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Routines shape many aspects of day-to-day consumption. While prior research has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines—which the authors define as repeated behaviors with recurring, temporal structures—for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. The authors propose a new approach for doing so, which they apply in the context of ridesharing. They model customer-level routines with Bayesian nonparametric Gaussian processes, leveraging a novel kernel that allows for flexible yet precise estimation of routines. These Gaussian processes are nested in inhomogeneous Poisson processes of usage, allowing the authors to estimate customers' routines and decompose their usage into routine and nonroutine parts. They show the value of detecting routines for customer relationship management in the context of ridesharing, where they find that routines are associated with higher future usage and activity rates, and more resilience to service failures. Moreover, the authors show how these outcomes vary by the types of routines customers have, and by whether trips are part of the customer's routine, suggesting a role for routines in segmentation and targeting. [ABSTRACT FROM AUTHOR]

Ride-sharing services have attracted significant interest due to overcrowding, limited energy resources, and environmental concerns. This study proposes a mathematical programming model that integrates user preferences into the ride-sharing problem with transfer. Passenger transfer in ride-sharing problems addresses the limitations of the matching process, especially in less-populated areas. It allows passengers to be dropped off at meeting points and continue with another driver. Moreover, considering user preferences in ride-sharing systems is crucial for enhancing efficiency and user satisfaction. Accordingly, we propose a Preference-Driven Matching Algorithm for the matching process. Our proposed algorithm captures user preferences and provides potential matches. In addition, we introduce an Iterative Enhance-and-Optimize Algorithm capable of producing high-quality solutions within short computational times. We evaluate the efficiency of these approaches across various instances, focusing on real-scale scenarios. Based on the results, the model with preferences demonstrates effective performance compared to other methods. Our findings underscore the importance of user preferences in optimizing ride-sharing problems and highlight the trade-off between efficiency and user satisfaction. By incorporating user preferences, our approach results in higher user satisfaction, a more responsive and efficient system with reduced response times, and increased demand and revenue by servicing more users. [ABSTRACT FROM AUTHOR]

The present study aims to explore the factors that influence the utilization of intercity transportation services, with a specific focus on ridesharing and bus services, assessing the dimensions of reliability, comfort, responsiveness, safety, social interaction, driver empathy, and value. To this end, it uses responses from 110 users of ridesharing, and 200 users of conventional intercity bus services. Using a non-linear method, Penalty-reward Contrast Analysis, the results show that the investigated dimensions have a distinct influence on the propensity to continue using each of these services, explaining 32% of using conventional road transport, and 65% of ridesharing. We confirmed the non-linearity between customer perception of the surveyed dimensions' performance and their propensity to use the service, with differences and similarities between bus and ridesharing services. A low performance of the dimensions of safety and comfort, and a high performance of reliability, impact both services. But there are significant differences in the impact of perceived value, responsiveness, driver empathy, and social interaction. These findings allow service providers to better understand customers' needs and provide better service. [ABSTRACT FROM AUTHOR]

Ridesharing services have been becoming a prominent solution to reduce road traffic congestion and environmental pollution in urban areas. Existing ridesharing services fall apart in ensuring the social comfort of the riders. We formulate a Community aware Ridesharing Group Set (CaRGS) query that satisfies the spatial and social constraints of the riders and finds a set of ridesharing groups with the maximum number of served riders. The CaRGS query utilizes user social data in community levels to ensure user privacy. We show that the problem of finding CaRGS query answer is NP-Hard and propose two heuristic approaches: a hierarchical approach and an iterative approach to evaluate CaRGS queries. We evaluate the effectiveness, efficiency, and accuracy of our solution through extensive experiments using real datasets and present a comparative analysis among the proposed algorithms. [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]

Batch service queueing systems are basically classified into two types: a time-based system in which the service facilities depart according to inter-departure times that follows a given distribution, such as the conventional bus system, and a demand-responsive system in which the vehicles start traveling provided that a certain number of customers gather at the waiting space, such as ride-sharing and on-demand bus. Motivated by the recent spreading of demand-responsive transportation, this study examines the M/M X / ∞ queue. In this model, whenever the number of waiting customers reaches a capacity set by a discrete random variable X , customers are served by a group. We formulate the M/M X / ∞ queue as a three-dimensional Markov chain whose dimensions are all unbounded and depict a book-type transition diagram. The joint stationary distribution for the number of busy servers, number of waiting customers, and batch size is derived by applying the method of factorial moment generating function. The central limit theorem is proved for the case that X has finite support under heavy traffic using the exact expressions of the first two moments of the number of busy servers. Moreover, we show that the M/M X / ∞ queue encompasses the time-based infinite server batch service queue (M/M G (x) / ∞ queue), which corresponds to the conventional bus system, under a specific heavy traffic regime. In this model, the transportation facility departs periodically according to a given distribution, G (x) , and collects all the waiting customers for a batch service for an exponentially distributed time corresponding to the traveling time on the road. We show a random variable version of Little's law for the number of waiting customers for the M/M G (x) / ∞ queue. Furthermore, we present a moment approach to obtain the distribution and moments of the number of busy servers in a GI/M/ ∞ queue by utilizing the M/M G (x) / ∞ queue. Finally, we provide some numerical results and discuss their possible applications on transportation systems. [ABSTRACT FROM AUTHOR]

The rapid expansion of ride-hailing service has imposed a huge amount of additional traffic on urban transportation systems. To reduce congestion, different regulatory policies have been announced to control the number of ride-hailing vehicles in metropolitan areas around the world. However, these measurements, shooting at ride-hailing vehicles only, deprive non-car owners of their private-car mobility rights (endowed by ride-hailing services), therefore are debatable in themselves. Instead of imposing specialized restrictions on ride-hailing vehicles, we propose to cure the deteriorating traffic conditions with classic traffic regulation policies implemented on all road space users. In this study, in a simple one corridor scenario with two different types of ride-hailing services, i.e., ride-sourcing and ride-sharing, we model travelers' mode choices at user equilibrium under no policy intervention to reveal the negative impacts of ride-hailing on urban transportation efficiency, and compare the different system optimum states with and without ride-hailing to demonstrate the great potential of ride-hailing in mitigating congestion. Based on the model, we then examine the impacts of three traffic regulation policies, i.e., road space rationing, congestion pricing and tradable credit schemes, and investigate the existence of traffic regulation policies that can achieve system optimum in the presence of ride-hailing. • This paper studies the impacts of two ride-hailing services on traffic condition. • This paper shows that ride-hailing services may increase or decrease congestion. • This paper studies the effectiveness of three classic traffic regulation policies. • This paper reveals the existence and properties of system optimal policies. [ABSTRACT FROM AUTHOR]

Early optimism for ridehailing services to complement existing public transit services and offer individuals another shared mobility service with reduced travel costs and improved travel times have largely proven to be unsubstantiated. This unwelcomed outcome, in part due to the popularity of ridehailing services among wealthier populations and restrictions on the less-expensive ridesharing service in some urban settings, has likely instead resulted in heightened disparities in access to this on-demand mobility option for historically-marginalized populations and under-resourced communities. This hypothesis is examined by estimating the macro-level socioeconomic and built environment determinants of ridehailing pick-ups and drop-offs in the Phoenix metro region with spatial lag of X modeling. A geographically weighted regression (GWR) model of vehicle miles traveled was then estimated using route-level ridehailing data from a third-party mileage tracking app to identify zonal attributes associated with this measure of vehicle-based exposure. Together, study findings highlight the benefits and drawbacks of greater ridehailing service activity, identifying a need for programs and interventions that safeguard and improve access to affordable high-quality mobility options for transportation disadvantaged neighborhoods. [ABSTRACT FROM AUTHOR]

Promoting green transportation development in the context of electric–carbon market synergy can help promote sustainable transport and tackle climate change. The sharing economy has given rise to innovative and successful business models in recent years. To occupy current and potential markets, many enterprises that participate in sharing economy activities have engaged in a fiercely competitive environment. It is an important way for enterprises to generate profits and improve competitiveness by encouraging consumers' continuous consumption or stimulating repurchase intentions. This study investigates the effects of consumer satisfaction on consumer repurchase intention (CRI) and how such effects are moderated by the consumer's risk perception and sustainability awareness in the case of ride-sharing services, which are viewed as a mode of green transportation service. The results of a survey of 358 Chinese consumers who have used ride-sharing services suggest that transaction-based and experience-based satisfaction have positive and significant effects on the CRI of ride-sharing services. Moreover, the results indicate that consumer risk perception negatively moderates the relationships between satisfaction and CRI, while consumer sustainability awareness plays different roles depending on the type of satisfaction (transaction-based versus experience-based). Finally, implications and suggestions for future studies are discussed. [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]

This paper presents a new ridesharing simulation model that accounts for dynamic driver supply and passenger demand, and complex interactions between drivers and passengers. The proposed simulation model explicitly considers driver and passenger acceptance/rejection on the matching options, and cancelation before/after being matched. New simulation events, procedures and modules have been developed to handle these realistic interactions. Ridesharing pricing bounds that result in high matching option accept rate are derived. The capabilities of the simulation model are illustrated using numerical experiments. The experiments confirm the importance of considering supply and demand interactions and provide new insights to ridesharing operations. Results show that higher prices are needed to attract drivers with short trip durations to participate in ridesharing, and larger matching window could have negative impacts on overall ridesharing success rate. Comparison results further illustrate that the proposed simulation model is able to replicate the predefined "true" success rate, in the cases that driver and passenger interactions occur. [ABSTRACT FROM AUTHOR]

Governments, regulatory bodies, and manufacturers are proposing plans to accelerate the adoption of electric vehicles (EVs), with the goal of reducing the impact of greenhouse gases and pollutants from internal combustion engines on human health and climate change. In this context, the paper considers a scenario where ride-sharing enterprises utilize a 100%-electrified fleet of vehicles, and seeks responses to the following key question: How can renewable-based EV charging be maximized without disrupting the quality of the ride-sharing services? We propose a new mechanism to promote EV charging during hours of high renewable generation, and we introduce the concept of charge request, which is issued by a power utility company. Our mechanism is inspired by a game-theoretic approach where the power utility company proposes incentives and the ride-sharing platform assigns vehicles to both ride and charge requests; the bargaining mechanism leads to prices and EV assignments that are aligned with the notion of Nash equilibria. Numerical results show that it is possible to shift the EV charging during periods of high renewable generation and adapt to intermittent generation while minimizing the impact on the quality of service. The paper also investigates how the users' willingness to ride-share affects the charging strategy and the quality of service. [ABSTRACT FROM AUTHOR]

This research provides empirical insights to reveal how value convergence occurs within Uber's sharing economy. Uber's business model is built on a digital platform that links private vehicle owners and their under-utilized assets to be economic revenue generating units for on-demand transport requests from potential clients. By analyzing adoption values for drivers and users of Uber, we identify antecedent operational and behavioral values for Uber adoption. We add to extant Industry 4.0 research and show value convergence emanating from economic and convenience motives. On the basis of our findings, we develop propositions for value convergence leveraging on cyber-physical systems embedded in Industry 4.0, and its application for theory and practice within the broader domains of the sharing economy. [ABSTRACT FROM AUTHOR]

We consider a ridesharing service in which no driver and rider's roles are pre-determined, but left to decide by the system to further reduce costs compared to the typical version with preassigned roles. Travelers are motivated to participate in the service by saving individual transportation costs and accept its rules. We first formally define it as a generalized ridesharing optimization problem (RSP), propose its transformation into a single-depot multiple traveling salesman problem with pickup and delivery constraints (SDMTSP-PD) and provide its mixed-integer program (MIP) formulation. We then develop a polynomial-time solution method based on optimal pair matching among participants, improved by a construction insertion-based heuristic to obtain approximate solutions to the SDMTSP-PD. Experiments show that this approach could solve the problem very fast and provide near-optimal solutions and that the proposed RSP model provides substantial system-wide travel cost saving (25%+) and vehicle-trip saving (50%) compared to the non-ridesharing system and perform better than companion services with preassigned roles (P-RSP). [ABSTRACT FROM AUTHOR]

Promoting sustainable transportation, ride-sourcing and dynamic ridesharing (DRS) services have transformative impacts on mobility, congestion, and emissions. As emerging mobility options, the demand for ride-sourcing and DRS services has rarely been simultaneously examined. This study contributes to filling this gap by jointly analyzing the demand for ride-sourcing and DRS services and examining how it varies across neighborhood-level built environment, transit accessibility and crime, behavioral, and sociodemographic factors. To achieve these objectives, unique geo-coded data containing millions of ride-sourcing and DRS trips in Chicago are spatially joined with up-to-date data on the built environment, transit accessibility, crime, active travel, and demographic factors. A novel Markov Random Field-based joint heterogeneous geo-additive copula framework is presented to simultaneously capture random, systematic, and spatial heterogeneity. Characterized by a Frank copula structure, the demand for ride-sourcing and DRS services exhibited a non-linear stochastic dependence pattern. With spatial heterogeneity and spillover effects, the stochastic dependence of ride-sourcing and DRS demand varied across time of day and was the strongest in compact and dense neighborhoods. Key aspects of the built environment related to urban design (pedestrian-oriented infrastructure), density, and land-use diversity were positively associated with ride-sourcing and DRS demand—suggesting that sustainable mobility goals can be achieved by continuing to invest in more walkable neighborhoods. Active travel and telecommuting were positively linked with ride-sourcing and DRS demand. Complementary and substitutive effects for transit accessibility were found. Results show that increasing transit accessibility in areas with low levels of accessibility (compared to those with high transit levels) could be more helpful in increasing the adoption of ride-sourcing and DRS services. Relative to ride-sourcing, the demand for DRS services appeared more responsive to improvements in pedestrian-infrastructure and transit accessibility. Quantification of non-linear associations with ceiling and overdose effects for the built environment, vehicle ownership, and transit accessibility provided deeper insights. The findings can help guide the development of policy interventions and investment decisions to further accelerate the adoption of mobility-on-demand systems. [ABSTRACT FROM AUTHOR]

The object of this study is the processes of transportation of grain cargoes based on the principles of ridesharing in railroad systems without observing the traffic schedule for freight trains. In order to study the influence of the model of railroad transportation of grain based on the principles of joint use on the operation of the railroad system, it is proposed to formalize this process under the conditions of the peak load period. It is proposed to formalize the transportation of grain using the ride-sharing service in the form of coalitions in congestion games. It is proposed to turn the game setup into a nonlinear optimization problem. As part of the research, mathematical modeling of the ridesharing service of railroad transportation of grain cargoes was carried out. Adequacy of the mathematical model was proven. It was established that compliance with the traffic schedule leads to an increase in non-productive downtime of railroad cars after loading, which reduces incentives for the formation of coalitions by shippers. However, according to the results of the simulation, under the conditions of traffic according to the schedule, taking into account the coordination of the shippers and carrier, the transportation indicators are significantly improved. This encourages shippers to form coalitions. It was found that the average duration of shipment transportation decreased by 14.9 % from the indicator according to the scenario of the current transportation model – without observing the schedule. A feature of the results within the framework of the study is that the proposed mathematical model makes it possible to adequately simulate the ride-sharing service of grain transportation in the railroad system. The field of practical application of the results is the railroad industry. The conditions for the practical application of the research results are the importance of implementing digital platforms of aggregators for the coordination of shippers and carriers. Current research will contribute to devising the improvements for grain logistics in railroad transport. [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]

It is believed that shared transport services (STSs) can reduce transport poverty and social exclusion. This paper proposes a definition of "social acceptability" and "social acceptance" and examines whether vulnerable groups accept STSs. The notions "acceptability" and "acceptance" were distinguished and four necessary conditions, especially for vulnerable groups, or the 4As were identified: "availability", "accessibility", "affordability", and "attractability". In the context of STSs, "social acceptability" is defined as the degree to which an individual intends to use a STS before experiencing it in everyday travel based on the expected availability, accessibility, affordability, and attractability of the service, while "social acceptance" also incorporates the use of a STS after experiencing it in everyday travel based on a minimum level of perceived availability, accessibility, affordability, and attractability. This paper further reviews the scientific literature in transport research regarding the "acceptability" or "acceptance" of STSs by vulnerable groups. While several studies include socio-economic and demographic variables (e.g. age, gender) to explain the "acceptability" of STSs, only a few studies specifically focus on vulnerable groups. More research on the "social acceptance" of STSs, especially shared scooters, ride-sharing, and apps and Mobility as a Service (MaaS), by vulnerable groups is needed. [ABSTRACT FROM AUTHOR]

With the emergence of new information and communication technology, mobile‐based ridesharing services have received more attention and have improved. They offer timely and convenient service and exert an enormous influence on pollution and congestion by allowing passengers to share overlapping routes. However, ridesharing services have also been discussed and called into question by people with respect to their attractiveness, their comfort, and passenger safety. This paper proposes a ridesharing algorithm with a perceived mechanism by introducing multiple dimensions of perceived value to overall assess passengers' perceptions. The approach involved can continuously track the psychological changes in passengers during travel, capture the dynamic and comprehensive perceptions of passengers, and make real‐time adjustments on this basis. Furthermore, the proposed partition‐based parallel strategy can ensure high‐quality services and further enhance computational efficiency. Finally, numerical experiments are conducted based on the urban transportation network and the actual trip data from Manhattan to evaluate the performance of the proposed algorithm. The results show that it is important to effectively capture the passengers' perception of ridesharing services and apply them to optimization mechanisms, which will contribute to improving the perception of ride experiences and attracting potential passengers. [ABSTRACT FROM AUTHOR]

This study examines the effects of an information sharing restriction policy that restricts taxi drivers' access to ride requests via ride-hailing apps. We show that the policy significantly decreases the ridership of an affected taxi fleet during times of enforcement but significantly increases the demands at some times of nonenforcement after launch. Furthermore, the traffic on public transportation, including metro, bus, ferry, and park & ride, and the congestion on the surface roads and expressways significantly increase after launching the policy during both enforcement and most nonenforcement times. We also show that the profitability of taxi fleet decreases after the restriction, which supports the notion that information sharing via ride-hailing apps enables them to match not only with more orders but also with those of higher marginal profit. These findings suggest that information sharing via ride-hailing apps can improve the utilization of existing taxi capacity, which further alleviates traffic during alternative times and the burden placed on alternative transportation modes. Policymakers and platform managers should dissect the value of information sharing from that of other aspects (e.g., changes in supply) in on-demand platforms and design policies that more specifically restrict the harmful aspects rather than restricting the use of such apps. A ride-hailing platform is an app-based, two-sided platform that matches riders with vehicles via information technology (IT). In 2015, the Shanghai government introduced a policy to restrict taxi drivers' access to and acceptance of ride requests via ride-hailing apps during certain hours. We conceptualize this policy shock as the restricting of information sharing enabled by IT and collect comprehensive data on various uses of transportation to gauge the economic benefits of this information sharing for existing capacity and its subsequent externalities on other transportation. Through a time series analysis, we identify significant decreases in the ridership of an affected taxi fleet during times of enforcement but significant increases at some times of nonenforcement postlaunch. Furthermore, the traffic on public transportation, via the city's transportation cards, and the congestion on the surface streets and expressways significantly increase after launching the policy during both enforcement and most nonenforcement times. These suggest that information sharing via ride-hailing apps can improve the utilization of existing taxi capacity, which further alleviates traffic during alternative times and the burden placed on alternative transportation modes. Interestingly, our mechanism analysis shows decreased profitability after the restriction, which supports the notion that information sharing via ride-hailing apps reduces drivers' search cost and thus enables them to match not only with more orders but also with those of higher marginal profit. This study contributes to the literature on ride-hailing platforms' impact and the economic value of information sharing and IT by dissecting the compound ride-hailing's impact to extract the value of information sharing enabled by IT and to reveal the underlying mechanism. Practically, we evaluate the policy studied, make postrevision suggestions for general contexts, and provide managerial insights on precise policymaking that best extracts the economic value of information sharing in ride-hailing and general forms of online two-sided platforms. History: D. J. Wu, Senior Editor; Yili (Kevin) Hong, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71729001, 71972047, 71831005, and 91746302]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1181. [ABSTRACT FROM AUTHOR]

The sharing economy is revolutionizing the way consumers use goods and services. The aim of this study is to understand consumer motivations to be satisfied and to continue using ridesharing services. With this aim, we modify and extend the expectation–confirmation model by including social value as an additional factor to those previously studied in the literature. Data were collected from 258 users of BlaBlaCar, one of the world leaders in ridesharing services. Social value positively affects satisfaction and has the second strongest total effect on continuance intention among the motivations in our model. Our results highlight that satisfaction of ridesharing users is driven by service quality, perceived usefulness, environmental impact, trust, and social value; and that all those factors joined for economic benefits affect continuance intention directly or indirectly through satisfaction. These results have important theoretical and managerial implications. [ABSTRACT FROM AUTHOR]

Purpose: Identifying the complementary effects of ride-sharing on public transit is critical to understanding the potential value of growing partnerships between public transit agencies and ride-sharing platforms. The purpose of this paper is to investigate whether and how ride-sharing services complement public transit. Design/methodology/approach: Taking advantage of a natural experiment whereby subway Line 2 opened after the entry of ride-sharing services in Xiamen, this study uses a difference-in-differences approach to identify the complementary effects of ride-sharing on public transit based on a proprietary fine-grained trip-level data set from a large ride-sharing platform. Findings: This study obtained the encouraging finding that ride-sharing has a significant complementary effect on the subway, as the number of ride-sharing pickups and drop-offs at subway stations increased by 130% and 117.9%, respectively, after the subway opening. Moreover, mechanism analysis shows that the complementary effect of ride-sharing services is stronger when connection distance is short (i.e. under 6 km) and when the transportation availability is limited (i.e. at night or in the areas with low transit supply and low population density). Practical implications: The findings provide guidelines for promoting cooperation between public transit agencies and ride-sharing platforms to build an efficient and sustainable urban transport system. Originality/value: This study is the first to examine the complementary effect of ride-sharing services on public transit via unique fine-grained ride-sharing trips data, and further reveal the underlying mechanism behind this effect. [ABSTRACT FROM AUTHOR]

Previous studies into the potential benefits of ride pooling failed to account for the trade-off that users likely make when considering a shared ride. We address this shortcoming by formulating user net benefit stemming from pooling as a compensatory function where the additional travel time and on-board discomfort need to be compensated by the price discount for a traveller to choose a pooled ride over a private ride. The proposed formulation is embedded in a method for matching travel requests and vehicles. We conduct a series of experiments investigating how the potential of ride-pooling services depends on demand characteristics, user preferences and the pricing policy adopted by the service provider. Our results suggest that the total vehicle mileage savings found by previous studies is only attainable when users are very willing to share their ride (i.e. attach low premium to private rides) and are offered a 50% discount for doing so. [ABSTRACT FROM AUTHOR]

Autonomous vehicles (AVs) have generated excitement for the future of transportation. Public transit agencies and companies (i.e., Uber) have begun developing shared autonomous transportation services. Most AV surveys focus on public opinion of perceived benefits and concerns of AVs but are not directly tied to field implementation of AVs. Experience and exposure to new technology affect adults' perceptions and level of technology acceptance. As such, the Autonomous RideShare Services Survey (ARSSS) was developed to assess adults' perceptions of AVs before and after being exposed to AVs. Face validity and content validity were established via focus groups and subject-matter experts (CVI = 0.95). Adults in the U.S. (N = 553) completed the ARSSS, and a subsample (N = 100) completed the survey again after two weeks. An exploratory and confirmatory factor analysis demonstrated that the ARSSS consists of three factors that can be used to reliably quantify users' perceptions of AVs: (a) Intention to Use, Trust, and Safety (r = 0.85, p < 0.001, ICC = 0.99); (b) Potential Benefits (r = 0.70, p < 0.001, ICC = 0.97); and (c) Accessibility (r = 0.78, p < 0.001, ICC = 0.96) of AVs. These are key factors in predicting intention to use and acceptance of AVs. Results from the ARSSS may inform the acceptance among users of these AV technologies. [ABSTRACT FROM AUTHOR]

This paper addresses how referral generation and referral value evolve throughout the customer's life cycle as a function of service usage, experience level, and past referral behavior. We look at the referral behavior of 400,000 users of a large ride-sharing platform over the duration of a year. The upshot is that users make more and higher value referrals as they become more experienced with the service and when they are using the service intensively. However, as users make referrals, they are more likely to run out of friends to refer, leading to fewer (and lower value) referrals in the future. Based on these results, we suggest how digital platforms can improve their referral programs by tailoring them to how referral generation and referral value evolve over time. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior, such as the relationship between past and future referrals, and accounting for unobserved heterogeneity across users. Online platforms often ask their users to refer friends in exchange for a reward. This paper addresses how referral generation and referral value evolve throughout the customer's life cycle as a function of service usage, experience level, and past referral behavior. Our analysis is based on a longitudinal data set that comprises the transactions and referral actions of 400,000 users in a ride-sharing platform over a year. The richness of our data set allows us to address two shortcomings from previous studies: modeling dynamic behavior (i.e., the relationship between past and future referrals) and accounting for unobserved heterogeneity across users. Our results show that users make more referrals when they have used the service recently and intensively. For example, users become 9% less likely to make referrals for each week they have not used the service. Furthermore, users make more high-value referrals as they become more experienced with the service. The referral generation and referral value of the top 10% most experienced users are more than 18% higher relative to when they first used the service. Finally, as users make more referrals, they become more likely to run out of friends to whom they can refer the service, leading to less referrals in the future. After users make their first referral, the probability of making additional referrals decreases by more than 78% and the value of subsequent referrals reduces by 19% on average. The results imply that platforms should consider tailoring their referral programs according to how referral generation and referral value evolve over time. History: Xiaoquan (Michael) Zhang, Senior Editor; Khim Yong Goh, Associate Editor. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive review of the significant elements in sustainable transportation systems with shared mobility. The main subsets of shared mobility includes ridesharing, carsharing, shared micromobility, on-demand ride services, and shared autonomous vehicles (SAVs). This paper revisits the related studies in the last decade since shared mobility spread via the emergence of smartphones and numerous applications. A framework is proposed to describe the shared mobility systems and understand the interactions among various elements in shared mobility systems, such as the supply and demand relationship between users and enterprises. Based on this framework, many fundamental research problems of shared mobility systems are summarized to cover travel behavior, enterprise supply, government policy, and sustainability. With the sustainability theme of shared mobility, we review the literature on the impacts of shared mobility on transportation sustainability. It is discovered that shared mobility has many advantages and improves urban transportation systems in many aspects, but these benefits vary with travel behavior and enterprise supply. We also discuss how government policies regulate travel behavior and enterprise supply in the sustainable development of urban transportation systems. [ABSTRACT FROM AUTHOR]

Purpose: The rising of the sharing economy (SE) has lowered the barrier of purchase price to accessing many different products, thus changing the consumer decision paradigm. This paper addresses the challenge of assessing the life cycle impacts of SE systems in the context of this new consumer decision-making process. The paper proposes a methodological framework to integrate consumer preferences into the Dynamic Life Cycle Assessment (dynamic-LCA) of SE systems. Methods: In the proposed consumer preference integrated dynamic-LCA (C-DLCA) methodological framework, system dynamics (SD) is used to combine consumer preference and the principal method, dynamic-LCA, which follows the ISO 14040 LCA framework. Choice-based conjoint analysis (CBCA) is chosen as the stated preference tool to measure consumer preference based on SE alternatives, attributes and attribute levels. CBCA integrates discrete choice experiments (DCE) and conjoint analysis features. Random utility theory is selected to interpret the CBCA results by employing multinomial logistics as the estimation procedure to derive the utilities. Derived utilities are connected in iterative modelling in the SD and LCA. Dynamic-LCA results are determined based on dynamic process inventory and DCE outcomes and then interpreted aligned with the SD policy scenarios. Results and discussion: The C-DLCA framework is applied to assess the GHG changes of the transition to car-based shared mobility in roundtrips to work in the USA. Carpooling and ridesourcing are selected as the shared mobility alternatives based on different occupancy behaviours. Powertrain system and body style are employed as the fleet technology attributes and the latter as an endogenous variable. Dynamic-LCA results are generated considering the high battery electrical vehicle (BEV) adoption as the policy scenario, and results are measured against a service-based functional unit, passenger-kilometre. The model outcomes show a significant reduction in aggregated personal mobility-related dynamic-GHG emissions by transitioning to car-based shared mobility. In contrast to the use phase GHG emissions, the production phase emissions show an increase. The results highlight the importance of integrating consumer preference and temporality in the SE environmental assessments. Conclusions: The proposed C-DLCA framework is the first approach to combine consumer preferences, SD and LCA in a single formulation. The structured and practical integration of conjoint analysis, SD and LCA methods added some standardisation to the dynamic-LCAs of the SE systems, and the applicability is demonstrated. The C-DLCA framework is a fundamental structure to connect consumer preferences and temporal effects in LCAs that is expandable based on research scope. [ABSTRACT FROM AUTHOR]

This paper presents an integrated approach, of a multi-criteria decision-making framework and fuzzy multi-objective programming to optimize dispatching and rebalancing for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby, a fleet of self-driving electric vehicles are coordinated to service on-demand travel requests and eventually allowing multiple passengers to share rides. Specifically, the fuzzy analytical hierarchy process and the Fuzzy technique for order of preference by similarity to ideal solution are first integrated in order to analyze customer preferences, prioritize their attitudes toward autonomous vehicles, and then to rank fleet vehicles according to these prioritizations. Next, leveraging the ranks of vehicles, we introduce a new Multi-Objective Possibilistic Linear Programming (MOPLP) model, considering realistic constraints and handling the uncertain nature of some critical data affecting RAMoD systems. Three conflicting goals are considered simultaneously which are (i) to minimize the lost customer requests, (ii) to minimize the total transportation cost and (iii) to improve customer satisfaction. This MOPLP model is converted to an equivalent crisp MOLP through applying appropriate strategies and the goal programming method is called to solve this MOLP and find an efficient compromise solution. Finally, the applicability and efficiency of the proposed approach are presented through an illustrative example. Collectively, this work provides a unified framework for controlling and analyzing RAMoD systems, which includes a wide range of modeling options (e.g., the inclusion of the uncertain future demand), and provides the first correlation between the dispatching and rebalancing decisions, and the process of analyzing customer preferences toward autonomous vehicles. [ABSTRACT FROM AUTHOR]

Ride sharing is a service that enables users to share trips with others, conserving energy, decreasing emissions and reducing traffic congestion. Selecting a suitable partner for a user based on the their trip data is essential for the service, but it also leads to privacy disclosure, e.g., the user's location and trajectory. Many privacy-preserving solutions for ride sharing services have been proposed, which are based on cryptographic technology and provide accurate matching services. However, these encryption-based algorithms are very complicated and difficult to calculate. In hot spots, such as stations, airports and sport gymnasiums, a large number of users may apply for a ride sharing service in short space of time, which will place huge pressure on the service provider. Using traditional matching methods increases the matching time and leads to a less favorable user experience. To solve these problems, we model them, aiming to maximize the vehicle's carrying capacity and propose a lightweight privacy-preserving ride matching scheme for selecting feasible partners during busy periods with a large number of requests. To achieve this, we make use of the homomorphic encryption technique to hide location data and design a scheme to calculate the distances between users in road networks securely and efficiently. We employ a road network embedding technique to calculate the distance between users. Moreover, we use travel time instead of space distance, which makes matching more accurate. Further, with the encrypted itineraries of users, the service provider selects potential ride share partners according to the feasibility of time schedules. We use ciphertext packing to reduce overhead, improving the efficiency of ride matching. Finally, we evaluate our scheme with simulation and demonstrate that our scheme achieves an efficient and accurate matching service. It only takes a few seconds to complete the matching, and the matching accuracy is higher than 85 percent in most cases. [ABSTRACT FROM AUTHOR]

This study aims to identify young adults' travel behavior using ridesharing services. We analyze data from an online survey of university students, regarding three free ridesharing services, including fixed-route, on-demand, and shared autonomous vehicles (SAVs). Ordinal regression and structural equation model (SEM) are employed to explore the frequency of service usage. Results indicate that most students had never taken a ride by available ridesharing services due to their preferences for using private vehicles and lack of service information. Regression results reveal that individuals' usual mode of transportation and residential location significantly influence ridesharing behavior. Our results also show significant associations between travel attitudes and students' travel behavior. We also found that shared on-demand and autonomous vehicle services could complement fixed-route services. Further research is needed on the link between young people's adoption of integrated ridesharing transportation services. [ABSTRACT FROM AUTHOR]

This study examines the ridesharing services in a developing economy, with the aim of revealing the factors influencing passengers' satisfaction and loyalty. This study is important as the ridesharing industry is growing at a tremendous rate and will ultimately attract many new players in the market. Offered abundant choices, consumers are exploring the best ridesharing services that meet their satisfaction. As the switching costs and brand loyalty are lower for on‐demand services, it is pertinent to understand ridesharing passengers' satisfaction and loyalty. The findings of this study are based on a questionnaire survey of 200 ridesharing passengers. The results reveal that both brand image and price significantly influence customer satisfaction. However, in terms of customer loyalty, only brand image is statistically significant. Price did not have a significant impact on customer loyalty. Overall, this research corroborated previous studies on the positive relationship of customer satisfaction and customer loyalty. The paper concludes by providing numerous theoretical and practical implications based on the findings of the study [ABSTRACT FROM AUTHOR]

The integration of automation and shared mobility services would significantly affect transportation demand, especially mode choice. However, little is known about how attitudes, travel attributes, and demographic factors affect the modal shift to shared autonomous vehicles (SAVs). A stated preference survey was designed to determine the preferences of car and transit users in relation to a modal shift to SAVs. The binary logit models' results revealed distinct behavior patterns and systematic heterogeneity among transit and private car users based on a representative sample of 607 individuals in 2021. The shifting behavior of both users is positively affected by attitudinal factors, including consumer innovativeness, perceived usefulness, sharing intention, and ecological awareness, while negatively affected by privacy concerns. In terms of travel-related attributes of SAVs, car users are eight times more sensitive to waiting times compared to transit users, who are three times more concerned with travel costs. Further, privacy concerns, the number of passengers sharing a trip, and the ratio of waiting time to travel time of SAVs were the major barriers to shifting the likelihood of car users' behavior. In light of these findings, based on the likely effects of SAVs on shifting behavior, a number of practical implications are suggested for more effective policy making. [ABSTRACT FROM AUTHOR]

The increasing number of private vehicles has caused severe traffic congestion and air pollution. As an innovative transportation mode, mobility services such as ridesharing have been considered as an alternative. However, it has been evidenced that most private vehicle owners would not switch from driving to using ridesharing because such options only attract those who never intend to own a car. Therefore, it is more meaningful for private vehicle owners to provide ridesharing services, also known as Hitch, which can improve urban traffic efficiency on the service supply side. This study outlines an empirical study which is conducted to assess drivers as a group, to examine the factors which influence their intention of ridesharing. A sample of 597 private vehicle owners in Tianjin, China, completed a questionnaire. Structural equation modeling (SEM) is implemented to analyze the proposed model, specially explore the mediating role of trust and habit. The results indicate that habit is the most important factor, which is mainly influenced by extrinsic motivation (i.e., instrumental motive). Furthermore, habit can increase trust in ridesharing, another important factor, which is mainly affected by intrinsic motivation (i.e., affective motive and responsibility). Therefore, this shows the relationship between intrinsic motivation and extrinsic motivation. The findings in this paper have strong implications for both governments and platform developers, to promote the transition from solo driving to ridesharing to improve traffic condition and environmental pollution. • We test the factors leading private vehicle owners to share a ride, on the service supply side. • This study analyzes trust and habit as mediators bridging motivations. • Motivation theory and material possession theory (MPT) are used. [ABSTRACT FROM AUTHOR]

Ridesharing has become popular over the last decade, bringing significant changes to residents' travel patterns and city traffic. Relevant to the growth and impact of ridesharing activities in populous urban areas are the side effects, such as air pollution and traffic congestion. Despite a number of studies on these side effects, few have focused on their underlying causes and explained the behavioral incentive. Drawing on the theory of averting behavior, this paper investigates the ridesharing choices of individuals who face but would prefer to avoid ambient air pollution. Such air pollution–averting behavior tends to increase the demand for ridesharing services, which further results in more motorized traffic and pollutant emissions. Utilizing trip records from DiDi company and data on air pollutants in Haikou, China, we find that an additional 10 μg/m3 of PM2.5 leads to an increase of 2.6% in the count of DiDi trip orders. Results also show that traffic congestion increases due to elevated DiDi usage and the effect varies with time. This could further worsen the air quality and bring other potential side effects. These empirical results offer unique insights into the impact of air pollution on ridesharing and traffic from the perspective of individual behavioral motivation. [ABSTRACT FROM AUTHOR]

Background: In the light of the sustainable development goals, a set of operationalizable criteria quantifying societal benefits versus costs is needed to prepare for the introduction of an incentive-driven, high adoption shared on-demand mobility service in a rural transportation area. Aiming to reduce still-existing uncertainty about the impacts of a market entry and to balance substantial monetary incentives for suppliers of pooled rides with the progress in net GHG emission reduction and accessibility gain, the framework is applicable at the transactional and/or aggregate level. Results: The presented set of indicators is decision-oriented, and applicable both at the transactional and macro level. Scenarios and user choice situations for two regulatory options of demand responsive transport—one in line with the current legislation in Germany, avoiding overlap with existing fixed-route scheduled public transport and one not strictly complementary which assumes direct competition—are formalized. By the help of a case study, the outcome of the different organizational models at three levels of incentivization can be systematically compared. The implementation effort of the assessment method is examined in view of the forthcoming sustainability compliance reporting in this sector. Conclusions: A system-optimal constrained public private DRT deployment offers the opportunity of a reduction of uneconomic routes and parallel services upon selection of eligible rides. As shown, a crowding out of existing, publicly financed offerings in an unsaturated mobility market should not be the primary concern. At the contrary, resorting to supply-side incentives, a proportion of the high volume of solo car trips could be consolidated while levels of service improve in total. However, this may be associated with considerable expense, as demonstrated by the quantity structure of the provided case study. [ABSTRACT FROM AUTHOR]

One of the key competitive advantages of sharing economy platforms stems from their superior IT capabilities, which enable operations that are more efficient and/or effective than the incumbent firms. However, given that many of the incumbents tend to be established market leaders or resource‐rich multi‐nationals, their general inability to acquire the appropriate digital options (ie, the IT capabilities that enable the launch of, and response to, competitive actions) to meet the challenge of the sharing economy platforms is puzzling. This study explores this phenomenon by posing the research question: How do the "forces at work" associated with the sharing economy paradigm impact the digital options of incumbent firms? Based on the case of Qiangsheng Taxi and how its IT capabilities have been affected by the emergence of ridesharing platforms, the evidence uncovered suggests that the sharing economy can influence the digital options of incumbent firms through a process of digital attrition, which may be induced by a blend of unfavorable contextual influences (ie, an unbalanced regulatory regime, evolving market preferences and the resourcing advantages of sharing economy platforms). A framework is inductively derived from the data collected that depicts digital attrition as a process of three phases: (a) deinstitutionalization, (b) technological incapacitation, and (c) competitive erosion. In doing so, our study suggests that digital attrition may culminate in IT‐induced competitive disadvantages for incumbent firms, which in turn, could exacerbate the unfavorable contextual influences to complete a vicious cycle that reinforces the negative influence of the sharing economy even further. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Action Research is the property of Verlag Barbara Budrich GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Dynamic ridesharing refers to services that arrange one-time shared rides on short notice. It underpins various real-world intelligent transportation applications such as car-pooling, food delivery and last-mile logistics. A core operation in dynamic ridesharing is the “insertion operator”. Given a worker and a feasible route which contains a sequence of origin-destination pairs from previous requests, the insertion operator inserts a new origin-destination pair from a newly arrived request into the current route such that certain objective is optimized. Common optimization objectives include minimizing the maximum/sum flow time of all requests and minimizing the total travel time of the worker. Despite its frequent usage, the insertion operator has a time complexity of $O(n^3)$ O (n 3) , where $n$ n is the number of all requests assigned to the worker. The cubic running time of insertion fundamentally limits the efficiency of urban-scale dynamic ridesharing based applications. In this paper, we propose a novel partition framework and a dynamic programming based insertion with a time complexity of $O(n^2)$ O (n 2) . We further improve the time efficiency of the insertion operator to $O(n)$ O (n) harnessing efficient index structures, such as fenwick tree. Evaluations on two real-world large-scale datasets show that our methods can accelerate insertion by 1.5 to 998.1 times. [ABSTRACT FROM AUTHOR]

Ridesharing services offer on-demand transportation solutions while improving the utilization of the available capacity of the vehicles on the road. Profit, travel time, and cost are the commonly optimized objectives in current ridesharing services. Existing multi-objective optimization-based services mainly focus on maximizing profit for service providers while minimizing the travel time for passengers. However, various personal preferences (e.g., co-riders' gender for a passenger or preferred area of service for a driver) should be considered when offering such services. Such preferences are often conflicting with one another and with the objectives such as cost and travel time. Therefore finding an optimized solution and satisfying such preferences simultaneously is challenging. To address this challenge, this paper proposes a Multi-agent, Multi-objective, Preference-based ridesharing model (MaMoP) that offers an optimized ridesharing solution while satisfying users' preferences simultaneously. MaMoP uses social-reasoning techniques to model preferences and their relations and employs evolutionary algorithms to find an optimized solution. [ABSTRACT FROM AUTHOR]

The advent of ridesharing introduced changes in transportation planning and raised issues regarding its acceptance and usage by potential users. This study contributes to the existing ridesharing literature by focusing on the implementation of ridesharing services in academic institutes. An online survey is deployed in the Region of Attica, Greece to explore the factors that may contribute towards using ridesharing to travel to and from academic institutes by using a service that is hosted by the institute and offered only to its registered students and employees. In total 844 respondents completed the survey. Identified factors for ridesharing passengers are grouped into: (1) reasons, (2) motives, and (3) constraints, and three models are developed by using ordinal logistic regression. An additional model is built for ridesharing drivers. Results for passengers suggest that reasons for ridesharing, such as, sustainability, socializing, and travel cost, and motives for ridesharing, such as the payment method, passenger awards, and number of passengers, are among significant factors that affect passengers to rideshare. Security and driver experience may hinder participation in ridesharing services in academic institutes. The study concludes with a discussion for transportation planners and service providers for implementing successful ridesharing services. [ABSTRACT FROM AUTHOR]

Within the context of a shared on-demand transport system, we study the problem of selecting the stopping points from which passengers should walk to their exact destinations (or from their exact origins). We focus on the single-vehicle case that must follow a predefined order of requests, posing the mathematical program, showing that it can be solved in polynomial time and proposing a heuristic that runs faster. We compare the optimal algorithm, the heuristic, and the routes that visit the exact request points, and we show that avoiding detours can reduce total costs by almost one fifth and vehicle costs by more than one third. The heuristic yields competitive results. Simulations over the real street network from Manhattan show that the time reduction achieved by the heuristic might be crucial to enable the system to operate in real-time. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

Ride-pooling (or ride-sharing) services combine trips of multiple customers along similar routes into a single vehicle. The collective dynamics of the fleet of ride-pooling vehicles fundamentally underlies the efficiency of these services. In simplified models, the common features of these dynamics give rise to scaling laws of the efficiency that are valid across a wide range of street networks and demand settings. However, it is unclear how constraints of the vehicle fleet impact such scaling laws. Here, we map the collective dynamics of capacity-constrained ride-pooling fleets to services with unlimited passenger capacity and identify an effective fleet size of available vehicles as the relevant scaling parameter characterizing the dynamics. Exploiting this mapping, we generalize the scaling laws of ride-pooling efficiency to capacity-constrained fleets. We approximate the scaling function with a queueing theoretical analysis of the dynamics in a minimal model system, thereby enabling mean-field predictions of required fleet sizes in more complex settings. These results may help to transfer insights from existing ride-pooling services to new settings or service locations. [ABSTRACT FROM AUTHOR]

Triggering the energy-efficient behavior of agents in firms simultaneously decreases costs and mitigates CO2 emissions. If firms use team tournaments to increase energy-efficient behavior and thus employee performance, they may face unintended consequences, like a bifurcation effect: Individuals drop out if they believe that they cannot win the contest. By contrast, high-performing employees may overexert themselves. Additionally, some individuals might be tempted to free-ride. In a field experiment with truck drivers, we analyze whether proportional sharing of the bonus within teams based on individual effort instead of egalitarian sharing reduces both bifurcation and free-riding during team tournaments. Our results reveal that (1) the team contest improves performance; (2) this increase in performance is overall slightly stronger under the proportional than under the egalitarian sharing rule, using ceteris paribus comparisons; and (3) the performance increase is mainly driven by the team member performing worse. [ABSTRACT FROM AUTHOR]