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Drive cycles in vehicle systems are important determinants for energy consumption, emissions, and safety. Estimating the frequency of the drive cycle quickly is important for control applications related to fuel efficiency, emission reduction and improving safety. Quantum computing has established the computational efficiency that can be gained. A drive cycle frequency estimation algorithm based on the quantum Fourier transform is exponentially faster than the classical Fourier transform. The algorithm is applied on real world data set. We evaluate the method using a quantum computing simulator, demonstrating remarkable consistency with the results from the classical Fourier transform. Current quantum computers are noisy, a simple method is proposed to mitigate the impact of the noise. The method is evaluated on a 15 qubit IBM-q quantum computer. The proposed method for a noisy quantum computer is still faster than the classical Fourier transform. [ABSTRACT FROM AUTHOR]

Collecting effective data is a fundamental step in developing transport networks and related research. Social media have become an emerging source of data for traffic analyses. In this paper, we demonstrate that the function of a city influences the utility of social media data in travel demand models by generating models for eight US cities with different functions. Data from Twitter and Foursquare, as well as other socio-demographic information, are considered as independent variables in Origin-Destination trip regression models generated via a Random Forest regression technique. Model performance with and without use of social media data are compared via 10-fold cross-validation. The results indicate that the accuracy of the models for all eight cities improved when independent variables based on social media data were included. The performance was most improved in metropolitan areas, followed by rural and tourist areas. Inspired by this finding, we conclude that the city function influences the utility of social media data in travel demand models. Meanwhile, we create models based on trip purpose and transport mode to explore other factors that may impact the efficiency of applying social media data in transport research. [ABSTRACT FROM AUTHOR]

The dominant challenge in one-way carsharing systems is the vehicle stock imbalance. Previous studies have proposed relocation approaches to handle it using optimization and simulation models. However, these models do not consider the interdependence between supply and demand in carsharing systems. In this paper, we develop an integrated optimization model to link supply and demand together. A discrete choice model that includes vehicle availability as a parameter directly affecting user's mode choice is introduced and incorporated within the optimization formulation. In this framework, carsharing travel demand is influenced by vehicle supply. The reaction of the demand further changes vehicle availability in the system. The incorporation of a discrete choice model with the Integer Linear Programming formulation leads to a nonlinear model. We propose a linearization scheme to reformulate it. We test the model in realistic case studies representative of an Australian carsharing operator. A sensitivity analysis on total travel demand, system capacity, one-way trip price, and vehicle availability coefficient is undertaken to evaluate their impacts on system profit. The results reveal that the pattern of profit over trip price varies across scenarios with different vehicle availability coefficients and travel demand. The profit-efficiency of enlarging carsharing network is also dependent on travel demand. We conclude that the interdependence between demand and supply should be considered when setting network development plans and pricing strategies in one-way carsharing systems. If there is a strong interaction between demand and supply, the supply of carsharing vehicles has a critical impact on system profit. [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]