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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>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Semi-Markov Process Based Localization Using Radar in Dynamic Environments</title>
      <link>https://trid.trb.org/View/1406249</link>
      <description><![CDATA[Automotive localization in urban environment faces natural long-term changes of the surroundings. In this work, a robust Monte-Carlo based localization is presented. Robustness is achieved through a stochastic analysis of previous observations of the area of interest. The model uses a grid-based Markov chain to instantly model changes. An extension of this model by a Lévy process allows statements about reliability and prediction for each cell of the grid. Experiments with a vehicle equipped with four short range radars show the localization accuracy performance improvement in a dynamic environment.]]></description>
      <pubDate>Wed, 15 Jun 2016 12:05:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406249</guid>
    </item>
    <item>
      <title>Research on Fuzzy Adaptive H∞ Robust Filter for Integrated Navigation System</title>
      <link>https://trid.trb.org/View/1365817</link>
      <description><![CDATA[This paper presents a novel integrated navigation algorithm based on the fuzzy adaptive Hinfin robust filter. By monitoring covariance between abstract and actual residual error, the definition of filtering performance factor is given. Based on the relationship between filtering performance factor, attenuation level and the parameter gamma in Hinfin robust filter, a fuzzy inference system is designed to choose gamma suitably and adaptively, so that there could be a balance between filtering accuracy and robustness performance accordingly. Analysis with practical train integrated navigation data validates the performance and practical value of the proposed algorithm.]]></description>
      <pubDate>Fri, 28 Aug 2015 14:00:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1365817</guid>
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    <item>
      <title>Dynamic congestion pricing with demand uncertainty: A robust optimization approach</title>
      <link>https://trid.trb.org/View/1237986</link>
      <description><![CDATA[In this paper, the authors consider dynamic congestion pricing in the presence of demand uncertainty. In particular, the authors apply a robust optimization (RO) approach based on a bi-level cellular particle swarm optimization (BCPSO) to optimal congestion pricing problems when flows correspond to dynamic user equilibrium on the network of interest. Such a formulation is recognized as a second-best pricing problem, and the authors refer to it as the dynamic optimal toll problem with equilibrium constraints (DOTPEC). The authors then present numerical experiments in which BCPSO is compared with two alternative robust dynamic solution approaches: bi-level simulated annealing (BSA) and cutting plane-based simulated annealing (CPSA), as well as a nominal dynamic solution and a robust static solution. The authors show that robust dynamic solutions improve the worst case, average, and stability of total travel cost in comparison with the nominal dynamic and the robust static solutions. The numerical results also show that BCPSO outperforms BSA and CPSA in terms of solution quality and computational efficiency.]]></description>
      <pubDate>Thu, 31 Jan 2013 09:08:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1237986</guid>
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    <item>
      <title>An Integrated Model for Evacuation Routing and Traffic Signal Optimization with Background Demand Uncertainty</title>
      <link>https://trid.trb.org/View/1129735</link>
      <description><![CDATA[Emergency evacuation in congested urban networks can be influenced by uncertain background travel demands, but this issue has not been fully investigated. In this study, an integrated model is proposed for determining flows on emergency evacuation routes and green ratios at signal intersection in the presence of uncertain background travel demands. The problem is formulated as a bi-objective bi-level programming model based on the concept of robust optimization. It is assumed that background travel demands belong to an ellipsoidal likelihood region whose parameters are determined by a singly-constrained gravity model. Aimed at maximizing the Background Traffic Impact Degree (BTID) in the lower-level model with a logit-based stochastic assignment constraint, background traffic corresponding to worst-case demands is determined by the Lagrange multiplier method. In the upper-level model, two objectives, minimizing both the BTID and total travel time of evacuation flows, are constructed to determine optimal evacuation flows and green ratios. The Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) is employed to determine the Pareto Solutions of this optimization problem. An example using Sioux Falls networks illustrates the validity of the algorithm. A field case involving Jianye networks around the Nanjing Olympics Sports Center shows the applicability of this algorithm.]]></description>
      <pubDate>Wed, 30 May 2012 15:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/1129735</guid>
    </item>
    <item>
      <title>Evaluating Methods and Algorithms for Multicriteria Bridge Management at the Network Level</title>
      <link>https://trid.trb.org/View/1093157</link>
      <description><![CDATA[In the current era of greater stakeholder participation, limited funding, and increased user awareness, bridge managers are using multiple-criteria techniques in their decision-making processes so that their decisions can be more accountable and transparent. Issues that arise in making such decisions include the multiplicity of the performance criteria and associated decision constraints and the large bridge inventories in most states. These issues translate into increased problem dimensionality, many variables, and excessive computational time. This paper presents alternative formulations and methods for decision problems, identifies alternative algorithms for implementing the solutions, and evaluates the performance of the algorithms. The algorithms include the incremental utility–cost ratio (IUC), Lagrangian relaxation, and pivot and complement methods. In investigating the suitability of these algorithms for agencywide bridge management, the study carried out computational experiments with field data. The criteria for evaluating the performance of the algorithms were computational speed, accuracy, simplicity, and robustness. The results were unequivocal: of the three heuristics, IUC consistently performed the best. IUC provided the simplest and quickest way to compute the optimal solution when small changes were made to the input parameters without the need to redo the entire optimization for each budget level.]]></description>
      <pubDate>Wed, 18 May 2011 10:51:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1093157</guid>
    </item>
    <item>
      <title>Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm</title>
      <link>https://trid.trb.org/View/933299</link>
      <description><![CDATA[In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.]]></description>
      <pubDate>Thu, 16 Sep 2010 12:43:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/933299</guid>
    </item>
    <item>
      <title>Empirical Evaluation of New Robust Travel Time Estimation Algorithms</title>
      <link>https://trid.trb.org/View/911353</link>
      <description><![CDATA[Travel times are key statistics for traffic performance, policy, and management evaluation purposes. Estimating travel times from local traffic speeds collected with loops or other sensors has been a relevant and lively research area. The most widespread and arguably most flexible algorithms developed for this purpose fall into the class of trajectory methods, which reconstruct synthetic vehicle trajectories on the basis of measured spot speeds and encompass various assumptions on which speeds prevail between traffic sensors. From these synthetic trajectories, average travel times can be deduced. This paper reviews and compares a number of these algorithms against two new trajectory algorithms based on spatiotemporal filtering of speed and 1/speed (slowness). On the basis of real data (from induction loops and an automated vehicle identification system), it is demonstrated that these new algorithms are more accurate (in terms of bias and residual error) than previous algorithms, and more robust with respect to increasing amounts of missing data.]]></description>
      <pubDate>Tue, 16 Mar 2010 06:13:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/911353</guid>
    </item>
    <item>
      <title>Evacuation Under Data Uncertainty: Robust Linear Programming Model</title>
      <link>https://trid.trb.org/View/881916</link>
      <description><![CDATA[This paper considers evacuation via surface transportation networks in an uncertain environment. The authors focus on uncertainty associated with significant infeasibility cost in evacuation, where loss of life or property may appear. In particular, the authors flexibly change the impact of infeasibility in the form of penalty function. We develop a robust linear programming model based on a robust optimization approach. The authors show that the robustness in evacuation is important and a robust solution may outperform a nominal deterministic solution in both quality and feasibility.]]></description>
      <pubDate>Wed, 29 Apr 2009 13:43:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/881916</guid>
    </item>
    <item>
      <title>Modelling Robust Flight-Gate Scheduling as a Clique Partitioning Problem</title>
      <link>https://trid.trb.org/View/874040</link>
      <description><![CDATA[The authors of this paper consider the problem of assigning flights to airport gates. The research examines a general case wherein an aircraft serving a flight may be assigned to different arrival and departure gates for processing, as well as for optional intermediate parking. Various objectives and assignment restrictions are considered in the research. For objectives, these include: 1) maximization of the total assignment preference score; 2) minimization of the number of unassigned flights during overload periods; 3) minimization of the number of tows; and 4) maximization of the robustness of the resulting schedule, taking flight delays into account. Restrictions include gate closures and shadow restrictions (gate assignments that potentially may block neighboring gates). The authors transform the flight-gate scheduling (FGS) problem into a graph problem -- the clique partitioning problem (CPP). They note several major advantages to this transformation, including the availability of effective algorithms for the CPP, increased simplicity in approach by placing data on the objectives and restrictions within a complete graph, and the easy integration of a robustness objective. The CPP is solved using a heuristic algorithm based on the ejection chain algorithm of Dorndorf and Pesch (1994). Results show the potential of the approach, as well as its superiority to the currently used ruled-based systems.]]></description>
      <pubDate>Tue, 25 Nov 2008 07:34:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/874040</guid>
    </item>
    <item>
      <title>An Effective Multirestart Deterministic Annealing Metaheuristic for the Fleet Size and Mix Vehicle-Routing Problem with Time Windows</title>
      <link>https://trid.trb.org/View/874035</link>
      <description><![CDATA[A new deterministic annealing metaheuristic (DA) is presented in this paper to address the fleet size and mix vehicle-routing problem, which include time windows. The objective of the problem is to use a heterogeneous capacitated vehicle fleet to deliver service to a set of customers at minimal total cost and within their respective time windows. After motivating and defining the problem, the authors give a mathematical formulation of the most studied variant reported in the literature, which takes the form of a mixed-integer linear program. They also suggest an alternative definition that offers relevance to industry and leads to the development of a linear mixed-integer formulation. Also suggested is a metaheuristic solution (MSDA) method to solve the two variants of the problem. The metaheuristic is comprised of three phases, including: 1) a savings-based heuristic, combining diversification strategies with learning mechanisms; 2) a new local search procedure designed to reduce the number of routes in the initial solution; and 3) a DA framework containing a set of four local search operators designed to guide the improvement process. New implementation strategies to provide time window feasibility checks are also proposed. Results of computational experiments on the 168 benchmark instances introduced by Liu and Shen (1999) show that the proposed method outperforms previously published results and yields 167 of the best known solutions, indicative of an algorithmic approach that is both robust and highly effective. Results for the new problem variant are also provided.]]></description>
      <pubDate>Tue, 25 Nov 2008 07:34:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/874035</guid>
    </item>
    <item>
      <title>Short-Haul Airline Crew Rostering by Using Inequality-Based Multiobjective Genetic Algorithm</title>
      <link>https://trid.trb.org/View/871536</link>
      <description><![CDATA[A novel method of inequality-based multiobjective genetic algorithm (MMGA) is presented for solving the airline crew rostering problem. This approach combines a traditional genetic algorithm with a multiobjective optimization method to address multiple objectives at the same time and to explore the optimal solution. With the method of inequalities, the objective can be modified to update different requirements. Therefore, the proposed MMGA approach has the advantages of global exploration and robustness. There are numerous local optima in airline crew rostering problems, so the problem is challenging to evaluate by using genetic algorithm-based approaches. Computational experiments show that the proposed method can obtain better and more robust results than those of manual rosters made by rosterers who have many years of experience. The research objective was to solve the complex airline crew rostering problem by using the MMGA approach and to demonstrate that this method can reduce the solution time. The proposed method is successfully applied to a domestic short-haul airline. It helps managers find a high-quality, feasible roster and reduce crew-related costs in crew rostering applications.]]></description>
      <pubDate>Thu, 02 Oct 2008 14:41:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/871536</guid>
    </item>
    <item>
      <title>Heuristic Approaches for the Fleet Size and Mix Vehicle Routing Problem with Time Windows</title>
      <link>https://trid.trb.org/View/844372</link>
      <description><![CDATA[A constructive insertion heuristic and a metaheuristic algorithm for fleet size and mix vehicle routing problem with time windows (FSMVRPTW) are presented in this paper. The problem looks at simultaneously determining both the composition and the routing of a fleet of heterogeneous vehicles serving a given set of customers. The research presents a set of minimum-cost routes that originate and terminate at a central depot where customers with known demands are served within specified time windows. Extensive computational experiments on benchmark instances show the proposed method to be both robust and efficient. The method outperforms previously published results.]]></description>
      <pubDate>Mon, 28 Jan 2008 08:09:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/844372</guid>
    </item>
    <item>
      <title>The Robust Traveling Salesman Problem with Interval Data</title>
      <link>https://trid.trb.org/View/836721</link>
      <description><![CDATA[This paper presents an extension of the traveling salesman problem, with travel times expressed as a range of possible values. To make the results of the computational experiments as realistic as possible, the authors apply the robust deviation criterion to optimize the interval data problem. The research identifies and explores theoretical aspects of new optimization problems. The discussion also encompasses a new mathematical formulation, along with some exact and heuristic algorithms.]]></description>
      <pubDate>Thu, 25 Oct 2007 10:27:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/836721</guid>
    </item>
    <item>
      <title>Influence of Plastic Deformations on Growth of Subsurface Rolling Contact Fatigue Cracks in Railway Wheels</title>
      <link>https://trid.trb.org/View/805667</link>
      <description><![CDATA[This article investigates a subsurface rolling contact fatigue crack in a railway wheel. Elasto-plastic finite-element simulations are used to evaluate the stress and strain fields. The authors also investigate the robustness and validity of the numerical model, along with searching for a suitable measure that characterizes crack propagation. Numerical results show that the effect of crack tip plasticity is small, which indicate the applicability of linear elastic fracture mechanics methods. Moreover, results are negative for the magnitudes of the stress intensity factor in mode I, K and subscript I, implying crack closure. The authors use the stress intensity factor range in mode II, increment K and subscript II as a measure of crack propagation. They also conduct studies to quantify the effects of altered contact load conditions and crack face friction.]]></description>
      <pubDate>Wed, 25 Apr 2007 13:45:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/805667</guid>
    </item>
    <item>
      <title>Calculation of Resistance to Motion of a High-Speed Train Using Acceleration Measurements in Irregular Coasting Conditions</title>
      <link>https://trid.trb.org/View/805666</link>
      <description><![CDATA[This article presents a study on the resistance to motion of the Korean high-speed train with calculations that have been evaluated using train speed measurements gathered from coasting tests. The speed range of these tests is from 30 to 300 km/h for a seven-car configuration, conducted under various track conditions. Several test runs of the Korean high-speed train have been performed on the Seoul-Busan high-speed line since August 2002. In this study, the authors have employed a modified method for regression and the normal regression method to calculate the running resistance of the train. This method is designed to eliminate errors due to the irregular test conditions of slope, train speed, and coasting distance. The authors also include an expanded discussion on the validity and robustness of the calculation methods. Based on the general resistance to motion equation for the Train à Grande Vittesse, initial estimates of resistance to motion for the Korean high-speed train are compared with measurements from the coasting test. Curve fits of the acceleration data using a second-order polynomial show that low-speed region error is relatively high. However, the authors note that fixing the constant and low-order coefficients can generate more robust curve-fitting results. The authors conclude that based on the resistance to motion equation for the Korea Train Express train, especially in the high-speed region, the Korean high-speed train has a lower actual resistance to motion than the estimated value.]]></description>
      <pubDate>Wed, 25 Apr 2007 13:45:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/805666</guid>
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