TRAVEL DEMAND INFERENCE FOR PUBLIC TRANSPORTATION SIMULATION

1 . A method for estimating travel demand in a transportation network, the method comprising:

receiving a dataset of trips taken in the transportation network each represented by an origin-destination pair and a departure time, wherein a trip can include a sequence of legs;

describing each trip by a vector of modalities;

for trips that include the sequence of legs, estimating boarding and alighting stops;

generating an empirical trip distribution for each modality for given origin-destination stops; and

fitting the empirical trip distribution to a specific family of probability distributions;

wherein at least one of the generating the empirical trip distribution for a given modality and fitting the empirical trip distribution to the probability distributions is performed with a processor.

2 . The method of claim 1 , wherein the modalities are each selected from a group consisting of: spatial, temporal, personal, and a combination of the above.

3 . The method of claim 2 further comprising:

determining the spatial modality by measuring a trip ratio of a distance connecting the origin to the destination to a sum of leg distances for each trip.

4 . The method of claim 3 , wherein the measuring the trip ratio includes:

in response to the trip being a roundtrip, computing the ratio as an arctangent using the equation:

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=

1

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D

i

-

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wherein D is a distance connecting an origin to a destination stop; and

in response to the trip including the sequence of legs, computing the ratio as the arctangent using the equation:

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2

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wherein a ij :Σ i D i is an estimation of the distance from an initial boarding stop to each of the alighting stops, and D is an estimated sum of all legs of the trip from an initial boarding stop to a last alighting stop.

5 . The method of claim 2 further comprising:

determining the temporal modality by measuring delays in transfer time of between the trip legs for each trip.

6 . The method of claim 1 , wherein the specific family of probability distributions includes a Beta distribution.

7 . The method of claim 1 further comprising:

identifying all origin-destination stops pairs in the transportation network;

grouping multiple origin-destination stop pairs in a cluster, by closeness of origin stops, destination stops or both;

merging empirical distributions for stop pairs in each cluster, for each modality;

fitting the distribution parameters and maintaining one parameter set for each cluster of origin-destination stop pairs.

8 . The method of claim 7 , wherein the grouping the multiple origin-destination stop pairs in the cluster includes:

identifying an equivalence relationship between neighboring stops in the transportation network, wherein the equivalence is determined by a set of change points achievable from the stops.

9 . The method of claim 7 , wherein the merging the empirical distributions includes:

generating a set of equivalence pairs forming oriented arcs in a stop graph; and

applying a transitivity property to the equivalence pairs to obtain the equivalence classes.

10 . The method of claim 6 , wherein the transitivity property includes a Floyd-Warshall algorithm.

11 . The method of claim 1 further comprising:

measuring uncertainty of one trip over another trip in the transportation network using a Kullback-Leibler divergence of the empirical trip distribution from a uniform trip distribution.

12 . The method of claim 8 , wherein the measuring the uncertainty includes:

plotting divergence values for the origin-destination pairs in the transportation network using a log-log scale, wherein a Kullback-Leibler value is inversely proportional to a level of uncertainty and is directly proportional to a strength that the one trip dominates over the other trip.

13 . The method of claim 1 further comprising:

fitting parameters of a selected distribution corresponding to the given origin-destination stop pair;

based on the detected distribution parameters, maintaining or removing from the dataset the each trip estimated to include the given origin-destination stop pair.

14 . A system for estimating travel demand in a transportation network, the system comprising:

a computer programmed to perform a method for estimating the travel demand and including the operations of:

receiving a dataset of trips taken in the transportation network each represented by an origin-destination pair and a departure time, wherein a trip can include a sequence of legs;

describing each trip by a vector of modalities;

for trips that include the sequence of legs, estimating boarding and alighting stops;

generating an empirical trip distribution for each modality for given origin-destination stops; and

fitting the empirical trip distribution to a specific family of probability distributions.

15 . The system of claim 14 , wherein the computer is further programmed to perform the operation of:

determining a spatial modality by measuring a trip ratio of a distance connecting the origin to the destination to a sum of leg distances for each trip.

16 . The system of claim 15 , wherein the measuring the trip ratio includes:

in response to the trip being a roundtrip, computing the ratio as an arctangent using the equation:

x

sp

=

2

π



tan

-

1



D

∑

i

=

1

n



D

i

-

D

wherein D is a distance connecting an origin to a destination stop; and

in response to the trip including the sequence of legs, computing the ratio as the arctangent using the equation:

x

sp

=

2

π



arctan





∑

i

=

1

n





a

ij

:

Σ

i



D

i



a

n

:

Σ

n



D

=

2

π



arctan





∑

i

=

1

n



∑

j



p

ij



D

ij

∑

j



p

j



D

j

wherein a ij :Σ i D i is an estimation of the distance from an initial boarding stop to each of the alighting stops, and D is an estimated sum of all legs of the trip from an initial boarding stop to a last alighting stop.

17 . The system of claim 14 , wherein the computer is further programmed to perform the operation of:

determining a temporal modality by measuring delays in transfer time of between the trip legs for each trip.

18 . The system of claim 14 , wherein the specific family of probability distributions includes a Beta distribution.

19 . The system of claim 14 , wherein the computer is further programmed to perform the operation of:

identifying all origin-destination stop pairs in the transportation network;

grouping multiple origin-destination stop pairs in a cluster, by closeness of origin stops, destination stops or both;

merging empirical distributions for stops pairs in each cluster, for each modality;

fitting the distribution parameters and maintaining one parameter set for each cluster of origin-destination stop pairs.

20 . The method of claim 4 , wherein the grouping the multiple origin-destination stop pairs includes:

identifying an equivalence relationship between neighboring stops in the transportation network, wherein the equivalence is determined by a set of change points achievable from the stops.

21 . The system of claim 15 , wherein the computer is further programmed to perform the operation of:

fitting parameters of a selected distribution corresponding to the given origin-destination stop pair;

based on the detected distribution parameters, maintaining or removing from the dataset the each trip estimated to include the given origin-destination stop pair.