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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]

An introduction is presented in which the editor discusses various reports within the issue on topics including transportdynamics, transportation system, and problems related to transportation such as congestion and vehicle emissions.

In this paper, we study the static bike repositioning problem where the problem consists of selecting a subset of stations to visit, sequencing them, and determining the pick-up/drop-off quantities (associated with each of the visited stations) under the various operational constraints. The objective is to minimize the total penalties incurred at all the stations. We present an iterated tabu search heuristic to solve the described problem. Experimental results show that this simple heuristic can generate high quality solutions using small computing times. [ABSTRACT FROM AUTHOR]

This paper proposes a bi-level transit network design problem where the transit routes and frequency settings are determined simultaneously. The upper-level problem is formulated as a mixed integer non-linear program with the objective of minimizing the number of passenger transfers, and the lower-level problem is the transit assignment problem with capacity constraints. A hybrid artificial bee colony (ABC) algorithm is developed to solve the bi-level problem. This algorithm relies on the ABC algorithm to design route structures and a proposed descent direction search method to determine an optimal frequency setting for a given route structure. The descent direction search method is developed by analyzing the optimality conditions of the lower-level problem and using the relationship between the lower- and upper-level objective functions. The step size for updating the frequency setting is determined by solving a linear integer program. To efficiently repair route structures, a node insertion and deletion strategy is proposed based on the average passenger demand for the direct services concerned. To increase the computation speed, a lower bound of the objective value for each route design solution is derived and used in the fitness evaluation of the proposed algorithm. Various experiments are set up to demonstrate the performance of our proposed algorithm and the properties of the problem. [ABSTRACT FROM AUTHOR]

This paper proposes a new scheduled-based transit assignment model. Unlike other schedule-based models in the literature, we consider supply uncertainties and assume that users adopt strategies to travel from their origins to their destinations. We present an analytical formulation to ensure that on-board passengers continuing to the next stop have priority and waiting passengers are loaded on a first-come-first-serve basis. We propose an analytical model that captures the stochastic nature of the transit schedules and in-vehicle travel times due to road conditions, incidents, or adverse weather. We adopt a mean variance approach that can consider the covariance of travel time between links in a space-time graph but still lead to a robust transit network loading procedure when optimal strategies are adopted. The proposed model is formulated as a user equilibrium problem and solved by an MSA-type algorithm. Numerical results are reported to show the effects of supply uncertainties on the travel strategies and departure times of passengers. [ABSTRACT FROM AUTHOR]

BACKGROUND A previous analysis in this trial showed that among patients with severe, symptomatic aortic stenosis who were at low surgical risk, the rate of the composite end point of death, stroke, or rehospitalization at 1 year was significantly lower with trans-catheter aortic-valve replacement (TAVR) than with surgical aortic-valve replacement. Longer-term outcomes are unknown. METHODS We randomly assigned patients with severe, symptomatic aortic stenosis and low surgical risk to undergo either TAVR or surgery. The first primary end point was a composite of death, stroke, or rehospitalization related to the valve, the procedure, or heart failure. The second primary end point was a hierarchical composite that included death, disabling stroke, nondisabling stroke, and the number of rehospitalization days, analyzed with the use of a win ratio analysis. Clinical, echo-cardiographic, and health-status outcomes were assessed through 5 years. RESULTS A total of 1000 patients underwent randomization: 503 patients were assigned to undergo TAVR, and 497 to undergo surgery. A component of the first primary end point occurred in 111 of 496 patients in the TAVR group and in 117 of 454 patients in the surgery group (Kaplan-Meier estimates, 22.8% in the TAVR group and 27.2% in the surgery group; difference, -4.3 percentage points; 95% confidence interval [CI], -9.9 to 1.3; P = 0.07). The win ratio for the second primary end point was 1.17 (95% CI, 0.90 to 1.51; P=0.25). The Kaplan-Meier estimates for the components of the first primary end point were as follows: death, 10.0% in the TAVR group and 8.2% in the surgery group; stroke, 5.8% and 6.4%, respectively; and rehospitalization, 13.7% and 17.4%. The hemodynamic performance of the valve, assessed according to the mean (±SD) valve gradient, was 12.8±6.5 mm Hg in the TAVR group and 11.7±5.6 mm Hg in the surgery group. Bioprosthetic-valve failure occurred in 3.3% of the patients in the TAVR group and in 3.8% of those in the surgery group. CONCLUSIONS Among low-risk patients with severe, symptomatic aortic stenosis who underwent TAVR or surgery, there was no significant between-group difference in the two primary composite outcomes. [ABSTRACT FROM AUTHOR]

Background: There are scant data on long-term clinical outcomes and bioprosthetic-valve function after transcatheter aortic-valve replacement (TAVR) as compared with surgical aortic-valve replacement in patients with severe aortic stenosis and intermediate surgical risk.Methods: We enrolled 2032 intermediate-risk patients with severe, symptomatic aortic stenosis at 57 centers. Patients were stratified according to intended transfemoral or transthoracic access (76.3% and 23.7%, respectively) and were randomly assigned to undergo either TAVR or surgical replacement. Clinical, echocardiographic, and health-status outcomes were followed for 5 years. The primary end point was death from any cause or disabling stroke.Results: At 5 years, there was no significant difference in the incidence of death from any cause or disabling stroke between the TAVR group and the surgery group (47.9% and 43.4%, respectively; hazard ratio, 1.09; 95% confidence interval [CI], 0.95 to 1.25; P = 0.21). Results were similar for the transfemoral-access cohort (44.5% and 42.0%, respectively; hazard ratio, 1.02; 95% CI, 0.87 to 1.20), but the incidence of death or disabling stroke was higher after TAVR than after surgery in the transthoracic-access cohort (59.3% vs. 48.3%; hazard ratio, 1.32; 95% CI, 1.02 to 1.71). At 5 years, more patients in the TAVR group than in the surgery group had at least mild paravalvular aortic regurgitation (33.3% vs. 6.3%). Repeat hospitalizations were more frequent after TAVR than after surgery (33.3% vs. 25.2%), as were aortic-valve reinterventions (3.2% vs. 0.8%). Improvement in health status at 5 years was similar for TAVR and surgery.Conclusions: Among patients with aortic stenosis who were at intermediate surgical risk, there was no significant difference in the incidence of death or disabling stroke at 5 years after TAVR as compared with surgical aortic-valve replacement. (Funded by Edwards Lifesciences; PARTNER 2 ClinicalTrials.gov number, NCT01314313.). [ABSTRACT FROM AUTHOR]

Background: Among patients with aortic stenosis who are at intermediate or high risk for death with surgery, major outcomes are similar with transcatheter aortic-valve replacement (TAVR) and surgical aortic-valve replacement. There is insufficient evidence regarding the comparison of the two procedures in patients who are at low risk.Methods: We randomly assigned patients with severe aortic stenosis and low surgical risk to undergo either TAVR with transfemoral placement of a balloon-expandable valve or surgery. The primary end point was a composite of death, stroke, or rehospitalization at 1 year. Both noninferiority testing (with a prespecified margin of 6 percentage points) and superiority testing were performed in the as-treated population.Results: At 71 centers, 1000 patients underwent randomization. The mean age of the patients was 73 years, and the mean Society of Thoracic Surgeons risk score was 1.9% (with scores ranging from 0 to 100% and higher scores indicating a greater risk of death within 30 days after the procedure). The Kaplan-Meier estimate of the rate of the primary composite end point at 1 year was significantly lower in the TAVR group than in the surgery group (8.5% vs. 15.1%; absolute difference, -6.6 percentage points; 95% confidence interval [CI], -10.8 to -2.5; P<0.001 for noninferiority; hazard ratio, 0.54; 95% CI, 0.37 to 0.79; P = 0.001 for superiority). At 30 days, TAVR resulted in a lower rate of stroke than surgery (P = 0.02) and in lower rates of death or stroke (P = 0.01) and new-onset atrial fibrillation (P<0.001). TAVR also resulted in a shorter index hospitalization than surgery (P<0.001) and in a lower risk of a poor treatment outcome (death or a low Kansas City Cardiomyopathy Questionnaire score) at 30 days (P<0.001). There were no significant between-group differences in major vascular complications, new permanent pacemaker insertions, or moderate or severe paravalvular regurgitation.Conclusions: Among patients with severe aortic stenosis who were at low surgical risk, the rate of the composite of death, stroke, or rehospitalization at 1 year was significantly lower with TAVR than with surgery. (Funded by Edwards Lifesciences; PARTNER 3 ClinicalTrials.gov number, NCT02675114.). [ABSTRACT FROM AUTHOR]