Development of Demand Forecasting Algorithms based on ARIMA Model Variations for Public Shared Bike Service in Seoul

Kim, Do Hee; Lim, Heejong

doi:10.37793/itpr.29.1.3

Cited by 2 publications

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“…Furthermore, we employed statistical models, such as ARIMA, its variants, and the Holt-Winters models, as benchmarks. According to Kim and Lim [26], ARIMAX, which incorporates daily precipitation and temperature as covariates, was the most accurate forecasting method among ARIMA variants. Two accuracy measurement metrics, namely the root mean squared error (RMSE) and the mean absolute percentage error (MAPE) were used (Equations ( 6) and ( 7)).…”

Section: Forecasting Demand Of Bikes By Districtsmentioning

confidence: 99%

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

2022

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Efficient and sustainable bike-sharing service (BSS) operations require accurate demand forecasting for bike inventory management and rebalancing. Probabilistic forecasting provides a set of information on uncertainties in demand forecasting, and thus it is suitable for use in stochastic inventory management. Our research objective is to develop probabilistic time-series forecasting for BSS demand. We use an RNN–LSTM-based model, called DeepAR, for the station-wise bike-demand forecasting problem. The deep-learning structure of DeepAR captures complex demand patterns and correlations between the stations in one trained model; therefore, it is not necessary to develop demand-forecasting models for each individual station. DeepAR makes parameter forecast estimates for the probabilistic distribution of target values in the prediction range. We apply DeepAR to estimate the parameters of normal, truncated normal, and negative binomial distributions. We use the BSS dataset from Seoul Metropolitan City to evaluate the model’s performance. We create district- and station-level forecasts, comparing several statistical time-series forecasting methods; as a result, we show that DeepAR outperforms the other models. Furthermore, our district-level evaluation results show that all three distributions are acceptable for demand forecasting; however, the truncated normal distribution tends to overestimate the demand. At the station level, the truncated normal distribution performs the best, with the least forecasting errors out of the three tested distributions.

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Section: Forecasting Demand Of Bikes By Districtsmentioning

confidence: 99%

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

2022

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“…Korea has many public bicycle rental systems in operation, such as Ttareungi in Seoul, Tashu in Daejeon, and Nubija in Changwon. In Seoul, public bicycle rental services have the highest awareness and satisfaction ratings among various urban environmental policies and services and are recognized as an effective environment related policy [8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Factors Affecting the Extra Journey Time of Public Bicycles

Jung,

Jung

2023

Sustainability

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Many countries worldwide are introducing public bicycle systems to reduce urban traffic and environmental problems. However, studies on the usage behavior of public bicycles have not considered the trip purpose of riders extensively due to data limitations. Therefore, this study defined the “extra journey time” from usage time and origin–destination (OD) expected time and clustered public bicycle usage behaviors. Subsequently, the effects of the location characteristics of the departure and arrival stations, road environmental factors, and weather conditions for each cluster were analyzed. Three clusters were obtained from the results. Riders in Cluster 1 were inferred to have used the bicycles to commute and for work purposes, and riders in Clusters 2 and 3 used the bicycles for leisure purposes. Moreover, the bike station location characteristics, road environmental factors, and weather conditions influenced the probability of classification into one cluster. In particular, bike lanes near the departure and arrival stations increased the probability of classification under Clusters 2 and 3. The trip patterns according to the extra journey time of public bicycles were classified under these clusters. Furthermore, the differences in the characteristics of the bicycle usage types were identified according to the location and meteorological factors affecting them.

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Development of Demand Forecasting Algorithms based on ARIMA Model Variations for Public Shared Bike Service in Seoul

Cited by 2 publications

References 0 publications

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

Analysis of Factors Affecting the Extra Journey Time of Public Bicycles

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