Development of optimal and cost effective bus scheduling using genetics algorithm (original) (raw)
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A Genetic Algorithm Based Bus Scheduling Model for Transit Network
… of the Eastern Asia Society for …, 2005
Farhan Ahmad KIDWAI Lecturer Department of Civil Engineering University of Malaya 50603 Kuala Lumpur Malaysia Fax: +60-3-7967-5318 E-mail: farhan@um.edu.my ... Kalyanmoy DEB Professor Department of Mech. Engineering Indian Institute of Technology Kanpur - ...
Optimizing Bus Lines Using Genetic Algorithm for Public Transportation
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2021
Abstract. Due to increasing human population, the need for quality public transportation has also increased. This study takes stop density, stop layout, and passenger population of those stops into consideration to offer a better regulated public transportation network design that can satisfy the increased demand. In this study, the boarding data is provided by the public transportation department of the city of Antalya, Turkey. Remaining required data was automatically generated using web services and stored in a PostgreSQL database hosted on a cloud server. After visualizing inputs such as bus routes, stop layout, and passenger density on Google Maps and KeplerGL, with the use of the K-Means algorithm, data was clustered to find ”hot” (i.e. attraction) areas on a macro scale. A novel means of connecting hot spots suggested by the outcome of the Genetic Algorithm was developed. To compare the effectiveness of the proposed approach with the existing network, current bus stops were m...
Optimization of Transfer Time and Initial Waiting Time for a Bus Network Using Genetic Algorithm
Genetic algorithms are search algorithm based on natural selection and genetics. It is first pioneered by John Holland in the 60’s and based on the Charles Darwin’s principle of “survival of the fittest”. The genetic algorithm method differs from other search methods in that it searches among a population of points and works with a coding of parameters set, rather than the parameter values themselves. GA Randomly generates an initial population of individual and a fitness function is used to evaluate individuals, and reproductive success varies with fitness. In this study optimization of Transfer Time (TT) and Initial Waiting Time (IWT) of existing city bus schedule of Indore city bus network for a given constraint is done. Problem has been formulated in the form of objective function by considering departure and arrival time of buses on different routes as variables and constraint. Data has been collected from Indore City Transport Services Limited (ICTSL) and problem is formulated for various buses and routes. Data based on particular route are considered as input for objective function and constraint. The same is considered as input of GA in MATLAB in the form of X1, X2, ..., Xn parameters and is optimized in the form of minimization problem. Based on the data considered and result obtained verification for minimization of TT & IWT has been done. It is concluded that GA can be considered as a tool to solve optimization problem for city bus route of Indore city.
Optimizing bus services with variable directional and temporal demand using genetic algorithm
Journal of Central South University, 2016
As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.
Linear programming model for scheduling bus rapid transit in Lagos State, Nigeria
Nigerian Journal of Technology
The amount of time users have to wait influences their mode of transportation choice. Commuters don’t like to wait in the bus station, especially when the weather is terrible and for the purpose of keeping appointments. Scheduling buses to the various bus terminals will meet the commuter’s needs. The aim of the study is to reduce the waiting time of commuters at bus station and to assign buses to each route. A mathematical model called linear programming (LP) was developed to schedule buses to improve the smooth process of the Bus Rapid Transit (BRT) system. The linear programming model was applied to the Lagos Metropolitan Area Transport Authority (LAMATA) data in Lagos State. The proposed method generated three (3) shifts from 6 – 11am in the morning, 11 – 4pm in the midday and 4 – 9pm in the evening subject to two (2) shifts of fifteen (15) hours to reduce the number of hours buses operate per day and also allocated buses for each route for weekdays with five (5) hours per each s...
Modeling and Analysis of Bus Scheduling Systems of Urban Public Bus Transport
Anbessa City Bus Service Enterprise (ACBSE) is the only public enterprise that provides transport services in and around the city of Addis Ababa. The enterprise uses a fixed bus schedule system to serve passengers in 110 routes. However, this type of bus assignment system created a problem in the company's operational and financial performances. The objective of this paper is to develop an optimum bus assignment method using Linear Programming (LP). After thorough analysis of the existing bus scheduling system, the LP model is developed and used to determine the optimal number of buses for each route in four shifts. The output of the LP-model is then validated with the performances of the existing systems. The findings of the study show that the new model reveals better performances on the operating costs, bus utilization and trips and distance covered compared with the existing scheduling system. The enterprise's bus utilization improved by the new system and cut costs on the one hand and improves the service quality to passengers on the other hand. The authors recommended the enterprise to adopt the new bus assignment system so that buses can be assigned based on the demand distribution of passengers for each route at a given shift.
Keywords: Makurdi town bus services Bus travel time multiple linear regression model ANN model ABSTRACT The lack of information on bus travel time in Makurdi town to enable trip makers plan for journeys is seen as a challenge in recent times. This study developed a multiple linear regression model for predicting bus travel time along bus routes in Makurdi town. Specifically, the study assessed bus travel time on routes without designated bus stops, examined geometric features of bus routes, assessed bus dwell time and travel speeds in a heterogeneous traffic stream on routes in Makurdi town. It developed and validated a model for the bus travel time. Field survey focused on the major bus routes in Makurdi town which included; High Level roundabout to School of Remedial Studies junction (HL-SRS), High Level roundabout to Federal Medical Centre junction (HL-FMC), Wurukum roundabout to Coca Cola Complex (W-CCC) and Wurukum roundabout to Welfare Quarters junction (W-WQ). Independent parameters examined on the sites for model development included; bus route length, bus travel speed, average dwell time at random stops for pickup and alighting of passengers, bus headway, the total number of cross and Tee intersections along the bus route, volume of motorcycles, private cars and trucks in the traffic stream, while the dependent variable was bus travel time. Based on the built model, 15 minutes approximately was established as the average bus travel time for all bus routes in Makurdi town assuming all other variables have zero magnitude. Goodness of fit test of the model yielded significant value for coefficient of determination (R 2 = 0.952) and the use of Artificial Neural Network (ANN) method for validating the model also confirmed it accuracy at 93% approximately. It was therefore concluded that, bus travel time on major routes in Makurdi town could be accurately estimated using the built multiple linear regression model provided all essential input parameters of the model are used. The establishment of designated bus stops along bus routes within Makurdi town to minimise bus dwell frequency and for accurate estimation of bus travel time, as well as erection of travel information bill boards along bus routes stating average bus travel time to inform commuters that have high value of travel time were recommended.
Dutse Journal of Pure and Applied Sciences
In this paper, new profit maximization for Kano State transport authority resulting from optimal allocation of buses to inter-state routes is considered taking into consideration all the constraints associated. The problem was modeled using linear programming and the TORA (a software for solving linear programming problems) was used to obtained the solution to the modeled problem. The maximum objective value of ₦2,203,900.00 was obtained daily after 16 iterations and this a better result when compared to the current traditional or intuitive schedule by the authority that yielded ₦2,036,000.00 daily. This recommended schedule will yield additional ₦167,900.00 daily and over ₦5,000,000.00 monthly when implemented.
School bus routing using genetic algorithms
Applications of Artificial Intelligence X: Knowledge-Based Systems, 1992
The school bus routing problem involves transporting students from predefined locations to the school using a fleet of school buses with varying capacity. The objective is to minimize the fleet size in addition to minimizing the distance traveled by the buses and the travel time of the students. As the school bus routing problem belongs to the NP-complete class of problems, search strategies based on heuristic methods are most promising for problems in this class. GENROUTER is a system that uses genetic algorithms, an adaptive heuristic search strategy, for routing school buses. The GENROUTER system was used to route school buses for two school districts. The routes obtained by GENROUTER system were superior to those obtained by the Cl-lOOSE school bus routing system and the current routes in use by the two school districts.
TELKOMNIKA Telecommunication Computing Electronics and Control, 2018
Currently, the existence of city transport is increasingly eliminated by private vehicles such as cars and motorcycles. This situation is further exacerbated by the behavior of city transport drivers who are less discipline in driving, or in picking up and dropping off their passengers. The bad behavior is partly caused by the low level of passenger occupancy. The drivers try to search for passengers as much as possible but often ignore the traffic rules. To overcome this problem, an optimal transport route with high passenger potential is required. Therefore, this study investigated the optimal route of city transport based 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 combined 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 be used in making better public transport routes.