Understanding the demand predictability of bike share systems: A station-level analysis

Zhuoli YIN, Kendrick HARDAWAY, Yu FENG, Zhaoyu KOU, Hua CAI

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Front. Eng ›› 2023, Vol. 10 ›› Issue (4) : 551-565. DOI: 10.1007/s42524-023-0279-8
RESEARCH ARTICLE

Understanding the demand predictability of bike share systems: A station-level analysis

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Abstract

Predicting demand for bike share systems (BSSs) is critical for both the management of an existing BSS and the planning for a new BSS. While researchers have mainly focused on improving prediction accuracy and analysing demand-influencing factors, there are few studies examining the inherent randomness of stations’ observed demands and to what degree the demands at individual stations are predictable. Using Divvy bike-share one-year data from Chicago, USA, we measured demand entropy and quantified the station-level predictability. Additionally, to verify that these predictability measures could represent the performance of prediction models, we implemented two commonly used demand prediction models to compare the empirical prediction accuracy with the calculated entropy and predictability. Furthermore, we explored how city- and system-specific temporally-constant features would impact entropy and predictability to inform estimating these measures when historical demand data are unavailable. Our results show that entropy and predictability of demands across stations are polarized as some stations exhibit high uncertainty (a low predictability of 0.65) and others have almost no check-out demand uncertainty (a high predictability of around 1.0). We also validated that the entropy and predictability are a priori model-free indicators for prediction error, given a sequence of bike usage demands. Lastly, we identified that key factors contributing to station-level entropy and predictability include per capita income, spatial eccentricity, and the number of parking lots near the station. Findings from this study provide more fundamental understanding of BSS demand prediction, which can help decision makers and system operators anticipate diverse station-level prediction errors from their prediction models both for existing stations and for new ones.

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Keywords

bike share systems / demand prediction / prediction errors / machine learning / entropy

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Zhuoli YIN, Kendrick HARDAWAY, Yu FENG, Zhaoyu KOU, Hua CAI. Understanding the demand predictability of bike share systems: A station-level analysis. Front. Eng, 2023, 10(4): 551‒565 https://doi.org/10.1007/s42524-023-0279-8

References

[1]
Bachand-Marleau, J Lee, B El-Geneidy, A (2012). Better understanding of factors influencing likelihood of using shared bicycle systems and frequency of use. Transportation Research Record: Journal of the Transportation Research Board, 2314( 1): 66–71
CrossRef Google scholar
[2]
Bao, J Xu, C Liu, P Wang, W (2017). Exploring bikesharing travel patterns and trip purposes using smart card data and online point of interests. Networks and Spatial Economics, 17( 4): 1231–1253
CrossRef Google scholar
[3]
Belgiu, M Drăguţ, L (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114: 24–31
CrossRef Google scholar
[4]
Benvenuto, D Giovanetti, M Vassallo, L Angeletti, S Ciccozzi, M (2020). Application of the ARIMA model on the COVID-2019 epidemic dataset. Data in Brief, 29: 105340
CrossRef Google scholar
[5]
Biau, G Scornet, E (2016). A random forest guided tour. Test, 25( 2): 197–227
CrossRef Google scholar
[6]
CazabetRBorgnatPJensenP (2017). Using degree constrained gravity null-models to understand the structure of journeys’ networks in bicycle sharing systems. In: 25th European Symposium on Artificial Neural Networks, Computational Intelligence, and Machine Learning. Bruges: ENSANN, 25
[7]
ChaiDWangLYangQ (2018). Bike flow prediction with multi-graph convolutional networks. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, WA: ACM, 397–400
[8]
ChenLZhangDWangLYangDMaXLiSWuZPanGNguyenTJakubowiczJ (2016). Dynamic cluster-based over-demand prediction in bike sharing systems. In: Proceedings of the International Joint Conference on Pervasive and Ubiquitous Computing. Heidelberg: ACM, 841–852
[9]
ChenPYuanHShuX (2008). Forecasting crime using the ARIMA model. In: 5th International Conference on Fuzzy Systems and Knowledge Discovery. Jinan: IEEE, 627–630
[10]
ChenTGuestrinC (2016). Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, CA: ACM, 785–794
[11]
El-Assi, W Salah Mahmoud, M Nurul Habib, K (2017). Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation, 44( 3): 589–613
CrossRef Google scholar
[12]
El SibaiRChabchoubYFrickerC (2018). Using spatial outliers detection to assess balancing mechanisms in bike sharing systems. In: 32nd International Conference on Advanced Information Networking and Applications (AINA). Krakow: IEEE, 988–995
[13]
Fano, R Hawkins, D (1961). Transmission of information: A statistical theory of communication. American Journal of Physics, 29( 11): 793–794
CrossRef Google scholar
[14]
Fishman, E (2016). Bikeshare: A review of recent literature. Transport Reviews, 36( 1): 92–113
CrossRef Google scholar
[15]
FishmanEAllanV (2019). Chapter six: Bike share. In: Fishman E, ed. Advances in Transport Policy and Planning. Volume 4. The Sharing Economy and the Relevance for Transport. Cambridge: Elsevier Inc., 121–152
[16]
HeSShinK (2020). Towards fine-grained flow forecasting: A graph attention approach for bike sharing systems. In: Proceedings of the Web Conference. Taipei: ACM, 88–98
[17]
HulotPAloiseDJenaS (2018). Towards station-level demand prediction for effective rebalancing in bike-sharing systems. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. London: ACM, 378–386
[18]
Hyland, M Hong, Z Pinto, H Chen, Y (2018). Hybrid cluster-regression approach to model bikeshare station usage. Transportation Research Part A: Policy and Practice, 115: 71–89
CrossRef Google scholar
[19]
Hyndman, R Khandakar, Y (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27( 3): 1–22
CrossRef Google scholar
[20]
Jia, W Tan, Y Liu, L Li, J Zhang, H Zhao, K (2019). Hierarchical prediction based on two-level Gaussian mixture model clustering for bike-sharing system. Knowledge-Based Systems, 178: 84–97
CrossRef Google scholar
[21]
KimDChoYKimDParkCChooJ (2022). Residual correction in real-time traffic forecasting. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management. Atlanta, GA: ACM, 962–971
[22]
KocerU U (2013). Forecasting intermittent demand by Markov chain model. International Journal of Innovative Computing, Information & Control, 9(8): 3307–3318
[23]
Kontoyiannis, I Algoet, P Suhov, Y Wyner, A (1998). Nonparametric entropy estimation for stationary processes and random fields, with applications to English text. IEEE Transactions on Information Theory, 44( 3): 1319–1327
CrossRef Google scholar
[24]
Kou, Z Cai, H (2019). Understanding bike sharing travel patterns: An analysis of trip data from eight cities. Physica A, 515: 785–797
CrossRef Google scholar
[25]
Kou, Z Cai, H (2021a). Comparing the performance of different types of bike share systems. Transportation Research Part D: Transport and Environment, 94: 102823
CrossRef Google scholar
[26]
KouZCaiH (2021b). Incorporating spatial network information to improve demand prediction for bike share system expansion. In: Proceedings of the 10th International Workshop on Urban Computing. Beijing
[27]
Kou, Z Wang, X Chiu, S Cai, H (2020). Quantifying greenhouse gas emissions reduction from bike share systems: A model considering real-world trips and transportation mode choice patterns. Resources, Conservation and Recycling, 153: 104534
CrossRef Google scholar
[28]
Li, Y Zheng, Y (2020). Citywide bike usage prediction in a bike-sharing system. IEEE Transactions on Knowledge and Data Engineering, 32( 6): 1079–1091
CrossRef Google scholar
[29]
LiYZhengYZhangHChenL (2015). Traffic prediction in a bike-sharing system. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems. Seattle, WA: ACM, 1–10
[30]
Lin, L He, Z Peeta, S (2018). Predicting station-level hourly demand in a large-scale bike-sharing network: A graph convolutional neural network approach. Transportation Research Part C: Emerging Technologies, 97: 258–276
CrossRef Google scholar
[31]
Lin, P Weng, J Hu, S Alivanistos, D Li, X Yin, B (2020). Revealing spatio-temporal patterns and influencing factors of dockless bike sharing demand. IEEE Access, 8: 66139–66149
CrossRef Google scholar
[32]
Liu, C Gao, X Wang, X (2022). Data adaptive functional outlier detection: Analysis of the Paris bike sharing system data. Information Sciences, 602: 13–42
CrossRef Google scholar
[33]
Lu, X Wetter, E Bharti, N Tatem, A Bengtsson, L (2013). Approaching the limit of predictability in human mobility. Scientific Reports, 3( 1): 2923
CrossRef Google scholar
[34]
Luo, H Zhao, F Chen, W Cai, H (2020). Optimizing bike sharing systems from the life cycle greenhouse gas emissions perspective. Transportation Research Part C: Emerging Technologies, 117: 102705
CrossRef Google scholar
[35]
Médard, de Chardon C Caruso, G (2015). Estimating bike-share trips using station level data. Transportation Research Part B: Methodological, 78: 260–279
CrossRef Google scholar
[36]
NiuZZhouMWangLGaoXHuaG (2016). Ordinal regression with multiple output CNN for age estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE, 4920–4928
[37]
NochaiRNochaiT (2006). ARIMA model for forecasting oil palm price. In: Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications. Penang: Academia, 13–15
[38]
Regue, R Recker, W (2014). Proactive vehicle routing with inferred demand to solve the bikesharing rebalancing problem. Transportation Research Part E: Logistics and Transportation Review, 72: 192–209
CrossRef Google scholar
[39]
Saboia, J L M (1977). Autoregressive integrated moving average (ARIMA) models for birth forecasting. Journal of the American Statistical Association, 72( 358): 264–270
CrossRef Google scholar
[40]
SenterH (2008). In: Kutner M H, Nachtsheim C J, Neter J, Li W, eds. Applied Linear Statistical Models. 5th ed. Boston: McGraw-Hill
[41]
Shaheen, S Guzman, S Zhang, H (2010). Bikesharing in Europe, the Americas, and Asia. Transportation Research Record: Journal of the Transportation Research Board, 2143( 1): 159–167
CrossRef Google scholar
[42]
Shannon, C E (1948). A mathematical theory of communication. Bell System Technical Journal, 27( 3): 379–423
CrossRef Google scholar
[43]
SinghviDSinghviSFrazierP IHendersonS GMahonyE OShmoysD BWoodardD B (2015). Predicting bike usage for New York City’s bike sharing system. In: AAAI Workshop: Computational Sustainability. Austin, TX: AAAI
[44]
Smith, D A Shen, Y Barros, J Zhong, C Batty, M Giannotti, M (2020). A compact city for the wealthy? Employment accessibility inequalities between occupational classes in the London metropolitan region 2011. Journal of Transport Geography, 86: 102767
CrossRef Google scholar
[45]
Song, C Qu, Z Blumm, N Barabási, A L (2010). Limits of predictability in human mobility. Science, 327( 5968): 1018–1021
CrossRef Google scholar
[46]
StraitsResearch (2021). Bike sharing market new research analysis and forecast 2030. Online Report
[47]
StrombergJ (2015). Bike share users are mostly rich and white. Here’s why that’s hard to change
[48]
US Census Bureau (2012). 2009–2011 American Community Survey 3-year Public Use Microdata Samples
[49]
Yang, Z Chen, J Hu, J Shu, Y Cheng, P (2019). Mobility modeling and data-driven closed-loop prediction in bike-sharing systems. IEEE Transactions on Intelligent Transportation Systems, 20( 12): 4488–4499
CrossRef Google scholar
[50]
YangZHuJShuYChengPChenJMoscibrodaT (2016). Mobility modeling and prediction in bike-sharing systems. In: Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services. Singapore: ACM, 165–178
[51]
YuBYinHZhuZ (2018). Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm: ACM, 3634–3640
[52]
Zhang, Y Thomas, T Brussel, M J G van Maarseveen, M F A M (2016). Expanding bicycle-sharing systems: Lessons learnt from an analysis of usage. PLoS One, 11( 12): e0168604
CrossRef Google scholar
[53]
ZhengV ZChoiSSunL (2023). Enhancing deep traffic forecasting models with dynamic regression. arXiv preprint, arXiv:2301.06650
[54]
Zhou, X (2015). Understanding spatiotemporal patterns of biking behavior by analyzing massive bike sharing data in Chicago. PLoS One, 10( 10): e0137922
CrossRef Google scholar
[55]
Zhou, Y Wang, L Zhong, R Tan, Y (2018). A Markov chain based demand prediction model for stations in bike sharing systems. Mathematical Problems in Engineering, 8028714
CrossRef Google scholar
[56]
Zhou, Y Yu, Y Wang, Y He, B Yang, L (2023). Mode substitution and carbon emission impacts of electric bike sharing systems. Sustainable Cities and Society, 89: 104312
CrossRef Google scholar

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s42524-023-0279-8 and is accessible for authorized users.

Competing Interests

The authors declare that they have no competing interests.

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