
Period 15-25 Period 25-120
0-2 min 2-4 min > 4 min 0-2 min 2-4 min > 4 min
No Control 57.76 29.60 12.64 63.46 27.68 8.86
HRT 79.24 20.29 0.47 87.30 12.62 0.08
78. Disobeying
Drivers
Similar
disobedience
across all drivers
A subset of
drivers never
obey
Technological
Disruption
Random signal
fail
Failure in the
signal receptor
equipment
Signal-less
zone
Homogeneous
distribution across
buses
Concentration in
certain buses
Concentration in
certain stops
5. Impact of implementation failures
80. Common disobedience rate across drivers
8000
9000
10000
11000
12000
13000
14000
15000
0%10%20%30%40%50%60%70%80%90%100%
TotalWaitingTime[Min]
Obedience rate
HRT, Beta=0,5
Sin Control
81. Full disobedience of a set of drivers
8000
9000
10000
11000
12000
13000
14000
15000
16000
0 1 2 3 4 5 6 7
TotalWaitingTime[Min]
Deaf Buses from a total of 15 buses
82. 6. Implementation
• The tool has been tested through two pilot plans in
buses of line 210 of SuBus from Transantiago (Santiago,
Chile) along its full path from 7:00 to 9:30 AM.
• We chose 24 out of 130 stops to hold buses
• One person in each of these 24 stops received text
messages (from a central computer) into their cell
phones indicating when each bus should depart from the
stop.
84. Implementation
Real time GPS
information of
each bus
Program optimizing
dispatch times for each
bus from each stop
Text messages were sent
automatically to each person
in each of the 24 stops
Buses are held according to
the text message instructions
(never more than one minute)
86. The results were very promising
even though the conditions were far
from ideal
87. Main results
• Transantiago computes an indicator for
regularity based on intervals exceeding twice
the expected headway (and for how much).
$ 10.000
$ 20.000
$ 30.000
$ 40.000
$ 50.000
$ 60.000
$ 70.000
$ 80.000
$ 90.000
$ 100.000
$ 110.000
Multas($CLP)
88. Main results: cycle times
2:24:00
2:31:12
2:38:24
2:45:36
2:52:48
3:00:00
3:07:12
3:14:24
3:21:36
3:28:48
3:36:00
5:52:48 6:00:00 6:07:12 6:14:24 6:21:36 6:28:48 6:36:00 6:43:12 6:50:24 6:57:36
Cycletime
Dispatch time
Piloto 1
Prueba10
Prueba12
Prueba13
Prueba15
Prueba16
Prueba17
No significant differences for cycle times
89. • Line 210 captured an extra 20% demand!
94.000
96.000
98.000
100.000
102.000
104.000
106.000
7.400 7.600 7.800 8.000 8.200 8.400 8.600 8.800
Demand for Line 210 (pax)
Demand on
All lines
(pax)
Unexpected result
90. 7. Conclusions
Developed a tool for headway control using Holding in real time reaching
simulation-based time savings of 60%
Huge improvements in comfort and reliability
The tool is fast enough for real time applications.
Two pilot plans have shown significant improvements in headway regularity.
During 2013 we will build a prototype to communicate directly to each driver.
91.
92. Other activities
• Three chilean operators will test our tool this year
• Raised interest from operators in Cali and Istanbul
• A research and development team is consolidating
• Pedagogic tool to teach bus headway control
93. Future of BRT:
Flexible Capacity Operations
Juan Carlos Muñoz and Ricardo Giesen
Bus Rapid Transit Centre of Excellence
Pontificia Universidad Católica de Chile
July 12, 2013
