Choice set generation of pedestrian route choiceusing data distribution of walking behavior
in urban space
Sachiyo Fukuyama
National Institute for Land and Infrastructure Management

International BinN research seminar #4
June 13, 2015

1

Problem of route choice set generation

・Alternatives exist inﬁnitely
・Observation of actual choice sets is diﬃcult
・Individualʼs choice set is small and diﬀerent individually

The problem is
how to generate the choice set
for unbiased parameter estimation and prediction

1

Problem of route choice set generation

Characteristics of pedestrian route choiceand needs for choice set
・Sensitivity to their surroundings→Important to include alternatives with various attributes
・Physical and mental limitation of reach→Important to limit the range of the choice set
for eﬃciency of estimation

2

Data oriented choice set generation

To generate the choice set
which contains alternatives with various attribute,and which reﬂect the pedestrian behavior
under the diﬃculty of modeling from actual choice set,
→ Use the behavioral patterns obtained fromindividual trace data
→ generalize the model by extracting common rulesfrom all of the related data

2

Data oriented choice set generation

Master set①Limit the range of master choice set
by the threshold of the behavioral
patterns obtained from data

②Sampling of alternatives basedon the data distribution: Importance sampling foreﬃciency of model estimation

3

Route choice set generation methods

Sampling methods based on path lengths
Frejinger et al. (2009)

A stochastic path generation approach

Sample each link from the origin to destination based on the link probability

Link sampling probability:
Weight of each link:based on its distanceto the shortest path,..
..
Frotterod and Bierlaire (2013) Metropolis-Hastings sampling of paths
Sample paths according to a given distribution from a general network

Draw an insertion nodeaccording to pre-specified distribution
Compute shortest paths
between the insertion nodeand fixed nodes

3

Route choice set generation methods

Choice set generation method based on attributes
Labeling approach, Ben-Akiva et al. (1984)
1. Label each route according to a criterion for which the path is optimum
2. Select the route whose labeled attribute is maximum/minimumGuo and Loo (2013)

A modiﬁed labeling approach

2 modiﬁcation;(1) A label is based on a linear generalized cost from all route attributes(2) Combine diﬀerent labels in the choice set generation (to reﬂect trade-oﬀs)

4Method in this research

Branch and boundPrato and Bekhor (2006)

Path generation by constructing a connection tree
between the origin and destination of a tripby processing sequences of links according to a branching rule+Sampling
Sampling of links based on the distribution of branching rules
Obtaining the distribution from observation data
: Probe person data

5DataProbe person data
Probe Person survey is the method for
tracking individual travel behavior in urban spaceby using an automatic position and time recording system
based on GPS and internet communications→ Provides us Individuals’ precise position and time data
→ Provides us long term data observed for same respondents
Web diary data-mode, -purpose,-visited facilities

Surveillance
period2 months
from 2005/11/19

Area

Shibuya, Tokyo

when moving

GPSdata
Location + Time(latitude, longitude)

press
the departure buttonpress
the arrival button

stop GPS recording

The number ofrespondents

39

The number oftrips422

5DataProbe person data
Example of GPS trace of a trip of an individual in Shibuya area

GPS dot dataoMap matching

Link trace datadExtracting
branching rules
500m

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Link sampling method

Sampling of links based on the distribution of branching rulesp(lx,y ) = f1(lx,y ) f2(lx,y ) f3(lx,y ) / Zip(lx,y )1

present nodeii

y1

Branching rules;① destination directivity

lx,y1p(lx,y )
2w

lw,xx

lx,y2lx,y3y3ip(lx,y )3y2② keep-direction rate
③ detour ratelx,yi : links to be chosenp(lx,yi) : probability distribution of lx,yi6Link sampling method① destination directivity
Tendency to head for the direction of the destination dTakegami and Tsukaguchi (2006)

Destination(d)y1lx,y1wxay

1

dirx,d : direction of the destination
at the present node x
Angle between lx,y and dirx,d6Link sampling method② keep-direction rate
Tendency to keep the same direction as the processed link lw,xTakegami and Tsukaguchi (2006)

y1lw,xw

xby1Angle between the direction of lw,x and lx,y

6Link sampling method③ detour rate
The rate of the length of each path subsegment on Po,y (path segment between o and y)
relative to the shortest path between its start and end nodes

Pr,s: Path subsegment between nodes r and s on the path segment Po,yL(lr,(r+1)): Length of the link lr,(r+1)

lr,(r+1)
Origin(o)ry1l (r+1),(r+2)

xl (r+2),ss
SP(r,s): Shortest Path between node r and node s

Destination

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Sampling algorithm

Slice Sampling
One of the methods of MCMC (Markov chain Monte Carlo)

Sampling from

Input auxiliary variable y andRepeat sampling from f(y|x) and f(x|y) : Gibbs sampling
Algorithm

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Data distribution① destination directivity

Assume the exponential distribution and estimate the parameters
Assume the rate parameter is proportional to Dod (the distance
between the origin and the destination)

8

Data distribution② keep-direction rate

1010

10

10

Assume the Bernoulli distribution;
keep the direction ( α < 45°) or change the direction ( 45°≦ α )Assume the success parameter is function of Dod (the distance
between the origin and the destination)

8

Data distribution③ detour rate
upper boundupper boundupper boundupper boundAssume the exponential distribution and estimate the parameters
Assume the rate parameter is proportional to Dod (the distance
between the origin and the destination)

8

Generated choice set

10 route samples

od500m9Conclusion

The data oriented method for pedestrian route
choice set generation is proposed;
method of link sampling according to the datadistribution of branching rules.
This approach is able to be applied for individual
choice set generation which reﬂects thecharacteristics of individual behavior, when we
have enough amount of individual trace data.

Thank you for your attention