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Analysis of E-Scooter Trips and Their Temporal Usage Patterns

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Abstract

With the recent rise of e-scooters as an alternative transportation choice, it is critical to understand their utilization trend in the micro-mobility and shared use sector. This paper analyzes three months of data acquired from the City of Indianapolis to provide an overview of temporal patterns and performance metrics of the e-scooter trips. More than 425,000 trips were made covering a total distance of approximately 475,000 miles. The maximum number of unique scooters in service on any day was 2988, occurring on November 3. The median number of unique scooters in service per day during the study period was 1654. Analysis showed that around 60% of the trips were less than 10 minutes and nearly 65% of trips were less than one mile. On average, 85% of scooters active on any given day were in use for less than 1-hour. The temporal patterns were very different from the conventional AM/PM traffic peaks with most scooter activity observed between 11 AM and 9 PM. Peak periods for weekdays were between 4-9 PM with more than 70 trips/minute, whereas for weekends, the peak periods were between 2-7 PM with an excess of 150 trips/minute. A series of graphics are included that can provide a framework for tabulating usage characteristics and comparing scooter usage to other modes, and regions of the country and world. Link to article: http://www.nxtbook.com/ygsreprints/ITE/G107225_ITE_June2019/index.php#/44
... The perception of e-scooters being fun objects (statement 2) is mainly based on the observed use times, trip purposes, and further survey results. Studies from the U.S. (Mathew et al., 2019; Portland Bureau of Transportation, 2019), France (6t Bureau de Recherche, 2019), and Germany (Tack et al., 2019) found that most e-scooter trips occur on weekends. In France, use on the two days of the weekend accounts for almost 40% of total demand (6t Bureau de Recherche, 2019). ...
... In France, use on the two days of the weekend accounts for almost 40% of total demand (6t Bureau de Recherche, 2019). In addition, many e-scooter trips occur during weekday evening hours, albeit in smaller numbers (Mathew et al., 2019;Portland Bureau of Transportation, 2019). According to the provider Lime, usage outside of rush hours increased by 9% during the COVID-19 pandemic (Lime, 2021). ...
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This paper explores which trips currently made in Germany by personal motorized transportation could be replaced by e-scooters and what effect this would have on greenhouse gas emissions. This potential for substitution is estimated on the basis of data from the national household travel survey in Germany. Our analysis shows that 13% of the daily car trips, corresponding to 2% of the car kilometers in Germany, are suitable for replacement. Based on these results, we show that saving potentials of greenhouse gas emissions are heavily dependent on the general conditions of the specific use case (e.g. e-scooter lifetime) and the type of vehicle replaced. At best, a saving potential of about 5.8 kt of CO 2eq per day could be achieved when trips with conventional cars are replaced by e-scooter driving. However, if battery electric cars are replaced, an increase in emissions may even occur under certain conditions.
... Using data from Singapore, Zhu et al. (2020) compared a DBS with a stationbased e-scooters service; establishing the e-scooters had a better performance in terms of utilization, as well as a more compact spatial distribution. Bai and Jiao (2020) conducted a comparison of usage patterns of e-scooters in Austin, TX, and Minneapolis, MN, and determined that escooters were predominantly used in the downtown areas and university campuses in both cities, but there were marked differences in the temporal distribution of trips., E-scooter trips in Indianapolis, IN, were also observed to be concentrated in downtown and campus areas, and the temporal distribution of e-scooter trips did not follow the standard morning/afternoon peak-hour patterns observed for other modes (Mathew et al., 2019). Caspi et al. (2020) used spatial regression analysis to examine the influence of the built environment, land use, and demographics on e-scooter trip generation. ...
... Cost concerns could be one of the explanations for the high concentration of e-scooter trips in UPRM and adjacent areas, as revealed by the analysis of the spatial patterns of MDES trip. The analysis shows that (Mathew et al., 2019;Bai and Jiao 2020;Caspi et al., 2020). The analysis of temporal patterns also aligns with travel behavior observed elsewhere; with most MDES trips occurred on weekdays and the standard morning/afternoon peak periods were not observed. ...
Article
A case study is presented of a dockless e-scooter rental service (MDES) in Mayagüez, Puerto Rico, a city within the understudied Latin American region. MDES trip data were used to examine the spatiotemporal patterns of e-scooter trips in the city, while survey data was collected to explore the characteristics of MDES users and nonusers, as well as the factors that influenced their demand for MDES trips. In addition, this study proposes a network-based approach to evaluate the level of spatial access and equity of dockless micromobility vehicles. Three measures are proposed to quantify spatial access at the level of locations (i.e., network nodes) as a function of the distance of each location to each e-scooter. As illustrated in the MDES case, the measures can be used to examine spatial access at the service area-, zonal-, building-, and point-levels, and to compute spatial access inequality indexes. The survey analysis indicated that female respondents were 1.7 times less likely to use MDES than males and that young populations groups more than two times more likely to be MDES users than the reference population group. The survey analysis also revealed that cost, safety, and built environment concerns were the main barriers to the use of MDES, and that the primary reasons for using the service were parking problems and traffic congestion. Among other things, the spatiotemporal analysis of the MDES trips data shows that 78% of trips started and ended at the city’s main university, that a significant proportion of trips were linked to neighborhoods with a high concentration of university students, and that demand for e-scooter trips dropped drastically when the university was not in session. The analysis of the MDES data revealed marked differences in spatial access within and between zones in the study region. On average, daily Atkinson inequality index values, which were computed using the proposed spatial access indicators, ranged from 0.45 to 0.80, which points to an unequal spatial access to MDES. The paper closes by discussing applications of the proposed methodology for the design of policies aimed at minimizing inequality in spatial access to dockless micromobility services.
... • Regulation, standards, and policies [16][17][18]; • Battery charging and charger distribution [19][20][21]; • Usage patterns and organisation in different cities [22][23][24]; • Accidents or security risks [25][26][27][28][29][30]; • Pollution or sustainability [31][32][33]. ...
Article
Means of transport should be able to fulfil their main function safely and comfortably for travellers and drivers. The effects of vibrations on ride comfort are in the frequency range of 0.5 to 80 Hz and can be analysed using the UNE-2631 standard. This type of analysis has been conducted for several means of transport (bicycles, motorcycles, cars, trucks, etc.), but the literature on e-scooter comfort is very scarce. Existing research describes methodologies, simulation models, and a few measurements related to e-scooter comfort. This paper presents, for the first time, a comfort analysis using an Arduino-based data acquisition system at a sampling frequency of 200 Hz (higher than that in previous studies). Acceleration and speed measurements were obtained by sensorising an e-scooter with inflated wheels without any additional damping systems, which is one of the commonly used e-scooter types. In this study, the comfort for two different speeds (20 and 28 km/h), two types of pavements (pavers and asphalt), and two drivers with different weights was investigated. The results indicate the lowest comfort values for higher velocities and paver pavement. Furthermore , the comfort values were extremely low for all scenarios. In addition, the results demonstrate the necessity of using a sampling rate of at least 80 Hz for this e-scooter model.
... Place of residence (city center, suburb, university campus) [39,40,[45][46][47] Population density [46,48] Rider satisfaction factors (battery capacity, customer service, ease of use pricing, safety (speed), safety (technical), e-scooter age, ease of use) [36] Demand-stimulating activities (daily meals/drinks, shopping and entertainment) [49] Urban landscape, characteristics of the area (parks, special zones, places of skiing, population density, business districts, land use) [27,40,44,46,[48][49][50] Places of use of e-scooter (city center, university campuses, business districts) [42,[47][48][49] Density of attractions [48] Forecast of future use of e-scooter [37] Niche for e-scooter [51] Switching to e-scooter from a car [52] Demand for e-scooter (potential demand, factors of increasing demand, demand forecast) [44,46,47,51] Frequency of demand [41,46,48,51] Frequency of use [39,40,[42][43][44]52] Travel time [41,42,52,53] Using a bicycle and car sharing [41,45] Replacement of other transportation modes by e-scooter (car, any personal transport, public transport, taxi) [37,39,40,44,53] Competition and comparison with other micro-mobile vehicles (bicycle) [53,54] Combination with other transport [39] Ownership or use of other vehicles (car, e-and regular bike, motorcycle) [43,45] Road infrastructure and recharging facilities [46,51,53,54] Road surface [39,54] Parking lots [39,46] Weather [41,53,55] Scooter design (luggage transportation) [45,53] Purpose of use (short trips, direct trips, entry/exit trips, the "last-mile" problem, entertainment, recreation, combined trips, trips to work/study, communication with close people, replacement of walking) [37][38][39][40]43,44,49,53,56] User behavior [40,47] Environmental awareness [45] User groups [39] Motivation factors, advantages of traveling by e-scooter (faster, more convenient, more fun, easier, cheaper) [37,40,41] Rider training, information support (security aspects) [39,57] Legislation and governance [40,53] Safety (wearing helmets, aggression of drivers, speed limit, road surface, road signs) [39,40,45,53,58] ...
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The contemporary urban environment faces such challenges as overloaded traffic, heavy pollution, and social problems, etc. The concept of the “smart city” allows solving some of these issues. One of the opportunities provided by the smart city is the development of micro-mobility and sharing services; contributing to the optimization of transport flows and decreasing carbon footprints. This study investigates the factors affecting the development of e-scooter sharing services and the attitudes of young urban residents towards using these services. The research applied a PLS-SEM (partial least squares structural equation modeling) analysis performed in SmartPLS3.7 software. The data were collected via focus groups and surveying a population aged 18–35. The authors partially based the research on the UTAUT model (the unified theory of acceptance and use of technology), taking such constructs as “intention to use”, “anxiety”, “attitude toward use”, “effort expectancy”, and “social influence”; they also introduced the new unique variables “internal uncertainty”, “e-scooter design”, “experience”, “perceived safety”, “infrastructure quality”, and “motivation to physical activity”. The main finding of the study was determining that the latent variables attitude towards sharing, anxiety, internal uncertainty, JTBD (jobs to be done), and new way of thinking have a direct or indirect effect on the intention to ride e-scooters in the future and/or to use sharing services. The obtained results permit making recommendations to businesses, municipal authorities, and other stakeholders on developing e-scooter sharing services as a contribution to the advancement of the smart city.
... He also found that there are dissimilar spatial and temporal patterns between e-scooter ridership and member bike-share ridership, while there are similar temporal patterns and dissimilar spatial patterns between e-scooter ridership and non-member bike-share ridership. Mathew et al. (2019) examined temporal usage patterns of e-scooters and found that the peak usage period is on the weekends between 14:00-19:00. For weekdays, the peak hour period is between 16:00-21:00 with the half number of active e-scooter trips compared to weekends. ...
Article
The emergence and growing popularity of e-scooters has created the need for researchers, policy-makers and urban planners to better understand user behaviors and travel patterns. In this paper, we examine the spatiotemporal patterns of e-scooter trips in 4 European cities: Paris, Malaga, Bordeaux, and Hamburg. We use a GPS dataset which includes position coordinates crossed with the country of registration of the user's bank card. Results suggest that riding frequencies and vehicle rotation are low and seem to be correlated. Average trip distance shows low variability and is of approximately 4.5 miles, while average trip duration is of 12 min. Tourists are major free-floating e-scooter users, ride during daytime, over longer distances, but at lower speeds. In all cities, the peak hour is observed in the afternoon (between 3 and 5 pm). Downtown, waterfront areas and availability of soft mobility infrastructure attract users. Usage is following relatively predictable patterns, especially when used for commuting.
... Vehicles of this type can be unblocked using a special application, and on completion of a journey can be left anywhere in the area designated by the operator [32]. E-scooters are small, one-person electric vehicles that fall within the category of micromobility [32,33], which also includes various light, individual vehicles such as e-bikes, skateboards and unicycles [34,35], segways, and the aforementioned e-scooters [32]. ...
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Shared micromobility is a new phenomenon being observed in urban transport. It is a response to the problems associated with congestion and environmental pollution. Small electric vehicles such as e-scooters are highly suitable for crowded city centres, often providing an alternative to private motor vehicles or public transport, and serve as a good first-and last-mile transport option. While they have become a feature of sustainable transport systems in cities, their impact on the environment often depends on the services offered by operators of this mode of personal transport. There are many tools available to measure the quality of transport, e-services and shared mobility services. However, no specific mechanism has been designed for vehicles in the field of shared e-scooters (research gap). The aim of the article is to verify whether the three dimensions identified by the authors: mobile application functions, device features, and customer service are valid for examining the quality of shared e-micromobility factors on the example of e-scooters. Based on the obtained results, the authors created the MMQUAL (MicroMobility QUALity) model, which accurately describes the quality of the studied phenomenon. The results of the study can serve as a platform for researchers interested in further exploring the issue and improving the proposed model. They may also be of commercial value to operators, who could use this tool to boost the competitiveness of their services by enhancing those features that have the greatest impact on their quality.
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Shared micromobillity has been extensively developed globally in the past few decades, but its impact on the environment remains unclear. This study quantitatively estimates the effects of global shared micromobillity programs on greenhouse gas (GHG) emissions using a life cycle assessment (LCA) perspective. Specifically, it takes major countries and cities around the world as examples to empirically analyze the impact of station-based bike-sharing (SBBS), free-floating bike-sharing (FFBS), free-floating e-bike sharing (FFEBS), and free-floating e-scooter sharing (FFESS) programs on the GHG emissions of urban transportation. The results show that, with the exception of SBBS, the other shared micromobillity programs have not achieved desirable GHG emissions reduction benefits. Contrarily to subjective expectations, although the rapid progress of technology in recent years has promoted the vigorous development of shared micromobility, it has brought negative impacts on the GHG emissions rather than the positive benefits claimed by related promoters and operators. The overcommercialization and low utilization rate makes shared micromobility more likely to be an environmentally-unfriendly mode of transportation. In addition, the regional differences in mode choice, operational efficiency, fleet scale, and market potential of shared micromobility and the corresponding impacts on GHG emissions vary greatly. Therefore, authorities should formulate appropriate shared micromobility plans based on the current conditions and goals of the region. This empirical study helps to better understand the environmental impact of the global shared micromobility program and offers valuable references for improving urban sustainability.
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The rapid rise of shared electric scooter (E-Scooter) systems offers urban areas a new micro-mobility solution. The focus on short-distance travel has made it a competitive option for addressing first-/last-mile travel needs. Nevertheless, its role as a first-/last-mile solution was understudied due to the lack of fine-grained trip data. This study aims at exploring the integration of shared E-Scooters with public transportation systems. Specifically, it compared the use of shared E-Scooters against shared bikes and taxis for connecting trips from/to metro stations. We analyzed massive amounts of trip-related data extracted through APIs. Multinomial logistic regression models were developed to uncover how the mode choices from/to metro stations vary in different contexts. The results show that the use of shared E-Scooters to connect trips from/to metro stations can be notably different from shared bikes and taxis. The preference of shared E-Scooters will vary depending on the land use and time period.
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E-scooters are considered a new type of micromobility that promotes sustainable transportation modes by shifting the private car mode to a Mobility as a Service. Irregular parking and high life cycle global warming impacts of e-scooters due to dockless operating system cause a tendency from dockless systems to charging stations. This article presents one of the first comprehensive spatial analysis studies aiming to develop a novel GIS-based multi-criteria decision support model for: 1) siting optimum e-scooter charging station locations that will integrate e-scooter system with the existing public transportation systems and point of interests, and 2) finding the most secure and convenient road network to connect the charging stations. Identifying the optimum road network for connecting the charging stations and developing a raster based spatial method for this purpose distinguish this study from the previous studies. The developed model was implemented by performing a case study in Karsiyaka, Izmir, Turkey. 35 suitable locations were determined for siting e-scooter charging stations and the optimal connectivity network connecting each station with its neighboring stations were identified. The model can be reproduced for other locations and used to develop innovative policies and plans for environmentally more sustainable and operationally effective shared e-scooter systems.
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This research examines the preliminary characteristics of early fatalities associated with shared e-scooters, a unique emerging mode of transportation. Using media and police reports, we identified 21 shared e-scooter fatalities which occurred in the United States from 2018 to 2020. We studied the cases and crash narratives in detail to document the characteristics present and identify potential risk factors. We found that most crashes (86%) involved motor vehicles and 28% of these were hit-and-runs. The majority of cases took place at night (81%), and adverse environmental conditions like precipitation and fog were present in 43% of cases. We suggest a framework of five possible crash configurations, and we find that two crash types represent a majority of fatalities. These two most common crash types occurred when a motor vehicle struck an e-scooter from behind, or when the e-scooter operator lost of control of the scooter. These results establish a useful point of reference for policymakers, e-scooter stakeholders, and future researchers by documenting the number and the characteristics of these fatalities. Our analysis suggests that many hazards, such as reduced visibility, poor surface conditions, and risky maneuvers, typically accumulate before a fatal e-scooter crash occurs. This may suggest multiple predictable points of conflict which could serve as places for intervention and areas of future study.
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