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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>A Discriminated Release Strategy for Parking Variable Message Sign Display Problem Using Agent-Based Simulation</title>
      <link>https://trid.trb.org/View/1491939</link>
      <description><![CDATA[With the development and application of intelligent transportation systems (ITS), parking guidance and information system (PGIS) has become a hot topic for research and applications. Parking variable message sign (parking VMS) is the most common form of PGIS currently in use, particularly in providing the space availability information to en-route drivers. The aim of this study is to model drivers' responses to the parking VMSs under various parking information in a dynamical way. To ensure the guiding efficiency and assist drivers to identify parking lots with vacant spaces, the display strategies of parking VMS were studied. A discriminated release strategy with a space occupancy indicator (SOI) was then proposed to determine the display content on the parking VMS, in which an agent-based simulation was introduced to incorporate both parking choice model and traffic assignment algorithm dynamically. Guiding reliability and display strategies were further analyzed to attain a discriminated release strategy with SOI effectively. The results indicate that the guiding reliability was improved by reducing the SOI value of the corresponding parking lot, and the relatively uniform guiding reliabilities of all parking lots were attained by modifying SOI values iteratively. Additionally, the performance by incorporating the waiting time into the parking choice model was investigated and recommended. The discriminated release strategy presented in this study will assist in designing and operating urban PGIS.]]></description>
      <pubDate>Tue, 27 Apr 2021 09:30:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1491939</guid>
    </item>
    <item>
      <title>A Dynamic Passenger Assignment Model for High-Speed Railway Networks Based on Constrained Continuous Capacity</title>
      <link>https://trid.trb.org/View/1573277</link>
      <description><![CDATA[For high-speed railway (HSR) networks, the space-time passenger flow distribution plays an important role for determining and optimizing the space-time resource allocation (e.g., train scheduling). This study develops a modeling framework to predict space-time passenger flow distribution for HSR networks with rigid capacity constraints. In particular, the service capacity is approximated as a continuous variable, which is time-and-space-dependent under given track network for the HSR system. The travel demand to be loaded to the HSR network has two features besides the origin and destination stations: ticket-booking time and desired departure time. Following the principle of first-booking-first-served (FBFS), an assignment model is formulated with the rigid capacity constraints, where a later ticket-booking time might result in less available travel options. A solution algorithm is designed for solving the ticket-booking-time-dependent assignment model. The model and its applicability are illustrated with a large-scale Chinese HSR network example.]]></description>
      <pubDate>Fri, 01 Mar 2019 15:51:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1573277</guid>
    </item>
    <item>
      <title>General solution scheme for the Static Link Transmission Model</title>
      <link>https://trid.trb.org/View/1574967</link>
      <description><![CDATA[Until the present day most static traffic assignment models are neither capacity constrained nor storage constrained. Recent studies have resulted in novel approaches that consider capacity constraints and sometimes storage constraints. We build upon the results of these works and the model formulated in our companion paper Bliemer and Raadsen (2018a) which comprises a static assignment model formulation that is both capacity constrained as well as storage constrained. The formulation of this model is derived from a continuous time dynamic network loading model proposed in Bliemer and Raadsen (2018b). The prospect of being able to capture spillback effects in static assignment provides new opportunities for making this modelling method more capable. It is well known that the absence of spillback typically results in significant underestimation of path travel times. This is especially true for paths that do not traverse bottleneck(s) directly, but that are affected by the space occupied of queues that are spilling back. Similar to Smith (2013) and Smith et al. (2013), Bliemer and Raadsen (2018a) did not provide a solution algorithm. In this paper, we take their model formulation and propose a general solution scheme suitable for large scale networks.]]></description>
      <pubDate>Mon, 17 Dec 2018 12:25:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1574967</guid>
    </item>
    <item>
      <title>Finding optimal solutions for vehicle routing problem with pickup and delivery services with time windows: A dynamic programming approach based on state–space–time network representations</title>
      <link>https://trid.trb.org/View/1411070</link>
      <description><![CDATA[Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). This paper first proposes a new time-discretized multi-commodity network flow model for the VRPPDTW based on the integration of vehicles’ carrying states within space–time transportation networks, so as to allow a joint optimization of passenger-to-vehicle assignment and turn-by-turn routing in congested transportation networks. The authors' three-dimensional state–space–time network construct is able to comprehensively enumerate possible transportation states at any given time along vehicle space–time paths, and further allows a forward dynamic programming solution algorithm to solve the single vehicle VRPPDTW problem. By utilizing a Lagrangian relaxation approach, the primal multi-vehicle routing problem is decomposed to a sequence of single vehicle routing sub-problems, with Lagrangian multipliers for individual passengers’ requests being updated by sub-gradient-based algorithms. The authors further discuss a number of search space reduction strategies and test the authors' algorithms, implemented through a specialized program in C++, on medium-scale and large-scale transportation networks, namely the Chicago sketch and Phoenix regional networks.]]></description>
      <pubDate>Wed, 27 Jul 2016 09:49:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1411070</guid>
    </item>
    <item>
      <title>System Optimal Dynamic Lane Reversal for Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/1406016</link>
      <description><![CDATA[Transformative technologies such as autonomous vehicles (AVs) create an opportunity to reinvent features of the traffic network to improve efficiency. The focus of this work is dynamic lane reversal: using AV communications and behavior to change the direction vehicles are allowed to travel on a road lane with much greater frequency than would be possible with human drivers. This work presents a novel methodology based on the linear programming formulation of dynamic traffic assignment using the cell transmission model for solving the system optimal (SO) problem. The SO assignment is chosen because the communications and behavior protocols necessary to operate AV intersection and lane reversal controls could be used to assign routes and optimize network performance. This work expands the model to determine the optimal direction of lanes at small space-time intervals. Model assumptions are outlined and discussed. Results demonstrate the model and explore the dynamic demand scenarios which are most conducive to increasing system efficiency with dynamic lane reversal.]]></description>
      <pubDate>Tue, 28 Jun 2016 16:16:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/1406016</guid>
    </item>
    <item>
      <title>Dynamic pricing, heterogeneous users and perception error: Probit-based bi-criterion dynamic stochastic user equilibrium assignment</title>
      <link>https://trid.trb.org/View/1245707</link>
      <description><![CDATA[A probit-based bi-criterion dynamic stochastic user equilibrium (BDSUE) model is presented to capture path choice behavior of heterogeneous users with distinct values of time (VOT) and different perception of travel costs in response to pricing and congestion in a transportation network. Across the population of travelers, the VOT is represented by a continuously distributed random variable, and path travel cost perception errors are multivariate normally distributed. The BDSUE problem is formulated as a fixed point problem in the infinite dimensional space, and solved by a column generation framework which embeds (i) a parametric analysis method (PAM) to transform the continuous problem to the finite dimensional space by finding breakpoints that partition the entire range of VOT into subintervals and define a multi-class dynamic stochastic user equilibrium (MDSUE) problem; (ii) a column generation algorithm to augment a feasible path set for each user class; (iii) a probit-based stochastic path flow updating scheme to solve a restricted MDSUE problem defined by the set of feasible paths using an averaging method; and (iv) dynamic network loading using a particle-based traffic simulator to capture traffic dynamics and determine experienced travel times for a given path flow pattern. Numerical experiments on a medium size network are conducted to explore convergence of the solution algorithm and to illustrate heterogeneous user responses to dynamic tolls.]]></description>
      <pubDate>Fri, 12 Apr 2013 12:15:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/1245707</guid>
    </item>
    <item>
      <title>Convergence of Time Discretization Schemes for Continuous-Time Dynamic Network Loading Models</title>
      <link>https://trid.trb.org/View/1130000</link>
      <description><![CDATA[This paper discusses the importance of obtaining convergence and choosing discretization schemes to numerically solve continuous-time dynamic network loading (DNL) models, using an α point-queue model as an example. The authors prove consistency, stability and convergence of the discretization schemes for solving the recently developed α point queue model. Also discussed is the implicit and explicit discretization schemes and their implications to DNL solution algorithms.]]></description>
      <pubDate>Fri, 15 Jun 2012 16:05:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/1130000</guid>
    </item>
    <item>
      <title>Evaluation of an equilibrium dynamic traffic assignment algorithm based on splitting rates</title>
      <link>https://trid.trb.org/View/926612</link>
      <description><![CDATA[Equilibrium models, which have for many years been the basis of travel forecasting in the form of static assignment models, are becoming more and more common in the area of dynamic traffic assignment (DTA) modeling. A DTAmodel is any model that assigns a time-varying origin-destination demand to a transport network in a time-varying way (e.g., expressed as time-varying path flows). This study is specifically concerned with DTA models thatare based on an underlying traffic model that satisfies the basic laws oftraffic flow theory as expressed in the well-known fundamental relationship between traffic flow, speed and density (often called the fundamental diagram of traffic). This mainly includes fluid-type traffic models (e.g. hydrodynamic) and traffic micro-simulation models. These types of traffic models can be combined with an assignment (or routing) algorithm in an iterative solution approach which converges to approximate dynamic user-equilibrium conditions. It should be noted that due to the inherent complexity introduced with this type of traffic model, these assignment algorithms arenecessarily heuristic in nature, though they are often inspired from well-known solution algorithms for the static assignment problem. The paper builds upon earlier work on an equilibrium assignment algorithm defined in the space of splitting rates (turning movement volumes by origin or destination), rather than path flows. It should be noted that splitting rate models have a structure that makes them well-suited for extension to en-route dynamic traffic assignment. The algorithm exploits the properties of the fundamental traffic flow relationship in computing a new set of splitting rates on each iteration. The splitting-rate computation explicitly considers the duration of the assignment time interval in conjunction with measures of link performance. The algorithm is used in conjunction with an efficient traffic simulation model, and is tested for the first time on real-world networks of significant size. The primary measures of performance are the number of iterations required to achieve dynamic equilibrium for a given scenario, and the proximity of the solution to true dynamic equilibrium conditions. For the covering abstract see ITRD E145999]]></description>
      <pubDate>Thu, 26 Aug 2010 08:39:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/926612</guid>
    </item>
    <item>
      <title>Congested transit networks: a schedule-based dynamic assignment model with explicit vehicle capacity constraints</title>
      <link>https://trid.trb.org/View/859905</link>
      <description><![CDATA[For high-frequency services, which are typical of urban areas, the treatment of congestion plays a key role in transit modelling. Congestion has usually been considered implicitly by the use of strictly increasing flow-dependent cost functions aiming at discouraging user boarding on vehicle overcrowded, even if the frontier of the research in this field moves towardsconsidering explicitly vehicle capacity constrains, for which users boardthe arriving run/line according to its residual capacity. The problem of taking into account congestion has been studied by several authors by using a frequency-based approach and by considering the concept of effective line frequency, which is a fictitious line frequency different from the real one, that is as lower as higher the congestion on the considered line is, trying to simulate the possibility of failing-to-board the overcrowded lines. As in the frequency-based approach services are represented by lines, single vehicles (runs) have not been explicitly considered; it implies an approximation in calculating single vehicle loads that is as relevant ashigher the variation of the demand profile (typical of the peak hours) is. This approximation also arises in the case of irregular arrival of vehicles at stops, for which peaks of boarding users at stops cannot be explicitly considered. For this reason, the use of a schedule based approach seems to be more adequate to investigate congestion in high-frequency transit networks by the use of explicit vehicle capacity constraints, as each run with its vehicle capacity can be considered, as well as a generic temporaldemand profile can be taken into account. This paper presents a schedule-based dynamic assignment model, which explicitly takes into account vehicle capacity. On the supply side, each run of transit services with its vehicle capacity is explicitly represented both in space and in time through the use of a diachronic network. On the demand side, the time-dependent characterisation of origin-destination trips is made by considering users which have desired arrival times (DAT) or desired departure times (DDT), defined by the starting or ending times of their activities; these target times are assumed to have a degree of flexibility (according to trip purpose, e.g. business, study, commuter-work, and so on), for which users can adaptwithin a certain range their arrival or departure in order to avoid or mitigate congestion effects. The core of the assignment model is the use of a joint departure time and path choice model, in which a path is defined by the choice of an access stop and a boarding run, defined in an explicit space-time dimension (schedule-based approach). The joint departure time and path choice model is based on a mixed pre-trip/en-route choice behaviour, in which some level of service (LOS) attributes (e.g waiting and traveltimes) and congestion (fail-to-board experiences), that are defined at single vehicle (run) level, are estimated by day-to-day learning processes (e.g. through exponential filters). In particular, pre-trip choices are relative to departure time and boarding stops, since they are considered before starting the trip and are mainly influenced by past experiences on congestion, while the en-route choice occurs at stops and concerns the decision to board a given arriving run which has a residual capacity and allows arriving at destination. The assignment model is defined through a dynamic process approach in which the within-day network loading procedure allocates users on each run of transit services (explicitly considered as a link of the diachronic network) according to users choices (made on the basis of experiences on LOS and congestion at single run level) and to the residual capacity of arriving vehicles at stops. If congestion arises, the formation and dispersion of queues at stops is solved through FIFO rules, and fail-to-board experiences, as well as the experimented LOS attributes, are part of the learning mechanism for the next-day user choices. Even if theoretical issues of the assignment model are under investigation, some experimental evidences on toy networks show the empirical convergence of the dynamic process towards a solution. An application example of the previouslydescribed schedule-based dynamic assignment model on a realistically-sized test network (a part of the transit network of Naples in the middle of Italy) has been carried out to explore the convergence of the assignment model, as well as to test the proposed approach and its potential use to assess effects of congestion and to support transit network operations planning. For the covering abstract see ITRD E137145.]]></description>
      <pubDate>Tue, 27 May 2008 09:35:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/859905</guid>
    </item>
    <item>
      <title>Spillback Congestion in Dynamic Traffic Assignment: A Macroscopic Flow Model with Time-Varying Bottlenecks</title>
      <link>https://trid.trb.org/View/840012</link>
      <description><![CDATA[In this paper, the authors propose a new model for the within-day Dynamic Traffic Assignment (DTA) on road networks where the simulation of queue spillovers is explicitly addressed, and a user equilibrium is expressed as a fixed-point problem in terms of arc flow temporal profiles, i.e., in the infinite dimension space of time’s functions. The model integrates spillback congestion into an existing formulation of the DTA based on continuous-time variables and implicit path enumeration, which is capable of explicitly representing the formation and dispersion of vehicle queues on road links, but allows them to exceed the arc length. The propagation of congestion among adjacent arcs will be achieved through the introduction of time-varying exit and entry capacities that limit the inflow on downstream arcs in such a way that their storage capacities are never exceeded. Determining the temporal profile of these capacity constraints requires solving a system of spatially non-separable macroscopic flow models on the supply side of the DTA based on the theory of kinematic waves, which describe the dynamic of the spillback phenomenon and yield consistent network performances for given arc flows. The authors also devise a numerical solution algorithm of the proposed continuous-time formulation allowing for “long time intervals” of several minutes, and give an empirical evidence of its convergence. Finally, the authors carry out a thorough experimentation in order to estimate the relevance of spillback modeling in the context of the DTA, compare the proposed model in terms of effectiveness with the Cell Transmission Model, and assess the efficiency of the proposed algorithm and its applicability to real instances with large networks.]]></description>
      <pubDate>Thu, 15 Nov 2007 10:33:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/840012</guid>
    </item>
    <item>
      <title>Issues in urban travel demand modelling. ICT implications and trip timing choice</title>
      <link>https://trid.trb.org/View/803966</link>
      <description><![CDATA[Travel demand forecasting is essential for many decisions, such as infrastructure investments and policy measures. Traditionally travel demand modelling has considered trip frequency, mode, destination and route choice. This thesis considers two other choice dimensions, hypothesised to have implications for travel demand forecasting. The first part investigates how the increased possibilities to overcome space that ICT (information and communication technology) provides, can be integrated in travel demand forecasting models. We find that possibilities of modelling substitution effects are limited, irrespective of data source and modelling approach. Telecommuting explains, however, a very small part of variation in work trip frequency. It is therefore not urgent to include effects from telecommuting in travel demand forecasting. The results indicate that telecommuting is a privilege for certain groups of employees, and we therefore expect that negative attitudes from management, job suitability and lack of equipment are important obstacles. We find also that company benefits can be obtained from telecommuting. No evidences that telecommuting gives rise to urban sprawl is, however, found. Hence, there is ground for promoting telecommuting from a societal, individual and company perspective. The second part develops a departure time choice model in a mixed logit framework. This model explains how travellers trade-off travel time, travel time variability, monetary and scheduling costs, when choosing departure time. We explicitly account for correlation in unobserved heterogeneity over repeated SP choices, which was fundamental for accurate estimation of the substitution pattern. Temporal constraints at destination are found to mainly restrict late arrival. Constraints at origin mainly restrict early departure. Sensitivity to travel time uncertainty depends on trip type and intended arrival time. Given appropriate input data and a calibrated dynamic assignment model, the model can be applied to forecast peak-spreading effects in congested networks. Combined stated preference (SP) and revealed preference (RP) data is used, which has provided an opportunity to compare observed and stated behaviour. Such analysis has previously not been carried out and indicates that there are systematic differences in RP and SP data (A). This report is also available via Internet at http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4092]]></description>
      <pubDate>Tue, 06 Mar 2007 08:48:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/803966</guid>
    </item>
    <item>
      <title>Revised Approaches for Estimation of Time-Varying Origin-Destination Flows Using Time-Series of Link Counts</title>
      <link>https://trid.trb.org/View/777231</link>
      <description><![CDATA[This paper presents two revised approaches for estimating time-varying Origin-Destination (O-D) flows in urban networks. Both approaches lend themselves to formulation as state-space models. An efficient parameter optimization model with the objective function to minimize the absolute deviations between measured and estimated link counts is first formulated for estimating real-time intersection turning flows, and is solved using genetic algorithm. Based on this, the first approach makes use of O-D flow deviations from historical data as state vectors, and constructs an additional set of system observation constraints with estimated intersection turning flows to improve the system observability. Since there may exist errors in DTA or simulation models, the second approach, also involves the constraints from intersection turning flows, further utilizes path flow deviations as state vectors, and accounts for the inherent errors in assignment/mapping matrix caused by DTA or traffic simulation. Intersection model is testified using field data, and both network models are evaluated using simulation experiments. The reported examples have clearly indicated that the proposed approaches are quite accurate, efficient and robust.]]></description>
      <pubDate>Fri, 21 Jul 2006 14:34:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/777231</guid>
    </item>
    <item>
      <title>Stochastic Quasi-Gradient Algorithm for the Off-Line Stochastic Dynamic Traffic Assignment Problem</title>
      <link>https://trid.trb.org/View/777957</link>
      <description><![CDATA[This paper proposes a stochastic quasi-gradient (SQG) based algorithm to solve the off-line stochastic dynamic traffic assignment (DTA) problem that explicitly incorporates randomness in O–D demand, as part of a hybrid DTA deployment framework for real-time operations. The problem is formulated as a stochastic programming DTA model with multiple user classes. Due to the complexities introduced by real-time traffic dynamics and system characteristics, well-behaved properties cannot be guaranteed for the resulting formulation and analytical functional forms that adequately capture traffic realism typically do not exist for the associated objective functions. Hence, a simulation-based SQG method that is applicable for a generalized differentiable (locally Lipschitz) non-convex objective function and non-convex constraint set is proposed to solve the problem. Simulation is used to estimate quasi-gradients that are stochastic to incorporate demand randomness. The solution approach is a generalization of the deterministic DTA solution methodology; under it, deterministic DTA models are special cases. Of practical significance, it provides a robust solution for the field deployment of DTA, or an initial solution for hybrid real-time strategies. The solution algorithm searches a larger feasible domain of the solution space, leading to a potentially more robust and computationally more efficient solution than its deterministic counterparts. These advantages are highlighted through simulation experiments.]]></description>
      <pubDate>Wed, 15 Mar 2006 08:24:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/777957</guid>
    </item>
    <item>
      <title>MODELLING FRAMEWORK FOR DYNAMIC MULTICLASS TRAFFIC ASSIGNMENT IN ITS ENVIRONMENT</title>
      <link>https://trid.trb.org/View/755503</link>
      <description><![CDATA[Intelligent transportation systems (ITS) use real-time data sources and information about traffic flow, speeds and travel times on roads, locations and causes of delays, and new construction and events not yet causing delays (but which could cause delays by the time drivers reach them).  This article reports on a study that considered the problems of dynamic traffic modeling and driver information systems.  The basic feature of the new dynamic model is a departure from the static assignment assumptions to include time-varying flows and incident events.  The authors describe a dynamic multiclass traffic assignment (DMTA) framework that uses traffic data associated with three backdrops: time, space, and defined "user classes."  The DMTA system includes modules for O-D (origin and destination) estimation and prediction, real-time network state simulation, consistency checking, updating functions, and resetting functions.  The methodological tools used for solving DMTA problems include a combination of mathematical programming, simulation, and heuristic methods.  The authors conclude that there is no analytical or mathematical formulation that can solve the actual size networks.  Thus, mathematical programming and optimal control DMTA approaches have significant limitations in developing usable models for general networks and ITS applications.]]></description>
      <pubDate>Mon, 02 May 2005 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/755503</guid>
    </item>
    <item>
      <title>LINK BASED TRAVEL TIME FUNCTIONS FOR DYNAMIC TRAFFIC ASSIGNMENT MODELS</title>
      <link>https://trid.trb.org/View/734090</link>
      <description><![CDATA[Unlike the static models, the distribution in time and space of the vehicles on a link is a matter of concern when considering dynamic traffic assignment models. Therefore, the Bureau of Public Roads (BPR) travel time function is no longer applicable in a time-dependent traffic network. Instead, many recently proposed dynamic assignment models assume different forms of dynamic link travel time functions. Yet, no well-accepted dynamic link travel time function exists and moreover no consensus has been reached for the suitable form of dynamic link travel time functions. Development of a set of time-dependent link travel time functions for dynamic assignment problems is becoming increasingly important. In order to transform the traffic flow data from roadside detectors into travel times for the purpose of short-term travel forecasting, dynamic link travel functions are also necessary. Link travel time or delay functions, have been extensively studied by traffic engineers. The choice of the dynamic link travel time functions involve several criteria: (1) the desired mathematical properties of the function to satisfy the condition for a unique solution of the model; (2) the cost and limited availablity of road data; (3) the computational effort required by the model; and (4) the desired accuracy of the travel time estimates generated by the model. The object of this paper is to review currently available delay models, show some of their properties, identify suitable functions and develop them into dynamic link travel time functions, which would be applicable in dynamic assignment.]]></description>
      <pubDate>Fri, 07 Mar 2003 00:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/734090</guid>
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