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    <title>Transport Research International Documentation (TRID)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Transport Research International Documentation (TRID)</title>
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      <title>A new one-level convex optimization approach for estimating origin-destination demand</title>
      <link>https://trid.trb.org/View/1237988</link>
      <description><![CDATA[Accurately estimating Origin-Destination (OD) trip tables based on traffic data has become crucial in many real-time traffic applications. The problem of OD estimation is traditionally modeled as a bilevel network design problem (NDP), which is challenging to solve in large-scale networks. In this paper, the authors propose a new one-level convex optimization formulation to reasonably approximate the bilevel structure, thus allowing the development of more efficient solution algorithms. This one-level approach is consistent with user equilibrium conditions, and improves previous one-level relaxed OD estimation formulations in the literature by 'equilibrating' path flows using external path cost parameters. This new formulation can, in fact, be viewed as a special case of the user equilibrium assignment problem with elastic demand, and hence can be solved efficiently by standard path-based traffic assignment algorithms with an iterative parameter updating scheme. Numerical experiments indicate that this new one-level approach performs very well. Estimation results are robust to network topology, sensor coverage, and observation error, and can achieve further improvements when additional data sources are included.]]></description>
      <pubDate>Thu, 31 Jan 2013 09:08:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/1237988</guid>
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      <title>Time-Dependent Origin–Destination Demand Estimation: Challenges and Methods for Large-Scale Networks with Multiple Vehicle Classes</title>
      <link>https://trid.trb.org/View/1093518</link>
      <description><![CDATA[This paper proposes a modified bi-level optimization algorithm to estimate the time-dependent origin–destination trip matrices for large-scale networks with multiple vehicle classes. Methodologies are presented to overcome the challenges caused by the scale of the problem. The upper-level problem, a bound-constrained quadratic problem, had many variables and parameters for a network with around 68,000 links, 28,000 nodes, and 3,700 zones. Techniques to reduce the number of variables and parameters are described in this study, along with an approach to reduce the time and memory requirement of the lower-level problem. Furthermore, the basic approach, which had been applied only to a single vehicle class, was extended and adapted in this study to estimate matrices for single-occupancy and high-occupancy vehicles jointly. Two solution packages, MINOS and KNITRO, were tested for the upper-level problem. The solution package KNITRO was run with an option to use an interior point–conjugate gradient algorithm, which was well suited to large-scale nonlinear problems. The modified bi-level algorithm was applied to estimate the time-dependent demand patterns for the New York City regional network.]]></description>
      <pubDate>Wed, 18 May 2011 10:51:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/1093518</guid>
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      <title>A Bilevel Genetic Algorithm for a real life location routing problem</title>
      <link>https://trid.trb.org/View/849830</link>
      <description><![CDATA[A fast, robust and efficient algorithm for solving the location routing problem (LRP) for one of the largest companies in Greece, which distributes wood products, is developed. To solve the company's problem, a new formulation of the LRP based on bilevel programming is proposed. Based on the fact that in the LRP, decisions are made at a strategic level and at an operational level, the authors formulate the problem in such a way that in the first level, the decisions of the strategic level are made, namely, the top manager finds the optimal location of the facilities: while in the second level, the operational level decisions are made, namely, the operational manager finds the optimal routing of vehicles. For this problem, a bilevel genetic algorithm is proposed. The algorithm manages to provide practical solutions and significant economic benefits for the company. The algorithm is also tested on a set of benchmark instances. The results of the algorithm are also very satisfactory for those instances and, for six of them, a new best solution has been found.]]></description>
      <pubDate>Mon, 03 Mar 2008 09:00:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/849830</guid>
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    <item>
      <title>Continuous Network Design with Emission Pricing as a Bi-Level Optimization Problem</title>
      <link>https://trid.trb.org/View/794895</link>
      <description><![CDATA[Traffic network design problem attempts to find the optimal network expansion policies under budget constraints. This can be formulated as a bi-level optimization problem” the upper level determines the optimal capacity expansion vector and the lower level determines the link flows subject to user equilibrium conditions. However, in the context of environmental concerns, driver’s route choice includes travel time as well as emission pricing. This study is an attempt to solve network design problems when the user is environment cautious. The problem is formulated as a bi-level continuous network design problem with the upper level problem determines the optimal link capacity expansions subject to user travel behavior. This behavior is represented in the lower level using the classical Wardropian user equilibrium principles. The upper level problem is an example of system optimum assignment and can be solved using any efficient optimization algorithms. Genetic Algorithm is used because of its modeling simplicity. The upper level will give a trail capacity expansion vector and will be translated into new network capacities. This then invokes the lower level with these new link capacities and the output is a vector of link flows which are passed to upper level. The upper level then computes the objective function and GA operators are applied to get a new capacity expansion vector and the process is repeated till convergence. The model is first applied to an example network and the optimum results are shown. Finally the model is applied to a large case study network and the results are presented.]]></description>
      <pubDate>Fri, 29 Dec 2006 11:07:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/794895</guid>
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    <item>
      <title>BILEVEL PARALLEL GENETIC ALGORITHMS FOR OPTIMIZATION OF LARGE STEEL STRUCTURES</title>
      <link>https://trid.trb.org/View/693094</link>
      <description><![CDATA[The authors present a cost optimization model for optimization of space-frame steel structures.  The structures are subjected to the design constraints of the American Institute of Steel Construction allowable stress design and load and resistance factor design specifications on high-performance multiprocessor machines using biologically inspired genetic algorithms.  The computational model has two stages.  First, parallel fuzzy genetic algorithms (GAs) are presented for optimization of steel structures using a distributed memory Message Passing Interface (MPI) with two different schemes.  The first scheme is the processor farming scheme and the second is the migration scheme. Next, two bilevel parallel GAs are presented for large-scale structural optimization through judicious combination of shared memory data parallel processing using the OpenMP Application Programming Interface (API) and distributed memory message passing parallel processing using MPI.  Speedup results are presented for parallel algorithms.]]></description>
      <pubDate>Mon, 17 Sep 2001 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/693094</guid>
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