Kapsch TrafficCom. Dynamic Pricing Leveraging Technology to Manage Pricing During Changing Conditions

Transcription

1 Kapsch TrafficCom Dynamic Pricing Leveraging Technology to Manage Pricing During Changing Conditions

2 Dynamically Priced Managed Lanes Shaping traffic through the value of time Straightforward modeling and system acceptance Behavior changes: Short-term incidents Long-term behavior Leverage technology for predictable flexibility and consistency Future work

3 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 time 1 Establish value of time 2 Surveys Traffic studies Demographic data Consultants Political climate 1 Brent, 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, Patterson, Tyler. Personal Interview: On Dynamic Pricing in WSDOT. June 19, Personal interview to discuss experiences with dynamic pricing in WSDOT's system, Kapsch TrafficCom, Austin.

4 Get It Right, Out of the Gate Using price to control traffic flow and capacity Simulation Acceptance testing Go Live! May entail period of human monitoring and controlling

5 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

6 Adaptation Flexibility for success Manual overrides and response: important, but not our topic Objective: 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

7 Accomplishing the Goal Approach for an adaptive algorithm Practical example Ratio of ML:GP occupancy Low ratio expected during nonpeak Ratio peak during high-demand Short-term & long-term data horizon

8 Set Points Set points provide a target for occupancy 30-Minute Ratio of ML to GP.8 5-Minute Sample Rate.2 0 Time

9 Set Points Managed traffic chases set points 30-Minute Ratio of ML to GP.8 Actual traffic.2 Minimum toll 0 Time

10 Set Points Application of information from current sample, in real-time 30-Minute Ratio of ML to GP Current slice, calculate Time Error ( Current state set point) Rate of change ( ) Integral (stored over time for comparison)

11 Determine Price Change Derivative (rate of change) Low error Error Current ratio - setpoint High error 1. Fuzzy logic lookup 2. Integral informs effectiveness of change: short-term impact to elasticity of price demand 3. Determine change in price 4. Store integral for historic reference effectiveness Error decreasing Error Increasing

12 Adaptation Points Proxies 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

13 Adaptation Points Flexibility 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

14 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

15 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

16 Flexible Algorithms Practical results of adaptability Sweet spot of utilization and traffic flow in ML Important to consider both ML & GP Ability to adapt as consumers adapt ensures performance Economical, practical, predictable, and effective Graphs courtesy of WSDOT I-405 Express Lanes

17 Importance of Simulation Hypothesize, test, validate Support of simulation which incorporates value of time is key Run scenarios Post-release, use to validate simulator logic

18 Future Steps Machine learning Better traffic & incident prediction: Refined expert systems Neural networks Better price determination: Markov decision process Neural networks

19 Conclusion Correct utilization of adaptive algorithms can: Improve long-term managed lane performance React effectively to short- and long-term behavior changes Perform in a cost-effective and predictable manner compared to active human management

20 Thank you for your attention. John Miller Vice President, Back Office Solution Management Kapsch TrafficCom Kapsch TrafficCom 211 E 7 th St Ste 800 Austin, TX USA Phone: john.miller@kapsch.net 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 CarrierCom AG. 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.