Optimization of Bus Route Planning in Urban Commuter Networks

Chien, Steven; Dimitrijevic, Branislav V.; Spasovic, Lazar N.

doi:10.5038/2375-0901.6.1.4

Cited by 42 publications

(17 citation statements)

References 8 publications

(8 reference statements)

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“…Ceder et al [24] designed a model to optimize the bus stop locations in a given route for uneven topography. Some other studies also carried out particularly focusing on urban areas [25,26].…”

Section: Generating the New Population And Go Up For Evaluatingmentioning

confidence: 99%

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Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

et al. 2021

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In this research, a feasible mechanism is developed to determine the optimum number of bus rapid transit (BRT) stations as well as their respective locations along the service corridor. To accomplish this, a mathematical model is developed and optimized by using three different evolutionary algorithms, namely particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), and the results are compared. The total cost function is composed of two main costs namely the operator’s cost, i.e., related to costs on service provider’s end, and the user’s cost, i.e., related to costs on commuters’ end. A functional numerical example with the commuters’ demand is worked out by minimizing the cost function, which demonstrates the applicability of the framework. In our case study, PSO outclassed GA and DE on the basis of convergence rate. Since our work has proved the robustness of PSO as compared to GA and DE, we conducted our sensitivity analysis keeping PSO as our benchmark algorithm to study the influence of various parameters on the optimal cost. The computational experiments reveal that the optimal cost is substantially affected by the variations in the commuters’ demand, commuters’ walking speed, and value of the users’ access and in-vehicle time. On the contrary, the acceleration/deceleration delays at a bus station, bus operating cost, and headway have an inconsiderable impact on the optimal cost.

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Section: Generating the New Population And Go Up For Evaluatingmentioning

confidence: 99%

“…Therefore, the total cost function can be estimated by summing up E o-inc , E o-mpc , E u-ac and E u-inv by using Eqs. (4), (5), (7) and (26), respectively, to determine the optimal BRT, bus rapid transit, stations' density and locations.…”

Section: Users' In-vehicle Cost E U-invmentioning

confidence: 99%

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

et al. 2021

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“…. In a similar study, Chien et al used the minimal value of total sys tem cost, including operator and user costs as a measure for determining an optimal bus route [9]. They developed 1203 method which applicable to irregular grid networks.…”

Section: Related Workmentioning

confidence: 99%

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in The City of Bandung

2018

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Currently, the existence of city transport is increasingly eliminated b y private vehicles such as cars and motorcycles. This situation is further exacerb ated b y the b ehavior of city transport drivers who are less discipline in driving, or in picking up and dropping off their passengers. The b ad b ehavior is partly caused b y the low level of passenger occupancy. The drivers try to search for passengers as much as possib le b ut often ignore the traffic rules. To overcome this prob lem, an optimal transport route with high passenger potential is required. Therefore, this study investigated the optimal route of city transport b ased on the passenger occupancy rate in the city of Bandung as the case study. The method employed for determining the optimal route is Genetic algorithm comb ined with Ordinary Kriging method used for the process of passenger prediction and fitness calculation. The optimal routes are those with higher occupancy rate. The analysis results showed that the use of the Genetic algorithm with a low numb er of generations succeed in creating new optimal routes even though the increase is not too high the maximum only reaches 4%.This result is certainly important enough to b e used in making b etter pub lic transport routes.

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“…Often, to reduce access impedence, tortuous routes are constructed, although they are likely to increase both in-vehicle time and operating cost (Chien et al 2001). However, according to the GIS output of this research, the length of the optimized path is 7.8km, which has no significant difference to the shortest path (7.1km); the new optimum route meets the criteria of increasing coverage and reducing route duplication, whereas the shortest path overlapped existing routes (see Figure 12).…”

Section: Route Evaluationmentioning

confidence: 99%

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Optimization of Bus Route Planning in Urban Commuter Networks

Cited by 42 publications

References 8 publications

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

Computation of Optimal Spacing and Density of Bus Rapid Transit Stations Using Evolutionary Algorithms

The Prediction of Optimal Route of City Transportation Based on Passenger Occupancy using Genetic Algorithm: A Case Study in The City of Bandung

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