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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>
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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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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Robust Optimization Approach for Transportation Network Design under Demand Uncertainty</title>
      <link>https://trid.trb.org/View/2203788</link>
      <description><![CDATA[This paper reviews the authors' recent developments on applying a robust optimization approach to transportation network design problems where future travel demands are uncertain and traffic flows on the underlying network are in user equilibrium. The authors assume that the travel demands belong to an uncertainty set instead of having them follow some probability distributions and then design the network against the worst-case scenario realized in the set. The problems are formulated as mathematical programs with complementarity constraints, which are efficiently solvable by a cutting-plane scheme. Numerical examples are provided to demonstrate that the designs from the robust optimization approach perform more stably and guard better against worst-case scenarios than those from traditional deterministic approach.]]></description>
      <pubDate>Thu, 18 Jul 2024 10:49:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2203788</guid>
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    <item>
      <title>A survey on the transit network design and frequency setting problem</title>
      <link>https://trid.trb.org/View/1990694</link>
      <description><![CDATA[Appropriate public transport systems are crucial in modern cities. Given the high costs that they represent and the impact they have on people’s lives, effective tools are required to support their design. With this in mind, the Transit Network Design problem (TNDP) and the Transit Network Design and Frequency Setting problem (TNDFSP) have been extensively studied in the domain of Operations Research. However, due to the complexity of these problems, multiple simplifications are typically made when modelling and designing solution algorithms. Therefore, still no optimization techniques are available to address these problems in practice. Moreover, different studies address different versions of the problem, with varying assumptions and constraints, complicating the comparison of results or solution approaches. This paper presents an extensive survey of studies addressing the TNDP and the TNDFSP. It discusses the different assumptions, constraints, objectives, solution approaches and testing instances that have been considered in the literature. Furthermore, a detailed analysis is done regarding the case studies considered for the TNDFSP. Moreover, the variants of the passenger assignment subproblem that have been applied within the TNDP and the TNDFSP are discussed. The analysis shows that extensive research has been done regarding these problems. However, it also identified the significant gap that still exists between theory and practice, even in the studies addressing case studies.]]></description>
      <pubDate>Thu, 21 Jul 2022 11:30:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/1990694</guid>
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      <title>Transit network design using a genetic algorithm with integrated road network and disaggregated O–D demand data</title>
      <link>https://trid.trb.org/View/1768698</link>
      <description><![CDATA[Evolutionary algorithms have been used extensively over the past 2 decades to provide solutions to the Transit Network Design Problem and the Transit Network and Frequencies Setting Problem. Genetic algorithms in particular have been used to solve the multi-objective problem of minimizing transit users’ and operational costs. By finding better routes geometry and frequencies, evolutionary algorithms proposed more efficient networks in a timely manner. However, to the knowledge of the authors, no experimentation included precise and complete pedestrian network data for access, egress and transfer routing. Moreover, the accuracy and representativeness of the transit demand data (Origin Destination matrices) are usually generated from fictitious data or survey data with very low coverage and/or representativity. In this paper, experiments conducted with three medium-sized cities in Quebec demonstrate that performing genetic algorithm optimizations using precise local road network data and representative public transit demand data can generate plausible scenarios that are between 10 and 20% more efficient than existing networks, using the same parameters and similar fleet sizes.]]></description>
      <pubDate>Tue, 27 Apr 2021 09:28:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/1768698</guid>
    </item>
    <item>
      <title>Integrating Fixed and Demand-responsive Transportation for Flexible Transit Network Design</title>
      <link>https://trid.trb.org/View/1759155</link>
      <description><![CDATA[In cities around the world, transit is currently provided with fixed route transportation only, whence the inherent limited Quality of Service (QoS) for travelers in suburban areas and during off-peak. On the other hand, it has been shown that completely replacing fixed-route with demand-responsive transit fails to serve the high transportation demand during peak hours. Therefore, it is still unclear how one can maximize the potential of demand-responsive transit by satisfying the complicated demand pattern varying with time and space. In this paper the authors propose Flexible Transit, a transit system design which gets the best from fixed-route and demand-responsive transit, depending on the demand observed in each sub-region of the urban conurbation and time-of-day. The goal is to provide high transportation capacity while guaranteeing high QoS, two objectives that are instead conflicting with classic fixed-schedule transportation. To this end, the authors first resort to microsimulation to show the limits of using either only fixed-route buses or only demand-responsive buses. This motivates the need of alternating between them instead, which the authors do in Flexible Transit. The authors then resort to Continuous Approximation to find the optimal design of flexible transit. The authors show that the flexible transit can significantly improve the user costs, in particular in suburban areas, while also reducing the overall cost of user and operator. The authors believe their findings suggest important policy insights in designing and planning of future transit systems, to take full advantage of demand-adaptive transportation modes.]]></description>
      <pubDate>Thu, 04 Feb 2021 16:48:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/1759155</guid>
    </item>
    <item>
      <title>Can travel time variability be ignored when solving the transit network design problem?</title>
      <link>https://trid.trb.org/View/1669851</link>
      <description><![CDATA[Due to the ever-increasing demand for transportation, and the resulting congestion in urban areas, the efficient design of transit systems nowadays is more relevant than ever before. In order to attract users, transit systems should deliver an added benefit. Improving transit systems can assist in encouraging the public to abandon their private vehicle and make an increasing use of transit. This in turn will support generating more sustainable and less congested transportation systems. Numerous studies have been dedicated to the Transit Network Design Problem (TNDP), each of which manifested other aspects of the problem, and suggested different solution approaches. A common division identifies a sequence of 5 decisions, covering different related aspects of the problem: the design of the routes, setting frequencies, timetable development, bus scheduling and driver scheduling. This paper discuss the first two stages, namely, designing the routes of the system and setting their frequencies, referred in several previous studies as the Transit Network Design and Frequencies Setting Problem. The main contribution of this study lies in proposing a new formulation for the TNDFSP, a formulation which is based on the solution of the user equilibrium, and therefore increases the reliability of the model by capturing travel time variability. Moreover, this study provides means for assessing the correctness of the assumption underlying previous models, suggesting that travel time variability can be neglected while solving the TNDFSP.]]></description>
      <pubDate>Fri, 20 Dec 2019 16:24:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/1669851</guid>
    </item>
    <item>
      <title>Metaheuristics for the transit route network design problem: a review and comparative analysis</title>
      <link>https://trid.trb.org/View/1669619</link>
      <description><![CDATA[This paper critically reviews applications of metaheuristics for solving the Transit Route Network Design Problem (TRNDP). A structured review is offered and prominent metaheuristics for tackling the TRNDP are evaluated, according to a benchmark network. The review findings yield a unified implementation framework, which contains common algorithmic components and different solution representations and methods, which are considered important for obtaining solutions of good quality. The paper concludes with identified gaps in research and opportunities for future research on the application of metaheuristic algorithms for solving the TRNDP.]]></description>
      <pubDate>Fri, 20 Dec 2019 16:23:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1669619</guid>
    </item>
    <item>
      <title>Integrated Railway Rapid Transit Network Design and Line Planning problem with maximum profit</title>
      <link>https://trid.trb.org/View/1605663</link>
      <description><![CDATA[The authors solve the Integrated Network Design and Line Planning Problem in Railway Rapid Transit systems with the objective of maximizing the net profit over a planning horizon, in the presence of a competing transportation mode. Since the profitability of the designed network is closely related with passengers’ demand and line operation decisions, for a given demand, a transit assignment is required to compute the profit, calculating simultaneously the frequencies of lines and selecting the most convenient train units. The proposed iterative solving procedure is governed by an adaptive large neighborhood search metaheuristic which, at each iteration, calls a branch-and-cut algorithm implemented in Gurobi in order to solve the assignment and network operation problems. The authors provide an illustration on a real-size scenario.]]></description>
      <pubDate>Mon, 29 Jul 2019 11:03:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/1605663</guid>
    </item>
    <item>
      <title>A conceptual framework to formulate transportation network design problem considering social equity criteria</title>
      <link>https://trid.trb.org/View/1631693</link>
      <description><![CDATA[In recent years, researchers have developed new methods to measure how transport decisions affect different groups of society. An example is the distribution of impacts (benefits and costs) from roadway investments, and the degree that the results are considered equitable (also called fair or just). Such decisions affect people’s ability to access services and activities, and therefore their economic opportunities and development. This study suggests ways of incorporating social equity measures in transportation network planning. It describes various equity impacts that can result from transportation planning decisions, discusses various social equity concepts and theories, reviews previous attempts to incorporate equity considerations into transport networks modeling, and suggests a framework for simultaneously optimizing network design and achieving social equity objectives. According to this framework, network design can be formulated using bi-level integer programming models corresponding to seven major social equity approaches along with the classical approach of “Total Travel Time Minimization.” An accessibility variable is used as the distributable benefit. This approach is more comprehensive and flexible than previous equity impact models. The proposed framework can be used to evaluate and optimize the equity impacts of various infrastructure investment decisions.]]></description>
      <pubDate>Wed, 10 Jul 2019 15:17:05 GMT</pubDate>
      <guid>https://trid.trb.org/View/1631693</guid>
    </item>
    <item>
      <title>Bike network design problem with a path-size logit-based equilibrium constraint: Formulation, global optimization, and matheuristic</title>
      <link>https://trid.trb.org/View/1629262</link>
      <description><![CDATA[This study focuses on the optimal network design problem of bike paths, which are on or adjacent to roadways but are physically separated from motorized traffic within the existing urban network. The problem seeks to maximize the total route utilities of cyclists and capture their actual route choice behavior using a path-size logit model. A mixed-integer nonlinear nonconvex model is developed for the problem and is reformulated and linearized into a mixed-integer linear program. The program is solved with a global optimization method and a matheuristic. Results are provided to illustrate the performance of these methods and the model properties.]]></description>
      <pubDate>Mon, 01 Jul 2019 09:20:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1629262</guid>
    </item>
    <item>
      <title>Service network design with mixed autonomous fleets</title>
      <link>https://trid.trb.org/View/1590507</link>
      <description><![CDATA[The authors propose a service network design problem for the tactical planning of parcel delivery with autonomous vehicles in SAE level 4. They consider a heterogeneous infrastructure wherein such vehicles may only drive in feasible zones but need to be guided elsewhere by manually operated vehicles in platoons. The authors' model decides on the fleet size and mix as well as on the routing of vehicles and goods. They observe cost savings and show that the strategies to coordinate a fleet using platooning depend upon the infrastructure, demand, and fleet mix. The authors discuss their results and identify areas for future research.]]></description>
      <pubDate>Fri, 26 Apr 2019 16:59:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/1590507</guid>
    </item>
    <item>
      <title>Benders Decomposition for the Design of a Hub and Shuttle Public Transit System</title>
      <link>https://trid.trb.org/View/1583755</link>
      <description><![CDATA[The BusPlus project aims at improving the off-peak hours public transit service in Canberra, Australia. To address the difficulty of covering a large geographic area, proposes a hub and shuttle model consisting of a combination of a few high-frequency bus routes between key hubs and a large number of shuttles that bring passengers from their origin to the closest hub and take them from their last bus stop to their destination. This paper focuses on the design of the bus network and proposes an efficient solving method to this multimodal network design problem based on the Benders decomposition method. Starting from a mixed-integer programming (MIP) formulation of the problem, the paper presents a Benders decomposition approach using dedicated solution techniques for solving independent subproblems, Pareto-optimal cuts, cut bundling, and core point update. Computational results on real-world data from Canberra’s public transit system justify the design choices and show that the approach outperforms the MIP formulation by two orders of magnitude. Moreover, the results show that the hub and shuttle model may decrease transit time by a factor of two, while staying within the costs of the existing transit system.]]></description>
      <pubDate>Thu, 21 Feb 2019 09:49:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/1583755</guid>
    </item>
    <item>
      <title>A dual approximation-based quantum-inspired genetic algorithm for the dynamic network design problem</title>
      <link>https://trid.trb.org/View/1577784</link>
      <description><![CDATA[In this paper, the authors formulate a dynamic transportation network design model in which traffic dynamics are modeled by the cell transmission model. In the formulation, transportation planners decide on the optimal capacity expansion policies of existing transportation network infrastructure with limited resources, while road users react to the capacity changes by selfishly choosing routes to maximize their own profit. Owing to the problem complexity, a majority of the research efforts have focused on tackling this problem using meta-heuristics. In this study, the authors incorporate a series of dual-variable approximation techniques into the paradigm of a quantum-inspired genetic algorithm (QIGA) and devise an efficient evaluation function based on these techniques. The proposed QIGA contains a series of enhancements compared to conventional genetic algorithms (GAs) and can be considered as a better alternative when solving problems with a complex solution space. The QIGA is applied to a synthetic network, a subnetwork of a real-world road network, and a realistic network to justify its theoretical and practical value. From the numerical results, it is found that in the same computational time, the QIGA outperforms the conventional GA by 3.86–5.63% in terms of the objective value, which can be significant, especially when network expansion of a large urban area is considered. Technical, computational, and practical issues involved in developing the QIGA are investigated and discussed.]]></description>
      <pubDate>Fri, 25 Jan 2019 10:34:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1577784</guid>
    </item>
    <item>
      <title>The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework</title>
      <link>https://trid.trb.org/View/1508307</link>
      <description><![CDATA[Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.]]></description>
      <pubDate>Mon, 23 Jul 2018 14:12:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1508307</guid>
    </item>
    <item>
      <title>A Network Design Problem Formulation and Solution Procedure for Intercity Transit Services</title>
      <link>https://trid.trb.org/View/1495654</link>
      <description><![CDATA[This study presents a formulation and solution procedure for the transit network design problem in an intercity context, wherein a transit service has multiple routes and serves multiple terminals in the origin and destination cities. The proposed solution procedure consists of three steps. First, a set of candidate terminals are selected, and in the second step, candidate routes are generated between those terminals using a k-shortest path algorithm. The third step is a mixed-integer optimization model that finds the optimal routes, terminals, frequencies, required fleet size and depots locations, given a set of constraints and the objective of minimizing the sum of total passenger travel time and vehicle deadheading time. The solution procedure was implemented for a newly conceived intercity transit service between Tucson and Phoenix in Arizona, US. It has multiple terminals in both urban areas, travels at a high speed on a dedicated guideway on intercity freeways and returns to a regular speed in urban areas. The final routes found by the model look reasonable, serving the most important trip production/attraction locations in the two cities. A sensitivity analysis was also performed by running the optimization model for different constraint values of the demand satisfaction ratio, the number of routes and terminals, and the required fleet size. The results showed that demand satisfaction has the greatest effect on the objective function, followed by the number of routes. The fleet size and the number of terminals were found not to have much influence on the final solution.]]></description>
      <pubDate>Wed, 31 Jan 2018 10:44:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495654</guid>
    </item>
    <item>
      <title>An Initial Route Set Generation Algorithm for the Transit Network Design Problem</title>
      <link>https://trid.trb.org/View/1495211</link>
      <description><![CDATA[This paper presents an initial route generation algorithm for the transit network design problem. The proposed algorithm is compared with the latest demand based route generation algorithm, in terms of both the average travel time and the total route length. The algorithm proposed can be used to generate initial solutions for a local search procedure or evolutionary algorithm. The initial solutions obtained for Mumford’s two larger instances evidence that the initial solutions obtained is better than both the initial solutions generated by former researchers, and the final solutions obtained by optimizing their initial solutions. This indicates that the proposed algorithm is capable of producing significantly better initial solutions for large networks. The comparison of computation time also shows that the proposed algorithm is very effective, as it only takes three seconds to generate an initial solution for the largest instance.]]></description>
      <pubDate>Wed, 24 Jan 2018 09:24:04 GMT</pubDate>
      <guid>https://trid.trb.org/View/1495211</guid>
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