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Understanding the psychological factors that influence people’s behavior in using sustainable modes of transportation, e.g., public transport, is crucial for promoting environmentally friendly behavior and mitigating climate change. This study examines the impact of the big-two personality traits (stability and plasticity) on households’ auto transport consumption (ATC) and public transport consumption (PTC), and their mediating role in the relationship between socioeconomic factors (e.g., age, income, education, marital status) and transport consumptions (i.e., ATC and PTC). A triple-hurdle model, including two binary logit models and one structural equation model, is developed using a comprehensive national household survey in Australia. Findings reveal that the stability trait is positively related to ATC (b = 0.253, p < 0.10) and negatively to PTC (b = −0.372, p < 0.001), while the plasticity trait shows a positive association with PTC (b = 0.351, p < 0.001) and is negatively related to ATC (b = −0.296, p < 0.001). The developed analytical framework supports policymakers to identify individuals with stability and plasticity traits, using socioeconomic factors, and to design more-targeted interventions to incentivize specific individuals to use public transport, thus contributing to global efforts toward a sustainable future.

Household energy consumption (HEC) is one of the major contributors to global emissions, making it a critical area for addressing sustainability challenges. The impact of personality traits on human behaviour is significant in shaping HEC patterns, and therefore, have important implications for sustainability policies. This study aims to investigate role of biologically predicted big-two personality traits (i.e., stability and plasticity), a higher order solution to five-factor traits and orthogonal traits, on HEC. To that end, a structural equation model is developed using a national household survey in Australia. The performance of the model is benchmarked against a five-factor (i.e., agreeableness, consciousness, emotional stability, extraversion and openness) personality trait model. The performance of the models is measured using six goodness-of-fit indices, all of which show a superior performance in the big-two traits model. The results indicate that a higher score in stability poses energy-intensive behaviour, while a higher plasticity score poses energy-saving behaviours. The plasticity trait is linked to environmentally friendly behaviours, while the stability trait is associated with environmentally unfavourable behavioural practices. The effects of socioeconomic status on HEC are mediated by stability and plasticity to identify those who are more likely to change their energy consumption habits as the target group for policy intervention. This study can assist policy makers to determine energy-intensive and energy-saving behaviours from the big-two traits, and to develop more effective and targeted sustainability policies that can help in reducing HEC and promote sustainable living in societies.

The emerging technology of autonomous vehicles (AVs) is envisaged to substantially alter transport systems and travel behaviour. However, this impact is conditional on the level and rate of AV market penetration. This study examines consumers’ consideration to adopt AVs, with a particular focus on the impact of informational cues from different communication channels. Data for our analysis is collected through an online survey conducted in December 2019, and our sample comprises 862 residents from Sydney, Australia. The survey asks respondents about their social networks, and their use of different mass and social media platforms. Respondents are subsequently presented a discrete choice experiment where we systematically vary informational cues about AVs from each of these communication channels, and respondents are asked to indicate their willingness to consider AVs under each scenario. This data is used to develop a latent class choice model of AV consideration as a function of the informational cues received from different communication channels. Our model also considers the level of trust that respondents associate with their social contacts when modelling the effect of word-of-mouth from these social contacts. The results reveal eleven segments in the sample that differ from one another in terms of sociodemographic attributes, evaluation of social contact trust, and the relative importance they attach to different communication channels. The findings show social media sentiment has the highest effect on AV consideration for 90 per cent of the sample. Also 3.8 per cent of the sample never consider adopting AVs, regardless of the informational cues.

Residential relocation decision making is a complicated process, and modelling this complex course of actions requires careful scrutinisation of different aspects. The relocation decision comprises several different decisions, including the reason for the relocation, relocation timing, and attributes of the desired residence. Among these decisions needing to be taken, the reason for relocation and its timing are decided earlier than others. Depending on the variant reasons and motivations for relocating, its timing may be accelerated or decelerated. Relocation usually occurs because of a multiplicity of reasons, which necessitates using a multivariate model for relocation decision making that is jointly modelled with the timing decision. A competing accelerated failure model to jointly formulate these decisions. The housing search literature emphasizes on the importance of considering financial risk acceptance level of decision makers in residential relocation decision models. Therefore, a binary logit model is used to model whether the decision maker is financially risk averse or not. This paper used longitudinal data collected in Australia from the Household, Income, and Labour Dynamics in Australia Survey. Further, the impact of group decision making on residential relocation is captured in this paper through the information provided in Household, Income, and Labour Dynamics in Australia Survey regarding the manner in which decisions are made within households.

Unmanned aerial vehicles (UAV) have made life easier in many ways, and their applications in civil practice are increasing rapidly. However, this benefit is not entirely risk-free, as unwanted accidents and incidents can cause serious harm and interrupt other aerial activities. In this paper, we investigate a dataset of UAV accidents and incidents in Australia and put up some precautionary exercises to reduce the risk of future events. To that end, univariate and bivariate distributions of past events are analysed, and the exploratory factor analysis technique is used to identify frequent accident and incident patterns. The findings show that equipment issues or/and lack of coordination between aerial activities are two of the accidents and incidents categories; therefore, necessitating regular safety inspections for UAVs and establishing an integrated monitoring system for aerial activities are expected to reduce the risk of accidents and incidents.