Maximizing Trip Coverage in the Location of a Single Rapid Transit Alignment

This article describes several heuristics for the construction of a rapid transit alignment. The objective is the maximization of the total origin-destination demand covered by the alignment. Computational results show that the best results are provided by a simple greedy extension heuristic. This conclusion is confirmed on the Sevilla data for scenarios when the upper bound for inter-station distance is greater than 1250 m. Otherwise, when those upper bounds are smaller (750mand 1000 m), an insertion heuristic followed by a post-optimization phase yields the best results. Computational times are always insignificant.Canadian Natural Sciences and Engineering Research Council OGP0039682Ministerio de Ciencia y Tecnología BFM2000-1052-C02-01Ministerio de Ciencia y Tecnología BFM2003-04062/MAT

Designing Rapid Transit Network Design with Alternative routes

The aim of this paper is to propose a model for the design of a robust rapid transit network. In this paper, a network is said to be robust when the effect of disruption on total trip coverage is minimized. The proposed model is constrained by three different kinds of flow conditions. These constraints will yield a network that provides several alternative routes for given origin–destination pairs, therefore increasing robustness. The paper includes computational experiments which show how the introduction of robustness influences network desig

Urban Rapid Transit Network Capacity Expansion

This paper examines a multi-period capacity expansion problem for rapid transit network design. The capacity expansion is realized through the location of train alignments and stations in an urban traffic context by selecting the time periods. The model maximizes the public transportation demand using a limited budget and designing lines for each period. The location problem incorporates the user decisions about mode and route. The network capacity expansion is a long-term planning problem because the network is built over several periods, in which the data (demand, resource price, etc.) are changing like the real problem changes. This complex problem cannot be solved by branch and bound, and for this reason, a heuristic approach has been defined in order to solve it. Both methods have been experimented in test networks

Improved rapid transit network design model: considering transfer effects

The rail rapid transit network design problem aims at locating train alignments and stations, maximizing demand coverage while competing with the current existing networks. We present a model formulation for computing tight bounds of the linear relaxation of the problem where transfers are also introduced. The number of transfers within a trip is a decisive attribute for attracting passengers: transferring is annoying and undesirable for passengers. We conduct computational experiments on different networks and show how we are able to solve more efficiently problems that have been already solved; sensitivity analysis on several model parameters are also performed so as to demonstrate the robustness of the new formulation

Improved rapid transit network design model: considering transfer effects

The rail rapid transit network design problem aims at locating train alignments and stations, maximizing demand coverage while competing with the current existing networks. We present a model formulation for computing tight bounds of the linear relaxation of the problem where transfers are also introduced. The number of transfers within a trip is a decisive attribute for attracting passengers: transferring is annoying and undesirable for passengers. We conduct computational experiments on different networks and show how we are able to solve more efficiently problems that have been already solved; sensitivity analysis on several model parameters are also performed so as to demonstrate the robustness of the new formulation

Modeling and solving line planning with integrated mode choice

We present a mixed-integer linear program (MILP) for line planning with integrated mode and route choice. In contrast to existing approaches, the mode and route decisions are modeled according to the passengers' preferences while commercial solvers can be applied to solve the corresponding MILP. The model aims at finding line plans that maximize the profit for the public transport operator while estimating the corresponding passenger demand with choice models. Both components of profit, revenue and cost, are influenced by the line plan. Hence, the resulting line plans are not only profitable for operators but also attractive to passengers. By suitable preprocessing of the passengers' utilities, we are able to apply any choice model for mode choices using linear constraints. We provide and test means to improve the computational performance. In experiments on the Intercity network of the Randstad, a metropolitan area in the Netherlands, we show the benefits of our model compared to a standard line planning model with fixed passenger demand. Furthermore, we demonstrate with the help of our model the possibilities and limitations for operators when reacting to changes in demand in an optimal way. The results suggest that operators should regularly update their line plan in response to changes in travel demand and estimate the passenger demand during optimization

Programación matemática para el diseño de líneas de transporte

El diseño de un modelo matemático exacto para la planificación de redes de tránsito rápido en una ciudad es posible, pero no es una tarea sencilla debido a la complejidad y la cantidad de variables involucradas. Como se ha introducido anteriormente, requiere una planificación cuidadosa y una consideración de varios factores, como las necesidades de transporte de la ciudad, el crecimiento futuro, las características topográficas y la demanda de transporte. El uso de modelos matemáticos en el diseño de una red de tránsito rápido en una ciudad es cada vez más común. Los modelos matemáticos pueden ayudar a los planificadores de transporte a analizar diferentes opciones de rutas, evaluar la demanda de pasajeros y determinar la capacidad necesaria para el sistema. Por otro lado, los modelos matemáticos también pueden ayudar a optimizar la ubicación de las estaciones, la frecuencia de los trenes y otros aspectosimportantes del diseño de la red de tránsito rápido. Además, estos modelos pueden ser útiles para simular diferentes escenarios y evaluar el impacto de posibles cambios en la red en términos de tiempos de viaje, capacidad, costes, y otros aspectos. No obstante, los modelos matemáticos avanzados que se han desarrollado para la planificación de redes de transporte son de alto coste, y requieren una cantidad significativa de recursos y tiempo para desarrollarse y mantenerse actualizados. Adicionalmente, la utilización de modelos matemáticos en el diseño de una red de tránsito rápido depende en gran medida de la disponibilidad de datos precisos y actualizados, así como de los recursos financieros y técnicos necesarios para llevar a cabo la planificación y construcción del sistema. Esto hace, que se dificulte su aplicación matemática, dado que es importante tener en cuenta las limitaciones y posibilidades de los solvers que resuelven los modelos matemáticos en función de las condiciones específicas de cada proyecto. Es por esta razón, que a lo largo de este capítulo se exponen algunos de los modelos ya creados y puestos a prueba que se han realizado a lo largo del tiempo. Empezaremos resumiendo varias revisiones que resuelven los problemas más comunes que se presentan en el diseño de redes, por lo tanto, se explicará a continuación qué recogen algunos autores en sus artículos publicados sobre la evaluación de la efectividad de las redes diseñadas. Los artículos de investigación descritos a continuación han sido seleccionados debido a su relevancia en relación con las contribuciones de la investigación que se expone en este documento. Además, se debe aclarar que a pesar de que se exponen artículos en orden cronológico, se omiten por cuestiones de espacio muchas investigaciones y otros artículos que han sido fundamentos de los presentes en este trabajo.Universidad de Sevilla. Grado en Ingeniería de Diseño Industrial y Desarrollo del Product

OPTIMIZATION OF STATION LOCATIONS AND TRACK ALIGNMENTS FOR RAIL TRANSIT LINES

Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, right-of-way type, and various other system characteristics. The existing studies overlook the complex interactions between railway alignments and station locations in a practical design process. This study proposes a comprehensive methodology that helps transit planners to concurrently optimize station locations and track alignments for an urban rail transit line. The modeling framework resolves the essential trade-off between an economically efficient system with low initial and operation cost and an effective system that provides convenient service for the public. The proposed method accounts for various geometric requirements and real-world design constraints for track alignment and stations plans. This method integrates a genetic algorithm (GA) for optimization with comprehensive evaluation of various important measures of effectiveness based on processing Geographical Information System (GIS) data. The base model designs the track alignment through a sequence of preset stations. Detailed assumptions and formulations are presented for geometric requirements, design constraints, and evaluation criteria. Three extensions of the base model are proposed. The first extension explicitly incorporates vehicle dynamics in the design of track alignments, with the objective of better balancing the initial construction cost with the operation and user costs recurring throughout the system's life cycle. In the second extension, an integrated optimization model of rail transit station locations and track alignment is formulated for situations in which the locations of major stations are not preset. The concurrent optimization model searches through additional decision variables for station locations and station types, estimate rail transit demand, and incorporates demand and station cost in the evaluation framework. The third extension considers the existing road network when selecting sections of the alignment. Special algorithms are developed to allow the optimized alignment to take advantage of links in an existing network for construction cost reduction, and to account for disturbances of roadway traffic at highway/rail crossings. Numerical results show that these extensions have significantly enhanced the applicability of the proposed optimization methodology in concurrently selecting rail transit station locations and generating track alignment