Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

Kaltenbrunner, Andreas; Meza, Rodrigo; Grivolla, Jens; Codina, Josep; Banchs, Rafael E.

doi:10.1016/j.pmcj.2010.07.002

Cited by 384 publications

(195 citation statements)

References 10 publications

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“…Some address the problem from a data mining point of view [15] with an exploratory data approach [16]. Afterwards, works like Kaltenbrunner et al [17] show how to implement prediction models based on time series analysis methods like Auto-Regressive Moving Average (ARMA).…”

Section: Bike Demand Prediction Modelsmentioning

confidence: 99%

Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems

et al. 2018

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Featured Application: The main application of this work is the analysis and prediction of the demand in bike sharing systems using their open/private data. A multi agent system is proposed and a case study is conducted using the data of a bicycle sharing system from a middle size city.Abstract: This paper proposes a multi agent system that provides visualization and prediction tools for bike sharing systems (BSS). The presented multi-agent system includes an agent that performs data collection and cleaning processes, it is also capable of creating demand forecasting models for each bicycle station. Moreover, the architecture offers API (Application Programming Interface) services and provides a web application for visualization and forecasting. This work aims to make the system generic enough for it to be able to integrate data from different types of bike sharing systems. Thus, in future studies it will be possible to employ the proposed system in different types of bike sharing systems. This article contains a literature review, a section on the process of developing the system and the built-in prediction models. Moreover, a case study which validates the proposed system by implementing it in a public bicycle sharing system in Salamanca, called SalenBici. It also includes an outline of the results and conclusions, a discussion on the challenges encountered in this domain, as well as possibilities for future work.

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Section: Bike Demand Prediction Modelsmentioning

confidence: 99%

Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems

et al. 2018

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“…In Wu et al [2011aWu et al [ , 2011b, it has been reported that the duty-cycled sensor nodes should be responsible for broadcasting beacons, and the temporal locality of human mobility should be learned and exploited when determining the frequency of broadcasting beacons. In this article, we focus on the challenges brought by the strong spatial locality of human mobility, which has been identified in Gonzalez et al [2008] and Kaltenbrunner et al [2010]. Based on smartphone users' mobility traces from the Nokia Mobile Data Challenge [Laurila et al 2012;Kiukkonen et al 2010], we have further validated the strong spatial locality in a more appropriate spatial granularity.…”

Section: Introductionmentioning

confidence: 89%

“…For instance, one water meter reading per month is enough for billing purposes, and one reading per hour could be enough for time-based pricing schemes. Finally, we assume that sensor nodes carry out DPF periodically since human mobility tends to follow some repeated patterns [Gonzalez et al 2008;Kaltenbrunner et al 2010;Wu et al 2013]. As for T dpf (the interval between two consecutive runs of DPF), it should be a multiple of (the epoch length of the repeated human mobility pattern) for avoiding unnecessary data exchange caused by the bursty contact arrivals within an epoch.…”

Section: Network Modelmentioning

confidence: 99%

Data pre-forwarding for opportunistic data collection in wireless sensor networks

Brown

Sreenan

2012

2012 Ninth International Conference on Networked Sensing (INSS)

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Opportunistic data collection in wireless sensor networks uses passing smartphones to collect data from sensor nodes, thus avoiding the cost of multiple static sink nodes. Based on the observed mobility patterns of smartphone users, sensor data should be preforwarded to the nodes that are visited more frequently with the aim of improving network throughput. In this article, we construct a formal network model and an associated theoretical optimization problem to maximize the throughput subject to energy constraints of sensor nodes. Since a centralized controller is not available in opportunistic data collection, data pre-forwarding (DPF) must operate as a distributed mechanism in which each node decides when and where to forward data based on local information. Hence, we develop a simple distributed DPF mechanism with two heuristic algorithms, implement this proposal in Contiki-OS, and evaluate it thoroughly. We demonstrate empirically, in simulations, that our approach is close to the optimal solution obtained by a centralized algorithm. We also demonstrate that this approach performs well in scenarios based on real mobility traces of smartphone users. Finally, we evaluate our proposal on a small laboratory testbed, demonstrating that the distributed DPF mechanism with heuristic algorithms performs as predicted by simulations, and thus that it is a viable technique for opportunistic data collection through smartphones.

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“…Thus, supporting the operation of the system by providing planning support for the distribution of bikes. Similar work, also focusing on Barcelona's Bicing system was done in [11]. The authors analyzed human mobility data in an urban area using the amount of available bikes at the stations.…”

Section: Related Workmentioning

confidence: 99%

Self-Organizing Redistribution of Bicycles in a Bike-Sharing System based on Decentralized Control

Preisler¹,

Dethlefs²,

Renz³

2016

Annals of Computer Science and Information Systems

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Abstract-Currently, bike-sharing systems undergo a rapid expansion due to technical improvements in the operation combined with an increased environmental and health awareness of people. When it comes to the acceptance of such systems the reliability is of great importance. It depends heavily on the availability of bicycles at the stations. But, in spite of truck-based redistribution efforts by the operators, stations still tend to become full or empty, especially in rush-hour situations. This paper builds upon an incentive scheme that encourages users to approach nearby stations for renting and returning bikes, thereby redistributing them in a self-organized fashion. A cooperativeness parameter is determined by the fraction of users that respond to an incentive by choosing the proposed stations. It uses a decentralized control process to calculate alternative rent and return stations for each of the stations. These alternatives are then proposed to the users when they approach an empty or full station. The approach is based on a decentralized control framework that allows to equipping different distributed software systems with the control capabilities needed to realize the coordination efforts required to achieve the desired self-organizing properties.

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Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system

Cited by 384 publications

References 10 publications

Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems

Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems

Data pre-forwarding for opportunistic data collection in wireless sensor networks

Self-Organizing Redistribution of Bicycles in a Bike-Sharing System based on Decentralized Control

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