Search

This study analyzes the relation between shared mobility services and greenhouse gases (GHGs) emissions by using a nationally representative sample of US young adults. We conduct a comprehensive analysis based on the data collected in the 2017 National Household Travel Survey (NHTS). These trip-level emissions are calculated following MOtor Vehicle Emission Simulators (MOVES) and Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model (GREET) workflows. We find that the weekday sample has a significantly higher value in daily GHGs emissions than the weekend sample. Controlling for other factors, shared micromobility services usage is found to have a significant impact on daily GHGs emissions for both weekday and weekend travel. Our analyses further indicate that carsharing complements public transit, and its users are more likely to reside in areas with better public transit supply. We find that the use of transportation network companies (TNCs) has a positive relationship with young adults’ GHGs emissions on weekdays only. The study results and implications may be useful for planners and professionals interested in tracking the impacts of new mobility services on transportation and the relevant environmental outcomes.

Mobility as a Service (MaaS) is expected to reorganise how we travel. To date, it is still unclear who will embrace these new mobility strategies, and how they will transform travellers’ behaviours. This study identifies the key drivers determining the adoption of MaaS in two European metropolitan areas, based on a survey taken in Madrid (Spain) and Randstad (The Netherlands). Overall, we found strong positive attitudes towards MaaS. The results highlight the role played by a set of demographic, socioeconomic, and travel-related variables, after their validation with a generalized ordered logit (gologit) model. The willingness to adopt MaaS appeared to be related to personal characteristics such as gender, age, education level, occupation, and household structure. Unexpectedly, the place of residence (city versus outskirts) was not found to be significant. The acceptance of MaaS was also influenced by individuals’ mobility patterns. We detected that private car users were not significantly attracted by MaaS, while individuals engaged with public transport and shared mobility services did intend to adopt it. Our final findings will help urban authorities and transport planners to predict the population segments with a higher (and lower) probability of using MaaS. Policy efforts could stimulate MaaS acceptability in the coming years by taking these insights into account.

New shared mobility services have become increasingly common in many cities and shown potential to address urban transportation challenges. This study aims to analyze the mobility performance of integrating bike-sharing into multimodal transport systems and develop a machine learning model to predict the performance of intermodal trips with bike-sharing compared with those without bike-sharing for a given trip using transit smart card data and bike-sharing GPS data from the city of Seoul. The results suggest that using bike-sharing in the intermodal trips where it performs better than buses could enhance the mobility performance by providing up to 34% savings in travel time per trip compared with the scenarios in which bus is used exclusively for the trips and up to 33% savings when bike-sharing trips are used exclusively. The results of the machine learning models suggest that the random forest classifier outperformed three other classifiers with an accuracy of 90% in predicting the performance of bike-sharing and intermodal transit trips. Further analysis and applications of the mobility performance of bike-sharing in Seoul are presented and discussed.

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.

In the context of sustainable mobility policies, carsharing services have gained importance as an alternative to personal vehicles. In an effort to increase the adherence to and use of such services, several studies have explored the key factors that determine use and membership. Although the ease with which individuals can access shared vehicles appears to be a central determinant, few studies have specifically investigated how to measure station and vehicle accessibility. To fill this gap, this study seeks to systematically assess and compare the contribution of different accessibility indicators to modeling carsharing membership rate, using 2016 data from the Montreal carsharing company Communauto and from the Canadian census. Three indicators of accessibility to in-station vehicles are generated: walking only, public transport only, and multimodal accessibility (walking and public transport), considering a variety of travel time thresholds and cost functions. A linear regression model is then generated to assess the contribution of the different indicators to modeling membership rates, while controlling for socio-economic and commuting characteristics. The results show that walking accessibility, within 20 minutes, and public transport accessibility, within 40 minutes, are both key determinants of membership rate and in a complementary manner. The influence of public transport accessibility is positive and highest when walking accessibility is low. The results also demonstrate that the use of a cumulative or weighted-opportunity indicator is equally sound from an empirical perspective. The study is of relevance to researchers and planners wishing to better understand and model the influence of vehicle accessibility.

In recent years, there has been a growing interest in studying the interactions between the fields of digital technology and transport and multiple terms to describe these interactions have been pr...

With rapid growth in on-demand delivery, ridehailing, and shared mobility use, cities are observing dramatic increases in demand for curbspace. In response, cities and private companies have proposed a diverse range of structural, policy and technology solutions to manage the curb lane more efficiently. Through structured interviews with public agency and private company staff and a review of existing pilot project evaluations and curb management guidelines, this study surveys contemporary approaches to curbspace management in 14 U.S. cities and documents the challenges and opportunities associated with them. A total of 17 public agencies (including public works departments, transportation agencies, and metropolitan planning organizations) in every census region of the U.S.A., and 10 technology companies were interviewed. The results show that the top curb management concerns among public officials are enforcement and communication, data collection and management, and interagency coordination. Interviewees reported success with policies such as allocating zones for passenger pick-ups and drop-offs, incentives for off-peak delivery, and requiring data sharing in exchange for reservable or additional curb spaces. Technology company representatives discussed new tools and technologies for curb management, including smart parking reservation systems, occupancy sensors and cameras, and automated enforcement. Both public and private sector staff expressed a desire for citywide policy goals around curb management, more consistent curb regulations across jurisdictions, and a common data standard for encoding curb information.

There is increasing evidence that ridehailing and other private-for-hire (PfH) services such as taxis and limousines are diverting trips from transit services. One question that arises is where and when PfH services are filling gaps in transit services and where they are competing with transit services that are publicly subsidized. Using weekday trip-level information for trips originating in or destined for the city center of Portland, OR from PfH transportation services (taxis, transportation network companies, limousines) and transit trip data collected from OpenTripPlanner, this study investigated the temporal and spatial differences in travel durations between actual PfH trips and comparable transit trips (the same origin–destination and time of day). This paper contributes to this question and to a growing body of research about the use of ridehailing and other on-demand services. Specifically, it provides a spatial and temporal analysis of the demand for PfH transportation using an actual census of trips for a given 2 week period. The comparison of trip durations of actual PfH trips to hypothetical transit trips for the same origin–destination pairs into or out of the central city gives insights for policy making around pricing and other regulatory frameworks that could be implemented in time and space.

A central feature of Mobility as a Service (MaaS) is the design of subscription plans, also known as mobility bundles. Despite the recognition of the importance of MaaS bundles compared to the Pay as you Go (PAYG) option, there is very little guidance in the literature on what a bundle that is attractive for users and financially viable for the operator might look like. With very few actual MaaS offers in real markets, and a lack of transparency in sharing how successful the few MaaS offers have been, the call for trials has grown throughout the world. The Sydney MaaS trial is the first in Australia to introduce MaaS bundles, using an incremental strategy of adding a bundle each month after a PAYG familiarity period. This paper sets out a framework within which we designed and introduced five bundles, using a co-creation and data-driven approach to bundle design. We present the findings on how successful bundles were in attracting MaaS users away from PAYG, and what this uptake might mean for achieving goals such as reduced transport emissions, notably those associated with private car use.

Ride-hailing services have gained popularity in recent years due to attributes such as reduced travel costs, traffic congestion, and emissions. However, with the impact of COVID-19, the ride-hailing market is estimated to lose its fair share of an uprising as a transportation mode. During normal and critical circumstances, ride-hailing service users express their concerns, habits, and emotions through posting on social platforms such as Twitter. Hence, Twitter, as an emerging data source, is an effective and innovative digital platform to observe the rider’s behavior in ride-hailing services. This study hydrates large-scale Twitter reactions related to shared mobility to perform comparative sentiment and emotion analysis to understand the impact of COVID-19 on transportation network services in pre-pandemic and during pandemic conditions. Amid pandemic, negative tweets (34%) associated with ‘sad’ (15%) and ‘anger’ (15%) emotions were most prevalent in the dataset.

Although metro systems are established in many Asian cities including Chengdu, they have yet to cover every corner of a city. Understanding the transfer behavior of passengers can provide insight into achieving efficient and sustainable urban transport systems. Combining shared mobility programs with metro to improve the weaknesses of traditional feeder modes is viewed as the most promising line of business in sustainable transportation for the near future. Therefore, this study aims to comprehend the factors affecting the usage regularity of shared mobility by deepening the knowledge on endogenous and exogenous effects, and integrating two modes, namely bike-sharing and ridesourcing. Two systems are cross-compared, first in respect of their travel characteristics. Then, a binary logistic model is employed to capture the influences of trip characteristics and travel environment characteristics on their usage frequency. Researchers found that trip distance is significantly associated with users’ mode options, indicating that bike-sharing and ridesourcing mainly serve short-distance and long-distance transfer users, respectively, although some users may be confused which feeder mode to choose for the journeys of 2 km to 4 km. There were also meteorological and temporal influences, with the competition and complementation of multiple shared mobility feeder modes being likely to change under extreme weather conditions, during peak hours, or on weekends. Besides, metro-shared mobility users value the accessibility of two kinds of transport service, which is affected by the metro station and its surrounding built environment. This study and the proposed policy implications are helpful for embracing a sustainable mobility design from general optimum.

Статья посвящена критике распространенных дискурсов об автоматизированных (беспилотных) транспортных средствах и совместной (шеринговой) мобильности, согласно которым в будущем эти группы технологий будут доминировать в сфере транспорта и вытеснят общественный транспорт. Автор подробно разбирает ложные тезисы в основе каждого из этих дискурсов и показывает, что даваемые ими обещания необоснованны или даже противоречат фактам. Ключевое из них — решение проблемы перегруженности городских дорог. Только общественный транспорт благодаря высокой заполняемости может эффективно использовать дорожное пространство, в то время как совместная мобильность чаще всего не подразумевает совместные поездки, а значит, и повышение заполняемости автомобилей. В заключение автор намечает перспективные тренды в сфере общественного транспорта — в частности, синтез элементов организации разных видов транспорта и дальнейшее внедрение шеринга и автоматизации.

Over the past 20 years, shared-mobility services have become important transportation options, as they provide on-demand, door-to-door mobility without requiring vehicle ownership. Although low-inc...

Highlights • Analyzing novel service innovations calls for combining systemic and micro-level analysis • We analyze the agency of sustainable shared urban mobility operators and local policymakers in the case of Finland • We stress the role of local policymakers in providing examples, enabling coordination and harnessing innovative actors • Local-level collaborating is crucial for the sustainability of mobility innovations Service-based alternatives to private vehicles and public transport are increasing. These innovations are hoped to boost the transition to sustainable mobility, addressing challenges such as congestion, air quality and the large share of CO2 resulting from traffic. To achieve this transition, there is an urgent need for collaboration between private companies and public authorities, especially locally. We claim that insufficient attention is paid to the agencies of the practical actors, such as car sharing operators, which is the key to understanding what this collaboration requires. Orchestrating the collaboration successfully is also a prerequisite for the sustainability of the services. We use interview data from car sharing operators and public authorities in Finnish cities to analyze the local-level implementation of shared mobility services. Our results show how the local public authorities play a crucial role in allowing the services to flourish but also ensure the services’ environmental and social sustainability.

The proliferation of mobile devices and the emergence of the Internet of Things are leading to an unprecedented availability of operational data. In this article, we study how leveraging this data in conjunction with data science methods can help researchers and practitioners in the development and evaluation of new operational policies. Specifically, we introduce a two-stage framework for exploratory data science consisting of a policy identification stage and an ex-ante policy assessment stage. We apply the framework to the context of free-floating carsharing—a novel mobility service that is an example of data-rich smart city services. Through data exploration, we identify a novel preventive user-based relocation policy and provide an ex-ante assessment regarding the feasibility of its implementation. We discuss practical implications of our approach and results for shared-mobility providers as well as the relationship between data science and operations management research.

Several converging trends appear to reshape the way citizens and goods move about. These trends are social, including urbanization and population growth, and technological, such as increased automation and connectivity. All these factors influence the market for connected, automated, shared and electric (CASE) vehicles, which presents many opportunities and challenges. The pace of the shift to a profoundly penetrated market for CASE vehicles is far from secure. Such transformation depends on the development of technologies, consumer attitudes, and policies. An expanding body of research has investigated the potential social and behavioral results of deploying CASE vehicles. However, most academic literature to date concentrates on technological issues linked to these vehicles. There are several teams from federal and state agencies, OEMs, academia, startups, and consortiums working on this complex subject. This study investigates several academic papers, as well as federal and industry reports, considering all the stakeholders mentioned above. Its aim is to present a comprehensive picture of the implementation barriers and drivers of CASE vehicle usage and provide suggestions to solve them. The findings confirm that several issues are currently affecting the implementation of CASE vehicles on the road. Although there have been significant partnerships and collaborations between CASE vehicle stakeholders, namely technology companies, federal-state agencies, and academic scholars, considerable work is still required to solve the remaining barriers facing CASE-related technologies. This would enable decision-makers to create effective policies for future transportation networks and increase the speed of CASE vehicle market penetration to enhance road network’s level of service.

In order to implement Mobility as a Service (MaaS), two main conditions are required: a consolidated public transport system and a varied shared mobility offer. Our study explores the latter condition in the case of Madrid. Through the Multi-Level Perspective framework, emerging shared mobility operators and their service characteristics are diagnosed, in order to explore how this is influencing MaaS developments at the niche level. Our findings show that Madrid has more than 30 services available and an approximate total fleet of almost 30 thousand vehicles, managed by 29 different operators. This dynamic ecosystem of mobility options is facilitating MaaS, as users begin to find it difficult when navigating through all the different applications raising users’ and authorities’ interest on the subject. However, although there are at least three ongoing MaaS initiatives in the city, there is no collaboration between them. The current state of cooperation supports what other authors have established as one of the main challenges to MaaS’ feasibility: poor governance frameworks for MaaS.

High costs of owning fully-automated or autonomous vehicles (AVs) will fuel the demand for shared mobility, with zero driver costs. Although sharing sounds good for the transport system, congestion can easily rise without adequate policy measures. Many or all public transit lines will continue to exist, and carefully-designed policies can be implemented to make good use of fixed public assets, like commuter- and light-rail lines. In this study, a shared AV (SAV) fleet is analyzed as a potential solution to the first-mile-last-mile (FMLM) problem for access to and from public transit. Essentially, SAVs are analyzed as collector-distributor systems for these mass-movers and compared with a door-to-door (D2D) service. Results from an agent-based simulation of Austin, Texas, show that SAVs have the potential to help solve FMLM transit problems when fare benefits are provided to transit users. Restricting SAV use for FMLM trips increases transit coverage, lowers average access and egress walking distance, and shifts demand away from park-and-ride and long walk trips. When SAVs are available for both D2D use and FMLM trips, high SAV fares help maintain transit demand, without which the transit demand may decrease significantly, affecting the transit supply and the overall system reliability. Policy makers and planners should be wary of this shift away from transit and may be able to increase transit usage using policies tested in this study.

Bike share, e-bike share, and e-scooter systems (shared micro-mobility) are gaining popularity throughout the United States and internationally, but the optimal system design has not been determined. This study investigated motivators and deterrents to the use of such systems in the Pacific Northwest with secondary data, participant observations, depth interviews, and an on-line survey to users and non-users. The survey was administered in all cities in Washington, Oregon, and Idaho that have shared micro-mobility systems. The strongest motivators reported were exercise and enjoyment. The strongest deterrents were weather, danger from automobile traffic, and insufficient bike lanes and paths. The latter two deterrents might be alleviated through continued improvements to infrastructure; however, the weather cannot be changed, and neither can hills. Data were fitted to the Theory of Reasoned Action and the resulting recommendation is to promote popular motivators of exercise and enjoyment and emphasize personal benefits more than social appearances.

Shared mobility is transforming transportation in major urban cities. This paper focuses on taxes and fees on ridesourcing services, particularly those revenue strategies levied on their usages, generally on a per trip basis. These revenue strategies are analyzed from three main aspects. First, the usage of revenues was assessed. The majority of localities use them as a mechanism to cover regulatory costs or fill budget gaps, with very few using the proceeds to improve transportation systems or mobility overall. Second, the different pricing schemes used across localities were looked into. Most localities have adopted a fixed fee/surcharge charged per trip. Only two localities have established differential fees depending on the type of ride, aiming to increase vehicle occupancy and reduce traffic congestion. Lastly, a media analysis was conducted to examine the rationale for imposing a revenue-raising strategy, perceptions of key stakeholders, and ongoing discussions. Most debates around the adoption of the revenue-raising strategy involved the legislative and executive branches of governments at different levels, transportation network companies (TNCs), taxi businesses, and so forth. Supporters argued that the measure contributes to customer safety and the enhancement of equitable transportation options for all residents, while opponents stated concerns about the disproportionate impact of the measure on the middle-class and low-income populations. The findings provide a framework of current practices to assist state and local governments to make informed decisions in relation to TNC taxes and regulations.

Mobility as a service (MaaS) seeks to integrate emerging shared mobility modes with existing public transportation (PT). Decisive to its uptake will be attractive subscription plans that cater for heterogeneous mobility needs. Research on willingness to pay for such plans has commenced, yet remains divided on a central question: how much to include of which mode, and how? Complementing previous research building on stated preference data, in this study revealed preference data is used to analyze the viability of different subscription plan components (PT, car-sharing, bike-sharing, taxi), modes of inclusion (budgets in minutes and season tickets) and subscription cycles (weekly, monthly). PT season tickets are found to be viable for 83% of all respondents. Interestingly, the viability of minute budgets of car- and bike-sharing depends on subscription cycle length. Using a monthly subscription cycle, car-/bike-sharing appears viable to include in a bundle for 35%/31% of all respondents, respectively. Using a weekly subscription cycle, these figures drop to 1.4%/0.4%, respectively, as weekly variation in demand is much higher than monthly variation. In contrast to many current MaaS pilots, taxi use remains too infrequent to include as recurring credit in MaaS plans. Rather, pay-as-you-go is the economically more sensible option for consumers. This research therefore challenges the idea of all-inclusive mobility flat rates and suggests a more modular design.

Background Transportation barriers prevent millions of people from accessing health care each year. Health policy innovations such as shared savings payment models (commonly used in accountable care organizations) present financial incentives for providers to offer patient transportation to medical care. Meanwhile, ridesourcing companies like Uber and Lyft have entered the market to capture a significant share of spending on non-emergency health care transportation. Our research examines the current landscape of innovative health care mobility services in the US. Methods We conducted an environmental scan to identify case examples of utilization of ridesourcing technology to facilitate non-emergency health care transportation and developed a typology of innovative health care mobility services. The scan used a keyword-based search of news publications with inductive analysis. For each instance identified, we abstracted key information including: stakeholders, launch date, transportation provider, location/service area, payment/booking method, target population, level of service, and any documented outcomes. Results We discovered 53 cases of innovation and among them we identified three core types of innovation or collaboration. The first and most common type of innovation is when a health care provider leverages ridesourcing technology to book patient trips. This involves both established and nascent transportation companies tailoring the ridesourcing experience to the health care industry by adding HIPAA-compliance to the booking process. The second type of innovation involves an insurer or health plan formally partnering with a ridesourcing company to expand transportation offerings to beneficiaries or offer these services for the first time. The third type of innovation is when a paratransit provider partners with a ridesourcing company; these cases cite increased flexibility and reliability of ridesourcing services compared to traditional paratransit. Conclusions Ridesourcing options are becoming a part of the mode choice set for patients through formal partnerships between ridesourcing companies, health care providers, insurers, and transit agencies. The on-demand nature of rides, booking flexibility, and integration of ride requests and payment options via electronic medical records appear to be the strongest drivers of this innovation.

Urban mobility options have increased in recent years, assisted by the widespread availability of smart device software apps, geo-positioning technology, and convenient electronic financial transactions. Multi-modal shared mobility consists of public transit systems and shared mobilities that support first/last mile travel, denoting the capability of Mobility as a Service (MaaS), and to stimulate additional non-private car travel demand. This paper reviews the supply and demand sides of implementation of multi-modal shared mobility. It found that an abundance of shared modes of car, bike, and e-scooter that are linked to public transport, can improve transport accessibility to meet specific public preferences, reduce social inequality, and minimise dilemmas from the demand side. This study introduces government policy innovations and other supporting system to improve the implementation of multi-modal shared mobility. Government policies play a key role in supporting shared mobility and technology development. However, governments do not have much information about new products such as shared mobility, which creates difficulties in subsidising multi-modal shared mobility services and potentially leads to policy failures around shared mobility schemes. This study suggests that policy entrepreneurship in collaboration with other partners, policy innovation, and the notions of merit goods and second-best policymaking can enable policy initiatives towards multi-modal shared mobility and provide supporting arguments if policies encounter failures. Implementing multi-modal shared mobility requires a collaborative partnership for a paradigm shift: service providers and government must jointly set a merit-based business model, with the support of organisations to achieve improved infrastructure provision, and smart technology applications. The findings will assist the community, business providers and government policymakers to promote multi-modal shared mobility as a pathway towards more efficient, environmentally sustainable, and socially responsive mobility solutions. Refereed/Peer-reviewed

The advent of road transport automation is suggested to be one of four key technological transitions that could amount to a major transformation in mobility practices. Specifically, fully Automated...

The introduction of mobile application-based ride hailing services represents a convergence between technologies, supply of vehicles, and demand in near real time. There is growing interest in quantifying the demand for such services from regulatory, operational, and system evaluation perspectives. Several studies model the decision to adopt ride hailing and the extent of the use of ride hailing, either separately or by bundling them into a single choice dimension, disregarding potential endogeneity between these decisions. Unlike developed countries, the literature is sparser for ride hailing in developing countries, where the demand may differ considerably because of differences in vehicle ownership, and availability and patronage of many transit and intermediate public transport (IPT) modes (the shared modes carrying 40% shares in some cases). This study aims to bridge these gaps in the literature by investigating three interrelated choice dimensions among workers in Chennai city: consideration of IPT modes, the adoption of ride hailing services and the subsequent usage intensity of ride hailing services. The main factors influencing these decisions are identified by estimating a trivariate probit model. The results indicate that sociodemographic and locational characteristics and the availability of IPT modes influence ride hailing adoption, whereas work-related constraints and perception of other modes affect its frequency. Work and non-work characteristics affect both the dimensions of ride hailing. Further, endogeneity is observed between ride hailing and IPT adoption after controlling for these variables, whereas evidence of endogeneity is absent among other dimensions. Mainly, the model separates the effect of the exogenous influences on the usage frequency level from their effect on the adoption of ride hailing services.

The dramatic development of shared mobility in food delivery, ridesharing, and crowdsourced parcel delivery has drawn great concerns. Specifically, shared mobility refers to transferring or delivering more than one passenger/package together when their traveling routes have common sub-routes or can be shared. A core problem for shared mobility is to plan a route for each driver to fulfill the requests arriving dynamically with given objectives. Previous studies greedily and incrementally insert each newly coming request to the most suitable worker with a minimum travel cost increase, which only considers the current situation and thus not optimal. In this paper, we propose a demand-aware route planning (DARP) for shared mobility services. Based on prediction, DARP tends to make optimal route planning with more information about requests in the future. We prove that the DARP problem is NP-hard, and further show that there is no polynomial-time deterministic algorithm with a constant competitive ratio for the DARP problem unless P=NP. Hence, we devise an approximation algorithm to realize the insertion operation for our goal. With the insertion algorithm, we devise a prediction based solution for the DARP problem. Extensive experiment results on real datasets validate the effectiveness and efficiency of our technique.

Ridesourcing is a shared mobility service with emerging operations around the world. Popular ridesourcing services use smartphone-based applications to connect riders with drivers and are operating successfully in Canadian municipalities today. Despite increasing adoption of ridesourcing, there is a significant gap in developing regulatory frameworks in different jurisdictions, specifically in understanding how to respond to public discourse when developing regulations. This study examines sixteen Canadian jurisdictions that have adopted or are considering regulations for ridesourcing services. It develops an inventory of ridesourcing regulations and compares them to issues emerging in the media through a thematic analysis. Ridesourcing services have generated considerable public discussions highlighting tensions among stakeholders, such as the taxi industry and its users. This study systematically reviews both the public discourse appearing in the media and the regulations developed by public agencies to understand the dynamics of their perspectives. The study reveals that common public discourse issues include licensing, safety, insurance, tracking, accessibility and social issues, such as gender equality and discrimination. The results of this qualitative analysis reveal that there are similarities in public discourse, but considerable variations exist across Canadian jurisdictions. It concludes that established ridesourcing regulations mention most issues found in public discourse but do not necessarily address them in practice. This study offers valuable insights for policymakers in developing regulations to guide technology-enabled ridesourcing services that are shaping the mobility market in many communities.

Automobile sector in India is gearing for sharing mobility, electric, smart and connected vehicles. Indian two-wheeler makers are either acquiring or investing in electric, IIoT and AI startups to compete in new scenario. This paper presents a brief overview of Indian two-wheeler industry and investment strategies of two-wheeler producer TVS Motors Private Limited in the context of emerging scenario and recent investments in startups. The paper analyses TVS's recent investments in electric vehicle, shared mobility (renting), IIOT and AI startups. It is concluded that TVS Motors is, in the first place, investing to be at the forefront of electric, smart, shared and connected vehicles market. The second reason is to overcome the limitation of internal innovation in new segments in terms of required time, needed knowledge and possibility of being the first mover in the new markets.

Automation, electrification, and shared mobility are 3 the Revolutions (3Rs) in the passenger transport sector that have resulted in efficiency improvements, cost reductions, and low carbon emissions. Although there is interest about their implementation in freight transportation, the development of multiple 3Rs initiatives in freight represent a challenge for academics and practitioners, especially to anticipate potential obstacles in last mile operations. Consequently, this paper discusses the challenges and opportunities for the introduction of the 3Rs in the last mile. Specifically, the paper provides an overview of different technologies, at different levels of market readiness, and explores their benefits and/or unintended consequences. The main contribution of this exploratory research is informing the current state of the 3Rs and providing insights to develop policies, plans or initiatives that promote these revolutions in the urban freight system.

This introductory paper to the Special Issue “Shared Mobility” aims (1) to present and differentiate the diversity of practices and services that constitute the shared mobility sector; (2) to emphasize the contribution of each published article; and (3) to identify knowledge gaps of knowledge and provide further research avenues. With the contribution from 29 authors affiliated to social sciences and transportation research institutions in seven countries (Sweden, Germany, Netherlands, Greece, Belgium, Norway, and Australia), new understandings of the potential, drivers, barriers, and limitations of diverse shared mobility solutions for a more sustainable society are presented. The common message across the special issue is that the shared mobility sector is constantly evolving, while aiming to attain sustainability goals. Several papers have taken a psychological approach to explain the adoption of shared mobility practices (e.g., carsharing), yet these findings may be context-dependent, which future research should further investigate (e.g., differences between platform-based and self-service modes). We also call for researchers to pay attention to how traditional transit services can be combined with newer shared mobility services (e.g., micro-mobility), but also to informal public transport systems, as we identify these as important developing areas.

Micromobility has a high potential to change mobility habits towards the use of sustainable transport modes. The shared mobility paradigm encourages the development of new mobility services, such as bike and e-scooter sharing, potentially reducing the need of car ownership, enlarging the accessibility of public transport and enriching the transport options needed to exploit Mobility as a Service solutions. While bike-sharing services have been used in urban areas for many years, shared e-scooter services (and private e-scooters) have been spreading only in the last few years. Due to the novelty of this mode, few attempts have been made for proper micromobility network planning. This paper proposes a multicriteria GIS-based analysis aimed at planning priority networks for e-scooters, focusing on safety, transport and land use characteristics. The case study is Catania, a medium-sized city in southern Italy, which suffers from a lack of adequate infrastructures for such sustainable modes of transport. By applying the methodology, it is possible to prioritise the road network elements that better fit the needs of e-scooters, thus paving the way for suitable infrastructures and network planning.

International audience; This paper proposes a generic shared mobility simulator based on an agent-based model - MATSim. The implemented module can simulate both human and engine powered vehicles, docked/station and dockless/free-floating services, and multiple operators. The module is employed in a case study based in the San Francisco Bay Area that focuses on potential benefits of docked bike-sharing services to serve as a first/last mile solution for public transit. The initial results indicate that the current BayWheels docked bike-sharing service can potentially reduce travel time for 1049 765 trips through intermodality compared to current transit travel times. By focusing our analysis on these trips that could benefit from bike-sharing as a first/last mile solution we present the quality of service indicators for different levels of adoption and two fleet sizes.

Despite a growing interest in using Mobility-as-a-Service (MaaS) as a tool to address rural transport problems, the question of how to organize such a concept remains unanswered. To address this knowledge gap, this article explores organizational elements of rural MaaS pilots. The analysis, which is based on participatory observation and interviews with actors involved in five pilots in rural areas of Sweden, reveals that the motives of the actors involved in rural MaaS both overlap with and diverge from the frequently stated objectives of urban MaaS developments. Both concepts center on complementing and extending public transport, but while urban MaaS is underpinned by the fight against climate change, congestion, and local pollution, the main objective of rural MaaS is to reduce transport poverty. The analysis, moreover, illustrates that despite the geographic differences, actors involved in rural MaaS pilots face similar organizational challenges as have been reported from urban MaaS developments. In both cases, actors struggle with finding their roles, mitigating uncertainties, distributing responsibilities, and negotiating business models. Finally, the analysis finds that rural MaaS puts higher expectations on user involvement than urban MaaS and identifies a risk that rural MaaS developments might contribute to spatial injustice since the studied pilots only supported rural communities with high social capital.

Crowd-shipping is a promising shared mobility service that involves the delivery of goods using non-professional shippers. This service is mainly intended to reduce congestion and pollution in city centers but, as some authors observe, in most crowd-shipping initiatives the crowd rely on private motorized vehicles and hence the environmental benefits could be small, if not negative. Conversely, a crowd-shipping service relying on public transport should maximize the environmental benefits. Motivated by this observation, in this study we assess the potentials of crowd-shipping based on metro commuters in the city of Brescia, Italy. Our contribution is twofold. First, we analyze the results of a survey conducted among metro users to assess their willingness to act as crowd-shippers. The main result is that most young commuters and retirees are willing to be crowd-shippers even for a null reward. Second, we assess the potential economic impact of using metro-based crowd-shipping coupled with a traditional home delivery service. To this end, we formulate a variant of the VRP model where the customers closest to the metro stations may be served either by a conventional vehicle or by a crowd-shipper. The model is implemented using Python with Gurobi solver. A computational study based on the Brescia case is performed to get insights on the economic advantages that a metro-based crowd delivery option may have for a retailing company., 20 pages, 11 figures

Korea’s shared mobility service, TADA, has been accused of violating the Passenger Transportation Services Act, despite being an innovative transportation service. A key reason for this is that TADA is yet to discover an approach to coexist with traditional transportation service companies. The TADA case shows that in addition to technical or business matters, a variety of issues can act as new barriers to the spread of innovation. Therefore, this study aims to understand the emerging barriers that need to be addressed in adopting breakthrough technologies, particularly for shared mobility services. The purpose of this study is to show that the introduction of innovative technology encounters not only technological barriers, as suggested in previous research, but also political, industrial, and legal barriers to diffusion that hinder innovation. Therefore, this study applied the text mining method to online news big data to confirm the existence of diffusion barriers for shared mobility services. The results demonstrate that not only technical efforts but also discussions with various stakeholders and efforts to minimize industrial and legal resistance are required to effectively spread innovative services.

Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers. Published version

As connected, electric, and autonomous vehicle (AV) services are developed for cities, the research is conclusive that the use of these services must be shared to achieve maximum efficiency. Yet, few agencies have prioritised designing an AV system that focuses on dynamic ridepooling, and there remains a gap in the understanding of what makes people willing to share their rides. However, in 2017, the Australian transport authority Transport for New South Wales launched over a dozen trials for on-demand, shared public transport, including AVs. In this paper, we investigate the user willingness-to-share, based on experiences from one of these trials. Four focus groups (19 participants in total) were held in New South Wales with active users of either the trialled on-demand dynamic ridepooling service (Keoride) or commercial ridepooling (UberPool). Through thematic analysis of the focus group conversations, the cost, comfort, convenience, safety, community culture, and trust in authority emerged as factors that influenced the willingness-to-share. When presented with driverless scenarios, the focus group participants had significant concerns about the unknown behaviour of their co-passengers, revealing sharing anxiety as a significant barrier to the adoption of shared AVs. This paper identifies previously disregarded factors that influence the adoption of AVs and dynamic ridepooling and offers insights on how potential users’ sharing anxiety can be mitigated.

Due to their small size and low energy demand, light electric vehicles (LEVs), such as electric moped scooters, are considered as a space efficient and eco-friendly alternative for mobility in cities. However, the growth of electric moped scooter sharing services raises the question of how environmentally friendly this business model is, considering the entire lifecycle. Due to the dynamic market and insufficient availability of public data on the business processes of sharing services only a few studies on the impact of shared electric mopeds are available. Especially there is a lack of research on the impacts of key operational logistic parameters of the sharing system. This paper aims to fill this gap by conducting a life cycle assessment using the example of an electric moped scooter manufactured and used in sharing services in Germany, based on different operating scenarios. The results show that e-moped sharing has a similar environmental impact on global warming potential, in terms of passenger kilometers, as public transport, especially if long product lifetimes as well as efficient operation logistics are realized.

This session will review and discuss the latest evidence on active travel, mobility-as-a-service, shared mobility, public transport and other ways to decarbonise transport in the urban environment.

Due to the current environmental and traffic-related problems of motorized individual transport (MIT), the importance of new, flexible, healthy, less pollutant, accessible mobility systems is growing. Bike and e-scooters have been shown to potentially mitigate these impacts. Therefore, we aim to explore the associated social characteristics of the users of bikes and e-scooters as shared options to identify the attributes of potential new customers and make them more competitive in the market. We explored social characteristics beyond the traditional sociodemographics, including psychographic, attitudinal, and behavioral attributes. These characteristics can help to understand deeper the interests, attitudes, and behavior of customers.Therefore, we conducted an online and offline survey in Munich, Germany with 408 respondents to evaluate who is using and who is not using bike sharing, shared e-scooters, and shared micromobility offers in general. Therefore shared micromobility user were classified as users of bike sharing and/ or shared e-scooter systems. The statistically significant parameters were then used to create classification models to predict users and non-users of shared micromobility. Results show that bike sharing is mainly used by high educated employed males with high income, who think that equity and adventure are important but not tradition. Bike sharing users feel that bikes are convenient, relaxing, and fun, which is not the case for private cars. They use bike sharing as well as public transport and other shared mobility options. Moreover, shared e-scooter users have values oriented to wealth and adventure but not tradition and they enjoy using other shared modes. Naive Bayes models helped to predict potential bike sharing users with an accuracy of 72% and shared e-scooter with 83%. The highest accuracy was scored by behavioral characteristics followed by sociodemographics and psychographic parameters.To the best of the authors’ knowledge, this would be one of the few studies on shared micromobility considering social characteristics beyond sociodemographics.

The mobility of people is at the center of transportation planning and decision-making of the cities of the future. In order to accelerate the transition to zero-emissions and to maximize air quality benefits, smart cities are prioritizing walking, cycling, shared mobility services and public transport over the use of private cars. Extensive progress has been made in autonomous and electric cars. Autonomous Vehicles (AV) are increasingly capable of moving without full control of humans, automating some aspects of driving, such as steering or braking. For these reasons, cities are investing in the infrastructure and technology needed to support connected, multi-modal transit networks that include shared electric Autonomous Vehicles (AV). The relationship between traditional public transport and new mobility services is in the spotlight and need to be rethought. This paper proposes an agent-based simulation framework that allows for the creation and simulation of mobility scenarios to investigate the impact of new mobility modes on a city daily life. It lets traffic planners explore the cooperative integration of AV using a decentralized control approach. A prototype has been implemented and validated with data of the city of Trento., Comment: 13 pages, 10 Figures, Early Access Article published at the IEEE Transactions on Intelligent Transportation Systems

We are proud to bring to you an issue focused on a pertinent topic-new mobility. The term new mobility was coined to represent four pivotal technologies that will change how we move not only ourselves but also our cargo: connected cars, autonomy, shared mobility, and electrification. The acronyms CASE or ACES are often used to convey these traits that new mobility entails.