Transit Route Network Design Research Articles

A Variable Neighbourhood Search-Based Algorithm for the Transit Route Network Design Problem

The transit route network design problem (TRNDP) has long attracted research attention, with many metaheuristic approaches proposed for its solution. So far, and despite the promising performance of Variable Neighbourhood Search (VNS) variants for vehicle routing problems, the performance of the algorithm on the TRNDP remains unexplored. In this context, this study develops a VNS-based algorithm for the problem at hand. The performance of the algorithm is tested using benchmark networks used in bus transit network design and compared with some of the most recent and efficient methods from the literature. Results show that the algorithm yields superior results over existing implementations in short computational times.

A Review of Bus Transit Route Network Design Problem Considering Developing Nations

A Review of Bus Transit Route Network Design Problem Considering Developing Nations

Robust Electric Transit Route Network Design Problem Considering Energy Storage Technology and Demand Charges: Model and Application

Robust Electric Transit Route Network Design Problem Considering Energy Storage Technology and Demand Charges: Model and Application

Robust electric transit route network design problem (RE-TRNDP) with delay considerations: Model and application

Electric bus networks are steadily gaining ground as the prominent option for urban public transport. However, in contrast to conventional transit systems operated by diesel buses, electric bus networks are particularly vulnerable with respect to energy supply, both in terms of power level availability and the unobstructed access to charging points. Indeed, power fluctuations can prevent buses from adequately recharging at designated points, affecting extended areas of operation. Similarly, queue formation at terminal stops can lead to poor schedule adherence and excessive delays. In this context, this study addresses research gaps by presenting a realistic and flexible design framework for fully electric public transport networks, tackling both the route network and the charging infrastructure design. To handle the uncertainty associated with power supply, Robust Optimization (RO) is employed for solving the charging infrastructure location problem while maintaining computational tractability. Queuing delays due to charging are also modeled and minimized. To address the integrated design of route networks and wireless chargers, RO is coupled with Multi-Objective Particle Swarm Optimization within a bi-level methodological framework. Different scenarios for power supply variability are considered. Results show that depending on policy priority, the cost of robustness significantly changes.

Optimal route design of electric transit networks considering travel reliability

AbstractTravel reliability is the most essential determinant for operating the transit system and improving its service level. In this study, an optimization model for the electric transit route network design problem is proposed, under the precondition that the locations of charging depots are predetermined. Objectives are to pursue maximum travel reliability and meanwhile control the total cost within a certain range. Constraints about the bus route and operation are also considered. A Reinforcement Learning Genetic Algorithm is developed to solve the proposed model. Two case studies including the classic Mandl's road network and a large road network in the context of Zhengzhou city are conducted to demonstrate the effectiveness of the proposed model and the solution algorithm. Results suggest that the proposed methodology is helpful for improving the travel reliability of the transit network with minimal cost increase.

Integrating Transit Route Network Design and Fast Charging Station Planning for Battery Electric Buses

There are currently rising applications of battery electric buses in public transportation system, given their energy efficiency and societal benefits. However, limited range and time-consuming charging processes significantly reduce the operational efficiency of battery electric buses system. Thanks to fast charging technology, battery electric buses can run continuously through rational planning of fast charging system. Therefore, to achieve an effective and economic electric transit route network, electric transit route network and fast charging stations should be simultaneously and systematically deployed. This study develops a bi-level programming framework to formulate the electric transit route network design problem. Where, the upper-level model is to determine the route structure and charging station location to optimize the total costs of both user and operator, the lower-level passenger assignment model is to calculate user cost for upper-level model. Then, the proposed model is solved by a modified genetic algorithm (GA), which can search all possible route structures through specifically designed genetic operators. A numerical example from Swiss road network is employed to demonstrate the applicability of bi-level model and performance of modified GA.

Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology.

An infeasible start heuristic for the transit route network design problem

ABSTRACT This paper develops an efficient heuristic for the transit network design problem, formulated as an integer programming problem. The model includes a preliminary step of route set generation, followed by an iterative procedure that simultaneously selects the best routes and corresponding headways. The iterative procedure is performed by an infeasible start algorithm that first assigns all candidate routes with the maximal frequency, and then iteratively eliminates routes and decreases frequencies of the less attractive ones. Routes are evaluated through a frequency-based transit assignment model that considers online information. The proposed model is applied to the Winnipeg transit network. The transit network found by the suggested model comprised fewer, faster and more frequent lines that serve high volume of passengers, compared to the given transit network. The running time of the algorithm is very short compared to existing methods, and its simplicity enables high level and detailed calibration.

A Multi-Objective Optimization and Hybrid Heuristic Approach for Urban Bus Route Network Design

This study proposed a hybrid optimization model for urban bus transit route network design problem (TRNDP). Although several mathematical methods had been developed to make the problem tractable, the methods relied on excessive assumptions, which resulted in over-simplification or idealization of the problems. In light of these considerations, a multi-level and multi-mode network design method was introduced in this study. A multi-level network consisted of three levels: skeleton network, arterial network, and feeder network. The different levels of network were associated with different modes of public transportation (such as subway, light rail transit, trolleybus, BRT, normal bus, and community branch) based on the features of bus routes, city sizes, etc. In addition, according to the respective features of urban transit route network structures, we developed different optimization models for different levels. Finally, the proposed methodology was applied to case studies of the city of Zhaoyuan in China and the transit network of Mandl benchmark. The results showed that the total travel time for the proposed method was significantly lower than that of the competing method, with a 21.51% reduction. In addition, the proposed method provided 85.23% direct travelers, 14.65% travelers with one transfer, 0.12% travelers with two transfers, and no unsatisfied demand, which were better than the results from the compared method.

Integrated Multimodal Transit Route Network Design with Feeder Systems

The rapid introduction of mass transit systems in cities around the world to counter traffic congestion and population growth has brought up the need of feeder systems which bridges the accessibility gap between the mass transit system and the users. This study aims to provide a way to reorganise the existing public transit system once a new system gets introduced in a city. This allows the existing and the new system to run in complement rather than in competition thus preventing losses to the existing system. The methodology is divided into two main components, generating a set of plausible routes and then finding the best route set along with associated frequencies using Genetic Algorithm. A cost objective function comprising of user costs and operator costs is minimized for finding the most feasible solution. A new methodology for public transit assignment is introduced and the solution route set satisfies both feeder and non-feeder demand. The methodology is tested for a large sized network with upcoming metro network namely, Mumbai Metropolitan Region, Maharashtra, India. The network design characteristics and routing is presented in this study.

Integrated transit route network design and infrastructure planning for on-line electric vehicles

The emergence of electromobility along with recent developments in wireless power transfer (WPT) technology offer potentials to improve the carbon footprint of bus transport, while offering quality services. Indeed, the deployment of fast charging stations and dynamic charging roadway segments (lanes) can ensure fast energy transmission to electricity-powered buses, mitigating existing energy-related concerns and limitations. Existing models for public transport network design cannot adequately capture the dependence between electric vehicle charging infrastructure requirements and route operational characteristics. In this context, this paper investigates the combined Transit Route Network Design and Charging Infrastructure Location Problem and proposes a bi-level formulation to handle both planning stages. At the upper level, candidate route sets are generated and evaluated, while at the lower-level wireless charging infrastructures are optimally deployed. A multi-objective Particle Swarm Optimization (MO-PSO) algorithm embedded with an integer programming solver is employed to handle the complexity of the problem and the conflicting design objectives related to passengers and operators. The resulting model is applied to an established benchmark network to assess the tradeoffs arising between user-oriented and operator-oriented solutions and highlight the complex decision process associated with the deployment of electric public transport networks.

Metaheuristics for the transit route network design problem: a review and comparative analysis

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.

A Transit Route Network Design Problem Considering Equity

As concerns about environmental quality, social equity, and economic efficiency are increasing, efforts on improving the sustainability of public transportation are being made all over the world. This study aims to propose a transit route network design problem that considers modal and spatial equities. The equities are accommodated by using two indexes that can simultaneously reflect mobility and accessibility. A decision-making process for designing a transit route network consists of the selection of the target line, selection of the target node, the determination of an alternative line, and the implementation of a procedure for setting frequency. The model is configured through bi-level modeling based on an iterative process to calculate the modal split and the traffic and transit assignments with changes in the transit route network. While the frequency of each line is determined by a genetic algorithm in the upper model, the modal split and traffic and transit assignments are implemented in the lower model. This transit route network design model and the associated algorithms are applied to a sample network. As a result, an improved solution with equity and the lower total cost is identified based on a comparison with the existing transit route network.

Electric Transit Route Network Design Problem: Model and Application

Electric buses have long been recognized as a promising direction for offering sustainable public transportation services. While range and battery performance constraints have hindered the widespread adoption of electric buses in the past, technological advances make them a prominent and attractive option for public transportation in the future. Still, operational constraints and the need for additional (charging) infrastructure highlight the need for introducing appropriate decision-making tools, tailor-made for supporting the design of transit networks operated by electric buses. This paper focuses on developing and testing a comprehensive route design model for the case of a transit network, operated exclusively by an electric bus fleet (Electric Transit Route Network Design Problem—E-TRNDP). The model is formulated as a bi-level optimization problem, which attempts to jointly design efficient transit routes and locate required charging infrastructure. A multi-objective, particle swarm optimization algorithm, coupled with a mixed linear—integer programming model is used to solve the model. An existing benchmark network is used as a test-bed for the proposed model and solution process; results illustrate that the proposed model and solution method yield realistic design outcomes in an acceptable time frame.

Multi-Objective Transit Route Network Design as Set Covering Problem

Many past researchers have ignored the multi-objective nature of the transit route network design problem (TrNDP), recognizing user or operator cost as their sole objective. The main purpose of this study is to identify the inherent conflict among TrNDP objectives in the design process. The conventional scheme for transit route design is addressed. A route constructive genetic algorithm is proposed to produce a vast pool of candidate routes that reflect the objectives of design, and then, a set covering problem (SCP) is formulated for the selection stage. A heuristic algorithm based on a randomized priority search is implemented for the SCP to produce a set of nondominated solutions that achieve different tradeoffs among the identified objectives. The solution methodology has been tested using Mandl's benchmark network problem. The test results showed that the methodology developed in this research not only outperforms solutions previously identified in the literature in terms of strategic and tactical terms of design, but it is also able to produce Pareto (or near Pareto) optimal solutions. A real-scale network of Rivera was also tested to prove the proposed methodology's reliability for larger-scale transit networks. Although many efficient meta-heuristics have been presented so far for the TrNDP, the presented one may take the lead because it does not require any weight coefficient calibration to address the multi-objective nature of the problem.

Sustainable urban transit network design

Sustainability is a requirement for modern public transportation networks, as these are expected to play a critical role in environment-friendly transportation systems. This paper focuses on developing an efficient model for solving a sustainable oriented variant of the Transit Route Network Design Problem. The model incorporates sustainable design objectives, considers emission-free (electric) vehicles and introduces a direct route design approach with route structure and directness control. An application in a real world case, highlights the performance and benefits of the proposed model.

English

A growing concern for public transit is its inability to shift passenger’s mode from private to public transport. The transit route network design (TRND) problem seeks a set of bus routes and schedules that is optimal in the sense that it maximizes the utility of an urban bus system for passengers while minimizing operator cost In this paper various data is collected to finding an optimal route length of VTCOS bus’s selected route, passenger capacity, total trip, boarding and alighting survey of passenger. TRANSCAD software provides useful tools such as road network links, shortest paths, O-D Matrices. This paper present some route of Anand city are very low passenger, so running empty buss and some route heavy passenger so increase in number of busses is advised. Our study area suggested route no. 2 & 7 is more reliable and suitable optimization route for the public transportation system.

Simple and Effective Solution Methodology for Transit Network Design Problem

Transit Network Design Problem (TNDP) is the most important component in Transit planning and operation, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a simple and effective solution methodology for the TNDP, which goes beyond previous traditional sophisticated approaches. The solution methodology adopted in this research for the TNDP is based on partitioning the solution into two consecutive stages; Transit route Network Design Problem “TrNDP” stage and frequency setting stage. In the first stage; a deterministic solution for TrNDP is tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. In the second stage; bus frequencies are optimized among bus routes (obtained in stage 1) via Genetic Algorithm, for a total bus fleet size representing operator's main cost. The adopted solution methodology has been tested through Mandl’s benchmark transit network problem. The test results showed that the methodology developed in this research is able to provide and effective solution in terms of the number of constructed routes, the direct demand coverage, and the total travel time. General Terms Transportation engineering, optimization, transit route design, frequency setting, integer programming, genetic algorithm.

A Novel Solution Methodology for Transit Route Network Design Problem

A Novel Solution Methodology for Transit Route Network Design Problem

Transit Route Design Solved with Wireless Data Collection Algorithms

The transit route network design problem and its variants, aiming for economically viable and efficient transit routes in urban areas, has been attracting many contributions in recent years and continues to be an active research field. This activity is mainly caused by the intractability of the underlying optimization problems in large-scale transportation networks, generally leading to the use of heuristic schemes to build good practical solutions. A relationship between the transit route network design problem and the problem of data collection in wireless sensor networks is presented, and an innovative solution approach is proposed based on a recently proposed algorithm in that domain. The original solution algorithm is adapted to urban transportation networks and tests are carried out on large-scale data sets on a single-route variant of the transit route network design problem. The results obtained show that the solution approach can be efficiently tuned to meet specific route design objectives and provide balanced solutions.