A method and system are disclosed for learning a demand model and simulation parameters from validation information. Validation information is received from automatic fare collection systems and trips are reconstructed from the validation information. Origins, destinations, and arrival/departure times are estimated from the reconstructed trips. A demand model is then generated from the origins, destinations, and times. Assignment model parameters are then learned from the received validation information and demand model via iterative simulations. Infrastructure changes are made to a simulated transportation network based on the assignment and demand model using the learned parameters. A simulated response of the transportation network to the infrastructure change is then output.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for learning transportation models of an associated transportation network, comprising: receiving validation information from at least one ticketing validation system, the validation information including at least one of a timestamp, a location, or a ticket identification of a traveler on the associated transportation network; reconstructing each of a plurality of trips in accordance with the received validation information; estimating an origin and a destination from the reconstructed plurality of trips; estimating at least one of an arrival time or a destination time corresponding to the reconstructed trip; and generating a demand model of the associated transportation network in accordance with the estimated origin, destination and time, wherein at least one of the receiving, reconstructing, estimating, and generating is performed by a computer processor. 2. The method of claim 1, further comprising: retrieving, from an associated database, an assignment model of the associated transportation network; simulating the transportation network in accordance with the assignment model to acquire origins, destinations and times; and reconstructing a set of simulated trips derived from the simulated origins, destinations, and times. 3. The method of claim 2, further comprising: comparing the set of simulated trips to a corresponding set of reconstructed trips; determining, from the comparing, a difference between the set of simulated trips and the corresponding set of reconstructed trips; and in response to the determined difference, iteratively adjusting at least one parameter value associated with the assignment model. 4. The method of claim 3, further comprising: selecting at least one new infrastructure for the associated transportation network; parameterizing the assignment model in accordance with the at least one iteratively adjusted parameter value; simulating the parameterized assignment model in accordance with the selected at least one new infrastructure; and outputting a simulated response of the associated transportation network corresponding to a result of the parameterized assignment model simulation. 5. The method of claim 4, wherein simulating the parameterized assignment model further comprises: simulating, for each of a plurality of vehicles of the associated transportation network, vehicle behavior responsive to the at least one new infrastructure; simulating, for each of a plurality of travelers, a plurality of decision points corresponding to travel on the associated transportation network responsive to the at least one new infrastructure; and outputting the simulated response of the associated transportation network in accordance with the simulated vehicle behavior and the simulated traveler decision points. 6. The method of claim 5, wherein the plurality of decision points include a stop, a time to leave for arrival at a stop, a speed to walk to a stop, a line at a stop, a vehicle to board at a stop, a stop from which to depart a vehicle. 7. The method of claim 1, wherein the associated transportation network is a public transportation network. 8. The method of claim 1, wherein the estimating an origin and destination further comprises for each user having historical data associated therewith, modelling an origin and a destination from a linear combination of a distribution of stops around each of a first observed boarding and a last observed alighting, wherein a weight assigned to each stop distribution is determined in accordance with a frequency of observations of the stop. 9. The method of claim 1, wherein estimating the time further comprises: for each user without a travel history, determining an arrival time by adding to an observed time of a last alighting an estimated walking duration from a stop corresponding to the last alighting to a final destination, and determining a departure time by adding to an observed time of a first boarding an estimated walking duration from an origin to a stop associated with the first boarding; and for each user with a travel history, determining an arrival time by adding to an observed time of a last alighting an estimated walking duration from a stop associated corresponding to the last alighting to a final destination for each instance of a trip in the travel history corresponding thereto, and determining a departure time by adding to an observed time of a first boarding an estimated walking duration from an origin to a stop corresponding to the first boarding for each instance of a trip in the travel history corresponding thereto. 10. A computer program product comprising a non-transitory recording medium storing instructions, which when executed on a computer causes the computer to perform the method of claim 1. 11. A system comprising memory storing instructions for performing the method of claim 1, and a processor in communication with the memory which implements the instructions. 12. A system for learning transportation models on an associated transportation network, comprising: a demand model construction component comprising: a trip reconstruction module configured to receive validation information from at least one ticketing validation system and reconstruct a plurality of trips on the associated transportation network; an origin and destination estimator configured to estimate an origin and a destination for each of the plurality of reconstructed trips, and a time estimator configured to estimate at least one of an arrival time or a destination time for each of the plurality of reconstructed trips; an assignment model component comprising: a traveler model component including a trip planner, a vehicle model component, and a simulator; a memory; and a processor in communication with the memory which stores instructions that are executed by the processor to: generate a demand model in accordance with estimated origins, destinations, and times, retrieve, from an associated database, an assignment model corresponding to the associated transportation network, input, into the retrieved assignment model, a vehicle model of the associated transportation network, the vehicle model corresponding to operations of vehicles on the associated transportation network, simulate the assignment model to determine a set of simulated trips, determine a difference between the set of simulated trips and the plurality of reconstructed trips, iteratively simulate the assignment model with different parameter values responsive to a determined difference, and output a set of parameter values for predictive modeling of the transportation network. 13. The system of claim 12, wherein the memory further stores instructions executed by the processor to: select at least one new infrastructure for the associated transportation network; parameterize the assignment model in accordance with the at least one iteratively adjusted parameter value; simulate the parameterized assignment model in accordance with the selected at least one new infrastructure; and output a simulated response of the associated transportation network corresponding to a result of the parameterized assignment model simulation. 14. The system of claim 13, wherein the memory further stores instructions executed by the processor to: simulate, for each of a plurality of vehicles of the associated transportation network, vehicle behavior responsive to the at least one new infrastructure; simulate, for each of a plurality of travelers, a plurality of decision points corresponding to travel on the associated transportation network responsive to the at least one new infrastructure; and output the simulated response of the associated transportation network in accordance with the simulated vehicle behavior and the simulated traveler decision points. 15. The system of claim 14, wherein the plurality of decision points include a stop, a time to leave for arrival at a stop, a speed to walk to a stop, a line at a stop, a vehicle to board at a stop, a stop from which to depart a vehicle. 16. The system of claim 12, wherein the origin and destination estimator is further configured to: for each user having historical data associated therewith, model an origin and a destination from a linear combination of a distribution of stops around each of a first observed boarding and a last observed alighting, wherein a weight assigned to each stop distribution is determined in accordance with a frequency of observations of the stop. 17. The system of claim 12, wherein the time estimator is further configured to: for each user without a travel history, estimate an arrival time by adding to an observed time of a last alighting an estimated walking duration from a stop corresponding to the last alighting to a final destination, and estimate a departure time by adding to an observed time of a first boarding an estimated walking duration from an origin to a stop associated with the first boarding; and for each user with a travel history, estimate an arrival time by adding to an observed time of a last alighting an estimated walking duration from a stop associated corresponding to the last alighting to a final destination for each instance of a trip in the travel history corresponding thereto, and estimate a departure time by adding to an observed time of a first boarding an estimated walking duration from an origin to a stop corresponding to the first boarding for each instance of a trip in the travel history corresponding thereto. 18. A computer-implemented method for learning transportation models of associated public transportation network, comprising: receiving validation information from at least one ticketing validation system, the validation information including at least one of a timestamp, a location, or a ticket identification of a traveler on the associated transportation network; reconstructing each of a plurality of trips in accordance with the received validation information; estimating an origin and a destination from the reconstructed plurality of trips, wherein for each user having historical data associated therewith, determining the origin and the destination from a linear combination of a distribution of stops around each of a first observed boarding and a last observed alighting; estimating at least one of an arrival time or a destination time corresponding to the reconstructed trip; and generating a demand model of the associated transportation network in accordance with the estimated origin, destination and time. 19. The computer-implemented method of claim 18, further comprising: retrieving, from an associated database, an assignment model of the associated transportation network; simulating the transportation network in accordance with the assignment model to acquire origins, destinations and times; reconstructing a set of simulated trips derived from the simulated origins, destinations, and times; comparing the set of simulated trips to a corresponding set of reconstructed trips; determining, from the comparing, a difference between the set of simulated trips and the corresponding set of reconstructed trips; and in response to the determined difference, iteratively adjusting at least one parameter value associated with the assignment model. 20. The computer-implemented method of claim 19, further comprising: selecting at least one new infrastructure for the associated transportation network; parameterizing the assignment model in accordance with the at least one iteratively adjusted parameter value; simulating, for each of a plurality of vehicles of the associated transportation network, vehicle behavior responsive to the at least one new infrastructure; simulating, for each of a plurality of travelers, a plurality of decision points corresponding to travel on the associated transportation network responsive to the at least one new infrastructure and parameterized assignment model; and outputting the simulated response of the associated transportation network in accordance with the simulated vehicle behavior and the simulated traveler decision points.