A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings
from the City of Austin, TX
- URL: http://arxiv.org/abs/2006.04033v1
- Date: Sun, 7 Jun 2020 03:27:44 GMT
- Title: A Comparative Analysis of E-Scooter and E-Bike Usage Patterns: Findings
from the City of Austin, TX
- Authors: Mohammed Hamad Almannaa, Huthaifa I. Ashqar, Mohammed Elhenawy,
Mahmoud Masoud, Andry Rakotonirainy, and Hesham Rakha
- Abstract summary: We investigate how average trip speed change depending on the day of the week and the time of the day.
Users tend to ride e-bikes and e-scooters with a slower average speed for recreational purposes compared to when they are ridden for commuting purposes.
- Score: 5.107549049636038
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: E-scooter-sharing and e-bike-sharing systems are accommodating and easing the
increased traffic in dense cities and are expanding considerably. However,
these new micro-mobility transportation modes raise numerous operational and
safety concerns. This study analyzes e-scooter and dockless e-bike sharing
system user behavior. We investigate how average trip speed change depending on
the day of the week and the time of the day. We used a dataset from the city of
Austin, TX from December 2018 to May 2019. Our results generally show that the
trip average speed for e-bikes ranges between 3.01 and 3.44 m/s, which is
higher than that for e-scooters (2.19 to 2.78 m/s). Results also show a similar
usage pattern for the average speed of e-bikes and e-scooters throughout the
days of the week and a different usage pattern for the average speed of e-bikes
and e-scooters over the hours of the day. We found that users tend to ride
e-bikes and e-scooters with a slower average speed for recreational purposes
compared to when they are ridden for commuting purposes. This study is a
building block in this field, which serves as a first of its kind, and sheds
the light of significant new understanding of this emerging class of
shared-road users.
Related papers
- Evaluating the effects of Data Sparsity on the Link-level Bicycling Volume Estimation: A Graph Convolutional Neural Network Approach [54.84957282120537]
We present the first study to utilize a Graph Convolutional Network architecture to model link-level bicycling volumes.
We estimate the Annual Average Daily Bicycle (AADB) counts across the City of Melbourne, Australia using Strava Metro bicycling count data.
Our results show that the GCN model performs better than these traditional models in predicting AADB counts.
arXiv Detail & Related papers (2024-10-11T04:53:18Z) - The Growth of E-Bike Use: A Machine Learning Approach [57.506876852412034]
E-bike usage in the U.S. resulted in a reduction of 15,737.82 kilograms of CO2 emissions in 2022.
E-bike users burned approximately 716,630.727 kilocalories through their activities in the same year.
arXiv Detail & Related papers (2023-07-15T03:34:10Z) - Are footpaths encroached by shared e-scooters? Spatio-temporal Analysis
of Micro-mobility Services [19.15684785810306]
We employ a combination of methods that analyse both spatial and temporal characteristics related to e-scooter trips.
Population density is the topmost important feature, and it associates with e-scooter usage positively.
We found that the effect of humidity is more important than precipitation in predicting hourly e-scooter trip count.
arXiv Detail & Related papers (2023-04-18T04:27:56Z) - A Wearable Data Collection System for Studying Micro-Level E-Scooter
Behavior in Naturalistic Road Environment [3.5466525046297264]
This paper proposes a wearable data collection system for investigating the micro-level e-Scooter motion behavior in a Naturalistic road environment.
An e-Scooter-based data acquisition system has been developed by integrating LiDAR, cameras, and GPS using the robot operating system (ROS)
arXiv Detail & Related papers (2022-12-22T18:58:54Z) - Predicting Citi Bike Demand Evolution Using Dynamic Graphs [81.12174591442479]
We apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
In this paper, we attempt to apply a graph neural network model to predict bike demand in the New York City, Citi Bike dataset.
arXiv Detail & Related papers (2022-12-18T21:43:27Z) - Detection of E-scooter Riders in Naturalistic Scenes [2.1270496914042987]
This paper presents a novel vision-based system to differentiate between e-scooter riders and regular pedestrians.
We fine-tune MobileNetV2 over our dataset and train the model to classify e-scooter riders and pedestrians.
The classification accuracy of trained MobileNetV2 on top of YOLOv3 is over 91%, with precision and recall over 0.9.
arXiv Detail & Related papers (2021-11-28T05:59:36Z) - Efficiency, Fairness, and Stability in Non-Commercial Peer-to-Peer
Ridesharing [84.47891614815325]
This paper focuses on the core problem in P2P ridesharing: the matching of riders and drivers.
We introduce novel notions of fairness and stability in P2P ridesharing.
Results suggest that fair and stable solutions can be obtained in reasonable computational times.
arXiv Detail & Related papers (2021-10-04T02:14:49Z) - Do e-scooters fill mobility gaps and promote equity before and during
COVID-19? A spatiotemporal analysis using open big data [7.0445529434309515]
E-scooters have both competing and complementary effects on transit and bikesharing services.
Price premium is greater during the COVID-19 pandemic but the associated travel-time savings are smaller.
E-scooters complement bikesharing and transit by providing services to underserved neighborhoods.
arXiv Detail & Related papers (2021-03-11T03:29:21Z) - Micromobility in Smart Cities: A Closer Look at Shared Dockless
E-Scooters via Big Social Data [6.001713653976455]
Dockless electric scooters (e-scooters) have emerged as a daily alternative to driving for short-distance commuters in large cities.
E-scooters come with challenges in city management, such as traffic rules, public safety, parking regulations, and liability issues.
This paper is the first large-scale systematic study on shared e-scooters using big social data.
arXiv Detail & Related papers (2020-10-28T19:59:45Z) - On the Data Fight Between Cities and Mobility Providers [64.10012625591345]
The Los Angeles Department of Transportation has put forth a specification that requests detailed data on scooter usage from scooter companies.
We argue that L.A.'s data request for using a new specification is not warranted as proposed use cases can be met by already existing specifications.
We propose an algorithm that enables formal privacy and utility guarantees when publishing parked scooters data.
arXiv Detail & Related papers (2020-04-20T06:01:44Z) - Learning by Cheating [72.9701333689606]
We show that this challenging learning problem can be simplified by decomposing it into two stages.
We use the presented approach to train a vision-based autonomous driving system that substantially outperforms the state of the art.
Our approach achieves, for the first time, 100% success rate on all tasks in the original CARLA benchmark, sets a new record on the NoCrash benchmark, and reduces the frequency of infractions by an order of magnitude compared to the prior state of the art.
arXiv Detail & Related papers (2019-12-27T18:59:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.