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This study aims to examine the differential impact of ride-hailing services (RHS) on private and commercial vehicle ownership from five metropolitan cities in India.

Using vehicle ownership data from five metropolitan cities over period 1991 to 2020, a panel corrected standard errors model was estimated to model the association between RHS and vehicle ownership.

The results indicate that advent of RHS has led to a significant reduction in private vehicle ownership rates and a corresponding increase in addition of intermediate public transport. The net effects of RHS on road congestion and pollution levels need to be studied in detail.

The findings of this study can potentially assist policymakers and mobility planners in efforts to decarbonise and decongest urban transport.

This study sets precedence in analysing the impact of RHS on private and commercial vehicle independently. Further, to the best of the author’s knowledge, this is the first study to examine this association for the city of Delhi and Kolkata.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

The study evaluates the accident-adjusted dynamic efficiency of public bus operators providing bus transportation services in eight major metropolitan cities of India.

The slack-based measure (SBM)–undesirable window analysis approach is used to gauge the dynamic efficiency levels and identify the sources of inefficiency in bus transportation services. This innovative approach integrates the SBM model developed by Tone (2001, 2004) and the window analysis approach of Charnes et al. (1985). The main advantage of this approach is that one can explicitly incorporate the number of accidents in the production technology specification as an undesirable (bad) output and potently handle the issue of the “curse of dimensionality” in a small sample like ours.

The key empirical findings suggest wide variations in average efficiency levels across sample bus operators in metropolitan cities. The Chennai Transport Corporation is observed as the most efficient and consistent bus operator due to its most stable efficiency performance. The results additionally unveil that the role of managerial inefficiency was diminutive, and the scale-related issues were the real cause of sub-optimal or supra-optimal behaviour of sample bus operators in the resource-utilisation process.

There is an urgent requirement for effective policy intercessions to mitigate the sizeable observed inefficiency in the production process and resolve scale-related issues of public bus operators offering transit services in major metropolitan cities of India.

This paper is maybe the first to assess the dynamic efficiency of public bus transit systems in India's major metropolitan cities after treating accidents.

The concept of green urban mobility has emerged as one of the best approaches for promoting environmental-friendly transportation in local communities. Green urban mobility aims to reshape public transportation system and enhance mobility, with emphasis on deploying digital technologies to promote sustainable public transportation. Therefore, this study aims to analyze existing public transportation policies by exploring how local communities can facilitate green urban mobility by developing a sociotechnical urban-based mobility model highlighting key factors that impact regions transitioning toward sustainable transportation.

This study investigates “the role of data for green urban mobility policies toward sustainable public transportation in local communities” in the form of a systematic literature review and insights from Norway. Secondary data from the literature and qualitative analysis of the national transport plan document was descriptively analyzed to provide inference.

Findings from this study provides specific measures and recommendations as actions for achieving a national green mobility practice. More important, findings from this study offers evidence from the Norwegian context to support decision-makers and stakeholders on how sustainable public transportation can be achieved in local communities. In addition, findings present data-driven initiatives being put in place to promote green urban mobility to decrease the footprint from public transportation in local municipalities.

This study provides green mobility policies as mechanisms to be used to achieve a sustainable public transportation in local communities. Practically, this study advocates for the use of data to support green urban mobility for transport providers, businesses and municipalities administration by analyzing and forecasting mobility demand and supply in terms of route, cost, time, network connection and mode choice.

This study provides factors that would promote public and nonmotorized transportation and also aid toward achieving a national green urban mobility strategy. Socially, findings from this study provides evidence on specific green urban mobility measures to be adopted by stakeholders in local communities.

This study presents a sociotechnical urban-based mobility model that is positioned between the intersection of “human behavior” and “infrastructural design” grounded on the factors that influence green urban mobility policies for local communities transiting to a sustainable public transportation. Also, this study explores key factors that may influence green urban mobility policies for local communities toward achieving a more sustainable public transportation leading to a more inclusive, equitable and accessible urban environment.

This paper aims to determine the significance of tourist walkability on three community-based tourism sustainability indicators, namely, the economic, social and environmental benefits and costs impacting community’s quality of life through the lens of the triple bottom line approach with the institutional theory.

This study views institutions as either enabling or restricting the sustainable community-based tourism because institutions influence resource integration and value assessment by the beneficiary. Moreover, institutions also lead the co-creation of sustainable community-based tourism among various stakeholders. Drawing on this conceptualisation, the notion of sustainable community-based tourism is filtered through the lens of institutional theory. Thus, this work approaches sustainable community-based tourism as a dynamic process of co-creating a tourist destination formed by different actors’ and institutions within the ecosystem of the tourist destination. Meanwhile, the triple bottom line benefits and costs experienced by the overall community would produce net effects on the residents’ perceptions of sustainable tourism.

This paper classifies both tangible and intangible costs and benefits because of tourist walkability and its triple bottom line trade-offs experienced by tourists and residents. This paper penetrates new grounds by reviewing the triple bottom line impacts of tourist walkability on residents’ quality of life. Government policies as mediating variable and national culture and individual personalities of tourists and residents as moderating variables were discussed. A conceptual framework named Tourist Walkability Sustainable Tourism Impact on Residents (TWSTIR) is proposed. Finally, a Sustainable Community-based Tourism Strategic (SCBTS) model which is based on the two dimensions of intensity of tourist walkability and residents’ quality of life is proposed.

Research limitations may include a lack of assessment on political, technological and legal issues, and therefore, future research is warranted in these three areas. Some emotions and attitudes of the residents may not be captured since the Gross National Index (Gross National Happiness) may have its inherent blind spots.

This paper would be of interest to the scholarly world, as its original idea and concluding research agenda are burrowing into a new sub-field of tourism research. In view of growth and degrowth of sustaining community-based tourism, the SCBTS model is presented to provide directions for tourism policymakers and entrepreneurs to formulate and implement appropriate strategy for the tourist walkability activity per se and investment in the accompanying infrastructure.

This paper also presents the sacrifices and inequities in the communities and the relevance of government policies, national culture and individual personalities of tourists and residents, in which the attention of tourism policymakers and the communities that thrive on the travel and tourism industry should not be neglected.

The idea and discussion of this paper is original. This paper burrows into a new sub-field of tourism research. Tourist walkability needs more attention from the scholars, as this tourist activity can have positive and negative effects on residents’ quality of life. The TWSTIR framework is developed to discuss the relationships of tourist walkability, triple bottom line concept and residents’ quality of life within the sustainable community-based tourism scope. The SCBTS model is presented for tourism policymakers and entrepreneurs to perform appropriate strategy for the tourist walkability activity and investment in the accompanying infrastructure.

The purpose of the chapter was to examine the impact of customer care chatbots on customer satisfaction levels in the mobile telephony industry in Zimbabwe, with a special focus on the company Econet Wireless. This chapter shows the conceptual framework used. An online questionnaire was administered to a sample of 100 Econet Wireless subscribers who were selected using probability stratified random sampling from Zimbabwe’s 10 provinces. The research data were collected and analysed for correlation, and a multiple regression analysis was carried out to identify the relationship between customer satisfaction and the three customer service improvements brought in by the introduction of customer service chatbots. The study discovered that there is a positive relationship between customer satisfaction levels and each of the three customer service improvements brought in by customer service chatbots, namely customer service convenience, speed of response, and omnichannel strategies. This study thereby proves that the introduction of customer service chatbots in the mobile telephony industry in Zimbabwe can lead to an improvement in customer satisfaction levels. However, addressing service quality only as a determinant of customer satisfaction in isolation is not sufficient to fully improve customer satisfaction levels. Therefore, organisations that seek to improve their customer satisfaction should consider strategies that address all determinants of customer satisfaction, namely price, product quality, service quality, situational factors, and personal factors. This study contributes to the body of knowledge, particularly regarding the use of artificial intelligence (AI) for customer service in developing economies.

The study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.

This study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.

The study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.

The model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.

To help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.

To estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.

The proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

The study examines the impact of emotional intelligence on service innovation capabilities. Furthermore, it explored the mediating role of diversity climate and the moderating role of innovation culture.

An online questionnaire helped to collect data from 257 public sector employees in the United Arab Emirates (UAE). The proposed hypotheses were analysed using structural equation modelling.

Building on the ability model, the study found that employees' emotional intelligence has a positive impact on diversity climate; that diversity climate does not mediate the relationship between emotional intelligence and service innovation capabilities and that innovation culture has a moderating effect between diversity climate and service innovation capabilities.

The paper clarifies the emotional intelligence of the workforce and its ability to influence innovation culture and diversity climate in public organisations, ultimately benefiting service innovation capability research. As such, the study contributes to the literature by proposing and analysing some antecedents of service innovation capabilities in the context of public organisations. The study also offers policymakers information on what prevents innovation, which they can use to raise the bar on service quality requirements in the public sector.