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Civil engineering, specifically transport engineering, is a continually evolving profession. Recent developments in technology have resulted in more automated and visual problem-solving techniques, involving the use of computer programs and simulation, as practitioners and researchers move away from traditional pen and paper approaches. Accordingly, teaching undergraduate university students the basic principles of transport planning, traffic engineering, and highway design effectively is fundamental to the sustainability of the profession. It also is a challenging and dynamic task for educators because enhanced accessibility to technology has changed the way students understand and learn the material being delivered at tertiary education institutions. This paper presents the development of, and feedback from, the implementation of a series of blended learning initiatives (interactive polling exercises, online quizzes, supplementary learning videos, and authentic real-world design project) within an introductory large class-size transport planning and geometric design subject. The process of developing the blended learning initiatives was documented to clearly highlight the benefits and challenges in the transformation process. In addition, qualitative student feedback and student performance between 2016 and 2018 were reviewed to understand the impacts of the transformation. The initiatives were well received; students valued self-paced learning and the exposure to real-world design exercises. From an educator's perspective, blending made it feasible to deliver complex content whilst offering tailored learning opportunities across the cohort. Although further comprehensive experiments and statistically oriented research are necessary, this case study adds to a growing body of literature that indicates the potential value of blended learning initiatives, especially in the context of large class-size university subjects.

Mobility-as-a-service (MaaS) has promised to integrate multiple service providers to deliver a multimodal mobility service to commuters through a seamless digital platform. An efficient digital network architecture is imperative to achieve secure and reliable interactions among the involved parties. From the service provider's perspective, by defining the mobility ownership for customers, MaaScan bring a personalized transport system to complement or even fully replace the concept of private vehicle ownership. This paper paves the initial steps to develop a distributed architecture to realize a MaaS digital network using blockchain technology. Therefore, a comprehensive blockchain-enabled MaaS platform with related industries is illustrated in this paper. To do so, first, we define crypto-tickets as means of ownership of the service and devise a straightforward smart contract to be executed in the blockchain network, which allows the customers to exchange the service ownership in a privacy-preserved manner. Then, we evaluate our blockchain-based ownership scheme's efficacy against the traditional membership plans using a simplified simulation instance in MATLAB. Finally, we discuss the blockchain potentials in personalizing the ownership of the service, congestion management, and data trading between various stakeholder of a comprehensive MaaS platform. [ABSTRACT FROM AUTHOR]

This paper presents the joint analysis of departure time choice for non-work activities and latent preferences toward the active and non-active mode. Utilizing the concept of smart growth design of neighborhoods, scheduling the travel activities considering active transport through smart phone applications is one of the efficient ways to promote active mode usage. The hypothesis of the current study states that departure time decision-making for non-work activities is influenced by disposition toward active modes such as cycling in addition to the socio-economic factors and dependency on mandatory activities. Information from this model is expected to provide the empirical support to the policies regarding the promotion of active mode usage. Departure time choice behavior with disposition toward non-active modes, such as car is also examined. Integrated choice latent variable modeling is used as methodological approach. The results of these two models are then compared and discussed in detail. Results indicate that both the models have contrary results to each other with respect to latent preferences for respective modes. Some socio-economic factors came to be more significant than latent preferences. Individuals who prefer active modes such as bicycle have more probability to choose morning departures to evening ones, while the opposite behavior is true for the ones who have latent preferences toward non-active modes. It is also found that car and bicycle mode preferences possess opposing elasticities. With the increase in latent preferences for active travel mode, probability for morning departure increases while opposite is true for latent preference for car. [ABSTRACT FROM AUTHOR]

Many tourism travel decisions are interrelated. From modelling viewpoint incorporating these interrelations is important for understanding the consequences of exogenous shocks, as well as the intended and unintended consequences of policy. This paper addresses two tourist choices in the temporal dimension that were previously only handled as if they were mutually exclusive. The paper adopts a discrete-continuous model framework to simulate tourists' behaviours in selecting travel seasons (choice model) and associated trip duration (accelerated failure time model) and estimates the correlation between these two tourism decisions conditional on the covariates. The empirical analysis is based on Australian domestic tourism data and reveal the factors influencing both decisions as well as those that influence only one or the other. The model results, which is tested with data from select years between 1999-2018, including years of significant exogenous shocks, show mixed evidence of stability and changes in the parameters. Building on this evidence, the paper concludes with underlying temporal choice behaviours of tourists that may be of relevance during- and post-COVID19 environment. [ABSTRACT FROM AUTHOR]

Unlike the past studies targeting overall mobility patterns, we examined a wide range of probability density functions among which Weibull found to better explain the human mobility rather than the widely tested power-law distribution. We further examined a variety of factors affecting human mobility such as people's trip attributes and socio-demographics traits. Moreover, the models show that social media active people travel mainly short distances; while people traveling to and from areas with higher economic and social advantages are more likely to explore a wider area within a city. [ABSTRACT FROM AUTHOR]

Equity in public transport services is becoming a high priority of transport authorities. Although studies regarding accessibility disparity give some insight into transportation equity, to date there has been no profound application of relative accessibility in equity assessment that combines with public transport need related to social status. In addition, accurate estimation of travel times for building relative accessibility still presents a challenge. Therefore, this research aims to investigate and map spatial distributions of public transport-related equity by examining relative accessibility to CBD, public transport needs, and population density. Google Maps Application Programming Interface is used for a realistic estimation of travel times. Results indicate that most socially disadvantaged groups experience high service levels of public transport to CBD, Sydney. However, there are still 16 regions that require further improvement of public transport services. These results can support urban planners in promoting transport equity among different socio-economic groups. [ABSTRACT FROM AUTHOR]

This article proposes a bi-criteria formulation to find the optimal location of light rapid transit stations in a network where demand is elastic and budget is constrained. Our model is composed of two competing objective functions seeking to maximize the total ridership and minimize the total budget allocated. In this research, demand is formulated using the random utility maximization method with variables including access time and travel time. The transit station location problem of this study is formulated using mixed integer programming and we propose a heuristic solution algorithm to solve large-scale instances which is inspired by the problem context. The elastic demand is integrated with the optimization problem in an innovative way which facilitates the solution process. The performance of our model is evaluated on two test problems and we carry out its implementation on a real-world instance. Due to the special shape of the Pareto front function, significant practical policy implications, in particular budget allocation, are discussed to emphasize the fact that the trade-off between cost and benefit may result in large investments with little outcomes and vice versa. [ABSTRACT FROM AUTHOR]