3,118 research outputs found
A low dimensional model for bike sharing demand forecasting
Big, transport-related datasets are nowadays publicly available, which makes
data-driven mobility analysis possible. Trips with their origins, destinations
and travel times are collected in publicly available big databases, which
allows for a deeper and richer understanding of mobility patterns. This paper
proposes a low dimensional approach to combine these data sources with weather
data in order to forecast the daily demand for Bike Sharing Systems (BSS). The
core of this approach lies in the proposed clustering technique, which reduces
the dimension of the problem and, differently from other machine learning
techniques, requires limited assumptions on the model or its parameters. The
proposed clustering technique synthesizes mobility data quantitatively (number
of trips) and spatially (mean trip origin and destination). This allows
identifying recursive mobility patterns that - when combined with weather data
- provide accurate predictions of the demand. The method is tested with
real-world data from New York City. We synthesize more than four million trips
into vectors of movement, which are then combined with weather data to forecast
the daily demand at a city-level. Results show that, already with a
one-parameters model, the proposed approach provides accurate predictions.Comment: 2019 IEEE. Personal use of this material is permitted. Permission
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Incentives and Redistribution in Homogeneous Bike-Sharing Systems with Stations of Finite Capacity
Bike-sharing systems are becoming important for urban transportation. In such
systems, users arrive at a station, take a bike and use it for a while, then
return it to another station of their choice. Each station has a finite
capacity: it cannot host more bikes than its capacity. We propose a stochastic
model of an homogeneous bike-sharing system and study the effect of users
random choices on the number of problematic stations, i.e., stations that, at a
given time, have no bikes available or no available spots for bikes to be
returned to. We quantify the influence of the station capacities, and we
compute the fleet size that is optimal in terms of minimizing the proportion of
problematic stations. Even in a homogeneous city, the system exhibits a poor
performance: the minimal proportion of problematic stations is of the order of
(but not lower than) the inverse of the capacity. We show that simple
incentives, such as suggesting users to return to the least loaded station
among two stations, improve the situation by an exponential factor. We also
compute the rate at which bikes have to be redistributed by trucks to insure a
given quality of service. This rate is of the order of the inverse of the
station capacity. For all cases considered, the fleet size that corresponds to
the best performance is half of the total number of spots plus a few more, the
value of the few more can be computed in closed-form as a function of the
system parameters. It corresponds to the average number of bikes in
circulation
Deep Learning for Short-Term Prediction of Available Bikes on Bike-Sharing Stations
Bike-sharing is adopted as a valid option replacing traditional public transports since they are eco-friendly, prevent traffic congestions, reduce any possible risk of social contacts which happen mostly on public means. However, some problems may occur such as the irregular distribution of bikes on related stations/racks/areas, and the difficulty of knowing in advance what the rack status will be like, or predicting if there will be bikes available in a specific bike-station at a certain time of the day, or if there will be a free slot to leave the rented bike. Thus, providing predictions can be useful to improve the service quality, especially in those cases where bike racks are used for e-bikes, which need to be recharged. This paper compares the state-of-the-art techniques to predict the number of available bikes and free bike-slots in bike-sharing stations (i.e., bike racks). To this end, a set of features and predictive models were compared to identify the best models and predictors for short-term predictions, namely of 15, 30, 45, and 60 minutes. The study has demonstrated that deep learning and in particular Bidirectional Long Short-Term Memory networks (Bi-LSTM) offers a robust approach for the implementation of reliable and fast predictions of available bikes, even with a limited amount of historical data. This paper has also reported an analysis of feature relevance based on SHAP that demonstrated the validity of the model for different cluster behaviours. Both solution and its validation were derived by using data collected in bike-stations in the cities of Siena and Pisa (Italy), in the context of Sii-Mobility National Research Project on Mobility and Transport and Snap4City Smart City IoT infrastructure
Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon
Albuquerque, V., Andrade, F., Ferreira, J. C., Dias, M. S., & Bacao, F. (2021). Bike-sharing mobility patterns: a data-driven analysis for the city of Lisbon. EAI Endorsed Transactions on Smart Cities, 5(16), 1-20. [169580]. https://doi.org/10.4108/eai.4-5-2021.169580New technologies applied to transportation services in the city, enable the shift to sustainable transportation modes making bike-sharing systems (BSS) more popular in the urban mobility scenario. This study focuses on understanding the spatiotemporal station and trip activity patterns in the Lisbon BSS, based in 2018 data taken as the baseline, and understand trip rate changes in such system, that happened in the following years of 2019 and 2020. Furthermore, our paper aims to understand the COVID-19 pandemic impact in BSS mobility patterns. In this paper, we analyzed large datasets adopting a CRISP-DM data mining method. By studying and identifying spatiotemporal distribution of trips through stations, combined with weather factors, we looked at BSS improvements more suitable to accommodate users’ demand. Our major contribution was a new insight on how people move in the city using bikes, via a data science approach using BSS network usage data. Major findings show that most bike trips occur on weekdays, with no precipitation, and we observed a substantial growth of trip count, during the observed time frame, although cut short by the pandemic. We believe that our approach can be applied to any city with available urban mobility data.publishersversionpublishe
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