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This study is the first in the literature to examine the Braess paradox considering parking behavior in the autonomous vehicle (AV) environment, based on which the network design problem for the autonomous transportation system (NDP‐ATS) is modeled. First, we introduce the AV commuting pattern considering the self‐driving process for the parking purpose. We then illustrate the existence of two distinct Braess paradoxes that can occur in AV traffic networks with regards to parking space addition and network capacity expansion. They show that these two types of "improvement" measures might deteriorate the network system performance in an AV situation. Second, motivated to avoid the deterioration due to the introduced Braess paradoxes, we develop a bi‐level programming model for NDP‐ATS. The lower‐level program addresses the network‐equilibrium traffic assignment for AVs. The upper‐level program aims to minimize the network‐wide travel cost considering both occupied and empty AV trips by selecting the optimal design option accounting for road capacity enhancement and parking facility deployment simultaneously. To solve the developed bi‐level model, the simplicial homology global optimization algorithm is employed along with the Frank–Wolfe algorithm and the CPLEX. Finally, the efficacy of the modeling framework is validated through case studies on the Sioux Falls city network and the Anaheim network. The results show that the developed methodology is capable of producing high‐quality solutions with respect to savings in system‐level travel costs for AV traffic. The results also highlight the impacts of parking fees on the system performance for the optimized network. Additionally, it is found that the two objectives, which minimize the total system travel cost and minimize that for empty AV trips only, are conflicting for certain scenarios. The methodological approach introduced in this work serves as a critical modeling device for infrastructure development and policy assessment for AV traffic. [ABSTRACT FROM AUTHOR]

Mathematical relations between supplier capacities, the resulting material supply shortages, together with the impact of material delays on construction projects are not well defined. In response to this, this paper presents a novel multi-objective mixed integer linear programming model that considers the selection of suitable suppliers, inventory management practices, order quantities and the possibility of splitting a material order as integrated decisions to be optimised. The trade-off between the overall procurement cost and the weighted lateness, a measure of material delay impacts, is optimised. Material prices, supplier capacities, and resulting delays are treated as fuzzy scenario-based parameters. The proposed model is tested on a numerical example and computation experiments validate the model performance. An extensive sensitivity analysis is carried out and results suggest that by considering high variations in uncertain supplier capacities, the model would generate lower procurement cost and show less significant delay impacts. Whereas greater variations in uncertain material prices cause the total procurement cost to grow 55%; greater variations in uncertain delay durations also drastically increase the weighted lateness by over 70%. This highlights the importance of having high quality estimates for uncertain parameters. Additionally, the analysis also indicates that a minimum overall satisfaction level of 0.9338 can be achieved depending on the model user's strategies, and the proposed scenario-adjusted problem outperforms problems modelled under deterministic market conditions. The major contribution of this paper lies in the development of a fuzzy scenario-based model to solve the supplier selection and material purchasing problem in construction supply chains. • A novel optimisation model for construction supply chain supplier selection, material ordering and inventory decisions. • Material prices, supplier capacities and estimated delay durations are modelled as scenario-based fuzzy parameters. • Splitting a material order into two separate shipments is permitted when supply shortages are expected. • Managerial insights are offered to aid the parameter tuning for the fuzzy scenario-based programming. [ABSTRACT FROM AUTHOR]

• 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]