Discrete Choice with Social and Spatial Network Interdependencies: An Empirical Example Using Mixed Generalized Extreme Value Models with Field and Panel Effects

Dugundji, Elenna R.; Walker, Joan L.

doi:10.3141/1921-09

Cited by 130 publications

(78 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, Paez and Scott (2007) and Wilton et al (2011) found that the decision of an individual to telecommute is heavily influenced by others deciding to telecommute. Social interaction effects have also been recognized in other settings, such as modal choice decisions (Dugundji and Walker 1921;Goetzke 2008), leisure travel (Axhausen 2005), participation in social activities (Carrasco and Miller 2006), and even illicit parking behavior (Fukuda and Morichi 2007). A guiding philosophy in these surveys is that the incorporation of social interaction variables leads to a more behaviorally realistic representation of the choice process, and consequently to a better explanatory power.…”

Section: Introductionmentioning

confidence: 99%

Incorporating social interaction into hybrid choice models

2014

View full text Add to dashboard Cite

The aim of this paper is to develop a methodological framework for the incorporation of social interaction effects into choice models. The developed method provides insights for modeling the effect of social interaction on the formation of psychological factors (latent variables) and on the decision-making process. The assumption is based on the fact that the way the decision maker anticipates and processes the information regarding the behavior and the choices exhibited in her/his social environment, affects her/ his attitudes and perceptions, which in turn affect her/his choices. The proposed method integrates choice models with decision makers' psychological factors and latent social interaction. The model structure is simultaneously estimated providing an improvement over sequential methods as it provides consistent and efficient estimates of the parameters. The methodology is tested within the context of a household aiming to identify the social interaction effects between teenagers and their parents regarding walking-loving behavior and then the effect of this on mode to school choice behavior. The sample consists of 9,714 participants aged from 12 to 18 years old, representing 21 % of the adolescent population of Cyprus. The findings from the case study indicate that if the teenagers anticipate that their parents are walking lovers, then this increases the probability of teenagers to be walking-lovers too and in turn to choose walking to school. Generally, the findings from the application result in: (a) improvements in the explanatory power of choice models, Transportation (2014) 41:1263-1285 DOI 10.1007 (b) latent variables that are statistically significant, and (c) a real-world behavioral representation that includes the social interaction effect.

show abstract

Section: Introductionmentioning

confidence: 99%

Incorporating social interaction into hybrid choice models

2014

View full text Add to dashboard Cite

show abstract

“…Araghi, Kroesen, Molin, and Van Wee (2014) underline this, as they found that people's willingness to compensate for their flight-related carbon emissions depends on the collective participation rate. Dugundji and Walker (2005) proposed and tested several methods in order to capture these social interdependencies within discrete choice modelling, which seem to be superior to the traditional approach.…”

Section: Perspective On Individual Route Choice Behaviourmentioning

confidence: 99%

From user equilibrium to system optimum: a literature review on the role of travel information, bounded rationality and non-selfish behaviour at the network and individual levels

et al. 2016

View full text Add to dashboard Cite

Travel information continues to receive significant attention in the field of travel behaviour research, as it is expected to help reduce congestion by directing the network state from a user equilibrium towards a more efficient system optimum. This literature review contributes to the existing literature in at least two ways. First, it considers both the individual perspective and the network perspective when assessing the potential effects of travel information, in contrast to earlier studies. Secondly, it highlights the role of bounded rationality as well as that of non-selfish behaviour in route choice and in response to information, complementing earlier reviews that mostly focused on bounded rationality only. It is concluded that information strategies should be tailor-made to an individual's level of rationality as well as level of selfishness in order to approach system-optimal conditions on the network level. Moreover, initial ideas and future research directions are provided for assessing the potential of travel information in order to improve network efficiency of existing road networks. ARTICLE HISTORY

show abstract

“…From a travel behavior perspective, the role of social networks has received increasing attention, tackling different questions, such as the social influence in travel related decisions (12,13), social activity-travel generation (14,15), and the spatial distribution of social networks (4,16,17). However, little is known regarding the influence of the social context on activity-scheduling decisions, considering not only the intrinsic negotiation process that occurs in activities such as social, but also the importance of "with whom" in key scheduling decisions.…”

Section: The Social Dimension Of Activity Scheduling Behaviourmentioning

confidence: 99%

Social Context of Activity Scheduling

Habib

Carrasco

Miller

2008

Transportation Research Record

View full text Add to dashboard Cite

Activity-based approaches to travel demand modeling are increasingly moving from theoretical to operational models. Agent-based microsimulation models are a promising approach as they explicitly conceive travel as an emergent phenomenon from people's activity characteristics and, more explicitly, from their activity-scheduling processes. Activity-scheduling processes are influenced by individuals’ characteristics as well as by the people with whom they interact. Thus, the activity-scheduling process has an intrinsic social context. With social activities used as a case study, the objective of this paper is to investigate empirically the relationship between social context (measured by with whom respondents interacted) and two key aspects of activity scheduling: start time and duration. Econometric models of the combined decisions of with whom to participate and when to start or how much time to spend are estimated to investigate the correlations between “with whom” and start time and duration decisions. Data collected by a 7-day activity diary survey were used for model development. Findings suggest that social context has a relevant role in activity-scheduling processes. For example, with whom people socialize influences social activity-scheduling processes more than do travel time or distances to social travel. In addition to theoretical understanding of the questions investigated here, the models serve as an empirical support for agent-based microsimulation models that could incorporate the role of social networks in activity-scheduling attributes.

show abstract

Discrete Choice with Social and Spatial Network Interdependencies: An Empirical Example Using Mixed Generalized Extreme Value Models with Field and Panel Effects

Cited by 130 publications

References 26 publications

Incorporating social interaction into hybrid choice models

Incorporating social interaction into hybrid choice models

From user equilibrium to system optimum: a literature review on the role of travel information, bounded rationality and non-selfish behaviour at the network and individual levels

Social Context of Activity Scheduling

Contact Info

Product

Resources

About