Method and system for providing dynamic pricing algorithm with embedded controller for high occupancy toll lanes

• US 10,311,651 B2
• Filed: 02/29/2012
• Issued: 06/04/2019
• Est. Priority Date: 02/29/2012
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Inventors: Zou, Guangyu, Gil, Alvaro E., Kulkarni, Rakesh, Costanza, Daniel W.
• Primary Examiner(s): Yesildag, Mehmet
• Assistant Examiner(s): Byrd, Uche
• Application Number:
  US13/407,931
• Publication Number:
  US 20130226668A1
• Time in Patent Office: 2,652 Days
• Field of Search: 705 711- 742
• 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 high occupancy toll lane method, said method comprising:

sensing the presence of a vehicle with a sensing device comprising a loop detector;

capturing an image of said vehicle with an imaging device comprising a video camera;

determining vehicle input flow for said toll lane according to said sensed vehicles and captured images with a special purpose computer configured for toll lane management;

obtaining an input-output transfer function of a vehicle flow with respect to said toll lane according to said vehicle input flow, utilizing a traffic simulation module with said special purpose computer;

utilizing a feedback controller, avoiding an unstable traffic condition on said toll lane due to a time delay between the input flow and an output flow with respect to said toll lane, due to a price regulation and due to a transient larger than a certain amount caused by an integral part of said feedback controller due to traffic with said special purpose computer;

deriving a driver behavior preference model according to a relationship between a toll rate and a plurality of characteristics of said toll lane and a general purpose lane, based on communication from the loop detector and video camera, wherein said plurality of characteristics comprise a proportion of vehicles in said toll lane compared to a total number of vehicles, vehicle density flow, travel times on said toll lane compared to travel time on said general purpose lane, and value of time with said special purpose computer; and

setting said toll rate in real-time according to said feedback controller and said behavior preference model in order to satisfy a desired performance metric with said special purpose computer.

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 with said special purpose computer.

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 with said special purpose computer;

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 with said special purpose computer; and

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

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 with said special purpose computer;

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 with said special purpose computer; 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 with said special purpose computer.

6. The method of claim 1 further comprising obtaining said discrete transfer function utilizing a system identification model wherein said system identification model relates to the means by which the discrete transfer function is obtained with said special purpose computer.

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 with said special purpose computer; 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 with said special purpose computer.

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 with said special purpose computer.

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 with said special purpose computer;

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 with said special purpose computer; 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 with said special purpose computer.

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 lane.

15. A system for high occupancy toll lane management, said system comprising:

a special purpose computer comprising;

a sensor comprising a loop detector for sensing the presence of a vehicle;

an imaging device comprising a video camera for capturing an image of said vehicle;

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;

determining vehicle input flow for said toll lane according to said sensed vehicles and captured images;

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

utilizing a feedback controller, avoiding an unstable traffic condition on said toll lane due to a time delay between the input flow and an output flow with respect to said toll lane, due to a price regulation and due to a transient larger than a certain amount caused by an integral part of said feedback controller due to traffic;

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, based on communication from the loop detector and video camera, wherein said plurality of characteristics comprise a proportion of vehicles in said toll lane compared to a total number of vehicles, vehicle density flow, travel times on said toll lane compared to travel time on said general purpose lane, and value of time; and

setting said toll rate in real-time according to said feedback controller and said behavior preference model 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 system comprising:

a special purpose computer comprising;

a sensing device comprising a loop detector for sensing the presence of a vehicle;

an image capturing device comprising a video camera for capturing an image of said vehicle; and

a non-transitory processor-readable medium storing code representing instructions to cause a computer to perform a process to provide feedback based dynamic pricing for a high occupancy toll lane, said code comprising code to;

determine vehicle input flow for said toll lane according to said sensed vehicles and captured images;

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

utilizing a feedback controller, avoid an unstable traffic condition on said toll lane due to a time delay between the input flow and an output flow with respect to said toll lane, due to a price regulation and due to a transient larger than a certain amount caused by an integral part of said feedback controller due to traffic;

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 based on communication from the loop detector and video camera, wherein said plurality of characteristics comprise a proportion of vehicles in said toll lane compared to a total number of vehicles, vehicle density flow, travel times on said toll lane compared to travel time on said general purpose lane, and value of time; and

,

setting said toll rate in real-time according to said feedback controller and said behavior preference model in order to satisfy a desired performance metric.