Feeder Bus Network Design Problem: a New Metaheuristic Procedure and Real Size Applications

Ciaffi, F; Cipriani, Ernesto; Petrelli, Marco

doi:10.1016/j.sbspro.2012.09.796

Cited by 56 publications

(30 citation statements)

References 9 publications

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“…The feeder bus is a public transit service for the connection of local areas, where the demand for bus services has to be gathered and transferred to the main stop, which is usually a transit hub or terminal, such as a rapid rail transit station or an express bus terminal (Ciaffi, Cipriani, & Petrelli, 2012). Feeder buses also provide services to transport passengers between the outskirts of a city to the urban centre.…”

Section: Good Design Of Feeder Bus Systemmentioning

confidence: 99%

Service Catchment of Mass Rapid Transit (Mrt) Feeder Bus: A Preliminary Study of T461 Route Taman Kajang Utama

Lin

Ponrahono

2019

PMJ

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The planning and development of rail services require various considerations. Land availability, land use, catchment, route matching, infrastructure fitting, barrier free and micro-climate friendly designs are some of the factors heeded prior to such installations. A deviation between designated and highly demanded service area in urban sprawl zones of the city has been occurring in many Malaysian cities. These gaps have led to the mismatch between origin/destination of passengers and planned locations of train stations and its feeder bus stops. As such, rail services become less accessible to populations with the highest demands. This paper discusses the preliminary findings from a pilot study which seeks to calibrate the research instrument and validate preliminary findings before actual data collection for the purpose of determining the service catchment of the T461 feeder bus in Kajang MRT Station. The Garmin GPS device acts as the research instrument to obtain coordinates of locations where passengers board and alight feeder buses. On-board surveys and comparison analyses are methods that have been used to obtain the optimum GPS coordinates of the bus stop locations. The preliminary findings indicate that the research instrument is ready to be used for actual data collection and geospatial analysis to determine the service catchment of the T461 feeder bus service.

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Section: Good Design Of Feeder Bus Systemmentioning

confidence: 99%

Service Catchment of Mass Rapid Transit (Mrt) Feeder Bus: A Preliminary Study of T461 Route Taman Kajang Utama

Lin

Ponrahono

2019

PMJ

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“…Their method consists of a set of heuristics that include a routine for route generation and a GA to find a suboptimal set of routes with the associated frequencies. Ciaffi et al [31] deal with a TRND problem related to the feeder routes. A heuristic algorithm generates two complementary sets of feasible routes, and a GA is then utilized to find a suboptimal set of routes with the associated frequencies.…”

Section: B Ga In Public Transportation Operationmentioning

confidence: 99%

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

Zuo

Chen

Tan

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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It is complex and difficult to perform the vehicle scheduling of urban bus lines, which is important to reduce the operational cost and improve the quality of public transportation services. One has to assign vehicles to cover a set of trips contained in a timetable while minimizing multiple objectives that may conflict with each other. Existing approaches combine these objectives in a weighted fashion to form a single objective and then use a single-objective optimization approach to solve it. However, they can only produce one solution, and it is not easy to assign a proper weight for each objective to obtain a superior solution that can balance different objectives. In this paper, a methodology is presented to create a set of Pareto solutions for this problem. First, a set of candidate vehicle blocks is generated. Then, multiple block subsets are selected from this candidate set by an improved multiobjective genetic algorithm combined with a departure-time adjustment procedure to obtain multiple Pareto solutions. To encode a solution, we propose a coding scheme that has a relatively short coding length and low decoding complexity. This approach is applied to a real-world vehicle scheduling problem of a bus line in Nanjing, China. Experiments show that this approach is able to quickly produce satisfactory Pareto solutions that outperform the actually used experience-based solution.

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“…Starting from the results of this procedure application, described extensively in Ciaffi et al (2012) [8], this section focus the analysis on the frequency setting problem showing the results of a sensitivity analysis with respect to the lines frequency. Generally speaking, a sensitivity analysis is used to determine how "sensitive" a model is to changes in the value of the parameters of the model and to changes in the structure of the model.…”

Section: Numerical Tests About the Frequency Setting Assessmentmentioning

confidence: 99%

“…The analysis is carried out using 8 different values of headway (4,8,10,12,15,20,30, 60 minutes) and their different combination so generating more than 200 different scenarios. A sub-set of 31 scenarios, containing the most significant ones, has been used to analyzing performances of bus network with 3 different levels of origin-destination demand (the actual one and two lower values).…”

Section: Numerical Tests About the Frequency Setting Assessmentmentioning

confidence: 99%

A new methodology for the public transport network design

Ciaffi

Cipriani

Petrelli

et al. 2014

The 9th International Conference "Environmental Engineering 2014"

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The present paper deals with the bus network design problem. Such problem is formulated as an optimization problem involving the minimization of all resources and costs related to the public transport system. The optimization problem is subject to user equilibrium on public transport network as well as to the bus capacity constraints and a set of feasibility constraints on route length and line frequency. The objective function is defined as the sum of operator's costs and users' costs. The input data are the public transport demand matrix, the characteristics of road network, the operating and users unit costs. Outputs are routes and frequencies for the lines of the public transport network. The performances of the network are estimated by a hyperpath transit assignment model, which reproduces the choice behaviour of transit users. The solving procedure consists of a set of heuristics, which includes a first routine for the definition of the roads and the zones to be served, a second step for the routes generation and then a genetic algorithm for finding a sub-optimal set of routes and associated frequencies. The GA is implemented in the C# language as a parallel genetic algorithm while the fitness evaluation requires computing, for each solution generated, the two terms of the objective function by simulating the public transport network with the EMME software. The proposed procedure will be implemented on a real large size network (two districts in the city of Rome), in order to compare its effectiveness with the performances of the existing transit network and to provide an extensive sensitivity analysis in bus frequency changes.

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Feeder Bus Network Design Problem: a New Metaheuristic Procedure and Real Size Applications

Cited by 56 publications

References 9 publications

Service Catchment of Mass Rapid Transit (Mrt) Feeder Bus: A Preliminary Study of T461 Route Taman Kajang Utama

Service Catchment of Mass Rapid Transit (Mrt) Feeder Bus: A Preliminary Study of T461 Route Taman Kajang Utama

Vehicle Scheduling of an Urban Bus Line via an Improved Multiobjective Genetic Algorithm

A new methodology for the public transport network design

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