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The COVID-19 pandemic has caused severe health and economic impacts globally. Strategies to safely reopen economies, travel and trade are a high priority. Until a reliable vaccine is available, non-pharmaceutical techniques are the only available means of disease control. In this paper, we aim to evaluate the extent to which social distancing (SD) and facemask (FM) use can mitigate the transmission of COVID-19 when restrictions are lifted. We used a microsimulation activity-based model for Sydney Greater Metropolitan Area, to evaluate the power of SD and FM in controlling the pandemic under numerous scenarios. The hypothetical scenarios are designed to picture feasible futures under different assumptions. Assuming that the isolation of infected cases and the quarantining of close contacts are in place, different numerical tests are conducted and a full factorial two-way MANOVA test is used to evaluate the effectiveness of the FM and SD control strategies. The main and interactive effects of the containment strategies are evaluated by the total number of infections, percentage of infections reduction, the time it takes to get the pandemic under control, and the intensity of active cases.

As a critical factor in ensuring the growth of the electronic auction (e-auction) domain, the privacy and security of the participants (sellers and buyers) must always be guaranteed. Traditionally, auction data, including participant details, are stored in a third party (auctioneer) database. This leads to a high risk of a single point of failure in terms of privacy and security. Toward this end, blockchain technology has emerged as a promising decentralized communication paradigm to address such risks. This paper presents a blockchain-based auction framework as a decentralized e-auctioning framework for location-aware services. In particular, the framework consists of three components: pre-auctioning, main auctioning, and post-auctioning processes with four algorithms. Our primary focus is on location-aware services, such as storage space rental, apartment rental, transport hire, and parking space rental. The trading volumes are expected to be high; hence, simplifying the design is a crucial requirement. In addition to the benefits of eliminating the centralized entity (the auctioneer), fees are redistributed among participants as rewards. Further, we incorporate a service quality monitoring method that ranks the services provided by participants. This ranking allows participants to determine the rank of other participants with whom they wish to trade. We have conducted several experiments to evaluate the proposed framework’s cost feasibility and to ensure the ease of the business flow.

We study the interrelation of normative beliefs, which are an individual’s perception of the beliefs of others regarding a specific behaviour, and modality styles, which represent the part of an individual’s lifestyle that is characterised by the use of a certain set of modes. In recent years, travel behaviour research has increasingly sought to understand the effect of social influence on mobility-related behaviour. One stream of literature has adopted representations rooted in social psychology to explain behaviour as a function of latent psycho-social constructs including normative beliefs. Another stream of literature has employed a lifestyle-oriented approach to identify segments or modality styles within a population that differ in terms of their orientation towards different modes of transport. Our study proposes an integrated conceptual framework that combines elements of these two streams of literature. Modality styles are hypothesised to be a function of normative beliefs towards the use of different modes of transport; mobility-related attitudes and behaviours are in turn hypothesised to be functions of modality styles. The conceptual model is operationalised using a latent class and latent variable model and empirically validated using data collected through an Australian consumer panel. We demonstrate how this integrated model framework may be used to understand the relationship between normative beliefs, modality styles and travel behaviour. In addition, we show how the model can be applied to predict how extant modality styles and patterns of travel behaviour may change over time in response to concurrent shifts in normative beliefs.

Some agent-based models have been developed to estimate the spread progression of coronavirus disease 2019 (COVID-19) and to evaluate strategies aimed to control the outbreak of the infectious disease. Nonetheless, COVID-19 parameter estimation methods are limited to observational epidemiologic studies which are essentially aggregated models. We propose a mathematical structure to determine parameters of agent-based models accounting for the mutual effects of parameters. We then use the agent-based model to assess the extent to which different control strategies can intervene the transmission of COVID-19. Easing social distancing restrictions, opening businesses, speed of enforcing control strategies, quarantining family members of isolated cases on the disease progression and encouraging the use of facemask are the strategies assessed in this study. We estimate the social distancing compliance level in Sydney greater metropolitan area and then elaborate the consequences of moderating the compliance level in the disease suppression. We also show that social distancing and facemask usage are complementary and discuss their interactive effects in detail.

Recent research has contrasted the travel patterns of young adults of Generation Y (or, synonymously, the Millennial Generation) with the travel patterns of earlier generations of young adults such as those belonging to Generation X. Young adults of Generation Y are found to drive less and in some contexts are found to exhibit more multimodal travel patterns and to use public transit more often. Potential causes for these observed shifts in transport mode use have also been theorised: One view is that period effects in the form of contemporaneous changes in socio-cultural, socio-economic and socio-technical factors are responsible for the observed shifts in transport mode use; another view is that members of Generation Y have inherently different preferences and values due to formative socio-cultural, socio-economic and historical experiences. Motivated by this yet-to-be-resolved dialectic, this paper uses a hierarchical Bayesian multivariate Poisson log-normal model to examine intergenerational differences in transport mode use among young adults. The model is applied to 23 waves of the German Mobility Panel and captures between-cohort and between-period variation of parameters of interest. The trained model informs a counterfactual prediction exercise aiming to decompose intergenerational differences in transport mode use into demography-, cohort-, and period-specific effects. Our findings suggest that all three sets of effects account for intergenerational differences in transport mode use, while the absolute and relative importance of each set of effects vary across transport modes. For the period from 1998 to 2016, two thirds of the decline in car use can be ascribed to period effects; nearly all of the increase in public transit use and 42% of the increase in bicycling can be ascribed to cohort effects.

In the transport policy development process, four-step models are commonly used to estimate transport costs and flows based on representations of travel demands and networks. However, these models typically do not account for broader changes in the economy, which may significantly shift travel patterns in the case of larger transport projects. LUTI models are often applied to simulate changes in land-use patterns, and regional production function models have been used to estimate changes in production, but these methods rely on fixed economic parameters that may not capture the structural economic changes induced by large transport projects. In a separate line of development, computable general equilibrium (CGE) models, which simulate entire economies, have been increasingly applied to estimate the magnitude and distribution of economic impacts from transport improvements both spatially and through markets, including GDP and welfare. Some CGE models are linked with transport network models, but none incorporate detailed networks or generate a complete set of travel demands. This paper presents an integrated CGE and transport model that generates household and freight trips and simulates a detailed road network for different time periods, such that the transport submodel can be calibrated and run as a conventional transport model. The model provides a tool for the rapid strategic assessment of transport projects and policies when economic responses cannot be assumed to remain static. In the model, the CGE submodel simulates the behaviour of households and firms interacting in markets, where their behaviour takes trip costs into account. The model then generates trips as a derived demand from agent activities and assigns them to the road network according to user equilibrium, before feeding back trip costs to the CGE submodel. The model is then tested by simulating the WestConnex motorway project under construction in Sydney, with results showing significant increases in welfare for regions close to the improvements. Further development of the model is required to incorporate land-use and mode choice.

Travel demand can be viewed as a weighted and directed graph where nodes are the origins and destinations and links represent the trips between nodes. This paper presents a network-theoretic methodology to evaluate and validate travel demand models. We apply the proposed method on three disaggregate travel demand models from Melbourne, Australia. Statistical properties of the modeled networks are compared against the observed networks over time. The new approach reveals the network structure and connectivity of the modeled trips that are not usually captured by traditional evaluation and validation methods. Results demonstrate the complexity involved in the development, evaluation, and validation of travel demand models, which calls for advanced evaluation techniques reflecting a wide range of attributes of the observed and modeled data, travelers, mobility patterns, and complex network characteristics.

Traditionally, transport planning model systems are estimated and calibrated in an unstructured way, which does not allow for interactions among included parameters to be considered. Furthermore, the computational burden of model systems plays a key role in choosing a calibration approach, and usually forces modellers to calibrate demand-side and network models separately. Also, trial-and-error methods and expert opinion are currently the backbones of transport model calibration, which leaves room for error in the calibrated parameters. This paper addresses these challenges and suggests a structured approach for determining optimal calibrated transport model parameters. This approach involves joint estimation and calibration of demand and network models, with a major focus on avoiding any manipulation of the OD matrix. The approach can be applied to static or dynamic traffic assignments. The approach is applied by calibrating GTAModel—an example of a large-scale agent-based model system from Toronto, Canada.

Purpose Enhancing sustainability of the supply process of construction materials is challenging and requires accounting for a variety of environmental and social impacts on top of the traditional, mostly economic, impacts associated with a particular decision involved in the management of the supply chain. The economic, environmental, and social impacts associated with various components of a typical supply chain are highly sensitive to project and market specific conditions. The purpose of this paper is to provide decision makers with a methodology to account for the systematic trade-offs between economic, environmental, and social impacts of supply decisions. Design/methodology/approach This paper proposes a novel framework for sustainability assessment of construction material supply chain decisions by taking advantage of the information made available by customized building information models (BIM) and a number of different databases required for assessment of life cycle impacts. Findings The framework addresses the hierarchy of decisions in the material supply process, which consists of four levels including material type, source of supply, supply chain structure, and mode of transport. The application is illustrated using a case study. Practical implications The proposed framework provides users with a decision-making method to select the most sustainable material alternative available for a building component and, thus, may be of great value to different parties involved in design and construction of a building. The multi-dimensional approach in selection process based on various economic, environmental, and social indicators as well as the life cycle perspective implemented through the proposed methodology advocates the life cycle thinking and the triple bottom line approach in sustainability. The familiarity of the new generation of engineers, architects, and contractors with this approach and its applications is essential to achieve sustainability in construction. Originality/value A decision-making model for supply of materials is proposed by integrating the BIM-enabled life cycle assessment into supply chain and project constraints management. The integration is achieved through addition of a series of attributes to typical BIM. The framework is supplemented by a multi-attribute decision-making module based on the technique for order preference by similarity to ideal solution to account for the trade-offs between different economic and environmental impacts associated with the supply decisions.

Calibration of a transport planning model system is a complex process. While trial-and-error methods and modelling expertise are still the backbone of calibration of transport models, analytical approaches automating the calibration process can improve the accuracy of the models. Introducing a model to guide modellers in the calibration process of large-scale transport planning model systems is the core of this study, where a systematic model for choosing the most appropriate models and parameters is discussed. The effectiveness of the proposed model is investigated by comparing three scenarios which are built on the Travel/Activity Scheduler for Household Agents model as a large-scale agent-based model system.