# 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

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**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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**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