PREDICTION OF ACTUAL LOADS FROM FARE COLLECTION DATA
A system and method are provided for predicting passenger loads at vehicle stops on a transportation network. The method includes providing a classifier which has been trained to predict passenger loads at vehicle stops on a transportation route, based on reconstructed passenger loads for vehicle stops on the route. Transaction data is acquired for passengers boarding at vehicle stops on the transportation route. Reconstructed passenger loads are computed for vehicle stops on the route based on the transaction data. With the trained classifier, a passenger load for at least one of the vehicle stops on the transportation route is predicted, based on the reconstructed passenger load for the vehicle stop.
1 . A method for predicting passenger load comprising:
providing a classifier which has been trained to predict passenger loads at vehicle stops on a transportation route, based on reconstructed passenger loads for vehicle stops on the route;
acquiring transaction data for passengers boarding at vehicle stops on the transportation route;
with a processor, computing reconstructed passenger loads for vehicle stops on the route based on the transaction data; and
with the trained classifier, predicting a passenger load for at least one of the vehicle stops on the transportation route, based on the reconstructed passenger load for the vehicle stop.
2 . The method of claim 1 , wherein the classifier is trained with actual passenger loads for respective vehicle stops on the route and a plurality of factors, the plurality of factors including the reconstructed passenger load for a given vehicle stop on the transportation route.
3 . The method of claim 2 , wherein at least one of the plurality of factors is selected from:
the reconstructed passenger load for a previous vehicle stop on the transportation route;
an estimated number of passengers boarding at the given vehicle stop on the transportation route, the estimated number being based on the transaction data;
an aggregate of estimated numbers of passengers boarding at vehicle stops; and
a non-linear function of the reconstructed passenger load for the given vehicle stop on the transportation route.
4 . The method of claim 2 , wherein the classifier is a Bayesian hierarchical multiple linear regression classifier.
5 . The method of claim 1 , wherein the providing of the classifier comprises training the classifier.
6 . The method of claim 5 , further comprising acquiring counts of passengers boarding and alighting at vehicle stops on at least one route of a transportation network and respective reconstructed passenger loads for the vehicle stops.
7 . The method of claim 1 , wherein the acquiring transaction data for passengers boarding at vehicle stops on the transportation route comprises acquiring identifiers for electronic tickets used by at least some of the passengers boarding at the vehicle stops.
8 . The method of claim 1 , wherein the computing reconstructed passenger loads comprises predicting alighting stops for the passengers, based on other boarding stops on routes of a transportation network for at least some of the passengers.
9 . The method of claim 1 , wherein the computing reconstructed passenger loads comprises predicting alighting stops for the passengers, the predicting including applying a set of heuristics.
10 . The method of claim 1 , wherein the computing reconstructed passenger loads comprises, for each vehicle stop in a sequence of vehicle stops on the route, computing a sum of a number of passengers already on board plus number of passengers boarding minus number of passengers alighting.
11 . The method of claim 1 , further comprising outputting at least one of the predicted passenger load and information based thereon.
12 . A computer program product comprising a non-transitory recording medium storing instructions, which when executed on a computer, cause the computer to perform the method of claim 1 .
13 . A system comprising memory which stores instructions for performing the method of claim 1 and a processor, in communication with the memory, for executing the instructions.
14 . A system for predicting passenger load comprising:
a classifier which has been trained to predict passenger loads at vehicle stops on a transportation route, based on reconstructed passenger loads for vehicle stops on the route;
a trip reconstruction component which predicts alighting stops for passenger trips based on transaction data for passengers boarding at vehicle stops on the transportation route;
a load reconstruction component which computes reconstructed passenger loads for vehicle stops on the route, based on the boarding stops and the predicted alighting stops;
a load prediction component which uses the trained classifier to predict a passenger load for at least one of the vehicle stops on the transportation route, based on the reconstructed passenger load for the vehicle stop; and
a processor which implements the trip reconstruction component, load reconstruction component, and load prediction component.
15 . The system of claim 14 , further comprising a training component which trains the classifier based on reconstructed passenger loads for vehicle stops in a transportation network which includes the transportation route and actual passenger load data based on counts of passenger boardings and alightings at the respective vehicle stops.
16 . The system of claim 15 , further comprising an automated passenger load computation component which computes the actual passenger load data based on the counts of passenger boardings and alightings.
17 . The system of claim 14 , further comprising an output component which outputs the predicted passenger load or information based thereon.
18 . The system of claim 14 , wherein the trip reconstruction component predicts alighting stops for passenger trips using a set of heuristics.
19 . A method for generating a system for predicting passenger load comprising:
acquiring transaction data for passengers boarding at vehicle stops on a plurality of transportation routes in a transportation network;
computing reconstructed passenger loads for the vehicle stops on the plurality of transportation routes, based on the transaction data;
acquiring count data for passengers boarding at the vehicle stops on the plurality of transportation routes;
computing actual passenger loads for the vehicle stops on the plurality of transportation routes, based on the count data;
training a classifier to predict passenger loads at vehicle stops on a transportation route in the transportation network, the training being based on the reconstructed passenger loads and actual passenger loads on the plurality of transportation routes;
storing the trained classifier in memory for predicting a passenger load for one of the vehicle stops on one of the transportation routes, based on the a new reconstructed passenger load for the vehicle stop.
20 . A system comprising memory which stores instructions for performing the method of claim 19 and a processor, in communication with the memory, which executes the instructions.