Influence of the built environment on E-scooter sharing ridership: A tale of five cities

Electric scooter (e-scooter) sharing systems (ESSs) have been widely adopted by many cities around the world and have attracted a growing number of users. Although some studies have explored the usage characteristics and effects of the built environment on ESS ridership using one city as an example, few studies have considered multiple cities to obtain generalizable and robust results. To fill this research gap, we collect the ESS trip data of five cities in the U.S., namely Austin, Minneapolis, Kansas City, Louisville, and Portland, and explore the effects of the built environment on ESS ridership after controlling for socioeconomic factors. The temporal distributions of e-scooter ridership of different cities are similar, having a single peak period on weekdays and weekends between 11:30 and 17:30. In terms of spatial distribution, the ESS ridership is higher in universities and urban centers compared to other areas. Multilevel negative binomial model results show that ESS trips are positively correlated with population density, employment density, intersection density, land use mixed entropy, and bus stop density in the census block group. E-scooter ridership is negatively correlated with the median age of the population in the census block group and distance to the city center. The findings in this article can help operators understand the factors that affect the ridership of shared e-scooters, determine the changes in ridership when the built environment changes, and identify high-ridership areas when ESS is implemented in new cities.     

Survey of E-scooter users in Vienna: Who they are and how they ride

In many cities around the world, electric (e-)scooters have emerged as a new means of transportation. They are often advertised as supporting modal shift towards more sustainable transportation and as a tool for enabling more equity in mobility. However, the environmental impact depends on how they are used and what kinds of trips they replace. Integration of e-scooters into urban transport systems also implicates discussions on fair road space allocation. In our study, we assess the socio-economic profiles and usage patterns of e-scooter users in Vienna, Austria. We differentiate between two basic groups of e-scooter users (renters and owners) and apply two different methods. Firstly, based on an online survey, we examine the age, gender and education of e-scooter users and we look into which kinds of trips (commuting, shopping or leisure) and which other means of transportation are replaced by e-scooter trips. Secondly, we analyse data from field observations at cycle paths in Vienna in order to determine the share of e-scooter riders and their gender distribution. We find that e-scooter users are more likely to be young, male, highly educated and residents of Vienna. According to the survey, there are considerable differences in usage between owners of private scooters and users of sharing schemes. Whereas in both groups, e-scooter trips mostly replace walking and public transport as a mode, e-scooter owners also show a considerable mode-shift from private car trips. These results implicate that e-scooter riders are additional users of cycling infrastructure. This puts further pressure on the current allocation of road space, which provides little space for active modes of transport. We conclude that city policies should address this competitive relationship adequately by allocating more space to safe and convenient cycling infrastructure and traffic-calmed zones. This could not only help ease the current challenges due to e-scooters but also provide better conditions for walking and cycling and thereby at the same time contribute to a more sustainable and equitable urban transport system.     

Notes on the practices and appearances of e-scooter users in public space

While the legalisation of and policies around e-scooters remain the cause of much debate worldwide, this article sheds lights on e-scooter users’ current practices and their interactions with pedestrians in the city. Taking an ethnomethodological approach to public space and mobility, we use video recordings of e-scooter riders to show, firstly, how riders dismount and then move to acquire rights to continue moving, thereby ‘playing’ with traffic rules, in order to weave rapidly through congested urban environments. Secondly, we examine how e-scooter riders and pedestrians deal with the potentially unexpected appearance of e-scooters via displays of attention, adjustments of speed, and the relative rights and obligations established via category-relevant spaces. The findings offer insights into the integration of e-scooters as one of what may be many new forms of electric powered micro-mobility in urban space.     

Spatial variation in shared ride-hail trip demand and factors contributing to sharing: Lessons from Chicago

As ride-hailing becomes more common in cities, public agencies increasingly seek transportation network company (TNC) service data to understand (and potentially regulate) demand and service response. Despite the increase in ride-hailing or TNC demand and subsequent research into its determinants, there remains little research on shared TNC trips and the spatial distribution of trip demand across demographic and land use variables. Using Chicago as a case study, shared TNC trip data from 2019 was used to estimate the count and ratio of shared ride services, based on built environment, demographic, location, time of day, and trip details. Findings reveal that trip length, day of week designation, density of pedestrian and multi-modal infrastructure, and underlying socioeconomic characteristics of the origin zones influence the proportion and count of shared ride-hail trips. Of concern is that those using transit or active modes may be taking more ride-hailing trips, but these Chicago-region results indicate that the provision of pedestrian infrastructure and remoteness to transit stops result in fewer shared trips.     

An empirical analysis of dockless bike-sharing utilization and its explanatory factors: Case study from Shanghai,China

Revealing dockless bike-sharing utilization pattern and its explanatory factors are essential for urban planners and operators to improve the utilization and turnover of public bikes. This study explores the dockless bike-sharing utilization pattern from the perspective of bike using GPS-based bike origin-destination data collected in Shanghai, China. In this paper, utilization patterns are captured by decoupling several spatially cohesive regions with intensive bike use via non-negative matrix factorization. We then measure the utilization efficiency of bikes within each sub-region by calculating Time to booking (ToB) for each bike and explore how the built environment and social-demographic characteristics influence the bike-sharing utilization with ordinary least squares (OLS) regression and geographically weighted regression (GWR) models. The matrix factorization results indicate that the shared bikes mainly serve a certain area instead of the whole city. In addition, the GWR model shows higher explanatory power (Adjusted R² = 0.774) than the OLS regression model (Adjusted R² = 0.520), which suggests a close relationship between bike-sharing utilization and the selected explanatory variables. The coefficients of the GWR model reveal the spatial variations of the linkage between bike-sharing utilization and its explanatory factors across the study area. This study can shed light on understanding the demand and supply of shared bikes for rebalancing and provide support for operators to improve the dockless bike-sharing utilization efficiency.     

Neighbourhood effects on station-level transit use: Evidence from the Taipei metro

While large high-density metropolitan areas with extensive transit networks experience greater use of rail transit than elsewhere, less is known about the neighbourhood effects that affect station use. This study applies the 5D model to analyse neighbourhood effects within 600 m of transit stations in the Taipei metropolitan area. The area is separated into three concentric zones, with separate functions for each zone. While population density, destination attractiveness, and distance to intermodal connections are important in all three zones, design features depend on their location vis-à-vis the centre. Intersection density is important in the downtown core, while bike share facilities affect station use in the intermediate ring. A geographically weighted regression (GWR) reveal that most 5D variables exhibit spatial serial dependence. The key GWR result is that population density has the greatest effect on station use in peripheral residential neighbourhoods.     

Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi

A Mixed Geographically Weighted Regression (GWR) model is applied to explore the effects of shared mobility trips on taxi and public transit ridership at the macro-level. Several essential variables, including socioeconomic, transportation, network, and land use data, are set as the causal factors. The experiment is conducted using the smart card data, vehicle GPS trajectories, and vehicle order data collected in Shenzhen City, China. We show that the Mixed GWR outperforms the basic GWR in model fitting and capturing the unobserved heterogeneity. The spatial analysis reveals that bike-sharing addresses the “last-mile” and “first-mile” problems to bus and metro in the urban periphery. It substitutes the bus and taxis in short-distance journeys in the city center. However, the over-placement of bike-sharing in some regions limits the flexibility of bike-sharing connections to the metro. In the city center, ride-hailing fills the gaps in bus coverage and competes with the metro. In the peripheral areas, ride-hailing replaces buses and improves the accessibility to metro stations. The transportation policy increases the cooperation between ride-hailing and taxis citywide, although competitions in few regions need to be solved. The abovementioned results provide policy suggestions to optimize the allocation of local transportation resources.     

Spatial variations in active mode trip volume at intersections: a local analysis utilizing geographically weighted regression

Geographically weighted regression (GWR) models have been employed in previous studies regarding vehicular travel demands, but few studies have locally modeled walking travel demands at intersections to address the issue of spatially varying relationships. Harnessing a comprehensive collection of walking and bicycling traffic counts over 10 years in Chittenden County, Vermont, US, along with socioeconomic characteristics, transit accessibility indices, land use attributes and characteristics of intersections and roadway networks, this study utilizes GWR models to identify whether there are spatially varying relationships between active mode travel demands and ambient built-environment attributes. One Ordinary Least Square (OLS) model and two GWR models were parametrically calibrated: a full GWR model of all local variables and a mixed GWR model of both global and local variables. K-fold cross-validation method is used to select variables that significantly influence the volume of active travel modes in the OLS model. The uniform set of variables is investigated in two GWR models. Only residuals of the mixed GWR model exhibit spatial independence. The prediction accuracy of the three models is respectively compared by means of the k-fold cross-validation method. Results show that the mixed GWR model has higher prediction accuracy, while the other two models have roughly the same level of performance. We find that not all independent variables possess a spatially varying relationship with active mode volumes. The flexibility of the mixed GWR model that allows some independent variables to be global strengthens its prediction power. With these findings, transportation planners can dynamically estimate bicycle and pedestrian volumes at widespread intersections, and this geographical realism would facilitate local transportation planning, facility design, safety enhancement and operation analysis, as well as instilling new insights into interdisciplinary spatial research domain.     

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou,China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use.     

Uncovering the spatiotemporal patterns of CO2 emissions by taxis based on Individuals' daily travel

Traffic-related carbon dioxide (CO₂) emissions have become a major problem in cities. Especially, the CO₂ emissions induced by taxis account for a high proportion in total CO₂ emissions. The availability of taxi trajectory data presents new opportunities for addressing CO₂ emissions induced by taxis. Few previous studies have analyzed the impact of human trips on CO₂ emissions. This paper investigates trip-related CO₂ emission patterns based on individuals' travel behavior using taxi trajectory data. First, we propose a trip purpose inference method that takes into account the spatiotemporal attractiveness of POIs to divide human trips into different types. Further, we reveal the spatiotemporal patterns of CO₂ emissions from various types of trips, including temporal regularity and periodicity as well as spatial distribution of “black areas”. Finally, comparative analysis of CO₂ emissions for different kinds of trips based on trip behavior is conducted using three variables, namely trip distance, trip duration and trip speed. This study is helpful for us to understand how to make travel and cities more sustainable through modifying people's trip behaviors or taxi trips.     

Exploring the spatial variations of transfer distances between dockless bike-sharing systems and metros

Dockless bike-sharing is emerging as a convenient transfer mode for metros. The riding distances of bike-sharing to or from metro stations are defined as transfer distances between dockless bike-sharing systems and metros, which determine the service coverages of metro stations. However, the transfer distances have rarely been studied and they may vary from station to station. Therefore, this study aims to explore the influencing factors and spatial variations of transfer distances between dockless bike-sharing systems and metros. First, a catchment method was proposed to identify bike-sharing transfer trips. Then, the Mobike trip data, metro smartcard data, and built environment data in Shanghai were utilized to calculate the transfer distances and travel-related and built environment variables. Next, a multicollinearity test, stepwise regression, and spatial autocorrelation test were conducted to select the best explanatory variables. Finally, a geographically weighted regression model was adopted to examine the spatially varying relationships between the 85th percentile transfer distances and selected explanatory variables at different metro stations. The results show that the transfer distances are correlated with the daily metro ridership, daily bike-sharing ridership, population density, parking lot density, footway density, percentage of tourism attraction, distance from CBD, and bus stop density around metro stations. Besides, the effects of the explanatory variables on transfer distances vary across space. Generally, most variables have greater effects on transfer distances in the city suburbs. This study can help governments and operators expand the service coverage of metro stations and facilitate the integration of dockless bike-sharing and metros.     

The early days of shared micromobility: A social practices approach

Urban mobility may be entering a period of substantive changes as new transport technologies (facilitated by developments in electrification, automation, and web 2.0 technologies for distributed, real-time transactions) provide new possibilities for movement. The recent arrival of shared electric scooters in some cities has been the topic of much conversation, particularly in relation to appropriate spaces for e-scooter use, and the safety of e-scooter users and pedestrians. This paper, however, takes a wider look at the early days of shared micromobility in New Zealand cities. Mobility is intricately connected to the wider social and cultural configurations of daily life, including its power relations, equalities and inequalities, and the spatial relations between people, places and opportunities. This paper draws on an online survey completed by residents of four New Zealand cities in which shared electric scooters became available in late 2018 or early 2019. Using a social practices approach, it explores early changes in the materials, competencies, and meanings associated with urban mobility as a response to the electric scooter trial. It discusses the disruptive potential of these changes, both for urban transport and for wider social relations.     

Carpool to work: Determinants at the county-level in the United States

Using US county-level data for 2017, this paper adopts a data-driven approach to study the main factors influencing carpooling for home-to-work trips. The potential explanatory variables quantify demographic, situational and judgmental characteristics of the counties. Since the proportion of workers carpooling for their commute is not (spatially) randomly distributed at the county level, the inclusion of spatial effects improves considerably the model's fit. The Spatial Autoregressive models show that eight variables (four related to demographics, three to situational and one to judgmental) do the best job of explaining the rates of carpooling. A Spatial Quantile Autoregression is further applied as a flexible and interpretable method to address the fact that some of the leading variables have varying effects on counties with different levels of carpooling. For instance, our results suggest that the agglomeration effect, measured by an increase in population density, has a gradual change in trend pattern because it encourages ridesharing among counties with low levels of carpooling, whereas it deters shared trips in high intensity carpooling areas. Alternatively, a decrease in car ownership, a variable strongly associated to counties' income, will lead to the largest increase in employees using carpooling for their home-to-work travels, and this relationship do not vary across quantiles.     

Toward a spatial understanding of active transportation potential among a university population

Universities and surrounding communities stand to benefit when active travel mode choices are elevated. Despite this, there is little research on travel mode choice at commuter universities and, in particular, the nonlinear spatial relationships among active travel potential and various contextual and compositional factors. The purpose of this study was to examine and visualize linkages among personal, household, density, diversity, and design factors, and active travel (bicycling, walking, and mass-transit modes) among a commuter-university population residing throughout southeastern Michigan, USA. This was accomplished by employing exploratory spatial data analysis (ESDA), ordinary least squares (OLS) regression, and a geographically weighted regression (GWR) model. The GWR model outperformed the traditional OLS model in terms of goodness of fit (R² = .534 and R² = .461, respectively). A novel cartographic mapping technique was employed to depict where statistically significant parameter estimates negatively or positively influenced active travel. The main finding was that personal, household, density, diversity, and design estimates varied in both magnitude and spatiality throughout the university's study area. Interestingly, distance was not a universal barrier to active travel potential. These variations emphasize the importance of promoting active transportation through localized interventions as well as coordinating efforts among universities and surrounding communities.     

Docked vs. dockless equity: Comparing three micromobility service geographies

Docked bike-share programs have proliferated worldwide, but studies find that the distribution of docked stations is geographically unequal. New dockless systems offer more flexibility compared to docked systems, but it remains unclear if dockless systems can address existing geographic inequities. This study examines all 32 US cities with both docked and dockless micromobility (bikeshare and e-scooter) programs and develops three service geography indicators to compare the geographic equity of docked versus dockless systems. We first use Lorenz curves and Gini indices to examine the overall spatial distribution of micromobility; we then use logistic and Tobit regressions to investigate how service geography corresponds to neighborhood characteristics. Results show that the distribution of docked systems is extremely unequal, and that dockless systems greatly reduce geographical inequalities relative to docked. Low-density areas and neighborhoods with low median household incomes, smaller shares of young people, and fewer zero-car households have limited micromobility service. Docked services are less prevalent in communities of color, and the implementation of dockless systems yields mixed outcomes for racial equity. Importantly, designated service areas do not always translate into available micromobility vehicles. Policymakers should use program design and performance metrics to address the mismatch between designated and actual service geographies and to ensure that micromobility services benefit marginalized communities.     

Do rural older adults take fewer trips than their urban counterparts for lack of a ride?

Aging and the presence of one or more illnesses result in limited travel for many adults age 65 and over. Yet, the need to get to essential, social, and non-emergency medical destinations endures. At some point in their life, older adults become dependent on family/friends, or rely on for-profit/not-for-profit transportation services for their mobility needs, while some do not go on certain trips. Researchers have studied out-of-home activity and mobility of older adults using data on trips taken. There is a gap, however, in understanding trips not taken in the older adult population in rural versus urban locations. Our objectives in this paper are: (i) to investigate unmet travel needs of older adults by relying on responses for trips not taken; (ii) to examine how personal abilities, living situation, and socio-demographic factors are associated with trips not taken to various destinations; and (iii) to compare the likelihood of trips not taken due to lack of a ride in urban versus rural locations across the age and income spectrum. Our data come from a phone survey conducted across the province of Alberta, Canada, in 2017–18 (n = 1390). We specify ordinal logistic models where the dependent variable is how often a respondent did not undertake a trip due to not having a ride to various trip destinations. We find that rural seniors are more likely to not take trips compared to older adults in cities, holding all else equal including driving cessation, worsening health, and disability. Rural seniors who live alone or in low-density housing are also more likely to not take trips compared to urban older adults. Household income, however, tempers these location preferences. Our findings suggest that rural older adults can be supported through income transfers, community-based low-cost travel, and moving to higher-density residential locations.     

Commuting patterns in the metropolitan region of Rio de Janeiro. What differences between formal and informal jobs?

Limiting commuting trips in major cities is important from the environmental, social and economic standpoints. In order to design policies that aim to change commuting practices it is, however, necessary to have acquired a good understanding of the trips in question and their determinants. However, these trips have been subjected to very little study in the cities of developing countries. This paper is concerned with the Rio de Janeiro Metropolitan Area (RJMA), and sets out to test the influence of “classical” socioeconomic and spatial variables on the distance and duration of the commuting trips of the region's inhabitants, especially those with the lowest incomes. The main original feature of this research is that it includes jobs in the informal sector. The results show that, all other things being equal, commuting distances and times are shorter for the informal sector, and people walk more from their homes to their place of work because jobs in the informal sector are more dispersed than jobs in the formal sectors. The notable exception is personal and household services for which employees (who are mainly women) live a long way from the city center where wealthy families (and their jobs) are concentrated.     

A spatial analysis of demand patterns on a bicycle sharing scheme: Evidence from London

This paper investigates the spatial demand for bikesharing through the application of a series of trip generation models for the London Bicycle Sharing Scheme (LBSS). The production of trips from and the arrival of trips at scheme stations are evaluated in reference to how they connect with features of the built environment, demographics of the resident and workplace populations, and attributes of the scheme's structure. A spatial econometrics approach is taken to specify the models, with four different time windows considered throughout the day for all trips taken during 2016. The built environment features show a consistent pattern of results in the model, indicating that proximity to cycling infrastructure, rail stations, parks, university facilities, as well as the density of shops and conventional roads in the vicinity of stations is linked with trip generation rates. The presence of males and Caucasians are associated with higher station demand, aligning with other work on the introduction of new mobility solutions elsewhere, though we do find that greater distances to work tend to depress use. Trip generation is also reduced at the minority of stations located south of the River Thames, indicating that the presence of natural barriers can affect the operation of schemes. The results carry implications for scheme integration in other cities.     

Determinants and spatial variability of after-school travel by teenagers

This research work targeted teenagers to investigate after-school travel patterns and factors affecting their modal choice characteristics in Okinawa, Japan, where teenagers are not allowed to drive. The analysis is based on the cross-sectional data collected for discrete choice modeling of students’ travel in the prefecture. In this study, high school students’ trips from school to home have been analysed using a multinomial logit model. This has revealed the impact of individual, modal, and spatial variables on the mode choice decisions and return trip patterns. A comparative analysis made of downtown and suburb schools exposed differences in travel patterns induced by spatial factors and some existing constraints in modal choice options.     

Car ownership and urban development in Chinese cities: A panel data analysis

Growth in car ownership has significant impacts on the use of urban space and management of urban environments, which makes it a topic of increasing interest especially for developing countries such as China. The dynamics of and factors influencing ownership in Chinese cities need careful investigation. Using fixed effects models applied to annual panel data (1994–2012; 293 cities) this study aimed to achieve the following: 1) assess the relationships between car ownership and average annual income per capita, population, built-up area, road area per capita, urban population density, number of taxis and bus passenger volume; 2) examine the variation of these relationships across geographical regions (East, middle, and West China) and city sizes (cities with small, medium, large, and super-large populations). The results showed that car ownership was positively associated with average annual income per capita, built-up area, road area per capita, urban population density, and number of taxis at the national level. All associations, except with the number of taxis, varied significantly across geographical regions. Built-up area, road area per capita, and number of taxis had different associations with car ownership depending on city sizes. The findings improve the understanding of relationships between car ownership and urban environments vis-a-vis variations in income and infrastructure per capita, population density, and transportation alternatives. These results have important policy implications for managing cars and health problems related to cars in China.