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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]

Ridesharing can help reduce traffic congestion, greenhouse gas emissions and increase accessibility to transportation in major metropolitan areas across the United States. A robust rideshare system needs to take uncertainties such as traffic congestion and passenger cancellations into account. In this report, the authors propose a data-driven stochastic rideshare system that integrates those sources of uncertainties. Instead of assuming a probability distribution, the approach learns the underlying distribution in travel times and passenger cancellations from historical data. The authors first provide a mathematical model of the problem. Later they propose a stochastic average approximation approach for solving the routing and flexible pickup and drop-off selection problem. They also propose a Branch-and-Price heuristic and Adaptive Large Neighborhood Search-basedmetaheuristic to solve the underlying rideshare routing problem. To validate the approach, the authors construct test cases based on the New York City taxicab dataset. Numerical results show that the proposed branch and price-based solution approach can efficiently solve small instances while being close to the true optimum. On the other hand, the ALNS-based approach can solve medium to large instances with a small computational time budget while being robust to uncertainties. The proposed approach can help transportation officials and rideshare planners design more robust rideshare systems to alleviate traffic congestion in California.View the NCST Project Webpage

Pooled trips in private vehicles, or pooling, can lead to smaller environmental impacts and more efficient use of the limited roadway capacity, especially during peak hours. However, pooling has not been well adopted in part because of difficulties in coordinating schedules among various travelers and the lack of flexibility to changes in schedules and locations. In the meantime, ridehailing (RH) provides pooled services at a discounted fare (compared to the single-travel-party option) via advanced information and communication technology. This study examines individuals’ preferences for/against pooled RH services using information collected among travelers answering a set of questions related to their last RH trip. In doing so, both trip attributes and rider characteristics are considered. Taste heterogeneity is modeled in a way that assumes the presence of unobserved groups (i.e., latent classes), each with unique preferences, in a given sample of RH riders (N=1,190) recruited in four metropolitan regions in Southern U.S. cities from June 2019 to March 2020. The researchers find two latent classes with qualitatively different preferences, choosy poolers and non-selective poolers, regarding their choice in favor of/against pooling based on wait time, travel costs, purpose, and travel party size of the last RH trip. Personal characteristics are also identified, specifically age and three attitudes (travel satisfaction, environmentalism, and travel multitasking), which account for individuals’ class membership. This research contributes to the literature by explicitly modeling taste heterogeneity towards pooled ridehailing. In addition, unlike existing studies either at the person level or employing stated-preference data, a trip-level analysis is performed in connection with revealed preferences, which generates more realistic and relevant implications to policy and practice.View the NCST Project Webpage

The blockchain-based traceability system (BTS) can reduce product losses in the perishable goods market, which yields a loss-reduction effect, and offer authentic information, which triggers a premium effect. In a dual-channel perishable goods supply chain (DPGSC), including one supplier and retailer each, the supplier can operate the price of the direct channel, different or non-different from that of the traditional channel. To study the adoption of BTS in the supplier-led DPGSC, we employed a game-theoretical model to capture BTS adoption strategy under the differential and non-differential pricing policies and proposed an improved cost-sharing contract to boost the DPGSC performance. The results indicated that both the supplier and the retailer are willing to improve product quality, broaden the saleable region, and lower the production cost. The adoption of BTS mainly depends on the losses-reduction effect, premium effect, production cost, and tag cost, which is affected more by the premium effect than by the loss-reduction effect. Furthermore, their adoption is not always easier when the production cost is higher. Except for symmetric base demands, they benefit asynchronously from BTS. Synchronised adoptions can be realised only when the improved cost-sharing contract generates more profits and introduces excess momentum. Highlights Blockchain-based traceability system handles product losses and forged information Adoption decision-making processes under two different pricing policies are studied In the adoption process, the premium effect outweighs the loss-reduction effect Contract may introduce insufficient momentum, excess momentum, or new inefficiency Once excess momentum is formed, supply chain members' adoptions become synchronised [ABSTRACT FROM AUTHOR]

Using prices induced by dual variables of a centralized optimization problem induces welfare-optimal equilibria among strategic drivers. We reveal a stark deficiency of such static pricing algorithms: it is possible for them to induce additional equilibria with arbitrarily low social welfare. Moreover, small perturbations to the marketplace, such as those caused by idiosyncratic randomness or model misspecification, can cause the welfare-optimal equilibrium to be Pareto-dominated (in terms of driver utility) by suboptimal equilibria. We show that dynamic pricing solves this problem. We describe a dynamic pricing algorithm that resolves the centralized optimization problem in each time period and show that it satisfies a new equilibrium robustness property, which guarantees that every induced (approximate) equilibrium is (approximately) welfare optimal. We also propose a novel two-level model of ridesharing networks with strategic drivers and spatiotemporal dynamics that lets us retain macroscopic uncertainty, such as correlated shocks caused by weather or other public events, when analyzing a large market limit in which idiosyncratic sources of uncertainty vanish. Funding: J. M. Cashore was supported by an NSERC PGS D Fellowship. P. Frazier was supported by AFOSR [Grant FA9550-19-1-0283]. É. Tardos was supported by AFOSR [Grant FA9550-19-1-0183] and [NSF Grants CCF-1408673 and CCF-1563714]. Supplemental Material: The online companion is available at https://doi.org/10.1287/moor.2022.0163. [ABSTRACT FROM AUTHOR]

Dynamic stochastic matching problems arise in a variety of recent applications, ranging from ridesharing and online video games to kidney exchange. Such problems are naturally formulated as Markov decision processes (MDPs) that are, however, intractable in general. To improve tractability, we investigate the linear programming-based approach to approximate dynamic programming. This approach can provide both feasible control policies and bounds on the MDPs' optimal policy value, which can be used to establish optimality gaps. However, the approximate linear programs (ALPs) resulting from this approach can often be difficult to solve. To address this computational challenge, we derive novel ALP reformulations that can be used for a broad class of dynamic stochastic matching problems that incorporate, among others, possible match failures and certain restrictions on feasible matchings. We show that these ALP reformulations can be solved efficiently and applied to a broad class of dynamic matching problems. In addition, our numerical results indicate that our ALP reformulations can produce tight bounds that allow us to establish near-optimal policy performance for a broad set of problem instances. Thus, ALP reformulations can present an attractive alternative for applications that involve dynamic stochastic matching. History: Accepted by Nicola Secomandi, Area Editor for Stochastic Models & Reinforcement Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0203) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0203). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The paper argues that learning analytics as a research field can benefit from a theory‐informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent descriptions of sensemaking, eclectically combining the paradigms that have underlying differences. I then propose a conceptualization of sensemaking that overcomes the differences between these theories and explains how the concepts of activity system, the definition of the situation and affordances can be used to capture individual differences in sensemaking. The paper concludes with a preliminary framework and examples demonstrating its utility in raising new theoretical questions, informing design principles and providing shared language for researchers in learning analytics.Practitioner notesWhat is already known about this topicSensemaking happens when individuals try to explain unknown situations.Learning analytics uses sensemaking as a lens to understand dashboard use.Systematic analysis of sensemaking is essential for learning analytics.What this paper addsThe paper notes that noticing and perceiving are commonly examined in learning analytics on dashboard use.The paper suggests a revision of fundamental assumptions in sensemaking.A paper proposes a toy model of sensemaking that includes operationalization of the definition of the situation, activity where sensemaking happens and processes of noticing and perceiving affordances.Implications for practice and/or policyLearning analytics must examine sensemaking of data about teaching and learning in a systematic manner.Internal perceptions of the social environment and activity that are informed by the data need to be considered in evaluating dashboard use. [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]

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

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]

With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. [ABSTRACT FROM AUTHOR]

The transportation landscape is witnessing profound changes due to technological advancements, necessitating proactive policy responses to harness innovation and avert urban mobility disruption. The sharing economy has already transformed ridesharing, bicycle-sharing, and electric scooters, with shared autonomous vehicles (SAVs) poised to reshape car ownership. This study pursues two objectives: firstly, to establish a market segmentation for shared ride services and secondly, to evaluate the environmental impact of ridesharing in different contexts. To mitigate potential biases linked to stated preference data, we analysed the navette service, utilized by a research institute in Europe, closely resembling future SAVs. The market segmentation relied on hierarchical cluster analysis using employee survey responses, while the environmental analysis was grounded in the 2019 navette service data. Our analysis revealed four unique employee clusters: Cluster 1, emphasizing active transportation and environmental awareness; Cluster 2, showing openness towards SAVs given reliable alternatives are available; Cluster 3, the largest segment, highlighting a demand for policy support and superior service quality; and Cluster 4, which places a premium on time, suggesting a potential need for strategies to make the service more efficient and, consequently, discourage private car use. These findings highlight a general willingness to adopt shared transport modes, signalling a promising transition to shared vehicle ownership with significant environmental benefits achievable through service design and policy measures.

Human beings have created numerous laws, sayings and proverbs that still influence behaviors and decision-making processes of people. Some of the laws, sayings or proverbs are used by people to understand the phenomena that may take place in daily life. For example, Murphy's law states that "Anything that can go wrong will go wrong." Murphy's law is helpful for project planning with analysis and the consideration of risk. Similar to Murphy's law, the old saying "Two heads are better than one" also influences the determination of the ways for people to get jobs done effectively. Although the old saying "Two heads are better than one" has been extensively discussed in different contexts, there is a lack of studies about whether this saying is valid and can be applied in evolutionary computation. Evolutionary computation is an important optimization approach in artificial intelligence. In this paper, we attempt to study the validity of this saying in the context of evolutionary computation approach to the decision making of ridesharing systems with trust constraints. We study the validity of the saying "Two heads are better than one" by developing a series of self-adaptive evolutionary algorithms for solving the optimization problem of ridesharing systems with trust constraints based on the saying, conducting several series of experiments and comparing the effectiveness of these self-adaptive evolutionary algorithms. The new finding is that the old saying "Two heads are better than one" is valid in most cases and hence can be applied to facilitate the development of effective self-adaptive evolutionary algorithms. Our new finding paves the way for developing a better evolutionary computation approach for ridesharing recommendation systems based on sayings created by human beings or human intelligence. [ABSTRACT FROM AUTHOR]

Geographic flow clustering analysis can effectively reveal human behavioral patterns in movement. Traditional methods for studying human movement patterns are mostly based on first-order quantity analyses of point data, such as hotspots, density or clustering. Currently, relatively few second-order spatial analysis methods based on geographic flows exist. Thus, we developed a new geographic flow method based on spectral clustering and applied it to trajectory data analysis. This article uses the bike-sharing trajectories data in Shanghai in August 2016, spectral clustering analysis was conducted on the group flow patterns before, during and after rainfall, on weekdays and weekends and in the morning and evening peak. Spectral clustering was verified to exhibit better clustering effect by comparing the clustering indices of different clustering methods. This study enriches the analysis method of geographical flows, and the human mobility patterns revealed by its analysis can provide references for formulating urban green travel policies. [ABSTRACT FROM AUTHOR]

This study introduces a new policy for managing parking scarcity called Parking Allocation and Ride-Sharing System (PARS). In PARS, a centralized algorithm allocates parking spacesto drivers who are willing to participate in a coordinated carpool. The algorithm is used to optimize the creation of carpools going to and returning from a particular venue and simultaneously reserve parking for these carpools at the venue. An efficient mixed integer linear programming (MIP) formulation is presented and two heuristics, namely Ride Decomposition (RD) and Quick Converge (QC), are proposed and compared via internally generated experiments. Experimental results show that a commercial solver is able to solve the MIP with thousands of individuals to optimality in minutes. For larger instances, the RD and QC heuristic algorithms can solve the problem, on average, 42.23% and 86.39% faster than the commercial solver and provide solutions that are 3.61% and 3.49% from optimal, respectively. [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 mobility services furnish convenient transportation alternatives for individuals without vehicle ownership or a preference against driving. Shared mobility could benefit developing countries by providing a cost-effective alternative, enhancing accessibility, reducing congestion, and creating multiple job opportunities. In this study, a comprehensive analysis to assess shared mobility options as an avenue to sustainable transportation in Amman, Jordan, is presented. The study employs a multifaceted methodology, including a survey questionnaire, preliminary analysis, Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis, and Structural Equation Model (SEM). The data were collected from a diverse group of Amman residents using a survey composed of 29 questions. The survey included demographic information, travel behavior, willingness to adopt shared mobility, perceived benefits, and possible barriers. These data were analyzed using Structural Equation Modeling (SEM), providing an in-depth understanding of the interrelationships among the variables studied. This study concludes by contributing to the ongoing discussion on sustainable urban transportation in Jordan and providing a road map for policymakers, urban planners, and transportation service providers. The presented findings provide an empirical basis for guiding future strategies and interventions toward sustainable urban development in Amman and potentially other urban contexts with comparable characteristics. Key findings reveal a significant potential for shared mobility to enhance urban transportation sustainability. Specifically, a notable positive perception among Amman residents was observed, with an average willingness to switch to shared mobility for daily commuting scoring 4.68 on a 7-point Likert scale. Moreover, a statistical analysis indicates that factors such as reduced costs, improved service reliability, and better environmental sustainability, notably influence the adoption of shared mobility services. [ABSTRACT FROM AUTHOR]

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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]

In a typical traffic scenario, automated vehicles are required to interact with surrounding traffic participants, e.g. human-driven vehicles, pedestrians, etc. In this paper, a novel adaptive authority allocation strategy considering the social behaviours of surrounding vehicles is proposed for the shared steering control (SSC) of automated vehicles. First, a Koopman-based potential-field-driven distributed model predictive control (K-PF-DMPC) method is proposed for the modelling of vehicle interaction to describe surrounding vehicle's social behaviour. This method effectively deals with the nonlinearity embedded in the non-cooperative game-based vehicle interaction model, so that the analytical form of Nash equilibrium can be derived for a fast online solution. Then, the SSC system of the automated vehicle is implemented by a weighted summation method. To balance the driving performance and intervention degree while considering the surrounding vehicles' social behaviours, a hybrid fuzzy strategy (HFS) is proposed for allocating the control authority between the driver and automation. The value of authority allocation coefficient is calculated by fusing the outputs from two shared fuzzy controllers, i.e. the enhanced and weakened shared fuzzy controllers, in accordance to the belief of the surrounding vehicle's type. Several numerical simulations and driver-in-the-loop simulator experiments are conducted for validation. Results show that the proposed strategy can provide appropriate steering intervention to the driver owing to the consideration of the surrounding vehicle's social behaviour and obtains the best driver-automation collaboration performance compared with the comparative groups. [ABSTRACT FROM AUTHOR]

The proliferation of shared electric scooters (E-scooters) has brought convenience to urban transportation but has also introduced challenges such as disorderly parking and an imbalance between supply and demand. Given the current inconsistent quantity and spatial distribution of shared E-scooters, coupled with inadequate research on deployment stations selection, we propose a novel maximal covering location problem (MCLP) based on distance tolerance. The model aims to maximize the coverage of user demand while minimizing the sum of distances from users to deployment stations. A deep reinforcement learning (DRL) was devised to address this optimization model. An experiment was conducted focusing on areas with high concentrations of shared E-scooter trips in Chicago. The solutions of location selection were obtained by DRL, the Gurobi solver, and the genetic algorithm (GA). The experimental results demonstrated the effectiveness of the proposed model in optimizing the layout of shared E-scooter deployment stations. This study provides valuable insights into facility location selection for urban shared transportation tools, and showcases the efficiency of DRL in addressing facility location problems (FLPs). [ABSTRACT FROM AUTHOR]

Ridesharing is an effective approach to alleviate traffic congestion. In most existing works, drivers and passengers are assigned prices without considering the constraints of desired benefits. This paper investigates ridesharing by formulating a matching and pricing problem to maximize the total payoff of drivers, with the constraints of desired benefit and quality of experience. An efficient algorithm is proposed to solve the formulated problem based on coalitional double auction. Secondary pricing based strategy and sacrificed minimum bid based strategy are proposed to support the algorithm. This paper also proves that the proposed algorithm can achieve a Nash-stable coalition partition in finite steps, and the proposed two strategies guarantee truthfulness, individually rational and budget balance. Extensive simulation results on the real-world dataset of taxi trajectory in Beijing city show that the proposed algorithm outperforms the existing ones, in terms of average total payoff of drivers while meeting the benefits of passengers. [ABSTRACT FROM AUTHOR]

Driver fatigue is a major cause of road crashes. However, there is limited knowledge regarding the potential consequences of driver fatigue in taxi (conventional and app-based), ride-hailing, and ridesharing services. Driver fatigue is likely to be significantly exacerbated in this population due to the multi-task characteristics of their jobs; thus, conducting a comprehensive study on driver fatigue in these transportation sectors is of utmost importance. This systematic review summarises the current state of knowledge about the causes and consequences of driver fatigue. We also suggested some potential control mechanisms for driver fatigue in taxi and ride-hailing services along a fatigue risk trajectory. We included studies published prior to September 2022 in three databases (Web of Science, Scopus, and PubMed) using a predefined search strategy. Eligible studies were critically appraised using the Joanna Briggs Institute (JBI) critical appraisal checklists. A total of 18 studies met our eligibility criteria as scoped from the 414 initially identified studies. Eight contributing factors to driver fatigue were revealed including long working hours, short rest breaks, limited driving experience, job demand, poor sleep, algorithmic management, traffic congestion, and additional workload. Furthermore, our review identified risk factors for driver fatigue in taxi and ride-hailing services, including road safety, work pressure and driver's health, optimism bias, job precariousness, and lack of additional benefits. Findings to date suggest that driver fatigue in taxi and ride-hailing industries is as serious as, or more serious than, in other transportation sectors. Understanding the working conditions of these drivers is critical to establish effective policies and practices for reducing crash-related driver fatigue. [ABSTRACT FROM AUTHOR]

Many ridesharing platforms operate with dual capacities. They employ full-timers, in addition to independent service providers (also known as self-schedulers). Unlike self-schedulers, fulltimers work exclusively for a platform and have limited flexibility. While allocating demand between the two capacities, a platform could prioritize its own full-timers over self-schedulers (preferential policy). This preferential treatment is considered discriminatory and, hence, illegal, even though it may maximize profits. Ridesharing platforms, therefore, have to explore alternative operating policies to overcome this legal hurdle. This study is a step towards helping platform managers identify the best alternative (or at the least the secondbest alternative) to the controversial preferential policy. It explores two alternative non-discriminatory policies—contingency capacity policy (self-schedulers get higher priority in demand allocation) and common-pool policy (equal preference to the two capacities). A major finding of this study is that the contingency capacity policy, the most altruistic among the three policies, can deliver optimal profits for asset-light platforms (platforms with fewer full-timers). Managers of asset-light platforms could leverage the altruistic nature of the contingency capacity policy to attract more self-schedulers to join the platform and derive a significant competitive advantage. [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]

Based on the anonymized dataset of one million shared scooter rides collected by the Tel Aviv municipality, we investigate the behavior of the shared e-scooter users in Tel Aviv and the resulting dynamics of the scooters' spatio-temporal patterns. The users' choice of the shared e-scooters as a transportation mode follows fast and frugal heuristics: the user who activated the shared scooter's application continues with the ride when the closest available scooter is close enough, at up to 50-100 meters from her. Riders strongly prefer bike paths for riding and essentially deviate from the shortest path between the origin and destination and choose the routes with the higher percentage of bike paths. The longer the route, the higher the fraction of the bike paths in the rider's route and the difference between the shortest and chosen paths. We also demonstrate that the operators fail to match the supply to the demand and the scooters are oversupplied in the central part of the city, where the demand is high, and undersupplied in the rest of the city, where the demand is essentially lower. Based on the analysis we propose a new policy of spatial adjustment between the demand and supply. [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]

Providing accurate information about bus travel times can help passengers plan their itinerary and reduce waiting time. However, due to various uncertainty factors and the sparsity of single-route data, traditional travel time predictions cannot accurately describe the credibility of the prediction results, which is not conducive to passengers waiting based on the predicted results. To address the above issues, this paper proposes a bus travel time prediction intervals model based on shared road segments, multiple routes' driving style similarity, and the bootstrap method. The model first divides the predicted route into segments, dividing adjacent stations shared by multiple routes into one section. Then, the hierarchical clustering algorithm is used to group all drivers in multiple bus routes in this section according to their driving styles. Finally, the bootstrap method is used to construct a bus travel time prediction interval for different categories of drivers. The travel time data sets of Shenyang 239, 134, and New Area Line 1 were selected for experimental verification. The experimental results indicate that the quality of the prediction interval constructed using a data set fused with multiple routes is better than that constructed using a single-route data set. In the two cases studied, the MPIW of the three time periods decreased by 101.04 s, 151.72 s, 33.87 s, and 126.58 s, 127.47 s, 17.06 s, respectively. [ABSTRACT FROM AUTHOR]

With the emergence of the sharing economy, a significant change in consumer behavior can be observed worldwide, which has a considerable impact on various industries. The rise of the sharing economy has changed the way people experience transport services, with ridesharing being a catalyst for change. In Romania, the debut of Uber in 2015 sparked controversy and led to legal regulations that were adapted to local specificities, highlighting the adaptability of ridesharing platforms to different legal frameworks. In the context of this development, the views and perceptions of Generation Z will be crucial in determining the direction in which this conflict between disruptive models and traditional players in the transport sector develops. The article deals with business models based on collaborative consumption, with a focus on ridesharing, and examines the attitudes, perceptions, and behavior of Romanian youths (aged 18–26) towards these models. The aim of the study is to determine the opinion of young Romanians on collaborative consumption in transport services—ridesharing (Uber case)—and their attitude towards the ethical controversies related to Uber's business model. A quantitative research approach was chosen, and an exploratory study was conducted using a questionnaire, with the non-probabilistic sample consisting of relevant observation units aged 18–26 years. The results show that almost 90% of the young Romanians surveyed use Uber and are satisfied with the quality, convenience, and speed of the service. Despite the positive attitude, there is a paradoxical tendency among respondents to regulate ridesharing services in a similar way to traditional taxis. Ethical considerations show that respondents tend to neutralize perceptions and justify the emergence of new models as normal and beneficial for competition and consumers. [ABSTRACT FROM AUTHOR]

Traffic congestion is a significant problem in major metropolitan areas in the United States. According to the Urban Mobility Report, in 2019 commuters on average lost about 54 hours in traffic congestion. To combat this, major infrastructure projects have been undertaken. However, expansion projects cannot keep up with the increase in usage of personal vehicles and thus fail to address the traffic congestion problem. Carpool ridesharing has shown some promise in combatting this traffic congestion problem. In this system, the drivers are regular commuters who take detours to pick up and drop off passengers to decrease their transportation costs. This system increases the efficiency of the transportation system by providing flexible commutes to people, thus reducing the need for each commuter to use their own personal vehicle. The researchers developed three approaches to rideshare routing. The researchers conducted a computational study using a San Francisco taxicab dataset to determine the effectiveness of the three approaches. To show the impact of flexible meeting points, the researchers also conducted experimental simulations with and without walking and performed sensitivity analyses.View the NCST Project Webpage

Travel behavior has dramatically shifted during the COVID-19 pandemic as social distancing measures and stay-at-home orders are encouraged to prevent the spread of infection. Shared mobility, which includes transit, ride-hailing, and shared ride-hailing, typically involves contact with strangers and is now viewed as a potential risk. To understand current trends and predict potential modal shifts in a post-COVID-19 world, this research designed, developed, and deployed a short online survey about comfort with and usage of shared transportation. The survey was distributed through multiple recruitment methods to adults in the Atlanta metro area. Data collected from the survey (n = 787) revealed preferences related to ride-sharing environments and safety procedures, frequency of travel on different modes before and during the pandemic, and level of comfort related to the usage of different modes. Despite reopening the economy in Georgia, this research found that the actual usage of private ride-hailing and transit has dramatically decreased. In addition to the usage, the indicated comfort level with usage of shared mobility has decreased since the pandemic. Looking to the future, regression models and data analysis indicated that although there would be an increase in comfort for all shared modes when a vaccine is available, it does not return to the pre-pandemic levels.

In major metropolitan areas such as Los Angeles County, ride-sharing systems can help reduce traffic congestion and increase the efficiency of the transportation system. This research project proposes three different solution approaches for solving the ride share routing problem with flexible pickup and drop-off points. The first is a dynamic programming-based route enumeration procedure that can be used to solve small-sized problems; the other two are branch and price-based heuristics for solving large problems. The researchers first provide a mixed integer nonlinear model for routing and pickup and drop-off points selection which they later decompose into a master and subproblem for solving. To validate the performance of their approaches and gather valuable insights about the ridesharing system, the researchers perform numerical experiments on a San Francisco Taxicab dataset. Results show that the approaches are efficient, solving instances with up to 300 nodes within 130 CPU seconds. For these datasets, incorporating flexible meeting points (i.e., pickup and drop-off points) can reduce the total travel time of the rideshare system by 18%. Sensitivity analysis shows that it can also decrease the time passengers wait time for rides by 43%. The methodologies in this study can help transportation planners design more efficient rideshare systems with less waiting, better passenger service, and less travel time.View the NCST Project Webpage

Sustainable concepts for on-demand transportation, such as ridesharing or ridehailing, require advanced technologies and novel dynamic planning and prediction methods. In this paper, we consider the prediction of taxi trip durations, focusing on the problem of the estimated time of arrival (ETA). ETA can be used to compute and compare alternative taxi schedules and to provide information to drivers and passengers. To solve the underlying hard computational problem with high precision, machine learning (ML) models for ETA are the state of the art. However, these models are mostly black box neural networks. Hence, the resulting predictions are difficult to explain to users. To address this problem, the contributions of this paper are threefold. First, we propose a novel stacked two-level ensemble model combining multiple ETA models; we show that the stacked model outperforms state-of-the-art ML models. However, the complex ensemble architecture makes the resulting predictions less transparent. To alleviate this, we investigate explainable artificial intelligence (XAI) methods for explaining the first- and second-level models of the ensemble. Third, we consider and compare different ways of combining first-level and second-level explanations. This novel concept enables us to explain stacked ensembles for regression tasks. The experimental evaluation indicates that the considered ETA models correctly learn the importance of those input features driving the prediction. [ABSTRACT FROM AUTHOR]

Mobility-on-demand (MoD) systems consist of a fleet of shared vehicles that can be hailed for one-way point-to-point trips. The total distance driven by the vehicles and the fleet size can be reduced by employing ridesharing, i.e., by assigning multiple passengers to one vehicle. However, finding the optimal passenger-vehicle assignment in an MoD system is a hard combinatorial problem. In this work, we demonstrate how the VGA method, a recently proposed systematic method for ridesharing, can be used to compute the optimal passenger-vehicle assignments and corresponding vehicle routes in a massive-scale MoD system. In contrast to existing works, we solve all passenger-vehicle assignment problems to optimality, regularly dealing with instances containing thousands of vehicles and passengers. Moreover, to examine the impact of using optimal ridesharing assignments, we compare the performance of an MoD system that uses optimal assignments against an MoD system that uses assignments computed using insertion heuristic and against an MoD system that uses no ridesharing. We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57% compared to an MoD system without ridesharing. Furthermore, we found that the optimal assignments result in a 20% reduction in vehicle distance driven and 5% lower average passenger travel delay compared to a system that uses insertion heuristic. [ABSTRACT FROM AUTHOR]

Shared, autonomous electric vehicles (SAEVs) are expected to enter the market in the coming decades. Using MATSim, we simulate a use case where SAEVs are introduced in multiple suburban zones at the outskirts of Vienna (Austria), which are characterized by relatively low population density, but have access to at least one rail-based public transport stop. For all combinations of different fleet sizes and fare levels, we find that a relatively small share of car trips by residents of these zones (7%–14%) are replaced by SAEVs, generating CO 2 emissions reductions of 5%–11%. Moreover, 23%–35% of trips previously undertaken by foot or bicycle are replaced by SAEVs, as well as 10%–20% of public transport trips. The potential of SAEVs to reduce the use and ownership of private vehicles in suburban areas therefore seems to be rather limited, which is also reflected in our finding that one SAEV usually replaces only 2–4 private vehicles. The potential becomes somewhat larger when the usage and ownership of private cars is assumed to become more expensive, leading to 17%–20% of car trips being replaced by SAEVs and generating CO 2 emissions reductions of up to 32%. • We simulate the introduction of SAEVs in suburban areas with public transport access. • Even with lare fleet sizes and low prices, only few car trips are replaced by SAEVs. • SAEVs mainly replace trips undertaken by foot, bicycle, or public transport. • Higher car usage & ownership costs increase switches from car to SAEV. • One SAEV replaces only 2–4 private vehicles, as little sharing takes place. [ABSTRACT FROM AUTHOR]

Considering the disruption risk and congestion deterioration caused by recurrent outbreaks of COVID-19 in several ports, this study developed a port capacity decision three-stage game model by adopting a sharing approach, which comprised a combined port management committee and a two-port system. The former determines the capacity-sharing rate, and the latter sequentially chooses its terminal handling charge (THC). Through backward induction, the capacity-sharing decisions among multiple extrema are derived, and then the "siphon effect" and "spillover effect" of capacity-sharing are analyzed. A case study of Shanghai and Ningbo Zhoushan Ports is presented to further illustrate the findings. • Three-stage game model is developed by first adopting a capacity sharing approach in the post-COVID-19 pandemic era. • The three-stage game model focuses on the imbalanced capacity and congestion deterioration. • A combined port management committee and a two-port system are considered. • The optimal capacity-sharing decisions among multiple extremums are derived. • "Siphon effect" and "spillover effect" of capacity-sharing are comparatively analyzed. [ABSTRACT FROM AUTHOR]

Rideshare passengers reasonably expect that the companies soliciting their business will take care of them in the event of a crash. But the reality is that in many states, including Florida, passengers that are injured by an uninsured motorist, by an underinsured motorist, or in a hit-and-run crash may be liable for the subsequent medical costs with no avenue for adequate reimbursement. Only twenty-six states require rideshare companies to provide their passengers with uninsured motorist coverage to mitigate the financial fallout of a car crash. This Note examines the current insurance regulations that Florida and other states impose upon rideshare companies, explores how specific aspects of Florida law expose rideshare passengers to additional risk, advocates for the legislature to mandate uninsured motorist coverage, and discusses the potential increase in costs consumers may experience. If private companies do not take it upon themselves to protect their passengers, it falls upon the government to shift financial risks to those who are best suited to bear them. [ABSTRACT FROM AUTHOR]

Future adoption of shared automated vehicles (SAVs) should lead to several societal benefits, but both automated vehicles (AVs) and ridesharing must overcome their barriers to acceptance. Previous research has investigated age differences in ridesharing usage and factors influencing the acceptability and acceptance of AVs. Further complicating our understanding of SAV acceptance, much of the public lack accurate knowledge and/or actual experience regarding AVs. In this study, we employed a 3 (age group) × 4 (video condition) longitudinal mixed experimental design to investigate age differences in anticipated SAV acceptance after viewing different types of introductory videos related to AVs (educational, experiential, or both) or currently available ridesharing provided by transportation network companies (control). Younger, middle-aged, and older adults were randomly assigned to watch (1) an educational video about SAV technologies and potential benefits, (2) an experiential video showing an SAV navigating traffic, (3) both the experiential and educational videos or (4) a control video explaining how current ridesharing services work. Attitudes toward SAVs (intent to use, trust/reliability, perceived usefulness, perceived ease of use, safety, desire for control, cost, authority, media, and social influence) were measured before and after viewing the video(s). Significant differences in how SAV attitudes changed were found between the educational and experiential video conditions relative to the control video and between different age groups. Findings suggest that educational and/or experiential videos delivered in an online format can have modest but significant improvements to their viewers' attitudes toward SAVs—particularly those of older adults. [ABSTRACT FROM AUTHOR]