Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing
Abstract
:1. Introduction
- ①
- Where are the bottlenecks of bike-sharing travel in a city? How can one find them by distinguishing between endogenous and exogenous demand?
- ②
- What are reasons for bottlenecks’ formation in bike-sharing travel? How can one determine the most key elements?
2. Literature Review
2.1. Influencing Factors on Bike-Sharing Travel
2.2. Traffic Bottleneck Modeling
3. Method
3.1. Establishing Analysis Areas
3.2. Identifying Bike-Sharing Travel Bottlenecks
3.3. Investigating Bottlenecks’ Causes
4. Case Study
4.1. Study Area
4.2. Data Description
5. Results and Discussion
5.1. Bottleneck Identification Demonstration
5.2. Demonstration of the Fault Tree–Bayesian Network
X1: High altitude | X2: Heavily heaved terrain | X3: Heavy rain |
X4: Poor air | X5: High income | X6: Serious traffic congestion |
X7: Serious Home-Work separation | X8: Few bus stops | X9: Few bus lines |
X10: Small density of bus lane | X11: Few subway station | X12: Low road density |
X13: Few branch roads | X14: Few car parking | X15: Low sidewalk density |
X16: Low density of bike lanes | X17: Low green area ratio | X18: Excessive intersections |
5.3. Interpretation of Bottleneck Causes
6. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Cho, S.H.; Shin, D. Estimation of Route Choice Behaviors of Bike-Sharing Users as First- and Last-mile Trips for Introduction of Mobility-as-a-Service (MaaS). KSCE J. Civ. Eng. 2022, 26, 3102–3113. [Google Scholar] [CrossRef]
- Hofmann, C.; Staehr, T.; Cohen, S.; Stricker, N.; Haefner, B.; Lanza, G. Augmented Go & See: An approach for improved bottleneck identification in production lines. Procedia Manuf. 2019, 31, 148–154. [Google Scholar] [CrossRef]
- Hale, D.; Chrysikopoulos, G.; Kondyli, A.; Ghiasi, A. Evaluation of data-driven performance measures for comparing and ranking traffic bottlenecks. IET Intell. Transp. Syst. 2021, 15, 504–513. [Google Scholar] [CrossRef]
- Wang, X.; Cheng, Z.; Trépanier, M.; Sun, L. Modeling bike-sharing demand using a regression model with spatially varying coefficients. J. Transp. Geogr. 2021, 93, 103059. [Google Scholar] [CrossRef]
- Gebhart, K.; Noland, R.B. The impact of weather conditions on bikeshare trips in Washington, DC. Transportation 2014, 41, 1205–1225. [Google Scholar] [CrossRef]
- Kim, M.; Cho, G.-H. Analysis on bike-share ridership for origin-destination pairs: Effects of public transit route characteristics and land-use patterns. J. Transp. Geogr. 2021, 93, 103047. [Google Scholar] [CrossRef]
- Du, Y.; Deng, F.; Liao, F. A model framework for discovering the spatio-temporal usage patterns of public free-floating bike-sharing system. Transp. Res. 2019, 103, 39–55. [Google Scholar] [CrossRef]
- Zk, A.; Hua, C. Understanding bike sharing travel patterns: An analysis of trip data from eight cities. Phys. A Stat. Mech. Its Appl. 2019, 515, 785–797. [Google Scholar]
- Zi, W.; Xiong, W.; Chen, H.; Chen, L. TAGCN: Station-level demand prediction for bike-sharing system via a temporal attention graph convolution network. Inf. Sci. 2021, 561, 274–285. [Google Scholar] [CrossRef]
- Yu, S.; Liu, G.; Yin, C. Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations. Sustain. Cities Soc. 2021, 74, 103162. [Google Scholar] [CrossRef]
- Ma, X.; Ji, Y.; Yuan, Y.; Van Oort, N.; Jin, Y.; Hoogendoorn, S. A comparison in travel patterns and determinants of user demand between docked and dockless bike-sharing systems using multi-sourced data. Transp. Res. Part A Policy Pract. 2020, 139, 148–173. [Google Scholar] [CrossRef]
- Wei, Z.; Zhen, F.; Mo, H.; Wei, S.; Peng, D.; Zhang, Y. Travel Behaviours of Sharing Bicycles in the Central Urban Area Based on Geographically Weighted Regression: The Case of Guangzhou, China. Chin. Geogr. Sci. 2021, 31, 54–69. [Google Scholar] [CrossRef]
- Soltani, A.; Mátrai, T.; Camporeale, R.; Allan, A. Exploring Shared-Bike Travel Patterns Using Big Data: Evidence in Chicago and Budapest. In Computational Urban Planning and Management for Smart Cities; Springer: Cham, Switzerland, 2019. [Google Scholar] [CrossRef]
- El-Assi, W.; Mahmoud, M.S.; Habib, K.N. Effects of built environment and weather on bike sharing demand: A station level analysis of commercial bike sharing in Toronto. Transportation 2017, 44, 589–613. [Google Scholar] [CrossRef]
- Kim, K. Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations. J. Transp. Geogr. 2018, 66, 309–320. [Google Scholar] [CrossRef]
- Lin, P.; Weng, J.; Liang, Q.; Alivanistos, D.; Ma, S. Impact of Weather Conditions and Built Environment on Public Bikesharing Trips in Beijing. Netw. Spat. Econ. 2020, 20, 1–17. [Google Scholar] [CrossRef]
- Cao, Z.; Gao, F.; Li, S.; Wu, Z.; Guan, W.; Ho, H.C. Ridership exceedance exposure risk: Novel indicators to assess PM2.5 health exposure of bike sharing riders. Environ. Res. 2021, 197, 111020. [Google Scholar] [CrossRef]
- Zhao, J.; Fan, W.; Zhai, X. Identification of land-use characteristics using bicycle sharing data: A deep learning approach. J. Transp. Geogr. 2020, 82, 102562. [Google Scholar] [CrossRef]
- Faghih-Imani, A.; Eluru, N.; El-Geneidy, A.M.; Rabbat, M.; Haq, U. How land-use and urban form impact bicycle flows: Evidence from the bicycle-sharing system (BIXI) in Montreal. J. Transp. Geogr. 2014, 41, 306–314. [Google Scholar] [CrossRef]
- Osama, A.; Sayed, T.; Bigazzi, A.Y. Models for estimating zone-level bike kilometers traveled using bike network, land use, and road facility variables. Transp. Res. Part A Policy Pract. 2017, 96, 14–28. [Google Scholar] [CrossRef]
- Osama, A.; Sayed, T. Evaluating the impact of bike network indicators on cyclist safety using macro-level collision prediction models—ScienceDirect. Accid. Anal. Prev. 2016, 97, 28–37. [Google Scholar] [CrossRef]
- Mateo-Babiano, I.; Bean, R.; Corcoran, J.; Pojani, D. How does our natural and built environment affect the use of bicycle sharing? Trans. Res. Part A Policy Pract. 2016, 94, 295–307. [Google Scholar] [CrossRef] [Green Version]
- Yeran, S.; Amin, M.; Xuke, H.; Weikai, W. Investigating Impacts of Environmental Factors on the Cycling Behavior of Bi-cycle-Sharing Users. Sustainability 2017, 9, 1060. [Google Scholar]
- Yang, R.; Long, R. Analysis of the Influencing Factors of the Public Willingness to Participate in Public Bicycle Projects and Intervention Strategies—A Case Study of Jiangsu Province, China. Sustainability 2016, 8, 349. [Google Scholar] [CrossRef] [Green Version]
- Zhang, Y.; Thomas, T.; Brussel, M.; van Maarseveen, M. Exploring the impact of built environment factors on the use of public bikes at bike stations: Case study in Zhongshan, China. J. Transp. Geogr. 2017, 58, 59–70. [Google Scholar] [CrossRef]
- Campbell, A.A.; Cherry, C.R.; Ryerson, M.S.; Yang, X. Factors influencing the choice of shared bicycles and shared electric bikes in Beijing. Trans. Res. Part C Emerg. Technol. 2016, 67, 399–414. [Google Scholar] [CrossRef] [Green Version]
- Ma, X.; Ji, Y.; Yang, M.; Jin, Y.; Tan, X. Understanding bikeshare mode as a feeder to metro by isolating metro-bikeshare transfers from smart card data. Transp. Policy 2018, 71, 57–69. [Google Scholar] [CrossRef]
- Chen, C.-F.; Cheng, W.-C. Sustainability SI: Exploring Heterogeneity in Cycle Tourists’ Preferences for an Integrated Bike-Rail Transport Service. Netw. Spat. Econ. 2016, 16, 83–97. [Google Scholar] [CrossRef]
- Ji, Y.; Fan, Y.; Ermagun, A.; Cao, X.; Wang, W.; Das, K. Public bicycle as a feeder mode to rail transit in China: The role of gender, age, income, trip purpose, and bicycle theft experience. Int. J. Sustain. Transp. 2017, 11, 308–317. [Google Scholar] [CrossRef]
- Yang, M.; Zhao, J.; Wang, W.; Liu, Z.; Li, Z. Metro commuters’ satisfaction in multi-type access and egress transferring groups. Transp. Res. Part D Transp. Environ. 2015, 34, 179–194. [Google Scholar] [CrossRef]
- Yang, M.; Liu, X.; Wang, W.; Li, Z.; Zhao, J. Empirical Analysis of a Mode Shift to Using Public Bicycles to Access the Suburban Metro: Survey of Nanjing, China. J. Urban Plan. Dev. 2016, 142, 05015011. [Google Scholar] [CrossRef]
- Zhao, P.; Li, S. Bicycle-metro integration in a growing city: The determinants of cycling as a transfer mode in metro station areas in Beijing. Transp. Res. Part A Policy Pract. 2017, 99, 46–60. [Google Scholar] [CrossRef]
- Zhuang, Y.; Liu, Z.; Schadschneider, A.; Yang, L.; Huang, J. Exploring the behavior of self-organized queuing for pedestrian flow through a non-service bottleneck. Phys. A Stat. Mech. Its Appl. 2021, 562, 125186. [Google Scholar] [CrossRef]
- Qu, Q.-K.; Chen, F.-J.; Zhou, X.-J. Road traffic bottleneck analysis for expressway for safety under disaster events using blockchain machine learning. Saf. Sci. 2019, 118, 925–932. [Google Scholar] [CrossRef]
- Li, C.; Yue, W.; Mao, G.; Xu, Z. Congestion Propagation Based Bottleneck Identification in Urban Road Networks. IEEE Trans. Veh. Technol. 2020, 69, 4827–4841. [Google Scholar] [CrossRef]
- Chiou, S.-W. An Efficient Bundle-Like Algorithm for Data-Driven Multi-objective Bi-Level Signal Design for Traffic Networks with Hazardous Material Transportation BT—Data Science and Digital Business; Márquez, F.P.G., Lev, B., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 191–220. [Google Scholar]
- Zhang, Y.; Zhou, Y.; Lu, H.; Fujita, H. Cooperative multi-agent actor–critic control of traffic network flow based on edge computing. Futur. Gener. Comput. Syst. 2021, 123, 128–141. [Google Scholar] [CrossRef]
- Nagatani, T. Traffic flow stabilized by matching speed on network with a bottleneck. Phys. A Stat. Mech. Its Appl. 2020, 538, 122838. [Google Scholar] [CrossRef]
- Monache, M.L.D.; Goatin, P. A numerical scheme for moving bottlenecks in traffic flow. Bull. Braz. Math. Soc. New Ser. 2016, 47, 605–617. [Google Scholar] [CrossRef] [Green Version]
- Qi, H.; Liu, M.; Wang, D.; Chen, M. Spatial-Temporal Congestion Identification Based on Time Series Similarity Considering Missing Data. PLoS ONE 2016, 11, e0162043. [Google Scholar] [CrossRef] [Green Version]
- Dong, H.; Ma, S.; Guo, M.; Liu, D. Research on Analysis Method of Traffic Congestion Mechanism Based on Improved Cell Transmission Model. Discret. Dyn. Nat. Soc. 2012, 2012, 854654. [Google Scholar] [CrossRef] [Green Version]
- Wang, W.-X.; Wang, B.-H.; Zheng, W.-C.; Yin, C.-Y.; Zhou, T. Advanced information feedback in intelligent traffic systems. Phys. Rev. E 2005, 72, 066702. [Google Scholar] [CrossRef]
- Lee, K.; Hui, P.M.; Wang, B.-H.; Johnson, N.F. Effects of Announcing Global Information in a Two-Route Traffic Flow Model. J. Phys. Soc. Jpn. 2001, 70, 3507–3510. [Google Scholar] [CrossRef]
- Li, X.; Zhang, J.; Li, Z.; Han, X. Influence of Different Management Measures on Traffic Bottleneck Induced by the Reduction of Lanes. Inf. Technol. J. 2012, 11, 388–391. [Google Scholar] [CrossRef] [Green Version]
- D’Ariano, A.; Pacciarelli, D.; Pranzo, M. Assessment of flexible timetables in real-time traffic management of a railway bottleneck. Transp. Res. Part C Emerg. Technol. 2007, 16, 232–245. [Google Scholar] [CrossRef]
- Yang, H.; Bell, M.; Meng, Q. Modeling the capacity and level of service of urban transportation networks. Transp. Res. Part B Methodol. 2000, 34, 255–275. [Google Scholar] [CrossRef]
- Wilson, S.E.; Bunko, A.; Johnson, S.; Murray, J.; Wang, Y.; Deeks, S.L.; Crowcroft, N.S.; Friedman, L.; Loh, L.C.; MacLeod, M.; et al. The geographic distribution of un-immunized children in Ontario, Canada: Hotspot detection using Bayesian spatial analysis. Vaccine 2021, 39, 1349–1357. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Y.; Min, J.; Liu, C.; Li, Y. Hotspot Detection and Spatiotemporal Evolution of Catering Service Grade in Mountainous Cities from the Perspective of Geo-Information Tupu. ISPRS Int. J. Geo-Inf. 2021, 10, 287. [Google Scholar] [CrossRef]
- Yu, W. Discovering Frequent Movement Paths From Taxi Trajectory Data Using Spatially Embedded Networks and Association Rules. IEEE Trans. Intell. Transp. Syst. 2019, 20, 855–866. [Google Scholar] [CrossRef]
- Bandyopadhyaya, R.; Mitra, S. Fuzzy Cluster–Based Method of Hotspot Detection with Limited Information. J. Transp. Saf. Secur. 2014, 7, 307–323. [Google Scholar] [CrossRef]
- Aitchison, J.; Lauder, I.J. Kernel Density Estimation for Compositional Data. Appl. Stat. 2018, 34, 129–137. [Google Scholar] [CrossRef]
- de Oña, J.; Mujalli, R.O.; Calvo, F.J. Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accid. Anal. Prev. 2010, 43, 402–411. [Google Scholar] [CrossRef]
- Kumar, M.; Kaushik, M. System failure probability evaluation using fault tree analysis and expert opinions in intuitionistic fuzzy environment. J. Loss Prev. Process Ind. 2020, 67, 104236. [Google Scholar] [CrossRef]
- Liu, P.; Yang, L.; Gao, Z.; Li, S.; Gao, Y. Fault tree analysis combined with quantitative analysis for high-speed railway accidents. Saf. Sci. 2015, 79, 344–357. [Google Scholar] [CrossRef]
- Bobbio, A.; Portinale, L.; Minichino, M.; Ciancamerla, E. Improving the analysis of dependable systems by mapping fault trees into Bayesian networks. Reliab. Eng. Syst. Saf. 2001, 71, 249–260. [Google Scholar] [CrossRef]
- Duan, R.-X.; Zhou, H.-L. A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks. Energy Procedia 2012, 17, 1376–1382. [Google Scholar] [CrossRef] [Green Version]
- You, B.; Lian, F.; Meng, X. An Analysis of Crash Factors for Freeways in Mountain Areas Based on Fault Tree and Bayesian Network. J. Trans. Inf. Saf. 2019, 37, 44–51. [Google Scholar]
- Jun, H.-B.; Kim, D. A Bayesian network-based approach for fault analysis. Expert Syst. Appl. 2017, 81, 332–348. [Google Scholar] [CrossRef]
- Ding, Y.; Zhang, M.; Qian, X.; Li, C.; Chen, S.; Wang, W. Using the geographical detector technique to explore the impact of socioeconomic factors on PM2.5 concentrations in China. J. Clean. Prod. 2019, 211, 1480–1490. [Google Scholar] [CrossRef]
- Zhou, Y.; Kong, Y.; Sha, J.; Wang, H. The role of industrial structure upgrades in eco-efficiency evolution: Spatial correlation and spillover effects. Sci. Total Environ. 2019, 687, 1327–1336. [Google Scholar] [CrossRef]
- Zerzour, O.; Gadri, L.; Hadji, R.; Mebrouk, F.; Hamed, Y. Semi-variograms and kriging techniques in iron ore reserve categorization: Application at Jebel Wenza deposit. Arab. J. Geosci. 2020, 13, 820. [Google Scholar] [CrossRef]
- Yost, R.S.; Uehara, G.; Fox, R.L. Geostatistical Analysis of Soil Chemical Properties of Large Land Areas. I. Semi-variograms. Soil Sci. Soc. Am. J. 1982, 46, 1033–1037. [Google Scholar] [CrossRef]
- Mazumdar, J.; Paul, S.K. Socioeconomic and infrastructural vulnerability indices for cyclones in the eastern coastal states of India. Nat. Hazards 2016, 82, 1621–1643. [Google Scholar] [CrossRef]
- Singh, P.; Sharma, A.; Sur, U.; Rai, P.K. Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environ. Dev. Sustain. A Multidiscip. Approach Theory Pract. Sustain. Dev. 2021, 23, 5233–5250. [Google Scholar] [CrossRef]
Event ID | Ip(i) | Event ID | Ip(i) | Event ID | Ip(i) | Event ID | Ip(i) | Event ID | Ip(i) |
---|---|---|---|---|---|---|---|---|---|
X1 | 1 | X5 | 1 | X9 | 1 | X13 | 0.0083 | X17 | 1 |
X2 | 1 | X6 | 1 | X10 | 1 | X14 | 0.0171 | X18 | 1 |
X3 | 1 | X7 | 1 | X11 | 1 | X15 | 0.0073 | ||
X4 | 1 | X8 | 1 | X12 | 0.0104 | X16 | 1 |
Basic Elements | |||||||||
---|---|---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | |
Prior probability | 14.8 | 11.0 | 17.4 | 18.1 | 21.9 | 20.0 | 23.9 | 34.8 | 29.7 |
Inferred probability | 15.3 | 11.4 | 18.0 | 18.8 | 22.7 | 20.7 | 24.8 | 36.1 | 30.8 |
Importance order | 14 | 17 | 12 | 11 | 8 | 10 | 7 | 2 | 3 |
Basic elements | X10 | X11 | X12 | X13 | X14 | X15 | X16 | X17 | X18 |
Prior probability | 8.7 | 36.8 | 21.3 | 26.5 | 12.9 | 30.3 | 28.4 | 15.5 | 12.3 |
Inferred probability | 8.77 | 37.0 | 21.3 | 26.5 | 12.9 | 30.3 | 29.4 | 16.1 | 12.7 |
Importance order | 18 | 1 | 9 | 6 | 15 | 4 | 5 | 13 | 16 |
Basic Elements | |||||||
---|---|---|---|---|---|---|---|
X1 | X2 | X3 | X4 | X5 | X6 | X7 | |
Mutual info | 0.00824 | 0.00599 | 0.00983 | 0.01027 | 0.01272 | 0.01148 | 0.01406 |
Percent | 3.77 | 2.74 | 4.5 | 4.7 | 5.82 | 5.25 | 6.43 |
VRXi(10−2) | 0.02124 | 0.01511 | 0.02576 | 0.02703 | 0.03429 | 0.03057 | 0.03841 |
Basic elements | X8 | X9 | X10 | X11 | X12 | X13 | X14 |
Mutual info | 0.02206 | 0.01816 | 0.00451 | 0.02262 | 0.00000 | 0.00000 | 0.00000 |
Percent | 10.1 | 8.3 | 2.06 | 10.3 | 0.000216 | 0.000162 | 0.000388 |
VRXi(10−2) | 0.06527 | 0.05166 | 0.01121 | 0.06730 | 0.00000 | 0.00000 | 0.00000 |
Basic elements | X15 | X16 | X17 | X18 | |||
Mutual info | 0.00000 | 0.01721 | 0.00866 | 0.00675 | |||
Percent | 9.52 × 10−5 | 7.87 | 3.96 | 3.09 | |||
VRXi(10−2) | 0.00000 | 0.04850 | 0.02243 | 0.01715 |
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Chao, S.; Jian, L. Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing. ISPRS Int. J. Geo-Inf. 2022, 11, 551. https://doi.org/10.3390/ijgi11110551
Chao S, Jian L. Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing. ISPRS International Journal of Geo-Information. 2022; 11(11):551. https://doi.org/10.3390/ijgi11110551
Chicago/Turabian StyleChao, Sun, and Lu Jian. 2022. "Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing" ISPRS International Journal of Geo-Information 11, no. 11: 551. https://doi.org/10.3390/ijgi11110551