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• Developed an approach to approximate the demand of a carsharing station. • Improved the estimation accuracy of the model by including social media data and spatio-temporal correlated data. • Applied a spatio-temporal autocorrelation model. • Developed a correlation analysis to account for the interactions among variables. As an emerging urban mobility service, carsharing has become increasingly popular worldwide. To understand customers' needs and optimize the design of service networks, the usage of carsharing vehicles whose trips are recorded by the operators has been applied in research estimating carsharing demand. However, as a form of spatio-temporal correlated data, the underlying spatio-temporal information included in such carsharing records has not been investigated in existing models of carsharing demand. Meanwhile, due to the supply limitation of carsharing stations, some demand cannot be fulfilled and thus remains unrecorded in the operational data. Unrealized demand may lead to underestimation of carsharing demand and therefore an incorrect vehicle deployment strategy by the service providers. In view of these issues, this paper develops an innovative approach to estimating the actual demand at a carsharing station with operational data from GoGet , the largest carsharing company in Australia. The accuracy of the estimation is improved by adding spatio-temporal correlated variables as well as variables from emerging data sources such as social media. To explore the latent space-and-time correlated information, spatio-temporal autoregressive and moving-average models have been applied. Based on the results of the analysis, the paper also provides recommendations related to the operation policies of the service providers. [ABSTRACT FROM AUTHOR]

• Literature suggests that value of time (VOT) may decrease due to advent of AVs. • Reduction in VOT may impact households' residential location choices. • This expectation was tested empirically using stated choice methods. • Empirical results do not suggest drastic changes in VOT due to AVs. The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a consequence, the generalised cost of car travel may decrease, and car users may become less sensitive to travel time. By facilitating private motorised mobility, AVs may eventually impact land use and households' residential location choices. This paper seeks to advance the understanding of the potential impacts of AVs on travel behaviour and land use by investigating stated preferences for combinations of residential locations and travel options for the commute in the context of autonomous automobile travel. Our analysis draws from a stated preference survey, which was completed by 512 commuters from the Sydney metropolitan area in Australia and provides insights into travel time valuations in a long-term decision-making context. For the analysis of the stated choice data, mixed logit models are estimated. Based on the empirical results, no changes in the valuation of travel time due to the advent of AVs should be expected. However, given the hypothetical nature of the stated preference survey, the results may be affected by methodological limitations. [ABSTRACT FROM AUTHOR]

Highlights • We integrate the NFD with a feedback-control-based toll optimization framework. • Various tolling regimes are studied including a new joint distance and delay toll. • We apply the proposed framework on a large-scale DTA model of Melbourne, Australia. • Results show that the joint tolls can reduce the uneven distribution of congestion. Abstract Given the efficiency and equity concerns of a cordon toll, this paper proposes a few alternative distance-dependent area-based pricing models for a large-scale dynamic traffic network. We use the Network Fundamental Diagram (NFD) to monitor the network traffic state over time and consider different trip lengths in the toll calculation. The first model is a distance toll that is linearly related to the distance traveled within the cordon. The second model is an improved joint distance and time toll (JDTT) whereby users are charged jointly in proportion to the distance traveled and time spent within the cordon. The third model is a further improved joint distance and delay toll (JDDT) which replaces the time toll in the JDTT with a delay toll component. To solve the optimal toll level problem, we develop a simulation-based optimization (SBO) framework. Specifically, we propose a simultaneous approach and a sequential approach, respectively, based on the proportional-integral (PI) feedback controller to iteratively adjust the JDTT and JDDT, and use a calibrated large-scale simulation-based dynamic traffic assignment (DTA) model of Melbourne, Australia to evaluate the network performance under different pricing scenarios. While the framework is developed for static pricing, we show that it can be easily extended to solve time-dependent pricing by using multiple PI controllers. Results show that although the distance toll keeps the network from entering the congested regime of the NFD, it naturally drives users into the shortest paths within the cordon resulting in an uneven distribution of congestion. This is reflected by a large clockwise hysteresis loop in the NFD. In contrast, both the JDTT and JDDT reduce the size of the hysteresis loop while achieving the same control objective. We further conduct multiple simulation runs with different random seed numbers to demonstrate the effectiveness of different pricing models against simulation stochasticity. However, we postulate that the feedback control is not applicable with guaranteed convergence if the periphery of the cordon area becomes highly congested or gridlocked. [ABSTRACT FROM AUTHOR]

• Assess interest in various MaaS subscription bundles using Sydney trial data. • Establish the role of financial savings on MaaS bundle uptake. • Identify key drivers of user's choices between subscriptions and PAYG. • MaaS with PAYG option only is unlikely to grow the market. • MaaS could obtain a sizeable uptake with appropriate incentive and subscription bundles. Australia's first Mobility as a Service (MaaS) trial commenced in April 2019 in Sydney. A key objective of the trial is to assess interest in various MaaS subscription plans through bundling public transport, ride share, car share and car rental with varying financial discounts and monthly subscription fees, in contrast to pay as you go (PAYG). This paper develops a mixed logit choice model to investigate the participants' choice between PAYG and four subscription plans (or bundles) that were incrementally introduced over a 5-month period. This is the first paper to model real uptake as previous studies are based on stated preference data. New evidence is provided on what role financial savings, estimated using tracking technology embedded in the MaaS app, play in the context of modal offerings and a monthly subscription fee as well as socio-demographic and seasonal effects. Behaviourally, we present evidence on the extent of take up of each bundle relative to PAYG as well as elasticity estimates for all exogenous influences and estimates of willingness to pay and scenario assessment, particularly for how much someone would have to save over a previous month's cost outlay to be willing to subscribe to a particular bundle in a subsequent month. Within the context of the trial, the findings suggest a substantial market for mobility bundles but PAYG is an option preferred by many, particularly those with varying travel needs. We are, however, not in a position yet to conclude that these choices necessarily align with a contribution to societal sustainability goals. [ABSTRACT FROM AUTHOR]

• A hybrid latent variable ordered logit model for the frequency of ride-hailing use. • Multi-variate models for autonomous and pooled ride-hailing adoption. • Examining the regional and metropolitan dimension in the use and adoption. • Investigating latent psychological and observed factors for different trip purposes. • Exploring impacts of ride-hailing on other transport modes and travel behaviour. While ride-hailing has become ubiquitous in most metropolitan areas, it is still unclear how it prevails in the regional areas, especially after the emergence of new services of pooling or self-driving rides. This paper contributes to this knowledge gap by examining the regional/metropolitan dimension in the factors influencing the frequency of ride-hailing use and the demand for pooled and autonomous vehicle (AV) rides in Australia. Through individual-level survey data, we found that there is indeed a divide between factors affecting not only the frequency of using ride-hailing in the status quo but also in the future adoption of new services of pooled and AV-based services. Using the hybrid discrete choice model, we identify several latent psychological constructs that influence the use of ride-hailing services, namely being pro-technology, pro-pooling, pro-AV, anti-driving and security-cautious traits. Moreover, the results of multivariate models point to different observed and latent characteristics influencing the adoption of pooled and AV ride-hailing services for various trip purposes. In line with the literature, it was found that the socio-demographic characteristics that affect the frequency of use or adopting pooled and AV services in future include gender, age, income, education attainment, employment status, car ownership, and the current travel behaviour. We also found that ride-hailing in Australia has played more as a substitution role for public transport, private car and taxi than as a complementary role or feeder of the public transport system, both in the metropolitan and regional areas. [ABSTRACT FROM AUTHOR]

• Younger male adults in higher income brackets appear to have the highest WTP. • Women are more inclined towards solar panel equipped EVs. • Average WTP for every added driving range by solar panels is $18.13 per km. • Average WTP for the solar panel to match the colour of vehicle is $1,384. • Non-EV owners are more sensitive to fuel saving benefits of solar panel equipped EVs. Cities globally are moving toward electrified public and private transport. Metropolitan areas are now seeking ways to mitigate emission and are heading towards sustainable ways of using clean/green energy sources by integrating and implementing new technology and science innovations into the transport systems. This study explores consumer preferences for installing photovoltaic solar cells on electric vehicles. In this paper, we have applied a double hurdle model to account for the low market penetration of electric cars in Australia and dealt with the high non-purchase choice. Data are obtained from nearly 1,900 individuals from eight major cities in Australia to explore consumer preferences regarding electric vehicles with photovoltaic solar cells option. The proposed model specification accounts for the panel impact generated from multiple tasks for each respondent, and the correlation between the two decisions on electric vehicle purchase and selecting the photovoltaic add-on. Furthermore, the systematic taste heterogeneity towards price variables is considered by introducing interactive sociodemographic terms to the utility functions. This study provides insights into the willingness-to-pay values for different features of electric vehicles and the solar panel add-on. The results show on average consumers are willing to pay a premium of $18.13 on purchase price for every daily additional kilometre in driving range generated by solar panels. The average willingness to pay for the solar panel to match the colour of vehicle is $1,021.09 for women, and $1,803.92 for men. This study informs policymakers and industries about the feasibility of such an emerging technology. [ABSTRACT FROM AUTHOR]

• Developing a novel approach for using trip sequences instead of separate trips. • Developing a unique similarity measure for the trip sequences. • Focusing only on the temporal attributes of the trip sequences. • Running the method on real-world case studies. • Evaluating the performance of the developed method. • Comparing the outcome with the existing literature. • Achieving a considerable improvement in inferring the trip purpose. Smart card transactions are known as a rich and continuous source of public transit data, but they miss some important attributes about trips and passengers. One of these missing attributes is the trip purpose attribute. This paper proposes a novel method to infer the trip purpose attribute from the sequences of trips of passengers instead of separate trips. The proposed method infers the trip purpose attribute (a missing attribute in the smart card data) from the temporal attributes (available attributes in the smart card data). First, the relation between the temporal attributes and the trip purpose attribute is learnt by discovering clusters of passengers in the Household Travel Survey dataset while each passenger is represented by one sequence of trips. Then, the discovered clusters are utilized to infer the trip purpose of smart card transactions by allocating each passenger to the closest clusters. The proposed method is implemented on the smart card and HTS datasets from southeast Queensland, Australia. The evaluation results showed a considerable improvement in inferring the trip purpose compared to the results published in the literature. Notably, the effect of considering the trip sequence was more significant than considering land use variables. [ABSTRACT FROM AUTHOR]