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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 paper develops a bottleneck model in which the capacity of the bottleneck is assumed to be stochastic and follow a general distribution that has a positive upper bound. The user equilibrium principle in terms of mean trip cost is adopted to formulate commuters' departure time choice in the stochastic bottleneck. We find that there exist five possible equilibrium departure patterns, which depend on both commuters' unit costs of travel time, schedule delay early and late, and the uncertainty of the stochastic capacity of the bottleneck. All possible equilibrium departure patterns are analytically derived. Both the analytical and numerical results show that increasing the uncertainty of the capacity of the bottleneck leads to an increase of commuters' individual mean trip cost. In addition, both a time-varying toll scheme and a single-step coarse toll scheme are designed within the proposed stochastic bottleneck model. We provide an analytical method to determine the detailed toll-charging schemes for both toll strategies. The numerical results show that the proposed toll schemes can indeed improve the efficiency of the stochastic bottleneck in terms of decreasing mean total social cost, and the time-varying toll scheme is more efficient than the single-step coarse toll scheme. However, as the uncertainty of the capacity of the bottleneck increases, the efficiency of the time-varying toll scheme decreases, whereas the efficiency of the single-step coarse toll scheme fluctuates slightly. [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]

This study defines the price of anarchy for general reliability-based transport network design problems, which is an indicator of inefficiency that reveals how much the design objective value exceeds its theoretical minimum value due to the risk averse and selfish routing behavior of travelers. This study examines a new problem, which is a reliability-based continuous network design problem under cost recovery. In this problem, the variations of system travel time and path travel times, the risk attitudes of the system manager and travelers, congestion toll charges, capacity expansions, and cost recovery constraint are explicitly considered. The design problem is formulated as a min-max problem with the reliability-based user equilibrium constraint. It is proved that the price of anarchy for this problem is bounded above, and the upper bound is independent of travel time functions, demands, and network topology. The upper bound is related to the travel time variations, the value of reliability, and the value of time. [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 investigates a new cash transportation problem, which is a variant of the capacitated vehicle routing problem. To better satisfy the demands of customers (e.g., banks, large retailers, shopping centers, automated teller machines, etc.), the combination of different cash denominations (i.e., $100, $50, $20, etc.) is considered. The robbery risk, which is measured by both the amount of cash being carried and the distance covered by the vehicle carrying the cash, is limited by a risk threshold. The problem is formulated as mixed‐integer linear programming to minimize the total cost, which consists of the total travel cost and the total penalties due to the unmet expected demand of customers. A combined hybrid tabu search metaheuristic is proposed to solve this problem. Tabu search is adopted for determining routing decisions while three methods, namely the exact method, the greedy method, and the mixed method, are proposed to be embedded within the tabu search to determine the denomination combination strategy. The numerical studies show that the proposed method makes a good tradeoff between the solution quality and the computation time. Experimental results also reveal the effects of the unit penalty and the risk threshold. [ABSTRACT FROM AUTHOR]

In this article, a bi-level transportation network design problem with environmental considerations is investigated. To explicitly reflect various requirements of environmental sustainability from planners, total emissions costs and total excessive noise cost are minimized along with total system travel time while performing optimal capacity expansion. To leave space for additional information on decision making for planners and provide a more generalized description of solution optimality, the Pareto optimization approach is adopted. The study proposes a multiobjective variant of a new metaheuristic named Chemical Reaction Optimization as the optimization tool to solve the formulated network design problem. Pareto front approximations have been successfully acquired for each scenario tested using the Sioux Falls network. The results show that the relations between pairwise objectives can change significantly (e.g., from conflicting to aligned) with regard to different demand situations. The newly proposed multiobjective metaheuristic succeeds to produce better approximations of the Pareto front than NSGA-II with comparable runtime for the considered road network. [ABSTRACT FROM AUTHOR]

Optimal immunological homoeostasis determines the long-term recovery of patients in the postoperative period. The functional adaptability of monocytes plays a pivotal role in adjusting the host's response to an insult, immunostasis and long-term health, and may help to determine successful recovery. We undertook a longitudinal analysis of the functional adaptability of monocytes in 20 patients undergoing heart surgery with cardiopulmonary bypass, as a model of severe stress. Using each patient's pre-cardiopulmonary bypass data as a baseline, we investigated the characteristics of peripheral blood monocytes' functional plasticity in-vitro before elective bypass, and three months afterwards. Approximately 30% of subjects showed diminished monocyte plasticity, as demonstrated by decreased monocyte differentiation into dendritic cells three months after bypass. Diminished monocyte functional plasticity was related to over-production of macrophage colony-stimulating factor. Adding a neutralising antibody to macrophage colony-stimulating factor corrected the monocytes' differentiation defect. Finally, patients with reduced monocyte plasticity had significantly elevated serum C-reactive protein, with a concomitant increase in cytomegalovirus IgG antibody titres, suggestive of the acquisition of immuno-suppressive traits. Our study shows that severe surgical stress resulted in a lasting immunological defect in individuals who had seemingly recovered. [ABSTRACT FROM AUTHOR]

A state-of-the-art review of flow observability, estimation, and prediction problems in traffic networks is performed. Since mathematical optimization provides a general framework for all of them, an integrated approach is used to perform the analysis of these problems and consider them as different optimization problems whose data, variables, constraints, and objective functions are the main elements that characterize the problems proposed by different authors. For example, counted, scanned or “a priori” data are the most common data sources; conservation laws, flow nonnegativity, link capacity, flow definition, observation, flow propagation, and specific model requirements form the most common constraints; and least squares, likelihood, possible relative error, mean absolute relative error, and so forth constitute the bases for the objective functions or metrics. The high number of possible combinations of these elements justifies the existence of a wide collection of methods for analyzing static and dynamic situations. [ABSTRACT FROM AUTHOR]

In this study, we investigate the problem of locating a facility in continuous space when the weight of each existing facility is a known linear function of time. The location of the new facility can be changed once over a continuous finite time horizon. Rectilinear distance and time- and location-dependent relocation costs are considered. The objective is to determine the optimal relocation time and locations of the new facility before and after relocation to minimize the total location and relocation costs. We also propose an exact algorithm to solve the problem in a polynomial time according to our computational results. [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.

Nowadays, the decision makers in the transportation industry are being urged to incorporate environmental costs into road network design decision making because road traffic affects the environment and health. The design of a road network should not only be cost-effective but also environmentally sustainable. This article proposes a new network design problem (NDP) that takes both vehicle emissions and noise into account. This proposed environmentally sustainable NDP is formulated as a discrete bilevel program. The lower-level problem is formulated as user-equilibrium assignment. The upper-level problem determines the optimal road capacity expansion to minimize the total costs of emissions, noise, and travel time with the considerations of budgetary and capacity improvement constraints. The proposed problem is solved by an enhanced version of a new meta-heuristic named Chemical Reaction Optimization (CRO), and its parameters are tuned by our proposed tuning procedure. Two benchmark road networks with different demand levels are used to evaluate the performance of the enhanced CRO and illustrate the properties of the problem. The results show that there were tradeoffs between emissions, noise, and travel time costs, and that the enhanced CRO outperformed Genetic Algorithm (GA) on more than half of the testing scenarios and had a comparable performance on certain test scenarios compared with GA. [ABSTRACT FROM AUTHOR]

In this paper a novel variational inequality (VI) formulation of the dynamic user optimal (DUO) route choice problem is proposed using the concept of approach proportion. An approach proportion represents the proportion of travelers that select a turning or through movement when leaving a node. Approach proportions contain travelers' route information so that the realistic effects of physical queues can be captured in a formulation when a physical-queue traffic flow model is adopted, and so that route enumeration and path-set generation can be avoided in the solution procedure. In addition, the simple structure of the approach proportion representation allows us to decompose the constraint set for solving large-scale DUO route choice problems. This paper also discusses the existence and uniqueness of the solutions to the VI problem and develops a solution algorithm based on the extragradient method to solve the proposed VI problem. This solution algorithm makes use of the decomposition property of the constraint set and is convergent if the travel time functions are pseudomonotone and Lipschitz continuous. It is not necessary to know the Lipschitz constant of the travel time functions in advance. Finally, numerical examples are given to demonstrate the properties of the proposed model and the performance of the solution algorithm. [ABSTRACT FROM AUTHOR]

Recent growth in demand for proactive real-time transportation management systems has led to major advances in short-time traffic forecasting methods. Recent studies have introduced time series theory, neural networks, and genetic algorithms to short-term traffic forecasting to make forecasts more reliable, efficient, and accurate. However, most of these methods can only deal with data recorded at regular time intervals, which restricts the range of data collection tools to presence-type detectors or other equipment that generates regular data. The study reported here is an attempt to extend several existing time series forecasting methods to accommodate data recorded at irregular time intervals, which would allow transportation management systems to obtain predicted traffic speeds from intermittent data sources such as Global Positioning System (GPS). To improve forecasting performance, acceleration information was introduced, and information from segments adjacent to the current forecasting segment was adopted. The study tested several methods using GPS data from 480 Hong Kong taxis. The results show that the best performance in terms of mean absolute relative error is obtained by using a neural network model that aggregates speed information and acceleration information from the current forecasting segment and adjacent segments. [ABSTRACT FROM PUBLISHER]

This article proposes a nonlinear complementarity problem (NCP) formulation for the risk-aversive stochastic transit assignment problem in which in-vehicle travel time, waiting time, capacity, and the effect of congestion are considered as stochastic variables simultaneously and both their means and variances are incorporated into the formulation. A new congestion model is developed and captured in the proposed NCP formulation to account for different effects of on-board passengers and passengers waiting at stops. A reliability-based user equilibrium condition is also defined based on the proposed generalized concept of travel time budget referred to as effective travel cost, and is captured in the formulation. A column generation based algorithm is proposed to solve the NCP formulation. A survey was conducted to validate that the degree of risk aversion of transit passengers affects their route choices. Numerical studies were performed to demonstrate the problem and the effectiveness of the proposed algorithm. The results obtained show that underestimating the congestion effect and ignoring the risk aversion behavior can overestimate the patronage of transit service, which have profound implications on the profit of the operators involved and the development of transit network design models. [ABSTRACT FROM AUTHOR]

This article proposes optimization frameworks for discrete road network design considering the land-use transport interaction over time. Unlike existing models, the optimization frameworks can determine the optimal designs automatically without trial-and-error once the objective(s) is/are clearly defined. Moreover, these frameworks allow the evaluation of the impacts of the optimal designs on the related parties including landowners, toll road operators, transit operators, and road users, and help network planners and profit-makers with decision making by eliminating many alternative designs. A numerical study is set up to examine road network design's effects on these related parties under three road construction schemes: exact cost recovery, build-operate-transfer, and cross-subsidization. The results show that the changes in landowner profits are not the same after implementing any scheme. These unequal changes raise the issue of the landowner equity. This implies that the government has to consider trade-offs between parties’ objectives carefully. [ABSTRACT FROM AUTHOR]

The paper develops a short-term space-time traffic flow forecasting strategy integrating the empirical-based seasonal autoregressive integrated moving average (SARIMA) time-series forecasting technique with the theoretical-based first-order macroscopic traffic flow model—cell transmission model. A case study in Dublin city center which has serious traffic congestion is performed to test the effectiveness of the proposed multivariate traffic forecasting strategy. The results show that the forecasts at the junctions only deviate around 10% at a maximum from the original observations and seem to indicate that the proposed strategy is one of the effective approaches to predict the real-time traffic flow level in a congested network especially at the locations where no continuous data collection takes place. [ABSTRACT FROM AUTHOR]

This article investigates whether there are strategic interactions between private traveler information service providers (ISPs) and private toll road operators (TOs), whose objectives are profit-driven. For this purpose, a multiclass stochastic user equilibrium assignment model capturing the elastic market penetration of traveler information services is developed through the nonlinear complementarity problem (NCP) approach, and a numerical study is set up. The result indicates that TO and ISP can have strong interactions and be competitive in nature. This article also provides models to determine their optimal strategies in both regulated and unregulated markets, and assist decision-making. These models are demonstrated and validated in a simple example. [ABSTRACT FROM AUTHOR]

How reliable the transport network is greatly affects the mobility and economy of a country. To assess the reliability of the network accurately and design a reliable transport network properly, one should consider the risk taking and elastic behaviour of travel demand. This paper develops a Nonlinear Complementarity Problem (NCP) formulation considering this behaviour. The NCP formulation is transformed to a smooth and unconstrained mathematical program via a Fischer function and then solved by the proposed modified αBB (branch and bound) algorithm, while guaranteeing finite convergence to a point arbitrarily close to the global minimum (ɛ-convergence). To speed up the computation, a sampling method is suggested. A small numerical study is set up to illustrate the properties and the performance of the algorithm. The results show that the bound improving strategy, the sampling size on estimating the value of α for the convex lower bounding function, and choice of α have a strong influence of the rate of convergence. [ABSTRACT FROM AUTHOR]

This study develops an analytical dynamic mixed-equilibrium model to describe the transportation system performance with Route Planning and Guidance (RPG) services. The model considers travelers' departure time and route choices, and their propensity to subscribe to the service at different charge levels. We investigate the effect of the service charge on three parties: (i) equipped drivers; (ii) society in general; and (iii) the transportation management agency. The results show that the objectives of these parties are not always accommodative and may conflict with each other. Specifically, a numerical example illustrates that, under certain conditions, the society overall benefits from the service because all travelers are better off in terms of total generalized travel cost (i.e., travel time cost plus schedule delay cost). The surprising result, however, is that the network may become more severely congested. This seemingly paradoxical phenomenon is due to the much lower schedule delay cost incurred by travelers with RPG services, which more than compensates for the higher travel time associated with a more congested network. These results indicate the importance of considering RPG services from a more comprehensive perspective. In particular, it is important to include schedule delay cost as part of the benefit assessment of RPG services, in addition to the common measure of travel time cost. [ABSTRACT FROM AUTHOR]