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The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents' perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

To accurately predict the regional spread of Covid-19 infection, this study proposes a novel hybrid model which combines a Long short-term memory (LSTM) artificial recurrent neural network with dynamic behavioral models. Several factors and control strategies affect the virus spread, and the uncertainty arisen from confounding variables underlying the spread of the Covid-19 infection is substantial. The proposed model considers the effect of multiple factors to enhance the accuracy in predicting the number of cases and deaths across the top ten most-affected countries and Australia. The results show that the proposed model closely replicates test data. It not only provides accurate predictions but also estimates the daily behavior of the system under uncertainty. The hybrid model outperforms the LSTM model accounting for limited available data. The parameters of the hybrid models were optimized using a genetic algorithm for each country to improve the prediction power while considering regional properties. Since the proposed model can accurately predict Covid-19 spread under consideration of containment policies, is capable of being used for policy assessment, planning and decision-making., Comment: As requested by the dear moderator, to assess the statistical significance of the reduction in RMSE in hybrid models compared to LSTM, each module was evaluated 500 times after hype-parameter tuning, and the corresponding RMSE distribution was used to estimate 95% confidence interval (CI) and t-test p-values comparing significant differences between different stages

A significant part of the United Nations World Heritage Sites (WHSs) is located in developing countries. These sites attract an increasing number of tourist and income to these countries. Unfortunately, many of these WHSs are in a poor condition due to climatic and environmental impacts; war and tourism pressure, requiring the urgent need for restoration and preservation (Tuan & Navrud, 2007). In this study, we characterise residents from Shiraz city (visitors and non-visitors) willingness to invest in the management of the heritage sites through models for the preservation of heritage and development of tourism as a local resource. The research looks at different categories of heritage sites within Shiraz city, Iran. The measurement instrument is a stated preference referendum task administered state-wide to a sample of 489 respondents, with the payment mechanism defined as a purpose-specific incremental levy of a fixed amount over a set period of years. A Latent Class Binary Logit model, using parametric constraints is used innovatively to deal with any strategic voting such as Yea-sayers and Nay-sayers, as well as revealing the latent heterogeneity among sample members. Results indicate that almost 14% of the sampled population is unwilling to be levied any amount (Nay-sayers) to preserve any heritage sites. Not recognizing the presence of nay-sayers in the data or recognizing them but eliminating them from the estimation will result in biased Willingness to Pay (WTP) results and, consequently, biased policy propositions by authorities. Moreover, it is found that the type of heritage site is a driver of WTP. The results from this study provide insights into the WTP of heritage site visitors and non-visitors with respect to avoiding the impacts of future erosion and destruction and contributing to heritage management and maintenance policies.

This study explores how individuals evaluate different shared parking features, as an emerging concept. In this paper, we develop a latent class behavioural model for a sample of 1,008 residents in New South Wales, Australia to explore different segments of taste and preference for shared parking. The outcome of this study extends our understanding about the emerging concept of shared parking by quantifying monetary value, in terms of willingness-to-pay, of different features of this new emerging phenomenon. This study contributes to the literature by providing firsthand insights about how demand for shared parking is formed and how different policy implication can be made accordingly. The results indicate that pricing persists to be the sole most effective parking policy, however non-price-related attributes can help decision makers to make more informative decision when it comes to different pricing schemes. The study also highlights that shared parking can perform a significant role in accommodating the surplus arising from the excessive parking demand and inadequate supply up to almost 40% of demand. The outcome of this study is anticipated to inform and equip city authorities with advanced knowledge about such an emerging phenomenon which can help developing regulations before the concept is out of control. Information from this study can also guide the private sector and the wider community to identify and develop opportunities related to shared parking Refereed/Peer-reviewed

Electromobility options, especially electric vehicles (EVs) are more environmentally friendly, efficient and economical than conventional vehicles (CVs). There is a low market penetration of EVs and associated vehicle-to-grid (V2G) technologies in Australia compared to some European countries like Norway. Thus, transport planners and electrical grid policymakers need to investigate commuters’ perceptions and behaviours related to EVs and V2G and the effects of incentives, as commuters will eventually switch from CVs to EVs. Our study investigated commuters’ perceptions of and attitudes to V2G technology adoption at workplaces. The main findings are: 1) Commuters are willing to reduce their upfront costs when adopting EVs and associated V2G technologies. 2) Commuters want to reduce their daily transportation costs by using V2G technologies and care less about transport sustainability. 3) The greatest concerns of employers are that the cost of the V2G technology which could be too high and that its internal rate of return is unclear. Refereed/Peer-reviewed

The restrictive measures implemented in response to the COVID-19 pandemic have triggered sudden massive changes to travel behaviors of people all around the world. This study examines the individual mobility patterns for all transport modes (walk, bicycle, motorcycle, car driven alone, car driven in company, bus, subway, tram, train, airplane) before and during the restrictions adopted in ten countries on six continents: Australia, Brazil, China, Ghana, India, Iran, Italy, Norway, South Africa and the United States. This cross-country study also aims at understanding the predictors of protective behaviors related to the transport sector and COVID-19. Findings hinge upon an online survey conducted in May 2020 (N = 9,394). The empirical results quantify tremendous disruptions for both commuting and non-commuting travels, highlighting substantial reductions in the frequency of all types of trips and use of all modes. In terms of potential virus spread, airplanes and buses are perceived to be the riskiest transport modes, while avoidance of public transport is consistently found across the countries. According to the Protection Motivation Theory, the study sheds new light on the fact that two indicators, namely income inequality, expressed as Gini index, and the reported number of deaths due to COVID-19 per 100,000 inhabitants, aggravate respondents’ perceptions. This research indicates that socio-economic inequality and morbidity are not only related to actual health risks, as well documented in the relevant literature, but also to the perceived risks. These findings document the global impact of the COVID-19 crisis as well as provide guidance for transportation practitioners in developing future strategies.

This study investigates the potential public demand for investing in electric vehicles infrastructure using a stated preference method. Availability of electric vehicle fast charging stations can improve consumer penetration and acceptance level of purchasing electric vehicles. The outlook of passenger transport is expected to shift to using electricity as the main fuel source which requires a significant amount of energy through the electricity grid and provision of appropriate public charging infrastructure to help support commuter usage. To quantify the preference of users towards an energy related policy, a discrete choice experiment using a virtual payment system was designed to increment an annual levy amount for specific purpose over a set of years. The results from a sample of 1,180 households in New South Wales Australia, revealed that depending on the policy setting, 74.2% of the population would be willing to pay some amount of levy. Moreover, we found that on average NSW households are willing to pay $31.9 as annual levy to help raise the fund to develop and install fast charging station state-wide. Refereed/Peer-reviewed