Estimating Primary Demand in Bike-sharing Systems

Goh, Chong Yang; Yan, Chiwei; Jaillet, Patrick

doi:10.2139/ssrn.3311371

Cited by 12 publications

(5 citation statements)

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“…Finally, we provide a method to estimate the shifted demand of the system, and obtain in the end the actual user demand for each station. This shifted demand is also considered in the recent work by Goh et al (2019). The authors propose a rank-based choice model to reveal the primary user demand of the system, which differs from our work as we follow a linear programming approach to estimate the probability of walking between stations.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Mellou

Jaillet

2019

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Mellou

Jaillet

2019

SSRN Journal

Self Cite

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“…Several removals and insertion operators have been proposed to diversify and intensify the search [19]. Goh et al proposed a method for estimating the primary demand using a rank-based demand model which accounts for choice substitutions by treating each observed trip as the best available option in a latent ranking over origin-destination (OD) pairs [20]. Yang et al proposed a spatiotemporal bicycle mobility model based on historical bike-sharing data and devised a traffic prediction mechanism on a per-station basis with sub-hour granularity [8].…”

Section: Related Workmentioning

confidence: 99%

Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City

Lin

Gao

et al. 2020

Sensors

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As a kind of transportation in a smart city, urban public bicycles have been adopted by major cities and bear the heavy responsibility of the “last mile” of urban public transportation. At present, the main problem of the urban public bicycle system is that it is difficult for users to rent a bike during peak h, and real-time monitoring cannot be solved adequately. Therefore, predicting the demand for bicycles in a certain period and performing redistribution in advance is of great significance for solving the lag of bicycle system scheduling with the help of IoT. Based on the HOSVD-LSTM prediction model, a prediction model of urban public bicycles based on the hybrid model is proposed by transforming the source data (multiple time series) into a high-order tensor time series. Furthermore, it uses the tensor decomposition technology (HOSVD decomposition) to extract new features (kernel tenor) from higher-order tensors. At the same time, these kernel tenors are directly used to train tensor LSTM models to obtain new kernel tenors. The inverse tensor decomposition and high-dimensional, multidimensional, and tensor dimensionality reduction were introduced. The new kernel tenor obtains the predicted value of the source sequence. Then the bicycle rental amount is predicted.

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“…To assess this issue, common approaches regard various data cleaning techniques. For example, [3,26,27,28] attempt to avoid the bias induced by censored observations by filtering out the time periods where censoring might have occurred, before modeling. As a further example, [27] substitute the censored observations with the mean of the historical (non-censored) observations regarding the same period.…”

Section: Common Approaches To Handling Demand Censorshipmentioning

confidence: 99%

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

Gammelli¹,

Peled²,

Rodrigues³

et al. 2020

Preprint

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Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly accounting for this inherent distinction, predictive models of demand would necessarily represent a biased version of true demand, thus less effectively predicting the needs of service users. To counter this problem, we propose a general method for censorship-aware demand modeling, for which we devise a censored likelihood function. We apply this method to the task of shared mobility demand prediction by incorporating the censored likelihood within a Gaussian Process model, which can flexibly approximate arbitrary functional forms. Experiments on artificial and real-world datasets show how taking into account the limiting effect of supply on demand is essential in the process of obtaining an unbiased predictive model of user demand behavior.

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Estimating Primary Demand in Bike-sharing Systems

Cited by 12 publications

References 50 publications

Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Dynamic Resource Redistribution and Demand Estimation: An Application to Bike Sharing Systems

Short-Term Rental Forecast of Urban Public Bicycle Based on the HOSVD-LSTM Model in Smart City

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

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