# Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis

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## Abstract

**:**

## 1. Introduction and Motivation

## 2. Literature Review

## 3. Approach

#### 3.1. Data Collection and Trip Inference

Algorithm 1 Trip Inference Algorithm. |

#### 3.2. Validation of Trip Inference

## 4. Analysis of the Aachen Case Study

#### 4.1. Results

#### 4.1.1. Spatial

#### 4.1.2. Temporal

#### 4.1.3. Impact on Public Transportation

#### 4.2. Discussion

## 5. Conclusions and Future Work

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Commision of the European Union. EU Transport in Figures: Statistical Pocketbook 2021; Publications Office of the European Union: Luxembourg, 2021. [Google Scholar]
- Population Division, Department of Economic and Social Affairs, United Nations. World Urbanization Prospects: The 2018 Revision; United Nations: New York, NY, USA, 2019. [Google Scholar]
- Philipsen, R.; Ziefle, M.; Biermann, H.; Brell, T. On the Road Again—Explanatory Factors for the Users’ Willingness to Replace Private Cars by Autonomous on-Demand Shuttle Services; Technical Report RWTH-2020-07253; Springer International Publishing: Berlin/Heidelberg, Germany, 2020. [Google Scholar]
- Abduljabbar, R.L.; Liyanage, S.; Dia, H. The Role of Micro-Mobility in Shaping Sustainable Cities: A Systematic Literature Review. Transp. Res. Part D Transp. Environ.
**2021**, 92, 102734. [Google Scholar] [CrossRef] - O’Hern, S.; Estgfaeller, N. A Scientometric Review of Powered Micromobility. Sustainability
**2020**, 12, 9505. [Google Scholar] [CrossRef] - Boglietti, S.; Barabino, B.; Maternini, G. Survey on E-Powered Micro Personal Mobility Vehicles: Exploring Current Issues towards Future Developments. Sustainability
**2021**, 13, 3692. [Google Scholar] [CrossRef] - Reck, D.J.; Haitao, H.; Guidon, S.; Axhausen, K.W. Explaining Shared Micromobility Usage, Competition and Mode Choice by Modelling Empirical Data from Zurich, Switzerland. Transp. Res. Part C Emerg. Technol.
**2021**, 124, 102947. [Google Scholar] [CrossRef] - Heumann, M.; Kraschewski, T.; Brauner, T.; Tilch, L.; Breitner, M.H. A Spatiotemporal Study and Location-Specific Trip Pattern Categorization of Shared E-Scooter Usage. Sustainability
**2021**, 13, 12527. [Google Scholar] [CrossRef] - Shaheen, S.A.; Zhang, H.; Martin, E.; Guzman, S. China’s Hangzhou Public Bicycle: Understanding Early Adoption and Behavioral Response to Bikesharing. Transp. Res. Rec. J. Transp. Res. Board
**2011**, 2247, 33–41. [Google Scholar] [CrossRef][Green Version] - Fishman, E.; Washington, S.; Haworth, N. Bike Share’s Impact on Car Use: Evidence from the United States, Great Britain, and Australia. Transp. Res. Part D Transp. Environ.
**2014**, 31, 13–20. [Google Scholar] [CrossRef][Green Version] - Campbell, A.A.; Cherry, C.R.; Ryerson, M.S.; Yang, X. Factors Influencing the Choice of Shared Bicycles and Shared Electric Bikes in Beijing. Transp. Res. Part C Emerg. Technol.
**2016**, 67, 399–414. [Google Scholar] [CrossRef][Green Version] - McKenzie, G. Spatiotemporal Comparative Analysis of Scooter-Share and Bike-Share Usage Patterns in Washington, D.C. J. Transp. Geogr.
**2019**, 78, 19–28. [Google Scholar] [CrossRef] - Bai, S.; Jiao, J. Dockless E-scooter Usage Patterns and Urban Built Environments: A Comparison Study of Austin, TX, and Minneapolis, MN. Travel Behav. Soc.
**2020**, 20, 264–272. [Google Scholar] [CrossRef] - Jiao, J.; Bai, S. Understanding the Shared E-scooter Travels in Austin, TX. ISPRS Int. J.-Geo-Inf.
**2020**, 9, 135. [Google Scholar] [CrossRef][Green Version] - Caspi, O.; Smart, M.J.; Noland, R.B. Spatial Associations of Dockless Shared E-Scooter Usage. Transp. Res. Part D Transp. Environ.
**2020**, 86, 102396. [Google Scholar] [CrossRef] [PubMed] - Feng, C.; Jiao, J.; Wang, H. Estimating E-Scooter Traffic Flow Using Big Data to Support Planning for Micromobility. J. Urban Technol.
**2020**, 29, 139–157. [Google Scholar] [CrossRef] - Almannaa, M.H.; Ashqar, H.I.; Elhenawy, M.; Masoud, M.; Rakotonirainy, A.; Rakha, H. A Comparative Analysis of E-Scooter and e-Bike Usage Patterns: Findings from the City of Austin, TX. Int. J. Sustain. Transp.
**2021**, 15, 571–579. [Google Scholar] [CrossRef] - Huo, J.; Yang, H.; Li, C.; Zheng, R.; Yang, L.; Wen, Y. Influence of the Built Environment on E-scooter Sharing Ridership: A Tale of Five Cities. J. Transp. Geogr.
**2021**, 93, 103084. [Google Scholar] [CrossRef] - Noland, R.B. Scootin’ in the Rain: Does Weather Affect Micromobility? Transp. Res. Part A Policy Pract.
**2021**, 149, 114–123. [Google Scholar] [CrossRef] - Espinoza, W.; Howard, M.; Lane, J.; Van Hentenryck, P. Shared E-scooters: Business, Pleasure, or Transit? arXiv
**2019**, arXiv:1910.05807. [Google Scholar] - Mathew, J.K.; Liu, M.; Bullock, D.M. Impact of Weather on Shared Electric Scooter Utilization. In Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 27–30 October 2019; IEEE: Piscataway, NJ, USA, 2019; pp. 4512–4516. [Google Scholar] [CrossRef]
- Luo, H.; Zhang, Z.; Gkritza, K.; Cai, H. Are Shared Electric Scooters Competing with Buses? A Case Study in Indianapolis. Transp. Res. Part D Transp. Environ.
**2021**, 97, 102877. [Google Scholar] [CrossRef] - Noland, R.B. Trip Patterns and Revenue of Shared E-Scooters in Louisville, Kentucky. Transp. Find.
**2019**. [Google Scholar] [CrossRef] - Hosseinzadeh, A.; Algomaiah, M.; Kluger, R.; Li, Z. Spatial Analysis of Shared E-Scooter Trips. J. Transp. Geogr.
**2021**, 92, 103016. [Google Scholar] [CrossRef] - McKenzie, G. Urban Mobility in the Sharing Economy: A Spatiotemporal Comparison of Shared Mobility Services. Comput. Environ. Urban Syst.
**2020**, 79, 101418. [Google Scholar] [CrossRef] - Zou, Z.; Younes, H.; Erdoğan, S.; Wu, J. Exploratory Analysis of Real-Time E-Scooter Trip Data in Washington, D.C. Transp. Res. Rec. J. Transp. Res. Board
**2020**, 2674, 285–299. [Google Scholar] [CrossRef] - Younes, H.; Zou, Z.; Wu, J.; Baiocchi, G. Comparing the Temporal Determinants of Dockless Scooter-share and Station-based Bike-share in Washington, D.C. Transp. Res. Part A Policy Pract.
**2020**, 134, 308–320. [Google Scholar] [CrossRef] - Xu, Y.; Yan, X.; Sisiopiku, V.P.; Merlin, L.A.; Xing, F.; Zhao, X. Micromobility Trip Origin and Destination Inference Using General Bikeshare Feed Specification (GBFS) Data. arXiv
**2020**, arXiv:2010.12006. [Google Scholar] - Hawa, L.; Cui, B.; Sun, L.; El-Geneidy, A. Scoot over: Determinants of Shared Electric Scooter Presence in Washington D.C. Case Stud. Transp. Policy
**2021**, 9, 418–430. [Google Scholar] [CrossRef] - Zhu, R.; Zhang, X.; Kondor, D.; Santi, P.; Ratti, C. Understanding Spatio-Temporal Heterogeneity of Bike-Sharing and Scooter-Sharing Mobility. Comput. Environ. Urban Syst.
**2020**, 81, 101483. [Google Scholar] [CrossRef] - Engdahl, H.; Englund, C.; Faxér, A.; Habibi, S.; Pettersson, S.; Sprei, F.; Voronov, A.; Wedlin, J. Electric Scooters’ Trip Data Collection and Analysis. In Proceedings of the 33rd Electric Vehicle Symposium (EVS33), Portland, Oregon, 14–17 June 2020; p. 11. [Google Scholar]
- Zhao, P.; Haitao, H.; Li, A.; Mansourian, A. Impact of Data Processing on Deriving Micro-Mobility Patterns from Vehicle Availability Data. Transp. Res. Part D Transp. Environ.
**2021**, 97, 102913. [Google Scholar] [CrossRef] - Noland, R.B. Bikeshare Trip Generation in New York City. Transp. Res. Part A Policy Pract.
**2016**, 94, 164–181. [Google Scholar] [CrossRef] - Jiang, S.; Guan, W.; He, Z.; Yang, L. Exploring the Intermodal Relationship between Taxi and Subway in Beijing, China. J. Adv. Transp.
**2018**, 2018, 3981845. [Google Scholar] [CrossRef] - Nawaro, Ł. E-Scooters: Competition with Shared Bicycles and Relationship to Public Transport. Int. J. Urban Sustain. Dev.
**2021**, 13, 614–630. [Google Scholar] [CrossRef] - Zuniga-Garcia, N.; Machemehl, R. Dockless Electric Scooters and Transit Use in an Urban/University Environment. In Proceedings of the 99th Annual Meeting of the Transportation Research Board, Washington, DC, USA, 12–16 January 2020; p. 20. [Google Scholar]
- Ziedan, A.; Shah, N.R.; Wen, Y.; Brakewood, C.; Cherry, C.R.; Cole, J. Complement or Compete? The Effects of Shared Electric Scooters on Bus Ridership. Transp. Res. Part D Transp. Environ.
**2021**, 101, 103098. [Google Scholar] [CrossRef] - Baltra, G.; Imana, B.; Jiang, W.; Korolova, A. On the Data Fight between Cities and Mobility Providers. arXiv
**2020**, arXiv:2004.09072. [Google Scholar]

**Figure 1.**Extract-Transform-Load (ETL) data pipeline developed for this research. Data is obtained from publicly available sources such as the micromobility provider’s API, then transformed into trips and finally loaded for visualization.

**Figure 2.**(

**a**) Overview of the service area of micromobility in Aachen. Blue areas show e-scooter area, gray pins show bike-sharing stations, and red areas are no parking zones. (

**b**) Overview of the gathered dataset, the trip’s filtering, and the trip inference results.

**Figure 3.**Workflow of the public transit first-mile/last-mile impact assessment analysis. Categorization of complement, competing, and extension based on the speed of available public transportation routes and the location of available stops.

**Figure 4.**Histogram showing the (

**a**) number of e-scooter trips and (

**b**) number of e-bike trips inferred with 1- and 10-min resolution data grouped by distance.

**Figure 5.**Kernel density estimation showing hotspots of supply from (

**a**) e-scooters, and (

**b**) e-bikes in Aachen. Boxplot (

**c**) of the trip distances of customer trips for e-scooters and e-bikes.

**Figure 6.**Histogram of customer trip distance distribution in Aachen for (

**a**) e-scooters and (

**b**) e-bikes.

**Figure 7.**E-bike trip’s (

**a**) origins and (

**b**) destinations in the morning and (

**c**) origins and (

**d**) destinations in the afternoon.

**Figure 8.**E-scooter trip’s (

**a**) origins and (

**b**) destinations in the morning and (

**c**) origins and (

**d**) destinations in the afternoon.

**Figure 11.**Temporal analysis of (

**a**) e-scooter and (

**b**) e-bike hourly, (

**c**) e-scooter and (

**d**) e-bike weekly, and (

**e**) e-scooter and (

**f**) e-bike monthly mean number of trips.

**Figure 12.**Kernel density estimation of distance to the nearest public transportation station from trip’s origin and destination for (

**a**) e-scooters and (

**b**) e-bikes.

**Figure 13.**Boxplot of trip durations (

**a**) if performed with micromobility or assumed public transportation and (

**b**) overview of whether public transport or walking is faster per hour of the day.

**Table 1.**List of data-driven e-scooters studies, categorized by continent, city, and publication year.

Continent | City | References |
---|---|---|

North America | Austin | [13,14,15,16,17,18,19] |

Atlanta | [20] | |

Indianapolis | [21,22] | |

Kansas City | [18] | |

Louisville | [18,23,24] | |

Minneapolis | [13,18] | |

Portland | [18] | |

Washington | [12,25,26,27,28,29] | |

Asia | Singapore | [30] |

Europe | Berlin | [8,31] |

Stockholm, Paris, Madrid | [31] | |

Zurich | [7,32] |

**Table 2.**Descriptive statistics with a 1-min and 10-min resolution over a timespan of 3 weeks. For the 10-min resolution the deviation to the 1-min resolution is given in percentages.

One-Min Resolution | Ten-Min Resolution | |||
---|---|---|---|---|

E-Scooter | E-Bike | E-Scooter | E-Bike | |

Total number of trips | 44,257 | 3736 | 45,935 (3.65%) | 3429 (8.22%) |

Number of customer trips | 34,410 | 3603 | 39,761 (15.55%) | 3376 (6.3%) |

Number of charging trips | 4920 | 0 | 5384 (9.43%) | 0 (0%) |

Number of rebalancing trips | 4927 | 133 | 790 (83.97%) | 53 (60.15%) |

Mean trip distance | 1946 m | 3327 m | 1721 m (11.56%) | 3322 m (0.15%) |

Mean trip duration $\Delta {t}_{k}$ | 665 s | 960 s | 1082 s (62.71%) | 1420 s (47.92%) |

Mean trip duration $\Delta {t}_{est}$ | 418 s | 719 s | 371 s (11.24%) | 718 s (0.14%) |

Mean trip duration $\Delta {t}_{approx}$ | 606 s | 883 s | 546 s (9.99%) | 888 s (0.57%) |

Total fleet size | 1878 | 264 | 1803 (3.99%) | 264 (0%) |

Total used fleet size | 1749 | 258 | 1757 (0.46%) | 257 (0.39%) |

**Table 3.**Descriptive statistics for the micromobility supply and demand in Aachen in a year with aggregated mean values.

E-Scooters | E-Bikes | |
---|---|---|

Total number of customer trips | 664,614 | 47,040 |

Total number of charging trips | 115,356 | 0 |

Total number of rebalancing trips | 13,383 | 936 |

Total number of deploying trips | 2495 | 106 |

Mean trip distance | 1680 m | 3219 m |

Mean trip duration $\Delta {t}_{approx}$ | 534 s | 865 s |

Mean available fleet size | 1163 | 213 |

Total number of vehicles observed | 3931 | 289 |

Total number of vehicles observed with swappable battery | 2650 | 0 |

E-Scooters | E-Bikes | |
---|---|---|

Average service days per vehicle | 131.13 days | 314.94 days |

Average number of trips per vehicle | 155 | 155 |

Aggregated trip distance per vehicle | 261.34 km | 497.80 km |

Aggregated trip duration $\Delta {t}_{approx}$ per vehicle | 23.07 h | 37.02 h |

**Table 5.**Land-use statistics and respective e-scooter and e-bike trips starting and ending in those areas.

Land Use Type | Area | Trip Share | Land Use Filter | ||
---|---|---|---|---|---|

Total | Percent | Origin | Dest. | ||

Residential | $14.52$${\mathrm{km}}^{2}$ | $64.90\%$ | $56.12\%$ | $56.35\%$ | Residential Buildings and Garages |

Commercial | $2.462$${\mathrm{km}}^{2}$ | $11.00\%$ | $14.54\%$ | $14.99\%$ | Commercial, Industrial, and Retail |

Recreational | $0.924$${\mathrm{km}}^{2}$ | $4.13\%$ | $4.77\%$ | $4.73\%$ | Allotments, Parks, Forests, Meadow, Greenfield, Flowerbed, Religious, Village Green |

Public Area | $4.465$${\mathrm{km}}^{2}$ | $19.95\%$ | $24.57\%$ | $23.93\%$ | Brownfield, Cemetery, Construction, Railway, Road, Civic, Grass, Farmland, Farmyard |

**Table 6.**Comparison of customer trips based on their relation to public transport. The factor f defines how much faster a micromobility trip must be compared to the respective public transportation in order for the trip to be classified as complementing. This means that the factor f defines the reasonability of the public transportation route.

f | Mode | Compete | Extend (First- or Last-Leg) | Complement (Stop) | Complement (Connection) |
---|---|---|---|---|---|

1.5 | E-scooters | 165,447 (24.90%) | 111,919 (16.85%) | 7834 (1.18%) | 379,338 (57.08%) |

E-bikes | 8806 (18.72%) | 774 (1.65%) | 925 (1.96%) | 36,533 (77.66%) | |

2.0 | E-scooters | 334,501 (50.48%) | 74,003 (11.14%) | 5386 (0.81%) | 249,648 (37.57%) |

E-bikes | 21,415 (45.53%) | 659 (1.40%) | 745 (1.58%) | 24,219 (51.49%) | |

3.0 | E-scooters | 569,447 (85.70%) | 21,569 (3.25%) | 1579 (0.23%) | 71,943 (10.82%) |

E-bikes | 41,016 (87.20%) | 308 (0.65%) | 41 (0.09%) | 5673 (12.06%) |

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## Share and Cite

**MDPI and ACS Style**

Schwinger, F.; Tanriverdi, B.; Jarke, M. Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis. *Sustainability* **2022**, *14*, 8247.
https://doi.org/10.3390/su14148247

**AMA Style**

Schwinger F, Tanriverdi B, Jarke M. Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis. *Sustainability*. 2022; 14(14):8247.
https://doi.org/10.3390/su14148247

**Chicago/Turabian Style**

Schwinger, Felix, Baran Tanriverdi, and Matthias Jarke. 2022. "Comparing Micromobility with Public Transportation Trips in a Data-Driven Spatio-Temporal Analysis" *Sustainability* 14, no. 14: 8247.
https://doi.org/10.3390/su14148247