Paper Info
Title
Genetic Search Of Pickup And Delivery Problem Solutions For Self-Driving Taxi Routing
Abstract
Self-driving cars belong to rapidly growing domain of cyber-physical systems with many open problems. In this paper, we study routing problem for taxis. In mathematical terms, it is well-known Pickup and Delivery problem (PDP). We use with the standard small-moves technique, which is to apply small changes to a solution for PDP in order to obtain a better one; and an approach that works with small-moves as mutations in genetic algorithms. We propose a strategy-based framework for managing set of small changes and suggest different strategies. We tested algorithms for routing on real-world dataset on taxi orders to airports in United Kingdom. The results show that algorithms using mixed strategies outperform algorithms using a single small move.
Year
DOI
Venue
2016
10.1007/978-3-319-44944-9_30
ARTIFICIAL INTELLIGENCE APPLICATIONS AND INNOVATIONS, AIAI 2016
Keywords
Field
DocType
Self-driving car, Autonomous car, Routing, Pickup and delivery, Genetic algorithms, City taxi
Computer science,Taxis,Artificial intelligence,Genetic search,Pickup,Machine learning,Genetic algorithm
Conference
Volume
ISSN
Citations 
475
1868-4238
1
PageRank 
References 
Authors
0.35
14
3
Name
Order
Citations
PageRank
Viacheslav Shalamov110.35
Andrey Filchenkov24615.80
Anatoly Shalyto39820.06