Environmental impacts of shared mobility: a systematic literature review of life-cycle assessments focusing on car sharing, carpooling, bikesharing, scooters and moped sharing.

Evidence about the environmental impacts of shared mobility is fragmented and scattered. In this article a systematic literature review is presented. The review focuses on assessments that use Life-Cycle Assessment to quantify the environmental impacts of car sharing, carpooling, bikesharing, and scooter/moped sharing. The results of these assessments were analyzed, as well as the factors that influence these impacts. Business-to-consumer car sharing, peer-to-peer car sharing, carpooling, bikesharing, and scooter/moped sharing can all cause gains and losses in terms of changing the environmental impacts of passenger transportation. The findings presented here refute unconditional claims that shared mobility delivers environmental benefits. Factors that influence changes in environmental impacts from passenger transportation from shared mobility include travel behaviour, the design of shared mobility modes, and how such schemes are implemented, as well as the local context. Local governments and shared mobility organisations can benefit from the analysis presented here by deepening their understanding of these factors and considering the life-cycle phase where the greatest impacts are caused.

The shareability potential of ride-pooling under alternative spatial demand patterns.

In In this study, we set out to explore how various spatial patterns of travel demand drive the effectiveness of ride-pooling services. To do so, we generate a broad range of synthetic, yet plausible demand patterns. We experiment with the number of attraction centres, the dispersion of destinations around these centres, and the trip length distribution. We apply a strategic ride-pooling algorithm across the generated demand patterns to identify shareability potential using a series of metrics related to ridepooling. Our findings indicate that, under a fixed demand level, vehicle-hour reduction due to ride-pooling can range between 18 and 59%. These results depend on the concentration of travel destinations around the centre and the trip length distribution. Ride-pooling becomes more efficient when trips are longer and destinations are more concentrated. A shift from a monocentric to a polycentric demand pattern is found to have a limited impact on the prospects of ride-pooling.

Impacts of COVID-19 on Bike-Share Usage: The case of Daejeon, Korea.

After COVID-19 began spreading through fecal-oral routes, crowded cities introduced social distancing policies. Mobility patterns in urban also changed because of the pandemic and the policies to reduce the infection of it. This study investigates the impact of COVID-19 and related policies such as social-distancing by comparing bike-share demand in Daejeon, Korea. By using big data analytics and data visualization, the study measures differences in bike-sharing demand between 2018-19, before the pandemic, and 2020-21, during the pandemic. According to results, (1) bike-share users tend to travel long distances and cycle more than before the pandemic, (2) bike users choose cycling not for commuting but for transportation during the pandemic, and (3) the pandemic has broadened the spatial borders bike-usages. These results provide meaningful implications for urban planners and policymakers by identifying differences in the ways people use public bikes during the pandemic era.

Equity Implications of Ride-Hail Travel during COVID-19 in California.

COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and-for those who cannot work from home-to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode-ride-hailing-fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.

Mobility Dynamics amid COVID-19 with a Case Study in Tennessee.

The year 2020 has marked the spread of a global pandemic, COVID-19, challenging many aspects of our daily lives. Different organizations have been involved in controlling this outbreak. The social distancing intervention is deemed to be the most effective policy in reducing face-to-face contact and slowing down the rate of infections. Stay-at-home and shelter-in-place orders have been implemented in different states and cities, affecting daily traffic patterns. Social distancing interventions and fear of the disease resulted in a traffic decline in cities and counties. However, after stay-at-home orders ended and some public places reopened, traffic gradually started to revert to pre-pandemic levels. It can be shown that counties have diverse patterns in the decline and recovery phases. This study analyzes county-level mobility change after the pandemic, explores the contributing factors, and identifies possible spatial heterogeneity. To this end, 95 counties in Tennessee have been selected as the study area to perform geographically weighted regressions (GWR) models. The results show that density on non-freeway roads, median household income, percent of unemployment, population density, percent of people over age 65, percent of people under age 18, percent of work from home, and mean time to work are significantly correlated with vehicle miles traveled change magnitude in both decline and recovery phases. Also, the GWR estimation captures the spatial heterogeneity and local variation in coefficients among counties. Finally, the results imply that the recovery phase could be estimated depending on the identified spatial attributes. The proposed model can help agencies and researchers estimate and manage decline and recovery based on spatial factors in similar events in the future.

Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data.

The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alabamians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local transportation plans that consider the impacts of the pandemic on future travel intentions.

Exploring the role of ride-hailing in trip chains.

Ride-hailing can potentially provide a variety of benefits to individuals who need to chain several activities together within a single trip chain, relative to other travel modes. Using household travel diary/survey data, the goal of this study is to assess the role ride-hailing currently plays within trip chains. Specifically, the study aims to determine, within trip chains, who uses ride-hailing services, for what trip/activity purposes, and to/from what types of areas, as well as the characteristics of trip chains that involve ride-hailing segments. To meet these objectives, the study estimates a binary logit model using 2017 National Household Travel Survey data, where the dependent variable denotes the inclusion of at least one ride-hailing trip within a trip chain. Similar to the non-trip-chaining ride-hailing literature, this study indicates that trip chains with ride-hailing legs are positively associated with travelers who are younger, live in high-income households, frequently use transit, and reside in high-density areas. However, this study includes novel findings indicating statistically significant relationships between ride-hailing and trip chains that end in healthcare and social/recreational activities. Moreover, trip chains with ride-hailing tend to have fewer stops and longer activity durations than trip chains without ride-hailing. This study also includes nested logit choice models, wherein the dependent variable denotes the primary mode (ride-hailing, transit, personal vehicle, or non-motorized transport) of a trip chain. These model results provide additional insights into the role of ride-hailing within trip chains, as they allow for cross-mode comparisons. The paper discusses the potential transportation planning and policy implications of the model results as well as future research directions.

A latent class analysis to understand riders' adoption of on-demand mobility services as a complement to transit.

On-demand app-based shared mobility services have created new opportunities for complementing traditional fixed-route transit through transit agencies' efforts to incorporate them into their service provision. This paper presents one of the first studies that rigorously examine riders' responses to a pilot aimed at providing such a transit-supplementing service. The study conducts latent class analysis on riders of the Via to Transit program, a mobility pilot in the Seattle region where on-demand service was offered to connect transit riders to light rail stations. The analysis identifies three distinct rider groups with heterogenous responses to the on-demand service: (1) riders who previously used private cars or ride-hailing; (2) riders who were pedestrians and bikers but switched likely because of safety concern; (3) mostly socio-economically disadvantaged riders who previously relied on the bus, but switched to the new service for the convenience and speed. These results point to rich transportation policy implications, which can inform decision-making by public transit agencies as they are exploring alternative ways to deliver the mobility services. Supplementary Information: The online version contains supplementary material available at 10.1007/s11116-022-10351-3.

Mode Split Equilibrium Microsimulation Approach for Early-Stage On-Demand Shared Automated Mobility.

The initial hype around Automated Vehicle (AV) technologies has subsided, and it is now being realized that near-term deployment of AV technologies will be in the form of low-speed shared automated shuttles in geofenced districts with a high density of trip demand. A concept labeled 'Automated Mobility Districts' (AMD) has been coined to define such deployments. A modeling and simulation toolkit that can act as a decision support tool for early-stage AMD deployments is desired for answering the questions such as (i) for a series of given conditions, such as the amount of travel demand and automated shuttle fleet configuration, what is the expected mode split for shared automated vehicle (SAV) services? (ii) for that mode share of SAVs, what level-of-service and network performance can be anticipated? To answer these research questions, an innovative and integrated framework of multi-mode choice and microscopic traffic simulation model is presented to obtain the equilibrium of mode split for various modes in AMDs, based on real-time traffic simulation data. The proposed framework was tested using travel demand and road network data from Greenville, South Carolina, considering a car, walk, and two SAV on-demand ridesharing modes in a proposed AMD. Results from the study demonstrated the efficacy of the proposed framework for solving the mode split equilibrium in an AMD. In addition, sensitivity analyses were conducted to understand the impact of factors such as waiting times and fleet resources on mode share equilibrium for SAVs.

The Impact of COVID-19 on Travel Mode Choice Behavior in Terms of Shared Mobility: A Case Study in Beijing, China.

Shared mobility is growing rapidly and changing the mobility landscape. The COVID-19 pandemic has complicated travel mode choice behavior in terms of shared mobility, but the evidence on this impact is limited. To fill this gap, this paper first designs a stated preference survey to collect mode choice data before and during the pandemic. Different shared mobility services are considered, including ride hailing, ride sharing, car sharing, and bike sharing. Then, latent class analysis is used to divide the population in terms of their attitudes toward shared mobility. Nested logit models are applied to compare travel mode choice behavior during the two periods. The results suggest that shared mobility has the potential to avoid the high transmission risk of public transport and alleviate the intensity of private car use in the COVID-19 context, but this is limited by anxiety about shared spaces. As the perceived severity of the pandemic increases, preference for ride hailing and ride sharing decreases, and a price discount for ride hailing is more effective than that for ride sharing at maintaining the ridership despite the impact of COVID-19. These findings contribute to understanding the change in travel demand and developing appropriate strategies for shared mobility services to adapt to the pandemic.

How Does Ride-Hailing Influence Individual Mode Choice? An Examination Using Longitudinal Trip Data from the Seattle Region.

This study investigates the impacts of ride-hailing, which we define as mobility services consisting of both conventional taxis and app-based services offered by transportation network companies, on individual mode choice. We examine whether ride-hailing substitutes for or complements travel by driving, public transit, or walking and biking. The study overcomes some of the limitations of convenience samples or cross-sectional surveys used in past research by employing a longitudinal dataset of individual travel behavior and socio-demographic information. The data include three waves of travel log data collected between 2012 to 2018 in transit-rich areas of the Seattle region. We conducted individual-level panel data modeling, estimating independently pooled models and fixed-effect models of average daily trip count and duration for each mode, while controlling for various factors that affect travel behavior. The results provide evidence of substitution effects of ride-hailing on driving. We found that cross-sectionally, participants who used more ride-hailing tended to drive less, and that longitudinally, an increase in ride-hailing usage was associated with fewer driving trips. No significant associations were found between ride-hailing and public transit usage or walking and biking. Based on detailed travel data of a large population in a major US metropolitan area, the study highlights the value of collecting and analyzing longitudinal data to understand the impacts of new mobility services.

A Multi-Scale Entropy Approach to Study Collapse and Anomalous Diffusion in Shared Mobility Systems.

In this paper, we study the phenomena of collapse and anomalous diffusion in shared mobility systems. In particular, we focus on a fleet of vehicles moving through a stations network and analyse the effect of self-journeys in system stability, using a mathematical simplex under stochastic flows. With a birth-death process approach, we find analytical upper bounds for random walk and we monitor how the system collapses by super diffusing under different randomization conditions. Using the multi-scale entropy metric, we show that real data from a bike-sharing fleet in the city of Salamanca (Spain) present a complex behaviour with more of a 1/f signal than a disorganized system with a white noise signal.

Secondary task engagement, risk-taking, and safety-related equipment use in German bicycle and e-scooter riders - An observation.

The behavioral safety of e-bike and e-scooter riders is a significant concern in traffic safety. In an observational study in Braunschweig, Germany, 4,514 bicycle and e-scooter riders were observed concerning their used vehicles type, secondary task engagement, use of additional safety equipment, and traffic rule violation. Overall, 13.4% of all riders were engaged in any secondary task, wearing headphones or earphones being the most frequent behavior (6.7%), followed by conversations with other cyclists (3.7%). Banned mobile phone use was low (0.8%). Secondary task engagement was positively correlated with traffic rule violations and at-fault conflicts and negatively with the use of additional safety equipment. Cluster analysis on vehicle types and behaviors revealed five groups of riders, two with relatively high numbers of risky behaviors: young and middle-aged, predominantly male riders of conventional bicycles, and a group of demographically similar users of electric bikes and e-scooters. Campaigns targeted at these specific groups may help reduce risky behaviors.

To Use or Not Use Car Sharing Mobility in the Ongoing COVID-19 Pandemic? Identifying Sharing Mobility Behaviour in Times of Crisis.

Car sharing services have expanded in order to meet the new necessities of mobility worldwide in an innovative way. Before the COVID-19 pandemic, car sharing was a very popular mode of transportation among young adults in big cities. However, during this ongoing pandemic and with public transportation considered a super-spreading transmitter, the usage of car sharing is unclear. Therefore, the aim of this study, which is explorative in nature, is to investigate the usage, advantages, drivers, and barriers to car sharing during this ongoing pandemic era. To this end, 66 interviews were conducted among users of car sharing during the COVID-19 pandemic. The findings provide key information for the planning of car sharing operations and public transportation in the context of avoiding COVID-19 infection and respecting the recommendations of local governments. In addition, new emerging profiles of car sharing users in the ongoing pandemic are identified. This research provides relevant insights for both business practice and policy makers.

Case study on the use and adaptation of SEAT MÓ motorbike sharing service in Barcelona in COVID-19 pandemic year.

Worldwide, cities are combating the negative impacts of extensive private motorised vehicle travels and are thus striving to accelerate a shift towards the predominant use of sustainable mobility options. Thereby, shared mobility services are emerging transportation modes, which opt as a complementary mean of transport in urban cities. Amid the current advancements of shared mobility services, the SARS-CoV-2 pandemic accelerated an immense shift in consumer demand and preferences, consequently posing new challenges. In this instance, the testing and validation of implementable hygienic measures has become a key factor for service providers to continuously ensure an increase in clientele. Hence, this research aims to identify the service factors required to accelerate the use of shared micromobility, thereby setting strong focus on hygienic measures. The aforementioned is fulfilled by the means of a quantitative study. Results have indicated an interest in hygiene; however, respondents' main requirements lie in factors that are not particular to the service provider.

Impact of Covid-19 on willingness to share trips.

This study relied on primary data from transportation users to investigate the impact of the Covid-19 pandemic on shared mobility types. The study used ordinal logistic regression models to explore the relationship between Covid-19 spread-mitigative protocols and the willingness to share trips with family, friends, and strangers. Travellers who were moderately affected by social distancing had [0.356 (95% confidence interval (CI) = 0.189 - 0.669; p = 0.0001)] times the rate willing to share public vehicles and [0.492 (95% CI = 0.268 - 0.900; p = 0.05)] times the rate willing to share private cars than commuters who mostly affected. Commuters with a minor extent of means of transportation change at (α < 0.0001) significance level had 0.330 times the rate willing to share personal cars with family and friends. At the same time, 0.434 times the rate (α < 0.01) willing to share public vehicles with strangers compared to commuters who had a major extent of means of transportation change. The prevalence rates of change were higher during Covid-19 than precovid, showing that the pandemic set an impetus for a modal shift from public to private vehicular use, with a probable effect on willingness to share trips postcovid, ceteris paribus. Consequently, the study concludes that the transportation regulators could continue to sensitise travellers, regulate passenger spacings, monitor and enforce gears to make shared mobility more appealing to people during and postcovid periods.

Changes in private car ownership associated with car sharing: gauging differences by residential location and car share typology.

While a large body of literature shows that car share encourages low car ownership, the evidence is rather limited in the context of different types of car share (fleet-based versus peer-to-peer) and geographic settings (inner versus middle suburbs). This study was an in-depth investigation of the impact of (round-trip) car share on ownership, including forgone or delayed purchasing across different car share systems. An online survey was conducted with car share members (n = 651) and non-members (n = 290) in Melbourne, Australia. All respondents had a shared car available within a 10-min walk of their home. The first part of the paper compared member and non-member householders (socio-demographically and geographically adjusted) and found that members owned significantly fewer cars than non-members. In the second part of the paper, a quasi-longitudinal comparison of car share members was conducted. One in three households reduced car ownership, and most reductions occurred in the year prior to joining car share. Fleet-based car share members reported a larger reduction in car ownership compared to peer-to-peer car share members. Residents of inner and middle suburbs of Melbourne reported similar "net" reductions in car ownership, the reasons differed. Residents in densely populated inner suburbs used car share to avoid or delay car ownership while middle suburb residents used car share to avoid purchasing a second car. Findings provide valuable insights for transport policy settings which have the potential to influence car share availability and thereby support broader policy objectives to reduce dependency on private car ownership and use.

In search of sustainable and inclusive mobility solutions for rural areas.

Background: Despite emerging research on novel mobility solutions in urban areas, there have been few attempts to explore the relevance and sustainability of these solutions in rural contexts. Furthermore, existing research addressing rural mobility solutions typically focuses on a specific user group, such as local residents, second-home owners, or tourists. In this paper, we study the social inclusivity, economic viability, and environmental impacts of novel mobility solutions in rural contexts based on published scholarly literature. When doing so, we bring both permanent and temporary residents of rural areas under one research framework. Methods: We used grey literature to identify and categorise novel mobility solutions, which have been applied in European rural areas and are suitable for travelling longer distances. By using six service flexibility variables, we reached four categories of novel mobility solutions: semi-flexible demand-responsive transport, flexible door-to-door demand-responsive transport, car-sharing, and ride-sharing. We analysed the social inclusivity, economic viability, and environmental impacts of those categories based on criteria and evidence identified from scholarly literature by including the perspectives of both permanent and temporary residents of rural areas. Results: Our findings revealed that while single novel mobility solutions are seldom applicable for all rural travellers, strong spatial and temporal synergies exist when combining different solutions. The need for a connected and flexible set of mobility solutions sensitive to the temporal and spatial patterns of mobility needs is inevitable. Accessible and easily understandable information on routing, booking, and ticketing systems, as well as cooperation, shared values, and trust between various parties, are key success factors for sustainable rural mobility. Conclusion: Integration of the needs of various user groups is essential when aiming to achieve the provision of environmentally, socially, and economically sustainable mobility solutions in rural areas. Supplementary Information: The online version contains supplementary material available at 10.1186/s12544-022-00536-3.

Should bike-sharing continue operating during the COVID-19 pandemic? Empirical findings from Nanjing, China.

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has triggered a worldwide outbreak of pandemic, and transportation services have played a key role in coronavirus transmission. Although not crowded in a confined space like a bus or a metro car, bike-sharing users are exposed to the bike surface and take the transmission risk. During the COVID-19 pandemic, how to meet user demand and avoid virus spreading has become an important issue for bike-sharing. METHODS: Based on the trip data of bike-sharing in Nanjing, China, this study analyzes the travel demand and operation management before and after the pandemic outbreak from the perspectives of stations, users, and bikes. Semi-logarithmic difference-in-differences model, visualization methods, and statistic indexes are applied to explore the transportation service and risk prevention of bike-sharing during the pandemic. RESULTS: Pandemic control strategies sharply reduced user demand, and commuting trips decreased more significantly. Some stations around health and religious places become more important. Men and older adults may be more dependent on bike-sharing systems. The declined trips reduce user contacts and transmission risk. Central urban areas have more user close contacts and higher transmission risk than suburban areas. Besides, a new concept of user distancing is proposed to decrease transmission risk and the number of idle bikes. CONCLUSIONS: This paper is the first research focusing on both user demand and transmission risk of bike-sharing during the COVID-19 pandemic. This study evaluates the mobility role of bike-sharing during the COVID-19 pandemic, and also provides insights into curbing the viral transmission within the city.

Injuries related to electric scooter and bicycle use in a Washington, DC, emergency department.

OBJECTIVE: E-scooter use has grown rapidly in the United States. Its rise in popularity has coincided with the promotion of cycling in many cities, but more needs to be known about how these transportation modes compare to determine if cycling should serve as an appropriate benchmark for policy decisions and safety expectations regarding e-scooters. METHODS: We examined characteristics of adults seeking treatment in a Washington, DC, emergency department (ED) for injuries associated with riding e-scooters during 2019 (n = 99) or bicycles during 2015-2017 (n = 337). RESULTS: E-scooter incidents less frequently involved moving vehicles (13.1% vs. 37.7%) or occurred on roads (24.5% vs. 50.7%) than cycling incidents. A smaller proportion of injured e-scooter riders were ages 30-49 (32.3% vs. 48.4%) and a larger proportion were 50 and older (34.3% vs. 22.6%) or female (45.5% vs. 29.1%). Distal lower extremity injuries were more common among e-scooter riders (13.1% vs. 3.0%; RR, 2.76; 95% CI, 1.79-3.54), and injuries to the proximal upper extremity (9.1% vs. 20.5%; RR, 0.49; 95% CI, 0.24-0.92) or chest, abdomen, and spine (3.0% vs. 14.0%; RR, 0.24; 95% CI, 0.07-0.70) were less common. Head injury rates were similar, but e-scooter riders more often experienced concussion with loss of consciousness (4.0% vs. 0.6%; RR, 3.03; 95% CI, 1.20-4.09) and were far less likely to wear helmets (2.0% vs. 66.4%). Estimated ED presentation rates per million miles traveled citywide were higher among e-scooter riders than cyclists (RR, 3.76; 95% CI, 3.08-4.59). CONCLUSIONS: E-scooters and bicycles are both popular forms of micromobility, but the characteristics of riders injured on them, the ways in which they become injured, and the types of injuries they sustain differ substantially. E-scooter rider injury rates, though currently high, may decrease as they gain experience; however, if the number of new users continues to climb, they will persist in using the ED more often than cyclists per mile that they travel.