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

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

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

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

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

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

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

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

Mobility as a Service is a potential game changer in the transition from individual motorized to sustainable and multimodal mobility in urban areas. These innovative concepts are an opportunity for existing forms of sustainable mobility to reach a broader user base and extend their service offerings. Although there are some practical examples of carsharing applications in such concepts, there is a lack of data-driven research about Mobility as a Service in practice and, above all, findings about the influence on usage behavior within such systems. We analyze usage data of the "Mobil-Flat", a subscription-based mobility offer that integrates carsharing, public transportation, and bike sharing into a single service in a medium-sized German city, to determine the impact of Mobility as a Service on carsharing use. This results in a structured overview of the business model and the implications of such concepts on user behavior and acceptance in the context of carsharing, based on a real-world dataset. [ABSTRACT FROM AUTHOR]

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

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers' decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies. [ABSTRACT FROM AUTHOR]

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

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

With the growth of cars and car-sharing applications, commuters in many cities, particularly developing countries, are shifting away from public transport. These shifts have affected two key stakeholders: transit operators and first- and last-mile (FLM) services. Although most cities continue to invest heavily in bus and metro projects to make public transit attractive, ridership in these systems has often failed to reach targeted levels. FLM service providers also experience lower demand and revenues in the wake of shifts to other means of transport. Effective FLM options are required to prevent this phenomenon and make public transport attractive for commuters. One possible solution is to forge partnerships between public transport and FLM providers that offer competitive joint mobility options. Such solutions require prudent allocation of supply and optimised strategies for FLM operations and ride-sharing. To this end, we build an agent- and event-based simulation model which captures interactions between passengers and FLM services using statecharts, vehicle routing models, and other trip matching rules. An optimisation model for allocating FLM vehicles at different transit stations is proposed to reduce unserved requests. Using real-world metro transit demand data from Bengaluru, India, the effectiveness of our approach in improving FLM connectivity and quantifying the benefits of sharing trips is demonstrated. [ABSTRACT FROM AUTHOR]

Greenhouse gas emission reduction in the passenger transport sector is a main challenge for China’s climate mitigation agenda. Electrification and shared mobility provide encouraging options for carbon emissions reduction in road transport. Based on an integrated scenario-based assessment framework, a provincial-level projection is made for vehicle growth and CO2 emissions in China under shared socioeconomic pathways (SSPs). This work illustrates how passenger car electrification and sharing contribute to China’s “30·60” climate goals (peaking of CO2 emissions by 2030 and carbon neutrality by 2060). The results demonstrate that China is en route to achieving the goal of a 2030 carbon peak (1.0Gt CO2) under current conditions, and could reach peak emissions around 2026 with optimistic growth in EVs and shared mobility. Compared with no policy action, the single EV policy (shifting from ICEVs to EVs) can reduce 71% of emissions by 2060, thus narrowing but not closing the mitigation gap to carbon neutrality in passenger cars (302 Mt CO2). Shared mobility can provide further emission reduction support, reducing emissions by 83% in 2060. Comprehensive climate actions (including electrification, sharing mobility to reduce car use, and improving vehicle efficiency and fuel carbon intensity) are needed to achieve deep decarbonization to net-zero by 2060 in the passenger transport sector. [ABSTRACT FROM AUTHOR]

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

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

Promoting alternative travel modes to private cars is a crucial aspect of sustainable transport policies. The integration of different transport modes and services, facilitated by mobile apps and payment systems, has expanded travel options and fostered multimodal travel behaviour. Despite the increasing prevalence of multimodal transport in practice, there exists a shortage of systematic academic research on this subject. This paper seeks to fill this gap by presenting an overview of key themes related to multimodality in travel behaviour, providing valuable insights for both researchers and practitioners. Leveraging an in-depth analysis of 182 relevant papers extracted from the Web of Science database, this paper intricately formulates a comprehensive conceptual framework that delves into the definition and measurement of multimodality. Furthermore, it synthesises recent perspectives and insights pertaining to the factors shaping multimodality and its socio-environmental impacts. The paper suggests several potential research directions in multimodality, including a focus on the supportive environment for multimodal behaviour (termed multimodal-oriented development, MMOD), an examination of the socio-environmental effects of multimodality and MMOD, and an investigation of regional variations in knowledge and practices related to multimodality. By exploring these research avenues, this paper aims to advance our understanding of multimodal travel behaviour and contribute to the development of effective strategies and policies promoting sustainable transportation systems. [ABSTRACT FROM AUTHOR]

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

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

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

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

New transport technologies have historically influenced how cities are shaped, while urban form is known to influence travel behaviour. But this reciprocal relationship is seldom operationalized in practice due to the disciplinary gap between urban design and transport planning. The two are often linked through a 'predict and provide' workflow, which is problematic in the context of emerging technologies such as AVs. This paper argues for a more iterative design and transport simulation workflow, through design experiments. One design experiment is illustrated using Sketch MATSim, to investigate the impact of network design on the performance of shared automated vehicles. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

A variety of shared mobility hubs, offering shared mobility and other services, have emerged in many cities across the globe. This paper provides a literature review on of the definition and categorisation of shared mobility hubs, guidance for the design of these hubs, and develops a multidimensional typology for shared mobility hubs. The typology, named the SmartHubs Integration Ladder, is based on three integration dimensions: physical, digital, and democratic. The literature review shows that digital and democratic (participation) integration dimensions, and universal design principles are typically missing in shared mobility hub concepts, definitions and planning practice. This implies that existing shared mobility hubs will not reach their full potential in terms of user and societal value. The "smarter" shared mobility hubs are physically, digitally, and democratically, the more user and societal value can potentially be created. [ABSTRACT FROM AUTHOR]

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

Recent developments indicate that emerging technologies like connected and autonomous vehicles (CAVs) and mobility-on-demand services and their possible integrations can revolutionize travel behavior. Recent research studies also establish the importance of peer inputs and media advertisements in expediting the adoption of such emerging technologies. This study quantifies individuals' ranking preference towards multiple CAV-based travel modalities (owned, ride-hailing, carpooled, and public transport) based on psychological constructs-capturing the perceptions of the CAV-related information received from their peers and advertisements-using a statewide survey and hybrid choice model. Results indicate individuals' increased concern towards their social status and barriers associated with CAVs when adopting a CAV-based travel mode. Also, individuals prioritize peer input over media advertisements and CAV-based ride-hailing services over other modalities. This study's findings and inferred implications will offer policymakers and public agencies valuable information to measure end-users ' inclinations to rank and adopt CAV-based travel modalities. [ABSTRACT FROM AUTHOR]

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

Shared autonomous vehicles (SAVs) represent a rapidly growing technological field with features believed to potentially support the development of more sustainable mobility systems in city regions. To succeed as a sustainable mode of mobility, however, many current car owners must replace their private vehicles with SAVs. Based on a survey of citizens in Oslo and 12 other Norwegian cities, as well as in-depth interviews with citizens living in a location where SAV is tested, this study explores the relationships between people's everyday mobility patterns and their future interest in using SAVs. The study located six different mobility practices and found a positive interest in the use of SAVs among people who adhere to a shared mobility type of practice, while the resistance was strongest among people who adhere to different car-based mobility practices. Also, those whose current mobility practice involves the use of electric cars had little interest in replacing their cars with SAVs. The study points to some paradoxes and challenges that are critical to handle if an upscaling of SAVs is to be part of a future sustainable mobility mode. [ABSTRACT FROM AUTHOR]

As ride-sharing emerges as a new mobility choice, modeling and control of large-scale multimodal systems in automated and shared environment recently has received significant attention. This paper developed a modeling approach for capturing the demand-supply relationship and the traffic flow dynamics in such ride-sharing systems at an aggregated network level. Private cars, taxis, and single- and multioccupancy ride-sharing vehicles are considered. A macroscopic fundamental diagram-based traffic flow model was constructed to describe the spatiotemporal physics of traffic. Multimodal meeting functions are utilized for passenger-vehicle matching in the shared transportation system. The proposed model enabled us to formulate an optimization model for region-level dispatching control strategies and relocating taxis and ride-sharing vehicles for different objectives. The results of experimental study reveal that (1) the proposed model can reproduce traffic dynamics and multimodal interactions under various traffic conditions, travel demands, and service intensities; and (2) the developed control theory-based dispatching strategies can improve the efficiency of all modes in the shared transportation system, and reduce the travel cost for riders and promote the level of service for passengers. [ABSTRACT FROM AUTHOR]

Shared mobility systems have been adopted in urban areas around the world to relieve parking problems caused by private vehicles. Among these systems, dockless electric scooter sharing systems (DESSSs) have been implemented in many large cities due to their advantages of environmental friendliness and convenience. However, the repositioning of dockless scooters is vital for the quality of service of these systems because the demand for shared scooters is temporally and spatially imbalanced. An optimization model and solution algorithm are developed in this study to solve the operator-based dynamic repositioning problem of DESSS. Dynamic repositioning refers to the scenario where the operator repositions scooters while users utilize scooters. A mixed-integer programming model for the dynamic repositioning problem of DESSS is proposed for the first time along with a heuristic algorithm in this study. The proposed methodology, which is tested using numerical examples, demonstrates high potential in assisting DESSS operations. [ABSTRACT FROM AUTHOR]

Ridesharing is a shared mobility service in which passengers and drivers with similar origins and destinations are matched to travel in the same vehicle. This service utilises unused seats in vehicles and multi-passenger rides to reduce the cost of travel. To promote ridesharing, both service providers and policymakers should carefully analyse passenger adoption behaviour to support future decision-making and planning. In this paper, 80 studies on passenger ridesharing behaviour published since 2004 are reviewed. The motivating factors and barriers are analysed and classified in terms of demographic factors, psychological factors, and situational factors, and boundary conditions are included. The work provides a corresponding research framework on ridesharing behaviour. Finally, the current literature gaps are summarised and research recommendations are provided. This study provides a comprehensive and systematic research basis for ridesharing studies, and presents important theoretical and practical contributions to guide sustainable ridesharing behaviour. [ABSTRACT FROM AUTHOR]

This paper reviews the most recent literature on the optimization of car‐sharing systems. Unlike other surveys, we do not focus on a single aspect of car‐sharing systems, but we consider a wide range of optimization problems with a global view. Our aim is threefold. First, we revise the classic decision problems arising in car‐sharing systems with conventional and electric vehicles, such as the fleet size, the station location, and the vehicle relocation. Then, we discuss some of the most recent decision problems arising in more innovative car‐sharing systems, like those with autonomous vehicles, with mixed fleets, and the multi‐modal and the time‐dependent ones. Finally, we focus on new perspectives arising in the optimization of some innovative car‐sharing services that are not yet (or little) investigated in the literature and that are worthy of attention from the operations research point of view. For each of them, we also highlight new and challenging open decision problems that deserve academic attention. [ABSTRACT FROM AUTHOR]

In the Fourth Industrial Revolution era, the sharing economy has brought a new way of social value creation. However, the impact of COVID-19 in recent years has hampered shared consumption practices but also generated new service directions based on changing market demand. To explore the motivation and behavior of people participating in the sharing economy in the current social context in detail, we empirically examined the impact of consumers' perception of shared mobility on their continuance and normative use behavior via community identification through surveyed online/offline 585 Chinese frequent users of shared mobility. Our study shows that the perception of usefulness, ease of use, and social benefits positively affect consumer behavior through community identification, with innovative consumers and role models acting as positive moderators. The results of our study have implications for how enterprises in the COVID-19 pandemic can take advantage of the sharing economy's optimal allocation of idle resources to integrate new service sections for improved service quality. It also provides a reference for enterprises' responses to public health emergencies. [ABSTRACT FROM AUTHOR]

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations. [ABSTRACT FROM AUTHOR]

Ridesplitting is a pooled version of a ridesourcing service that can reduce emissions per trip compared with exclusive ridehailing. However, it only takes a small share of the ridesourcing market currently. Fiscal and social incentives are potential leverage tools to attract passengers from solo hailed rides. This study aims to quantitatively identify the effects of carbon credits (CC) and monetary rewards (MR) on people's willingness to adopt ridesplitting. The theory of planned behavior realized by structural equation modeling decomposes the factors which affect passengers' intention and actual action on choosing ridesplitting. Confirmatory factor analysis suggests that pro-environmental attitudes do not directly force people to ridesplitting. Path effect analysis shows that subjective norms and perceived behavioral control significantly affect ridesplitting intentions. Immediate effect regression reveals that CC has greater direct effects on ridesplitting intention than MR (4.5 times more effective). Moderating effect analysis shows the MR effect reduces while the CC effect enhances when increasing the incentive value. The results encourage policymakers and operation designers to consider better marketing schemes combining monetary and social incentives to promote ridesplitting and to gain better fiscal and environmental benefits from ridesourcing systems. [ABSTRACT FROM AUTHOR]

Shared mobility is one of the smart city applications in which traditional individually owned vehicles are transformed into shared and distributed ownership. Ensuring the safety of both drivers and riders is a fundamental requirement in shared mobility. This work aims to design and implement an adequate framework for shared mobility within the context of a smart city. The characteristics of shared mobility are identified, leading to the proposal of an effective solution for real-time data collection, tracking, and automated decisions focusing on safety. Driver and rider safety is considered by identifying dangerous driving behaviors and the prompt response to accidents. Furthermore, a trip log is recorded to identify the reasons behind the accident. A prototype implementation is presented to validate the proposed framework for a delivery service using motorbikes. The results demonstrate the scalability of the proposed design and the integration of the overall system to enhance the rider's safety using machine learning techniques. The machine learning approach identifies dangerous driving behaviors with an accuracy of 91.59% using the decision tree approach when compared against the support vector machine and K-nearest neighbor approaches. [ABSTRACT FROM AUTHOR]

The aim of this study was to use machine learning model for prediction of running costs of public transport buses in Karachi, which is the most widely used mode of shared mobility in this city. To achieve this objective, classification and regression tree (CART) models have been used. Later on, the prediction models were used to determine the optimum operating parameters of public transport buses in Karachi. An interview study was conducted to acquire their operational and cost parameters. The dataset comprised for operational and maintenance parameters of 146 buses of various specifications. Running costs were calculated on the basis of number of passengers, hours of service and distance traveled. From the CART models, it was found that the minimum total weekly distance traveled by the vehicle should be 297 km, below which costs start to increase. Similarly, optimum values could also be found, from CART models, for other parameters, such as; number of passengers, number of hours in which the vehicle was running, and type of vehicle. The findings of this study would be helpful in designing future shared mobility public transit options, such as metro. [ABSTRACT FROM AUTHOR]

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Sudden changes in urban mobility were caused due to the COVID-19 pandemic. The impacts are yet to be furtherly measured and analyzed. Our article uses GPS records provided by three different micromobility operators in Madrid to study how the pandemic affected their service usage and its relationships with land use. Thus, spatio-temporal travel patterns are compared between pre-COVID 19 (from January 2019 to February 2020) and COVID times (from March to December 2020). Additionally, multiple regression analyses are conducted to assess how the two scenarios differentiate in relation to micromobility trips, generated or attracted, to or from different land uses, and during morning or afternoon peak hours. Results show that the most pandemic-resilient shared mode is bike-sharing, and that COVID-19 has caused a downfall in micromobility trips of approximately 10%, which is relatively lower compared to the 80% ridership drop reported by the public transport system. Our models reveal that residential and commercial areas gained importance after the pandemic, while workplace locations (office and industrial), educational and transport facilities lost relevance with teleworking and online studying. These findings could help authorities to plan future policies and improve the infrastructure needed to promote micromobility services. [ABSTRACT FROM AUTHOR]

Shared mobility is defined as an act, which involves sharing a ride, sharing travel costs or sharing a vehicle by different users simultaneously. They are an increasingly popular public transportation mode in Indian cities and towns, often called share-autos or shared cabs. Share-autos are three-wheeled vehicles, while Shared cabs are fourwheeled. This transportation model differs from typical intermediate public transportation models, such as taxis and auto-rickshaws in terms of operation and service. Share-cabs are an informal and unregulated form of transportation in India. They have a higher passenger occupancy than share-autos, and they do not provide doorto-door private transportation service. They operate on semi-fixed routes and they allow passengers to board and alight at any point along the routes operated. While the patronage for share-cabs is increasing, transport authorities have yet to decide whether the mode should be encouraged to complement more significant public transport modes like buses and trains or, be banned by concluding it as a competition to public transport modes. In this study, questionnaire surveys were conducted to understand how the four main stakeholders of share-cabs, namely the main users, other road users, drivers, owners and authorities perceived share-cabs as a viable mode. An indexing system is proposed to measure the stated preference about share-cabs sustainability, which is based on stakeholders’ opinions. On a scale of 1 to 5, with 5 representing a ‘very good’ opinion about the share-cabs, a value of 3.01 resulted from the analysis of questionnaire surveys. This survey shows that share-cabs could be a sustainable transportation mode, at present on a moderate level giving scope for improvement based on standardizing and regulation of operations. [ABSTRACT FROM AUTHOR]

The promotion of urban mobility by integrating people-and-goods transportation has attracted increasing attention in recent years. Within this framework, diversified forms such as co-modality, freight on transit, and crowdshipping have been proposed, piloted or implemented. The success of the implementation and market penetration depends on not only the novelties of the concept but also the planning and operational efficiency. Thus, a comprehensive review focusing on the operation of integrated people-and-goods transportation systems and associated critical decisions and subproblems is performed. Different practical forms in which people and goods are transported in an integrated manner are identified. The critical decisions associated with each form and subproblem are discussed, along with corresponding models and solution approaches. Notably, because integrated transportation systems are in the early exploration stage at present, new forms are expected to emerge. Therefore, this paper proposes a general framework to realise the planning and operation of new forms in the future. The decisions and subproblems identified from existing forms are fed to the proposed general framework to identify two key research opportunities: to improve or extend existing research and to conduct pioneering research to fill the gaps in the frameworks for operating potential forms of integrated people-and-goods transportation. [ABSTRACT FROM AUTHOR]