Paper Info
Title
Crowd-Based Ecofriendly Trip Planning
Abstract
In recent years we have witnessed a growing interest in trip planning systems aiming at organizing daily travel schedules in smart cities. Such systems use specialized engines to find optimal means of transport between two geospatial endpoints to provide recommendations to citizens for short routes across the city. At the same time, alternative means of transportation, such as bike sharing systems, have enjoyed tremendous success since they offer a green and facile solution for daily commuters and tourists. However, one major challenge of the bike sharing systems is that the distribution of bikes among the stations can be quite uneven during rush hours or due to topography. This often results in shortage of bikes and increasing numbers of disappointed users. Existing works in the literature are limited since they only focus on predicting the demand or apply a-posteriori methods for balancing the load of stations. Furthermore, none of these works consider the benefit of these systems in concert. In this work, we present "MOToR" (MultimOdal Trip Rebalancing), a system that builds upon the OpenTripPlanner framework to incorporate dynamic transit schedule data while balancing the availability of bikes among the bike stations. Our experimental evaluation shows that our approach is practical, efficient and outperforms state-of-the-art methods for route planning.
Year
DOI
Venue
2018
10.1109/MDM.2018.00018
2018 19th IEEE International Conference on Mobile Data Management (MDM)
Keywords
Field
DocType
trip planning,bike sharing systems,ecofriendly routes
Geospatial analysis,Route planning,Trip planning,Computer science,Transport engineering,Schedule,Economic shortage,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-5386-4134-7
0
0.34
References 
Authors
13
4
Name
Order
Citations
PageRank
Dimitrios Tomaras132.47
Vana Kalogeraki21686124.40
Thomas Liebig320320.77
Dimitrios Gunopulos47171715.85