Travel-Activity Choice Set Generation
within the Discrete-Continuous Extreme Value Models
using Probe Person data
2012.10.26 Travel behavior and network model courses

Sachiyo Fukuyama,PhD Student, Dept.of Urban Engineering,
The University of Tokyofukuyama@bin.t.u-tokyo.ac.jp1	Outline

1. Research background
2. Purpose of the study
3. Frame of the study
4. Data
1) Probe Person data
2) Basic analysis3) Virtual network
5. Choice set generation
6. Choice model
7. Estimation result
8. Conclusion2	Research backgroundEﬀective approaches for reactivating the city central areas

have been required and attempts to renovate the places
to be attractive are getting much attention.
But the problem is whether people will go there or not.
For the places being used frequently and continuously,
considering the relation between the places and daily life
activity patterns is necessary.

3	Research background

Activity-based models
The demand for travel is derived from the demand for activity
Discrete choice models

Rule-based models

Choice behavior is generated
based on utility-maximization

Choice behavior is generated
based on huristic rules

Bowman and Ben-Akiva(2000)PETRA(Fosgerau,2001)
METRO(Bradley et al.,1998) etc.

ALBATROSS(Arentze and Timmermans, 2005)
TASHA(Roorda et al., 2008) etc.

The size of choice set
become enormous

Not easy to understandthe characteristics of parameters

4	Purpose of the study

1) To propose eﬃcient and realistic way for the choice set
generation in location choice situations.
2) To ﬁnd common patterns of the spatial distribution of
the location choice as domains of daily activities, which
are assumed to be formed by acceccibility andstrengthened by familiarity.
3) To ﬁnd the characteristics of the places where people
stay longer by estimating the value of places by
duration of stay as well as frequency.

5	Frame of the study

Trajectory based approach
Applying MDCEV(Multiple discrete-continuous extremevalue) model (Bhat 2005),
to handle the choice of multiple alternatives simultanelusly
and allocation of time-budget

Generating the choice set
in trajectory-based and data-oriented

Using Probe Person data,
individualsʼ precise position and time data for long term

Using the Virtual Networkfor analyzing GPS data without road network data6	

Probe Person data

Probe Person survey
is the method for tracking individual travel behavior in urban
space by using an automatic position and time recording
system based on GPS and internet communications→ Provides us Individualsʼ precise position and time data
→ Provides us long term data observed for same respondents
GPS satellite

Database

tracking trajectoryby GPS or base station
Workplace

Base station

Surveillance
periodfrom 2 to 4 weeks,
10 terms in
2007/11/12-2008/1/27

Area

Matsuyama urban area

HomeShop
Restaurant
input into a cellphone at depature

PP Survey

input into a cellphone at arrivalex) observe 3 activities

The number ofrespondents*

109

The number oftours*276* After data cleaning and extracting tours by car7	

Basic analysis
SP(O,H)≦35

SP(O,H)>35

SP: Total link costof the shortestroute
O: Oﬃce
H: Home
S: Stopping place

Stopping places distribute around theshortest routes between the oﬃce andhome in the case of SP(O,H)>35.
The diﬀerences between SP(O,S)+SP(S,H)
and SP(O,H) get shorter as SP(O,H) longer.
8	Virtual Network

Using the Virtual Network for analyzing GPS data
without road network data
→ Avoiding the bias caused by map matching→ Avoiding the computational burden of map matching→ Saving the cost of development of the road network data
① Divide the area into mesh cells as
nodes and set virtual links between
adjacent nodes② Relate each GPS point with nodes③ Compute each link cost by counting
the points passing the link9	

Choice set generation
Sampling alternatives of stopping nodes
in trajectory-based and data-oriented

sd : destination
link l = (v,w) ∈ E v
E v : set of outgoing links from vC(l) : generalized cost of link l
SP(v1,v 2 ) : generalized cost of the shortest
path between nodes v1 and v 2b1,b2 : shape parameters

Chosen routeOD pair€a biased random
walk algorithm(Frejinger et al. 2009)

Sampling of routes

weightω (l b1,b2 ) = 1 − (1 − x lb1 ) b2
xl =Sampling ofstop nodes
Chosen nodesSP(v,sd )C(l) + SP(w,sd )probability€q(l E v ,b1,b2 ) =ω (l b1,b2 )∑ω (m b ,b )12m∈E v

Choice set€10	Choice set generation

4 patterns of sampling:
1. Sampling of routes based on the probability distribution
by the distance to the shortest path and,-a: Sampling of stop nodes based on the distribution of
the stop nodes in each individualʼs real datato consider the diﬀerence between individuals-b: Samling of stop nodes based on the distribution of
the stop nodes in the real data of all members
to consider the tendency to ovwrall distribution-c: Sampling of stop nodes randomly
to see the eﬀects of a and b
2. Sampling of stop nodes randomly
11	Choice model

MDCEV ( Multiple discrete-continuous extreme value model )Bhat ( 2005, 2008 )Random utility function:αk⎧⎪⎛ t⎫⎪⎞
γkk
U(t) = ∑ [ exp( β 'z k + ε k )] • ⎨⎜ + 1⎟ −1⎬⎪⎩⎝ γ k⎪⎭k αk⎠

α k : satiation parameterγ k : satiation and translation parameterzk : explanatory variables
β : parameter
ε k : error termt k : time spent in activity purpose k€€In this research, variables are1) distance from home to the stop node2) distance from oﬃce to the stop node3) average of duration and frequency of visit at each node4) diﬀerence between total link cost of the route and the shortest path12	Estimation results13	Estimation results

3 cases considering route lengths (case1-a,b,c) result worse than thecase generated choice set randomly(case2).
This may be the eﬀect of moderate dispersion by random sampling.
But in case 2 the parametersʼ signs are not stable when computing
with the number of alternatives varied.

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Couclusion1) Proposing a trajectory-based and data-oriented approachfor choice set generation in location choice situations.
2) Proposing the location choice model with considering
the tendency of the spatial distribution of daily activities
and bringing duration of stay in the value estimation ofof the places by applying MDCEV model.
3) As a result, though the accuracy of the model was still low,
we got the stable and logically appropriate model for
future study.

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