Exploring the links between personal networks, time use, and the spatial distribution of social contacts

Although the study of the role of the social context in travel behavior and activity patterns has recently gained attention, the empirical evidence supporting the relationship between social networks and the temporal and spatial characteristics of social activities is still limited. With this motivation, this paper studies the link between “longer term” (social networks) and “shorter term” (social activities) social decisions, by exploring the intertwined relationship between the individuals’ personal networks attributes, and the spatiotemporal characteristics of their daily social activities. The paper contributes to the literature by adding two key aspects to the study of the role of social networks on travel behavior: the social networks’ structure, and the spatiality of all individuals participating on the social activities. Based on data which link people’s personal networks and time use, and using a structural equation modeling approach, the paper studies the influence of individual and interactional attributes on the duration, distance, and number of people involved in social daily activities. The results show that aspects such as tie social closeness, gender and age similarity, and network density, help to understand social activity duration and distance, complementing traditional socio-demographic aspects such as income, occupation, and accessibility to services. In this way, socio-demographic attributes are not enough to explain the spatiotemporal dimension of daily activities which makes necessary to include variables related to the social context to explain with a higher level of accuracy both the duration and distance traveled to the activity.     

Analysis of social networks,social interactions,and out-of-home leisure activity generation: Evidence from Japan

Transportation - This article analyses the connection between social networks, social interactions and out-of-home leisure activity generation in the context of Japanese society. A multilevel...     

Decision makers and socializers, social networks and the role of individuals as participants

Models explaining and predicting human travel behavior have gone through many changes in the past few decades. As researchers attempt to explain more and predict with more accuracy, the inclusion of social interactions in modeling and simulation is being recognized as a necessity. Among these efforts, researchers have focused on issues such as the composition of social networks, and the constraints and influences that others have on spatial decisions. An important aspect that has been understudied however is the variability or heterogeneity of individuals both as social network members and as participants in these social networks. Understanding the role individuals play in decision-making in different social networks can further define our models to include more accurate representations of human behavior. This research explores the differences between social network composition, and the decision roles members play within different social networks specifically when deciding where to participate in activities. A survey was conducted in Santa Barbara, California on social network involvement, network attributes and decision-making roles within each network. Two separate latent class cluster analysis models were developed to classify social network involvement and roles. Results show that there are clearly different types of social involvement and roles within networks. Further data collection and analysis will be used to better understand how these decision-making roles manifest themselves in activity decision-making.     

Studying the relationship between activity participation,social networks,expenditures and travel behavior on leisure activities

Transportation - In the context of an increasing interest in understanding travel for non-mandatory activities, such as recreation and socializing, this work focuses on studying the relationships...     

Involvement in clubs or voluntary associations, social networks and activity generation: a path analysis

Leisure activities have received increasing attention from travel behavior researchers over the past decade. However, these activities are often treated as a single category, neglecting their differences. Whereas most leisure activities are flexible, club activities are usually scheduled longer in advance and are more fixed in time, location and company. Hence, trip-generating properties of club activities are likely to differ from those of other leisure activities. As very little is known about involvement in clubs or voluntary associations in relation to trip generation, voluntary association activities deserve further research in relation to travel. Therefore, in this paper a path analysis is conducted, analyzing the relationships between participation in clubs or voluntary associations, trip frequencies, and social network characteristics. The analyses are based on data collected in 2011 in Eindhoven in the Netherlands in a survey among 516 respondents. The results show interesting relationships between the social context and involvement in clubs. They indicate that people become club members through their social networks, and frequent club activities increase social network size. Family oriented people were found to go less often to clubs. Club membership and the frequency of going to club activities were also found to be affected by socio-demographics, such as gender, age, education, work, presence of young children in the household and owning a season ticket for public transport.     

A latent class accelerated hazard model of social activity duration

Over the past decade, activity scheduling processes have gained increasing attention in the field of transportation research. However, still little is known about the scheduling of social activities even though these activities account for a large and growing portion of trips. This paper contributes to this knowledge. We analyze how the duration of social activities is influenced by social activity characteristics and characteristics of the relationship between the respondent and the contacted person(s). To that end, a latent class accelerated hazard model is estimated, based on social interaction diary data that was collected in the Netherlands in 2008. Chi-square tests and analyses of variance are used to test for significant relations between the latent classes and personal and household characteristics. Findings suggest that the social activity characteristics and the characteristics of the relationship between the socializing persons are highly significant in explaining social activity duration. This shows that social activities should not be considered as a homogenous set of activities and it underlines the importance of including the social context in travel-behavior models. Moreover, the results indicate that there is a substantial amount of latent heterogeneity across the population. Four latent classes are identified, showing different social activity durations, and different effects for both categories of explanatory variables. Latent class membership can be explained by household composition, socio-economic status (education, income and work hours), car ownership and the number of interactions in 2 days.     

The interactions between online shopping and personal activity travel behavior: an analysis with a GPS-based activity travel diary

Accompanying the widespread use of the Internet, the popularity of e-commerce is growing in developing countries such as China. Online shopping has significant effects on in-store shopping and on other personal activity travel behavior such as leisure activities and trip chaining behavior. Using data collected from a GPS-based activity travel diary in the Shangdi area of Beijing, this paper investigates the relationships between online shopping, in-store shopping and other dimensions of activity travel behavior using a structural equation modelling framework. Our results show that online buying frequency has positive effects on the frequencies of both in-store shopping and online searching, and in-store shopping frequency positively affects the frequency of online searching. Frequent online purchasers tend to shop in stores on weekends rather than weekdays. We also found a negative effect of online buying on the frequency of leisure activities, indicating that online shopping may reduce out-of-home leisure trips.     

A two-stage stochastic optimization model for the transfer activity choice in metro networks

This research focuses on finding the best transfer schemes in metro networks. Using sample-based time-invariant link travel times to capture the uncertainty of a realistic network, a two-stage stochastic integer programming model with the minimized expected travel time and penalty value incurred by transfer activities is formulated. The first stage aims to find a sequence of potential transfer nodes (stations) that can compose a feasible path from origins to destinations in the transfer activity network, and the second stage provides the least time paths passing by the generated transfer stations in the first stage for evaluating the given transfer schemes and then outputs the best routing information. To solve our proposed model, an efficient hybrid algorithm, in which the label correcting algorithm is embedded into a branch and bound searching framework, is presented to find the optimal solutions of the considered problem. Finally, the numerical experiments are implemented in different scales of metro networks. The computational results demonstrate the effectiveness and performance of the proposed approaches even for the large-scale Beijing metro network.     

The kinematic wave model with finite decelerations: A social force car-following model approximation

This paper derives a five-parameter social force car-following model that converges to the kinematic wave model with triangular fundamental diagram. Analytical solutions for vehicle trajectories are found for the lead-vehicle problem, which exhibit clockwise and counter-clockwise hysteresis depending on the model’s parameters and the lead vehicle trajectory. When coupled with a stochastic vehicle dynamics module, the model is able to reproduce periods and amplitudes of stop-and-go waves, as reported in the field. The model’s stability conditions are analysed and its trajectories are compared to real data.     

A model of joint activity participation between household members

A proportional shares model of daily time allocation is developed and applied to the analysis of joint activity participation between adult household members. The model is unique in its simultaneous representation of each decision maker's decisions concerning independent activity participation, allocation of time to joint activities, and the interplay between individual and joint activities. Further, the model structure ensures that predicted shares of joint activity outcomes be the same for both decision makers, an improvement over models that do not make interpersonal linkages explicit. The empirical analysis of travel diary data shows that employment commitments and childcare responsibilities have significant effects on tradeoffs between joint and independent activities. In addition, evidence is presented for the continued relevance of gender-based role differences in caring for children and employment participation.     

The interaction between public transportation and other social activities: A systems approach

This paper describes a quantitative method of evaluating public transportation systems in areas in which the frequencies of transportation services are low. The starting point of the evaluation is an overall view of the interaction between public transportation, private transportation, and the area with its physical and socio-economic variables. In the present paper the problem concerning the evaluation of the public transportation system is treated. A behavioural approach to travelling patterns is utilized to describe the benefit to the users of public transportation leading to the formulation of a measure for transportation service—from an individual point of view. It is emphasized that the social point of view is taken into account in a total model and finally the methods set up are discussed.     

Social networks,social influence and activity-travel behaviour: a review of models and empirical evidence

ABSTRACT

The study of social networks in activity-travel research has recently gained momentum because social activities and social influence were relatively poorly explained in activity-based models of travel demand. Over the last decade, many scholars have shown interest in identifying personal social networks that constitute an important source of explanation of activity-travel behaviour. This paper seeks to review two research streams: social networks and activity-travel behaviour, and social influence and travel decisions. We classify models, summarise empirical findings and discuss important issues that require further research.     

A study on possibility of commuting trip using private motorized modes in cities around the world: Application of multilevel model

The focus of the current research was to evaluate how the individual’s social characteristics and urban infrastructure impacts the usage of Private Motorized Modes (PMM). Based on individual and urban characteristics a multilevel analysis was conducted on the possibility of commuting trip by private motorized modes on the rush time of 78 cities around the world. Also the selected cities were classified through a principal component analysis, and based on the classification the impact of and urban variables on the possibility of commuting trips made by private motorized modes (PCTP) was verified. Results showed a diverse range of variables related to the usage of PMM, as well as the urban structure and railway lengths being an important variable in travel behavior.     

The effects of social networks on choice set dynamics: Results of numerical simulations using an agent-based approach

Activity-based analysis has slowly shifted gear from the analysis of daily activity patterns to the analysis and modeling of dynamic activity-travel patterns. In this paper, we address one type of dynamics: the formation and adaptation of location choice sets under influence of dyad relationships within social networks. It extends the dynamic model developed in earlier work, which simulates habitual behavior versus exploitation and exploration as a function of discrepancies between dynamic, context-dependent aspiration levels and expected outcomes. Principles of social comparison and knowledge transfer are used in modeling the impact of social networks through information exchange, adaptations of spatial choice sets and formation of common aspiration levels. We demonstrate model properties using numerical simulation with a case study of shopping activities.     

Strategic assessment of capacity consumption in railway networks: Framework and model

In this paper, we develop a new framework for strategic planning purposes to calculate railway infrastructure occupation and capacity consumption in networks, independent of a timetable. Furthermore, a model implementing the framework is presented. In this model different train sequences are generated and assessed to obtain timetable independence. A stochastic simulation of delays is used to obtain the capacity consumption. The model is tested on a case network where four different infrastructure scenarios are considered. Both infrastructure occupation and capacity consumption results are obtained efficiently with little input. The case illustrates the model’s ability to quantify the capacity gain from infrastructure scenario to infrastructure scenario which can be used to increase the number of trains or improve the robustness of the system.     

Representing and estimating interactions between activities in a need-based model of activity generation

Although several activity-based models made the transition to practice in recent years, modeling dynamic activity generation and especially, the mechanisms underlying activity generation are not well incorporated in the current activity-based models. For instance, current models assume that activities are independent, but to the extent that different activities fulfill the same underlying needs and act as partial substitutes, their interactions/dependencies should be taken into account. For example, recreational, leisure, and social activities tend to be partly substitutable since they satisfy a common need of relaxation, and when undertaken together with others, social needs will be satisfied as well. This paper describes the parameter estimation of a need-based activity generation model, which includes the representation of possible interaction effects between activities. A survey was carried out to collect activity data for a typical week and a specific day among a sample of individuals. The diary data contain detailed information on activity history and future planning. Estimation of the model involves a range of shopping, social, leisure, and sports activities, as dependent variables, and socioeconomic, day preference, and interaction variables, as explanatory variables. The results show that several person, household, and dwelling attributes influence activity-episode timing decisions in a longitudinal time frame and, thus, the frequency and day choice of conducting the social, leisure, and sports activities. Furthermore, interactions were found in the sense that several activities influence the need for other activities and some activities affect the utility of conducting another activity on the same day.     

Impact of ICT access on personal activity space and greenhouse gas production: evidence from Quebec City, Canada

This paper presents an approach to investigating the impact of information and communication technologies (ICTs) on travel behaviour and its environmental effects. The paper focuses on the spatial dispersion of out-of-home activities and travel (activity space) and greenhouse gas emissions (GHGs) at the level of the individual. An original method, combining spatial analysis in a geographic information system with advanced regression techniques, is proposed to explore these potentially complex relationships in the case of access to mobile phones and the internet, while taking into account the influence of socio-economics and built environment factors. The proposed methodology is tested using a 7-day activity-based survey in Quebec City in 2003?C2004, a juncture of particular interest because these ICTs had recently crossed the threshold of 40?% (mobile phone) and 60?% (home-based internet) penetration at the time. The study period also largely pre-dates the era of mobile internet access. Among other results, socio-demographic factors were found to significantly affect both ICT access and travel out-comes. The built environment, represented by neighbourhood typologies, also played an important role. However, it was found that after controlling for the self-selection effect, built environment and socio-demographics, those who had a mobile phone available produced 30?% more GHGs during the observed week than those who did not. This higher level of GHG pro-duction was accompanied by a 12?% higher measure of activity dispersion. On the other hand, having internet access at home was associated with lower GHGs (?19?%) and lesser activity dispersion (?25?%). Possibly, mobile phones enable individuals to cover more space and produce more emissions, while the internet provides opportunities to stay at home or avoid motorized travel thus reducing emissions. The estimated effects of having a mobile phone were not only negative but also larger in magnitude from the environmental point of view than those of fixed internet access. However, the results of this study also suggest that access to mobile phones and internet may have substantial and compensatory effects at the individual level that are undetected when using model structures that do not take into account that unobserved factors may influence both ICT choices and travel outcomes.     

A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling

The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.     

Investigating heterogeneity in social influence by social distance in car-sharing decisions under uncertainty: A regret-minimizing hybrid choice model framework based on sequential stated adaptation experiments

The present study is designed to investigate social influence in car-sharing decisions under uncertainty. Social influence indicates that individuals’ decisions are influenced by the choices made by members of their social networks. An individual may experience different degrees of influence depending on social distance, i.e. the strength of the social relationship between individuals. Such heterogeneity in social influence has been largely ignored in the previous travel behavior research. The data used in this study stems from an egocentric social network survey, which measures the strength of the social relationships of each respondent. In addition, a sequential stated adaptation experiment was developed to capture more explicitly the effect of social network choices on the individual decision-making process. Social distance is regarded as a random latent variable. The estimated social distance and social network choices are incorporated into a social influence variable, which is treated as an explanatory variable in the car-sharing decision model. To simultaneously estimate latent social distance and the effects of social influence on the car-sharing decision, we expand the hybrid choice framework to incorporate the latent social distance model into discrete choice analysis. The estimation results show substantial social influence in car-sharing decisions. The magnitude of social influence varies according to the type of relationship, similarity of socio-demographics and the number of social interactions.