A taxi fleet management system is presented .The system consists of Clustering , Neuro fuzzy systems and Particle Swarm Optimization methodologies. The proposed system aims at maximizing revenue of cabs as individual entities and the cab aggregator simultaneously. Clustering of pick up requests is carried out using a variant of DBSCAN which uses Delaunay triangulation to recognise fare hotspots. Neuro Fuzzy system is used to evaluate the eligibility of taxis to contest for these hotspots .The Neuro Fuzzy System is trained using Particle Swarm Optimization method. Intelligent swarming of taxis according to their eligibilities for the hotspots is performed to maximize revenue of both cab aggregators and cabs.
Keywords: PSO, TSK Model, Taxi Fleet Management, Neuro Fuzzy Systems, Clustering, Fleet Management , Particle Swarm Optimization , Swarm Intelligence.
[1]
Xueni Zhang,et al.
An Improved Clustering Algorithm Based on Multi-swarm Intelligence
,
2016,
2016 International Symposium on Computer, Consumer and Control (IS3C).
[3]
Peng Gao,et al.
Discovering Spatial Patterns in Origin‐Destination Mobility Data
,
2012,
Trans. GIS.
[4]
Shih-Fen Cheng,et al.
Density Peaks Clustering Approach for Discovering Demand Hot Spots in City-scale Taxi Fleet Dataset
,
2015,
2015 IEEE 18th International Conference on Intelligent Transportation Systems.
[5]
Russell C. Eberhart,et al.
Swarm intelligence for permutation optimization: a case study of n-queens problem
,
2003,
Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).