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In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted by other researchers, it has been possible to structure the ecosystem and trends in the Urban Transportation Concept, also mentioning likely future trends. Based on the literature analysis of the Sustainable Urban Transport, the authors of the study found that a large group of researchers deal with technical solutions and innovative business models, while the essential behavioral aspects are examined in less detail. Extensive literature analysis allowed the authors to select several solutions to achieve the transformation towards sustainable transportation in urban areas: new vehicle technologies and their environmental factors' analysis, geographic information systems, the analytic hierarchy process method, the time series analysis of road traffic accidents using multiplicative models, electrification and use of Friedman Analysis of Variance by Ranks, as well as innovations in sharing mobility.
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The aim is to analyse the business results of the Lyft platform during the Covid 19 pandemic. Research and analysis of business results are the basis for comparison with the business results of other corporations in the sharing economy model. Selected financial indicators were used in the analysis, which were put in relation with selected ride-hailing indicators. The research was done using the financial analysis of parameters from the corporation's profit and loss account, that is, statistical regression of the ride-hailing trend in the selected time period. The research results indicate a strong impact of the Covid 19 pandemic on the overall activities of the corporation. In the first quarter of 2020, during the lockdown in the USA, the corporation's revenues and the number of rides dropped sharply. The corporation's revenues continuously grew until the emergence of the Covid 19 pandemic, and fell sharply with the advent of the lockdown model. The same trend was shown with the number of ride-hailing activities. After emerging from the crisis, revenues are recovering as well as the number of ride-hailing.
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Mobility and transportation activities in smart cities require an increasing amount of energy. With the frequent energy crises arising worldwide and the need for a more sustainable and environmental friendly economy, optimizing energy consumption in these growing activities becomes a must. This work reviews the latest works in this matter and discusses several challenges that emerge from the aforementioned social and industrial demands. The paper analyzes how collaborative concepts and the increasing use of electric vehicles can contribute to reduce energy consumption practices, as well as intelligent x-heuristic algorithms that can be employed to achieve this fundamental goal. In addition, the paper analyzes computational results from previous works on mobility and transportation in smart cities applying x-heuristics algorithms. Finally, a novel computational experiment, involving a ridesharing example, is carried out to illustrate the benefits that can be obtained by employing these algorithms.
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The number of cars on Polish roads is increasing year by year. Currently, Poland is in second place in Europe in terms of the number of cars per 1000 inhabitants [1]. This causes problems in finding a place to park. In addition, during the pandemic, there was a problem with semis, which caused a sharp increase in the price of used cars and longer waiting times for new vehicles. The aim of this article is to find out the opinion of Polish residents on the Car-Sharing service during the CoVID-19 pandemic and how the pandemic has affected Car-Sharing not only in Poland, but also in Europe as a whole. For this purpose, a survey was conducted. The research found that about 8% of people in Poland use the Car-Sharing service and that the pandemic had little impact on how this service was used. If someone needed to use this service, the pandemic was not an obstacle for them. [ FROM AUTHOR]
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The judges for the 2021 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics. The prestigious Wagner Prize-awarded for achievement in implemented operations research, management science, and advanced analytics-emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year's winning application describes the design and deployment of Eva, the Greek COVID-19 testing system used as Greece was opening up for tourism in 2020. The remaining four papers describe the stochastic modeling and mixed-integer programming system used to optimize the Atlanta police patrol zones for better police balance and reduced response time to emergency calls;Lyft's new priority dispatch system, which solves the ride-sharing productivity paradox whereby increases in efficiency do not benefit the drivers;the application of advanced analytics to assist local and federal law enforcement organizations in their efforts to disrupt sex-trafficking networks;and the development of a new after-sales service concept, which increases chip availability for ASML's customers.
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Bike-sharing holds promise for available and healthy mobility services during COVID-19 where bike sharing users can make trips with lower health concerns due to social distancing compared to the restricted transportation modes such as public transit and ridesharing services. Leveraging the trip data of the Divvy bike-sharing system in Chicago, this study exploresspatially heterogeneous effects of built environment on bike-sharing usage under the pandemic. Results show that the average weekly ridership declined by 52.04%. To account for the spatially heterogeneous relationship between the built environment and the ridership, the geographically weighted regression (GWR) model and the semiparametric GWR (S-GWR) model are constructed. We find that the S-GWR model outperforms the GWR and the multiple linear regression models. The results of the S-GWR model indicate that education employment density, distance to subway, COVID-19 cases, and ridership before COVID-19 are global variables. The effects between ridership and the built environment factros (i.e., household density, office employment density, and the ridership) vary across space. The results of this study could provide a useful reference to transportation planners and bike-sharing operators to determine the high bike-sharing demand area under the pandemic,thus adjusting station locations, capacity, and rebalancing schemes accordingly.
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Short-term car rental services, i.e., carsharing, is a solution that has been developing better and better in urban transport systems in recent years. Along with intensive expansion, service providers have to face an increasing number of challenges to compete with each other. One of them is meeting the expectations of customers about the fleet of vehicles offered in the system. While this aspect is noticed in the literature review mainly in terms of fleet optimization and management, there is a research gap regarding the appropriate selection of vehicle models. In response, the article was dedicated to identifying the vehicles that were best suited to carsharing systems from the point of view of frequent customers. The selection of appropriate vehicles was treated as a multi-criteria decision issue, therefore the study used one of the multi-criteria decision support methods—ELECTRE III. The work focuses on researching the opinions of users (experts) who often use carsharing services in Poland. The study included a list of the most popular vehicles in Europe in 2021, including classic, electric, and hybrid cars, and a list of 11 evaluation criteria. The research results indicate for frequent users the advantage of conventional drive vehicles over electric and hydrogen vehicles. Moreover, they indicate that the best vehicles are relatively large cars (European car segments C and D) with the greatest possible length, boot capacity, engine power, number of safety systems, and quality. On the other hand, the least important issues are the number of seats in the vehicle and the number of doors. Interestingly, the vehicles selected by frequent users questioned the concept of small city cars, which occupied a small public space on which carsharing was supposed to focus. The results obtained support the operators of carsharing services in making fleet decisions.
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As an important part of the sharing economy, the usage of car sharing increases world widely with the help of developments in the technology. Especially after COVID-19 the demand for private car ownership and car sharing systems increased tremendously. Therefore, its market share attracts new investors and causes existing service providers to enlarge their service area. In this article, a novel multi-objective location-dependent two-stage stochastic optimization model is proposed to determine the most appropriate locations for car sharing system and allocate the demand to these locations. The model is applied to determine the best locations among 15 candidates, and three objectives are considered, which are the minimization of total cost that comprises locating costs minus income from satisfying the demand, minimization of CO<formula><tex>$_2$</tex></formula> emission occurs by the usage of car sharing system's cars and minimization of average unsatisfied demand. Both location-independent and location-dependent demands are taken into account. The proposed model delivers a more precise decision process framework for problems include stochasticity and multiobjectivity, and it easily can be implemented to any region, providing region sensitive parameters. IEEE
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Finding a sustainable mobility solution for the future is one of the most competitive challenges in the logistics and transportation sector nowadays. Researchers, universities, and companies are working intensively to provide novel mobility options that can be environmentally friendly and sustainable. While autonomous car-sharing services have been introduced as a very promising solution, an innovative alternative is arising using self-driving bikes. Shared autonomous cargo bike fleets are likely to increase the livability and sustainability of the city as the use of cargo bikes in an on-demand mobility service can replace the use of cars for short-distance trips and enhance connectivity to public transportation. However, more research is still needed to develop this new concept. To address this research gap, this paper examines the on-demand shared-use autonomous bikes service (OSABS) from a fleet management perspective. In fact, such a system requires good management strategies in order to ensure its efficiency. Through an agent-based simulation of a case study in Magdeburg, we investigate various parameters that can influence the performance and the service quality of OSABS such as the rebalancing frequency and the relocation type. Tests were performed for two different operational areas: the inner city and the complete city of Magdeburg. We conclude with different management insights for an optimized functioning of the system.
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Transportation demand management is a successful complement to urban infrastructure. The emergence of shared mobility strategies such as car sharing offers sustainable mobility in urban areas. Car sharing has launched in different cities worldwide to mitigate severe transportation problems such as traffic congestion, air pollution, and traffic safety. Therefore, this study aims to investigate the intentions and preferences of travelers toward car sharing services in Djibouti, Africa. The data was collected through an online stated preference (SP) survey. The SP survey included the awareness of car sharing services, attributes related to transport modes, and demographic characteristics. A total of 600 respondents were received. In this study, we employed the multinomial logit (MNL) model to travel mode choice modeling and compared the results with the AdaBoost algorithm. The MNL model results showed that generic attributes such as travel time, travel cost, maintenance charges, and membership fees were found significant. In addition, several demographic characteristics like gender, education, and income were also found significant. The modeling and prediction performances of the MNL model and AdaBoost algorithm were compared using multi-class predictive errors. According to the goodness-of-fit results, the AdaBoost algorithm achieved overall higher prediction accuracy than the MNL model. This study could be helpful to transport planners and policymakers for the implementation of car-sharing services in urban areas.
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The new normal of the world has been shaped by the COVID-19 outbreak by avoiding public transportation in order to prevent the spread of the disease. Due to the high financial burden of purchasing a car, new business models have been developed in order to make possible of utilizing vehicles to meet the transportation needs in pay-per-use base concept called “servicizing” or “servicization” which is based on presenting a product as a service, and selling the functionality of that product instead of the product itself. In order to meet the increasing demand for individual vehicle use, the existing car rental service providers have provided a new mobile application controlled business model which makes the rental process easier. The aim of this study is to evaluate the customers’ preferences of purchasing, renting through an agency, or mobile application supported new pay-as-you-go business model use, in order to determine which criterion is prominent in the decision-making process, and to identify the weights of these criteria. Due to the uncertain and indeterminate attitudes of the customers in decision making, the data were collected as neutrosophic data sets and analyzed with a novel neutrosophic Analytic Hierarchy Process (nAHP) approach. The study provides implications both theoretically and practically in terms of revealing new servicization possibilities and analyzing real user judgments © 2022, Neutrosophic Sets and Systems.All Rights Reserved.
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Purpose>The primary goal of this study is to determine the predictors of on-demand ridesharing intention in an emerging economy. For this purpose, the study uses the theoretical underpinnings of the theory of planned behavior (TPB).Design/methodology/approach>The study surveyed 347 frequent users of ridesharing services using a set of pre-validated scales. The resulting data were analyzed using covariance-based structural equation modeling (CB-SEM).Findings>The results of SEM analysis disclosed that the significant factors contributing to ridesharing intention are awareness of environmental consequences, subjective norm, perceived behavioral control and attitude (towards ridesharing).Practical implications>This empirical research provides statistically robust insights for developing marketing strategies that attract more individuals toward ridesharing services.Originality/value>This research has remarkable significance as it is one of the pioneering studies that critically examine the determinants of ridesharing intention from a South Asian emerging economy. Further, the extended TPB framework proposed in this study explains 71.4% variance in ridesharing intention, which is significantly higher than existing studies, with none of them explaining more than 70% variance.
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Vehicle sharing, electrification, and automation, as the triple revolutions in urban transportation, have been under debate towards a new transport paradigm. In this regard, carsharing services, as a potential solution for sustainable urban transport, have gained momentum within the context of sustainable cities in recent years. This research, as the first attempt in the literature, aims to render a comprehensive map of the body of knowledge in the carsharing field of research through conducting a systematic bibliometric analysis. To achieve that, a total of 729 peer-reviewed journal articles from the Web of Science database were scrutinized using keyword, text mining, and bibliographic coupling analyses. The analyses revealed four main research themes building the carsharing literature, including (1) collaborative consumption and carsharing business models development in the context of sustainable urban transport, (2) carsharing adoption with a special focus on user behavior, intention, and preferences, (3) carsharing operational challenges, considering infrastructure and fleet management, and (4) technological advancement towards deployment of shared autonomous vehicles and mobility as a service. The results showed that the carsharing literature lacks (i) a well-established and comprehensive long-term sustainability assessment framework, (ii) inclusive and integrative marketing and training plans, as well as effective incentives, (iii) a holistic analysis of the role of carsharing in the achievement of Sustainable Development Goals, (iv) reliable circular economy indicators designed to measure the circularity of carsharing to help transitioning towards a circular economy, and (v) a timely broad analysis on the implications of the COVID-19 pandemic and the future of carsharing post pandemic era, which call for more investigations in the future. The provided insights support both researchers and policy-makers by shedding light on carsharing services research by providing a state-of-the-art of carsharing studies and developments up to date, uncovering the emergent research themes and trends, and identifying research gaps for future studies towards better positioning carsharing services in sustainable cities developments.
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The shared mobility services market is growing and changing very rapidly. Many novelties are introduced to the systems, ranging from improvements to the services already offered to services referred to as innovative. Since the following years are to bring significant development of mobility as a service (MaaS) systems, data sharing, and cooperation on the mobility market, the article is dedicated to check whether the current business models of the industry are ready for the open innovations implementations. The article aimed to analyze the business models of shared mobility systems along with their presentation in the form of CANVAS models and to investigate whether the models contain aspects of open innovation. Moreover, the article presents its own value-added open business model prepared for the whole shared mobility market. The paper also identifies a set of open innovations that can be implemented by all types of shared mobility operators. It proposed the basis that operators can use when developing their own open business models. The developed research is an original contribution to filling the research gap concerning the approach to open innovation by operators of all types of shared mobility services available on the market. The results show that car-sharing service providers are the biggest opponents of open innovation. On the other hand, the most ‘open’ systems are bike-sharing services. The conducted research may support operators in the process of transforming their businesses into more accessible for users. It also helps to develop the open innovation concept to create more sustainable shared mobility systems along the lines of collaborative economy assumptions.
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Car sharing services have expanded in order to meet the new necessities of mobility worldwide in an innovative way. Before the COVID-19 pandemic, car sharing was a very popular mode of transportation among young adults in big cities. However, during this ongoing pandemic and with public transportation considered a super-spreading transmitter, the usage of car sharing is unclear. Therefore, the aim of this study, which is explorative in nature, is to investigate the usage, advantages, drivers, and barriers to car sharing during this ongoing pandemic era. To this end, 66 interviews were conducted among users of car sharing during the COVID-19 pandemic. The findings provide key information for the planning of car sharing operations and public transportation in the context of avoiding COVID-19 infection and respecting the recommendations of local governments. In addition, new emerging profiles of car sharing users in the ongoing pandemic are identified. This research provides relevant insights for both business practice and policy makers.
Subject(s)
COVID-19 , Pandemics , Automobiles , COVID-19/epidemiology , Humans , SARS-CoV-2 , Transportation , Young AdultABSTRACT
Carsharing is regarded as a new mode of transportation that can meet the diversity of travel demands. Carsharing systems have different operating modes, and one-way systems are more widely used since cars can be dropped off at any station. However, their planning involves a series of joint decisions regarding the number, size, and location of stations, as well as the fleet size. This paper develops a data-driven mixed-integer linear programming (MILP) model for planning one-way carsharing systems that consider the spatial distribution of demand and the interacting decisions between stations. The characteristics of existing stations and their spatiotemporal correlations are an important part of the model. To solve the MILP model, the extension of the Benders decomposition algorithm is adopted. The practicality of the proposed approach is demonstrated in a case study in Beijing, China. The results show that the existing planning of carsharing could result in a serious waste of resources. In contrast, the proposed method can obtain effective results in a reasonable time. The location results corresponding to a different rate of satisfied demand show that increasing the parking spots to improve the interaction between stations can effectively reduce the cost of operations. It should be noted that this paper only considers the benefit of operators. Future works will be carried out to optimize the one-way carsharing system by considering the benefits of operators, as well as the benefits of users and society. In addition, the impact of COVID-19 will be taken into account in future modeling and case studies.
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So-called “new mobility” innovations could transform transportation systems, including the deployment of shared, electric, and automated vehicles. However, researchers are still learning about who is currently using these technologies (“realized demand”) and who is interested in future usage (“latent demand”). We explore these patterns via a representative survey including three of Canada's largest metropolitan regions: Vancouver, Toronto, and Montreal (n = 3658, June 2020). Realized demand is assessed as respondents who use or own the technology (pre-pandemic), while latent demand is assessed as respondents who report interest in using the technology (post-pandemic). For most technologies, latent demand is higher than realized demand, indicating the potential for market growth. Exceptions are ride-hailing and pooled ride-hailing in Toronto and Montreal, where latent demand is lower, likely due to concerns relating to the COVID-19 pandemic. There is regional variation in realized and latent demand for shared mobility: ride-hailing usage is highest in Toronto and car-sharing usage and interest are highest in Vancouver (each corresponding to regional availability). Further, latent demand for electric vehicles and fully automated electric vehicles are highest in Montreal. Otherwise, latent demand levels for the remining technologies are similar across regions. Regression models indicate that latent demand for each technology is associated with respondents' travel patterns, demographics, values, lifestyles, and environmental concern. Regional effects hold in descriptive and regression analyses, for example higher car-sharing latent demand among Vancouver respondents and higher electric vehicle latent demand among Montreal residents, suggesting regional context plays a role in explaining latent demand.
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This paper explores the spatial spillover effect of shared mobility on urban traffic congestion by constructing spatial econometric models. Based on panel data of 94 Chinese cities from 2016 to 2019, this study analyses the spatial correlation of shared mobility enterprise layout and geographical correlation of urban transport infrastructure and examines their influence mechanism. From the perspective of geographic spatial distribution, congestion has positive spatial correlation among Chinese cities, and it has different directions and centripetal forces across regions. The shared mobility enterprises in a region have same direction distribution with traffic congestion, but the centripetal forces of the aggregation effect are different. The econometric results include the fact that bike-sharing has reduced congestion significantly, but the overall impact of car-sharing is not clear. Neither bike-sharing nor car-sharing can offset the traffic congestion caused by economic activities and income growth. From the perspective of spillover effects, congestion has been influenced by bike-sharing, economic development, population, and public passengers in surrounding areas. In terms of spatial heterogeneity, bike-sharing relieves congestion in the Pearl River Delta region while having no significant effect in other regions. Meanwhile, car-sharing has aggravated congestion in the Yangtze River Delta but eased traffic jams in the Pearl River Delta.
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Service platform has developed rapidly in car-sharing, consumers often buy or own cars but not fully utilize and share them. Since the coronavirus pandemic has affected sales and people's attitudes towards car-sharing, which brought both opportunities and challenges to the platform and changed the operating mode of manufacturers, some traditional manufacturers have motivated to cooperate with third-party platform. In this paper, we develop an analytical framework to examine the pricing decisions and optimal mode selection of manufacturer under the COVID-19 epidemic. Considering the supply chain consists of a manufacturer and a third-party sharing platform. We analyze three scenarios including no sharing, customers-to-customers, and mixed sharing, then employ a game theoretic approach to get equilibrium solutions and analytically derive the optimal mode choice. Our analysis shows that when the operation and maintenance cost is low, manufacturer will join the third-party platform, and the sharing price increase in operation and maintenance cost, while the selling price decrease in operation and maintenance cost. When the value perception factor less than the threshold, the manufacturer will retain sales channel, and the selling demand decrease in value perception factor in the growing market, the sharing demand has the same trend, vice versa. Furthermore, we find that if the operation and maintenance cost is low and value perception factor is high, mixed sharing is the best choice for the manufacturer, while the manufacturer will choose no car-sharing when the value perception factor is relatively low.