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Dynamic PricingLeveraging Technology to Manage Pricing During Changing Conditions
Kapsch TrafficCom
Dynamically Priced Managed Lanes
Shaping traffic through the value of timeStraightforward modeling and system acceptanceBehavior changes: Short-term – incidents Long-term – behavior
Leverage technology for predictable flexibility and consistencyFuture work
www.kapsch.net |
Get It Right, Out of the Gate
Elasticity of price demand: quick primer How consumer behavior responds to
changes in price
May appear positive, especially at outset, but demand is elastic over time1
Establish value of time2
Surveys
Traffic studies
Demographic data Consultants
Political climate
1Brent, Daniel A., and Austin Gross. "Dynamic Road Pricing and the Value of Time and Reliability (Revised June 2017)." DEPARTMENT OF ECONOMICS WORKING PAPER SERIES2016, no. 07 (June 2017): 3-4. Accessed June 02, 2017. http://faculty.bus.lsu.edu/papers/pap16_07.pdf.2Patterson, Tyler. Personal Interview: On Dynamic Pricing in WSDOT. June 19, 2017. Personal interview to discuss experiences with dynamic pricing in WSDOT's system, Kapsch TrafficCom, Austin.
http://faculty.bus.lsu.edu/papers/pap16_07.pdf
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Get It Right, Out of the Gate
Using price to control traffic flow and capacitySimulationAcceptance testingGo Live! May entail period of human monitoring and controlling…
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We Had It Right…Don’t expect “set it and forget it”
Reasons to adapt: Patrons change behavior – long-term
adaptation Incidents – short-term adaptation Politics
The importance of flexibility: Adaptation strategy: conservative or
aggressive? Manual adaptation Automation Agility
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AdaptationFlexibility for successFlexibility for success
Manual overrides and response: important, but not our topicObjective: automation that is Flexible: can be configured to adapt
to changes in elasticity of price Insightful: reacts well under both
expected and unexpected conditions
Reactive: able to respond to the real world
Effective: provides predictable influence over traffic patterns, without excessive jitter or manual intervention
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Accomplishing the GoalApproach for an adaptive algorithm
Practical exampleRatio of ML:GP occupancy Low ratio expected during non-
peak Ratio 0.6 - 0.8 peak during
high-demand
Short-term & long-term data horizon
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Set Points
Set points provide a target for occupancy
30-Minute Ratio of ML to GP
Time0
.2
.8
5-Minute Sample Rate
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30-Minute Ratio of ML to GP
Time0
.2
.8
Actual traffic
Minimum toll
Set Points
Managed traffic chases set points
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30-Minute Ratio of ML to GP
Time0
.2
.8Current slice, calculate • Error (∆Current state – set point)
• Rate of change ( )
• Integral (stored over time for comparison)
Set Points
Application of information from current sample, in real-time
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Determine Price Change
↓↓↓ ↓↓ ↓ 0↓↓ ↓ 0 ↑↓ 0 ↑ ↑↑0 ↑ ↑↑ ↑↑↑
ErrorCurrent ratio - setpointD
erivative (rate of change)
Low error High error
1. Fuzzy logic lookup2. Integral informs effectiveness of change: short-term impact to elasticity of price demand3. Determine change in price4. Store integral for historic reference – effectiveness
Error decreasing
Error Increasing
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Adaptation PointsProxies for Changing Behavior
Fuzzy logic output – how much change for each cell?
Elasticity of price demand impact: Small integral over time == toll rate effective.
No change needed Large positive integral over time == larger toll
changes needed. Large negative integral over time == smaller
toll changes needed.
Storage of integral informs need to adapt across long-term
Integral can be used: Direct impact to fuzzy logic result
As a factor in a PID controller
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Adaptation PointsFlexibility of Blending Models
Models can break down: Low traffic
Low traffic punctuated by spikes
Technology should have ability to blend algorithms: Over time
As a ratio
Different segments or trips
Time-of-day
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What About Incidents?Accident in the Managed Lane:
Upstream traffic slows
Ratio of ML:GP gets larger; derivative large
Price increases rapidly in response, limiting new entrants into the ML system
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What About Incidents?Accident in the General Purpose Lane:
Upstream traffic slows
Ratio of ML:GP gets smaller; derivative large
Price decreases rapidly in response, encouraging new entrants into the ML system.
Increased utilization of the ML, helping to manage overall traffic flow
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Flexible Algorithms
“Sweet spot” of utilization and traffic flow in MLImportant to consider both ML & GPAbility to adapt as consumers adapt ensures performanceEconomical, practical, predictable, and effective
Graphs courtesy of WSDOT I-405 Express Lanes
Practical results of adaptability
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Importance of Simulation
Support of simulation which incorporates value of time is keyRun scenariosPost-release, use to validate simulator logic
Hypothesize, test, validate
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Future StepsMachine learning
Better traffic & incident prediction: Refined expert systems Neural networks
Better price determination: Markov decision process Neural networks
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Conclusion
Correct utilization of adaptive algorithms can: Improve long-term managed lane performanceReact effectively to short- and long-term behavior changesPerform in a cost-effective and predictable manner compared to
active human management
Thank youfor your attention.John MillerVice President, Back Office Solution Management
Kapsch TrafficCom
Kapsch TrafficCom211 E 7th St Ste 800Austin, TX 78701 USAPhone: +1 512 450 6305E-Mail: [email protected]
Please Note:The content of this presentation is the intellectual property of Kapsch AG and all rights are reserved with respect to the copying, reproduction, alteration, utilization, disclosure or transfer of such content to third parties. The foregoing is strictly prohibited without the prior written authorization of Kapsch CarrierComAG. Product and company names may be registered brand names or protected trademarks of third parties and are only used herein for the sake of clarification and to the advantage of the respective legal owner without the intention of infringing proprietary rights.
Dynamic PricingDynamically Priced Managed LanesGet It Right, Out of the GateGet It Right, Out of the GateWe Had It Right…AdaptationAccomplishing the GoalSet PointsSet PointsSet PointsDetermine Price ChangeAdaptation PointsAdaptation PointsWhat About Incidents?What About Incidents?Flexible AlgorithmsImportance of SimulationFuture StepsConclusionThank you�for your attention.