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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 rapid growth of private car ownership has led to significant issues such as traffic congestion and environmental pollution. Ridesharing has emerged as a promising solution to alleviate the negative impacts associated with private car usage. This paper focuses on the stability of ridesharing systems and establishes a single-driver multiple-rider ridesharing matching model. To solve this model, a filtering algorithm for the pre-matching set and a fast-solving algorithm for stable matching scheme are proposed. Furthermore, we introduce the concept of subsidy distance upper limit into the ridesharing system. Remarkably, our findings indicate that with a limit of 0.1km, the distance saved generated by the subsidy amounts to 560.5% of the total subsidy. To validate our approach, we simulate ridesharing demand data using real taxi data, and design computational experiments to prove the computational efficiency of the filtering algorithm and fast-solving algorithm. The impact of various parameters on ridesharing systems is also explored. [ABSTRACT FROM AUTHOR]

In a congested urban road network, it is important to reduce passenger travel time and delay by implementing appropriate traffic signal controls. Previous studies have mainly focused on vehicle travel times or queue lengths around signals. However, as ridesharing has become popular in recent years, a new line of research delves into prioritizing ridesharing vehicles in terms of convenience for people rather than for cars. This paper proposes a control method based on dynamic programming that reduces the passenger travel time in a distributed manner by taking into account the number of passengers in each vehicle. This method also alleviates vehicle spillback and extremely long wait times for vehicles. Finally, the performance of the proposed method is verified with simulations. [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]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

Ride-sharing has transformed people's travel habits with the development of various ride-sharing platforms, which can enhance the utilization of transportation resources, alleviate traffic congestion, and reduce carbon emissions. However, the development of a general and efficient matching framework is challenging due to the dynamic real-time conditions and uncertainty of ride-sharing problems in the real world. Additionally, previous research has identified limitations in terms of model practicability and algorithmic solution speed. To address these issues, a two-stage dispatching approach for one-to-many ride-sharing with sliding time windows is proposed. The dynamic ride-sharing problem is formally defined, and an integer programming model is constructed to solve it. A multi-rider distance and time constraint algorithm uses a distance matrix and sliding time windows to preprocess data before matching is proposed, thereby optimizing data quality and improving computational efficiency. The ride-sharing process is divided into a reservation order matching stage based on path similarity and a real-time order matching stage based on path distance degree. A two-stage collaborative mechanism is designed to guide the collaboration of the two stages. Furthermore, numerical experiments are conducted using two real-world datasets from developing and developed country regions to verify the efficiency and practicability of the proposed approach. [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 platforms compensate drivers using a fixed commission system that does not systematically reward effective drivers, which reduces platform engagement. Unsurprisingly, driver transaction activity is intermittent and service unpredictable. Influenced by agency theory, we propose a variable commission that jointly accounts for drivers' transactions and service performance. To alleviate disengagement, we propose a customer-oriented engagement framework that challenges the notion of the sole monetary focus of drivers. We compare the effects of variable and fixed commission schemes on consequences such as driver net revenue and referral value, mediated by attitudinal outcomes. In a 3-month cluster-randomized field experiment with 3,367 ridesharing drivers across 16 cities and two population tiers, we show improvements in driver satisfaction and emotional connectedness accentuated by goal-oriented feedback. Variable commission with goal-oriented feedback translates to a 24.5% rise in revenue, a 19.5% increase in referral value, and a 43.21% lower churn. A cost–benefit analysis reinforces these results. [ABSTRACT FROM AUTHOR]

In ride-sharing, commuters with similar itineraries share a vehicle for their trip. Despite its clear benefits in terms of reduced congestion, ride-sharing is not yet widely accepted. We propose a specific ride-sharing variant, where drivers are completely inflexible. This variant can form a competitive alternative against private transportation, due to the limited efforts that need to be made by drivers. However, due to this inflexibility, matching of drivers and riders can be substantially more complicated, compared to the situation where drivers can deviate. In this work, we propose a four-step procedure to identify the effect of such a ride-sharing scheme. We use a dynamic mesoscopic traffic simulator which computes departure-time choices and route choices for each commuter. The optimal matching of potential drivers and riders is obtained outside the simulation framework through an exact formulation of the problem. We evaluate the potential of this ride-sharing scheme on a real network of the Paris metropolitan area for the morning commute. We show that even with inflexible drivers and when only a small share of the population is willing to participate in the ride-sharing scheme, ride-sharing can alleviate congestion. Further improvements can be obtained by increasing the capacity of the vehicles or by providing small monetary incentives, but without jeopardizing the inflexibility of the drivers. Thereby, we show that ride-sharing can lead to fuel savings, CO 2 emission reductions and travel time savings on a network level, even with a low participation rate. [ABSTRACT FROM AUTHOR]

Shared e-scooter systems have become an alternative for micromobility users in cities since 2017. The success of the shared e-scooter service can be related to the provision of more last-mile flexibility and convenience to users. They can also be seen as a replacement for private cars and on-demand ridesharing, especially in highly crowded urban environments. Therefore, the main objective of this research is to disclose the main characteristics that determine shared e-scooter usage and their policy implication in a medium-sized city in the North of Portugal. To meet this purpose, a survey was conducted, and statistical analyses were performed to correlate the sociodemographic characteristics of respondents with the willingness to use shared e-scooters. Results show that gender, origin of the trip, and the main mode of transport affect the usage of e-scooters, thus specific policies should be developed to decrease these inequalities. Special attention needs to be given to the creation and expansion of dedicated areas for e-scooter ridership, zoning, and some traffic calming measures to promote a safe, user-centric, and more pleasant environment for shared e-scooter riders. [ABSTRACT FROM AUTHOR]

Nonprofit ridesharing presents an underutilized opportunity for increasing transportation sustainability, as well as users' economic and social benefits, while reducing adverse environmental impacts. However, while app-based ridesharing has achieved only modest uptake, mostly limited to longer trips, in certain contexts, self-organized ridesharing (SORS) has shown significant achievements, even for the challenging short- and mid-range commuting trips. Still, knowledge regarding the key elements and mechanisms behind successful SORS is partial, hindering the ability to effectively leverage SORS diffusion and scaling potential. Following the premise that successful initiatives may provide learning opportunities, this study aims at narrowing this knowledge gap by performing a comparative cross-cultural systematic study of eight diverse SORS cases. The study uses a recently designed conceptual framework and multiple research methods (mainly online data exploration, personal interviews, observations, and documents analysis) to comprehensively examine SORSs' attributes and evolution stages and processes. Study findings highlight repeated patterns as well as the role of context, official actors, and local practices in shaping initiative's dynamics towards success. The study offers general and specific policy recommendations for supporting the early development, growth, and diffusion of SORS, as well as reflections on post pandemic ridesharing. [ABSTRACT FROM AUTHOR]

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%. [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]

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]

Expansion of business activities comes with various environmental and natural disasters, hence agitating the economies and academia to present the counter and effective solution. Researchers also pinpointed prior that there exists a dark side of business expansion such as lacking of green innovative activities and dynamic capabilities which are creating resistance to achieve sustainability. Besides, literature also claims that green innovation is a crucial segment to achieve competitive edge that leads to sustainable entrepreneurship; however, it is not easier to be achieved as green innovation along green capabilities makes a complex network which consists of embedded partners, hence, also in dire need of green knowledge sharing as well to make the process smooth. In this lieu, the study aims to scrutinize the effectiveness of green knowledge sharing, green dynamic capabilities, and green service innovation on green entrepreneurship in the presence of gender role as a moderator. Empirical estimations calculated through PLS methodology reveal that green knowledge sharing, green dynamic capabilities, and green service innovation has a positive and significant linkage with green entrepreneurship in Vietnam. The outcomes also exposed that the male gender role significantly moderates the said relationship in Vietnam. Findings confirm that adoption sustainability practices such as green knowledge sharing, innovation, and dynamic capabilities are helpful to fulfill sustainable entrepreneurship demand; hence, further research is needed to extend the framework along with existing concepts. [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]

With the popularization of Internet technologies and shared mobility services, online ridesharing has developed rapidly in numerous cities worldwide. However, perhaps owing to the lack of empirical data, there is a lack of comprehensive and comparative studies on the two major online ridesharing modes, namely, ridesplitting and carpooling, vis-à-vis operational performance discrepancies. Thus, we conduct an empirical study using the massive amount of actual operating data provided by DiDi Chuxing. Based on an analysis of the operating characteristics of ridesplitting and carpooling, this study proposes an approach to estimate ridesharing fuel-saving and distance-saving performance by combining the vehicle operating information and fuel economy indicators of various transportation modes. Furthermore, the operational performance discrepancies between the two major ridesharing modes are compared through an analysis of the user characteristics and interactive effects between ridesharing and subway systems. The results show that the average fuel-saving and distance-saving ratios of ridesplitting are lower than those of carpooling. From the perspective of the transportation system's fuel economy, ridesharing is not considered to be fuel-saving, and its scale should be reasonably regulated. According to driver classification, carpooling is more suitable for commuting and intercity transportation. In addition, ridesplitting and carpooling can be employed as feeders into subway networks in suburban areas. These findings are believed likely to be beneficial for facilitating the sustainable and standardized development of these two ridesharing modes. [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]

Sharing logistics platform can effectively realize the sharing of idle logistics resources and maximize the utility of logistics resources utilization by publishing and matching the supply and demand information of logistics resource. For sharing logistics platform, subsidy strategy plays a significant role in winning market competition. Therefore, how to establish an effective subsidy scheme is a burning issue. By analyzing the problems in the transaction orders of drivers and consignor on the sharing logistics platform, this paper estimates the subsidy strategy on the following three aspects: (1) establish a dynamic model of freight supply and demand matching according to the freight cost and determine the impact of different subsidy policies on the platform usage; (2) evaluate the solution of different subsidy policies to the matching problem of vehicles and goods, and determine the optimal subsidy scheme based on the evaluation results; (3) establish the subsidy model based on platform maximum benefit and social welfare optimization and analyze a practical case of "Huochebang" (a Sharing logistics platform). The analysis results show that the model constructed in this paper can be well applied to the subsidy problem of sharing logistics platform and assist the platform to establish the most suitable subsidy policy to achieve the optimal economic benefits. [ABSTRACT FROM AUTHOR]

Improvements in mobile information and communication technologies (ICT) and the increasing presence of innovative shared mobility services such as car-sharing, bike-sharing and ride-sourcing have required transportation modelers to forecast and consider matching processes between agents. The transportation modeling community increasingly views these scenarios as two-sided markets, where mobility service providers (suppliers) endogenously adjust their aspirations and behavior in relation to a dynamic traveler demand response. We adopt this two-sided market perspective in modeling the matching process for ridesourcing matching, where drivers match with riders through a bidding process. Through experiments from an agent-based simulation model, we investigate the convergence and matching stability under varying initial assumptions about system characteristics, such as potential payoff or profit and transaction price. These experiments also show that convergence towards a (relatively) stable state may be self-coordinating but is impacted by the total size of the market. Additionally, with respect to the optimal core matching solution and the nucleolus payoff allocations, systems have more difficulty reaching these states with increasing market size, across wider potential profits and across different transaction price mechanisms. Interestingly, by controlling and minimizing the range of the final transaction price, we can minimize the deviation from the nucleolus profit allocation. Designing and managing shared mobility systems must consider these conditions. [ABSTRACT FROM AUTHOR]

Governments around the world have been facing the challenges of regulating the ever-increasing prevalence of ridesharing platforms, leading to a proliferation of academic and policy debates about the social value of such platforms. However, the overall social value is still unclear because when analyzing the net benefit provided by ridesharing platforms, prior studies have focused on either supply side (i.e., drivers) or demand side (i.e., passengers) users without considering nonusers. Conversely, some studies that have considered the spillover effect of a ridesharing platform on nonusers have not simultaneously considered the net benefit for users. In this study, we propose that a hedonic pricing model can be used as a reasonably straightforward approach to collectively quantify the social value of a ridesharing platform for both users and nonusers. Using this approach, we exploit plausibly exogenous variation in the staggered entry of Uber into different metropolitan areas between 2010 and 2016 and find that the entry of Uber leads to, on average, a 2.8% increase in median housing prices per square foot within a metropolitan area. We also find that there is substantial spatial heterogeneity, with benefits concentrated in areas with greater dependence on public transportation and higher levels of traffic delay. By quantitatively computing the social value of a ridesharing platform with a hedonic pricing model, our study helps government regulators generate policies that are more likely to promote public welfare and become less susceptible to political whims. [ABSTRACT FROM AUTHOR]

Self-driving cars enable dynamic shared mobility, where customers are independent of schedules and fixed stops. This study aims to investigate the potential effects shared mobility can have on future transportation. We simulate multiple scenarios to analyze the effects different service designs might have on vehicle kilometers, on the required number of shared vehicles, on the potential replacement of private cars, and on service metrics such as waiting times, travel times, and detour levels. To demonstrate how simulation can be used to analyze future mobility, we present a case study of the city of Gothenburg in Sweden, where we model travel demand in the morning hours of a workday. The results show that a significant decrease of vehicle kilometers can be achieved if all private car trips are replaced by rideshare and that shared vehicles can potentially replace at least 5 private cars during the morning peak. [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]

The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. [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]

On-demand mobility systems in which passengers use the same vehicle simultaneously are a promising transport mode, yet difficult to control. One of the most relevant challenges relates to the spatial imbalances of the demand, which induce a mismatch between the position of the vehicles and the origins of the emerging requests. Most ridepooling models face this problem through rebalancing methods only, i.e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected. This paper introduces two types of techniques for anticipatory routing that affect how vehicles are assigned to users and how to route vehicles to serve such users, so that the whole operation of the system is modified to reach more efficient states for future requests. Both techniques do not require any assumption or exogenous knowledge about the future demand, as they depend only on current and recent requests. Firstly, we introduce rewards that reduce the cost of an assignment between a vehicle and a group of passengers if the vehicle gets routed towards a high-demand zone. Secondly, we include a small set of artificial requests, whose request times are in the near future and whose origins are sampled from a probability distribution that mimics observed generation rates. These artificial requests are to be assigned together with the real requests. We propose, formally discuss and experimentally evaluate several formulations for both approaches. We test these techniques in combination with a state-of-the-art trip-vehicle assignment method, using a set of real rides from Manhattan. Introducing rewards can diminish the rejection rate to about nine-tenths of its original value. On the other hand, including future requests can reduce users' traveling times by about one-fifth, but increasing rejections. Both methods increase the vehicles-hour-traveled by about 10%. Spatial analysis reveals that vehicles are indeed moved towards the most demanded areas, such that the reduction in rejections rate is achieved mostly there. [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]

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of ridesharing. We take a multi-objective approach, evaluating 10 metrics related to global efficiency, complexity, passenger, and platform incentives, in settings designed to closely resemble reality in every aspect, focusing on vehicles of capacity two. To the best of our knowledge, this is the largest and most comprehensive evaluation to date. We (i) identify CARs that perform well on global, passenger, or platform metrics, (ii) demonstrate that lightweight relocation schemes can significantly improve the Quality of Service by up to 50 % , and (iii) highlight a practical, scalable, on-device CAR that works well across all metrics. [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]

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 planning of on-demand services requires the formation of vehicle schedules consisting of service trips and empty trips. This paper presents an algorithm for building vehicle schedules that uses time-dependent demand matrices (= service trips) as input and determines time-dependent empty trip matrices and the number of required vehicles as a result. The presented approach is intended for long-term, strategic transport planning. For this purpose, it provides planners with an estimate of vehicle fleet size and distance travelled by on-demand services. The algorithm can be applied to integer and non-integer demand matrices and is therefore particularly suitable for macroscopic travel demand models. Two case studies illustrate potential applications of the algorithm and feature that on-demand services can be considered in macroscopic travel demand models. [ABSTRACT FROM AUTHOR]

Governments in Africa are licensing major global ride-hailing firms to launch operations in the continent. This is often presented as a refreshing development for the continent to leverage technology to address its twin problems of inefficient urban transport and rising youth unemployment. Interviews with ride-hailing adopters (drivers, riders, and car owners) and researchers in Ghana suggest, however, that whereas the technology is driving up the standards of road transport experience, the benefits are accessible to a select few (largely, the younger, highly educated and relatively high income-earning class). The lopsided power relations underlying the ride-hailing industry have also meant that the economic opportunities it avails disproportionately benefit a few powerful players (e.g. ride-hailing firms and car owners) while stimulating 'turf wars' among online and traditional taxi drivers; deepening existing gender inequalities in access to income-earning opportunities in the commercial passenger transport sector; encouraging unhealthy driving practices, shifts from shared public transport, and inundation of the roads with more private cars. While it will be imprecise to say that the private gains of ride-hailing outstrip the public costs and, therefore, the technology is detrimental to Ghana's development, the considered evidence raises the need for sustained scrutiny of the hailing of technological interventions as though they are the magic bullets for socio-economic transformation in Africa. Overall, the paper argues that dismantling the power structures underlying Africa's urban challenges will require more than splashing 'smart' apps and other tech wizardries around. Indeed, the lessons from Ghana's ride-hailing industry suggest that such exclusively technical solutions could easily take root and pattern after existing strictures of unjust power structures in ways that could exacerbate the social and environmental problems they are supposed to address. [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]

On-Demand Mobility Services (ODMS) have gained considerable popularity over the past few years. Travelers use mobile phone applications to easily request a ride, update trip itinerary and pay the ride fare. This paper describes a novel methodology for integrated ride matching and vehicle routing for ODMS with ridesharing and transfer options. The methodology adopts a hybrid heuristic approach, which enables solving medium to large problem instances in near real-time. The solution of this problem will be a set of routes for vehicles and a ride match for each passenger. The heuristic (1) promptly responds to individual ride requests, and (2) periodically re-evaluates the generated solutions and recommend modifications to enhance the overall solution quality by increasing the number of served passengers and total profit of the system. The results of a set of experiments considering hypothetical and real-world networks show that the methodology can provide efficient solutions while satisfying the real-time execution requirements. In addition, the results show that the Transportation Network Company (TNC) could serve more passengers and achieve higher profitability if more passengers are willing to rideshare or transfer. Also, activating a rollback procedure increases the number of served passengers and associated profits. [ABSTRACT FROM AUTHOR]

Gender discrimination continues to plague organizations. While the advent of the Internet and the digitization of commerce have provided both a mechanism by which goods and services can be exchanged, as well as an efficient way for consumers to voice their opinions about retailers (i.e., via online rating systems), recent work has begun to uncover significant biases that manifest during the review process. In particular, it has been suggested that the gig-economy's elimination of previously anonymous arm's-length transactions may re-introduce bias into perceptions of quality. In this work, we build upon research that has identified biases based on ascriptive characteristics in rating systems of ridesharing platforms. In doing so, we conduct an online experiment using a series of factorial vignettes to consider not only willingness to transact but post-transaction perceptions of quality. Findings suggest that female drivers are not penalized ex ante for taking on gender incongruent roles, i.e., driving, and experience no penalty when a rider reports a high-quality experience. However, conditional upon a lower quality experience, there is a disproportionate penalty for female drivers. Strikingly, we also observe that historic high quality does not serve as a buffer against such penalties, as prior literature would suggest. These findings underscore the challenges platforms face in ensuring their participants are being evaluated in a fair and impartial way, and highlight the difficulties present when non-employees (customers) are rating non-employees (drivers) of the firm. [ABSTRACT FROM AUTHOR]

Aim: Ride-sharing is an innovative on-demand transport service that aims to promote sustainable transport, reduce car utilization, increase vehicle occupancy and public transport ridership. By reviewing ride-sharing studies around the world, this paper aims to map major aspects of ride-sharing, including online platforms, user factors and barriers that affect ride-sharing services, and extract useful insights regarding their successful implementation. Method: A systematic literature review is conducted on scientific publications in English language. Articles are eligible if they report a study on user factors affecting ride-sharing use and/or barriers preventing ride-sharing implementation; ride-sharing online platforms in these articles are also recorded and are further explored through their official websites. A database is built that organizes articles per author, year and location, summarizes online platform attributes, and groups user factors associated with the likelihood to ride-share. Findings: The review shows that the term "ride-sharing" is used in the literature for both profit and non-profit ride-sharing services. In total, twenty-nine ride-sharing online platforms are recorded and analyzed according to specific characteristics. Sixteen user factors related to the likelihood to ride-share are recorded and grouped into sociodemographic, location and system factors. While location and system factors are found to follow a pattern among studies, mixed findings are recorded on the relationship between sociodemographic factors and ride-sharing. Factors that may hinder the development of ride-sharing systems are grouped into economic, technological, business, behavioral and regulatory barriers. Conclusion: Opportunities exist to improve the quality of existing ride-sharing services and plan successful new ones. Future research efforts should focus towards studying ride-sharing users' trip purpose (i.e., work, university, shopping, etc.), investigating factors associated to ride-sharing before and after implementation of the service, and perform cross-case studies between cities and countries of the same continent to compare findings. [ABSTRACT FROM AUTHOR]

On-demand peer-to-peer ridesharing services provide flexible mobility options and are expected to alleviate congestion by sharing empty car seats. An efficient matching algorithm is essential to the success of a ridesharing system. The matching problem is related to the well-known dial-a-ride problem, which also tries to find the optimal pickup and delivery sequence for a given set of passengers. In this paper, we propose an efficient dynamic tree algorithm to solve the on-demand peer-to-peer ridesharing matching problem. The dynamic tree algorithm benefits from given ridesharing driver schedules and provides satisfactory runtime performances. In addition, an efficient pre-processing procedure to select candidate passenger requests is proposed, which further improves the algorithm performance. Numerical experiments conducted in a small network show that the dynamic tree algorithm reaches the same objective function values of the exact algorithm, but with shorter runtimes. Furthermore, the proposed method is applied to a larger size problem. Results show that the spatial distribution of ridesharing participants influences the algorithm performance. Sensitivity analysis confirms that the most critical ridesharing matching constraints are the excess travel times. The network analysis suggests that small vehicle capacities do not guarantee overall vehicle-kilometer travel savings. [ABSTRACT FROM AUTHOR]

Ridesharing is the sharing of trip segments from one place to another among multiple travelers, obviating others' needs to drive themselves. By having more than one occupant sharing a vehicle, ridesharing aims to reduce personal resources and costs, such as fuel and trip-related costs, and driver stress. The objective of this paper is to model the social preferences of rideshare passengers. We identify challenges and barriers people face in ridesharing with respect to whom they share the ride with and model these social preferences to determine the probability of matching for rideshare demand forecasting. An online survey instrument was designed and distributed among the people residing in the United States to uncover their preferences for ridesharing, in addition to the attributes of potential rideshare passengers. Furthermore, using the survey data, a discrete choice model with latent variables was estimated to uncover the relationship between social preferences and matching. We identified 13 attitudinal dimensions characterizing social preference from the survey responses. These 13 variables were further distilled into four latent variables using factor analysis. Four models were estimated for each latent dimension to predict the probabilities of a person pleasantly experiencing his/her shared rides in social aspects from his/her attributes and preferences. Based on the estimated choice model, we developed a matching index derived from preference probabilities that give a compatibility ratio between riders. [ABSTRACT FROM AUTHOR]

Simulation studies suggest that pooled on-demand services (also referred to as Demand Responsive Transport, ridesharing, shared ride-hailing or shared ridesourcing services) have the potential to bring large benefits to urban areas while inducing limited time losses for their users. However, in reality, the large majority of users request individual rides (and not pooled rides) in existing on-demand services, leading to increases in motorised vehicle miles travelled. In this study, we investigate to what extent fare discounts, additional travel time, and the (un)willingness to share the ride with (different numbers of) other passengers play a role in the decision of individuals to share rides. To this end, we design a stated preference study targeting Dutch urban individuals. In our research, we (1) disentangle the sharing aspect from related time–cost trade-offs (e.g. detours), (2) investigate preference heterogeneity regarding the studied attributes and identify distinct market segments, and (3) simulate scenarios to understand the impact of the obtained parameters in the breakdown between individual and pooled services. We find that less than one third of respondents have strong preferences against sharing their rides. Also, we find that different market segments vary not only in their values of the willingness to share, but also in how they perceive this willingness to share (per-ride or proportional to the in-vehicle time). Further, the scenario analysis demonstrates that the share of individuals who are willing to share rides depends primarily on the time–cost trade-offs, rather than on the disutility stemming from pooling rides per se. [ABSTRACT FROM AUTHOR]

In recent years, Transportation Network Companies (TNC) such as Uber and Lyft have embraced ridesharing: a passenger who requests a ride may decide to save money in exchange for the inconvenience of sharing the ride with someone else and incurring a delay. When matching passengers, these services attempt to optimize cost savings. But a possible scenario is that while passenger A is matched to passenger B, if matched to passenger C then both A and C would have saved more money. This leads to the concept of "fairness" in ridesharing, which consists of finding the Nash equilibrium in a ridesharing plan. In this paper we compare the optimum plan (i.e., benefit maximized at a global level) and the fair plan in both static and dynamic contexts. We show that in contrast to the theoretical indications, the fair plan is almost optimum. Furthermore, the fairness concept may help attract more passengers to rideshare and thus further reduce vehicle miles traveled. If social preferences are included in the total benefit, we demonstrate that the optimum ridesharing plan may be unboundedly and predominantly unfair in a sense that will be formalized in this paper. [ABSTRACT FROM AUTHOR]

Ride-sharing—the combination of multiple trips into one—may substantially contribute towards sustainable urban mobility. It is most efficient at high demand locations with many similar trip requests. However, here we reveal that people's willingness to share rides does not follow this trend. Modeling the fundamental incentives underlying individual ride-sharing decisions, we find two opposing adoption regimes, one with constant and another one with decreasing adoption as demand increases. In the high demand limit, the transition between these regimes becomes discontinuous, switching abruptly from low to high ride-sharing adoption. Analyzing over 360 million ride requests in New York City and Chicago illustrates that both regimes coexist across the cities, consistent with our model predictions. These results suggest that even a moderate increase in the financial incentives may have a disproportionately large effect on the ride-sharing adoption of individual user groups. Ride-sharing, combining similar concurrent trips into one, may support sustainable urban mobility yet lacks broad adoption. Storch et al. reveal how collective interactions in shared rides explain essential characteristics of ride-sharing adoption patterns e.g. observed in New York City and Chicago. [ABSTRACT FROM AUTHOR]

Disinfection is a common practice to inhibit pathogens, yet success is limited by microbial adaptation and our poor knowledge of viral transmission, notably in the current COVID-19 pandemic. There is a need for alternative disinfection strategies and techniques that are adapted to the actual behavior of humans living in densely populated mega-cities. Here, high public circulation in shared passenger vehicles such as taxis, buses and personal cars represents a major risk of viral transmission due to confined space and commonly touched surfaces. Actual regulatory guidelines are not fully successful because they rely both on passengers' willingness to wear face masks and on drivers' willingness to disinfect cars after each shift or each ride with symptomatic individuals. Here we propose that passive solar heating, a sustainable technique that has been used in agronomy to kill weeds and soil pathogens, could inactivate the virus in vehicles during warm-to-hot weather within few minutes to half an hour at 50–60 °C. We measured temperatures in a white compact-size sedan left in a parking lot under direct sunlight. Air temperatures increased from 30 to 42–49 °C after 30 min and then reached a plateau at 52–57 °C after 90 min. Temperatures were about 3 °C higher in front versus back of the car and about 5 °C higher at face height compared to knee height. Since COVID-19 is inactivated in 30 min at 56 °C, our findings confirm that hot air generated passively by solar heating in enclosed spaces is a promising strategy of disinfection with benefits of no added costs, chemicals or worktime. Though this technique appears limited to hot climate, possible heating systems that work during parking time might be developed by vehicle makers to extend the technique to cold climates. [ABSTRACT FROM AUTHOR]

The increasing passengerization of transportation through shared ride services and driverless vehicles has the potential to vastly change the transportation system. Although values are sometimes considered in the design of information tools and through attitudes toward travel, the systematic approach of value sensitive design (VSD) should be used in the design of transportation infrastructure to create a sustainable transportation future. [ABSTRACT FROM AUTHOR]

Due to its significant economic and environmental impact, sharing the ride among a number of drivers (i.e., car pooling) has recently gained significant interest from industry and academia. Hence, a number of ride sharing services have appeared along with various algorithms on how to match a rider request to a driver who can provide the ride sharing service. However, existing techniques have several limitations that affect the quality of the ride sharing service, and hence hinder its wide applicability. This paper proposes SHAREK*; a scalable and efficient ride sharing service that overcomes the limitations of existing approaches. SHAREK* allows riders requesting the ride sharing service to indicate the maximum price they are willing to pay for the service, the maximum waiting time before being picked up and the maximum arrival time for arriving the destination. In the mean time, SHAREK* computes the price of the service based on the distance of the rider trip and the detour that the driver will make to offer the service. Then, SHAREK* returns a set of drivers that can make it to the rider within its price and temporal constraints. Since there could be many of such drivers, SHAREK* internally prunes those drivers that are dominated by others, i.e., they provide higher price and higher waiting time (or arrival time) than other drivers. To realize its efficiency and scalability, SHAREK* employs a set of early pruning techniques that minimize the need for any actual shortest path computations. [ABSTRACT FROM AUTHOR]

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

Prior studies have provided evidence of discrimination between drivers and passengers in the context of ridehailing. This paper extends prior research by investigating passenger-to-passenger discriminatory attitudes in the context of ridesharing. We conducted a survey of 1110 Uber and Lyft users in the US using Mechanical Turk, 76.5% of whom have used uberPOOL or Lyft Shared rides, and estimated two structural equation models. The first model examines the influence of one's demographic, social and economic characteristics on discriminatory attitudes toward fellow passengers in ridesharing, and how such influence varies by the targets of discrimination (i.e., race and class). The second model examines the influence of one's generic social dominance orientation on discriminatory attitudes in the ridesharing context. We find that discriminatory attitudes toward fellow passengers of differing class and race in the shared ride are positively correlated with respondents that are male or are women with children. A respondent's race does not have a significant effect on discriminatory attitudes, but white respondents that live in majority white counties are more likely to hold discriminatory attitudes with regard to race (no effect is observed regarding class preferences). The same is true of respondents that live in counties in which a larger share of the electorate voted for the Republican candidate in the 2016 presidential election. Conversely, higher-income respondents appear more likely to hold discriminatory attitudes regarding class, but no effect is observed regarding racial preferences. We also find that one's generic social dominance orientation strongly influences his/her discriminatory attitudes in ridesharing, supporting the claim that behavior in shared mobility platforms reflects long-standing social dominance attitudes. Further research is required to identify policy interventions that mitigate such attitudes in the context of ridesharing. [ABSTRACT FROM AUTHOR]

Route planning and recommendation have drawn increasing attention recently as traveling is now an essential part of our daily life. However, existing route recommendation researches focus on single transportation mode (i.e., taking taxi or riding bicycle only), which leave recommending routes with multiple transportation modes largely untouched. Inspired by the observation of the first/last-mile congestion problem when traveling with vehicles, in this paper we study a novel route recommendation problem, namely Route Recommendation with Hybrid Transportation Modes (RRHTM), which aims to minimize the travel time for a trip by taking taxis and/or shared bicycles. To address this problem, we propose a two-phase data-driven framework called Predictive Hybrid Route Recommendation (PHRR), which integrates a prediction and recommendation phase. The first phase proposes different learning models (i.e., Deep Neural Network model, and Transfer and Multi-Task learning model based on Deep Neural Network) to predict the travel time by taxi/bicycle for a given trip and the station status in Bicycle Sharing System. In the route recommendation phase, we develop a network-based travel time comparison algorithm to efficiently find the optimal route. Moreover, an evaluation criterion called Empirical Risk of Travel Time (ERTT) is designed to evaluate the reliability of our recommendation results. Finally, we verify the effectiveness of proposed approaches through extensive experiments based on real datasets. [ABSTRACT FROM AUTHOR]

Personal automobility is critical for accessing economic and social opportunities in the auto-oriented built environment of the United States. Despite declines over the past 40 years, household carpooling remains the most popular alternative mode to solo-driving regardless of demographic group. While carpooling provides a degree of automobility, carpool-dependent passengers often suffer from practical and other disadvantages. This paper explores gender gaps and ethnicity in personal automobility levels, particularly among Hispanics. The analysis explicitly considers drivers' access to household vehicles and non-drivers' access to household carpooling. The research finds that Hispanic females, especially immigrants, are low in automobility, both in the probability of being a driver and in access to household vehicles. The large gender gap is specific to Hispanics, and differs from gaps found for non-Hispanic Whites or Blacks. The gender gap decreases, but persists, as immigrant Hispanics stay longer in the U.S., gain or maintain employment, or become college-educated. Surprisingly, the gender gap in personal automobility level exists even among U.S. native Hispanics. Gender gaps in personal automobility among different ethnicity groups, particularly Hispanics, merit more study, especially as commercial car-sharing and ridesharing services become more common. [ABSTRACT FROM AUTHOR]

A discussion of the sustainability and travel behaviour impacts of ride-hailing is provided, based on an extensive literature review of studies from both developed and developing countries. The effects of ride-hailing on vehicle-kilometres travelled (VKT) and traffic externalities such as congestion, pollution and crashes are analysed. Modal substitution, user characterisation and induced travel outputs are also examined. A summary of findings follows. On the one hand, ride-hailing improves the comfort and security of riders for several types of trips and increases mobility for car-free households and for people with physical and cognitive limitations. Ride-hailing has the potential to be more efficient for rider-driver matching than street-hailing. Ride-hailing is expected to reduce parking requirements, shifting attention towards curb management. On the other hand, results on the degree of complementarity and substitution between ride-hailing and public transport and on the impact of ride-hailing on VKT are mixed; however, there is a tendency from studies with updated data to show that the ride-hailing substitution effect of public transport is stronger than the complementarity effect in several cities and that ride-hailing has incremented motorised traffic and congestion. Early evidence on the impact of ride-hailing on the environment and energy consumption is also concerning. A longer-term assessment must estimate the ride-hailing effect on car ownership. A social welfare analysis that accounts for both the benefits and costs of ride-hailing remains unexplored. The relevance of shared rides in a scenario with mobility-as-a-service subscription packages and automated vehicles is also highlighted. [ABSTRACT FROM AUTHOR]

Ridesharing has emerged as an alternative transportation mode along road networks around the world. Rideshare matching problem is vital to improve the sustainable development of ridesharing systems. This paper aims to address the stable two-sided satisfied matching problem considering the participants' psychological perception. First of all, we investigate the elements that influence passengers' and drivers' ridesharing experience by means of semi-structured research interviews and questionnaire survey. Two ridesharing perception evaluation systems are originally established to get the preference orders of passengers and drivers separately. Then, rideshare matching-related definitions are stated and rideshare matching linguistic information processing is also elaborated in detail based on preference utility function, disappointment function, as well as elation function. Furthermore, we propose a stable two-sided satisfied matching model on account of fuzzy linguistic information processing about ridesharing, which is able to reflect participants' psychological factors. To verify the validity of our model, we present a two-sided matching case based on hypothetical rideshare matching platform. The analytical results indicate that the use of stable two-sided satisfied matching method based on fuzzy linguistic information enables to substantially satisfy both drivers' and passengers' expectation and improve the sustainability of ridesharing systems. [ABSTRACT FROM AUTHOR]