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A new approach to calculate gains Session : Transit Transit Signalisation Priority (TSP)
| Date post: | 21-Jan-2018 |
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A new approach to calculate gains
Session : Transit
Transit
Signalisation
Priority (TSP)
Authors
François Bélisle, Eng. , B.Sc., M.A.
Stephan Kellner, Eng., P.Eng., MS
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Presentation outline
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Introduction
Basic operation principles
Typical Impacts and gain evaluation
Proposed Methodology
Typical Gains
Conclusion
Introduction
4
Transit Signalisation Priority
(TSP)
− Operational strategy to
facilitate transit vehicle
movements (1)
− Enhances transit
movements without
undue impact on other
users
Basic operation principles
5
Vehicle detected upstream
Requests
− All or some requests are
accepted
− Request transmitted to
traffic controller
Detected vehicle passes thru
intersection
Confirmation transmitted to
controller
Return to normal operation
Basic operation principles
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On-board interface
Emitter
Check-in detection zone
Check-out detection zone
Traffic controller
(w/ or w/o interface)
Optional: connection to
centralized traffic control
centre (CTC)
Operation strategies
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Passive systems (w/o detection)
− Optimisation to enhance
transit operation
• Cycles
• Timings
• Offsets
May be sufficient for certain
agencies and certain
intersections
Operation strategies
8
Active systems (w/ detection)
− Green extension
− Red truncation
− Phase rotation
− Addition of transit phases
May be too costly for certain
problems and agencies
May have too much impact on
other users
Typical Impact evaluation
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Different tools can be used to
evaluate TSP gains
Each tool has its advantages and
disadvantages
Disadvantages induce a bias in
TSP impacts and gains
Most importantly, future
schedules aren’t know at the time
of analysis
Example of disadvantages
Synchro : public transit support is
limited
VISSIM :
Phases Rotation is impossible
in practice
Rings and TSP don’t get along
well
Punctual delay during a trip is
hard to quantify
Generally micro-simulation
Requires lots of data and
resources
Proposed Methodology: a new application of an old idea
Reviewing the Space-Time diagram
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Because of the disadvantages
and the complexity of existing
micro-simulation models, WSP
has developed its own
mesoscopic model
The model is mesoscopic in the
sense that it doesn’t take into
account interactions between
vehicles
Since the model proposed is not
a micro-simulation, the model
requires less data: only traffic
signal and basic PT data
Proposed Methodology: a new application of an old ideaReviewing the Space-Time diagram
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From existing bus traces in
Space-Time (GPS, simulation,
etc.) , the model evaluates what
would have happened if TSP had
been applied
Bus traces can be modified to
included dynamic information,
such as passengers stepping on
or off of a bus
Other vehicles can also be
modelled but interactions
between vehicles is not
considered
Only traffic signals are
adapted
Proposed Methodology: a new application of an old ideaReviewing the Space-Time diagram
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In the following example, one can
see in the Space-Time diagram
that there are missed bus
opportunities in both directions
Is TSP useful in this case ? What
gains could be expected if TSP
was used at the beginning of the
phase ? At the end ? At which
signals ? How long should a TSP
phase be to have a significant
gain?
What if…?
Proposed Methodology: a new application of an old ideaReviewing the Space-Time diagram
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What if…?
Our idea is to see what happens
by just “shifting” the space-time
curve at a specific traffic signal
where TSP could be applied
The method measures the effect
of applying TSP on all buses
crossing the intersection
We make the assumption that
having a TSP priority does not
change the buses’ space-time
curve afterwards
Proposed Methodology: a new application of an old ideaReviewing the Space-Time diagram
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Gains can than be easily
computed
Synchro uses a similar concept
for cars, but not for buses
Moreover, because our proposed
methodology is implemented in
Python, it can easily optimize the
given situation (Where should the
TSP phase be ? For how long ?,
etc.) Which Synchro does not
address, either for buses or cars
The method can give
detailed statistical results
Proposed Methodology: a new application of an old ideaReviewing the Space-Time diagram
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How does this method
compare to other methods ?
No comparison test has been
made on the same traffic signal
configuration using this method
and a fully TSPed micro-
simulation
However, optimized traffic signals
using this method shows “typical”
gains with respect to other
comparable projects where a
fully TSPed micro-simulation
was used
Gains: a general Outlook
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“Analysis of Transit Signal
Priority Using Archived TriMet
Bus Dispatch System Data”
Kimpel et. Al. 2005
Analysis of 6 bus lines in the
greater Portland, Oregon
region
More than 13 000 observations
without TSP et 9000 after its
implementation
Every period of the day
and week is covered
Conclusion : « Our study shows
that the expected benefits of TSP
are not consistent across routes
and time periods, nor are they
consistent across the various
performance measures. »
Gains depend on the specific
issues of the problem at hand and
an appropriate analysis must be
made
Conclusion
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TSP can have positive
impact on transit operations
(regularity)
Analysis of transit needs and
agency procedures is
important
The new “what-if”
methodology is simple and
doesn’t require much data
While simpler, the model
gives more detailed results
References
1. Transit Signal Priority: A Planning and Implementation Handbook. ITS
America. 2005.
2. Transit Signal Priority: Advanced Control Logic to Really Benefit Transit.
Peter G. Furth, Northeastern University. Boston, MA. 2009.
3. TCRP 110: Commonsense Approaches for Improving Transit Bus
Speeds. TRB 2014.
4. Kimpel et. Al. “Analysis of Transit Signal Priority Using Archived TriMet
Bus Dispatch System Data” 2005
5. Transit Signal Priority Systems Application and Technology Investigation.
Christopher G. Hedden. NJDOT. 2009.
