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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]

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]

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]