- Email: [email protected]
Travel, social networks and time use
Chapter 15
Travel, social networks and time use: modeling complex real-life behavior Chiara Calastri Institute for Transport Studies and Choice Modelling Centre, University of Leeds, Leeds, UK
Chapter outline 1. Introduction and motivation 2. Treatment of context effects 2.1 Modeling availability and consideration 2.2 Social interactions and social network evolution 2.2.1 Social interactions 2.2.2 Social network evolution 3. Modeling discrete-continuous decisions
279 281 282 282 283 284
3.1 The nested MDCEV model: application and forecasting 3.2 Accommodating correlation in MDCEV models 4. Improvement of data collection approaches 5. Summary Acknowledgments References
287 290 294 294 295 295
287
1. Introduction and motivation Discrete choice modeling has been used as a mathematical tool to model and predict travel choices for more than forty years. Although theoretical developments of complex model structures took place at early stages, only recent progress in computing power made this technique widespread and facilitated further advances. The continuously changing patterns of human behavior, and the consequent need to collect adequate data to capture it, drove some of the more recent model developments, addressing challenges such as the incorporation of environmental and social factors in more sophisticated models and their measurement in surveys. But while much progress has been made, gaps remain in many areas. This chapter will briefly focus on three of them, namely the Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00015-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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treatment of context effects, the modeling of discrete and continuous decisions, and the improvement of data collection approaches. To date, the majority of applications of choice models are centered on rational agents maximizing individual utility functions, which depend on their socio-demographic characteristics and on attributes of the choice alternatives. In reality, individuals are embedded in complex environments, which shape the way they perceive the world around them, and consequently how they make choices. For example, let us consider the choice of how to spend free time on a Saturday afternoon. The traditional approach would define all the possible activities as the choice set and consider the attributes of these activities and the socio-demographics of the decision maker as determinants of the choice. In reality, only some activities will be available to an individual, maybe because he/she lives in a specific geographic area. Many existing applications have dealt with this, at least to some extent. However, availability and consideration may also vary across choices for the same person. For example, a person may not consider performing some of the activities if the weather conditions are not favourable for it. In addition, an activity might be performed with friends or family members, whose presence might relate with the choice or duration of the activity. Finally, if someone has performed an activity the previous day or even that very morning, he/she might want to avoid performing it again and rather do something different. It is clear that while this example refers to time use, these elements apply to many other choices. Moreover, this example is not meant to present an exhaustive summary of all the aspects potentially influencing choice, but it highlights those that will be discussed in this chapter. One of the issues mentioned in this example is that of availability and consideration of alternatives. Information about availability is generally easy to capture in travel behavior surveys. In revealed preference surveys, respondents are asked about what travel modes are available to them. Even when an alternative is available, it might not be considered because of search costs (Stigler, 1961), personally imposed thresholds and inertia. The definition of the choice set or the decision itself could also be somehow related to an individual’s social environment. The existing literature has provided valuable insights into how social network effects can be integrated into choice models. Several contributions focused on social influence effects (Brock and Durlauf, 2001), where the number of individuals in the reference group making a certain choice (Dugundji and Gulya´s, 2008; Fukuda and Morichi, 2007) or the past actions of proximate contacts (Pa´ez and Scott, 2007) have an impact on decisions. While social influence is the most intuitive effect of social networks on choices, it is worth noting that further insights about decision-making can be gained by understanding how social networks themselves operate. For example, while some attention has been devoted to looking at changes in car ownership over time (Prillwitz et al., 2006) and it has been recognized that there is an impact of social networks on the decision to
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own a car (Belgiawan et al., 2014), the process through which social networks form and change over time, which involve discrete decisions and outcomes, has not been widely studied in choice modeling. Another aspect, particularly relevant in the case of time use applications, has to do with correlation across decisions over time. If a person does his/her large shopping on Saturday, he/she will probably not do it on Sunday as well. In the case of time use (but not only), the choice is characterized by the fact that, associated with the discrete choice, there is a continuous choice. For example, an activity is chosen together with the time that will be spent performing it. As in real life these choices are made jointly, they should be modeled with an appropriate framework that recognizes such behavioral process. The state-ofthe-art tool for modeling discrete continuous choices is the Multiple DiscreteContinuous Extreme Value (MDCEV) model (Bhat, 2008). The richness of behavior described above is in line with general observations about real world behavior. However, for choice modeling, we need to capture such complexities in data suitable for modeling purposes. A major gap has opened in the field of choice modeling in recent years. Notwithstanding some exceptions (for example the work of Chandra Bhat and Joan Walker), many recent methodological advances, in particular the study of heterogeneity, have focused on stated preference (SP) and stated choice (SC) data. Despite their appeal, it is not clear that very behaviorally rich (and especially longitudinal) effects can be captured with SP data. In recent years, an increasing number of scholars have recognized the value of using RP data, notwithstanding the many difficulties associated with it, such as frequent high collinearity and limited variation among attributes (Brownstone et al., 2000). Moreover, it is often more difficult to identify the choice set in the presence of RP data, and the attributes of the unchosen alternatives are not always known. On the other side, these data reflect real-life conditions and should be used if one aims to capture and model the complexity of real-life behavior. The domain of RP data is rapidly growing to include the so-called “ubiquitous” data sources, collected via mobile phones, smart cards, CCTV. These new sources constitute an invaluable resource in that they record repeated choices. Investigating ways to access, handle and model them is an important challenge in the field of choice modeling. In the remainder of this chapter, the three main themes will be discussed, and the main contributions put forward in the thesis will be outlined, together with references for further details.
2. Treatment of context effects This section will briefly describe the contributions in two different research themes: the incorporation of availability and consideration in choice models of travel behavior, and the modeling of choices related to the social environment, and their potential effects on travel and other decisions.
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2.1 Modeling availability and consideration As mentioned above, the issue of availability and consideration of choice alternatives has been explored in the travel behavior literature, but with the advent of new forms of RP data such as GPS and smart-card data, dealing with these aspects is even more difficult. Indeed, these types of ubiquitous data are often collected for purposes other than travel behavior analysis and modeling, and for this reason relevant information such as mode availability is not often collected. In these cases, the missing information need to be derived and the dataset needs to be augmented to be suitable for modeling. Such datasets can be an invaluable resource, as they can come at virtually no cost and are generally characterized by repeated measurement from the same people. An example of an attempt to exploit such a dataset for choice modeling is the one of the Tag My Day project. This mode choice dataset, collected in Italy with the sole purpose of plotting traffic flows, collected GPS data from a random sample of people who recorded their trips during summer 2014. Information about cost, weather, gradient and other variables were added to the dataset. With the aim to investigate whether and how GPS data collected for purposes other than modeling can be used for our analyses, we proposed a latent class approach which treats mode availability and consideration in a probabilistic manner, with the former being at the person level and the latter at the trip level. Due to missing information about availability, this had to be inferred through other available socio-demographics, a method that proved not only effective but superior to using stated availability. Moreover, the inclusion of consideration sets provided improvements in model fit. As described in detail in Calastri et al. (2017a), despite the data limitations, performing data cleaning and enriching and developing models making different assumptions in terms of availability and consideration resulted in successful estimation of mode choice models that provided reasonable values of travel time (VTT). We also showed that GPS data without associated indepth information about respondents can be successfully used in our analyses.
2.2 Social interactions and social network evolution The study of the role of social networks on travel decisions is in its infancy, and gaps exist in different dimensions, pertaining to the formation and evolution of social networks, the way in which different members of a social network interact and the relationship between a person’s social network and his/her choices. The work presented at IATBR drew from Calastri et al. (2017c) and Calastri et al. (2018b), which look into the processes of social interactions and social network formation and evolution over time, respectively. Social interactions have mainly been modeled using multi-level and regression models, which present some limitations for the correct representation of these
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processes (Calastri et al., 2017c). Apart from a limited number of attempts looking at social network formation (Arentze et al., 2013) and evolution (Sharmeen et al., 2016) (but without modeling the maintenance of social contacts over time), choice models have not been used to study these processes. Not only could choice models help gain a better understanding of social network dynamics, but scholars from different applied fields who are proficient in choice modeling could be encouraged to incorporate the effects of social networks in their models and possibly jointly model network evolution with other long term decisions. Conversely, social influence on choices has received more attention in travel behavior research, although existing work often includes social network variables in the utility assuming these have a causal effect on choices, while this might not necessarily be the case.
2.2.1 Social interactions The determinants of communication patterns between individuals and their social network members can be seen as a discrete-continuous choice, in the sense that a person can choose the mode of interaction with a given network member, and for each mode a given frequency. The process can therefore be modeled with the MDCEV model, which is applied to this new area of analysis. As mentioned above, while previous research efforts looking at this decision mainly used multi-level models or multi-level path analysis, the MDCEV represents a more suitable framework that jointly deals with the continuous and discrete choice dimension and can accommodate corner solutions. This framework also allows us to measure satiation from different modes, something that was not accommodated in previous studies. We make use of “ego-centric” social network data collected in Switzerland (see Kowald and Axhausen, 2014) originally collected as a snowball sample: each of the 40 “ego-seeds” initially recruited was asked to report the names and other information about their social contacts, who were asked to do the same in turn. The sample used for analysis included 638 egos and 13,500 alters. The g-specification of the MDCEV model was adopted as it fitted the data better and the overall number of social interactions over a year was used as the individual-specific budget, making this an allocation model. Our findings show that both the respondent’s (ego) characteristics as well as those of the relationship between him/her and each social contact (ego-alter) have a significant impact on communication patterns. In particular, differently from similar studies which made use of social network data, the simultaneous modeling technique adopted in the present paper allows us to observe that dyad level variables have a much more significant effect on communication frequency than ego-level ones, a result that supports the need to make use of measures related to the similarities and differences between egos and alters to understand interaction patterns.
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As an example, Fig. 15.1 shows the impact of the distance (in km) between the ego and the alter’s home location on the utility of interacting by the different modes. The impact on face-to-face is larger than on other modes, followed by SMS, phone and email. Utility decreases with distance for face-to-face, phone and SMS, while the opposite happens with email, a mode that seems to be used for different purposes and perhaps with different people as opposed to the first three. We also find that when the age difference between the ego and an alter increases (i.e. one of the two is likely to be quite old), face-to-face and phone are more likely to be used as opposed to SMS and email. People are also more likely to communicate by any mode with those who they consider to be emotionally close. The model results highlight a strong underlying preference for face-to-face contact (especially with core contacts). This is an important conclusion given the on-going debate on potential substitution effects between ICT based modes of communication and most traditional ones. We performed an illustrative forecasting example (using the Pinjari and Bhat (2010a) forecasting algorithm) which shows how a model of the type used here can be used to gain insights into the likely changes in travel patterns resulting from changes in the composition and characteristics of a social network. In particular, we selected a friend of each ego’s who lived less than 5 km away and increased this distance by 10%. The main results of this exercise show that the overall frequency of interaction is inelastic with respect to the change, and that while the computed distance elasticity is small and negative for the relocated alter, the overall elasticity is positive (though also small), meaning that there is a positive impact on the overall distance traveled.
2.2.2 Social network evolution As mentioned above, we can gain a better understanding of social interactions, one dimension of decisions making related to social networks, by the
FIG. 15.1 Effect of distance on the utility of each mode.
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application of advanced choice models. Another process which possibly cannot be strictly classified as a decision but can most definitely be analyzed by the same tools as it manifests as a discrete outcome is social network formation and evolution over time. In particular, it is possible to model whether, in a given time span, social contacts are maintained or lost over time. Given the nature of the problem, panel data are needed to study this process: in the social network literature, some studies have tried to understand the magnitude and reasons for network changes in small groups (Wasserman and Faust, 1994) or using limited sample sizes (Wellman et al., 1997) and generally linked changes to important life events. In our case, we made use of a 2-wave dataset, collected in 2008 and in 2012 in Concepcio´n, Chile, including an in-depth questionnaire, a time use survey and a name generator and name interpreter based on the design in Carrasco et al. (2008). A total of 240 people took part in each wave, but after data cleaning the overlapping useable sample included 94 egos only, each reporting an average of 20.34 alters each. This binary outcome, which is modeled through a binary logit model, can be thought of as a function of socio-demographic characteristics and life course changes of the ego as well as of ego-alter characteristics in the first wave. We then introduce random heterogeneity in this simple model to account for differences across egos in retention of social contacts. We also incorporate the concept of emotional strength of a relationship in a hybrid model as a latent component. The strength of the relationship is a concept drawn from the sociology literature, which explains a number of indicators, such as the emotional closeness as stated by the egos, the frequency of interaction, the joint participation in social activities and the social capital, i.e. exchanging help when needed. The modeling framework is shown in Fig. 15.2. As shown in detail in Calastri et al. (2018b), this allows us to separately account for different layers of heterogeneity, both at the level of the ego and across the alters. This approach also allows us to gain interesting insights into which factors affect the retention process directly and which ones indirectly, by having an impact on the strength of relationship. In line with previous studies, we find an overall tendency to lose contacts over time, and the significant effect of sociodemographic variables related to specific life-course moments. As an example, we find that if the ego is a student or went to university between the two waves, he/she is less likely to retain social contacts. As expected, we find a positive effect of gender homophily on retention. The effect of some variables is only significant on the strength: this includes reduced relationship strength for older respondents, those not employed and homemakers. Network size and density have a negative impact on latent strength. Importantly, we find a positive and significant effect of relationship strength on the retention of social contacts, as expected. Thanks to the complexity of the models that allow us to incorporate different layers of heterogeneity in the models, our findings underline the
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FIG. 15.2 Modeling framework for the social contact retention model.
relevance of random egolevel heterogeneity in retention, as well as both ego and ego-alter level heterogeneity in strength. The former means that some egos will retain more social contacts than others, while the latter implies that some egos will be more prone to establishing strong relationships than others (variation across egos) and that even within a specific ego’s network, certain ties will be stronger than others (variation across alters, for an ego). Not only is this work one of the first to apply choice modeling to study social network evolution, but also it does so by developing a comprehensive framework allowing for an appropriate treatment of relationship strength and random heterogeneity. These two contributions dealing with social interactions and social network evolution show the importance of using comprehensive frameworks that can accommodate the complexity of the choice process. In both cases, it is crucial to include in the model both ego and ego-alter level variables. Moreover, the results of Calastri et al. (2017c), highlighting that there is a strong preference for face-to-face interaction, especially with core social contacts, are reflected in the ones from Calastri et al. (2018b), which indicate that emotionally stronger contacts are more likely to be retained in the network over time. The overall picture that we obtain, showing that some social contacts are emotionally stronger, have more frequent face-to-face interactions and are more likely to remain in the network, is an important finding in light of the current research on potential substitution effects between ICT based modes of communication and more traditional ones, and the role of distance in social relationships. These insights and their implications have a direct impact on the demand for travel for social purposes and potentially for long-term social influence on choices.
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3. Modeling discrete-continuous decisions The MDCEV model, preferred tool for analyzing discrete-continuous choices, has been applied by scholars mainly in the field of travel behavior to contexts in which a discrete and a continuous dimension of choice are present, such as choice of vehicle and time traveled/mileage/expenditure (Jian et al., 2017) or activity participation and time allocation (Rajagopalan et al., 2009). In recent years, other fields have also made use of this model and applied it to different problems, although most applications only made use of the “base” model (Bhat, 2008), despite several developments of increasingly complexity have been proposed. Indeed, some of these advanced models such as the nested version of MDCEV (Pinjari and Bhat, 2010b) and the extension allowing to accommodate complementarity and substitution (Bhat et al., 2015) developed by Chandra Bhat and his colleagues are rarely applied. Further testing of these methods with different datasets and contributions that could potentially improve their applicability are therefore needed. A very important issue that needs addressing is suggesting ways to account for correlations across discrete-continuous choices so as to be able to observe substitution patterns across them. In what follows, two contributions to this area of research are briefly introduced. The first one focuses on applying and forecasting with the socalled MDCNEV model, i.e. the nested version of MDCEV, in a time use context where the influence of social networks on activity choices is also considered. The second one proposes the use of a mixed MDCEV model to capture correlations across different days of the week and explores different approaches to forecast with such a model.
3.1 The nested MDCEV model: application and forecasting Time allocation is a leading topic in travel behavior research, and it has been long recognized to be one particularly suitable for the application of discretecontinuous models, due to its behavioral complexity. Starting from the 1950s, models able to accommodate a discrete and a continuous dimension of choice have been developed (Dubin and McFadden, 1984; Heckman, 1976; Tobin, 1958) and subsequently applied to understand activity type or timing and duration (Bhat, 2005; Kapur and Bhat, 2007). The MDCEV model is the stateof-the-art tool for modeling activity choice and duration, but as the MNL model it assumes an i.i.d. distribution for the error term in the utility, not accounting for correlation across alternatives. To address this shortcoming, a nested version of the model was proposed by (Pinjari and Bhat, 2010b). We apply this to the time use data from Concepcin, Chile, studying the choice between 12 macro activities and the impact not only of socio-demographics and land-use variables, but also of social network effects, such as size of the network. Empirical testing of over 30 different nesting structures guided by intuition, statistical fitting and previous literature resulted in selecting the
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preferred structure as one including one nest for all the in-home activities, one for the out-of-home activities, and one for family activities. The nesting parameters for the first two (non-degenerate) nests highlighted a heightened correlation in the first nest, possibly due to the fewer unobserved factors affecting in-home activities. The model results provided interesting insights into time use behavior, of which we report a few examples. Men are less likely than women to perform household obligations: an expected results, as gender imbalance in household activities is found in most cultural contexts (Lachance-Grzela and Bouchard, 2010; Ruppanner, 2008). The lowest level of income is associated with higher likelihood of performing household obligations and family-oriented activities. These findings are expect as medium and high income Chilean households generally employ a housekeeper (Mora, 2006). We also find that internet access increases the likelihood of engaging in social activities: this confirms the finding of the literature on social networks and travel activities, which has suggested the presence of complementarities between access to communication technology and travel for social purposes (Schaap et al., 2016). Access to the internet has also a positive impact on the utility of studying, which is reasonable if we think of the importance of the web in the search for information. In this case, we acknowledge that there is a potential inverse causality, as we cannot exclude that someone would get access to the internet because they want to study. In terms of the continuous choice, we find that younger people (less than 26 year old) are likely to spend more time doing social activities than older people; while having a larger social network is associated with spending less time traveling. We find that the likelihood of working is positively affected by having one underage child, but the duration of this activity is lower than in the case of people without young children. Further results and comparisons across the MDCEV and MDCNEV model are reported in Calastri et al. (2017b). As described above, the efficient forecasting algorithm by Pinjari and Bhat (2010b) can be used to forecast with the MDCEV model, but it relies on the analyst producing draws from the underlying error structure of the model. This presents no difficulty with the MDCEV model but can be more complex with the MDCNEV model as it requires the production of generalized extreme value (GEV) draws. For this reason, we propose a simple way to draw from a GEV distribution (detailed in Calastri et al. (2017b)) and produce model forecasts. We produced several purely illustrative forecasting scenarios where we made arbitrary assumptions that impacted behavior relative to a given activity. For example, in the scenario reported in Table 15.1 we assume that everybody in the sample behaves as if they were women, and this has an impact on the utility of household obligations, belonging to the first nest. The Table shows the “true” time allocation values in the sample, the computed “base scenario” and then the forecasted time use in the MDCEV and in the MDCNEV models,
TABLE 15.1 Forecasting scenarios results. Assumption: everybody behaves as if they were women MDCEV
MDCNEV
Activity
Sample
Base scenario
Forecast
Change
Base scenario
Forecast
Change
1
Basic needs
28.6
26.55
26.21
1.26%
26.09
25.74
1.34%
1
Household obligations
2.6
2.65
3.30
24.42%
2.68
3.23
20.41%
1
In home recreation
0.8
0.78
0.77
1.61%
0.79
0.76
3.45%
1
Study
1.1
1.12
1.11
1.24%
1.10
1.07
2.83%
2
Drop off/Pick up
0.1
0.17
0.17
1.58%
0.17
0.17
0.75%
2
Out-of-home recreation
0.8
1.01
0.99
1.79%
1.13
1.12
0.75%
2
Services
0.5
0.65
0.64
1.87%
0.72
0.71
0.80%
2
Social
4.0
4.13
4.06
1.64%
4.36
4.33
0.76%
2
Shopping
0.5
0.77
0.75
2.23%
0.78
0.77
0.99%
2
Travel
3.3
4.35
4.28
1.53%
3.97
3.94
0.75%
2
Work
4.9
5.06
4.97
1.74%
5.39
5.35
0.83%
3
Family
0.8
0.76
0.75
1.85%
0.82
0.82
0.92%
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both as number of hours and as a percentage change from the base scenario. We can see that a change in the household obligations is compensated by a change in the time spent in the other activities, and that in the case of the MDCNEV model, the reduction generated by the compensation mechanism is higher for the activities included into the “in-home” nest. These results show the importance of accounting for the nesting structure in forecasting and how it affects time reallocation in line with expectations.
3.2 Accommodating correlation in MDCEV models In the discrete-continuous time use literature some studies have accommodated interactions between different days (Bhat and Misra, 1999; Yamamoto and Kitamura, 1999). One of the extensions of the MDCEV model accommodates substitutions and complementarities across goods (Bhat et al., 2015) but the patterns that the model estimation can give rise to is somehow constrained, so that complementarity must outweigh substitution. In addition, the model presents difficulties in estimation and has not been applied in the literature (except from the empirical application presented together with the model formulation). With more and more surveys collecting multi-day datasets that allow to observe regularities and habitual behavior, the importance of accommodating such behavioral processes in our models is striking. In particular, viable modeling and forecasting solutions for the MDCEV framework are needed. Indeed, the standard formulation of the MDCEV model is not adequate to model time allocation when multiple days of data are available for each individual as it does not permit the analyst to build links across days. The theoretical accommodation of these effects is not straightforward (especially when the budget constraints both at the day-level and multi-day level). For this reason, we apply a mixed MDCEV model with multi-variate distributions allowing for correlation between activities at the within-day and between-day level. The model is estimated using panel data, in particular we use 2 weeks of the well-known 6-week Mobidrive travel diary dataset, which we transform in activity diary. These include 10 macro-categories of activities, among which an outside good. Making use of Bayesian techniques, we estimate a mean and standard deviation for all the model parameters and the full covariance matrix. The estimation results show substantially different sensitivities during weekday and weekend days. We also observe substantial random heterogeneity, highlighting that different people have different sensitivities, both in terms of the participation and in the time invested in different activities. The estimation of the full covariance matrix allowed us to compute the correlations between different model parameters. For example, we obtained a high positive correlation between the baseline utility of working across different day types; while we observed a negative correlation between the
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time invested in work on weekdays and Saturdays and weekdays and Sundays. The former effect could represent a complementarity effect, as people who perform this activity are likely to do so in different days (in other words, people who “like” to work are more likely to do it on multiple day types), while the latter a substitution effect: if more time was invested in a weekday, less will be invested on the weekend. Of course, we cannot identify these effects with certainty. If we apply the Pinjari and Bhat (2010b) algorithm to the present model, this will account for the random heterogeneity across individuals as well as the correlations between the different parameters, but it would produce forecasts for time consumption for each separate day without building a link across them. For this reason, we suggested two modifications of this approach to account for these links. The detailed derivation can be found in Calastri (2017). In our example, reported in Table 15.2, we forecast with three days (Friday, Saturday and Sunday) from the base model and assume that the utility of the work activity is halved on Friday. This simply means that people derive reduced utility from working on Friday for whatever reason, possibly they have some other commitments; therefore they have to decide how to reallocate their time for that and the following days. We call the forecast obtained with the Pinjari and Bhat (2010b) algorithm Approach A: It is clear from the Table reporting the changes in the share of people taking part in the activities that there is a redistribution of the time subtracted to work to the other activities on Friday but no change in the other two days. In order to create a link across days in forecasting, we make use of the budget constraint: we forecast jointly with 30 activities (10 for each day), but as we disregard the day-specific 24hr constraints, we might get violations in the sum of time allocation for each day, and therefore we need to perform a rescaling to ensure that the 24hr constraint is met for each day: Tk;d d T $24: k;d ¼ 12 P Tk;d k¼1
This is what we call Approach 2. Then, instead of forecasting with 30 activities, we use 29, with a single outside good for the three days. Therefore, we forecast for 9 inside goods for each day and we then re-compute the outside goods. We again perform the rescaling as before, and name this Approach 3a. In a slightly different version (Approach 3b), we perform the rescaling according to the shares of time use observed in the data. Both Approach 2 and Approach 3 allow some time redistribution across different days, in particular showing that when people cannot work on Friday they will try to catch up on the weekend days. In Calastri (2017) we assess in details the goodness of each approach using different measures, and acknowledge that our suggested approaches are of course “ad hoc” and not in line with the modeling framework,
Change in share participating
Approach A
Approach B
Approach C1
Approach C2
Outside good
0.00%
0.00%
0.00%
0.00%
Work
L51.17%
L57.51%
L54.82%
L54.82%
School
2.72%
0.69%
0.90%
0.90%
Drop-off/pick-up
13.46%
6.96%
4.81%
4.81%
Daily shopping
9.73%
4.09%
3.87%
3.87%
12.43%
6.06%
4.96%
4.96%
Social
11.46%
4.75%
4.75%
4.75%
Leisure
9.71%
3.22%
3.45%
3.45%
Private business
10.84%
4.51%
4.39%
4.39%
Travel
0.41%
0.24%
0.16%
0.16%
Outside good
0.00%
0.00%
0.00%
0.00%
Work
0.00%
14.20%
11.33%
11.33%
School
0.00%
0.51%
2.50%
2.50%
Drop-off/pick-up
0.00%
5.43%
5.51%
5.51%
Daily shopping
0.00%
5.26%
4.67%
4.67%
0.00%
4.16%
4.31%
4.31%
Non daily shopping
Non daily shopping
Friday
Saturday
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TABLE 15.2 Percentage changes in the share participating (discrete choice) for each forecasting approach.
0.00%
4.20%
4.02%
4.02%
Leisure
0.00%
4.94%
4.35%
4.35%
Private business
0.00%
4.32%
5.10%
5.10%
Travel
0.00%
1.88%
1.27%
1.27%
Outside good
0.00%
0.00%
0.00%
0.00%
Work
0.00%
15.13%
11.84%
11.84%
School
0.00%
2.05%
1.41%
1.41%
Drop-off/pick-up
0.00%
6.33%
5.55%
5.55%
Daily shopping
0.00%
4.70%
4.95%
4.95%
0.00%
4.26%
4.82%
4.82%
Social
0.00%
5.85%
5.10%
5.10%
Leisure
0.00%
3.32%
3.59%
3.59%
Private business
0.00%
4.29%
5.04%
5.04%
Travel
0.00%
2.23%
2.04%
2.04%
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although they can accommodate a behavioral process that is of crucial importance for the correct modeling and forecasting of time use behavior.
4. Improvement of data collection approaches All the contributions described in this chapter make use of revealed preference (RP) data, ranging from GPS tracking data to name generator data to travel diaries. Capturing such complex real-life behavior can be challenging, especially because when asking respondents to recall detailed information about multiple aspects of their lives they might experience fatigue and recall issues; not to mention the possibility of errors related to data entry, depending on the survey format. The different issues encountered with these dataset have been treasured when developing a unified data collection within the “Choices and consumption: modeling long and short term decisions in a changing world” ERC project, funder of the PhD. The survey is made up of four different components: the first three were completed through an online interface while the last one consisted in using a smartphone tracking app for two weeks. The first component was a questionnaire, asking respondents a wide range of information about themselves, their families, their travel and energy habits and their homes. The second one was a life-course calendar, used to retrospectively obtain data about events and activities occurred during the life of respondents (Caspi et al., 1996); while the third was a name generator and name interpreter, to gather information about respondent’s social networks. Full details of the survey design can be found in Calastri et al. (2018a). A final sample size of 452 people completed the survey in all its components, and preliminary analysis of the data showed reasonable statistics. Interestingly, the number of trips recorded with the smartphone app was approximately 5.45 per day, a figure substantially larger than the ones usually reported in paper-and-pencil travel diaries, where under-reporting is a common issue. The data collected in the present survey will be used for complex modeling incorporating different dimensions, such as travel, social networks and activities to unveil the complex dynamics of real-life behavior.
5. Summary This chapter has summarized a number of theoretical and applied contributions put forward in the Ph.D. thesis “Capturing and modeling complex decision-making in the context of travel, time use and social interactions”. These are organized around three themes: the treatment of context effects, the modeling of discrete and continuous decisions, and the improvement of data collection approaches. I first discussed the use of ubiquitous data in transport, especially when such data sources are collected for purposes other than travel behavior modeling. In this case, an appropriate management of the data and the inclusion of availability and consideration of choice alternatives
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can improve the models and lead to meaningful values of travel time. The work on the modeling of social interactions and social network evolution over time was then introduced, applying new modeling techniques to analyze these research questions and obtaining results that shed light on the relationship between these processes and travel behavior choices. The research on discrete-continuous models discussed in this chapter is mainly aimed at accommodating heterogeneity and capturing correlation structures across days and activities in MDCEV models. While using different models in estimation, both contributions focus on forecasting, which is a crucial issue with discrete-continuous models, although current applications devote limited attention to this issue. The thesis proposes a method to easily draw from a GEV distribution that allows to forecast with the MD- CNEV model, and it presents a forecasting method to be used with multiple days of data. Finally, I briefly describe the design of the unified data collection conducted in Leeds, UK, aimed at capturing different dimensions of real-life behavior and overcoming some of the common limitations of RP data collections. While the contributions presented here and in more details in Calastri (2017) have hopefully addressed some gaps in the travel behavior literature, more work remains to be done in these areas. The modeling of social network choices and the understanding of their impact on travel behavior is at its infancy, and many other such soft factors which may affect decisions deserve investigation. Differently, the study of time use has received more attention from travel behavior researchers, but the MDCEV model is still a relatively new approach that could benefit from further developments to incorporate behavioral realism in its framework. Finally, while it is clear that different domains of life are interconnected and choices are not made in isolation, data collection is a great challenge, and the developments of new methods, also thanks to new technologies, together with the reassessment of existing ones, is a priority in our field.
Acknowledgments The work described in this chapter would have not been possible without the support of my advisors Prof. Stephane Hess and Dr. Charisma Choudhury and the financial support by the European Research Council through the consolidator grant 615596-DECISIONS.
References Arentze, T.A., Kowald, M., Axhausen, K.W., 2013. An agent-based random-utility maximization model to generate social networks with transitivity in geographic space. Social Networks 35 (3), 451e459. Belgiawan, P.F., Schmo¨cker, J.-D., Abou-Zeid, M., Walker, J., Lee, T.-C., Ettema, D.F., Fujii, S., 2014. Car ownership motivations among undergraduate students in China, Indonesia, Japan, Lebanon, Netherlands, Taiwan, and USA. Transportation 41 (6), 1227e1244.
296 PART | II New research methods and findings Bhat, C.R., 2005. A multiple discreteecontinuous extreme value model: formulation and application to discretionary time-use decisions. Transportation Research Part B: Methodological 39 (8), 679e707. Bhat, C.R., 2008. The multiple discrete-continuous extreme value (mdcev) model: role of utility function parameters, identification considerations, and model extensions. Transportation Research Part B: Methodological 42 (3), 274e303. Bhat, C.R., Castro, M., Pinjari, A.R., 2015. Allowing for complementarity and rich substitution patterns in multiple discreteecontinuous models. Transportation Research Part B: Methodological 81, 59e77. Bhat, C.R., Misra, R., 1999. Discretionary activity time allocation of individuals between inhome and out-of-home and between weekdays and weekends. Transportation 26 (2), 193e229. Brock, W.A., Durlauf, S.N., 2001. Discrete choice with social interactions. The Review of Economic Studies 68 (2), 235e260. Brownstone, D., Bunch, D.S., Train, K., 2000. Joint mixed logit models of stated and revealed preferences for alternative-fuel vehicles. Transportation Research Part B: Methodological 34 (5), 315e338. Calastri, C., 2017. Capturing and Modelling Complex Decision-Making in the Context of Travel, Time Use and Social Interactions (Ph.D. thesis). Institute for Transport Studies, University of Leeds (chapter 5). Calastri, C., dit Sourd, R.C., Hess, S., 2018a. We want it all: experiences from a survey seeking to capture social network structures, lifetime events and short-term travel and activity planning. Transportation 1e27. Calastri, C., Hess, S., Daly, A., Carrasco, J.A., Choudhury, C., 2018b. Modelling the loss and retention of contacts in social networks: the role of dyad-level heterogeneity and tie strength. Journal of Choice Modelling 29, 63e77. Calastri, C., Hess, S., Choudhury, C., Daly, A., Gabrielli, L., 2017a. Mode choice with latent availability and consideration: theory and a case study. Transportation Research Part B: Methodological. Calastri, C., Hess, S., Daly, A., Carrasco, J.A., 2017b. Does the social context help with understanding and predicting the choice of activity type and duration? an application of the multiple discrete-continuous nested extreme value model to activity diary data. Transportation Research Part A: Policy and Practice 104, 1e20. Calastri, C., Hess, S., Daly, A., Maness, M., Kowald, M., Axhausen, K., 2017c. Modelling contact mode and frequency of interactions with social network members using the multiple discreteecontinuous extreme value model. Transportation Research Part C: Emerging Technologies 76, 16e34. Carrasco, J.A., Hogan, B., Wellman, B., Miller, E.J., 2008. Collecting social network data to study social activity-travel behavior: an egocentric approach. Environment and Planning. B, Planning & Design 35 (6), 961. Caspi, A., Moffitt, T.E., Thornton, A., Freedman, D., Amell, J.W., Harrington, H., Smeijers, J., Silva, P.A., 1996. The life history calendar: a research and clinical assessment method for collecting retrospective event-history data. International Journal of Methods in Psychiatric Research 6, 101e114. Dubin, J.A., McFadden, D.L., 1984. An econometric analysis of residential electric appliance holdings and consumption. Econometrica: Journal of the Econometric Society 345e362. Dugundji, E.R., Gulya´s, L., 2008. Sociodynamic discrete choice on networks in space: impacts of agent heterogeneity on emergent outcomes. Environment and Planning B: Planning and Design 35 (6), 1028e1054.
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Fukuda, D., Morichi, S., 2007. Incorporating aggregate behavior in an individual’s discrete choice: an application to analyzing illegal bicycle parking behavior. Transportation Research Part A: Policy and Practice 41 (4), 313e325. Heckman, J.J., 1976. The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5 (4), 475e492. NBER. Jian, S., Rashidi, T.H., Dixit, V., 2017. An analysis of carsharing vehicle choice and utilization patterns using multiple discrete-continuous extreme value (mdcev) models. Transportation Research Part A: Policy and Practice 103, 362e376. http://www.sciencedirect.com/science/ article/pii/S0965856416309028. Kapur, A., Bhat, C., 2007. Modeling adults’ weekend day-time use by activity purpose and accompaniment arrangement. Transportation Research Record: Journal of the Transportation Research Board 2021, 18e27. Kowald, M., Axhausen, K.W., 2014. Surveying data on connected personal networks. Travel Behaviour and Society 1 (2), 57e68. Lachance-Grzela, M., Bouchard, G., 2010. Why do women do the lion’s share of housework? a decade of research. Sex Roles 63 (11e12), 767e780. Mora, C., 2006. The meaning of womanhood in the neoliberal age: class and age-based narratives of chilean women. Gender Issues 23 (2), 44. Pa´ez, A., Scott, D.M., 2007. Social influence on travel behavior: a simulation example of the decision to telecommute. Environment and Planning 39 (3), 647e665. Pinjari, A.R., Bhat, C., 2010a. An Efficient Forecasting Procedure for Kuhn-Tucker Consumer Demand Model Systems. Technical paper. Department of Civil & Environmental Engineering, University of South Florida. Pinjari, A.R., Bhat, C., 2010b. A multiple discreteecontinuous nested extreme value (mdcnev) model: formulation and application to non-worker activity time-use and timing behavior on weekdays. Transportation Research Part B: Methodological 44 (4), 562e583. Prillwitz, J., Harms, S., Lanzendorf, M., 2006. Impact of life-course events on car ownership. Transportation Research Record: Journal of the Transportation Research Board 1985, 71e77. Rajagopalan, B., Pinjari, A., Bhat, C., 2009. Comprehensive model of worker nonworkactivity time use and timing behavior. Transportation Research Record: Journal of the Transportation Research Board 2134, 51e62. Ruppanner, L., 2008. Fairness and housework: a cross-national comparison. Journal of Comparative Family Studies 509e526. Schaap, N.T., Hoogendoorn-Lanser, S., Berveling, J., 2016. On good neighbours and distant friends in the online era. In: Transportation Research Board 95th Annual Meeting. No. 16-1179. ´ ., Carrasco, J.-A., Arentze, T., Tudela, A., 2016. Modeling populationSharmeen, F., Cha´vez, O wide personal network dynamics using two-wave data collection method and origindestination survey. In: Transportation Research Board 95th Annual Meeting. No. 16-3387. Stigler, G.J., 1961. The economics of information. Journal of Political Economy 69 (3), 213e225. Tobin, J., 1958. Estimation of relationships for limited dependent variables. Econometrica: Journal of the Econometric Society 24e36. Wasserman, S., Faust, K., 1994. Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press. Wellman, B., Wong, R. Y.-l., Tindall, D., Nazer, N., 1997. A decade of network change: turnover, persistence and stability in personal communities. Social Networks 19 (1), 27e50. Yamamoto, T., Kitamura, R., 1999. An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non-working days. Transportation 26 (2), 231e250.
Travel, social networks and time use: modeling complex real-life behavior Chiara Calastri Institute for Transport Studies and Choice Modelling Centre, University of Leeds, Leeds, UK
Chapter outline 1. Introduction and motivation 2. Treatment of context effects 2.1 Modeling availability and consideration 2.2 Social interactions and social network evolution 2.2.1 Social interactions 2.2.2 Social network evolution 3. Modeling discrete-continuous decisions
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3.1 The nested MDCEV model: application and forecasting 3.2 Accommodating correlation in MDCEV models 4. Improvement of data collection approaches 5. Summary Acknowledgments References
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1. Introduction and motivation Discrete choice modeling has been used as a mathematical tool to model and predict travel choices for more than forty years. Although theoretical developments of complex model structures took place at early stages, only recent progress in computing power made this technique widespread and facilitated further advances. The continuously changing patterns of human behavior, and the consequent need to collect adequate data to capture it, drove some of the more recent model developments, addressing challenges such as the incorporation of environmental and social factors in more sophisticated models and their measurement in surveys. But while much progress has been made, gaps remain in many areas. This chapter will briefly focus on three of them, namely the Mapping the Travel Behavior Genome. https://doi.org/10.1016/B978-0-12-817340-4.00015-2 Copyright © 2020 Elsevier Inc. All rights reserved.
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treatment of context effects, the modeling of discrete and continuous decisions, and the improvement of data collection approaches. To date, the majority of applications of choice models are centered on rational agents maximizing individual utility functions, which depend on their socio-demographic characteristics and on attributes of the choice alternatives. In reality, individuals are embedded in complex environments, which shape the way they perceive the world around them, and consequently how they make choices. For example, let us consider the choice of how to spend free time on a Saturday afternoon. The traditional approach would define all the possible activities as the choice set and consider the attributes of these activities and the socio-demographics of the decision maker as determinants of the choice. In reality, only some activities will be available to an individual, maybe because he/she lives in a specific geographic area. Many existing applications have dealt with this, at least to some extent. However, availability and consideration may also vary across choices for the same person. For example, a person may not consider performing some of the activities if the weather conditions are not favourable for it. In addition, an activity might be performed with friends or family members, whose presence might relate with the choice or duration of the activity. Finally, if someone has performed an activity the previous day or even that very morning, he/she might want to avoid performing it again and rather do something different. It is clear that while this example refers to time use, these elements apply to many other choices. Moreover, this example is not meant to present an exhaustive summary of all the aspects potentially influencing choice, but it highlights those that will be discussed in this chapter. One of the issues mentioned in this example is that of availability and consideration of alternatives. Information about availability is generally easy to capture in travel behavior surveys. In revealed preference surveys, respondents are asked about what travel modes are available to them. Even when an alternative is available, it might not be considered because of search costs (Stigler, 1961), personally imposed thresholds and inertia. The definition of the choice set or the decision itself could also be somehow related to an individual’s social environment. The existing literature has provided valuable insights into how social network effects can be integrated into choice models. Several contributions focused on social influence effects (Brock and Durlauf, 2001), where the number of individuals in the reference group making a certain choice (Dugundji and Gulya´s, 2008; Fukuda and Morichi, 2007) or the past actions of proximate contacts (Pa´ez and Scott, 2007) have an impact on decisions. While social influence is the most intuitive effect of social networks on choices, it is worth noting that further insights about decision-making can be gained by understanding how social networks themselves operate. For example, while some attention has been devoted to looking at changes in car ownership over time (Prillwitz et al., 2006) and it has been recognized that there is an impact of social networks on the decision to
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own a car (Belgiawan et al., 2014), the process through which social networks form and change over time, which involve discrete decisions and outcomes, has not been widely studied in choice modeling. Another aspect, particularly relevant in the case of time use applications, has to do with correlation across decisions over time. If a person does his/her large shopping on Saturday, he/she will probably not do it on Sunday as well. In the case of time use (but not only), the choice is characterized by the fact that, associated with the discrete choice, there is a continuous choice. For example, an activity is chosen together with the time that will be spent performing it. As in real life these choices are made jointly, they should be modeled with an appropriate framework that recognizes such behavioral process. The state-ofthe-art tool for modeling discrete continuous choices is the Multiple DiscreteContinuous Extreme Value (MDCEV) model (Bhat, 2008). The richness of behavior described above is in line with general observations about real world behavior. However, for choice modeling, we need to capture such complexities in data suitable for modeling purposes. A major gap has opened in the field of choice modeling in recent years. Notwithstanding some exceptions (for example the work of Chandra Bhat and Joan Walker), many recent methodological advances, in particular the study of heterogeneity, have focused on stated preference (SP) and stated choice (SC) data. Despite their appeal, it is not clear that very behaviorally rich (and especially longitudinal) effects can be captured with SP data. In recent years, an increasing number of scholars have recognized the value of using RP data, notwithstanding the many difficulties associated with it, such as frequent high collinearity and limited variation among attributes (Brownstone et al., 2000). Moreover, it is often more difficult to identify the choice set in the presence of RP data, and the attributes of the unchosen alternatives are not always known. On the other side, these data reflect real-life conditions and should be used if one aims to capture and model the complexity of real-life behavior. The domain of RP data is rapidly growing to include the so-called “ubiquitous” data sources, collected via mobile phones, smart cards, CCTV. These new sources constitute an invaluable resource in that they record repeated choices. Investigating ways to access, handle and model them is an important challenge in the field of choice modeling. In the remainder of this chapter, the three main themes will be discussed, and the main contributions put forward in the thesis will be outlined, together with references for further details.
2. Treatment of context effects This section will briefly describe the contributions in two different research themes: the incorporation of availability and consideration in choice models of travel behavior, and the modeling of choices related to the social environment, and their potential effects on travel and other decisions.
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2.1 Modeling availability and consideration As mentioned above, the issue of availability and consideration of choice alternatives has been explored in the travel behavior literature, but with the advent of new forms of RP data such as GPS and smart-card data, dealing with these aspects is even more difficult. Indeed, these types of ubiquitous data are often collected for purposes other than travel behavior analysis and modeling, and for this reason relevant information such as mode availability is not often collected. In these cases, the missing information need to be derived and the dataset needs to be augmented to be suitable for modeling. Such datasets can be an invaluable resource, as they can come at virtually no cost and are generally characterized by repeated measurement from the same people. An example of an attempt to exploit such a dataset for choice modeling is the one of the Tag My Day project. This mode choice dataset, collected in Italy with the sole purpose of plotting traffic flows, collected GPS data from a random sample of people who recorded their trips during summer 2014. Information about cost, weather, gradient and other variables were added to the dataset. With the aim to investigate whether and how GPS data collected for purposes other than modeling can be used for our analyses, we proposed a latent class approach which treats mode availability and consideration in a probabilistic manner, with the former being at the person level and the latter at the trip level. Due to missing information about availability, this had to be inferred through other available socio-demographics, a method that proved not only effective but superior to using stated availability. Moreover, the inclusion of consideration sets provided improvements in model fit. As described in detail in Calastri et al. (2017a), despite the data limitations, performing data cleaning and enriching and developing models making different assumptions in terms of availability and consideration resulted in successful estimation of mode choice models that provided reasonable values of travel time (VTT). We also showed that GPS data without associated indepth information about respondents can be successfully used in our analyses.
2.2 Social interactions and social network evolution The study of the role of social networks on travel decisions is in its infancy, and gaps exist in different dimensions, pertaining to the formation and evolution of social networks, the way in which different members of a social network interact and the relationship between a person’s social network and his/her choices. The work presented at IATBR drew from Calastri et al. (2017c) and Calastri et al. (2018b), which look into the processes of social interactions and social network formation and evolution over time, respectively. Social interactions have mainly been modeled using multi-level and regression models, which present some limitations for the correct representation of these
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processes (Calastri et al., 2017c). Apart from a limited number of attempts looking at social network formation (Arentze et al., 2013) and evolution (Sharmeen et al., 2016) (but without modeling the maintenance of social contacts over time), choice models have not been used to study these processes. Not only could choice models help gain a better understanding of social network dynamics, but scholars from different applied fields who are proficient in choice modeling could be encouraged to incorporate the effects of social networks in their models and possibly jointly model network evolution with other long term decisions. Conversely, social influence on choices has received more attention in travel behavior research, although existing work often includes social network variables in the utility assuming these have a causal effect on choices, while this might not necessarily be the case.
2.2.1 Social interactions The determinants of communication patterns between individuals and their social network members can be seen as a discrete-continuous choice, in the sense that a person can choose the mode of interaction with a given network member, and for each mode a given frequency. The process can therefore be modeled with the MDCEV model, which is applied to this new area of analysis. As mentioned above, while previous research efforts looking at this decision mainly used multi-level models or multi-level path analysis, the MDCEV represents a more suitable framework that jointly deals with the continuous and discrete choice dimension and can accommodate corner solutions. This framework also allows us to measure satiation from different modes, something that was not accommodated in previous studies. We make use of “ego-centric” social network data collected in Switzerland (see Kowald and Axhausen, 2014) originally collected as a snowball sample: each of the 40 “ego-seeds” initially recruited was asked to report the names and other information about their social contacts, who were asked to do the same in turn. The sample used for analysis included 638 egos and 13,500 alters. The g-specification of the MDCEV model was adopted as it fitted the data better and the overall number of social interactions over a year was used as the individual-specific budget, making this an allocation model. Our findings show that both the respondent’s (ego) characteristics as well as those of the relationship between him/her and each social contact (ego-alter) have a significant impact on communication patterns. In particular, differently from similar studies which made use of social network data, the simultaneous modeling technique adopted in the present paper allows us to observe that dyad level variables have a much more significant effect on communication frequency than ego-level ones, a result that supports the need to make use of measures related to the similarities and differences between egos and alters to understand interaction patterns.
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As an example, Fig. 15.1 shows the impact of the distance (in km) between the ego and the alter’s home location on the utility of interacting by the different modes. The impact on face-to-face is larger than on other modes, followed by SMS, phone and email. Utility decreases with distance for face-to-face, phone and SMS, while the opposite happens with email, a mode that seems to be used for different purposes and perhaps with different people as opposed to the first three. We also find that when the age difference between the ego and an alter increases (i.e. one of the two is likely to be quite old), face-to-face and phone are more likely to be used as opposed to SMS and email. People are also more likely to communicate by any mode with those who they consider to be emotionally close. The model results highlight a strong underlying preference for face-to-face contact (especially with core contacts). This is an important conclusion given the on-going debate on potential substitution effects between ICT based modes of communication and most traditional ones. We performed an illustrative forecasting example (using the Pinjari and Bhat (2010a) forecasting algorithm) which shows how a model of the type used here can be used to gain insights into the likely changes in travel patterns resulting from changes in the composition and characteristics of a social network. In particular, we selected a friend of each ego’s who lived less than 5 km away and increased this distance by 10%. The main results of this exercise show that the overall frequency of interaction is inelastic with respect to the change, and that while the computed distance elasticity is small and negative for the relocated alter, the overall elasticity is positive (though also small), meaning that there is a positive impact on the overall distance traveled.
2.2.2 Social network evolution As mentioned above, we can gain a better understanding of social interactions, one dimension of decisions making related to social networks, by the
FIG. 15.1 Effect of distance on the utility of each mode.
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application of advanced choice models. Another process which possibly cannot be strictly classified as a decision but can most definitely be analyzed by the same tools as it manifests as a discrete outcome is social network formation and evolution over time. In particular, it is possible to model whether, in a given time span, social contacts are maintained or lost over time. Given the nature of the problem, panel data are needed to study this process: in the social network literature, some studies have tried to understand the magnitude and reasons for network changes in small groups (Wasserman and Faust, 1994) or using limited sample sizes (Wellman et al., 1997) and generally linked changes to important life events. In our case, we made use of a 2-wave dataset, collected in 2008 and in 2012 in Concepcio´n, Chile, including an in-depth questionnaire, a time use survey and a name generator and name interpreter based on the design in Carrasco et al. (2008). A total of 240 people took part in each wave, but after data cleaning the overlapping useable sample included 94 egos only, each reporting an average of 20.34 alters each. This binary outcome, which is modeled through a binary logit model, can be thought of as a function of socio-demographic characteristics and life course changes of the ego as well as of ego-alter characteristics in the first wave. We then introduce random heterogeneity in this simple model to account for differences across egos in retention of social contacts. We also incorporate the concept of emotional strength of a relationship in a hybrid model as a latent component. The strength of the relationship is a concept drawn from the sociology literature, which explains a number of indicators, such as the emotional closeness as stated by the egos, the frequency of interaction, the joint participation in social activities and the social capital, i.e. exchanging help when needed. The modeling framework is shown in Fig. 15.2. As shown in detail in Calastri et al. (2018b), this allows us to separately account for different layers of heterogeneity, both at the level of the ego and across the alters. This approach also allows us to gain interesting insights into which factors affect the retention process directly and which ones indirectly, by having an impact on the strength of relationship. In line with previous studies, we find an overall tendency to lose contacts over time, and the significant effect of sociodemographic variables related to specific life-course moments. As an example, we find that if the ego is a student or went to university between the two waves, he/she is less likely to retain social contacts. As expected, we find a positive effect of gender homophily on retention. The effect of some variables is only significant on the strength: this includes reduced relationship strength for older respondents, those not employed and homemakers. Network size and density have a negative impact on latent strength. Importantly, we find a positive and significant effect of relationship strength on the retention of social contacts, as expected. Thanks to the complexity of the models that allow us to incorporate different layers of heterogeneity in the models, our findings underline the
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FIG. 15.2 Modeling framework for the social contact retention model.
relevance of random egolevel heterogeneity in retention, as well as both ego and ego-alter level heterogeneity in strength. The former means that some egos will retain more social contacts than others, while the latter implies that some egos will be more prone to establishing strong relationships than others (variation across egos) and that even within a specific ego’s network, certain ties will be stronger than others (variation across alters, for an ego). Not only is this work one of the first to apply choice modeling to study social network evolution, but also it does so by developing a comprehensive framework allowing for an appropriate treatment of relationship strength and random heterogeneity. These two contributions dealing with social interactions and social network evolution show the importance of using comprehensive frameworks that can accommodate the complexity of the choice process. In both cases, it is crucial to include in the model both ego and ego-alter level variables. Moreover, the results of Calastri et al. (2017c), highlighting that there is a strong preference for face-to-face interaction, especially with core social contacts, are reflected in the ones from Calastri et al. (2018b), which indicate that emotionally stronger contacts are more likely to be retained in the network over time. The overall picture that we obtain, showing that some social contacts are emotionally stronger, have more frequent face-to-face interactions and are more likely to remain in the network, is an important finding in light of the current research on potential substitution effects between ICT based modes of communication and more traditional ones, and the role of distance in social relationships. These insights and their implications have a direct impact on the demand for travel for social purposes and potentially for long-term social influence on choices.
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3. Modeling discrete-continuous decisions The MDCEV model, preferred tool for analyzing discrete-continuous choices, has been applied by scholars mainly in the field of travel behavior to contexts in which a discrete and a continuous dimension of choice are present, such as choice of vehicle and time traveled/mileage/expenditure (Jian et al., 2017) or activity participation and time allocation (Rajagopalan et al., 2009). In recent years, other fields have also made use of this model and applied it to different problems, although most applications only made use of the “base” model (Bhat, 2008), despite several developments of increasingly complexity have been proposed. Indeed, some of these advanced models such as the nested version of MDCEV (Pinjari and Bhat, 2010b) and the extension allowing to accommodate complementarity and substitution (Bhat et al., 2015) developed by Chandra Bhat and his colleagues are rarely applied. Further testing of these methods with different datasets and contributions that could potentially improve their applicability are therefore needed. A very important issue that needs addressing is suggesting ways to account for correlations across discrete-continuous choices so as to be able to observe substitution patterns across them. In what follows, two contributions to this area of research are briefly introduced. The first one focuses on applying and forecasting with the socalled MDCNEV model, i.e. the nested version of MDCEV, in a time use context where the influence of social networks on activity choices is also considered. The second one proposes the use of a mixed MDCEV model to capture correlations across different days of the week and explores different approaches to forecast with such a model.
3.1 The nested MDCEV model: application and forecasting Time allocation is a leading topic in travel behavior research, and it has been long recognized to be one particularly suitable for the application of discretecontinuous models, due to its behavioral complexity. Starting from the 1950s, models able to accommodate a discrete and a continuous dimension of choice have been developed (Dubin and McFadden, 1984; Heckman, 1976; Tobin, 1958) and subsequently applied to understand activity type or timing and duration (Bhat, 2005; Kapur and Bhat, 2007). The MDCEV model is the stateof-the-art tool for modeling activity choice and duration, but as the MNL model it assumes an i.i.d. distribution for the error term in the utility, not accounting for correlation across alternatives. To address this shortcoming, a nested version of the model was proposed by (Pinjari and Bhat, 2010b). We apply this to the time use data from Concepcin, Chile, studying the choice between 12 macro activities and the impact not only of socio-demographics and land-use variables, but also of social network effects, such as size of the network. Empirical testing of over 30 different nesting structures guided by intuition, statistical fitting and previous literature resulted in selecting the
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preferred structure as one including one nest for all the in-home activities, one for the out-of-home activities, and one for family activities. The nesting parameters for the first two (non-degenerate) nests highlighted a heightened correlation in the first nest, possibly due to the fewer unobserved factors affecting in-home activities. The model results provided interesting insights into time use behavior, of which we report a few examples. Men are less likely than women to perform household obligations: an expected results, as gender imbalance in household activities is found in most cultural contexts (Lachance-Grzela and Bouchard, 2010; Ruppanner, 2008). The lowest level of income is associated with higher likelihood of performing household obligations and family-oriented activities. These findings are expect as medium and high income Chilean households generally employ a housekeeper (Mora, 2006). We also find that internet access increases the likelihood of engaging in social activities: this confirms the finding of the literature on social networks and travel activities, which has suggested the presence of complementarities between access to communication technology and travel for social purposes (Schaap et al., 2016). Access to the internet has also a positive impact on the utility of studying, which is reasonable if we think of the importance of the web in the search for information. In this case, we acknowledge that there is a potential inverse causality, as we cannot exclude that someone would get access to the internet because they want to study. In terms of the continuous choice, we find that younger people (less than 26 year old) are likely to spend more time doing social activities than older people; while having a larger social network is associated with spending less time traveling. We find that the likelihood of working is positively affected by having one underage child, but the duration of this activity is lower than in the case of people without young children. Further results and comparisons across the MDCEV and MDCNEV model are reported in Calastri et al. (2017b). As described above, the efficient forecasting algorithm by Pinjari and Bhat (2010b) can be used to forecast with the MDCEV model, but it relies on the analyst producing draws from the underlying error structure of the model. This presents no difficulty with the MDCEV model but can be more complex with the MDCNEV model as it requires the production of generalized extreme value (GEV) draws. For this reason, we propose a simple way to draw from a GEV distribution (detailed in Calastri et al. (2017b)) and produce model forecasts. We produced several purely illustrative forecasting scenarios where we made arbitrary assumptions that impacted behavior relative to a given activity. For example, in the scenario reported in Table 15.1 we assume that everybody in the sample behaves as if they were women, and this has an impact on the utility of household obligations, belonging to the first nest. The Table shows the “true” time allocation values in the sample, the computed “base scenario” and then the forecasted time use in the MDCEV and in the MDCNEV models,
TABLE 15.1 Forecasting scenarios results. Assumption: everybody behaves as if they were women MDCEV
MDCNEV
Activity
Sample
Base scenario
Forecast
Change
Base scenario
Forecast
Change
1
Basic needs
28.6
26.55
26.21
1.26%
26.09
25.74
1.34%
1
Household obligations
2.6
2.65
3.30
24.42%
2.68
3.23
20.41%
1
In home recreation
0.8
0.78
0.77
1.61%
0.79
0.76
3.45%
1
Study
1.1
1.12
1.11
1.24%
1.10
1.07
2.83%
2
Drop off/Pick up
0.1
0.17
0.17
1.58%
0.17
0.17
0.75%
2
Out-of-home recreation
0.8
1.01
0.99
1.79%
1.13
1.12
0.75%
2
Services
0.5
0.65
0.64
1.87%
0.72
0.71
0.80%
2
Social
4.0
4.13
4.06
1.64%
4.36
4.33
0.76%
2
Shopping
0.5
0.77
0.75
2.23%
0.78
0.77
0.99%
2
Travel
3.3
4.35
4.28
1.53%
3.97
3.94
0.75%
2
Work
4.9
5.06
4.97
1.74%
5.39
5.35
0.83%
3
Family
0.8
0.76
0.75
1.85%
0.82
0.82
0.92%
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290 PART | II New research methods and findings
both as number of hours and as a percentage change from the base scenario. We can see that a change in the household obligations is compensated by a change in the time spent in the other activities, and that in the case of the MDCNEV model, the reduction generated by the compensation mechanism is higher for the activities included into the “in-home” nest. These results show the importance of accounting for the nesting structure in forecasting and how it affects time reallocation in line with expectations.
3.2 Accommodating correlation in MDCEV models In the discrete-continuous time use literature some studies have accommodated interactions between different days (Bhat and Misra, 1999; Yamamoto and Kitamura, 1999). One of the extensions of the MDCEV model accommodates substitutions and complementarities across goods (Bhat et al., 2015) but the patterns that the model estimation can give rise to is somehow constrained, so that complementarity must outweigh substitution. In addition, the model presents difficulties in estimation and has not been applied in the literature (except from the empirical application presented together with the model formulation). With more and more surveys collecting multi-day datasets that allow to observe regularities and habitual behavior, the importance of accommodating such behavioral processes in our models is striking. In particular, viable modeling and forecasting solutions for the MDCEV framework are needed. Indeed, the standard formulation of the MDCEV model is not adequate to model time allocation when multiple days of data are available for each individual as it does not permit the analyst to build links across days. The theoretical accommodation of these effects is not straightforward (especially when the budget constraints both at the day-level and multi-day level). For this reason, we apply a mixed MDCEV model with multi-variate distributions allowing for correlation between activities at the within-day and between-day level. The model is estimated using panel data, in particular we use 2 weeks of the well-known 6-week Mobidrive travel diary dataset, which we transform in activity diary. These include 10 macro-categories of activities, among which an outside good. Making use of Bayesian techniques, we estimate a mean and standard deviation for all the model parameters and the full covariance matrix. The estimation results show substantially different sensitivities during weekday and weekend days. We also observe substantial random heterogeneity, highlighting that different people have different sensitivities, both in terms of the participation and in the time invested in different activities. The estimation of the full covariance matrix allowed us to compute the correlations between different model parameters. For example, we obtained a high positive correlation between the baseline utility of working across different day types; while we observed a negative correlation between the
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time invested in work on weekdays and Saturdays and weekdays and Sundays. The former effect could represent a complementarity effect, as people who perform this activity are likely to do so in different days (in other words, people who “like” to work are more likely to do it on multiple day types), while the latter a substitution effect: if more time was invested in a weekday, less will be invested on the weekend. Of course, we cannot identify these effects with certainty. If we apply the Pinjari and Bhat (2010b) algorithm to the present model, this will account for the random heterogeneity across individuals as well as the correlations between the different parameters, but it would produce forecasts for time consumption for each separate day without building a link across them. For this reason, we suggested two modifications of this approach to account for these links. The detailed derivation can be found in Calastri (2017). In our example, reported in Table 15.2, we forecast with three days (Friday, Saturday and Sunday) from the base model and assume that the utility of the work activity is halved on Friday. This simply means that people derive reduced utility from working on Friday for whatever reason, possibly they have some other commitments; therefore they have to decide how to reallocate their time for that and the following days. We call the forecast obtained with the Pinjari and Bhat (2010b) algorithm Approach A: It is clear from the Table reporting the changes in the share of people taking part in the activities that there is a redistribution of the time subtracted to work to the other activities on Friday but no change in the other two days. In order to create a link across days in forecasting, we make use of the budget constraint: we forecast jointly with 30 activities (10 for each day), but as we disregard the day-specific 24hr constraints, we might get violations in the sum of time allocation for each day, and therefore we need to perform a rescaling to ensure that the 24hr constraint is met for each day: Tk;d d T $24: k;d ¼ 12 P Tk;d k¼1
This is what we call Approach 2. Then, instead of forecasting with 30 activities, we use 29, with a single outside good for the three days. Therefore, we forecast for 9 inside goods for each day and we then re-compute the outside goods. We again perform the rescaling as before, and name this Approach 3a. In a slightly different version (Approach 3b), we perform the rescaling according to the shares of time use observed in the data. Both Approach 2 and Approach 3 allow some time redistribution across different days, in particular showing that when people cannot work on Friday they will try to catch up on the weekend days. In Calastri (2017) we assess in details the goodness of each approach using different measures, and acknowledge that our suggested approaches are of course “ad hoc” and not in line with the modeling framework,
Change in share participating
Approach A
Approach B
Approach C1
Approach C2
Outside good
0.00%
0.00%
0.00%
0.00%
Work
L51.17%
L57.51%
L54.82%
L54.82%
School
2.72%
0.69%
0.90%
0.90%
Drop-off/pick-up
13.46%
6.96%
4.81%
4.81%
Daily shopping
9.73%
4.09%
3.87%
3.87%
12.43%
6.06%
4.96%
4.96%
Social
11.46%
4.75%
4.75%
4.75%
Leisure
9.71%
3.22%
3.45%
3.45%
Private business
10.84%
4.51%
4.39%
4.39%
Travel
0.41%
0.24%
0.16%
0.16%
Outside good
0.00%
0.00%
0.00%
0.00%
Work
0.00%
14.20%
11.33%
11.33%
School
0.00%
0.51%
2.50%
2.50%
Drop-off/pick-up
0.00%
5.43%
5.51%
5.51%
Daily shopping
0.00%
5.26%
4.67%
4.67%
0.00%
4.16%
4.31%
4.31%
Non daily shopping
Non daily shopping
Friday
Saturday
292 PART | II New research methods and findings
TABLE 15.2 Percentage changes in the share participating (discrete choice) for each forecasting approach.
0.00%
4.20%
4.02%
4.02%
Leisure
0.00%
4.94%
4.35%
4.35%
Private business
0.00%
4.32%
5.10%
5.10%
Travel
0.00%
1.88%
1.27%
1.27%
Outside good
0.00%
0.00%
0.00%
0.00%
Work
0.00%
15.13%
11.84%
11.84%
School
0.00%
2.05%
1.41%
1.41%
Drop-off/pick-up
0.00%
6.33%
5.55%
5.55%
Daily shopping
0.00%
4.70%
4.95%
4.95%
0.00%
4.26%
4.82%
4.82%
Social
0.00%
5.85%
5.10%
5.10%
Leisure
0.00%
3.32%
3.59%
3.59%
Private business
0.00%
4.29%
5.04%
5.04%
Travel
0.00%
2.23%
2.04%
2.04%
Non daily shopping
Sunday
Travel, social networks and time use Chapter | 15
Social
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294 PART | II New research methods and findings
although they can accommodate a behavioral process that is of crucial importance for the correct modeling and forecasting of time use behavior.
4. Improvement of data collection approaches All the contributions described in this chapter make use of revealed preference (RP) data, ranging from GPS tracking data to name generator data to travel diaries. Capturing such complex real-life behavior can be challenging, especially because when asking respondents to recall detailed information about multiple aspects of their lives they might experience fatigue and recall issues; not to mention the possibility of errors related to data entry, depending on the survey format. The different issues encountered with these dataset have been treasured when developing a unified data collection within the “Choices and consumption: modeling long and short term decisions in a changing world” ERC project, funder of the PhD. The survey is made up of four different components: the first three were completed through an online interface while the last one consisted in using a smartphone tracking app for two weeks. The first component was a questionnaire, asking respondents a wide range of information about themselves, their families, their travel and energy habits and their homes. The second one was a life-course calendar, used to retrospectively obtain data about events and activities occurred during the life of respondents (Caspi et al., 1996); while the third was a name generator and name interpreter, to gather information about respondent’s social networks. Full details of the survey design can be found in Calastri et al. (2018a). A final sample size of 452 people completed the survey in all its components, and preliminary analysis of the data showed reasonable statistics. Interestingly, the number of trips recorded with the smartphone app was approximately 5.45 per day, a figure substantially larger than the ones usually reported in paper-and-pencil travel diaries, where under-reporting is a common issue. The data collected in the present survey will be used for complex modeling incorporating different dimensions, such as travel, social networks and activities to unveil the complex dynamics of real-life behavior.
5. Summary This chapter has summarized a number of theoretical and applied contributions put forward in the Ph.D. thesis “Capturing and modeling complex decision-making in the context of travel, time use and social interactions”. These are organized around three themes: the treatment of context effects, the modeling of discrete and continuous decisions, and the improvement of data collection approaches. I first discussed the use of ubiquitous data in transport, especially when such data sources are collected for purposes other than travel behavior modeling. In this case, an appropriate management of the data and the inclusion of availability and consideration of choice alternatives
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can improve the models and lead to meaningful values of travel time. The work on the modeling of social interactions and social network evolution over time was then introduced, applying new modeling techniques to analyze these research questions and obtaining results that shed light on the relationship between these processes and travel behavior choices. The research on discrete-continuous models discussed in this chapter is mainly aimed at accommodating heterogeneity and capturing correlation structures across days and activities in MDCEV models. While using different models in estimation, both contributions focus on forecasting, which is a crucial issue with discrete-continuous models, although current applications devote limited attention to this issue. The thesis proposes a method to easily draw from a GEV distribution that allows to forecast with the MD- CNEV model, and it presents a forecasting method to be used with multiple days of data. Finally, I briefly describe the design of the unified data collection conducted in Leeds, UK, aimed at capturing different dimensions of real-life behavior and overcoming some of the common limitations of RP data collections. While the contributions presented here and in more details in Calastri (2017) have hopefully addressed some gaps in the travel behavior literature, more work remains to be done in these areas. The modeling of social network choices and the understanding of their impact on travel behavior is at its infancy, and many other such soft factors which may affect decisions deserve investigation. Differently, the study of time use has received more attention from travel behavior researchers, but the MDCEV model is still a relatively new approach that could benefit from further developments to incorporate behavioral realism in its framework. Finally, while it is clear that different domains of life are interconnected and choices are not made in isolation, data collection is a great challenge, and the developments of new methods, also thanks to new technologies, together with the reassessment of existing ones, is a priority in our field.
Acknowledgments The work described in this chapter would have not been possible without the support of my advisors Prof. Stephane Hess and Dr. Charisma Choudhury and the financial support by the European Research Council through the consolidator grant 615596-DECISIONS.
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