Modeling bike availability in a bike-sharing system using machine learning

Ashqar, Huthaifa I.; Elhenawy, Mohammed; Almannaa, Mohammed; Ghanem, Ahmed N; Rakha, Hesham A.; House, Leanna

doi:10.1109/mtits.2017.8005700

Cited by 47 publications

(32 citation statements)

References 19 publications

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“…In Table 1, we present each category's percentage separately by city, as a previous study showed that there were close to no trips between the five cities (Ashqar et al 2017).…”

Section: Resultsmentioning

confidence: 99%

Identifying Optimum Bike Station Initial Conditions using Markov Chain Modeling

2019

Self Cite

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Bike sharing systems (BSSs) are being deployed in many cities because of their environmental, social, and health benefits. To maintain low rental costs, rebalancing costs must be kept minimal. In this paper, we use BSS data collected from the San Francisco Bay Area to build a Markov chain model for each bike station. The models are then used to simulate the BSS to determine the optimal station-specific initial number of bikes for a typical day to ensure that the probability of the station becoming empty or full is minimal and hence minimizing the rebalancing cost.

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“…In Table 1, we present each category's percentage separately by city, as a previous study showed that there were close to no trips between the five cities (Ashqar et al 2017).…”

Section: Resultsmentioning

confidence: 99%

Identifying Optimum Bike Station Initial Conditions using Markov Chain Modeling

2019

Self Cite

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“…The partition and identification of geographic hotspots are simple, and future studies can adopt more complex methods. The reallocation function does not include complex machine-learning techniques, which can be incorporated into future work, that considers factors such as weather conditions [34,35]. In addition, since the calculation in the data-processing section is based on simple Euclidean distance, future studies can be based on more precise methods like great circle distance.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

Dai

Zhu

et al. 2019

Sustainability

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Environmentally friendly shared transit systems have become ubiquitous at present. As a result, analyzing the ranges and tracts of human activities and gatherings based on bike share data is scientifically useful. This paper investigates the spatial and temporal travel characteristics of citizens based on real-time-extracted electric bikes (e-bikes) Global Positioning System (GPS) data from May to July in 2018 in the central area of Tengzhou City, Shandong Province, China. The research is conducive for the exploration of citizens’ changes in mobility behaviors, for the analysis of relationships between mobility changes and environmental or other possible factors, and for advancing policy proposals. The main conclusions of the study are as follows. First, in general, citizens’ travelling is featured by rides that are less than 10 min, shorter than 5 km, and with a speed between 5 km/h and 20 km/h. Second, in terms of temporal characteristics, monthly e-bike usage and citizens’ mobility are positively correlated with temperature in May and negatively correlated with temperature in July; an overall negative correlation is also manifested between the e-bike usage (mobility) and air quality index; daily usage reaches a trough on Tuesday and a peak on Friday, indicating the extent of mobility on respective days; e-bike usage and human outdoor behaviors are significantly lowered in rainy weather than in sunny weather; hourly rides reach a peak at 18:00 (more human activities) and a trough at 2:00 (less activities), and average hourly riding speed maximizes at 5:00 and minimizes around 8:00 and 17:00. Third, for spatial characteristics, destinations (D points) during morning rush hour and regions where e-bikes are densely employed are concentrated mainly in mid-north and middle parts of the central area (major human gatherings), and the rides have a diffusing pattern; e-bike origin–destination (O–D) trajectories radiate mostly towards the mid-north and the east during evening rush hour. In addition, 9.4% of the total trips to work areas during morning rush hour represent spillover commuting, indicating that separations between jobs and residential are not severe in the central area of Tengzhou City and commuting is relatively convenient.

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“…Bachand-marleau et al [9] investigated social economy and spatial factors, and further analyzed their influence on the usage frequency. Besides, the univariate regression algorithms and multivariate regression algorithms were used by Ashqar et al [10] to model available bikes at each station and at the spatially correlated stations of each region, respectively. While these studies failed to capture the complex spatial-temporal features, they did clarify the importance of external factors in prediction model.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction

Liu

Yang

2020

IEEE Access

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As a new type of short distance commuting, the station-free sharing bike effectively alleviates urban traffic congestion. Thus, they are deployed in a large scale in many cities. However, various complex factors, including spatial, temporal, and other external information, result in serious imbalance of supply and demand between regions, which makes accurate prediction a challenging issue. In this study, our primary objective is to accurately forecast supply and demand by leveraging multi-source datasets. Based on the visual analysis about spatial-temporal characteristics of GPS data in Shanghai, we presented the innovative methods of Area of Interest grading and Traffic Analysis Zone division of bike-sharing, and revealed the distribution characteristics of sharing bike trips. A multi-block hybrid model where three blocks were separately modeled according to different data types was proposed. Moreover, eight state-of-theart models and two variant models were developed as benchmarks to compare and evaluate the proposed model. The results suggested that MBH outperforms ten baselines with the highest accuracy. In addition, we conducted practical application of prediction results to validate that the proposed model could provide effective information for scheduling and rebalancing of bike-sharing system.

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Modeling bike availability in a bike-sharing system using machine learning

Cited by 47 publications

References 19 publications

Identifying Optimum Bike Station Initial Conditions using Markov Chain Modeling

Identifying Optimum Bike Station Initial Conditions using Markov Chain Modeling

Analysis of Spatial and Temporal Characteristics of Citizens’ Mobility Based on E-Bike GPS Trajectory Data in Tengzhou City, China

A Deep Learning Based Multi-Block Hybrid Model for Bike-Sharing Supply-Demand Prediction

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