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Pilot programs in California and beyond are exploring universal basic mobility (UBM), which calls upon government actors to ensure that everyone can access transportation services for basic needs. UBM addresses the problem of transport poverty, which is defined in various ways but is generally when transportation spending puts one below the poverty line or transportation is exceedingly time-consuming, unsafe, or unavailable. This research evaluated UBM-inspired pilot programs in Oakland and Bakersfield, via pre- and post-pilot surveys and interviews during the programs.Both pilots provided free-fare transportation services (shared micromobility in both cities and public transit services in Oakland) to populations vulnerable to transport poverty (residents of a low-income, minority-majority community in East Oakland and current and former foster youth in Bakersfield). Participants replaced car trips and/or walking with shared mobility and/or public transportation and reported improved access to jobs, food, health care, and social and recreational opportunities. They were able to go more places, more efficiently, and perhaps even enjoy the trip. The services helped participants carry out activities with more comfort and dignity and yielded social and cultural benefits. Lessons learned for program design and administration include the need for: providing some car-based services; clear communications throughout the program; training/support components for new mobility options; troubleshooting operations; and planning for turnover in program staff and fast changes in micromobility services.

To understand the extent to which micromobility services such as bike-share and scooter-share are enabling car-light lifestyles by replacing driving, we explore the trip-chaining patterns of micromobility users. We use travel diary data collected from micromobility users in 48 cities across the US. Our analysis incorporated 15,985 trip chains from 1,157 survey participants who provided at least seven days of travel diary data, and an imputed dataset of 35,623 trip chains from 1,838 participants from the same survey. Our analysis of both datasets shows that a considerable portion of car owners are leaving their cars at home when using micromobility. This suggests that, for a subset of users, micromobility can form part of a car-free or car-light day of travel, despite having a car available. Trip chains with less frequent car use are composed of a variety of different modes in combination with micromobility. Micromobility services are supportive of complex trip chains that include both work and non-work trips with reduced reliance on cars. The use of micromobility services tends to entirely replace shorter car trips on shorter-length trip chains. Our findings show the importance of considering the chain of trips rather than individual trips to understand the sustainability potential of micromobility services. The policy implications of these findings are improving methods of travel behavior analysis of shared mobility services.

During the early months of the pandemic, stay-at-home orders and concerns about infection catalyzed a shift toward online activities, such as remote work and e-shopping, resulting in a significant decrease in conventional travel. However, as the effects of the pandemic diminished, the pandemic-induced online activities began to subside, and conventional travel started to rebound. The challenge among transportation planners and policymakers is to determine the lasting effects of the pandemic and adjust the policies accordingly. In the same efforts to understand the evolving travel-related activities and inform policymaking, the 3 Revolutions Future Mobility Program at the University of California, Davis, conducted four waves of mobility surveys between Spring 2020 and Fall 2023. Key findings from the analysis of these data reveal that remote work and a combination of remote work and physical commuting (i.e., hybrid work) emerge as an enduring outcome of the pandemic. The pandemic accelerated the rise of e-shopping, both for grocery and non-grocery purchases, with findings demonstrating the critical influence of socio-demographic factors, including age, gender, and income, on e-shopping adoption and frequency. The findings show that socio-demographic factors such as work status, income level, and work arrangements are associated with household vehicle ownership changes and individual vehicle miles traveled (VMT). In particular, an increase in commute frequency reduces the likelihood of vehicle shedding, while amplifying the likelihood of vehicle acquisition. In the meantime, remote workers exhibit lower commuting VMT but higher non-commuting VMT compared to hybrid workers. The findings demonstrate a similarity between the percentage of respondents who used public transit, bikes, e-bikes, and e-scooters for commuting and non-commuting trips to some degree between 2019 and 2023. These insights underscore that adapting to shifting activity and transportation patterns is crucial for policymakers and planners to build a sustainable and inclusive post-pandemic future.

This article reviews shared mobility, a prominent urban transportation concept with considerable potential to contribute to more sustainable urban mobility. Shared passenger mobility spans diverse services, often leveraging technological advances and disruptions such as smartphones and data analytics to optimize transport resources. Given the broad range of services, a shared mobility taxonomy is proposed, accommodating evolving services. Key challenges for delivering efficient and effective shared mobility services with lower environmental impacts are also identified. Finally, the International Transport Forum transport demand models are used to analyze policy implications and potential effects quantitatively. This article presents a possible scenario for the global evolution of these services to 2050. Results emphasize shared mobility's role in transport decarbonization in the present and future and show that shared mobility may reduce resource use and mobility externalities (e.g., CO2, local pollutants, congestion, urban space use) but that the uptake will differ between Global South and Global North cities. [ABSTRACT FROM AUTHOR]

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the "soft strategy" and "hard strategy" in the framework of TDM are determined. In the soft strategy, we should focus on "shared mobility education" (shared mobility education, shared mobility publicity and shared mobility "environment" information) and "community organization" (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on "traffic planning and measures", "dedicated lanes", "parking facilities", and "financial subsidies". (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations. [ABSTRACT FROM AUTHOR]

This paper uses smart card data from Melbourne's public transport network to model and evaluate the impacts of a flexible on-demand transport system. On-demand transport is an emerging mode of urban passenger transport that relies on meeting passenger demand for travel using dynamic and flexible scheduling using shared vehicles. Initially, a simulation model was developed to replicate existing fixed-schedule bus performance and was then extended to incorporate on-demand transport services within the same network. The simulation results were used to undertake a comparative analysis which included reliability, service quality, operational efficiency, network-wide effectiveness, and environmental impacts. The results showed that on-demand buses reduced average passenger trip time by 30%, increased vehicle occupancy rates from 8% to over 50%, and reduced emissions per passenger by over 70% on an average weekday compared to fixed-schedule buses. This study also offers insights for successful on-demand transport implementation, promoting urban sustainability. It also outlines future research directions, particularly the need for accurate short-term passenger demand prediction to improve service provision and passenger experience. [ABSTRACT FROM AUTHOR]

Mobility hubs facilitate multimodal transport and have the potential to improve the accessibility and usability of new mobility services. However, in the context of increasing digitalisation, using mobility hubs requires digital literacy or even owning a smartphone. This constraint may result in the exclusion of current and potential users. Digital kiosks might prove to be a solution, as they can facilitate the use of the services found at mobility hubs. Nevertheless, knowledge of how digital kiosks may improve the experience of disadvantaged groups remains limited in the literature. As part of the SmartHubs project, a field test with a digital kiosk was conducted with 105 participants in Brussels (Belgium) and Rotterdam (The Netherlands) to investigate the intention to use it and its usability in the context of mobility hubs. This study adopted a mixed methods approach, combining participant observation and questionnaire surveys. Firstly, participants were asked to accomplish seven tasks with the digital kiosk while being observed by the researchers. Finally, assisted questionnaire surveys were conducted with the same participants, including close-ended, open-ended and socio-demographic questions. The results offer insights into the experience of the users of a digital kiosk in a mobility hub and the differences across specific social groups. These findings may be relevant for decision-makers and practitioners working in urban mobility on subjects such as mobility hubs and shared mobility, and for user interface developers concerned with the inclusivity of digital kiosks. [ABSTRACT FROM AUTHOR]

Free-floating car-sharing (FFCS) services allow users to rent electric vehicles by the minute without restrictions on pick-up or drop-off locations within the service area of the rental company. Beyond enlarging the choice set of mobility options, FFCS may reduce congestion and emissions in cities, depending on the service's usage and substitution patterns. In this paper, we shed light on this by analyzing the universe of FFCS trips conducted through a leading company in Madrid during 2019. We correlate FFCS usage patterns with data on traffic conditions, demographics, and public transit availability across the city. We find complementarities between FFCS and public transport in middle-income areas with scarce public transport options. Moreover, we find that the use of FFCS peaks earlier than overall traffic and is broadly used during the summer months. This suggests that FFCS may have smoothed road traffic in Madrid, contributing to a reduction in overall congestion. [ABSTRACT FROM AUTHOR]

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm. [ABSTRACT FROM AUTHOR]

Abstract Mobility hubs facilitate multimodal transport and have the potential to improve the accessibility and usability of new mobility services. However, in the context of increasing digitalisation, using mobility hubs requires digital literacy or even owning a smartphone. This constraint may result in the exclusion of current and potential users. Digital kiosks might prove to be a solution, as they can facilitate the use of the services found at mobility hubs. Nevertheless, knowledge of how digital kiosks may improve the experience of disadvantaged groups remains limited in the literature. As part of the SmartHubs project, a field test with a digital kiosk was conducted with 105 participants in Brussels (Belgium) and Rotterdam (The Netherlands) to investigate the intention to use it and its usability in the context of mobility hubs. This study adopted a mixed methods approach, combining participant observation and questionnaire surveys. Firstly, participants were asked to accomplish seven tasks with the digital kiosk while being observed by the researchers. Finally, assisted questionnaire surveys were conducted with the same participants, including close-ended, open-ended and socio-demographic questions. The results offer insights into the experience of the users of a digital kiosk in a mobility hub and the differences across specific social groups. These findings may be relevant for decision-makers and practitioners working in urban mobility on subjects such as mobility hubs and shared mobility, and for user interface developers concerned with the inclusivity of digital kiosks.

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.

Abstract Despite the current increased spread of modern mobility options in urban areas aimed at inducing changes in mobility choices, their integration into daily life remains sporadic, reducing their potential impact on the adoption of shared mobility services as a usual mode of transport. This paper provides a literature review of established models of psychological factors, including values, beliefs, and personal norms, particularly focused on pro-environmental behaviors influencing mobility choices and facilitating the adoption of more sustainable transportation habits, often supported by technological advancements. We started considering the recent increase in the complexity of cities and mobility needs, which induced policymakers to pay attention to the sustainability of people’s movements by promoting various kinds of shared mobility options. Then, we illustrated the most consolidated models of psychological factors linked to ecological behaviors, also summarizing the main results present in the literature regarding variables that influence mobility choices. The final goal was to highlight crucial aspects that should be considered to foster the effective use of new modes of transportation to help reduce the environmental impact caused by traffic.

In the pursuit of sustainable urbanization, Bike-Sharing Services (BSS) emerge as a pivotal instrument for promoting green, low-carbon transit. While BSS is often commended for its environmental benefits, we offer a more nuanced analysis that elucidates previously neglected aspects. Through the Dominant Travel Distance Model (DTDM), we evaluate the potential of BSS to replace other transportation modes for specific journey based on travel distance. Utilizing multiscale geographically weighted regression (MGWR), we illuminate the relationship between BSS’s environmental benefits and built-environment attributes. The life cycle analysis (LCA) quantifies greenhouse gas (GHG) emissions from production to operation, providing a deeper understanding of BSS’s environmental benefits. Notably, our study focuses on Xiamen Island, a Chinese “Type II large-sized city” (1–3 million population), contrasting with the predominantly studied “super large-sized cities” (over 10 million population). Our findings highlight: (1) A single BSS trip in Xiamen Island reduces GHG emissions by an average of 19.97 g CO2-eq, accumulating monthly savings of 144.477 t CO2-eq. (2) Areas in the southwest, northeast, and southeast of Xiamen Island, characterized by high population densities, register significant BSS environmental benefits. (3) At a global level, the stepwise regression model identifies five key built environment factors influencing BSS’s GHG mitigation. (4) Regionally, MGWR enhances model precision, indicating that these five factors function at diverse spatial scales, affecting BSS’s environmental benefits variably.

Shared mobility (SM) services are transitioning from the introduction stage to the growth stage, driven by the growth of the sharing economy, the promotion of smart cities, the diverse personal transportation modes, and the development of autonomous driving technologies. SM services, such as car sharing, car-hailing, shared bikes, and e-scooters, have emerged as solutions to address issues related to carbon neutrality and traffic congestion in densely populated areas. The purpose of this study is to investigate potential disparities in user perception and satisfaction among groups with or without driving experience when using SM services—through hypothesis testing using the two-proportion Z-test. Subsequently, a satisfaction analysis is conducted. This research creates foundational data for future SM services. The survey targeted 1041 residents living in Seoul, Gyeonggi Province, and Incheon, and was conducted over two weeks in March 2020. This study aims to derive associations between two groups using SM—those with and without driving experience. The results indicate that car sharing and bike sharing showed significant differences in user patterns based on driving experience, whereas e-scooters and car-hailing did not exhibit significant differences. This contradicts the assumption that people without a driver's license would use SM more frequently. Moreover, the results of each SM's analysis show different usage patterns and satisfaction between driver's license holders and nonholders. This study will serve as foundational data for researching strategies to reduce personal car ownership through the promotion of public transportation and SM services. Furthermore, it can be a basis for suggesting policy recommendations to facilitate future mobility systems. [ABSTRACT FROM AUTHOR]

The rapid growth of the sharing economy has propelled shared mobility to the forefront of the public's attention. Continuous advancements in autonomous driving technology also bring new opportunities and challenges to the shared mobility industry. This study comprehensively analyzes the impact of using land-based autonomous vehicles (AVs) to provide shared mobility services, utilizing SWOT analysis (Strengths, Weaknesses, Opportunities, and Threats), PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental), and Porter's Five Forces (the bargaining power of suppliers, the bargaining power of buyers, threats of new entrants, substitutes, and rivalry). The findings reveal that AVs can provide improved shared mobility services by increasing transportation safety, reducing emissions, reducing costs, enhancing traffic efficiency, and increasing customer satisfaction as well as the profitability of shared mobility services. However, challenges such as technological and policy uncertainties, safety concerns, high initial costs, inadequate public communication infrastructure, and the absence of standardized regulations can hinder the widespread adoption of AVs. The benefits are also restricted by the low market penetration rate of AVs. To promote AVs in the shared mobility market, this study also provides implications for AV stakeholders tailored to the evolving shared mobility market dynamics. [ABSTRACT FROM AUTHOR]

Despite the current increased spread of modern mobility options in urban areas aimed at inducing changes in mobility choices, their integration into daily life remains sporadic, reducing their potential impact on the adoption of shared mobility services as a usual mode of transport. This paper provides a literature review of established models of psychological factors, including values, beliefs, and personal norms, particularly focused on pro-environmental behaviors influencing mobility choices and facilitating the adoption of more sustainable transportation habits, often supported by technological advancements. We started considering the recent increase in the complexity of cities and mobility needs, which induced policymakers to pay attention to the sustainability of people's movements by promoting various kinds of shared mobility options. Then, we illustrated the most consolidated models of psychological factors linked to ecological behaviors, also summarizing the main results present in the literature regarding variables that influence mobility choices. The final goal was to highlight crucial aspects that should be considered to foster the effective use of new modes of transportation to help reduce the environmental impact caused by traffic. [ABSTRACT FROM AUTHOR]

The increasing popularity of moped scooter-sharing as a direct and eco-friendly transportation option highlights the need to understand travel demand for effective urban planning and transportation management. This study explores the use of machine learning techniques to forecast travel demand for moped scooter-sharing services in Madrid, Spain, based on origin–destination trip data. A comprehensive dataset was utilized, encompassing sociodemographic characteristics, travel attraction centers, transportation network attributes, policy-related variables, and distance impedance. Two supervised machine learning models, linear regression and random forest, were employed to predict travel demand patterns. The results revealed the effectiveness of ensemble learning methods, particularly the random forest model, in accurately predicting travel demand and capturing complex feature relationships. The feature scores emphasize the importance of neighborhood characteristics such as tourist accommodations, public administration centers, regulated parking, and commercial centers, along with the critical role of trip distance. Users' preference for short-distance trips within the city highlights the appeal of these services for urban mobility. The findings have implications for urban planning and transportation decision-making to better accommodate travel patterns, improve the overall transportation system, and inform policy recommendations to enhance intermodal connectivity and sustainable urban mobility. [ABSTRACT FROM AUTHOR]

Shared mobility platforms have built scalable digital marketplaces that facilitate the allocation and sharing of transportation and promote sustainable urban travel. Generation Z's attitude toward shared consumption is closely linked to their perceptions of the importance of sustainability. This study identifies Generation Z's awareness of shared mobility platforms in India and the factors that influence their use. Data were collected from 318 respondents from Generation Z in India and analyzed using partial least squares structural equation modeling. Findings indicate that Generation Z's intention to use shared mobility is influenced by environmental consciousness, social aspects, economic benefits, and perceived risks. Results also show that perceived risks have an indirect effect on intention, which is mediated by attitude. The novel conceptual model developed and tested in this research can be used to inform policies and business models for the adoption of shared mobility services for Generation Z, ultimately promoting more sustainable transportation systems and improved urban mobility. [ABSTRACT FROM AUTHOR]

Shared mobility services furnish convenient transportation alternatives for individuals without vehicle ownership or a preference against driving. Shared mobility could benefit developing countries by providing a cost-effective alternative, enhancing accessibility, reducing congestion, and creating multiple job opportunities. In this study, a comprehensive analysis to assess shared mobility options as an avenue to sustainable transportation in Amman, Jordan, is presented. The study employs a multifaceted methodology, including a survey questionnaire, preliminary analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis, and Structural Equation Model (SEM). The data were collected from a diverse group of Amman residents using a survey composed of 29 questions. The survey included demographic information, travel behavior, willingness to adopt shared mobility, perceived benefits, and possible barriers. These data were analyzed using Structural Equation Modeling (SEM), providing an in-depth understanding of the interrelationships among the variables studied. This study concludes by contributing to the ongoing discussion on sustainable urban transportation in Jordan and providing a road map for policymakers, urban planners, and transportation service providers. The presented findings provide an empirical basis for guiding future strategies and interventions toward sustainable urban development in Amman and potentially other urban contexts with comparable characteristics. Key findings reveal a significant potential for shared mobility to enhance urban transportation sustainability. Specifically, a notable positive perception among Amman residents was observed, with an average willingness to switch to shared mobility for daily commuting scoring 4.68 on a 7-point Likert scale. Moreover, a statistical analysis indicates that factors such as reduced costs, improved service reliability, and better environmental sustainability, notably influence the adoption of shared mobility services. [ABSTRACT FROM AUTHOR]

Background: Motorcycle taxi (MCT) is a form of widely used informal transportation. Over the last few years, MCT has developed from conventional public transportation into a modern business using digital technology. In these services, digitalization has received a positive response from the public and the increasing number creates many challenges. However, there is a gap in the discussion of new service models for shared mobility transportation. Objective: This research aimed to analyze MCT, providing an overview of the services as informal transportation. Methods: Bibliometric analysis was used to evaluate 366 articles published in the Scopus database between 2011-2023. In addition, annual publications and citation topics, most productive sources and influential articles, relevant affiliations, productive countries, location research, main topics, and future research options were identified. Results: Frequently occurring topics were reported with past and present academic developments related to MCT services. Based on the publication themes, the main topics were arranged into five clusters, namely (a) the development of more sustainable transportation services, (b) environmental and health impact, (c) road safety, (d) risky behaviors or risk factors as MCT drivers, and (e) utilization of MCT for medical or health services. Meanwhile, the research topics comprised themes about travel behavior, health, safety-security, customer satisfaction, and advanced mobility topics. Conclusion: This research increased knowledge about main topics, trends and future analysis options in MCT. The academic developments served as a guide to future topics. [ABSTRACT FROM AUTHOR]

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.

The vehicular congestion and the constant increase in the number of vehicles in urban areas represent significant issues due to the negative externalities associated with these phenomena. In this context, Bicycle-sharing systems have emerged as alternatives to mitigate the inconveniences linked to private vehicle use. Therefore, it is imperative to explore new options within shared bicycle systems, such as including e-bikes. The city of Cuenca is located in the Andes Mountain range 2550 meters above sea level, with an approximate population of 650,000 inhabitants, and is considered an intermediate city. Despite having a public bicycle system, it has not achieved the expected acceptance, according to reports from the City's Public Mobility Company. Consequently, in this study, we propose a comparative analysis of perceptions and operational parameters, such as speed and acceleration, between mechanical and e-bikes within the public bicycle system of the city of Cuenca, Ecuador. The results revealed that participants perceive more positive aspects when using e-bikes compared to conventional mechanical bicycles. Elements such as the enjoyment of the journey and the widespread recommendation of these stand out, findings that are supported by the data obtained from the analyzed operational parameters.

This report presents preliminary findings from the American Micromobility Panel, the largest study of shared micromobility services in the United States incorporating riders from multiple major operators. Micromobility services (bike-share and scooter-share) have recently emerged in many U.S. cities. Given that the substitution of bicycling, scooting, and other small vehicle travel for car travel will help cities reach numerous planning goals (e.g., accessibility, emissions, climate, health, equity, etc.), there is a need for understanding the effects of these mobility services. The purpose of this study was to examine the impact of micromobility services on travel behavior and outcomes such as mode shift, car ownership, access, equity, safety, and physical activity. The authors surveyed shared micromobility service users in 48 U.S. cities with two different surveys in Summer 2022: a 21-day smartphone-based travel diary (2206 participants with 183,483 trips), and an online follow-up survey of travel diary participants (657 valid responses). Car substitution rates, including private car and ride-hailing, show strong variation by city size and micromobility vehicle type. Through self-report, micromobility seems to have had at least a partial influence on the decision to purchase a car, perhaps as a part of a long-term car use reduction effort/plan. Participants showed positive attitudes toward using public transit, but a small portion of trips to access or egress from transit facilities were made by the participants. Instead, the participants more generally showed a transit substitution effect when using micromobility services. Results also suggest that bike-share and scooter-share use may be influenced in opposing ways by participant income. Half of participants had at least once experienced that they could not find an available vehicle nearby, suggesting a sizeable supply constraint on demand for the services to satisfy existing micromobility user needs. The effect of micromobility services on increasing physical activity was slight given the physical activity it often replaced. Additionally, concerning safety, participants tended to agree that bike-share is safer than scooter-share, and participants tended to agree with the view that using micromobility improved their mental health.View the NCST Project Webpage

Abstract Electric shared mobility hubs, called eHUBs, offer users access to a range of shared electric vehicles, including e‐bikes, e‐cargobikes, and e‐cars. Through the diversity of modes offered, eHUBs provide mobility solutions for different target groups and trip purposes. In this study, potential users’ willingness to use shared electric vehicles from eHUBs as either a commute or food shopping trip alternative was analysed using logistic regression methods. Results indicated that half of respondents were willing to use shared electric vehicles for at least a few of their regular commute or food shopping trips, although this proportion dropped substantially if considering the use of shared vehicles in combination with public transport. Across modes and trip purposes, holding a pro‐shared mobility attitude and belonging to the youngest age group strongly increased the willingness to use shared modes. Yet, while eHUBS may offer a potential alternative for at least some of people's regular commute or food shopping trips, cross‐mode shifts may be limited. That is, car drivers show a greater interest in shared e‐cars, whereas cyclists show a greater interest in e‐bikes and e‐cargobikes with public transport. Further influential factors, as well as implications for both shared mobility providers and local authorities, are discussed.

Urban mobility in the Global South presents unique challenges for public transport (PT) and emerging shared mobility (SM) services influenced by socio-economic factors, mobility attitudes, and urban form. This study examines the factors shaping mode choice behavior between PT and SM services in four communities each in Dubai and Lahore. Additionally, it analyses the impact of primary daily activities on PT and SM preferences in both cities. Data from 1,653 Dubai and 1,603 Lahore residents were analyzed using four binary logistic regression models. Results indicate that socio-demographic factors (age, gender, household car ownership, driver’s license possession, education level, and weekly commuting trips) and mobility attitudes (ride-sharing, ride-hailing, pro-personal car) significantly influence PT and SM choices. Key determinants vary by neighborhood type, including link-node ratio, intersection and road length density, and car and PT commute times. The influence of primary daily activities (work or study) on the choice of PT and SM modes was found to be moderate in Dubai. The study highlights the need for tailored urban and transport policies to promote sustainable modes, considering neighborhood characteristics and demographics.

Shared electric scooters (e-scooters) have rapidly gained prominence as a first/last-mile mobility solution globally, with over 66,000 systems operating in 88 cities across 21 countries in 2019. While recognized for their flexibility, accessibility, and environmental benefits, concerns such as safety, parking issues, and infrastructural challenges accompany the operation of shared e-scooter systems. This research investigates the evolving perceptions of e-scooter users in Riyadh, Saudi Arabia, comparing pre-survey results with a recent study following the official deployment of e-scooters as a transportation mode in 2022. The analysis reveals significant shifts in user behavior, preferences, and perceptions. The findings indicate increased familiarity with e-scooters, heightened usage rates, and notable changes in domestic e-scooter use. Furthermore, the study identifies variations in willingness to use e-scooters across genders. A notable shift is observed in riders' perceptions, transforming from viewing e-scooters primarily as entertainment tools to embracing them as a reliable mode of transportation. The results show that the percentage of female respondents using e-scooters increased from 3% to 13%, representing over four times the post-survey numbers. Additionally, the percentage of individuals perceiving e-scooters as safe decreased from 28.2% in the pre-survey to 14.9% in the current survey (post-survey) among those who had used e-scooters. The regression analysis demonstrates a historical uptrend in the utilization of e-scooters, juxtaposed with a discernible decline projected for forthcoming usage (odds ratio [OR] = 0.74). Intriguingly, there is evidence indicating an enhancement of riders' confidence towards e-scooters, as reflected by an augmented perception of safety (OR = 1.48). [ABSTRACT FROM AUTHOR]

Shared micromobility services have become increasingly prevalent and indispensable as a means of transportation across diverse geographical regions. Integrating shared micromobility with public transit offers opportunities to complement fixed-route transit networks and address first- and last-mile issues. To explore this topic, a systematic literature review was conducted to consolidate knowledge, analyze research achievements and best practices, and provide future research recommendations. This study examined 108 journal papers from the Web of Science (WoS) core collection from 2016 to 2022, along with grey literature. Citation and co-citation analyses were performed to build and illustrate the literature's bibliometric networks. This analysis categorized the literature into four major study themes: policy, sustainability, the interaction between shared micromobility and public transportation, and infrastructure. The implementation approaches of integrating shared micromobility and public transportation in different cities were classified into four categories: physical integration, payment and fee integration, informational integration, and institutional integration. The findings indicate that the relationship between shared micromobility and public transportation varies with spatial–temporal conditions and the population density of the city. Overall, integrating micromobility into public transit can offer faster and more cost-effective mobility options for most trips, contributing to urban resilience, a better air quality, lower greenhouse gas emissions, and livable communities. Based on these insights, further research is recommended to explore dynamic and context-specific strategies for successful shared micromobility and public transit integration, considering diverse urban settings and demographic factors. [ABSTRACT FROM AUTHOR]

Ridesharing effectively tackles urban mobility challenges by providing a service comparable to private vehicles while minimising resource usage. Our research primarily concentrates on dynamic ridesharing, which conventionally involves connecting drivers with passengers in need of transportation. The process of one-to-one matching presents a complex challenge, particularly when addressing it on a large scale, as the substantial number of potential matches make the attainment of a global optimum a challenging endeavour. This paper aims to address the absence of an optimal approach for dynamic ridesharing by refraining from the conventional heuristic-based methods commonly used to achieve timely solutions in large-scale ride-matching. Instead, we propose a novel approach that provides snapshot-optimal solutions for various forms of one-to-one matching while ensuring they are generated within an acceptable timeframe for service providers. Additionally, we introduce and solve a new variant in which the system itself provides the vehicles. The efficacy of our methodology is substantiated through experiments carried out with real-world data extracted from the openly available New York City taxicab dataset. [ABSTRACT FROM AUTHOR]

Sharing economy businesses (SEB) have attracted attention for their potential to reduce environmental impacts compared to conventional business models. While it is often assumed that SEB enable more sustainable modes of consumption, it is still a challenge to clarify the desirable linkage between consumers and business providers by means of services, products, and digital platforms, due to complexity and uncertainty in consumer behavior. To address this, a systematic review of literature that evaluates the environmental impacts of SEB, using shared mobility systems (SMS) as a case study, was conducted. An analysis is presented on how studies have addressed the above challenges, extracting key factors identified in literature to determine the net environmental effects of SMS, which can be useful in re-designing them for environmental sustainability. [ABSTRACT FROM AUTHOR]

Electric shared mobility hubs, called eHUBs, offer users access to a range of shared electric vehicles, including e‐bikes, e‐cargobikes, and e‐cars. Through the diversity of modes offered, eHUBs provide mobility solutions for different target groups and trip purposes. In this study, potential users' willingness to use shared electric vehicles from eHUBs as either a commute or food shopping trip alternative was analysed using logistic regression methods. Results indicated that half of respondents were willing to use shared electric vehicles for at least a few of their regular commute or food shopping trips, although this proportion dropped substantially if considering the use of shared vehicles in combination with public transport. Across modes and trip purposes, holding a pro‐shared mobility attitude and belonging to the youngest age group strongly increased the willingness to use shared modes. Yet, while eHUBS may offer a potential alternative for at least some of people's regular commute or food shopping trips, cross‐mode shifts may be limited. That is, car drivers show a greater interest in shared e‐cars, whereas cyclists show a greater interest in e‐bikes and e‐cargobikes with public transport. Further influential factors, as well as implications for both shared mobility providers and local authorities, are discussed. [ABSTRACT FROM AUTHOR]

Taxis and Transportation Network Companies (TNCs) are important components of the urban transportation system. An accurate short-term forecast of passenger demand can help operators better allocate taxi or TNC services to achieve supply–demand balance in real time. As a result, drivers can improve the efficiency of passenger pick-ups, thereby reducing traffic congestion and contributing to the overall sustainability of the program. Previous research has proposed sophisticated machine learning and neural-network-based models to predict the short-term demand for taxi or TNC services. However, few of them jointly consider both modes, even though the short-term demand for taxis and TNCs is closely related. By enabling information sharing between the two modes, it is possible to reduce the prediction errors for both. To improve the prediction accuracy for both modes, this study proposes a multi-task learning (MTL) model that jointly predicts the short-term demand for taxis and TNCs. The model adopts a gating mechanism that selectively shares information between the two modes to avoid negative transfer. Additionally, the model captures the second-order spatial dependency of demand by applying a graph convolutional network. To test the effectiveness of the technique, this study uses taxi and TNC demand data from Manhattan, New York, as a case study. The prediction accuracy of single-task learning and multi-task learning models are compared, and the results show that the multi-task learning approach outperforms single-task learning and benchmark models. [ABSTRACT FROM AUTHOR]

Nowadays, shared micromobility has become a trend in cities as an alternative to conventional automotive vehicles, especially for short-distance travel. It also plays an important role in the reduction of the number of automotive vehicles which results in a decrease of air pollution and traffic congestion. Shared micromobility is, however, influenced by the terrain characteristics. Varying elevation within a fleet operational area can cause imbalances in the use of micromobility stations if a steep terrain lies between stations. It also impacts the energy consumption of electric micromobility vehicles such as e-bicycles and e-scooters. Therefore, to simulate the state of charge (SOC) of traction batteries for micromobility close to reality, it is essential to include elevation data into the simulation model. This paper proposes a workflow for Simulation of Urban MObility (SUMO) comprising several steps with concrete implementation and validation in order to prepare and define the simulation model with micromobility stations and the integration of elevation data using a REST API. The integration of elevation and bike station data is validated with a defined vehicle type following a route in the hilly part of Stuttgart, Germany. A comparison of micromobility trips, with and without elevation data, was performed through a simulation by recording changes in energy consumption and driven altitude differences. The proposed workflow provides a basis for more complex use cases such as analysing micromobility business areas, improving vehicle.

Abstract Shared travel offers an important way to increase the accessibility of rail services. However, providing an integrated shared travel capability for rail travel is both a conceptual and technical challenge. This paper presents an overview of Ride2Rail, enabling ‘Easy use for all’ of rail through ridesharing as part of a multimodal journey. Ride2Rail has the overall objective of developing intelligent multimodal mobility, by facilitating the efficient combination of flexible and crowdsourced transport services, such as ridesharing, with scheduled transport. A requirements activity has set out the travel behaviour and system requirements for Ride2Rail. Development activities have covered the technical implementation of Ride2Rail, involving both development of the Ride2Rail functionalities and the Ride2Rail Driver Companion application, integrated within the wider Shift2Rail ecosystem. Demonstration activities have involved the preparation, implementation, execution and monitoring of Ride2Rail at four demonstration sites. This paper outlines the overall approach and findings of the Ride2Rail. This demonstrates the technical feasibility of integrating shared travel, including the architecture for a shared ride capability that can be readily integrated into pre-existing Mobility as a Service (MaaS) platform. Additionally, the paper reports positive user attitudes to this kind of shared travel, within the context of multimodal trips.

One of the biggest challenges for modern metropolises is the problem of public transportation. Barriers to the development of urban transport, the expansion of private vehicles, congestion of beehives, traffic jams, parking problems, and the negative impacts of transport on the environment are causing an increase in interest in shared micromobility. Electric scooters are one of these innovative solutions. This article aims to construct a model for the acceptance of electric scooters among students. The authors employed the technology acceptance model, and used the SmartPLS 4. Program to reconstruct the model. The basis of the model was a CAWI survey on a sample of 442 students. On this basis, the study verified the hypotheses on the relationships between the factors influencing the acceptance of electric scooters: demographic characteristics, perceived usefulness, ease of use, attitudes towards this solution, and behavioral intentions.

This study is based on an investigation of shared mobility in Chinese cities, which identifies the factors affecting the sustainable development of shared mobility based on the theoretical framework of TDM (travel demand management). Through a literature review and expert interviews, the FUZZY-DEMATEL-ISM-MICMAC integration model was used to screen 21 influencing factors from aspects that fit the research theme. Triangular fuzzy numbers are used to quantify the subjective scores of nine expert groups and weaken the subjective influence of expert scores. The logical relationships among DEMATEL technology-building factors and ISM technology-based factors are divided into levels. The MICMAC technique is used to divide the types of factors according to the driving power and dependency. The results show that (1) the influence factors of the “soft strategy” and “hard strategy” in the framework of TDM are determined. In the soft strategy, we should focus on “shared mobility education” (shared mobility education, shared mobility publicity and shared mobility “environment” information) and “community organization” (community organization and advocacy and organizational interaction). In the hard strategy, we should focus on “traffic planning and measures”, “dedicated lanes”, “parking facilities”, and “financial subsidies”. (2) The ISM recursive structure model is divided into five layers. Among them, shared mobility education, shared mobility operating technology, and organizational interaction are at the deep root level, which can continuously influence other factors in the long run. (3) In MICMAC, the number of related factors is large. When making decisions on these factors, managers should comprehensively consider the correlation of factors and adjust the use of factors from an overall perspective. This study can help managers identify the key factors affecting the sustainability of shared mobility and make targeted recommendations.

Any sharing mobility service aims to make urban mobility sustainable to help reduce environmental impacts and improve the quality of life for all in cities. Many transport services are not currently self-sustainable. The Life for Silver Coast (LifeSC) opened its mobility services on 22 May 2021 and offered electric mobility services during the summer for a few cities in Tuscany. E-bikes and e-scooters can be financially neutral, and even profitable, thanks to the low costs of the vehicles, but they only see a high utilization rate in winter. Shared electric cars, meanwhile, are not profitable. A new shared service that is viable must be profitable to become widely adopted and significantly contribute to sustainability. A few key characteristics have been identified, and one has been tested with a new business model that combines ride-sharing and car-sharing. The innovative Ride Sharing Algorithm (RSA) has been tested based on data from a potential city, Monterondo, where many commuters travel daily to Rome by train. The Italian census and local survey data allowed for the simulation of the scheduling of vehicle rides and an evaluation of the economic results, which could be positive if enough interest for such a system exists among the people, as at least 400 commuters from Monterotondo go to the train station daily in the morning and return in the afternoon. Such a transport demand would justify a new commercial sharing service by using the model tested with the RSA algorithm.

Emerging transportation services such as ridehailing, whose development and adoption have been enabled by information and communication technology, are transforming people’s travel and activity patterns. It is unclear what these changes mean for environmental sustainability, as researchers are still trying to understand how new mobility services might impact multimodal travel and reliance on private cars. A better understanding of emerging mobility patterns can improve travel demand forecasting tools, inform investment decisions, and help provide efficient, reliable, and accessible transportation solutions.Building on a multi-year study, researchers at the University of California, Davis surveyed 4,071 California residents in 2018 about their personal attitudes and preferences, lifestyles, travel patterns, vehicle ownership, adoption and use of new mobility services, and personal and household characteristics. This brief summarizes the results of multiple studies that have used this dataset to generate insights into the impact of ridehailing services on the use of other travel modes and on car ownership prior to the COVID-19 pandemic, as well as provides policy implications.View the NCST Project Webpage

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users' willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation. [ABSTRACT FROM AUTHOR]

Electric scooter sharing mobility services have recently spread in major cities all around the world. However, the bad parking behavior of users has become a major source of issues, provoking accidents and compromising urban decorum of public areas. Reducing wild parking habits can be pursued by setting reserved parking spaces. In this work, we consider the problem faced by a municipality that hosts e-scooter sharing services and must choose which locations in its territory may be rented as reserved parking lots to sharing companies, with the aim of maximizing a return on renting and while taking into account spatial consideration and parking needs of local residents. Since this problem may result difficult to solve even for a state-of-the-art optimization software, we propose a hybrid metaheuristic solution algorithm combining a quantum-inspired ant colony optimization algorithm with an exact large neighborhood search. Results of computational tests considering realistic instances referring to the Italian capital city of Rome show the superior performance of the proposed hybrid metaheuristic. [ABSTRACT FROM AUTHOR]

The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]

This study presents a novel examination of shared mobility's viability and impact in Amman, Jordan, framed within the context of sustainable urban transportation. A rigorous methodological approach that integrates advanced statistical models including Probit and Decision Tree analyses was utilized to evaluate the propensity of Amman's residents to adopt shared mobility solutions. Notably, the Ordered Probit Model provided superior model prediction compared to the multinomial logit model, evidenced by a better goodness of fit measure. The results showed that public transportation users would highly use shared mobility services based on cost and reliability, with service convenience emerging as a pivotal factor. The classification tree identified the convenience of the service as the most important factor in adopting shared mobility. The survey data revealed an initial adoption rate of 25.4%, indicating a significant inclination towards shared mobility among respondents. This is pivotal in understanding the current readiness and potential growth of shared mobility in the city. This study is one of the first to quantify the readiness and potential growth of shared mobility in a Middle Eastern urban setting. Furthermore, the impact of this adoption rate on CO2 emissions was conducted. Emission analysis is crucial for assessing the environmental benefits of transitioning towards shared mobility options and aligning with global sustainability goals. Finally, the study extrapolates strategic guidelines for advancing sustainable transportation in Amman, identifying shared mobility options with the highest potential for successful adoption and proposing strategies to foster their implementation. This research contributes a unique perspective to the discourse on urban mobility, particularly in developing urban contexts like Amman, offering valuable insights for policymakers and urban planners. [ABSTRACT FROM AUTHOR]

One of the most significant financial benefits of a shared mobility mode such as ridesharing is cost savings. For this reason, a lot of studies focus on the maximization of cost savings in shared mobility systems. Cost savings provide an incentive for riders to adopt ridesharing. However, if cost savings are not properly allocated to riders or the financial benefit of cost savings is not sufficient to attract riders to use a ridesharing mode, riders will not accept a ridesharing mode even if the overall cost savings is significant. In a recent study, the concept of discount-guaranteed ridesharing has been proposed to provide an incentive for riders to accept ridesharing services through ensuring a minimal discount for drivers and passengers. In this study, an algorithm is proposed to improve the performance of the discount-guaranteed ridesharing systems. Our approach combines a success rate-based self-adaptation scheme with an evolutionary computation approach. We propose a new self-adaptive metaheuristic algorithm based on success rate and differential evolution for the Discount-Guaranteed Ridesharing Problem (DGRP). We illustrate effectiveness of the proposed algorithm by comparing the results obtained using our proposed algorithm with other competitive algorithms developed for this problem. Preliminary results indicate that the proposed algorithm outperforms other competitive algorithms in terms of performance and convergence rate. The results of this study are consistent with the empirical experience that two people working together are more likely to come to a correct decision than they would if working alone. [ABSTRACT FROM AUTHOR]

Among the most crucial organizational challenges of free-floating carsharing is the question how to cope with regional demand imbalance. Because users are allowed to leave a rented car anywhere in the service district, it regularly occurs that too many cars are left behind in low-demand regions whereas other regions face a demand surplus. In this paper, we consider a countermeasure that has been overlooked by previous research: an optimization-based matching of carsharing supply and demand that not only addresses the profit promised by the current matches but also targets future demand imbalance. To account for such imbalances, we define regional demand levels that specify the projected number of requested cars per region and aim to reduce the deviations of the regions' actual car supply from these target levels. We present exact polynomial-time algorithms for this extended matching task that are suitable for real-time application on large carsharing platforms. In an extensive computational study, we compare optimization-based matching approaches with and without the consideration of demand imbalance and benchmark them with the status quo, the individual choice of carsharing users among available cars. Based on generated data with considerable demand variation among regions, our results indicate a clear advantage of our novel matching approach. In a further study based on a large carsharing data set, however, the proof of concept fails because the real-world regions are cut according to geographical characteristics instead of demand variation. To successfully relieve the strains of demand imbalance, our novel matching task thus requires a properly partitioned service district and reliable forecasts of the carsharing demands. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Grants BO 3148/8-1 and BR 3873/10-1]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0067. [ABSTRACT FROM AUTHOR]

To provide safety to users and not disturb traffic flow, autonomous vehicles in shared on-demand mobility services cannot stop everywhere; thus, pick-up and drop-off (PUDO) spots must be dedicated or established for them. Our research objective is to propose a method to locate the PUDO spots for shared autonomous vehicle-based mobility services as this topic has been barely studied. The number of required PUDO spots is calculated, and the location is chosen considering the urban environment, walking radius, vehicle occupancy, and time for boarding and alighting among other parameters. Different from some methods applied to shared mobility, we consider the existing infrastructure (e.g., parking spaces) as potential locations for PUDO spots. The method is applied to a study case, demonstrating the applicability and providing the main findings: (i) the required number of PUDO spots decreases if willingness to walk increases; (ii) with a 3-min walking radius, 83% of curbside parking spaces can be repurposed, and 100% is reached with a 10-min walking radius; (iii) the minimum of 55% of curbside parking spaces can be repurposed with 10-min walking radius and without locating PUDO spots in private parking. Using our method, cities can determine the quantity of PUDO spots and their locations, being prepared in advance for the required changes in the existing infrastructure as well as the freed-up space to be repurposed. [ABSTRACT FROM AUTHOR]

E-scooter sharing systems are a new mobility solution that is emerging in cities all throughout the world. Its rapid diffusion and disruptive nature have gained immediate attention from scholars, decision-makers, the public and the media (with strong arguments in favor of or against being put forward). To date, the scientific community has focused mostly on studying the determinants for adoption by end-users, the trip patterns, the safety, and the sustainability of such systems. This research work provides preliminary insights on the future role that these shared mobility systems can play in urban mobility. The study focuses on recent experiences in Portugal (more than a dozen cities and a dozen service providers) drawing on a literature review and an expert survey (N = 23) with local decision-makers, scholars, service providers and civic associations. The analysis provides a positive outlook on the future of shared e-scooters, observes that deployment has been taking place through waves of launch–test–withdraw/expand, and highlights which policies and actions should be prioritized so that these systems can make a truly significant contribution towards sustainable urban mobility and livable cities. [ABSTRACT FROM AUTHOR]

The United States faces urban issues like congestion and pollution due to heavy car dependency, with over 91% owning cars and 87% driving to work. In response to these challenges, various shared mobility solutions have been integrated into transportation systems, encompassing bike-sharing, e-scooter sharing, and ride-hailing, but have not seen widespread adoption. To this end, this study investigates the perception of these services in the context of benefits and barriers. A survey was conducted in Indianapolis, Indiana and distributed online, gathering 424 responses. Latent class analysis (LCA) was used to determine groups of individuals with similar perceptions. The LCA identified three classes regarding perceptions of shared mobility benefits, and three others in relation to barriers. Regarding benefits, the classes include “Casual observers of benefits,” predominantly comprising older, less active females; “Benefits proponents,” featuring a majority of young, well-educated, higher-income males who prioritize health; and “Non-believers in benefits,” primarily consisting of older individuals, often identifying as black, with lower incomes and less active commuting. In terms of barriers, the classes are “Indifferent about barriers,” mainly comprising younger, lower-income individuals; “Shared mobility bystanders,” primarily characterized by older individuals with infrequent work commutes; and “Barrier conscious,” mostly including younger, well-educated, racially diverse individuals with complex commuting patterns. In both the benefits and barriers categories, the largest classes consist of individuals with neutral perspectives on shared mobility, signifying that a substantial portion of the population has not fully embraced these services. The study recommends policies to promote shared mobility to this significant population segment.

Shared mobilities, particularly carpooling, foster a paradox in Africa. While bush taxis have experienced historic success for the past 50 years due to traditional sharing practices transposed into public transport, modernization through digital platforms is struggling to take root in a sustainable way. This observation, highlighted through maps, underscores the recurring failures of initiatives taken in West Africa. Indeed, over the past 15 years, more than half of the companies created in this sector have been abandoned. In Grand-Nokoué, Benin, a qualitative and quantitative survey of 230 users combined with a documentary analysis at the West African level, makes it possible to understand their difficulties. Data from an immersion in the field reveals three major factors: a more flexible relationship to time without a culture of rigorous planning, out of step with the rational logic of platforms; a deeply rooted community conception of travel based on social proximity, contrary to sharing with strangers; a sense of insecurity towards outsiders and a primacy of individual comfort, contrary to the spirit of carpooling. The working classes, versed in ancestral practices of mutual aid in transport, could play a key role in the sustainable territorial anchoring of the new services. However, innovative initiatives in the field of platforms require strong political support, which is not yet acquired here, and, concurrently, a "progressive" familiarization that is still very much in progress.

The determinants of smartphone use for urban transportation purposes in developing countries, particularly in the cities of the Middle East and North Africa, have not been thoroughly investigated so far. This study aims to clarify the individual, mobility, and perception-related correlates of smartphone use specifically for ordering vehicles via ride-sourcing in medium-sized and large cities as well as the megacities of the region. The four cities of Cairo and Alexandria in Egypt and Beirut and Jounieh in Lebanon were taken as case-study cities. An overall sample of 3,285 face-to-face interviews was undertaken between late 2022 and early 2023 using a questionnaire focusing on individual, household, mobility, and perceptions of the respondents. The research depends on a chi-square test of independence and Binary logistic regression to answer three research questions: Are the levels of smartphone use for transportation purposes significantly different in the countries of Egypt and Lebanon? What are the correlates of smartphone use for transportation purposes in the four case-study cities? What are the differences among the correlates of smartphone use for transportation purposes in the four case-study cities? The results of a Chi-square test of independence show that there is a highly significant difference between the smartphone use levels for transportation purposes among the four case-study cities: in subsamples in the Egyptian and Lebanese capitals, there is a stronger tendency to use smartphones for transportation. Moreover, the model fit based on Binary Logistic regression shows that age, trip generation for non-commute purposes, perceived ease of using ride-sourcing apps, perceived security when using public transportation, education status, and gender are significantly correlated with smartphone use. These explanatory variables function somehow differently in the case-study cities, but in general, there are strong similarities among the four cities. In conclusion, the results of this study can be used to reduce personal car use and strengthen shared mobility in the cities of the Middle East and North Africa region.

People who live in rural areas in California face unique transportation challenges due to long travel distances, infrequent transit service, the cost of car ownership, and limited access to app-based rideshare services that are common in more populated urban centers. Over the past eight years, UC Davis has partnered with the eight San Joaquin Valley Metropolitan Planning Organizations to identify and support development of three innovative mobility pilot concepts for the region.Researchers at the University of California, Davis evaluated these three pilot programs using survey and service usage data collected from their launch dates in 2019 and 2020 through November 2021 to understand the participant characteristics and outcomes of each pilot. This policy brief summarizes the key findings from that research.View the NCST Project Webpage