Schedule synchronization in public transit using the fuzzy ant system

Teodorović, Dušan; Lučić, Panta

doi:10.1080/0308106052000340387

Cited by 32 publications

(13 citation statements)

References 18 publications

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“…This information is modified during the algorithm run, depending on the solutions found by the ants. In [181], a fuzzy ACO approach is presented, which uses fuzzy logic to calculate an ant's utility to visit the next node. In particular, transition probabilities (usually given in a classical ACO in closed form) are computed by a fuzzy rule-based system.…”

Section: Fuzzy Ant Systemmentioning

confidence: 99%

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Herrera

Lozano

2009

Intelligent Systems Reference Library

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Abstract. There are two possible ways for integrating fuzzy logic and evolutionary algorithms. The first one involves the application of evolutionary algorithms for solving optimization and search problems related with fuzzy systems, obtaining genetic fuzzy systems. The second one concerns the use of fuzzy tools and fuzzy logic-based techniques for modelling different evolutionary algorithm components and adapting evolutionary algorithm control parameters, with the goal of improving performance. The evolutionary algorithms resulting from this integration are called fuzzy evolutionary algorithms. In this chapter, we shortly introduce genetic fuzzy systems and fuzzy evolutionary algorithms, giving a short state of the art, and sketch our vision of some hot current trends and prospects. In essence, we paint a complete picture of these two lines of research with the aim of showing the benefits derived from the synergy between evolutionary algorithms and fuzzy logic.

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Section: Fuzzy Ant Systemmentioning

confidence: 99%

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Herrera

Lozano

2009

Intelligent Systems Reference Library

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“…Due to a big dimension of the problem, standard integer programming methods not always could be used for solving it in the real-world conditions. For this reason, to obtain some rational solution, different heuristic techniques are applied: the ant colony model in combination with fuzzy logic methods [7] or genetic-based algorithms [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling demand for passenger transfers in the bounds of public transport network

Naumov¹

2018

EasyChair Preprints

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The passenger transfers optimization is one of the main directions to enhance the quality of public transport services. Modelling a demand for services of transfer nodes is quite a complex issue due to the stochastic nature of the transport process and the random nature of demand for trips of urban residents. However, this procedure is an essential stage for any problem related to optimization in transfer nodes, such as timetables scheduling for public transport lines, designing the public transport network or estimating the parameters of a given transfer node. The proposed model is based on the developed approach to perform the routing assignment stage in the classical four-stage urban planning procedure. Demand for trips is generated for each stop of a public transport system on the grounds of the stochastic variable of the time interval between passengers arrival to the respective stop. After defining of the destination stop, the route for the passenger’s trip is determined with the use of Dijkstra’s algorithm within the frame of a public transport network which is presented as a graph model with stops for vertices and route segments for edges. Transfer nodes are defined in the model as such graph vertices which are common for at least two lines of the public transport system. The author presents a class library implemented with the use of the Python programming language. On the basis of this library, the model for simulations of demand for transfers within the given public transport system was developed. The proposed approach to the demand modeling and the developed software were used for simulations of demand for transfers within the bounds of the public transport system of Bochnia (Poland). The brief results of these simulations are presented in the paper.

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“…Turns can be made at any junction where edges connect. There are n 2 possible turns at every network junction, where n is the number of edges connected at that junction [36].…”

Section: The Properties Of a Network Dataset In Arcgismentioning

confidence: 99%

Efficient Tour Planning for Tourist Sites Visitation in Lokoja, Nigeria: A Multi-Scenario Analysis Using GIS

Abubakar¹,

Idoko²,

Ocholi³

2017

JGIS

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This study used GIS to analyse various visitation sequence and routes for tourist sites in Lokoja considering multiple scenarios. This was done with a view to determining the most efficient visitation sequence vis-à-vis the routes for optimizing the overall visiting time and distance for the various scenarios considered. The main datasets used were the transportation network and the geographic coordinates of the tourist sites. These were collected through a comprehensive field survey of the study area. The ArcGIS 10.1 Network Analyst Extension was the main software used for this analysis. Five scenarios were considered comprising open and closed tours as well as Sequential Ordering Problems (SOP) and non-SOPs. In addition, for the first and second scenarios, an auxiliary scenario was considered that featured a hypothetical road block on an important arterial route. The fifth scenario considered the implication of a hypothetical flooding of some road sections along the bank of the River Niger. Furthermore, for each scenario, there were two optimization solutions: One that optimized the distance covered in visiting all the tourist sites and the other that optimized the time duration required to complete the site visitations. Optimal visitation sequences were determined by the network analyst as part of the solution of the respective scenarios. Sensitivity analysis was also performed to compare the consequences of time savings vis-à-vis distance savings across the various scenarios. In addition to the detailed navigation map produced for each scenario, a comprehensive navigation description guide was derived. The study reveals that scenarios where the site visitation sequence were optimized saved both time and distance markedly compared to scenarios with a predefined site visitation sequence. In all cases, shorter visitation durations were associated with time-optimized scenarios compared to distance-optimized ones while shorter visitation distances were 60associated with distance-optimized scenarios as compared to time-optimized scenarios. However, the disparities between distance-optimized and timeoptimized scenarios were negligible in some cases. Furthermore, the blockage of an important arterial route (including the flooding of a vulnerable road segment) resulted in a striking increase in the optimal distance and time required to visit all the tourist sites in Lokoja. Overall, the shortest cumulative travel time (of approximately 17.69 minutes) and distance (of 15,897.20 meters) were derived from the second scenario.

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Schedule synchronization in public transit using the fuzzy ant system

Cited by 32 publications

References 18 publications

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Modeling demand for passenger transfers in the bounds of public transport network

Efficient Tour Planning for Tourist Sites Visitation in Lokoja, Nigeria: A Multi-Scenario Analysis Using GIS

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