THE SOCIAL CONTEXT OF ACTIVITY-SCHEDULING: A DISCRETE-CONTINUOUS MODEL OF THE RELATIONSHIP BETWEEN WITH WHOM AND EPISODE START TIME AND DURATION

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1 THE SOCIAL CONTEXT OF ACTIVITY-SCHEDULING: A DISCRETE-CONTINUOUS MODEL OF THE RELATIONSHIP BETWEEN WITH WHOM AND EPISODE START TIME AND DURATION Khandker M. N. Habb Department of Cvl & Envronmental Engneerng Unversty of Alberta Markn/CNRL Natural Resources Engneerng Faclty Edmonton, Alberta T6G 2W2 Emal: khandker.nurulhabb@utoronto.ca Juan-Antono Carrasco (correspondng author) Department of Cvl Engneerng Unversdad de Concepcón P.O. Box 160-C Concepcón, Chle Tel: Fax: Emal: j.carrasco@udec.cl Erc J. Mller Department of Cvl Engneerng Unversty of Toronto 35 St. George St. Toronto, ON M5S 1A4, Canada Tel: Fax: Emal: mller@ecf.utoronto.ca Transportaton Research Record: Journal of the Transportaton Research Board Forthcomng 2008 Pre-prnt verson

2 Habb, Carrasco, and Mller 2 ABSTRACT Actvty-based approaches to travel demand modelng are ncreasngly movng from theoretcal to operatonal models. In ths context, agent-based mcro-smulaton models are a promsng approach snce they explctly conceve travel as an emergent phenomenon from peoples actvty characterstcs, and more explctly, from ther actvty schedulng processes. Behavorally, actvty-schedulng processes are nfluenced not only by ndvduals characterstcs, but also by the other people wth whom they nteract. Thus, the actvty schedulng process has an ntrnsc socal context. Usng socal actvtes as a case study, the objectve of ths paper s to emprcally nvestgate the relatonshp between the socal context (measured by wth whom the respondents nteracted) and two key aspects of actvty schedulng: start tme and duraton. Econometrc models of the combned decsons regardng wth whom to partcpate and when to start or how much tme to spend on are estmated to nvestgate the correlatons between wth whom and the start tme / duraton decsons. Data collected by a seven-day actvty dary survey are used for the model developments. Fndngs suggest that the socal context has a relevant role n actvty schedulng processes. For example, the nvestgatons ndcate that t s wth whom we socalze what nfluences the socal actvty schedulng processes more than the travel tme or the dstances to socal travel. Overall, and addtonally to the theoretcal understandng of the questons posed to nvestgate n ths paper, the models serve as an emprcal support for agent-based mcrosmulaton models that could ncorporate the role of socal networks n actvty schedulng attrbutes.

3 Habb, Carrasco, and Mller 3 1. INTRODUCTION Actvty-based approaches to model travel demand are ncreasngly movng from theoretcal to operatonal models (1). In ths context, agent-based mcrosmulaton models are a promsng approach snce they explctly conceve travel as an emergent phenomenon from peoples actvty characterstcs, and more explctly, from ther actvty schedulng processes. Behavorally, actvty-schedulng processes are not only nfluenced by ndvduals characterstcs, but also by the other people wth whom they nteract. Hence, the actvty schedulng process has an ntrnsc socal context. In prncple, agent-based mcrosmulaton models have the capablty of explctly representng nteractons among dfferent actors, modelng ther nfluence n ther respectve decson makng processes (2, 3). However, current models n practce stll rely heavly upon ndvdualstc assumptons because of the lack of knowledge about how the socal dmenson affects actvty-travel behavor n general, and actvty schedulng processes, specfcally. The objectve of ths paper s to nvestgate how the socal dmenson defned by wth whom actvtes are performed nfluences ndvduals actvty schedulng processes. The study explctly focuses on socal actvtes snce these are the paradgmatc examples of the mportance of wth whom n actvty-travel decson processes. Socal actvtes are an understuded area that s becomng ncreasngly mportant from the polcy perspectve due to ther relatvely long trp lengths and aspects such as socal coheson and qualty of lfe (4). However, smple statstcal data analyss may not necessarly reveal the extensve behavoral detals for such a complex phenomenon. Ths paper uses an advanced econometrc method to nvestgate how wth whom nfluences the start tme and duraton of socal actvty epsodes, wth the purpose of capturng the complex and multdmensonal nature of people s behavor. The study uses data from a Computerzed Household Actvty Schedulng Elctor (CHASE) survey conducted n Toronto n wth 271 households, ncludng 426 adults (5). CHASE focuses on capturng mult-day nformaton on observed actvty-travel patterns whle tracng the underlyng actvty schedulng process (6). In addton, the data also ncludes nformaton regardng temporal and spatal flexblty, and other key actvty attrbutes, ncludng wth whom the respondent s actvtes were performed. Usng these data, a dscrete-contnuous econometrc approach s employed to explore the relatonshp between the socal dmenson and socal actvty schedulng attrbutes. The combned estmaton of dscrete-contnuous models gves quanttatve evdence of how wth whom relates to start tme and tme allocaton to socal actvty epsodes. The models also use other soco-economc and actvty attrbutes that gve a better nsght about these behavoral processes. In addton to behavoral understandng, t s expected that such a model can be useful to model actvty schedulng-reschedulng processes n Toronto s operatonal actvtybased model, TASHA (7). Replacement of rules used to resolve schedulng conflcts by econometrc models should ncrease polcy senstvtes of the overall model. The organzaton of the paper s the followng: next secton dscusses the socal dmenson of actvty schedulng behavor, followed by a descrpton of data and methods, emprcal model results, and conclusons. 2. THE SOCIAL DIMENSION OF ACTIVITY SCHEDULING BEHAVIOUR Neglectng the socal dmenson n transportaton modelng s one of the most crucal and crtcal shortcomngs of exstng actvty-based models (8). Research n actvty-based models has spread and focused on a varety of aspects, helpng to understand better the dfferent characterstcs and attrbutes that consttute household and ndvdual actvty patterns. Modelng efforts have ncreasngly ncorporated the nsghts ganed by the greater understandng of actvty patterns (9). However, there s stll consderable room for

4 Habb, Carrasco, and Mller 4 mprovements, especally n the scope and behavoral explanaton these models gve. In ths context, understandng and modelng the nfluence of socal nteractons on actvty-travel decsons s a crucal need (10). In order to ncorporate formally the socal dmenson, researchers should take account of the exstng structure of socal relatons represented n the ndvdual s socal networks, for t s wthn ths structure that nterpersonal nteractons and socal travel decsons are made. The study of socal networks n actvty-travel behavor responds to the need to underpn our travel models wth a better understandng of the socal structures of daly lfe and, as we mplctly forecast/speculate about them when we predct travel behavor over long tme horzons, anyway as Axhausen (11: p.3), argues. Ths requrement s even more germane when a seres of possble transport questons are consdered, such as physcal spatal-temporal coherence / overlap (constrants), replacement of physcal and telecommuncaton-based contact, nteracton frequency and spatal reach, and nteracton and nformaton / knowledge transfer (11: p.10). In addton, the focus n socal actvtes s partcularly nterestng snce nteractons ntutvely play a motvator role n the behavoral processes of those actvtes. From a travel behavor perspectve, the role of socal networks has receved ncreasng attenton, tacklng dfferent questons, such as the socal nfluence n travel related decsons (12, 13), socal actvty-travel generaton (14, 15), and the spatal dstrbuton of socal networks (4, 16, 17). However, lttle s known regardng the nfluence of the socal context on actvty-schedulng decsons, consderng not only the ntrnsc negotaton process that occurs n actvtes such as socal, but also the mportance of wth whom n key schedulng decsons. For example, Goulas et al. (18) explctly consdered the role of wth whom, but mostly as a way of selectng dfferent actvty type groups rather than as studyng the role of ths attrbute on the schedulng process. Closer to the analyss performed n ths paper, Srnvasan and Bhat (19) study the companonshp characterstcs for lesure actvtes, usng the Amercan Tme Use survey. Ther results suggest sgnfcant mpacts of socoeconomcs of ndvduals on companon type choces for lesure actvtes (p.11) as well as an emprcal relatonshp between schedulng attrbutes such as duraton and day of the week, and wth whom. Ths need for a better understandng of the socal dmenson n actvty schedulng processes s even more crucal consderng that, at least n prncple, agent-based mcrosmulaton approaches to model actvty-travel have the capablty of ncorporatng nteracton somewhat explctly (2, 3). For example, Mller argues that the socal context of actvty-schedulng processes need to be consdered explctly, even though socal network formaton processes may or may not be endogenously represented wthn an operatonal model (3, 20, 21). Furthermore, hs agent-based theoretcal framework explctly defnes socal projects, whch encapsulates the key elements that defne socal actvty-travel processes, and where socal network attrbutes are an mportant part. Thus, n ths approach, wth whom actvtes are performed consttutes a potentally relevant pece of nformaton to be used to condton schedulng attrbutes explctly, such as start tme, duraton, and locaton. 3. DATA The frst wave of the Toronto Travel-Actvty Panel Survey (TAPS, see 5), whch uses the CHASE nstrument (6) was used n ths study. TAPS provdes mportant emprcal data to support the actvty schedulng process modelng pursued n Toronto s actvty-based models (3). The specfc survey was conducted n that cty between 2002 and 2003, wth 271 households (426 adults) partcpatng n the weeklong survey. The CHASE nstrument was desgned to collect nformaton about actvtes n both plannng and executon stages. For ths seven-day actvty dary survey, the partcpants are requred to record the ndvdual actvty

5 Habb, Carrasco, and Mller 5 nformaton pror to the startng of the day. The CHASE program tracks the actvty nformaton that s added frst, and then modfed, deleted and executed over tme. The frst tme added nformaton represents the agenda formaton, whch undergoes modfcaton or sometmes deleton for schedulng. The fnal scheduled observatons nclude the nformaton regardng schedulng pressure. CHASE dvdes all actvtes nto nne major groups, of whch socal s one; socal actvtes n ths paper are those self-classfed by the respondents as well as those that correspond to gong to restaurants and havng meals at home, were at least one nonhousehold member partcpates. Further detals about each of these categores can be found n Doherty et al (5). CHASE collects a varety of attrbutes related to the actvty type, the actor of the actvty and the household wthn whch the actor resdes. In addton to ths general nformaton, some specfc nformaton about the actvty s collected by actvely promptng the respondent n an End of Week Revew (EWR). EWR systematcally queres stated spatal and temporal flexbltes, normal duraton, and frequency of the actvty type of concern. A detaled descrpton of ths EWR component of CHASE s avalable n Doherty et al. (5). In addton to soco-economc and actvty specfc varable, ths paper ncorporates varables regardng the socal context where the actvty-travel decsons occur. From an emprcal perspectve, an effcent way of addressng the mpact of the socal context s by consderng the ndvdual s personal network nformaton. Personal networks consttute a useful approach to study the relevance of the socal structure n actvty-travel decsons. A personal network s my network for any gven ndvdual. In personal network analyss, the respondent ndvdual s referred to as an ego and all of the people wth whom he/she nteracts are referred to as alters. The number of alters wthn an ego s personal network ndcates the sze of the network. Measures of the sze of a personal network vary accordng to the purpose of study. For example, McCarty, et al. (22) estmated networks of about 250 tes n an Amercan sample. However, as scope condtons get more specfc, the number of network members decreases. There s a long tradton about methods to gather personal networks, manly from socology (23, 24), but also - more recently from the travel behavor feld (4, 25, 26). Most of these methods explctly elct the personal network from certan prompt questons, and then gather nformaton about travel behavor patterns. However, an alternatve method even present n the socal networks lterature conssts of buldng the personal networks from the respondents dary contacts (27) or, n other words, from wth whom ndvduals nteracted wthn a certan perod. Followng that approach, personal networks are devsed n ths paper based on nformaton of the people (alters) wth whom a respondent (ego) socalzes. Constructng these personal networks s feasble snce the CHASE nstrument records the specfc persons wth whom the respondent socalzed n each epsode. For the purposes of ths paper, alters are dvded n two categores: famly, whch correspond to both close and extended famly members; and the frends, whch correspond to all the other people who are not famly members. At the same tme, CHASE also records whether the alters are household members or not. The followng personal network varables are studed n ths paper: - Total number of famly members, total number of frends, total number of alters wth whom the respondent socalzed that week. All these varables measure the overall sze of the respondent s personal network n the perod studed. - Proporton of famly members and proporton of frends. Ths s the rato between the number of frends and the total number of people wth whom the respondent had socal actvtes. These varables measure the relatve mportance of each role n the respondent s contact; that s, whether the respondent s more famly- or frend-orented, or whether there s a balance n the knds of contacts s/he has. - Varablty of wth whom. For each alter, a varablty ndex s constructed calculatng the rato between the number of socal epsodes s/he had wth the respondent and the total

6 Habb, Carrasco, and Mller 6 number of epsodes that the respondent performed durng the week. The average varablty ndex of all the alter members from the respondent s personal network corresponds to the varablty of the wth whom varable. Ths varable measures the varety seekng on socal contacts, servng as a proxy of the fragmentaton of the respondent s personal network. A number close to one nvolves a low varablty of people, that s, the respondent tends to have socal epsodes mostly wth the same people for all the epsodes, whereas a number close to zero nvolves a hgh varablty of people, where most of the socal epsodes nvolved dfferent alters. After cleanng some observatons, 294 ndvduals n 208 households were selected for analyses. Total number of ndvdual socal epsodes for these 294 people was 1223, 124 of the sampled persons are male, and 170 are female. For further detals about these data the reader can consult Doherty et al. (5). 4. METHODS As dscussed before, the central queston of ths paper s how wth whom nfluences the start tme and duraton of socal actvty epsodes (see Fgure 1). The wth whom dmenson s classfed nto four optons: 1. Socalze wth famly members together wth household members 2. Socalze wth frends together wth household members 3. Socalze wth famly members wthout any household member 4. Socalze wth frends wthout any household member Start tme and duraton are consdered as contnuous varables. The start tme of an actvty epsode s expressed as the fracton of the twenty-four hours n a day, and the duraton s expressed n terms of the total mnutes spent. Thus, the hazard-based modelng approach s approprate for such contnuous start tme and duraton modelng. The overall method estmates jontly the effect of the dscrete wth whom choce, and the contnuous start tme and duraton decsons. Jont estmaton of these two types of decsons leads to the sample selecton econometrc structure proposed by several authors, notably Duncan (28) and Lee (29). Snce the wth whom decson opton s more than bnary, n both start tme and duraton decsons, the model becomes a contnuous tme hazard model wth multnomal logt sample selecton. The formulaton of such a model s descrbed below. The specfcaton for selectng wth whom s defned by the utlty functon: U = V + ε = β x + ε (1) wh w w w w w Where V w ndcates the ndrect utlty of alternatve (wth whom), x w s the vector of explanatory varables, β w s the vector of correspondng coeffcents, and ε w s the unobserved error term. The subscrpt w ndcates each observaton. Alternatve (wth whom) s chosen f and only f t gves maxmum utlty compared to all other alternatves: U w > max j= 1... M, j U wj { max j= 1... M, j } V = U ε w wj w (2) If the error term has a Type I Extreme Value dstrbuton, the choce model takes the form of multnomal logt model:

7 Habb, Carrasco, and Mller 7 exp( Vw ) Pr( w) = F( ε w ) = exp( V ) exp( V ) w + j wj (3) In the case of the contnuous component, the dependent varable s tme. For the epsode duraton, t corresponds to the total tme spent for the epsode; for the start tme, t corresponds to the tme from mdnght to the start tme of the epsode. Recognzng the nherent dynamcs of the behavoral processes a contnuous tme hazard model s used for epsode duraton and epsode start tme. Two possble types of contnuous tme hazard specfcatons are: proportonal hazard model and accelerated tme hazard model. Although both of them are nterchangeable, the proportonal specfcaton targets the hazard rate, where as the accelerated tme specfcaton targets the duraton per se. An accelerated tme hazard specfcaton s the most approprate approach n our case because t smplfes the lkelhood functon (30). Hence, the survval tme, t of the events s expressed as follows: ln( t ) = γ Z + α (5) Where Z s the vector of covarates of the wth whom alternatve, γ s the vector of correspondng coeffcents, and α s the unobserved error term. The dstrbuton functon of ths error term determnes the dfferent types of accelerated hazard model. In the case of the Normal dstrbuton assumpton, t takes the form of a certan lognormal accelerated tme hazard model. Consderng that α has a Normal dstrbuton wth mean and varance, t becomes: f( α ) = N( µσ, ) (5) The parameterzaton of the mean accommodates the covarate functon: µ = γ Z (6) Then, the hazard functon h(t ) of the accelerated tme hazard model has the followng expresson: f( t ) ht ( ) = St ( ) f( t ) = h( t )* S( t ) 1 1 f t = t Z 2 ( ) exp[ {ln( ) } ] 2 γ t 2 2 σ σ π (7) Where S(t ) s the survval functon and f(t ) s the frst dervatve of the cumulatve dstrbuton functon wth respect to tme. The jont estmaton of these dscrete and contnuous decsons requres assumng that the error terms ε w and α are correlated. Gven that these two error terms have dfferent types of dstrbuton, t s requred to defne the equvalent standard dstrbuton for dervng the jont probablty of dscrete and contnuous decsons. Accordng to Lee (29), any random varable can be expressed as a standard normal dstrbuton. Transformng the prevous error terms n (1) and (4) to correspondng standard Normal dstrbuted values, we get:

8 Habb, Carrasco, and Mller 8 ε α J * w * = J = J Where ( ε w ( α ) = Φ ) = Φ ( α ) = (ln( t ) γz 1 1 F( ε w F( α ) ) / σ ) (8) The combned decson process s modeled by assumng a bvarate dstrbuton: C( ε, α, ρ ) = B[ J ( ε ), J ( α ), ρ ] (9) w 1 w 2 Where ρ s the correlaton between the dscrete choce alternatve and the correspondng contnuous decson. For any ndvdual, the jont probabltes of observng a partcular start tme or duraton and correspondng wth whom can be wrtten as (29): Pr( T = t W = w ) = Pr( T = t ε J ( ε )) 1 w 1 ln( t ) γz J 1( εw ) ρ J 2 ( α ) = φ Φ σt σ 2 1 ρ Therefore, for the lognormal accelerated hazard model assumpton, the log-lkelhood functon of the combned estmaton becomes: N s= 1 M LL = D ln( ( φ(ln( t ) γz ) / σ ) D = 1 + D ln( Φ( ( J 1 ( ε w ln( t σ ) ρ ((ln( t ) ) γz ) / σ ))) / (1 ρ 2 ) ) ) (10) where φ ( ) and Φ ( ) ndcate the pdf and cdf of the Standard Normal dstrbuton, respectvely; σ s the varance or ancllary parameter of the accelerated tme hazard component; s ndcates the ndvdual observatons; ndcates the alternatve n the dscrete choce secton; and D s the ndcator varable of choosng alternatve. The log-lkelhood functon s estmated wth code wrtten n GAUSS usng the BFGS algorthm (31). The standard errors of the parameters are calculated usng the nverse of Hessan procedure. The combned decson of wth whom and duraton or start tme s shown n Fgure 1. Ths modelng structure represents the sample selecton model, where the duraton of correspondng epsode s only observed f the specfc alternatve of wth whom s chosen for the socal actvty partcpaton. The correlaton coeffcent and the alternate specfc varance ndcate the nfluence of the specfc alternatve of wth whom on the correspondng epsode duraton. For mathematcal dentfcaton purposes, one of the wth whom alternatves s to be consdered as the reference alternatve. The goodness of ft of the models s estmated usng adjusted lkelhood rato test (32): Loglkelhood at Convergence - No. of Parameters Adjusted Rho-Square=1- Loglkelhood of the Null Model (11) where the null model has no explanatory varables and the number of parameters ndcates the number of parameters n the fully specfed model over the number of parameters n the null model. When the null model does not contan any alternatve specfc constant of the dscrete choce component, then t becomes the adjusted lkelhood rato at zero. If the null model contans alternate specfc constant of the dscrete choce component, then t becomes the

9 Habb, Carrasco, and Mller 9 adjusted lkelhood rato ndex at sample share (32). In ths paper, the null model contans only the constant for hazard model. 5. ESTIMATED MODELS Ths secton presents results of the estmated models; Table 1 corresponds to the model for epsode duraton and Table 2 corresponds to the model for epsode start tme. In both tables, ρ represents the correlaton between the combned decsons of wth whom and the total duraton / start tme. Also n these tables, σ represents the varance of the duraton/start tme model for specfc opton of wth whom partcpaton. Consderng t values greater than or equal to 1.64 as the lmt of statstcal sgnfcance (.e. at the 90% level), the models are dscussed next. The statstcal sgnfcance of the error correlaton parameter ρ ndcates that the decson of wth whom and start tme/duraton are not ndependent of each other. As per equaton 9 and 10, t s clear that postve value of ρ ndcates negatve correlaton and vce versa. The model for epsode duraton shows that the duraton of socal epsodes s hghly correlated wth wth whom f household members are not nvolved n the actvty. Ths fndng ndcates that, when people partcpate n socal actvtes wthout other household members, they tend to spend more tme n socal actvtes. When household members are nvolved n the socal actvtes together, the actvty epsodes are usually shorter n duraton. On the other hand, correlaton coeffcent of the model for epsode start tme wth wth whom ndcates that nvolvement of more than one household member nfluences the socal actvty to start later n the day, possbly due to more ntensve coordnaton constrants. In terms of alternatve specfc varance of the duraton and start tme models, all parameters show hgh statstcally sgnfcant results. Accordng to the model formulatons, the hgher the value of the varance, the lower the duraton and the earler the start tme of the socal epsode. In the case of duraton, the mnmum duraton corresponds to the case when the respondent socalzes wth famly members but wthout any household member. The second lowest duraton corresponds to the case when the respondent socalzes wth frends but wthout any household member. Fnally, the duraton s hgher f the respondent socalzes wth household members. In the case of start tmes, later start tmes occur when the respondent socalzes wth famly members but wthout any household member. The second latest start tme occurs when the respondent socalzes wth frends but wthout any household member. Start tme s the earlest f ndvdual socalze wth household members. In both of these cases, t s very clear that the nvolvement of household members n socal actvtes sgnfcantly nfluences the tme allocaton decson. The hgher statstcal sgnfcance of σ n both models proves the necessty for combned estmaton of such dscrete-contnuous decson structure. In terms of decson regardng wth whom to socalze, the models show the relevance not only of the personal attrbutes of the respondents, but also of the characterstcs of ther personal networks. The same varables enter nto both duraton and start tme models wth almost the same parameter values. Males prefer to socalze more wth the frends than famly members, compared wth females. Household heads, ether male or female, are less lkely to socalze alone wth the frends wthout any household members. In addton, adults wth partners are more lkely to socalze wth frends together wth household members, than n the case of sngle parents of adult chldren lvng n the household. A hgher proporton of frends n the socal network ncreases the probablty of partcpatng n socal actvtes wth frends together wth the household members. On the other hand, a hgher total number of frends and a hgher number of alters n the personal network ncrease the probablty of partcpatng n socal actvtes wth frends wthout any household members. Fnally, a hgher number of famly members n the socal network

10 Habb, Carrasco, and Mller 10 ncrease the probablty of partcpatng n the socal actvtes wth famly members wthout any household members. Overall, consderng the alternate specfc constant of the wth whom selecton model, t s clear that people prefer socal actvtes wth frends or famly members but wthout household members. In the case of duraton, the constant coeffcent s very hgh wth a hgh t statstc, ndcatng that there s stll scope to ncorporate more varables to explan the process. However, t s clear that the hgher the number of people nvolved n the socal actvty, the longer the duraton, although a hgher number of household chldren tends to a shorter duraton. A hgher number of potental locaton for socalzaton nfluences longer duraton epsode. Duraton flexblty of the actvty also nfluences the duraton postvely. People tend to spend more tme for the socal actvty that requres longer travel tme to reach the actvty locaton. Full tme workers tend to spend more tme n socal actvtes than part tme workers or people workng at home. People wth hgher number of household automobles are more lkely to nvolve n longer duraton socal actvtes. Also, ntutvely, s the relatonshp between hgher weekly socal actvty frequences and shorter duratons for the ndvdual actvty epsode. In terms of start tme, the constant coeffcent ndcates that people prefer the afternoon (e = = 4:45 pm) to start the socal actvtes. However, when a hgher number of people s nvolved, start tmes tend to be earler. Smlarly, start tmes tend to be earler than 4:45 pm when there are a hgher number of potental socal actvty locatons and hgher duraton flexblty, as well as f the respondent has a relatvely hgh number of chldren lvng at the household. On the other hand, a hgher number of household automoble ncreases the optons to travel and hence nfluences the actvty to start later. It s mportant to note that nether the effect of dstance to travel nor the travel tme become statstcally sgnfcant n the start tme model. A possble explanaton s that t s the wth whom that defnes the start tme of the socal actvtes and not the travel tme to reach the actvty locaton. Then, as the models explctly ntegrated the wth whom decson nto the start tme selecton model, the varable ndcatng the travel tme to reach the actvty locaton becomes nsgnfcant. Both wth whom - duraton and wth whom - start tme models provde hgh goodnessof-ft (see Table 3 for a summary of ther goodness-of-ft). The start tme model has a hgher goodness-of-ft than the duraton model, although the number of statstcally sgnfcant parameters s hgher n the duraton model. A possble explanaton to ths phenomenon s that the selecton of wth whom s more nfluental on the start tme selecton compared to that of duraton. Ths also favors the argument that t s not the dstance or travel tme to defne how to schedule the socal actvtes but t s the wth whom we socalze. Ths aspect also reaffrms the need of ncorporatng socal dmenson n actvty schedulng model as well as treatng the socal actvty separately than other actvty types. 6. CONCLUSIONS Ths paper nvestgates a crtcal ssue n actvty schedulng decson: the socal dmenson of actvty schedulng. For prototype applcaton, socal actvty epsodes were selected for detaled analyses. The man objectve was to dentfy how the wth whom dmenson nfluences schedulng decsons, specfcally start tme and duraton of socal actvty epsodes. CHASE survey data collected n Toronto were used for the nvestgaton. The ndvdual s socal network s drawn from wth whom respondents performed socal actvtes n the seven-day survey perod. Based on ths socal network approach, four classfcatons of wth whom the ndvduals to partcpate the socal actvtes are used to capture the socal context of start tme and duraton of socal actvtes. Econometrc models of combned decsons regardng wth whom to partcpate and when to start or how much tme to spend on were estmated to nvestgate the correlatons

11 Habb, Carrasco, and Mller 11 between the wth whom decson and these schedulng decsons: start tme and duraton. One of the key fndngs was that when the socal dmenson s ncorporated explctly n the start tme model, the effect of travel tme and travel dstances becomes statstcally nsgnfcant. Ths fndng bolsters the argument that t s wth whom we socalze what defnes the socal actvty schedulng process and not travel tme or dstance. The models also provde other profound nsght nto the behavoral process of actvty schedulng and the socal dmenson. Sgnfcant correlatons exst between wth whom to partcpate and the start tme as well as duraton of the socal actvty epsodes. Overall, the contrbuton of ths paper s two-fold. In addton to the theoretcal understandng of the questons posed to nvestgate n ths paper, the models are useful to advance emprcally n prevous theoretcal deas developed regardng actvty schedulng modelng (3), whch could serve to mprove the exstng operatonal actvty schedulng model TASHA (7), ntegrated nsde the ILUTE framework (33). REFERENCES 1. Roorda, M.J., E.J. Mller, and K.M.N. Habb. Valdaton of TASHA: A 24-hour actvty schedulng mcrosmulaton model. Transportaton Research Part A, Vol. 42, 2007, pp Arentze, T. and H. Tmmermans. Socal networks, socal nteractons, and actvty-travel behavor: A framework for mcro-smulatons. Envronment and Plannng B, 2008, forthcomng. 3. Mller, E. J. Project-based actvty schedulng for household and person agents. Flow, Dynamcs and Human Interacton (Proceedngs of the 16th Internatonal Symposum on Transportaton and Traffc Theory), H. Mahmassan, Ed. Elsever, New York, Larsen, J., J. Urry, and K. W. Axhausen. Mobltes, Networks, Geographes. Ashgate Publshng Lmted, Aldershot, Doherty, S. T., E. Nemeth, M. J. Roorda, and E. J. Mller. Desgn and assessment of the Toronto Area computerzed household actvty schedulng survey. In Transportaton Research Record: Journal of the Transportaton Research Board, No. 1894, Transportaton Research Board of the Natonal Academes, Washngton, D.C., 2004, pp Doherty, S. T. and E. J. Mller. A computerzed household actvty schedulng survey. Transportaton, 21, 2000, pp Mller, E. J. and M. J. Roorda. Prototype model of household actvty/travel schedulng. In Transportaton Research Record: Journal of the Transportaton Research Board, No. 1831, Transportaton Research Board of the Natonal Academes, Washngton, D.C., 2003, pp Axhausen K.W. Socal networks and travel: Some hypotheses. In K Donaghy (ed.) Socal Aspects of Sustanable Transport: Transatlantc Perspectves, pp Ashgate, Aldershot, Tmmermans, H. (Ed). Progress on Actvty-Based Analyss. Elsever, New York, Bhat C.R. and T.K. Lawton. Transportaton n the New Mllennum: Passenger Travel Demand Forecastng, A1C02: Commttee on Passenger Travel Demand Forecastng, Transportaton Research Board of the Natonal Academes, Washngton, D.C., Axhausen, K.W. A Dynamc Understandng of Travel Demand: A Sketch. Arbetsberchte Verkehrs-und Raumplanung, 119. Insttut für Verkehrsplanung, Transporttechnk, Strassenund Esenbahnbau (IVT), ETH Zurch, Zurch, Dugundj, E. and J Walker. Dscrete choce wth socal and spatal network nterdependences: An emprcal example usng Mxed GEV models wth feld and panel effects. In Transportaton Research Record: Journal of the Transportaton Research

12 Habb, Carrasco, and Mller 12 Board, No. 1921, Transportaton Research Board of the Natonal Academes, Washngton, D.C., 2005, pp Páez, A. and D. Scott. Socal nfluence on travel behavor: A smulaton example of the decson to telecommute. Envronment and Plannng A, 39, 2007, pp Carrasco, J.A. and E. J. Mller. Explorng the propensty to perform socal actvtes: A socal networks approach. Transportaton, 33, 2006, pp Carrasco, J.A. and E. J. Mller. The socal dmenson n acton: A multlevel, personal networks model of socal actvty frequency. CD ROM, 86th Annual Meetng of the Transportaton Research Board. January 21-25, 2007, Washngton D.C. 16. Carrasco, J.A., E. J. Mller, and B. Wellman. Spatal and socal networks: The case of travel for socal actvtes. CD ROM. 11th Internatonal Conference on Travel Behavor Research. Kyoto, August 16-20, Carrasco, J.A., E. J. Mller, and B. Wellman. How far and wth whom do people socalze? Emprcal evdence about dstance between socal network members. In Transportaton Research Record: Journal of the Transportaton Research Board, Transportaton Research Board of the Natonal Academes, Washngton, D.C., 2008, forthcomng. 18. Goulas, K., T-G. Km, and M. Patten. On actvty type classfcaton and ssues related to the wth whom and for whom questons of an actvty dary: Prelmnary fndngs and pattern classfcaton. Progress n Actvty-Based Analyss, H. Tmmermans, Ed. Elsever, New York, Srnvasan, S., and C.R. Bhat. Companonshp for lesure actvtes: An emprcal analyss usng the Amercan Tme Use Survey. In Innovatons n Travel Demand Modelng Conference, Austn, Texas, May Mller, E. J. Propostons for Modellng Household Decson-Makng. In Integrated Land- Use and Transportaton Models: Behavoural Foundaons, M. Lee-Gosseln and S Doherty, Eds. Elsever, New York, Mller, E. J. An ntegrated framework for modellng short- and long-run household decson-makng. In Progress n Actvty-Based Analyss, H. Tmmermans, Ed. Elsever, New York, McCarty, C., P.D. Kllworth, H.R. Bernard, E.C. Johnsen, and G.A. Shelley. Comparng two methods for estmatng network sze. Human Organzaton, 60, 2000, pp Marsden, P. V. Networks data and measurement. Annual Revew of Socology, 16, 1990, pp Marsden, P. V. Recent developments n network measurement. In Models and Methods n Socal Network Analyss, P. Carrngton and J. Scott and S. Wasserman, Eds. Cambrdge Unversty Press, New York, Hogan, B., J.A. Carrasco, and B. Wellman. Vsualzng Personal Networks: Workng wth Partcpant-Aded Socograms. Feld Methods, 19, 2007, pp Carrasco, J.A., B. Hogan, B. Wellman, and E. J. Mller. Collectng socal network data to study socal actvty-travel behavour: An egocentred approach. Envronment and Plannng B, 2008, forthcomng. 27. Fu, Y-C. Contact dares: Buldng archves of actual and comprehensve personal networks. Feld Methods, 19, 2007, pp Duncan, G.M. Formulaton and statstcal analyss of the mssed, contnuous/dscrete dependent varable model n classcal producton theory. Econometrca, 48, 1980, pp Lee, L.-F. Generalzed econometrc models wth selectvty. Econometrca, 51, 1983, pp Lee, B., and H.J.P Tmmermans. A latent class accelerated hazard model of actvty epsode duraton. Transportaton Research Part B, 41, 2007, pp Aptech Systems GAUSS User s Manual. Maple Valley, CA, 2006.

13 Habb, Carrasco, and Mller Pendyala, R., and C.R. Bhat. An exploraton of the relatonshp between tmng and duraton of mantenance actvtes. Transportaton, 31, 2004, pp Salvn, P. and E. J. Mller. ILUTE: An operatonal prototype of a comprehensve mcrosmulaton model of urban systems. Networks and Spatal Economcs, 5, 2005, pp

14 Habb, Carrasco, and Mller 14 LIST OF TABLES AND PICTURES Fgure 1: Graphcal Presentaton of Jont Decson Process Table 1: Model for Epsode Duraton Table 2: Model for Epsode Start Tme Table 3: Summary of Goodness of Ft Measures

15 Habb, Carrasco, and Mller 15 Wth Whom Alternatve A Alternatve B Alternatve C Alternatve D ρ A=Correlaton ρ B=Correlaton ρ C=Correlaton ρ D=Correlaton σ A=Varance σ B=Varance σ C=Varance σ D=Varance Duraton / Start Tme Duraton / Start Tme Duraton / Start Tme Duraton / Start Tme Fgure 1: Graphcal Presentaton of Jont Decson Process

16 Habb, Carrasco, and Mller 16 Table 1: Model for Epsode Duraton Varable Name Parameter Std. Error t Base Alternatve: Wth Famly Members together wth Household Members Correlaton Between Dscrete and Contnuous Parts ρ ρ ρ Varances of Contnuous Hazard Model Part σ σ σ Utlty Functon of Multnomal Logt Components Alternatve1: Wth Frends together wth Household Member Male (Dummy) Proporton of Frends n Network Adult Wth Partner (Dummy) Alternatve2: Wth Famly wthout any Household Members Constant Total Famly Members n Network Varablty of Wth Whom Alternatve3: Wth Frends wthout any Household Members Constant Total Frends Members n Network Total Alters n Network HH Head (Dummy) Covarates of Lognormal Hazard Model Component Constant Total People Involved No. of Potental Locatons Duraton Flexblty( Dummy) Travel Tme (Mnutes) Total HH Chldren No. of HH Automobles Full Tme Worker (Dummy) Weekly Frequency

17 Habb, Carrasco, and Mller 17 Table 2: Model for Epsode Start Tme Varable Name Parameter Std. Error t Base Alternatve: Wth Famly Members together wth Household Members Correlaton Between Dscrete and Contnuous Parts ρ ρ ρ Varances of Contnuous Hazard Model Part σ σ σ Utlty Functon of Multnomal Logt Components Alternatve1: Wth Frends together wth Household Member Male (Dummy) Proporton of Frends n Network Adult Wth Partner (Dummy) Alternatve2: Wth Famly wthout any Household Members Constant Total Famly Members n Network Varablty of Wth Whom Alternatve3: Wth Frends wthout any Household Members Constant Total Frends Members n Network Total Alters n Network HH Head (Dummy) Covarates of Lognormal Hazard Model Component Constant Total People Involved No. of Potental Locatons Duraton Flexblty( Dummy) Total HH Chldren No. of HH Automobles Home Maker (Dummy) Weekly Frequency

18 Habb, Carrasco, and Mller 18 Table 3: Summary of Goodness of Ft Measures Duraton Start Tme Loglkelhood of Null Model Loglkelhood at Convergence Number of Parameters to be consdered for Adjustment Number of Observatons Adjusted Lkelhood Rato Index

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