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With the growing environmental awareness, people are concerned about the air pollution issues because these issues not only cause adverse environmental effects but also damage human’s health condit...

The doubly constrained entropy distribution/assignment (DEDA) problem that combines a gravity-based trip distribution (TD) problem and a traffic assignment (TA) problem has long been formulated as an optimization model and solved by two solution algorithms, i.e., partial- and full-linearization solution algorithms. As an alternative, this research first treats the DEDA problem by the augmented Lagrangian dual (ALD) method as the singly constrained entropy distribution/assignment (SEDA) problem, which in turn is addressed, via a tactical supernetwork representation, as an “extended” 1-origin-to-1-destination TA problem. A quick-precision TA solution algorithm, − called TAPAS (Traffic Assignment by Paired Alternative Segments), − is then adopted for solutions. The proposed approach is demonstrated with a numerical example for the correctness of the result, using Lingo 11 solver and the partial linearization solution (PLS) algorithm. Moreover, through the use of TAPAS in the innermost loop, the proposed approach also has the merit of generating unique path flow solution, which is very useful in route guidance under the intelligent transportation systems environment, among other academic applications. In addition, the proposed approach can be easily applied, or with minor modification at most, to various combined models in travel demand forecasting.

The traffic equilibrium problem (TEP) is an essential module in transportation planning. This paper studies an extended TEP version, i.e. the path-specific traffic equilibrium problem (PS-TEP), which has many applications in the real world. Due to inherent path interactions, the PS-TEP cannot be formulated as an optimization model but can be formulated as a variational inequality model. The corresponding user equilibrium conditions are characterized by the equal and minimum generalized path cost, which in turn can be expressed as a function of actual path costs and path-specific disutility. To interpret the PS-TEP as an extension of the TEP, an augmented network representation that includes a basic network (incurred actual path costs) and an additional virtual network (incurred path-specific disutility) is proposed. A path-based solution algorithm, called the gradient projection method, is modified for demonstration. Four path-specific cost schemes of the path-specific disutility are used for elaboration ...

A stochastic/dynamic user-optimal route choice problem that assumes time-dependent route travel times Gumbel distributed is formulated by a variational inequality and the corresponding equilibrium conditions are introduced. Two K -shortest-path based heuristics called K -shortest-path (KSP) and restricted K -shortest-path (RKSP) are proposed and make comparisons with a link-based heuristic called stochastic dynamic method (SADA). Numerical examples show that both the KSP and RKSP are superior to the SADA in terms of total CPU time. While both the KSP and RKSP have fully taken the advantage of computational efficiency, the latter is more practical by taking into account the thresholds of an overlapping ratio and route travel time difference.

A link-based fuzzy user-optimal route choice problem embedding link interactions is formulated using the variational inequality approach. To avoid prohibitive arithmetic computations and by taking a plausible point estimate of each link travel time interval at a certain α -cut level, this fuzzy problem reduces to a crisp user-optimal route choice model, which may be readily solved by the so-called diagonalization method. The corresponding user-optimal conditions are explicitly stated and demonstrated by means of a numerical example.

The JHE method has been recently proposed as an approximation to the vertex method for solving the fuzzy-weighted average problem. In this study, the usefulness of the JHE method is demonstrated via illustrative examples to be rather restricted and in practice cannot be regarded as an efficient alternative to the vertex method. The appropriateness of the JHE method only appears at the situation where the number of criteria exceeds 11 and the exactness of the solution is not a primary concern.

This paper describes the solution of the traffic assignment model under fuzzy information. The assumption about perfect network information is relaxed. The fuzzy algorithm, successive representative method, is proposed in this study. This algorithm assigns a set of O-D trip rates to a transportation network in which the link travel times are fuzzy. The assignment assumes that route choice is based on fuzzy link travel times. The fuzzy travel times are modeled not only as fuzzy numbers, but also as flow dependence. This dependence is accounted for by assuming that the fuzzy travel time for each link is a fuzzy mapping of the flow on that link. Four fuzzy operations are used in the successive representative method, including fuzzy addition, fuzzy multiplication, fuzzy comparison and defuzzification. Our algorithm is exemplified by the road network around Osaka. Sensitivity analysis of the algorithm are also presented.

We propose a fuzzy approach, which induces some methodologies as special cases, for solving the multiple objective linear programming problem. A numerical example is then provided for demonstration.

We propose a new algorithm for solving a fuzzy goal programming (FGP) problem with symmetrically triangular membership functions of fuzzy goals and priority structure. The efficiency of the algorithm is justified by showing its computational superiority over that by Tiwari et al.

This paper formulates the dynamic signal timing optimization (DSTO) problem as a bilevel model. In the upper level, total network travel time is minimized subject to some necessary signalisation constraints. In the lower level, the dynamic user-optimal route choice is formulated as a variational inequality model, which complies with the dynamic extension of Wardrop's first principle. The sensitivity analysis using a generalized inverse approach performed in the link variable is adopted to solve the DSTO problem. It is not required to generate further a nondegenerate extreme path solution from the link flow solution obtained by the FW method in the sensitivity analysis. This is the case as any path flow solution can be readily accommodated for use. Four heuristics with the same framework of the nested diagonalization (ND) method, but differing in updating decision variable or determining step size, are proposed and compared in terms of both computational performance and precision level. Numerical examples show that all four heuristics algorithms can obtain equilibrium solutions in compliance with the dynamic extension of War drop's first principle. However, considering a balance between precision level and computational performance, the Armijo-based method is recommended.

The paper incorporates both the link capacity and first-in-first-out constraints into the dynamic user equilibrium problem. The corresponding dynamic equilibrium conditions for each origin-destination pair and time interval state that the generalized route travel times experienced by travelers are equal and minimal. A nested Lagrangian method embedding the gradient projection algorithm is then proposed and demonstrated with a numerical example.