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Impact of Weather on Shared Electric Scooter Utilization
... Studying substitution patterns of shared and personal micromobility, researchers in Zurich [21] revealed that precipitation positively influenced mode choice for public transport and cars and negatively for micromobility modes, most so for shared e-bikes and e-scooters. In Indianapolis, the negative binomial model showed that snow, rain, wind speed, and freezing temperatures negatively affected the number of SES trips [6]. The most important predictors were rainfall, snowfall, and mean temperatures. ...
... The afternoon plateau in Munich indicates that e-scooters might be used for midday business and leisure [2]. This complies with the previous findings that shared e-scooters are more intensively used in the afternoon than in the morning [6], [13], [15], [17]. ...
... GP-1 for the 2020 and 2021 operation years shows that compared to Mondays, there were fewer trips on Sundays and holidays. This pattern is different from the one in North America, where the average daily usage is higher on weekends and special days such as public holidays [6], [12], [15], [16]. Since its launch in June 2019, the shared e-scooters demand grew to have its peak in autumn. ...
This paper analyzes the meteorological and temporal impacts on shared e-scooters (SES) over 27 months of service in Munich. The objective is to explore the factors associated with SES utilization (hourly usage counts, median ride distances, and booking durations), focusing on time-variant variables (weather, holiday, time of the year, week, and day). This study employs the negative binomial (NB) and Consul's generalized Poisson (GP-1) regressions for modeling SES hourly demand. The Poisson regression is used for hourly medians of SES ride distances and booking durations. Random forest models evaluate the relative importance of meteorological and temporal variables for SES usage. In Munich, the popularity of SES grew over time. The peak booking numbers were on Fridays, Saturdays, and afternoons. Longer rides were on the weekends and holidays than on working days. The most extended trips were around midnight, posing the issue of riders' visibility. The COVID-19 lockdown negatively impacted SES bookings. Compared to winter, more and longer rides were between July and November. The weather impacted e-scooter usage with fewer bookings and shorter rides when raining and humid and more and longer trips when warm. Negative weather impacts for e-scooters may be partially due to a reduction in recreational use as weather discourages many outside activities.
... Following our research questions, we extract the most important findings of related literature. Recent spatiotemporal studies were predominantly published on US cities. Austin (Texas) (e.g., [4,6,[12][13][14]) and Washington D.C. (e.g., [8,9,15,16]), Louisville (e.g., [17]), Minneapolis (Minnesota) (e.g., [4]), and Indianapolis (e.g., [18,19]) were the reference cities for many spatiotemporal studies. Apart from US cities, Zhu et al. [20] conducted a study on two city districts in Singapore. ...
... In contrast to many cities, the lowest ridership occurs from 6 am to 12 pm. Regarding temporal ridership patterns, Mathew et al. [18] identified weekday peak hours between 4 pm and 9 pm. Furthermore, their results show a slight peak at 9 am. ...
... Furthermore, their results show a slight peak at 9 am. Because of this marginal morning peak, Mathew et al. [18] conclude that there are no commuter trips. Underpinning the temporal findings of previous studies, Mathew et al. [18] determined more e-scooter activity at weekends than weekdays. ...
This study analyzes the temporally resolved location and trip data of shared e-scooters over nine months in Berlin from one of Europe's most widespread operators. We apply time, distance, and energy consumption filters on approximately 1.25 million trips for outlier detection and trip categorization. Using temporally and spatially resolved trip pattern analyses, we investigate how the built environment and land use affect e-scooter trips. Further, we apply a density-based clustering algorithm to examine point of interest-specific patterns in trip generation. Our results suggest that e-scooter usage has point of interest related characteristics. Temporal peaks in e-scooter usage differ by point of interest category and indicate work-related trips at public transport stations. We prove these characteristic patterns with the statistical metric of cosine similarity. Considering average cluster velocities, we observe limited time-saving potential of e-scooter trips in congested areas near the city center.
... For instance, McKenzie (2020) showed a recurring weekly pattern among the number of scooter trips of six providers in Washington, D.C. (McKenzie, 2020). Another study in Indianapolis, Indiana, found that total e-scooter trips reduced by 80% in wintertime, while the median trip distance and duration only decreased slightly (Mathew et al., 2019). Jiao and Bai (2020) analyzed temporal variation of e-scooter trips in Austin, Texas, and found considerable variation in monthly e-scooter trips. ...
... • e-scooters usage was influenced less by rainy weather • docked and dockless bikeshare systems are more influenced by the adverse weather (Mathew et al., 2019) Indianapolis, Indiana ...
... Linear Regression Models (Flynn et al., 2012), Negative Binomial Regression Models (Gebhart and Noland, 2014;Mathew et al.,2019;Jiao and Bai, 2020), Time Series Models (Gallop et al., 2011;Kaltenbrunner et al., 2010), Generalized Additive Models (GAM) (Hosseinzadeh et al., 2021a;Wang and Brown, 2011), and non-parametric machine learning approaches (Liu et al., 2016) are some of the most commonly used methods for modeling time-varying phenomena in transportation. GAM provides a rigorous modeling approach that accounts for temporal autocorrelation with an interpretable outcome. ...
This study explores how factors, including weather, day of the week, holidays, and special events, influence the trip frequency of two micromobility modes, shared e-scooters and bikeshare, in Louisville, Kentucky. Negative binomial generalized additive models (NBGAM) were estimated to model the trip frequency of each mode. NBGAM provides a rigorous modeling approach that accounts for temporal autocorrelation among variables. While results showed some differences exist between how various factors impact shared e-scooters and bikeshare trips, several similarities emerged between modes. Rain reduced trips for both, reducing bikeshare by 17% and shared e-scooters by 16%. Mondays, Thursdays, Friday, and Saturdays had increased use of both micromobility services though Tuesdays and Wednesdays only saw significant increases in bikeshare ridership. This study contributes to the existing literature in the micromobility realm by quantifying and comparing time-dependent relationships for e-scooters and bikeshare. Results of this study inform how providers distribute vehicles and how cities manage e-scooter policies.
... Noland (2019) finds that rain and snow reduce daily trips, while higher wind speeds are responsible for reducing e-scooter trip distances. Mathew et al. (2019) also conclude that precipitation amount and average temperature are essential variables in modeling the hourly number of e-scooter trips (Mehzabin Tuli et al., 2021). ...
... On the other side of the Atlantic Ocean, more precisely: in Washington, humidity, wind speed, and precipitation also negatively impacted the number of trips per hour, although warmer temperatures and better visibility were associated with more trips (Younes et al., 2020). Furthermore, in Indianapolis, the negative binomial model showed that snow, rain, wind speed, and freezing temperatures negatively affected the number of shared e-scooters trips (Mathew et al., 2019). In Chicago, the demand was higher on days with a higher average temperature, lower wind speed, and less precipitation (Mehzabin Tuli et al., 2021). ...
Based on Mobility Data Specification (MDS) data supplied by almost all operating sharing e-scooter companies in Munich, Germany, this study investigates how shared e-scooters have been used in the city.
This research examines how the aspects such as the frequency, duration, and distance of travel have changed. Indeed, variations over time, differences by weekday, and developments throughout the day are investigated more closely. Furthermore, the study addresses the effect of temperature and precipitation on the frequency of use across the entire period.
The analysis of over 8 million trips during 27 months reveals that since shared e-scooters were introduced in Munich, the number of rides using them has gradually risen yearly. Furthermore, the utilization of the services increases noticeably throughout the summer.
According to the investigations, the weather and temperature change significantly impact booking rates. The impact of temperature is directly correlated with the volume of rides. In the case of precipitation, it depends more on whether it is raining at the time or not, while the amount of rain plays a subordinate role. The study shows apparent differences in demand between weekdays and weekends or public holidays: During the week, the number of trips rises earlier, and there is a morning peak. Most journeys occur in the afternoon between 4 and 6 pm. The weekday bookings are generally consistent from Monday through Thursday, slightly higher on Fridays and Saturdays, and again decreasing on Sundays. Another study finding is that the typical journey time has stayed constant over time, ranging between seven and eight minutes.
... ABD'nin Indianapolis kentinde yapılan çalışmada 2018 ile 2019 tarihleri arasında kente ait yüzey sıcaklığı, hava sıcaklığı, görüş mesafesi, nem, yağış ve rüzgar gibi hava durumu ile ilgili verilerin yanında e-skuter ile yapılan yolculuklara ait başlangıç, bitiş noktaları ve kat edilen mesafe verileri kullanılmıştır. Verilerin analizi sonucunda kış aylarında yolculuk sayısının %30-80 oranında düştüğü, buna karşın ortalama yolculuk mesafesi ve süresinin etkilenmediği ortaya konulmuştur [20]. ...
... Bu bilgiler doğrultusunda, örneklem sayısının belirlenmesinde kullanılan formül Denklem 1'de verilmiştir [27]. Buna göre; öngörülen e-skuter kullanma oranı (p=0, 20), öngörülen e-skuter kullanmama oranı (q=0,80), z değeri (%5 anlamlılık düzeyi için 1,96), hata payı (e=0,05) değerleri için örneklem sayısı 246 olarak hesaplanmıştır. Belirlenen örneklem sayısına uygun olarak anket çalışmasına 314 kişi katılım göstermiştir. ...
... ABD'nin Indianapolis kentinde yapılan çalışmada 2018 ile 2019 tarihleri arasında kente ait yüzey sıcaklığı, hava sıcaklığı, görüş mesafesi, nem, yağış ve rüzgar gibi hava durumu ile ilgili verilerin yanında e-skuter ile yapılan yolculuklara ait başlangıç, bitiş noktaları ve kat edilen mesafe verileri kullanılmıştır. Verilerin analizi sonucunda kış aylarında yolculuk sayısının %30-80 oranında düştüğü, buna karşın ortalama yolculuk mesafesi ve süresinin etkilenmediği ortaya konulmuştur [20]. ...
... Bu bilgiler doğrultusunda, örneklem sayısının belirlenmesinde kullanılan formül Denklem 1'de verilmiştir [27]. Buna göre; öngörülen e-skuter kullanma oranı (p=0, 20), öngörülen e-skuter kullanmama oranı (q=0,80), z değeri (%5 anlamlılık düzeyi için 1,96), hata payı (e=0,05) değerleri için örneklem sayısı 246 olarak hesaplanmıştır. Belirlenen örneklem sayısına uygun olarak anket çalışmasına 314 kişi katılım göstermiştir. ...
Dünyada 2017 yılından beri kullanılan ve mikromobilite araçlarından biri olan paylaşımlı elektrikli skuterler (e-skuter), Türkiye’de 2019 yılında hayatımıza girmiştir. Yeni bir ulaşım türü olan e-skuterlerin incelendiği ulusal düzeydeki literatür çalışmaları nispeten yetersiz görülmüştür. Bu doğrultuda optimum fayda sağlamak için paylaşımlı e-skuter kullanımının detaylı şekilde araştırılması hedeflenmiştir. Çalışma kapsamında, Türkiye’nin nüfusu en yüksek ili olan İstanbul’da çevrimiçi anket yapılmış, elde edilen veri setinin analizinde ki-kare ve ağırlıklandırma olmak üzere iki tür yöntem kullanılmıştır. İlk olarak katılımcıların kategorik olarak düzenlenen sosyo-ekonomik özellikleri ile paylaşımlı e-skuter kullanım durumları arasındaki ilişkilerin anlamlılığı incelenmiştir. Buna göre paylaşımlı e-skuteri tercih etme eğiliminin erkeklerde (%38), gençlerde (%43), yüksek eğitim seviyesinde (%38) ve aylık ulaşım gideri fazla olan kişilerde (%45) daha yüksek olduğu görülmüştür. İkinci olarak kullanım özelliklerine ait değişkenlerin birbirleri ile olan ilişkileri istatistiksel açıdan sorgulanmıştır. Buna göre yolculuk mesafesi, kullanım sıklığı, ortalama aylık skuter ulaşım gideri ve tercih edilen ikinci ulaşım türü parametrelerinin birbirleri ile ilişkilerinin anlamlı olduğu görülmüştür. Son olarak eskuter kullanan katılımcıların tercih etmelerinin (eğlenceli bir ulaşım türü olması-%30), e-skuter kullanmayan katılımcıların kullanmamalarının (güvenli bulunmaması-%14) ve kullanımlarını sağlayacak iyileştirmelerin (kendine ait yolunun olması-%35) ağırlıklandırma yöntemi ile birincil çıktıları sunulmuştur.
... Younes [27] compared dockless e-scooters with docked e-bikes and found significant differences between the modes, both temporally and spatially. Noland [19] and Mathew [21] found evidence of e-scooters and e-bike usage being negatively affected by weather factors such as low temperature, precipitation, and strong wind. E-scooters, however, are less affected by weather factors than e-bikes. ...
... The number of trips strongly rises in June 2021, most prominently shown in the e-bike data. However, the data may slightly hint at a lower micromobility usage in the winter months (e.g., in November 2021), probably due to more precipitation, as shown by [21]. In further research, we hope to obtain data that are not influenced so strongly by external factors such as travel restrictions. ...
Micromobility service systems have recently appeared in urban areas worldwide. Although e-bike and e-scooter services have been operating for some time now, their characteristics have only recently been analyzed in more detail. In particular, the influence on the existing transportation services is not well understood. This study proposes a framework to gather data, infer micromobility trips, deduce their characteristics, and assess their relation to a public transportation network. We validate our approach by comparing it to similar approaches in the literature and applying it to data of over a year from the city of Aachen. We find hints at the recreational role of e-scooters and a larger commuting role for e-bikes. We show that micromobility services in particular are used in situations where public transportation is not a viable alternative, hence often complementing the available services, and competing with public transportation in other areas. This ambivalent relationship between micromobility and public transportation emphasizes the need for appropriate regulations and policies to ensure the sustainability of micromobility services.
... Several studies (e.g., [24][25][26][27][28][29]) examine usage patterns of shared scooter services. In an analysis of more than 8000 scooters serving over 425,000 rides in Indianapolis, Matthew et al. looked at trip durations, distances, speeds, and schedules (i.e., time-of-day and dayof-the-week) [27]. ...
... A number of factors were found to affect the usage of e-scooters. Mathew et al. focus on the potential impacts of the weather in Indianapolis and find that in the winter, there are fewer scooter trips and slightly shorter distances and duration [29]. Additionally, there is even less demand when temperatures drop below freezing and during snowfall than during rain. ...
Shared e-scooters have the potential to increase access, complement transit, and replace automobiles, all while reducing emissions and congestion. However, there are concerns worldwide over the mode’s safety issues and risks. In this paper, we explore both the motivations and barriers to using e-scooters. Data are collected from a stated preference survey, using a sample consisting of mostly university staff and students in Singapore. Three logit models with varying specifications of e-scooters’ speed and lane use and one’s prior experience of conflict with a personal mobility device (PMD) are estimated. Overall, the three models have a very comparable fit (adjusted R^2 of about 0.55) and consistent results. The results indicate preferences for e-scooters if they are faster and off the sidewalk. However, a bad or unsafe experience with a PMD would negatively affect use to a greater degree, although it varies across individuals. Our study suggests diverting scooters off the sidewalk and increasing the speed may not always be effective in encouraging behavioral shifts toward this alternative mode. Other solutions such as improving the services and enhancing traffic safety should be explored and considered instead.
... Columns 4 and 5 of table 1 show average effects during non-winter months (March-October) and during winter months (November-February), respectively. We expect the treatment effect to be concentrated in the non-winter months, since providers tend to reduce the number of deployed e-scooters and e-scooters utilization drop considerably during winter, according to descriptive analyses and news reports (Mathew et al., 2019;O'Brien, 2021). The estimates indicate that e-scooter services significantly increase the total number of accidents by 11.5 ± 3.5% in the non-winter months (col. ...
... First, we use winter-time accidents as a sub-group of accidents that would likely show significant estimates, if our estimates were capturing differential general trends in urban traffic policies or behaviors. E-scooters are a considerably less attractive transportation mode in cold weather and companies tend to reduce the number of deployed scooters (Mathew et al., 2019;O'Brien, 2021). Therefore, shared e-scooters likely cause fewer accidents in winter. ...
We estimate the causal effect of shared e-scooter services on traffic accidents by exploiting variation in availability of e-scooter services, induced by the staggered rollout across 93 cities in six countries. Police-reported accidents in the average month increased by around 8.2% after shared e-scooters were introduced. For cities with limited cycling infrastructure and where mobility relies heavily on cars, estimated effects are largest. In contrast, no effects are detectable in cities with high bike-lane density. This heterogeneity suggests that public policy can play a crucial role in mitigating accidents related to e-scooters and, more generally, to changes in urban mobility.
... also analyzed e-scooter users in Zurich and found that they tend to be young, universityeducated males with full-time employment living in affluent households without children or 135 cars. Mathew et al. (2019) analyzed the impact of weather on e-scooter utilization using data from Indianapolis. The results from a negative binomial model suggested that the number of trips was reduced significantly during rain and snow events. ...
... In contrast, e-scooter trips seem to decrease for temperatures higher than 85 degrees Fahrenheit and lower than 37 degrees Fahrenheit. Mathew et al. (2019) found that e-scooter users in Indianapolis are particularly sensitive to lower temperatures. Our findings using data from Austin, where the temperatures are significantly higher than Indianapolis, suggest that this is also true for high 530 temperatures. ...
E-scooters are an alternative for short trips and are particularly suitable for solving the last-mile transit problem, yet their impact on transit is not well understood. There is a need to understand the e-scooter demand patterns and users’ characteristics to develop adequate policies and regulations. In this research, we consider the problem of modeling the interaction of e-scooters and bus transit services and provide an overview of e-scooter trips and user characteristics. We use a revealed-preference survey to evaluate the e-scooter usage in one of the highest-demand areas in the City of Austin, corresponding to a university campus. We explore population characteristics, mode shift, and mode interaction. Then, using publicly available datasets, we provide a causal analysis to evaluate the nature of the relationship between e-scooter and transit trips in the whole city. Assessing this relationship is challenging because several factors affect the demand of both types of trips (e.g., location of attractive zones), known as confounding variables. We develop a methodological framework to isolate the effects of confounding variables on transit trips using a two-stage regression procedure. The first stage aims to isolate confounding variables using a gradient boosting regression. The second stage models first and last-mile trips using a negative binomial and a zero-inflated negative binomial count model. The university survey indicated that 12 percent of the e-scooter users employed transit to complement their trips. Although small in magnitude, the data modeling results show that a statistically significant relationship was found on the university campus and downtown areas, supporting the survey results and extending the analysis to other areas of the city. However, the overall interaction between the two modes has a small magnitude. The proposed methodology can be used to identify areas with potential e-scooter and transit interaction.
... Rain and snow decreased the number of trips, rain reduced the distance of trips, and higher temperature was correlated with longer distances and faster speeds [52]. Mathew et al. (2019) used a negative binomial model to explore the impact of weather on e-scooter trips in Indianapolis, IN, USA. They found a negative association between e-scooter trips and snowfall, rainfall, visibility, wind speed, and freezing temperature. ...
... During winter, e-scooter trips reduced by 80% on average, while median distance and duration dropped only slightly. Trips were also more sensitive to snowfall rather than rainfall [53]. In another study in Indianapolis, IN, USA, Liu et al. (2020) captured a 76% decline in the number of trips during wintertime [54]. ...
The emergence of micromobility services in the form of dockless shared e-scooters has resulted in a wide range of behavioral changes in urban environments. In order to effectively steer these changes towards sustainability targets, the characteristics of e-scooter trips and users’ behaviors should be understood further. However, there is a lack of systematic literature reviews in this domain. To address this gap, we provide a two-fold systematic literature review. The first aspect focuses on the categorization of temporal and spatial patterns of shared e-scooter usage. The second aspect focuses on a deeper understanding of e-scooter users’ behaviors, utilizing the principles of persona design. The analysis of temporal patterns highlights the commonality of midday, evening, and weekend peak usage across cities, while spatial patterns suggest e-scooters are used for traveling to recreational and educational land use, as well as city center areas. The synthesis of findings on users’ behaviors has resulted in six categories, with four user types based on usage frequency (one time, casual, power, and non-adopters), and two motivation-based personas (users who are not satisfied with current mobility options and users who have had positive travel experience from e-scooter usage). The overall findings provide important lessons for evaluating this emerging mobility service, which should be considered for steering its development in public-private stakeholder networks.
... Shared electric scooters (e-scooters), as one type of shared micro-mobility, have become increasingly popular in cities worldwide (Gössling, 2020;Mathew et al., 2019). The stand-up e-scooter, consisting of an electric motor and a standing deck, is designed for a user to ride for a short distance in urban areas (Hollingsworth et al., 2019). ...
... On a given day, most of the trips occurred between 12 pm and 9 pm, with an evening peak from 4 pm to 7 pm and no morning peak (Fig. 3c). Our observation for the e-scooter usage pattern is consistent with another study focused on Indianapolis (Mathew et al., 2019). The e-scooter usage pattern varies at different time of the day, which may lead to temporally heterogeneous relationships between the two systems. ...
Shared e-scooter systems have been growing rapidly in many cities as a potential sustainable transportation mode. However, whether shared e-scooters compete with or complement existing public transportation is still unclear. This study proposes a modeling framework to identify the potential impacts of e-scooter trips on the existing bus system, considering the spatiotemporal availability of bus service. The impact of the potentially competing trips on transit ridership can then be verified using Difference-in-Differences models. The framework is applied to Indianapolis, Indiana as a case study. Our results show that about 27% of e-scooter trips could potentially compete with bus system and they are concentrated in downtown. The potential competing relationship can also lead to a bus ridership reduction. The complementary trips (29%) are mainly located outside of downtown where the bus coverage is low. Repositioning e-scooters to areas with limited bus service can better promote synergistic relationship between the two systems.
... Moreover the e-scooter daily patterns do not match the commuting patterns. In [14] the authors show that the number of bookings per hour is higher in good weather condition. These characteristics reinforce the need of specific models and tools to study this new type of mobility. ...
... The different number of daily trips justifies the difference in size among the cities, with Minneapolis having more than twice as much the e-scooters in Louisville (see Table I). 4 Some sudden falls are related to bad weather conditions that affects the willingness of customers to rent an e-scooter [14]. ...
... In this study, a battery structure based on a digital twin for electric mobility device batteries is proposed [11]. There are also studies analyzing the impact on the demand for bicycles or micro-mobility devices [6,[12][13][14]. Brandenburg et al. [15] analyzed the effect of weather conditions on bicycling activity. ...
Several mobility vehicle rental companies have emerged owing to the increased preference for shared mobility as a short-distance transit option. These shared-mobility vehicles must be strategically placed at different locations to enable easy access to customers. However, without prior knowledge of the occurrence of rental demand, it becomes challenging for companies to respond quickly. In this study, we analyzed the factors affecting rental demand for shared electric mobility vehicles by utilizing actual data from the company EV PASS and predicted rental demand to ensure that the vehicles were distributed effectively, allowing customers to receive timely service. We compared the performance of machine learning models such as the Extra Trees regressor, CatBoost regressor, and LightGBM (Light Gradient Boosting Machine) models in predicting the demand for shared mobility vehicles. Additionally, we explored the use of an ensemble technique called voting regressor to reduce errors with an R2 score of 0.7629, it outperformed all the individual models. The analysis revealed that factors including humidity, precipitation, and solar radiation have a significant influence on rental demand. Based on the findings of this study, companies can effectively manage equipment and personnel, providing better shared electric mobility rental services, leading to increased customer satisfaction.
... Moreover, the presence of snow influences everyday life in terms of mobility. Usage of bikes or scooters is reported to significantly depend on snow depths (Mathew et al., 2019;Yang et al., 2018). Also, the alternatives (trains, cars, planes) may be affected by high snow depths (Doll et al., 2014;Taszarek et al., 2020;Trinks et al., finer-scale representation of complex topography (Poschlod et al., 2018). ...
Snow dynamics play a critical role in the climate system as they affect the water cycle, ecosystems and society. Within climate modelling, the representation of the amount and extent of snow on the land surface is crucial for simulating the mass and energy balance of the climate system. Here, we evaluate simulations of daily snow depths against 83 station observations in southern Germany over the time period 1987 – 2018. Two simulations stem from high-resolution regional climate models, the Weather Research & Forecasting Model (WRF) at 1.5 km resolution and the COSMO-CLM (CCLM) at 3 km resolution. Additionally, the hydrometeorological snow model AMUNDSEN is run at the point scale of the climate stations based on the atmospheric output of CCLM. The ERA5-Land dataset (9 km) complements the comparison as state-of-the-art reanalysis land surface product. All four simulations are driven by the same atmospheric boundary conditions of ERA5. The WRF simulation features a cold bias of -1.2 °C and slightly overestimates snow depth (+0.4 cm) with a root-mean-square error (RMSE) of 4.3 cm. Snow cover duration slightly exceeds the observations (+6.8 d; RMSE = 20.5 d). The CCLM reproduces the winter climate very well, but shows a strong negative bias at snow depth (-2.5 cm; RMSE = 5.6 cm) and snow cover duration (-20.0 d; RMSE = 27.1 d). AMUNDSEN improves the reproduction of snow cover duration (+6.5 d; RMSE = 16.6 cm) and snow depth (+2.2 cm; RMSE = 6.2 cm). ERA5-Land shows a strong positive bias in mean winter snow depth (+3.6 cm; RMSE = 14.5 cm) and snow cover duration (+33.9 d; RMSE = 44.0 d). All models fail to skilfully predict white Christmas. For extreme events of snow dynamics such as annual maximum snow depths, maximum daily snow accumulation and melting, the ERA5L and CCLM show large biases in amplitude and deviations in seasonality. WRF and AMUNDSEN can improve the representation of extremes but still with considerable limitations. The high spatial resolution of convection-permitting climate models shows potential in reproducing the winter climate in southern Germany. However, the uncertainties within the snow modelling prevent a further straightforward use for impact research. Hence, careful evaluation is needed before any impact-related interpretation of the simulations, also in the context of climate change research.
... Previous studies (e.g., Mathew et al., 2019;Tuli and Mitra, 2021;Kimpton et al., 2022, Noland 2021Gabhardt et al., 2021) found weather variables as influencing factors in e-scooter usage. In order to compare the effects of weather variables on e-scooter usage during and before the COVID-19 pandemic, the study included the average temperature of the day ( • F), total precipitation (inch), average wind speed (mph), and percentage of humidity. ...
This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips reduced, the average trip distance and the average trip duration increased during the Pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the Pandemic compared to other parts of Austin; iii) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the Pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the Pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.
... The existing literature showed that the average trip distance for shared bikes and e-scooters is between 0.8 and 31.5 miles. A study by Mathew (2019) found that the mean trip length of shared e-scooters in Indianapolis is around 1.12 miles (95th percentile equals 3.69 miles). NACTO (2022) also showed a similar mean trip length for station-based bike sharing services (2.4-2.7 miles for casual users). ...
... Various causes have been discussed in the literature, such as trip purposes and trip distance, to discuss the usage of e-scooters in mobility (Caspi et al., 2020;Liao & Correia, 2020). For example, e-scooters have been frequently referred to as a remedy for the first-last mile trips (Baek et al., 2021;Crowe & Elkbuli, 2021;Gössling, 2020;Mathew et al., 2019;McKenzie, 2019). Also, On the other hand, e-scooters could partly/fully substitute motorised vehicles such as public transport and cars (Bai & Jiao, 2020a, 2020bLaa & Leth, 2020). ...
Although electric scooters (e-scooters) are gaining ground rapidly, research on analysing their users' experience lags far behind practice. Level of Service (LOS) is a promising approach to bridge the gap between research and practice via quantifying e-scooter riders' experience. We reviewed the state-of-the-art literature of e-scooters concerning their users' experience and proposed a preliminary framework for developing e-scooter LOS (SLOS). The findings suggest a lack of studies to evaluate SLOS, and e-scooters are rarely considered in the LOS estimation of other transport modes. Considering the impact of e-scooters in both modal substitute and supplement calls for unique SLOS indices in each scenario to reflect their user's experience realistically. Future studies should analyse the interaction of e-scooters with other road users, particularly pedestrians. This study highlights the importance of treating e-scooter as a distinct transport mode and contributes to matching policy and practice to integrate e-scooters into transport planning.
... Tablo Boston Danışmanlık Grubu tarafından yapılmış bir çalışma, e-skuter kullanımının uzun dönemde ekonomik analizini oluşturmuştur. Bird şirketinin verilerinin kullanıldığı çalışmada şehirlerin nüfus yoğunluğu, şehrin ne kadar bisiklet dostu olduğu, hava durumu ve genç nüfus yoğunluğu parametreleri kullanılarak sistemin etkisi değerlendirilmiştir [26,60]. ...
Micromobility devices such as shared bicycle and scooter systems, have come to the fore as
an alternative urban transportation system in recent years. The e-scooter system emerges
as a type of transportation that is widely used in the world in a short time due to the
flexibility and ease of use it provides. The use of e-scooters in the world literature is
investigated in terms of; demography, travel time, journey distance, the purpose of travel,
safety problems, environmental effects, legal regulations and applications. The most
important findings are that the user age range is between 16-60 years, they are generally
used in short distance (~2 km) and short-term (<11 min) journeys, and they cause a mode
shifting as a new type of transportation. In terms of safety, it has been observed that more
than half of the accidents are caused by the user. In order for e-scooters to be included in
urban mobility plans as a mode of transportation rather than being an entertainment and
leisure activity, the legal basis of the system should be established. In this study, the
evaluation and examination of the e-scooter as a type of urban transportation was made
with the data obtained from e-scooter studies and applications in the world and Turkey,
and suggestions for implementation and supervision were presented. By examining the
relevant legal regulations in the cities of the world, it has been suggested to introduce
different regulations according to the infrastructure of the cities in Turkey. Since there are
very few studies on the subject in Turkey, it is thought that this study will contribute to the
literature and form the basis for future studies.
... 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] ...
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.
... Shared e-scooter research is still at the beginning phase. Previous studies have examined the effects of shared e-scooters on micro-mobility (Esztergár-Kiss and Lopez Lizarraga, 2021;Shaheen and Cohen, 2019;Smith and Schwieterman, 2018), length (Espinoza et al., 2019;Mathew et al., 2019;Orr et al., 2019), accident and injury rates (Badeau et al., 2019;Beck et al., 2020;Choron and Sakran, 2019;Espinoza et al., 2019;Kobayashi et al., 2019;Mckenzie, 2019;Trivedi et al., 2019;Yang et al., 2020), regulation (Latinopoulos et al., 2021;Moran et al., 2020), user behavior (Usages et usagers des trottinettes, 2019; Abouelela et al., 2021;Aman et al., 2021;Christoforou et al., 2021;Hawa et al., 2021;Lee et al., 2021;Mckenzie, 2019;Nikiforiadis et al., 2021;Zuniga-Garcia et al., 2021), and environmental impacts (Chester, 2019;Hollingsworth et al., 2019;Moreau et al., 2020). A few studies have focused on user acceptance of shared e-scooters (Eccarius and Lu, 2020;Huang, 2020;Kopplin et al., 2021). ...
Shared e-scooters are a newly found type of electric vehicle, emerging from and utilized within micro-mobility systems across the globe. The effectiveness of their performance, however, remains contingent on the number of individuals willing to accept them. This study aims to determine those predictors which influence behavioral intention toward shared e-scooters. The suggested research model is based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2), along with price sensitivity and environmental awareness. Data was gathered through an online questionnaire of 467 participants in Turkey, from which we obtained 413 valid responses. Model explains the 60% variance in the behavioral intention to use e-scooters. Primary findings demonstrated that behavioral intention is significantly affected by social influence (R2=.319), effort expectancy (R2=.194), performance expectancy (R2=.179), and price sensitivity (R2=.154). As shared e-scooters are novel to the Turkish mobility system, we analyzed findings to help practitioners and policymakers develop strategies that will enhance interest in adopting shared e-scooters within micro-mobility systems.
... Literature showed that the weather conditions, such as temperature, precipitation, and wind speed, can greatly influence the usage of dockless scooters (Mathew et al., 2019;Noland, 2021;Younes et al., 2020). For example, the rainy and cold weather will significantly reduce the use of dockless scooter-sharing services (Noland, 2021). ...
Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide researchers with powerful tools to achieve this task, but research in this area is still lacking. This paper thus proposes a novel deep learning architecture named Spatio-Temporal Multi-Graph Transformer (STMGT) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model uses a graph convolutional network (GCN) based on adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph to attach spatial dependency to temporal input (i.e., historical demand). The output of GCN is subsequently processed with weather condition information by the Transformer to capture temporal dependency. Then, a convolutional layer is used to generate the final prediction. The proposed model is evaluated for two real-world case studies in Washington, D.C. and Austin, TX, respectively, and the results show that for both case studies, STMGT significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help the micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage dockless scooter-sharing operations.
(This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.)
... Providers indicate that the e-scooter demand in Germany is particularly declining on days with high precipitation, while cooler temperatures alone have little effect [30]. A study by Mathew et al. [31] on the impact of weather on shared electric scooter use in Indianapolis found that usage is affected more strongly by temperatures below freezing and snowfall than by rain. ...
Considering the controversial discussion about the sustainability and usefulness of e-scooters, in this study, we analyzed the substitution potential of e-scooters, especially with regard to car trips. Based on data from the national mobility survey in Germany (Mobility in Germany, MiD 2017), we identified trips that could be covered purely by an e-scooter. Thereby, trip length, trip purposes, weather conditions, and other influencing factors were taken into account. Our analysis showed that, in Germany, 10–15% of the motorized individual transport (MIT) trips could be made by e-scooter. Accompanied by a literature analysis, we then critically reflected on the overall potential of e-scooters and formulated recommendations for urban and transport planning.
... Providers indicate that the e-scooter demand in Germany is particularly declining on days with high precipitation, while cooler temperatures alone have little effect [30]. A study by Mathew et al. [31] on the impact of weather on shared electric scooter use in Indianapolis found that usage is affected more strongly by temperatures below freezing and snowfall than by rain. ...
Considering the controversial discussion about the sustainability and usefulness of e‐ scooters, in this study, we analyzed the substitution potential of e‐scooters, especially with regard to car trips. Based on data from the national mobility survey in Germany (Mobility in Germany, MiD 2017), we identified trips that could be covered purely by an e‐scooter. Thereby, trip length, trip purposes, weather conditions, and other influencing factors were taken into account. Our anal‐ ysis showed that, in Germany, 10–15% of the motorized individual transport (MIT) trips could be made by e‐scooter. Accompanied by a literature analysis, we then critically reflected on the overall potential of e‐scooters and formulated recommendations for urban and transport planning.
... Two other recent studies examined e-scooters and weather, both finding similar effects. One used data from Indianapolis, Indiana (Mathew et al., 2019) while the other used data from Washington, DC (Younes et al., 2020); this latter study also compared e-scooters with docked bikeshare usage, finding that e-scooters were less sensitive to weather conditions. None of these prior studies adequately controlled for serial correlation in the data, something that I do in this analysis; despite this shortcoming, results are generally similar. ...
The usage of shared e-scooters, dockless e-bikes, and docked bicycles are correlated with weather conditions to examine the relative impact on each mode, specifically number of trips taken, their duration, and distance. Data is obtained from the City of Austin data portal. Rain, temperature and wind conditions are obtained from NOAA and a variety of analysis methods are applied, specifically Prais-Winsten and Negative Binomial regressions as well as a Random Forest model to examine the full suite of weather variables and to avoid some of the distributional issues in the trip models. In addition, controls are included for holidays, days of the week, and special events in Austin (such as the SXSW festival); all are found to be critical control variables, with SXSW associated with large increases in trips. Results suggest that docked bicycle and e-bike usage is more sensitive to adverse weather conditions than e-scooter trips, though all are reduced in colder, rainier, and windier conditions, as well as extreme heat and high relative humidity.
... The tendency to use e-PMVs most during the afternoon and evening may also depend on the climatic conditions of the city. Mathew, Liu & Bullock [28] showed how meteorological variables (i.e., amount of precipitation, snowfall, wind speed, visibility, and average temperatures) significantly affected the number of trips per hour (30%-80% in winter months). A quite similar result is pointed out by Noland [33] and Hardt & Bogenberger [21]; the latter also added the scarce use of clothing suitable for the climate. ...
Nowadays, the diffusion of electric-powered micro Personal Mobility Vehicles (e-PMVs) worldwide—i.e., e-bikes, e-scooters, and self-balancing vehicles—has disrupted the urban transport sector. Furthermore, this topic has captured many scholars and practitioners’ interest due to multiple issues related to their use. Over the past five years, there has been strong growth in the publication of e-PMV studies. This paper reviews the existing literature by identifying several issues on the impact that e-PMVs produce from different perspectives. More precisely, by using the PRIMA’s methodological approach and well-known scientific repositories (i.e., Scopus, Web of Science, and Google Scholar), 90 studies between 2014 and 2020 were retrieved and analyzed. An overview and classification into endogenous issues (e.g., impact on transport and urban planning) and exogenous issues (e.g., impact on safety and the environment) are provided. While several issues are deeply investigated, the findings suggest that some others need many improvements. Therefore, the status quo of these studies is being assessed to support possible future developments.
... scooter trips in Washington DC for higher temperatures and visibility and decreasing numbers with humidity, wind 10 speed and rain. Mathew et al. (2019b), focusing on data from Indianapolis, observe reductions of trips up to 80% 11 and a two-thirds smaller utilization rate during winter months, as well as slightly lower average length and time 12 ...
In the last years, a new generation of shared micromobility systems has rapidly proliferated in urban areas. Their distinctive characteristics are dockless security systems, electric power assistance and a new device, the e-scooter. The technological advances combined with the lack of regulations reduce implementation costs and expand potential demand, which has encouraged their promotion by private companies. The unregulated spread of these services has opened several questions about their impacts in mobility, environment, infrastructure and urban space, and safety. This paper reviews evaluation reports from several cities and other research and technical publications to provide insights about these issues, including how cities have managed them. Based on this overview of shared e-scooter services, we observe that there are still knowledge gaps at different levels of analysis: understanding their travel patterns, their role in the global transport system, and guidelines and strategies for a competitive service design and operation.
... Sufficient infrastructure is essential to facilitate the effective integration of e-scooters into the urban mobility ecosystem and for users to commute safely and efficiently when passing nearby cars and/or pedestrians [31,32]. In addition, results showed that closed areas may be suitable for the launch of the system because the weather is typically very hot in summer (~45 °C), which may also hinder the deployment of e-scooters [31,33]. We hypothesize that deploying e-scooters in open areas would meet the demand of users who would use them during the early morning or after sunset. ...
This study explores the feasibility of launching an e-scooter sharing system as a new micro-mobility mode, and part of the public transportation system in the city of Riyadh, Saudi Arabia. Therefore, survey was conducted in April 2020 to shed light on the perception of e-scooter systems in Riyadh. A sample of 439 respondents was collected, where the majority indicated willingness to use the e-scooter sharing system if available (males are twice as likely to agree than females). Roughly 75% of the respondents indicated that open entertainment areas and shopping malls are ideal places for e-scooter sharing systems. Results indicated that people who use ride-hailing services such as Uber expressed more willingness to use e-scooters for various purposes. The study found that the major obstacle for deploying e-scooters in Saudi Arabia is the lack of sufficient infrastructure (70%), followed by weather (63%) and safety (49%). Moreover, the study found that approximately half of the respondents believed that COVID-19 will not affect their willingness to ride e-scooters. Two types of logistic regression models were built. The outcomes of the models show that gender, age, and using ride-hailing services play an important role in respondents' willingness to use e-scooter. Results will enable policymakers and operating agencies to evaluate the feasibility of deploying e-scooters and better manage the operation of the system as an integral and reliable part of public transportation.
... Thus, it could be very efficient and popular in Dubai and other places with similar climatic conditions. Extended summer season is a known deterrent to walking and conventional cycling (Weinert et al., 2007;Mathew et al., 2019). For that reason, e-bikes or e-scooters could be one solution to commute faster and shorter especially during summer time. ...
A key component of TOD planning is pedestrians’ access to transit stations. An often-underrated component of the urban fabric is alleyways. Alleys are largely neglected in assessments of walkability and network connectivity in TOD contexts. This paper examines the contribution of alleyways in enhancing the connectivity efficiency of twelve metro stations in Dubai. Prior studies identified four phases of Dubai’s evolution: Organic, Pre-suburban, Suburban, and Bigness. These phases represent variations in street networks’ and alleys’ configurations. This study quantifies alleyways’ contribution to pedestrian connectivity in each phase by comparing two network scenarios: i) street networks only and ii) streets plus alleyways. Pedestrian connectivity in this study measures both the distance and the directness of pedestrian routes. Distance is computed using the concept of pedestrian catchment areas (PCAs), while directness is measured using the concept of pedestrian route directness (PRD).
Findings show a decreasing trend over time in street network efficiency and an increase in overall street network connectivity when alleys are included in the analysis. Results indicate that alleys in many cases turn disconnected street networks into more efficient ones. Results highlight alleyways’ potential to promote walkability around transit stations. The article argues that alleys warrant a rebirth in urban design scholarship and practice.
While people's expectations and needs affect their behavior and preferences, technological developments increase the options they can choose from. One of these areas has been the field of transportation, which has been offering environmentally friendly options such as e-scooters and e-bikes in recent years. These new transportation options, called micromobility, are becoming increasingly popular, especially among young people. In this study, the opinions of university students about micromobility, with an emphasis on e-scooters, were investigated. Using a structured interview method, 10 female and 10 male students studying at a maritime university were asked about their opinions on the use of these vehicles. The results show that students are reluctant to use them mainly because they find the roads unsafe and the rules and regulations inefficient. From the gender point of view, it is revealed that the majority of female students don’t prefer them, especially on quiet roads, because of the harassment they might face. Further research on this topic can be carried out with the participation of more students using different methods.
Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide researchers with powerful tools to achieve this task, but research in this area is still lacking. This paper thus proposes a novel deep learning architecture named Spatio-Temporal Multi-Graph Transformer (STMGT) to forecast the real-time spatiotemporal dockless scooter-sharing demand. The proposed model uses a graph convolutional network (GCN) based on adjacency graph, functional similarity graph, demographic similarity graph, and transportation supply similarity graph to attach spatial dependency to temporal input (i.e., historical demand). The output of GCN is subsequently processed with weather condition information by the Transformer to capture temporal dependency. Then, a convolutional layer is used to generate the final prediction. The proposed model is evaluated for two real-world case studies in Washington, D.C. and Austin, TX, respectively, and the results show that for both case studies, STMGT significantly outperforms all the selected benchmark models, and the most important model component is the weather information. The proposed model can help micromobility operators develop optimal vehicle rebalancing schemes and guide cities to better manage dockless scooter-sharing operations.
This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. The study employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) panel models to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key findings are: i) while the daily e-scooter trips, total trip duration, total trip distance, and daily percentage of e-scooter share reduced, the average trip distance and the average trip duration increased during the pandemic; ii) the central part of Austin city experienced a major decrease in e-scooter usage during the pandemic compared to other parts of Austin; iii) areas with higher population density and land use mix with very good public transportation services and higher number of e-scooter devices were positively associated with e-scooter usage in both of the years; iv) areas with low median income and higher number of available e-scooter devices experienced a smaller decrease in daily total e-scooter trips, trip distance, and trip duration during the pandemic while the opposite result was found in areas with higher public transportation services. The results of this study provide policymakers with a timely understanding of the changes in shared e-scooter usage during the pandemic, which can help redesign and revive the shared micromobility market in the post-pandemic era.
Weather, climate, and daily human mobility patterns are inextricably linked, and so quantifying and examining these patterns is essential for smarter urban policy and design that are tailored to support our daily mobility needs and foreground urban sustainability. This study provides an empirical approach to better understanding the interface between weather, climate, and daily human mobility on >800,000 shared e-Scooter trips across subtropical Brisbane, Australia. We find that the number of eScooter trips increases with heat and declines with rain. However, results reveal that the ‘connectivities’ between land use types remain stable irrespective of weather conditions while trip distance contracts during inclement weather. As such, weather influences the appeal and distance of eScooter trips but seemingly not trip purpose.
Background:
The recent proliferation of electric standing scooters in major urban areas of the United States has been accompanied by injuries of varying severity and nature, representing a growing public health concern.
Objective:
Our aim was to characterize imaging utilization patterns for injuries associated with electric scooter (e-scooter) use, including their initial emergency department (ED) management.
Methods:
We conducted a retrospective review of the electronic medical record for all patients presenting to affiliated EDs for e-scooter-related injuries between July 2018 and April 2020. Demographics, date and time of presentation, imaging study type, resultant injury, and procedural details were recorded.
Results:
Ninety-seven patients were included; mean age was 27.6 years. Of these, 55 patients (57%) had injuries identified on imaging and 40% of all imaging studies were positive. Most identified injuries (61%) were musculoskeletal, with a small number of neurological (2%) and genitourinary (1%) injuries. The highest prevalence of presentations occurred in August; most patients (72%) presented between 3 pm and 1 am and granular peaks were between 12 am and 1 am and 5 pm and 6 pm.
Conclusions:
Patients presenting with e-scooter injuries have a high likelihood of injury to the radial head, nasal bone, and malleoli. Emergency physicians should be especially vigilant for injuries in these areas at presentation. Visceral injuries are uncommon but may be severe enough to warrant surgery.
Shared electric scooters (e-scooter) are booming across the world and widely regarded as a sustainable mobility service. An increasing number of studies have investigated the e-scooter trip patterns, safety risks, and environmental impacts, but few considered the energy efficiency of e-scooters. In this research, we collected the operational data of e-scooters from a major provider in Gothenburg to shed light on the energy efficiency performance of e-scooters in real cases. We first develop a multiple logarithmic regression model to examine the energy consumption of single trips and influencing factors. With the regression model, a Monte Carlo simulation framework is proposed to estimate the fleet energy consumption in various scenarios, taking into account both trip-related energy usage and energy loss in idle status. The results indicate that 40% of e-scooter battery energy was wasted in idle status in the current practice, mainly due to the relatively low usage rate (0.83) of e-scooters. If the average usage rate drops below 0.5, the wasted energy could reach up to 53%. In the end, we present a field example to showcase how to optimally integrate public transport with e-scooters from the perspective of energy efficiency. We hope the findings of this study could help understand and resolve the current and future challenges regarding the ever-growing e-scooter services.
Importance
Since September 2017, standing electric scooters have proliferated rapidly as an inexpensive, easy mode of transportation. Although there are regulations for safe riding established by both electric scooter companies and local governments, public common use practices and the incidence and types of injuries associated with these standing electric scooters are unknown.
Objective
To characterize injuries associated with standing electric scooter use, the clinical outcomes of injured patients, and common use practices in the first US metropolitan area to experience adoption of this technology.
Design, Setting, and Participants
This study of a case series used retrospective cohort medical record review of all patients presenting with injuries associated with standing electric scooter use between September 1, 2017, and August 31, 2018, at 2 urban emergency departments associated with an academic medical center in Southern California. All electric scooter riders at selected public intersections in the community surrounding the 2 hospitals were also observed during a 7-hour observation period in September 2018.
Main Outcomes and Measures
Incidence and characteristics of injuries and observation of riders’ common use practices.
Results
Two hundred forty-nine patients (145 [58.2%] male; mean [SD] age, 33.7 [15.3] years) presented to the emergency department with injuries associated with standing electric scooter use during the study period. Two hundred twenty-eight (91.6%) were injured as riders and 21 (8.4%) as nonriders. Twenty-seven patients were younger than 18 years (10.8%). Ten riders (4.4%) were documented as having worn a helmet, and 12 patients (4.8%) had either a blood alcohol level greater than 0.05% or were perceived to be intoxicated by a physician. Frequent injuries included fractures (79 [31.7%]), head injury (100 [40.2%]), and contusions, sprains, and lacerations without fracture or head injury (69 [27.7%]). The majority of patients (234 [94.0%]) were discharged home from the emergency department; of the 15 admitted patients, 2 had severe injuries and were admitted to the intensive care unit. Among 193 observed electric scooter riders in the local community in September 2018, 182 (94.3%) were not wearing a helmet.
Conclusions and Relevance
Injuries associated with standing electric scooter use are a new phenomenon and vary in severity. In this study, helmet use was low and a significant subset of injuries occurred in patients younger than 18 years, the minimum age permitted by private scooter company regulations. These findings may inform public policy regarding standing electric scooter use.
This study examines the potential for public e-scooter sharing systems to fill mobility niches within and between Chicago neighborhoods. It explores how availability of this micro-mode of transportation could influence travel time, cost, and the convenience of trips relative to other active and shared-use modes including walking, bicycling, bikeshare, and public transit.
To draw conclusions, it uses the Chaddick Institute’s multimodal travel model to evaluate approximately 30,000 randomly selected hypothetical trips between locations on the North, South, and West sides of the city. Different assumptions about the quantity and distribution of shared dockless e-scooters are considered to assess the sensitivity of the results. The analysis shows that:
• On trips between 0.5 and 2 miles, e-scooters would be a particularly strong alternative to private automobiles. For example, in parking-constrained environments within the North case study area, the introduction of e-scooters would increase the number of trips of these distances in which non-auto options are time-competitive with driving from 47% to 75%. The cost of using an e-scooter, inclusive of tax, would likely be around $1.10 per trip plus $1.33 per mile, making them cost effective on short-distance trips. By filling a gap in mobility, e-scooters have the potential to increase the number of car-free households in Chicago.
• Due to their higher relative cost on trips over three miles, e-scooters would likely not result in significant diversion from transit on these longer-distance trips, particularly services operating to and from jobs in the transit-rich Loop business district. Often, the use of scooters on these longer journeys would likely be short journeys to nearby transit stops, in some cases as a substitute for walking or feeder bus services.
• The benefits of e-scooters can differ widely between geographic areas that are only a few blocks apart due to the differential access of these areas to transit lines and bus routes.
• E-scooters would make about 16% more jobs reachable within 30 minutes compared to those currently accessible by public transit and walking alone. The gains tend to be markedly different across the North, South, and West study areas.
By fostering insights into how e-scooters could influence travel time, cost and convenience, these results can help set the stage for an informed discussion on the many tradeoffs associated with this micro-mode of transportation.
Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.
Bicycle usage can be affected by colder weather, precipitation, and excessive heat. The research presented here analyzes the effect of weather on the use of the Washington, DC, bikeshare system, exploiting a dataset of all trips made on the system. Hourly weather data, including temperature, rainfall, snow, wind, fog, and humidity levels are linked to hourly usage data. Statistical models linking both number of users and duration of use are estimated. Further, we evaluate trips from bikeshare stations within one quarter mile of Metro (subway) stations at times when Metro is operating. This allows us to determine whether Metro serves as a back-up option when weather conditions are unfavorable for bicycling. Results show that cold temperatures, rain, and high humidity levels reduce both the likelihood of using bikeshare and the duration of trips. Trips taken from bikeshare stations proximate to Metro stations are affected more by rain than trips not proximate to Metro stations and less likely when it is dark. This information is useful for understanding bicycling behavior and also for those planning bikeshare systems in other cities.
Hamilton, Ontario’s bike share system was launched officially on March 22, 2015. This study analyzes the effects of weather conditions, temporal variables, hub attributes (most of which are derived for 200 m buffers around hubs), and a one-day lag on daily ridership at the bike share’s hubs during its first year of operation. Two random intercept multilevel models are estimated – one for daily trip departures, the other for daily trip arrivals. All weather (temperature, precipitation) and temporal variables (daylight hours, university terms, weekdays, holidays) are statistically significant in both models. Conversely, variables measuring transportation infrastructure in the vicinity of hubs, including the amount of bike lanes, are largely insignificant, suggesting that these features of the built environment have little to no influence on ridership. Proximity to important locations in the city (McMaster University, Hamilton’s downtown) has a strong impact on ridership. Although population density was an important consideration when locating the hubs, population does not influence daily departures or arrivals. Employment in the vicinity of hubs, which serves as a surrogate for an area’s activities or its attractiveness, does influence ridership, as is the case for the one-day lag effect. While all of these variables are able to explain some of the differences in daily ridership activity between hubs, the random intercept models confirm that they do not explain all of it. In other words, there remain intrinsic differences between hubs that are not captured by the independent variables – differences that influence ridership.
The paper develops a method that quantifies the effect of weather conditions on the prediction of bike station counts in the San Francisco Bay Area Bike Share System. The Random Forest technique was used to rank the predictors that were then used to develop a regression model using a guided forward step-wise regression approach. The Bayesian Information Criterion was used in the development and comparison of the various prediction models. We demonstrated that the proposed approach is promising to quantify the effect of various features on a large BSS and on each station in cases of large networks with big data. The results show that the time-of-the-day, temperature, and humidity level (which has not been studied before) are significant count predictors. It also shows that as weather variables are geographic location dependent and thus should be quantified before using them in modeling. Further, findings show that the number of available bikes at station i at time t-1 and time-of-the-day were the most significant variables in estimating the bike counts at station i.
Preface
Introduction
Transportation is integral to developed societies. It is responsible for personal mobility which includes access to services, goods, and leisure. It is also a key element in the delivery of consumer goods. Regional, state, national, and the world economy rely upon the efficient and safe functioning of transportation facilities.
In addition to the sweeping influence transportation has on economic and social aspects of modern society, transportation issues pose challenges to professionals across a wide range of disciplines including transportation engineers, urban and regional planners, economists, logisticians, systems and safety engineers, social scientists, law enforcement and security professionals, and consumer theorists. Where to place and expand transportation infrastructure, how to safely and efficiently operate and maintain infrastructure, and how to spend valuable resources to improve mobility, access to goods, services and healthcare, are among the decisions made routinely by transportation-related professionals.
Many transportation-related problems and challenges involve stochastic processes that are influenced by observed and unobserved factors in unknown ways. The stochastic nature of these problems is largely a result of the role that people play in transportation. Transportation-system users are routinely faced with decisions in contexts such as what transportation mode to use, which vehicle to purchase, whether or not to participate in a vanpool or telecommute, where to relocate a business, whether or not to support a proposed light-rail project and whether to utilize traveler information before or during a trip. These decisions involve various degrees of uncertainty. Transportation-system managers and governmental agencies face similar stochastic problems in determining how to measure and compare system measures of performance, where to invest in safety improvements, how to efficiently operate transportation systems and how to estimate transportation demand. As a result of the complexity, diversity, and stochastic nature of transportation problems, the methodological toolbox required of the transportation analyst must be broad.
Approach
The third edition of Statistical and Econometric Methods offers an expansion over the first and second editions in response to the recent methodological advancements in the fields of econometrics and statistics, to address reader and reviewer comments on the first and second editions, and to provide an increasing range of examples and corresponding data sets.
This book describes and illustrates some of the statistical and econometric tools commonly used in transportation data analysis. Every book must strike an appropriate balance between depth and breadth of theory and applications, given the intended audience. This book targets two general audiences. First, it can serve as a textbook for advanced undergraduate, Masters, and Ph.D. students in transportation-related disciplines including engineering, economics, urban and regional planning, and sociology. There is sufficient material to cover two 3-unit semester courses in statistical and econometric methods. Alternatively, a one semester course could consist of a subset of topics covered in this book. The publisher’s web-site contains the numerous datasets used to develop the examples in this book so that readers can use them to reinforce the modeling techniques discussed throughout the text. The book also serves as a technical reference for researchers and practitioners wishing to examine and understand a broad range of statistical and econometric tools required to study transportation problems. It provides a wide breadth of examples and case studies, covering applications in various aspects of transportation planning, engineering, safety, and economics. Sufficient analytical rigor is provided in each chapter so that fundamental concepts and principles are clear and numerous references are provided for those seeking additional technical details and applications.
Data-Driven Methods vs. Statistical and Econometric Methods
In the analysis of transportation data, four general methodological approaches have become widely applied: data-driven methods, traditional statistical methods, heterogeneity models, and causal inference models (the latter three of which fall into the category of statistical and econometric methods and are covered in this text). Each of these methods have an implicit trade-off between practical prediction accuracy and their ability to uncover underlying causality. Data-driven methods include a wide range of techniques including those relating to data mining, artificial intelligence, machine learning, neural networks, support vector machines, and others. Such methods have the potential to handle extremely large amounts of data and provide a high level of prediction accuracy. On the down side, such methods may not necessarily provide insights into underlying causality (truly understanding the effects of specific factors on accident likelihoods and their resulting injury probabilities). Traditional statistical methods provide reasonable predictive capability and some insight into causality, but they are eclipsed in both prediction and providing causal insights by other approaches Heterogeneity models extend traditional statistical and econometric methods to account for potential unobserved heterogeneity (unobserved factors that may be influencing the process of interest). Causal-inference models use statistical and econometric methods to focus on underlying causality, often sacrificing predictive capability to do so.
Even though data-driven methods are often a viable alternative to the analysis of transportation data if one is interested solely in prediction and not interested in uncovering causal effects, because the focus of this book is uncovering issues of causality using statistical and econometric methods, data-driven methods are not covered.
Chapter topics and organization
Part I of the book provides statistical fundamentals (Chapters 1 and 2). This portion of the book is useful for refreshing fundamentals and sufficiently preparing students for the following sections. This portion of the book is targeted for students who have taken a basic statistics course but have since forgotten many of the fundamentals and need a review.
Part II of the book presents continuous dependent variable models. The chapter on linear regression (Chapter 3) devotes additional pages to introduce common modeling practice—examining residuals, creating indicator variables, and building statistical models—and thus serves as a logical starting chapter for readers new to statistical modeling. The subsection on Tobit and censored regressions is new to the second edition. Chapter 4 discusses the impacts of failing to meet linear regression assumptions and presents corresponding solutions. Chapter 5 deals with simultaneous equation models and presents modeling methods appropriate when studying two or more interrelated dependent variables. Chapter 6 presents methods for analyzing panel data—data obtained from repeated observations on sampling units over time, such as household surveys conducted several times to a sample of households. When data are collected continuously over time, such as hourly, daily, weekly, or yearly, time series methods and models are often needed and are discussed in Chapters 7 and 8. New to the 2nd edition is explicit treatment of frequency domain time series analysis including Fourier and Wavelets analysis methods. Latent variable models, discussed in Chapter 9, are used when the dependent variable is not directly observable and is approximated with one or more surrogate variables. The final chapter in this section, Chapter 10, presents duration models, which are used to model time-until-event data as survival, hazard, and decay processes.
Part III in the book presents count and discrete dependent variable models. Count models (Chapter 11) arise when the data of interest are non-negative integers. Examples of such data include vehicles in a queue and the number of vehicle crashes per unit time. Zero inflation—a phenomenon observed frequently with count data—is discussed in detail and a new example and corresponding data set have been added in this 2nd edition. Logistic Regression is commonly used to model probabilities of binary outcomes, is presented in Chapter 12, and is unique to the 2nd edition. Discrete outcome models are extremely useful in many study applications, and are described in detail in Chapter 13. A unique feature of the book is that discrete outcome models are first considered statistically, and then later related to economic theories of consumer choice. Ordered probability models (a new chapter for the second edition) are presented in Chapter 14. Discrete-continuous models are presented in Chapter 15 and demonstrate that interrelated discrete and continuous data need to be modeled as a system rather than individually, such as the choice of which vehicle to drive and how far it will be driven.
Finally, Part IV of the book contains massively expanded chapter on random parameters models (Chapter 16), a new chapter on latent class models (Chapter 17), a new chapter on bivariate and multivariate dependent variable models (Chapter 18) and an expanded chapter on Bayesian statistical modeling (Chapter 19). Models that deal with unobserved heterogeneity (random parameters models and latent class models) have become the standard statistical approach in many transportation sub-disciplines and Chapters 16 and 17 provide an important introduction to these methods. Bivariate and multivariate dependent variable models are encountered in many transportation data analyses. Although the inter-relation among dependent variables has often been ignored in transportation research, the methodologies presented in Chapter 18 show how such inter-dependencies can be accurately modeled. The chapter on Bayesian statistical models (Chapter 19) arises as a result of the increasing prevalence of Bayesian inference and Markov Chain Monte Carlo Methods (an analytically convenient method for estimating complex Bayes’ models). This chapter presents the basic theory of Bayesian models, of Markov Chain Monte Carlo methods of sampling, and presents two separate examples of Bayes’ models.
The appendices are complementary to the remainder of the book. Appendix A presents fundamental concepts in statistics which support analytical methods discussed. Appendix B provides tables of probability distributions used in the book, while Appendix C describes typical uses of data transformations common to many statistical methods.
While the book covers a wide variety of analytical tools for improving the quality of research, it does not attempt to teach all elements of the research process. Specifically, the development and selection of research hypotheses, alternative experimental design methodologies, the virtues and drawbacks of experimental versus observational studies, and issues involved with the collection of data are not discussed. These issues are critical elements in the conduct of research, and can drastically impact the overall results and quality of the research endeavor. It is considered a prerequisite that readers of this book are educated and informed on these critical research elements in order to appropriately apply the analytical tools presented herein.
Simon P. Washnington
Mathew G. Karlaftis
Fred L. Mannering
Panigiotis Ch. Anastasopoulos
Road Weather Severity Based on Environmental Energy
Bird scooters flying around town
- M Hall
Integrating Crowdsourced Probe Vehicle Traffic Speeds into Winter Operations Performance Measures
- M Mcnamara
- R Sakhare
- H Li
- M Baldwin
- D Bullock
Integrating Crowdsourced Probe Vehicle Traffic Speeds into Winter Operations Performance Measures
- mcnamara