Modeling travel behavior and sense of place using a structural equation model

Deutsch, Kathleen; Yoon, Seo Youn; Goulias, Konstadinos G.

doi:10.1016/j.jtrangeo.2012.12.001

Cited by 51 publications

(33 citation statements)

References 26 publications

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“…Another study on car use, but looking at a wider set of identities (Deutsch et al, 2013) investigated the role played by sense of place, two malls in Santa Barbara, California, USA on travel behaviour by using structural equations modelling (SEM) on survey data (n=719) collected in Santa Barbara (CA, USA). Place identity was one of these constructs and was measured by the following responses: 'reflects the type of person I am', 'says little about me', 'makes me feel I can be myself', and 'good reflection of my identity', which were combined into one factor score.…”

Section: Mann and Abrahammentioning

confidence: 99%

Identity and travel behaviour: A cross-sectional study on commute mode choice and intention to change

Heinen

2016

Transportation Research Part F: Traffic Psychology and Behaviou

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Introduction: Social and self-identities have been conceptualised to prevent travel behaviour change, as threats to one's identity may cause resistance to change. This study focuses on the role of social, transport, place, and self-identities on commute mode choice and intention to change mode choice.Method: Data were collected in June 2015 in Utrecht, the Netherlands. Invitations to participate were distributed by mail using data from the municipality, resulting in 1,062 adult participants.The outcome measures were the transport mode shares based on a 14-day travel-to-and-from-work record of trips (i) involving any car use, (ii) involving any bicycling, (iii) involving any walking, and (iv) involving any public transport use. The second series of outcome measures concerned the willingness to change the amount of car use, bicycle use and walking, determined by the question 'to what extent do you intend to change the use of … ?'. Identity was measured on a seven-point disagree/agree scale for 17 items by asking to what extent the respondent 'sees him/herself as … '. Separate multinomial regression models were estimated stepwise adjusting for socioeconomic and transport characteristics.Results: Multiple identity items were associated with the use of all commute modes. In the maximally adjusted models, identities associated with the respective modes remained significant. For example, whether someone identified themselves with being a cyclist corresponded with higher likelihood of cycling occasionally (relative risk ratio (RRR): 1.84; 95% confidence interval (CI):1.47-2.30), or always to work (RRR: 2.86; 95%CI: 2.16-3.79). In addition, we found that a family-oriented identity was negatively associated with occasional commuting by car, and a 'sporty' identity was negatively associated with always cycling to work.Transport identities were also associated with stated intentions to change as were several social, place, and self-identities. Identifying with being a car driver decreased the likelihood of intending to reduce car use, but it increased the likelihood of intending to increase car use, as did identifying with being careeroriented. Individuals that identified with being a cyclist were less likely to have an intention to reduce bicycle use, whereas countryside-lovers had greater intentions of increasing cycling. Individuals that identified themselves as pedestrians had a lower intention of decreasing their walking levels, and a higher intention of increasing them, as did those who identified themselves as being family-oriented. Discussion:The results confirm limited previous findings that identifying with users of a transport mode corresponds with its use. Nevertheless, questions around causality remain. The intention to change mode choice was associated with several identities, including transport-related identities, place-related identities, social/family-related identities, and self-identities. Future research should focus on the associations between identity and actual behaviour change to further our...

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Section: Mann and Abrahammentioning

confidence: 99%

Identity and travel behaviour: A cross-sectional study on commute mode choice and intention to change

Heinen

2016

Transportation Research Part F: Traffic Psychology and Behaviou

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“…and their corresponding measured variables and categorized the transport market based on these latent variables. Deutsch et al [31] analyzed the relationship between travel behavior of residents and their perception on surrounding environment, using structural equation model based on 719 sample data points from a survey in Santa Barbara and California, USA. Nurlaela and Curtis [32] established a mode choice model in downtown, which has not been developed and analyzed empirically before.…”

Section: Introductionmentioning

confidence: 99%

Mode Choice Model for Public Transport with Categorized Latent Variables

Chen

2017

Mathematical Problems in Engineering

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Mode choice model for public transport, which integrates structural equation model (SEM) and discrete choice model (DCM) with categorized latent variables, was presented in this paper. Apart from identifying those important latent variables that affect mode choice for public transport, the objective of this study was also to develop an improved disaggregative model that better explains travel behavior of those decision-makers in choosing public transport. After extensive observations, selective latent variable sets which consist of latent variable components were chosen together with explicit variables in formulating the utility functions. Data collected in Chengdu city, China, were used to calibrate and validate the model. Results showed that the impact of fare on mode choice of public transport escalated in the SEM-DCM integrated model compared with the traditional logit model. The goodness of fit for the integrated model with latent variable sets is 0.201 higher than that of the traditional logit model, which proves that latent variables have an obvious impact on mode choice behavior, and the SEM-DCM integrated model has higher accuracy and stronger explanatory ability. The results are especially helpful for public transport operators to achieve higher mode share split by improving the service quality of public transport in terms of providing more convenience and better service environment for public transport users.

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“…Another strength of SEM is that nonparametric SEM permits the estimation of the three effects without making any commitment to the form of the equations or to the distribution of the error terms. This has made the SEM method particularly useful for modelling social interactions in many disciplines (see examples in Ramkissoon et al, 2013 andDeutsch et al, 2013).…”

Section: Methods and Case Studiesmentioning

confidence: 99%