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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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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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    <item>
      <title>ASSESSING THE PERFORMANCE OF ARTIFICIAL NEURAL NETWORK INCIDENT DETECTION MODELS</title>
      <link>https://trid.trb.org/View/506852</link>
      <description><![CDATA[This paper explores the performance of a relatively new generation of algorithms for automated freeway incident detection using Artificial Neural Networks (ANNs). These new models have the potential to provide faster and more reliable incident detection times and fault-tolerant operation while being easy to implement on existing and new hardware platforms. The ANN incident detection models were trained on data obtained from two freeways in Melbourne, Australia. Two sources of data were used to assemble the training data sets. The first comprised speed, flow and occupancy data from dual-loop detector stations and the second was an incident log showing the approximate time of incidents. The off-line performance of the model is reported under both incident and non-incident conditions. The false alarm rates of the model are evaluated based on non-incident data that were collected from a freeway section which was video-taped for a period of 33 days. The results presented provide a comprehensive evaluation of the performance of the ANN model and confirm that neural network models can provide fast and reliable incident detection on freeways.]]></description>
      <pubDate>Thu, 09 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506852</guid>
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    <item>
      <title>RELIABILITY MEASURES OF AN ORIGIN AND DESTINATION PAIR IN A DETERIORATED ROAD NETWORK WITH VARIABLE FLOWS</title>
      <link>https://trid.trb.org/View/506853</link>
      <description><![CDATA[A flow network should be studied for evaluating the reliability of a degraded road network, in which the inconvenience of travel may bring the reduction of travel demand and network flow pattern may change. This paper aims to show reliability measures of an origin and destination (OD) pair in a road network when some links are possibly damaged by natural disasters and may be closed to traffic. The UE model with variable demand and strict link capacity constraints is applied to describe flows in a network with some disconnected links. An approximation algorithm is proposed for estimating the cumulative travel time distribution between an OD pair. Small scale examples are calculated to test the performance of the proposed algorithm.]]></description>
      <pubDate>Thu, 09 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506853</guid>
    </item>
    <item>
      <title>VARIATIONAL INEQUALITY MODEL OF IDEAL DYNAMIC USER-OPTIMAL ROUTE CHOICE</title>
      <link>https://trid.trb.org/View/506854</link>
      <description><![CDATA[An ideal dynamic user-optimal (DUO) route choice model is described for predicting dynamic traffic conditions, as required for off-line evaluation of Advanced Traffic Management Systems and Advanced Traveler Information Systems. The model is formulated as a variational inequality (VI), a general way of describing a dynamic network equilibrium. Although route-based VI models have an intuitive interpretation, their computational complexity makes them intractable for real applications. Consequently, the proposed model is formulated as a link-based variational inequality for use in large-scale implementations. Using the diagonalization technique with discrete time intervals, the model is solved to a specified level of convergence. Computational results for a real, large-scale traffic network are presented.]]></description>
      <pubDate>Thu, 09 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506854</guid>
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    <item>
      <title>TRAVEL TIMES COMPUTATION FOR DYNAMIC ASSIGNMENT MODELING</title>
      <link>https://trid.trb.org/View/506855</link>
      <description><![CDATA[Travel time is the commonly used cost function for dynamic road traffic assignment. The paper begins with a review of various travel time and aggregation methods used in simulation models. It further attempts to clarify the different definitions of travel times given in the literature: experienced travel time, predictive travel time, instantaneous travel time and to give mathematical definitions of these quantities, notably for macroscopic models. Finally, recursive computational procedures are given for the discretized case.]]></description>
      <pubDate>Thu, 09 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506855</guid>
    </item>
    <item>
      <title>FOUNDATIONS OF A THEORY OF DISEQUILIBRIUM NETWORK DESIGN</title>
      <link>https://trid.trb.org/View/506845</link>
      <description><![CDATA[This paper serves as a primer on a new class of models, so-called disequilibrium network design models. These models maintain the usual design objective of maximizing some measure of social welfare, but recognize that traffic on a network is not necessarily in equilibrium and that capacity changes to the network must induce transient phenomena not captured by the invocation of Wardrop's First Principle (user equilibrium). Such models by their vary nature avoid temporal versions of Braess's paradox known from static equilibrium design models.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506845</guid>
    </item>
    <item>
      <title>THE CONTINUOUS EQUILIBRIUM OPTIMAL NETWORK DESIGN PROBLEM: A GENETIC APPROACH</title>
      <link>https://trid.trb.org/View/506846</link>
      <description><![CDATA[A genetic algorithm (GA) program for providing a solution to the Continuous Equilibrium Network Design Problem (NDP) is introduced following a general discussion of the network design problem and genetic algorithms. A description of the current GA operators used in the program are described and early preliminary results shown. While the program is in its early stages of development the results have been encouraging and so further development is planned utilizing more of the characteristics of the continuous NDP to reduce the computational burden.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506846</guid>
    </item>
    <item>
      <title>CONTINUOUS EQUILIBRIUM NETWORK DESIGN PROBLEM WITH ELASTIC DEMAND: DERIVATIVE-FREE SOLUTION METHODS</title>
      <link>https://trid.trb.org/View/506847</link>
      <description><![CDATA[Three representative algorithms, namely the Hooke-Jeeves method, the equilibrium decomposed optimization heuristic and simulated annealing, which have been applied to the equilibrium network design problem (ENDP) with fixed demand, are compared by numerical examples. The comparison is extended to cover the ENDP with elastic demand. A bi-level formulation of the ENDP with elastic demand is presented and validated. Experiments on simple and complex networks reveal some interesting and important phenomena, for instance, these three algorithms can give very different results when different values of the algorithm's parameters are used. The simulated annealing approach can always obtain the best solution while its computation time requirement is the most too. The equilibrium decomposed optimization heuristic is the most computationally efficient, but the solutions it yields are not quite as good as those given by the Hooke-Jeeves method, in most cases. A warning and suggestions are given in the last section.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506847</guid>
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    <item>
      <title>A COLUMN GENERATION APPROACH TO BUS DRIVER SCHEDULING</title>
      <link>https://trid.trb.org/View/506848</link>
      <description><![CDATA[Mathematical programming approaches to solving the driver scheduling problem have become successful with improvements in computer technology but heuristics are also necessary to reduce many problems to a manageable size. A column generation method is described which allows much larger problems to be solved than is currently possible. The approach allows problems to be solved more quickly than with the current approach and encourages better solutions to be found due to the availability of a larger set of potential duties.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506848</guid>
    </item>
    <item>
      <title>STOCHASTIC NETWORK MODELS AND SOLUTION METHODS FOR DYNAMIC FLEET MANAGEMENT PROBLEMS</title>
      <link>https://trid.trb.org/View/506849</link>
      <description><![CDATA[Dynamic networks have been used in a variety of transportation and logistics problems that involve both the spatial and time dimensions. In real-world applications, most of these problems concern decision making in an uncertain environment. With the recent advances of information technology, making decisions by using real-time information is now possible. In this paper, the author first describes these problems in the contexts of vehicle allocation for truckload carriers and empty container repositioning for ocean shipping. Second, the stochastic network formulations of these problems are shown. Third, the author compares solution methods that take advantage of the network structure. Finally, directions for developing new solutions methods are proposed.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506849</guid>
    </item>
    <item>
      <title>PERIODIC SHIPPING STRATEGIES FOR THE MINIMIZATION OF THE LOGISTIC COSTS</title>
      <link>https://trid.trb.org/View/506850</link>
      <description><![CDATA[In this paper the authors study the often neglected relationship between inventory and transportation costs in several logistic networks. In particular, the authors first consider the problem of shipping several products in the single link case. In this problem a set of products has to be shipped from a common origin to a common destination; the aim is to determine a periodic shipping strategy which minimizes the sum of the transportation and inventory costs. A general framework of analysis is presented from which the authors derive the known approaches with a continuous frequency and with given frequencies as particular cases. Moreover, the authors consider two more complex logistic networks, the one origin-multiple destinations case and the sequences of links case, and they show a property of the inventory.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506850</guid>
    </item>
    <item>
      <title>AN ALGORITHM FOR THE COMBINED DISTRIBUTION AND ASSIGNMENT MODEL</title>
      <link>https://trid.trb.org/View/506851</link>
      <description><![CDATA[The authors consider the problem of simultaneously determining the distribution of trips between origins and destinations in a transportation network and the assignment of trips to routes in each origin-destination pair. They consider a model for such a problem where the distribution follows a gravity model and the assignment a user equilibrium model. The most well-known algorithm for this model is that of Evans (1976). Although this algorithm has been shown to be more efficient than the Frank-Wolfe method, which it generalizes, it builds the sequence of link flow solutions based on the same underlying algorithmic principle, and therefore also is subject to slow convergence. The authors propose to combine Evans' approach with the disaggregate simplicial decomposition (DSD) algorithm for updating the link flows; this leads to faster convergence, as well as other improvements. For example, the algorithm is less sensitive to inexact solutions of the entropy maximization subproblems; it further provides explicit route flows in each of the origin-destination pairs. Numerical results are presented for four small and medium scale network models.]]></description>
      <pubDate>Wed, 08 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506851</guid>
    </item>
    <item>
      <title>TRAFFIC EQUILIBRIUM IN A DYNAMIC GRAVITY MODEL AND A DYNAMIC TRIP ASSIGNMENT MODEL</title>
      <link>https://trid.trb.org/View/506844</link>
      <description><![CDATA[This paper is concerned with the analysis of traffic network equilibria in a dynamic gravity model and a dynamic logit-based trip assignment model. These two models have a similar mathematical form and so are put together for consideration. Both models were suggested by Dendrinos and Sonis (1990) as modifications of, respectively, the conventional static gravity model (Ortuzar and Willumsen, 1990) and the logit-based trip assignment model (Dial, 1971), but without giving any detailed analysis.]]></description>
      <pubDate>Tue, 07 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506844</guid>
    </item>
    <item>
      <title>DESCENT METHODS OF CALCULATING LOCALLY OPTIMAL SIGNAL CONTROLS AND PRICES IN MULTI-MODAL AND DYNAMIC TRANSPORTATION NETWORKS</title>
      <link>https://trid.trb.org/View/506892</link>
      <description><![CDATA[A bilevel descent method of optimizing signals and prices for a multi-modal network while taking account of travellers' choices (equilibrium) is specified within a framework which may, when developed, be efficient for large networks. Similar trilevel methods for the corresponding dynamic problem are also presented. Fairly complete proofs of convergence of the method to a local optimum are given for the steady state case but there remains a gap when the authors seek to prove convergence in a dynamic context; however, if the method converges (in a dynamic context) to the set of equilibria then (under natural conditions) it must also converge to the set of local optima.]]></description>
      <pubDate>Tue, 07 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506892</guid>
    </item>
    <item>
      <title>ANALYSIS OF TRAFFIC MODELS FOR DYNAMIC EQUILIBRIUM TRAFFIC ASSIGNMENT</title>
      <link>https://trid.trb.org/View/506893</link>
      <description><![CDATA[The authors investigate the properties and suitability of various traffic models for use in dynamic assignment using an analysis based upon a dynamic extension of Wardrop's equilibrium condition for route choice. The authors consider various requirements of plausible traffic behavior, notably conservation of traffic and dependence only on traffic downstream, and establish the crucial importance of the latter in the present context. General analytical results are complemented by calculations for simple example networks: this shows that the deterministic queuing and the kinematic wave models of traffic are suitable for this use, but that several other traffic models that are used widely give rise to dynamic assignments that have unacceptable characteristics.]]></description>
      <pubDate>Tue, 07 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506893</guid>
    </item>
    <item>
      <title>AN EFFICIENT ALGORITHM FOR THE CONTINUOUS NETWORK LOADING PROBLEM: A DYNALOAD IMPLEMENTATION</title>
      <link>https://trid.trb.org/View/506894</link>
      <description><![CDATA[The continuous dynamic network loading problem (CDNLP) consists in determining, on a congested network, time-dependent arc volumes, together with arc and path travel times, given the time varying path (departure) flow rates over a finite time horizon. This problem constitutes an intrinsic part of the dynamic traffic assignment problem. An efficient implementation is developed. Numerical examples are provided.]]></description>
      <pubDate>Tue, 07 Sep 1999 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/506894</guid>
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