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Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers' decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies. [ABSTRACT FROM AUTHOR]

This article develops a probabilistic approach for assessing transport network vulnerability. A novel performance measure is proposed to evaluate the expected impact when multiple transport network components fail simultaneously at various degrees. The proposed measure captures both the likelihood and consequence of a combination of transport network component failures. The most critical combination of transport network component failures is obtained by solving a bi-level optimization problem. The upper-level problem is to solve for the combination of transport network components together with their corresponding disruption levels, which induces the maximum reduction in the performance measure. The lower-level problem is to capture the response of travelers to network changes due to network component failures and is formulated as a traffic assignment problem. The clonal selection algorithm (CSA), a biologically inspired approach, is adopted to tackle the proposed bi-level optimization problem. Numerical results indicate that neglecting partial capacity degradation and its probability of occurrence could misestimate the worst scenario, and different vulnerability assessment approaches could identify similar critical components but our approach can discover some components that are not found by other existing approaches. Moreover, it is shown that the CSA outperforms the well-known genetic algorithm in terms of solution quality in a large network. [ABSTRACT FROM AUTHOR]

There has been a recent surge of research interest in quantifying the health and environmental impacts of increased cycling in urban environments. Although there is general agreement that the benefits of increased cycling outweigh the risks, most of the methodologies developed have had limited value for evaluating real-world transport policies. This is because they are based on hypothetical scenarios where increased cycling takes place but give no consideration to the courses of action which may help policymakers to achieve the scenarios. A useful extension to these methodologies would be one which allowed a user to find the optimal infrastructure design and/or policies which would maximize total societal benefit, taking into account the health and environmental impacts of cycling. In this study, a Network Design Problem is formulated for systematically designing cycling network layouts in order to maximize the net benefits to the network users and society. The problem is formulated as a mathematical program with equilibrium constraints (MPEC) and a solution approach based on a genetic algorithm (GA) is provided to solve the problem. The problem formulation and solution algorithm are tested using a numerical example. The GA algorithm was shown to efficiently converge to an optimal or near-optimal solution for the cycle network design. The proposed optimization framework may be adopted by transport authorities and/or urban planners as a decision support tool to help them to systematically identify the best design for a cycle network which balances the benefits and risks to all stakeholders. [ABSTRACT FROM AUTHOR]

This paper proposes a simultaneous route and departure time choice (SRDTC) problem with fixed demand in a degradable transport network. In this network, travelers face with stochastic travel times. Their selection of routes and departure times follows the UE principle in terms of the mean generalized route cost, which is defined as the probabilistic dynamic user optimal (PDUO) principle. The proposed PDUO-SRDTC problem is formulated as a variational inequality (VI) problem. As a special case of PDUO-SRDTC problem, the PDUO route choice (PDUO-RC) problem is also proposed and formulated as a VI problem. Network degradation is defined on the degradation of the outflow capacity of each link. A Monte Carlo-based stochastic link transmission model (MC-SLTM) is developed to capture the effect of physical queues and the random evolution of traffic states during flow propagation to estimate mean generalized route costs. Both the extragradient algorithm and the route-swapping method with a variable sample size scheme are developed to solve the proposed VI problems. Numerical examples are developed to illustrate the paradoxical phenomena of the problems and the effectiveness of the solution methods. Numerical results show that constructing a new road, enhancing link outflow capacity, or reducing outflow capacity degradation can lead to poor network performance and it is important to consider both network degradation and queue spillback when making transportation policies aimed at improving network performance. The results also demonstrate that the variable sample size scheme can give a quicker and better solution than the traditional fixed sample size scheme. [ABSTRACT FROM AUTHOR]

Dynamic user optimal simultaneous route and departure time choice(DUO-SRDTC) problems are usually formulated as variational inequality (VI) problems whose solution algorithms generally require continuous and monotone route travel cost functions to guarantee convergence. However, the monotonicity of the route travel cost functions can not be ensured even if the route travel time functions are monotone. In contrast to traditional formulations, this paper formulates a DUO-SRDT C problem (that can have fixed or elastic demand) as a system of nonlinear equations. The system of nonlinear equations is a function of generalized origin-destination (OD) travel costs rather than route flows and includes a dynamic user optimal (DUO) route choice subproblem with perfectly elastic demand and a quadratic programming (QP) subproblem under certain assumptions. This study also proposes a solution method based on the backtracking inexact Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, the extra gradient algorithm, and the Frank-Wolf e algorithm The BFGS method, the extra gradient algorithm, and the Frank-Wolf e algorithm are used to solve the system of nonlinear equations, the DUO route choice subproblem, and the QP subproblem, respectively. The proposed formulation and solution method can avoid the requirement of monotonicity of the route travel cost functions to obtain a convergent solution and provide a new approach with which to solve DUO-SRDTC problems. Finally, numeric examples are used to demonstrate the performance of the proposed solution method. [ABSTRACT FROM AUTHOR]