METHOD AND SYSTEM FOR PROVIDING DYNAMIC PRICING ALGORITHM WITH EMBEDDED CONTROLLER FOR HIGH OCCUPANCY TOLL LANES

• US 20130226668A1
• Filed: 02/29/2012
• Published: 08/29/2013
• Est. Priority Date: 02/29/2012
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Gil, Alvaro E., Kulkarni, Rakesh, Costanza, Daniel W., Zou, Guangyu
• Granted Patent: US 10,311,651 B2
• Time in Patent Office: Days
• US Class Current: 705/7.35
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/0837 Return transactions
• CPC Class Codes: G06Q 2240/00 Transportation facility access, e.g. fares, tolls or parking
• CPC Class Codes: G06Q 30/014 Providing recall services for goods or products
• CPC Class Codes: G06Q 30/016 After-sales
• CPC Class Codes: G06Q 30/0283 Price estimation or determination
• CPC Class Codes: G07B 15/063 using wireless information transmission between the vehicle and a fixed station identification of vehicles G08G

Abstract

A method and system for providing a feedback based dynamic pricing algorithm with an embedded controller for a HOT (High Occupancy Toll) lane. An input-output transfer function of a vehicle flow with respect to a HOT lane can be obtained utilizing a simulation module. A feedback controller combined with, for example, a Smith predictor can be designed to avoid an unstable behavior due to a time delay in the HOT lane, a price regulation, and a large transient caused by an integral part of the controller due to traffic jams. A driver behavior preference model can be derived based on a relationship between a toll rate and several characteristics of the HOT lane and a general purpose lane. The feedback controller and the behavior preference model can then be implemented to set the toll rate in real-time in order to satisfy a desired performance metric.

Claims

1. A method for providing a feedback based dynamic pricing algorithm for a high occupancy toll lane, said method comprising:

obtaining an input-output transfer function of a vehicle flow with respect to a to lane utilizing a traffic simulation module;

utilizing a feedback controller to avoid an unstable behavior due to a time delay with respect to said toll lane, a price regulation and a large transient caused by an integral part of said feedback controller; and

deriving a driver behavior preference model based on a relationship between a toll rate and a plurality of characteristics of said toll lane and a general purpose lane and thereafter implement said feedback controller and said behavior preference model to set said toll rate in real-time in order to satisfy a desired performance metric.

2. The method of claim 1 further comprising deriving said input-output transfer function of said vehicle flow offline utilizing historical data to define a model parameter.

3. The method of claim 1 further comprising deriving said input-output transfer function of said vehicle flow online utilizing a set of recent data.

4. The method of claim 1 wherein obtaining said input-output transfer function, further comprises:

collecting an input and output vehicle flow data from said toll lane and building a simulation model utilizing said traffic simulation module and said input vehicle flow data;

calibrating said simulation model until a simulated output is similar to a real output and thereafter batch-running said simulation model a plurality of times to obtain a group of pairs of input and output values for different vehicle'"'"'s density total flow and initial condition; and

converting said input and output vehicle flow from a time domain to a frequency domain utilizing a Fourier transformation.

5. The method of claim 4 further comprising:

deriving a series of complex number of an output by an input for each frequency to obtain a plurality of transfer functions;

computing a median of said plurality of transfer functions as a statistic transfer function and thereafter estimating said parameter of said plurality of transfer functions utilizing optimization to fit said statistic transfer function; and

converting and solving said statistic transfer function to a discrete transfer function to obtain an output vehicle flow model and an input vehicle flow model.

6. The method of claim 1 further comprising obtaining said discrete transfer function utilizing a system identification model.

7. The method of claim 6 wherein said system identification comprises an autoregressive polynomial model.

8. The method of claim 1 wherein designing said feedback controller, further comprises:

designing said feedback controller utilizing a Smith predictor to avoid an unstable behavior due to said time delay; and

configuring said feedback controller utilizing a proportional-integral-derivative controller and an anti-windup compensator to avoid interaction between said integral part of said controller and an actuator saturation.

9. The method of claim 8 further comprising designing said feedback controller utilizing a toll rate regulator and a saturation block to regulate said toll rate change within a time interval.

10. The method of claim 1 wherein deriving said driver behavior preference model, further comprises:

estimating said toll rate that is set with respect to said toll lane for a set of inputs by said driver behavior preference model;

defining a utility function for said toll lane and said general purpose lane and thereafter implement said utility function in said traffic simulation module and derive a function that relates said toll rate as a function of said set of inputs; and

choosing said toll lane by said driver if said utility function with respect to said toll lane is greater than said utility function for said general purpose lane.

11. The method of claim 10 further comprising choosing said general purpose lane by said driver if said utility function with respect to said general purpose lane is greater than said utility function for said toll lane.

12. The method of claim 1 further comprising tuning said controller utilizing a traffic simulator.

13. The method of claim 1 further comprising utilizing an estimated time delay obtained during modeling of said toll lane if a current time delay is unknown.

14. The method of claim 1 wherein said toll lane comprises a high occupancy toll gall lane.

15. A system for providing a feedback based dynamic pricing algorithm for a high occupancy toll lane, said system comprising:

a processor;

a data bus coupled to said processor; and

a computer-usable medium embodying computer code, said computer-usable medium being coupled to said data bus, said computer program code comprising instructions executable by said processor and configured for;

obtaining an input-output transfer function of a vehicle flow with respect to a toll lane utilizing a traffic simulation module;

utilizing a feedback controller to avoid an unstable behavior due to a time delay with respect to said toll lane, a price regulation and a large transient caused by an integral part of said feedback controller; and

deriving a driver behavior preference model based on a relationship between a toll rate and a plurality of characteristics of said toll lane and a general purpose lane and thereafter implement said feedback controller and said behavior preference model to set said toll rate in rear-time in order to satisfy a desired performance metric.

16. The system of claim 15 wherein said instructions are further configured for deriving said input-output transfer function of said vehicle flow offline utilizing historical data to define a model parameter.

17. The system of claim 15 wherein said instructions are further configured for deriving said input-output transfer function of said vehicle flow online utilizing a set of recent data.

18. The system of claim 15 wherein said instructions for obtaining said input-output transfer function are further configured for:

collecting an input and output vehicle flow data from said toll lane and building a simulation model utilizing said traffic simulation module and said input vehicle flow data;

calibrating said simulation model until a simulated output is similar to a real output and thereafter batch-running said simulation model a plurality of times to obtain a group of pairs of input and output values for different vehicle'"'"'s density total flow and initial condition; and

converting said input and output vehicle flow from a time domain to a frequency domain utilizing a Fourier transformation.

19. The system of claim 15 wherein said instructions are further configured for;

deriving a series of complex number of an output by an input for each frequency to obtain a plurality of transfer functions;

computing a median of said plurality of transfer functions as a statistic transfer function and thereafter estimating said parameter of said plurality of transfer functions utilizing optimization to fit said statistic transfer function; and

converting and solving said statistic transfer function to a discrete transfer function to obtain an output vehicle flow model and an input vehicle flow model.

20. A processor-readable medium storing code representing instructions to cause a process to perform a process to provide a feedback based dynamic pricing algorithm for a high occupancy toll lane, said code comprising code to:

obtain an input-output transfer function of a vehicle flow with respect to a toll lane utilizing a traffic simulation module;

utilize a feedback controller to avoid an unstable behavior due to a time delay with respect to said toll lane, a price regulation and a large transient caused by an integral part of said feedback controller; and

derive a driver behavior preference model based on a relationship between a toll rate and a plurality of characteristics of said toll lane and a general purpose lane and thereafter implement said feedback controller and said behavior preference model to set said toll rate in real-time in order to satisfy a desired performance metric.