Search

Ridepooling is a new mobility service mainly for people in cities and urban areas. By matching the routes of customers with similar start and end points while driving in an optimally pooled manner, meaningful reductions in road traffic and related emissions can be achieved. Such services must meet customers’ demands appropriately to achieve sustainable customer acceptance. Service providers face diverse customer expectations and prejudices that differ from those toward existing transportation modes. Today, most ridepooling trips are conducted with only one customer, confirming impressions of non-optimal operation. Using a survey-based approach, possible relevant constructs for the acceptance of and intention to use ridepooling services are analyzed. Testing constructs from the Unified Theory of Acceptance and Use of Technology 2 and environmental awareness, partial least squares analysis was performed with the software SmartPLS to investigate a dataset of 224 respondents. Results suggest that attitude toward use, perceived usefulness, and performance expectancy have an influence on the behavioral intention to use ridepooling services. In contrast, environmental awareness, price value, and effort expectancy do not have such an influence. The study expands the literature about customer acceptance of ridepooling service as well as new mobility services in general. Further, the paper provides research implications and recommendations for the development and implementation of the ridepooling concept for service providers.

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.

Free-floating car sharing (FFCS) offers greater flexibility than station-based car sharing but seems to affect car ownership less. This study looks into characteristics of people who changed or did not change car ownership over time and how an increase or decrease relates to FFCS membership, demographic and attitudinal factors. The study is based on FFCS users (n = 776) and non-users (n = 720) in Copenhagen surveyed two times within a 2.5-year period. Five population segments were created: car dependents, car avoiders, and car limiters who showed constant but different levels of car ownership; car aspirers who increased, and car sellers who decreased car ownership over time. The segments' profiles range from car dependents who show high affective car motives, high perceived mobility necessities and car dependency at the one end, and car avoiders who seem more driven by environmental norms and an instrumental relation to the car, at the other end of the scale. A multinomial regression predicting whether car owners increased or decreased the number of cars in the household during the project period found a positive effect of FFCS membership for decreasing car ownership. However, the effect was no longer significant when adding the intention to reduce car ownership at the time of the first survey. Main factors that remained significant for changed car ownership included a change in household composition, access to a private parking space and the initial number of cars in the household. The paper discusses strategies to increase the contribution of FFCS to car ownership reduction.

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.

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...

In this paper, we model and simulate special use cases of on-demand shared mobility services for the City of Ann Arbor, MI. We define shared mobility as any motor-vehicle-served transportation option between private vehicles and public transit, such as taxis, demand-responsive transit, and dynamic shuttles. Here, we present and evaluate a suite of four different service types that could potentially complement existing transportation services in Ann Arbor. A novel aspect of this study is that it tests scenarios that were developed in consultation with city planners looking for insights into real-world problems. This study used fleet simulation software to test four service configuration scenarios for a hypothetical on-demand shared mobility service: citywide shuttle, a corridor-based downtown shuttle, a park and ride shuttle, and a transit-complementary service. Three levels of demand were tested for each scenario: 3%, 9%, and 15% of all private vehicle trips in the city. Findings indicated that citywide on-demand shared mobility services struggled to achieve higher vehicle occupancies than private vehicles at approximately 1.4. Service configurations with aggregated trip density resulted in slightly improved occupancies, as found in downtown- and park and ride shuttle scenarios. More impactful was aggregating demand by moving from “many-to-many” routing as with citywide floating services to “many-to-one” routing as with downtown- or park and ride shuttle services, which increased vehicle occupancy from 1.4 to almost 2. Lastly, we also discuss the potential benefits of reduced congestion and parking needs.

The global expansion of ridehailing platforms has been accompanied by a diversification of service offerings as platforms fit within new urban contexts. While ridehailing has been of great interest to transportation researchers, analysis of its adoption and use in developing cities that differentiates between service offerings is lacking. To help address this knowledge gap, this study analyzes primary survey data collected from frequent users of the DiDi Chuxing ridehailing platform in three Mexican cities: Mérida, Toluca de Lerdo, and Aguascalientes. It investigates how ridehailing fits into the travel behavior of its users, explicitly differentiating between express (exclusive) and comparte (pooled) services. Findings were that (i) frequent use of ridehailing is positively correlated with use of public transport—city-run and privately-operated buses—and taxi, but negatively correlated with use of private car and motorcycle; and (ii) ridehailing trips are more likely to substitute public transport and taxi trips, but that the mode substitution depends on the service offering, with high substitutability between express and comparte. This degree of substitutability suggests that there is potential to encourage ridehailing users to pool trips, increasing the occupancy rate of ridehailing vehicles and reducing their negative impacts on congestion. Among the many factors involved in choosing between exclusive and pooled services, study participants rated safety, travel time, travel time reliability, and price as key determinants, with a highly elastic relation between travel time and price. These results inform efforts by urban transportation policymakers and ridehailing operators to encourage pooling in the Latin American context.

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

Conventional fixed-structure transit service is unable to satisfy the increasingly personalized passenger demand. Shared mobility companies enable the transformation of conventional transit with advanced technology. This paper proposes a framework to design an application-based demand responsive transit (DRT) system to serve zone-to-zone passenger requests. The proposed DRT service operates between zone pairs to satisfy “door-to-door” and “reservation-based” requests. A two-phase problem is formulated, including (i) conceptual planning phase and (ii) discrete route design phase. In the conceptual planning phase, a continuous approximation model is proposed to optimize operational efficiency by deciding the resource requirements and operational features with given service area and demand density. In the route design phase, a customized bus routing model is established to generate profitable routes, by adopting a combined solution approach with adapted savings and simulated annealing (SA) method. The theoretical framework is applied in a pilot area at Huangdao College Town in Qingdao, China. An implementable design including the layout plan, candidate stops, and operation headways is developed from the conceptual plan model. Groups of sample data sets are used to demonstrate the applicability of the proposed design framework. Results show that the proposed DRT system is promising in relation to providing better service than the previous first-come-first-serve bus pooling service.

The last decade witnessed a rapid rise in shared mobility in China. However, there is lack of understanding how the shared mobility market developed, how shared mobility reshapes daily travel patte...

There has been a rapid rise in papers modelling the impacts of autonomous vehicles. Drawing on a review of this literature, we analyse and discuss the messages conveyed by these studies from a poli...

Regions worldwide are adopting and exploring low-speed automated electric shuttle (AES) service as an on-demand shared mobility service in dense geofenced urban areas. Building on this concept, the...

We propose a model for optimizing the last-mile delivery of n packages from a distribution center to their final recipients, using a strategy that combines the use of ride-sharing platforms (e.g., Uber or Lyft) with traditional in-house van delivery systems. The main objective is to compute the optimal reward offered to private drivers for each of the n packages such that the total expected cost of delivering all packages is minimized. Our technical approach is based on the formulation of a discrete sequential packing problem, in which bundles of packages are picked up from the warehouse at random times during the interval [Formula: see text]. Our theoretical results include both exact and asymptotic (as [Formula: see text]) expressions for the expected number of packages that are picked up by time T. They are closely related to the classical Rényi’s parking/packing problem. Our proposed framework is scalable with the number of packages.

Vehicle sharing services (bikeshare, carshare, and e-scooters) offer the potential to improve mobility and accessibility for disadvantaged populations. This article reviews research related to equity and vehicle sharing, focusing on race/ethnicity, income, gender, age, and disability. We find evidence of disparities in use of shared vehicles, which is only partly explained by lack of physical proximity. Some studies reveal additional barriers to use, particularly for bikesharing. More research on barriers is necessary, particularly for carsharing. We identify several programs aimed at reducing barriers, though evidence of their impact is necessary to inform practice.

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.

Car-sharing has experienced a significant boom in recent years, with estimates suggesting that car-sharing programs are now operating in over 30 countries worldwide, serving around five million users. The potential to reduce individual vehicle ownership rates is frequently cited as a motive for promoting car-sharing. While some previous studies have argued that customers are indeed willing to reduce the total number of vehicles owned after becoming car-sharing members, the reliability of these findings is tenuous given that many are based on self-selected samples of car-sharing users, resulting in biased estimates. In theory, the availability of car-sharing programs could have limited effect on the general public’s car ownership decisions–or at least have no effect on a large portion of travelers. Whether or not traveler decision processes are significantly influenced by specific attributes of different car-sharing options (e.g., access time, vehicle size, fuel type, etc.) remains an unanswered question, as there are limited quantitative studies on this issue. To contribute to filling this research gap, this paper discusses the findings of a study of 1,500 private households across major Australian cities. A nested logit model is used to investigate the impacts of car-sharing on respondents’ household vehicle ownership decisions. In contrast to the results of some previous studies, we find that the stated availability of car-sharing appears to have minimal impact on respondents’ decision to own a vehicle or not, leading to important policy implications. We agree with prior investigations that car-sharing could potentially reduce private car ownership. However, because this study finds limited impact of the availability of car-sharing on vehicle ownership, and because the majority of respondents did not self-identify as car-sharing users, education and awareness campaigns could be important factors in improving the general public’s preferences towards car-sharing and fully realizing car-sharing’s benefits to society.

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.

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.

Carpooling rates in America have been falling for decades. But new technologies may offer solutions to traditional carpooling barriers and usher in a new chapter in shared car travel. Ride-hail services connect riders to drivers through smartphone applications. The largest ride-hail companies, Uber and Lyft, offer shared carpool (rideshare) services to connect riders traveling in the same directions and at the same times. Although researchers have recently begun to understand who uses ride-hail services, few have yet investigated ridesharing. To fill this gap, I ask and answer two questions. First, what factors are associated with where ridesharing occurs? Second, what factors are associated with who rideshares? To answer both questions, I use trip-level data of 6.3 million Lyft trips, including 1.9 million Lyft Shared trips, taken in Los Angeles County in 2016. Findings reveal that while about one-third of Lyft trips are on Lyft Shared, these rideshare trips are made by a small fraction of ride-hail users. Just one-third of ride-hail users made even one rideshare trip over the three-month study period, and just ten percent of all Lyft riders made 94 percent of rideshare trips. People living in dense and lower-income neighborhoods share a higher proportion of ride-hail trips compared to riders living in other neighborhoods. Less ridesharing occurs in racial and ethnically diverse neighborhoods compared to neighborhoods where clear racial or ethnic majorities exist. Cities seeking to increase sharing in ride-hail services should focus efforts on attracting non-users, including pricing to encourage shared rather than solo car trips. Implemented policies should avoid undercutting demand for transit or active travel, which remain the most efficient modes on our streets.

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

Using shared mobility data collected from different sources and multinomial models, this paper investigates its role in serving transit-dependent populations and whether shared mobility can reduce ...

Shared mobility services such as ride-hailing services, bikesharing services, and carsharing services are relatively new forms of transportation where users gain access to a specific type of transportation for a short period of time, usually using a mobile phone-based application (app). Much has been written about whether these services are fundamentally transforming urban transportation systems or if they are simply the latest fad. However, little empirical research has been done on the effects of these services on people’s travel behavior. This paper uses the 2017 National Household Travel Survey (NHTS) to assess the association between the usage of shared mobility services and individual’s trip making behavior. We fit negative binomial regression models to NHTS data to understand if the usage of shared mobility services is potentially influencing the number of trips people make on weekdays and weekends. We then perform hypothesis testing to confirm the significance of our results. We find that ride-hailing app usage does have a statistically significant association with the number of trips made per day on both weekends and weekdays, although the effect appears to be slightly more significant on weekdays than weekends. Based on our model, bikesharing and carsharing do not appear to have an association with daily trip generation behavior on either weekends or weekdays.

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.

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.

This work studies how the usage of shared mobility services could be influenced by latent factors. An integrated choice and latent variable model is adopted to explore the effects of three attitudinal and perceptual factors on bike- and car-sharing choices while simultaneously investigating the causes associated with each of the latent variables. A group of Chinese commuters’ stated preference mode choice data are collected. It is found that the probability to choose bike-sharing could be positively affected by “willingness to be a green traveler” and “satisfaction with cycling environment,” and car-sharing choice is positively correlated with “advocacy of car-sharing service.” By taking into account the interaction effects between the latent variables and travel time of the two services, a significant difference is discovered on the estimated value of travel time savings (VTTS) compared with other more restrictive model specifications. The finding highlights the importance to derive different VTTS for travelers with differentiated attitudes and perceptions as the tastes toward travel time spent could vary substantially. In other words, there would be different trade-off preferences across attitudinal groups, according to which transport service operators could customize their strategies on prices and levels of service offered.

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.

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.

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.

International audience; Electric scooter (e-scooter) sharing has recently known a wide success in many cities all around the world. Nevertheless, it has also generated issues due to risky and improper behaviour of its users. Wild parking, namely parking without caring about the rules of the road, has in particular become a major issue and has induced an increasing number of cities to impose bans and fines to e-scooter sharing. To tackle wild parking, we introduced the figure of the beautificator, an agent hired by a sharing company with the specific task to reposition e-scooters for guaranteeing urban decorum. In this paper, we propose to increase the effectiveness of the beautificators by integrating Unmanned Aerial Vehicles (UAVs) in their activities: remotely controlled UAVs equipped with cameras are deployed to fly across the sharing service area for identifying e-scooters that require beautification with priority. Thanks to the UAVs, the beautificators do not have to operate blindly, touring locations of parked e-scooters without knowing their parking condition, but can readily learn which e-scooters require their immediate attention. We formulate the problem of optimally scheduling the joint actions of beautificators and UAVs, taking into account beautification constraints, battery limits of UAVs and the possibility of swapping exhausted UAV batteries. For tackling this problem, we propose a mixed integer programming model and a heuristic for accelerating the convergence to the optimum of a state-of-the-art optimization solver, reporting results of computational tests over realistic instances.

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

The current COVID-19 pandemic situation has a very significant impact on urban mobility, as evidenced by fundamental changes in passengers’ behavior. In many cases, passengers have switched to other modes of transport to minimize the risk of infection (particularly cycling and walking). This paper aims to point out the analysis results of the impacts of a bike-sharing system in the city of Košice before and during the COVID-19 pandemic. Additionally, this paper describes an analysis of the current state of bike-sharing in the Slovak Republic. We have stated a timeline of significant dates related to the COVID-19 pandemic. COVID-19 had a significant impact on people’s mobility and bike-sharing, as evidenced by the graphs and results in this paper.

Modeling congestion effects arising from multiple travel modes, shared mobility modes in particular, is non-trivial because of the complex interactions among diverse agents and distinct traffic flow compositions. This research aims to provide a theoretical framework of generic traffic network equilibria to unify these services and hopefully become a step stone to modeling shared mobility services in congested road network. In the proposed framework, we mainly focus on three modes: driving solo, ridesharing, and e-hailing service. The four types of traffic flows are: personal vehicle drivers, e-hailing drivers, ridesharing riders, and e-hailing passengers. The first two flows contribute to traffic congestion while the latter two do not. To capture their interactions, a super extended network is created with four copied networks each of whom represents one type of traffic flow. The equilibrium of new mobility systems can then be reformulated as a quasi-variational inequality and solution existence is discussed. The numerical results are tested in both Braess network and Sioux Falls network to illustrate the impact of different parameters on equilibrium outcomes, including modal cost, system travel time and deadhead miles. The results of this model will help assist transportation planners in making policy and regulation decisions regarding shared mobility services.

The increasing availability and adoption of shared vehicles as an alternative to personally-owned cars presents ample opportunities for achieving more efficient transportation in cities. With private cars spending on the average over 95\% of the time parked, one of the possible benefits of shared mobility is the reduced need for parking space. While widely discussed, a systematic quantification of these benefits as a function of mobility demand and sharing models is still mostly lacking in the literature. As a first step in this direction, this paper focuses on a type of private mobility which, although specific, is a major contributor to traffic congestion and parking needs, namely, home-work commuting. We develop a data-driven methodology for estimating commuter parking needs in different shared mobility models, including a model where self-driving vehicles are used to partially compensate flow imbalance typical of commuting, and further reduce parking infrastructure at the expense of increased traveled kilometers. We consider the city of Singapore as a case study, and produce very encouraging results showing that the gradual transition to shared mobility models will bring tangible reductions in parking infrastructure. In the future-looking, self-driving vehicle scenario, our analysis suggests that up to 50\% reduction in parking needs can be achieved at the expense of increasing total traveled kilometers of less than 2\%., Comment: IEEE Transactions on Intelligent Transportation Systems, 2018

Shared mobility is widely recognized for its contribution in reducing carbon footprint, traffic congestion, parking needs and transportation-related costs in urban and suburban areas. In this context, the use of carpooling in home-work commute is particularly appealing for its potential of lessening the number of cars and kilometers traveled, consequently reducing major causes of traffic in cities. Accord- ingly, most of the carpooling algorithms are optimized for reducing total travel time, cost, and other transportation-related metrics. In this paper, we analyze carpooling from a new perspective, investigating the question of whether it can be used also as a tool to favor social integration, and to what extent social benefits should be traded off with transportation efficiency. By incorporating traveler's social character- istics into a recently introduced network-based approach to model ride-sharing opportunities, we define two social-related carpooling problems: how to maximize the number of rides shared between people belonging to different social groups, and how to maximize the amount of time people spend together along the ride. For each of the problems, we provide corresponding optimal and computationally effi- cient solutions. We then demonstrate our approach on two datasets collected in the city of Pisa, Italy, and Cambridge, US, and quantify the potential social benefits of carpooling, and how they can be traded off with traditional transportation-related metrics. When collectively considered, the models, algorithms, and results presented in this paper broaden the perspective from which carpooling problems are typically analyzed to encompass multiple disciplines including urban planning, public policy, and social sciences.

Emerging technologies and services stand poised to transform the transportation system, with large implications for energy use and mobility. The degree and speed of these impacts depend largely on who adopts these innovations and how quickly. Leveraging data from a novel survey of San Francisco Bay Area residents, we analyze adoption patterns for shared mobility, electrified vehicle technologies, and vehicle automation. We find that ride-hailing and adaptive cruise control have penetrated the market more extensively than have electrified vehicles or car-sharing services. Over half of respondents have adopted or expressed interest in adopting all levels of vehicle automation. Overall, there is substantial potential for market growth for the technologies and services we analyzed. Using county fixed effects regressions, we investigate which individual and location-level factors correlate to adoption and interest. We find that, although higher-income people are disproportionately represented among current adopters of most new technologies and services, low- to middle-income people are just as likely to have adopted pooled ride-hailing. Younger generations have high interest in automated and electrified vehicles relative to their current adoption of these technologies, suggesting that young people could contribute substantially to future market growth—as they are doing for ride-hailing. We find no evidence that longer commutes present a barrier to plug-in electric vehicle adoption. Finally, women are less likely than men to adopt and/or be interested in adopting most new transportation technologies, with the exception of ride-hailing; designing or marketing technologies with women’s preferences in mind could contribute to future market expansion.

Shared mobility can contribute to tackling many of the pressing problems from transport and initiate a system change. But who is attracted by shared mobility and who not? How is this related to the attractiveness of electric driving? What are the perceptions underlying those preferences? We try to answer these questions based on two survey studies from Germany with a focus on carsharing. First, we present findings from an online survey (n = 1548) in one of Germany’s showcase regions for electric vehicles (EVs) to analyse the acceptance of carsharing in society. The data analysis shows that perceived compatibility with daily life is the most important factor related to the attitudes towards carsharing and that social norms also play an important role. Second, we analyse early adopters of electric carsharing, i.e. a combination of both innovations in more detail. We draw on a survey (n = 947) from field trials in Germany. We find that – extending the results of study 1 – the users are a socio-demographically specific group. A segmentation revealed that carsharing with EVs is particularly attractive for younger people who (i) live as a couple but without cars or (ii) are starting a family and use carsharing as a supplement to their own cars. The findings from the second study are in line with the first one and also emphasise that the affinity for carsharing and EVs is closely connected. We conclude the paper by discussing the implications of our findings for the transition to low-carbon mobility.

A self-driving, fully automated, or “autonomous” vehicle (AV) revolution is imminent, with the potential to eliminate driver costs and driver error, while ushering in an era of shared mobility. Dynamic ride-sharing (DRS), which refers to sharing rides with strangers en route, is growing, with top transportation network companies providing such services. This work uses an agent-based simulation tool called MATSim to simulate travel patterns in Austin, Texas in the presence of personal AVs, and shared AVs (SAVs), with DRS and advanced road-pricing policies in place. Fleet size, pricing, and fare level impacts are analyzed in depth to provide insight into how SAVs may best be introduced to a city or region. Results indicate that the cost-effectiveness of traveling with strangers overcomes inconvenience and privacy issues at moderate-to-low fare levels, with high fares being more detrimental than the base case. A moderately sized Austin, Texas fleet (one SAV for every 25 people) serves nearly 30% of all trips made during the day. The average vehicle occupancy of this fleet was around 1.48 [after including the 12.7% of SAV vehicle-miles traveled (VMT) empty/without passengers], with a 4.5% increase in VMT. This same fleet performs better when road-pricing is enforced in the peak periods (4 h a day), moderating VMT by 2%, increasing SAV demand and in turn fleet-manager revenue. SAVs are able to earn around $100 per SAV per day even after paying tolls, but only at low-fare levels.

Emergent technologies like autonomous/connected vehicles and shared mobility platforms are anticipated to significantly affect various aspects of the transportation network such as safety, mobility, accessibility, environmental effects, and economics. Transit agencies play a critical role in this network by providing mobility to populations unable to drive or afford personal vehicles, and in some localities carry passengers more efficiently than other modes. As transit agencies plan for the future, uncertainty remains with how to best leverage new technologies. A survey completed by 50 transit agencies across the United States revealed similar yet different perceptions and preparations regarding transportation network companies (TNCs) and autonomous transit (AT) systems. Transit agencies believe TNC market share will grow, either minimally or rapidly (72%), within the next 5 years and have either a negative (43%) or positive (35%) impact on their transit system. Only 30% of agency boards instructed the agency to work with TNCs, despite no perceived transit union support. For AT systems, 22% of agencies are studying them, 64% believe the impacts of AT over the next 10–20 years will be positive, but fewer agencies are influenced to consider new technologies because of AT systems (38%) compared with TNCs (72%). Surprisingly, transit administration is mostly unsure about driver and transit unions’ perceptions of these technologies. In addition, a significant number of transit agencies do not believe they should play a role in ensuring TNCs are safe and equitable and that TNCs should not have to adhere to the same regulations (50%, 28% respectively).

Last-mile connectivity is an important factor in enabling greater integration and accessibility of mass transit networks to the largest number of urban residents. With the rise of new mobility enterprises, characterized by mobile applications delivering real-time information and on-demand and shared mobility, there is potential for integration with public and mass transit to bridge the last-mile gap. This paper presents and analyzes the results from a case study in which new mobility enterprises are piloted as last-mile solutions at a metro station in Bangalore. The solutions have a high perceived time savings among users, and it is found that there is a measurable modal shift from personal vehicles to these solutions for bridging the last mile gap. At the same time, the case study shows that there is need for supporting regulatory frameworks and greater multimodal integration for enabling public-private collaboration for seamless and sustainable urban mobility.

Shared mobility is an increasingly popular phenomena already inscribed in the landscape of urban transport. The response to it in the area of last-mile urban freight delivery is crowd-logistics based on ride-sourcing. Due to great interest in this topic in literature in recent years, the authors decided to review existing findings on crowd-logistics and its impact on sustainable city logistics and embed it in European regulatory context with particular reference to the Polish legal system. The findings presented have a preliminary character. This article stresses the significance of legal risks and barriers in the development of sustainable solutions for urban freight transport and indicates further research directions in the area.

Shared mobility refers to the shared use of a motor vehicle, bicycle, or other low-speed transportation modes, which has significant influences on transportation systems. Among different types of shared mobility, ridesplitting is developing the most rapidly because of the emergence of transportation network companies. In this study, we present exploratory evidence of on-demand ridesplitting's impacts on the vehicle use and purchase willingness using the emerging ridesourcing real-world data (i.e., app-based, on-demand ride services like Uber and DiDi Chuxing) in Hangzhou, China. We explore ridesplitting users' travel habits and their transportation modal shift behavior if the ridesplitting services are unavailable, and how ridesplitting impacts the use of public transit and private cars. In summer 2017, an online survey was conducted with respondents who had completed ridesourcing journeys within one month before the investigation. We compare the survey results with two-week ridesourcing order data provided by DiDi Chuxing, 20% of which are ridesplitting orders (including DiDi Hitch and Express ridesplitting). Considering the modal shift from public transit to ridesplitting, the findings indicate that (I). In the short term, ridesplitting services reduce the number of vehicles on road (decreased by 3,051 veh/day, nearly 2.6p of the vehicle ownership in the urban area of Hangzhou). (II). In the intermediate term with the development of ridesplitting, the total decreased number of vehicles is estimated to be 4,129 veh/day (nearly 3.6p of the vehicle ownership). An interesting phenomenon is that Hitch's influences on the vehicle usage in the intermediate term is not obvious as the influence in the short term due to travelers' modal shift from public transit. (III). In the long term, ridesplitting will reduce the car purchase willingness and influence people's travel behavior to some extent. This paper shines light on quantitatively exploring the influence of on-demand ridesplitting on the vehicle use and purchase willingness.

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

Shared micro-mobility services have rapidly gained popularity yet challenged city administrations to develop adequate policies while scientific insight is largely missing. From a transportation equity perspective, it is particularly important to understand user correlates, as they are the beneficiaries from public investment and reallocation of public space. This paper provides an up-to-date account of shared micro-mobility adoption and user characteristics in Zurich, Switzerland. Our results suggest that shared micro-mobility users tend to be young, university-educated males with full-time employment living in affluent households without children or cars. Shared e-scooter users, in particular, are younger, yet more representative of the general population in terms of education, full-time employment, income and gender than bike-sharing users. This suggests that shared e-scooters may contribute to transportation equity, yet their promotion should be handled with care as life-cycle emissions exceed those of bike-sharing and equity contributions might be skewed as many users are students., Transportation Research Part D: Transport and Environment, 94, ISSN:1361-9209, ISSN:1879-2340

Moped-style scooters are one of the most popular systems of micro-mobility. They are undoubtedly good for the city, as they promote forms of environmentally-friendly mobility, in which flexibility helps prevent traffic build-up in the urban centers where they operate. However, their increasing numbers are also generating conflicts as a result of the bad behavior of users, their unwarranted use in public spaces, and above all their parking. This paper proposes a methodology for finding parking spaces for shared motorcycle services using Geographic information system (GIS) location-allocation models and Global Positioning System (GPS) data. We used the center of Madrid and data from the company Muving (one of the city’s main operators) for our case study. As well as finding the location of parking spaces for motorbikes, our analysis examines how the varying distribution of demand over the course of the day affects the demand allocated to parking spaces. The results demonstrate how reserving a relatively small number of parking spaces for scooters makes it possible to capture over 70% of journeys in the catchment area. The daily variations in the distribution of demand slightly reduce the efficiency of the network of parking spaces in the morning and increase it at night, when demand is strongly focused on the most central areas.