Abstract
Bicycle sharing systems are becoming increasingly popular in cities around the world as they are an inexpensive and sustainable means of transportation. Promoting the use of these systems substantially improves the quality of life in cities by reducing pollutant emissions and traffic congestion. In these systems, bikes are made available for shared use to individuals on a short-term basis. They allow people to borrow a bike in one dock and return it to any other station with free docks belonging to the same system. The occupancy level of the stations can be constantly monitored. However, to achieve a satisfactory user experience, all the stations in the system must be neither overloaded nor empty when the user needs to access the station. The aim of this paper is to analyze occupancy level data acquired from real systems to determine situations of dock overload in multiple stations which could lead to service disruption. The proposed methodology relies on a pattern mining approach. A new pattern type called Occupancy Monitoring Pattern is proposed here to detect situations of dock overload in multiple stations. Since stations are geo-referenced and their occupancy levels are periodically monitored, occupancy patterns can be filtered and evaluated by taking into consideration both the spatial and temporal correlation of the acquired measurements. The results achieved on real data highlight the potential of the proposed methodology in supporting domain experts in their maintenance activities, such as periodic re-balancing of the occupancy levels of the stations, as well as in improving user experience by suggesting alternative stations in the nearby area.
Highlights
In recent years, municipalities have fostered alternative ways of public transportation in order to reduce pollution and traffic congestion [1,2,3,4,5]
To automatically detect recurrent dock overload conditions in multiple stations, we propose a new type of pattern, anmed Occupancy Monitoring Pattern (OMP)
In Section 3.2.3, we describe the algorithm used in the BELL framework to mine the OMPs including nearby stations according to the spatial constraint maxdist as well their criticality and intermittence values
Summary
Municipalities have fostered alternative ways of public transportation in order to reduce pollution and traffic congestion [1,2,3,4,5]. Bicycle sharing systems [6,7] are a notable example of eco-friendly transportation systems, where citizens can rent bicycles on a short-term basis. Bikes are retrieved from stations spread throughout the city and each station has a maximum capacity as it is equipped with a fixed number of docks. Citizens can rent a bicycle parked at any station and return it to any other station with free docks. To achieve a satisfactory user experience, system managers should carefully monitor the level of occupancy of the stations. If a station is frequently overloaded at peak hours, a re-balancing action should be scheduled in order to move some of the parked bicycles to any station located in the neighborhood. In case the problem is more severe, managers may decide to expand the station to fit the increasing demand.
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