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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]

We introduce a modeling framework for stochastic rider‐driver matching in many‐to‐one ridesharing systems, in which drivers have to be selected before the exact rider demand is known. The modeling framework allows for the use of driver booking fees and penalties for unmatched drivers, therefore supporting different system operating modes. We model this problem as a two‐stage stochastic set packing problem. To tackle the intractability of the stochastic problem, we introduce three model approximations and evaluate them on a large set of benchmark instances for three different system operating modes. Our computational experiments show the superiority of some model approximations over others and provide valuable insights on the impact of penalties and booking fees on the system's profitability and user satisfaction. [ABSTRACT FROM AUTHOR]

Both electric vehicles and Tokamak power supply use multiple parallel converters to realize high current operation in four quadrants. A finite‐time current sharing control algorithm based on disturbance compensation is proposed to improve the converter system's dynamic and steady‐state performance so that the system can achieve the required load current and branch‐current sharing in a finite time. In the proposed algorithm, this paper adopts a finite‐time observer for unknown disturbances in the converter system and load circuit due to disturbances, such as unknown inductance and resistance in the converter system. The results show that the load current control overshoot is minimal, and the dynamic performance of each branch current is higher than the traditional control algorithm. A finite‐time current sharing control algorithm based on disturbance compensation is proposed so that the converter system can achieve the required load current and branch current sharing in a finite time. [ABSTRACT FROM AUTHOR]

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

There are great challenges in the driving of human–machine shared control vehicles (HSCVs) in uncertain environments. Aiming at solving the collision avoidance problem of HSCV considering the uncertainty of surrounding vehicles (SVs), a human–machine cooperative control method for collision avoidance based on stochastic non‐cooperative game (SNCG) is proposed. This method realizes the safe driving of HSCV combined with the prediction of surrounding environment and constructs the interaction model between a driver and an automation system based on the game theory. Firstly, a vehicle–driver coupling model with driver's lateral and longitudinal operation characteristics is established. Next, utilizing the prediction equation, the prediction of the SV motion considering uncertainty is realized. Then, the safety constraints of HSCV are established by considering the chance constraints and the saturation characteristics of the actuators. Finally, based on the distributed stochastic model predictive control (DSMPC) method, the coupled optimization problem with chance constraints is constructed. To solve the coupled optimization problem with chance constraints, the idea of Nash equilibrium solution and the method of chance constraints tightening is adopted. The simulation results illustrate that the proposed method ensures the safe driving of HSCV under uncertain environments and realize the cooperative control between the automation system and the driver. [ABSTRACT FROM AUTHOR]

In this article, we characterize the relationship between spatial pricing and capacity based on distributed service design (DSD) decisions in a two‐sided sharing economy platform. We leverage theoretical tenets on two‐sided markets and on spatial pricing and capacity management in the sharing economy to inform a set of empirical and simulation models. Empirically, we use data on 156,520 observations of dynamic pricing and capacity distribution within Uber's San Francisco region. Estimation of a spatial econometric model reveals that the number of active drivers in neighboring zones negatively impacts the price in focal zones. Simultaneously, we find that spatial proximity is a significant factor in determining the distribution of prices when service demand levels are sufficiently high. We leverage this simultaneity finding to advance the literature on the sharing economy by incorporating operational considerations such as distributed capacity into service design. We link these econometric results with profit and welfare using a simulation that tests a variety of DSD pricing strategies under varying elasticity and revenue‐sharing conditions. Our findings offer guidance to firms managing two‐sided sharing economy platforms on tracking demand‐ and supply side price elasticity levels as well as revenue sharing spread when seeking to maximize profit, welfare, or both. [ABSTRACT FROM AUTHOR]

Surge pricing has been used to coordinate supply and demand in the ride‐sharing industry, but its causal effects on driver behavior remain unclear. This motivates us to examine how surge pricing causally affects driver earnings and labor supply by leveraging a unique quasi‐experiment, in which a leading ride‐sharing company in China introduced surge pricing in two cities at different times. Using a difference‐in‐differences design with the causal forest method, we find that surge pricing led to increases in drivers' weekly revenue. Decomposing the weekly revenue into "intensive margin" and "extensive margin" factors, we discover two countervailing effects at play: a cherry‐picking effect and a competition effect, and the daily revenue decreased because the latter dominated. Consequently, the increased weekly revenue can be explained by the extensive margin: drivers worked on more days to compensate for the decreased daily revenue, a result consistent with the income targeting behavior. Finally, we examine heterogeneous treatment effects across drivers, and find that surge pricing enticed more part‐time drivers to flood the market and crowd out full‐time drivers, and that the increase in the drivers' weekly revenue was primarily driven by part‐time drivers. Therefore, the benefit of surge pricing was unevenly distributed across drivers. Highlights: While surge pricing enlarged the size of the pie by enhancing overall operational efficiency, the induced increase in the driver pool resulted in a smaller slice per driver and decreased daily revenue for all drivers.The impact of surge pricing differed significantly across driver segments: full‐time drivers became worse off and earned less due to intensified competition from part‐time drivers; part‐time drivers earned more by increasing the weekly number of working days.When designing a new policy, platforms should be mindful of participants' bounded rationality and heterogeneous responses to the policy. [ABSTRACT FROM AUTHOR]

Detecting shared bicycles is an essential and challenging task. Deep learning has been widely used in object detection tasks in urban scenes, such as vehicle detection. However, deep learning algorithms still face many difficulties and challenges in shared bicycle detection. For example, the problem of large deformation of shared bicycles and the problem of small targets because the camera is far away from the shared bicycles. In order to solve these problems, this study introduces the feature fusion module and deformable convolution into the object detection network, which improves the efficiency of shared bicycle detection. This study proposes an enhanced faster R‐CNN network (A classic two‐stage object detection network) for shared bicycle detection and a shared bicycle dataset (SBD) is constructed for model training and testing. Compared with the original faster R‐CNN, the mean average precision (mAP) of the enhanced method on SBD is improved by 13%, which indicates that the method provided in this study is more suitable for detecting shared bicycles. This study also conducts experiments on the Microsoft Common Objects (COCO) dataset, where this method achieves 40.2% of the mAP, which is 5.8% higher than faster R‐CNN before improvement. [ABSTRACT FROM AUTHOR]

Car use contributes significantly to environmental pollution, leading people to rethink their transport habits. Previous studies on car use reduction relied on established theories related to values and self‐regulation to examine this decision. In this paper, we offer an alternative approach and examine the intention to reduce car use through the prism of identity theory, and propose environmental and moral identities, together with environmental concern, as the main antecedents to the importance of car use reduction and intention to reduce car use. Further, we consider ride‐sharing intention as an outcome of the decision to reduce car use. The proposed conceptual model is tested on a sample of consumers from the United Kingdom using an online consumer panel. The results offer partial support for the influence of moral identity, environmental identity, and environmental concern on the importance to reduce car use and intention. The links among the importance of and intention to reduce car use, along with ride‐sharing intentions, are supported. Finally, managerial implications are discussed, and future research directions are suggested. [ABSTRACT FROM AUTHOR]

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

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

There has been rapid growth in on‐demand ride‐hailing platforms that serve as an intermediary to match individual service providers (drivers) with consumer demand (riders). Several major players of this market have introduced carpool services that allow passengers heading toward the same direction to share a ride at a discounted fare. In this article, we develop an analytical model to study the pricing issues of ride‐sharing platforms in the presence of carpool services, and their economical and social implications. We show that the carpool service should be provided when its quality and/or the pooling efficiency is high. When the platform finds it optimal to offer the carpool service option, the platform achieves a larger market coverage and the riders are able to enjoy more affordable rides without compromising on service quality. Our analysis reveals that the provision of carpool services benefits the platform and the riders in general, but may hurt the drivers. [ABSTRACT FROM AUTHOR]

In recent years, innovative ride‐sharing systems have gained significant attention. In such systems, dynamic fleet management covers demand and fulfillment control to determine which stochastically incoming requests are to be satisfied and how vehicle resources are utilized for their fulfillment, respectively. Demand and fulfillment control can be implemented ranging from straightforward myopic to more sophisticated anticipatory. In this paper, our aim is twofold: (1) we want to classify how policies implement demand and fulfillment control in the related literature on dynamic fleet management; (2) we want to explore the effectiveness of demand and fulfillment control under varying conditions in order to identify benefits and risks for ride‐sharing systems. To this end, we define policies that differ in the optimization of demand and/or fulfillment control through the exploitation of either confirmed or complete information. Our experimental results demonstrate that demand and fulfillment control affect the performance and service quality of ride‐sharing systems quite differently. [ABSTRACT FROM AUTHOR]

To solve the last mile problem in rural areas, we deal with compact ride‐sharing model. For reducing the computational cost, we use a simple GA method and compared the performance of the parameters. To simplify the problem, our algorithm use a simple linear distance and minimize the total traveling distance. A data set used for GA was based on the Inami area in Nanto City, Toyama Prefecture, that is a real case study region. The performance comparison experiment of the algorithm by changing parameters was carried out. Experimental results show that the algorithm is likely to give correct answers for up to four vehicles. It was also shown that the combination of population size and elite proportion for GA could reduce computational costs while ensuring accuracy. In addition, by ride‐sharing was actually carried out using the obtained experimental result, it was shown that it could be applied to the actual route even if the calculation was carried out in the linear distance. [ABSTRACT FROM AUTHOR]

Ride‐sharing platforms, such as Uber and Didi Chuxing, widely use differential pricing strategies to encourage platform participation. However, it remains unclear whether differential pricing strategies in ride sharing are reasonable when users care about price fairness. Moreover, platforms face a unique dilemma when considering differential pricing: should it be used for customers, drivers, or both? On the demand side, platforms must deal with taxi competition, while the supply side contends with government regulation; it is this dilemma that motivates our work. Using a stylized model that includes price fairness, market conditions, and government policies, we consider four ride‐sharing platform pricing strategies: uniform pricing, differential customer pricing, differential driver pricing, and bilateral differential pricing. We present results from three perspectives. For platforms, we obtain feasible regions in all four pricing strategies. If the maximal number of potential drivers is below certain thresholds, a platform will use differential driver pricing. Conversely, differential customer pricing is optimal when the maximal number of potential drivers is sufficiently large. Moreover, when customers have stronger fairness concerns, platforms tend to use differential driver pricing to reduce competition. From the government perspective, when a platform charges customers differently, the government can achieve a win‐win solution for sustainable development and market stability by setting a proper taxi rate. Interestingly, we find that, for the demand and supply sides, an increase in fairness concerns may hurt customers and drivers under certain conditions. [ABSTRACT FROM AUTHOR]

The aim of this study is to examine three types of trust in services provided in the sharing economy toward the goal of increasing consumers' intention to use a ride‐sharing service. The three trust factors for sharing services are trust in drivers, trust in users, and trust in platforms. It is hypothesized that related to consumers' consumption lifestyles would influence their trust in a sharing service; in particular, previous researchers found that anti‐consumption influenced consumers to participate in the sharing economy. Based on previous findings, the attempt with this study is to determine whether an anti‐consumption would affect formation of the three types of trust with regards to the ride‐sharing service. Specifically, the differing effects of anti‐consumption on trust formation in a ride‐sharing service are compared between the United States and India. Data are collected from 253 online surveys from the United States and 266 from India, and data analysis reveals that the anti‐consumption affects the formation of all three types of trust in the ride‐sharing service. In the United States in particular, non‐voluntary anti‐consumption has a greater effect on trust formation than it did in India. [ABSTRACT FROM AUTHOR]

Here, a mixed‐integer linear programming model is developed to represent a transportation system of students traveling from/to a university campus. The concept of ridesharing is used and the mechanism of combinatorial auctions is incorporated within a routing‐based model. The mathematical model is based on the vehicle routing problem along with appropriate constraints accommodating features that express the auction clearing phase. A hybrid heuristic‐based optimization framework, that takes advantage of meta‐heuristic algorithms to improve an initial solution, is also developed to solve large‐sized instances of the problem. Three meta‐heuristics, namely particle swarm optimization, dragonfly algorithm, and imperialist competitive algorithm, are implemented in the proposed framework, whose performances are assessed and compared. Moreover, two improvement heuristic procedures that attempt to improve the outcomes of the foregoing meta‐heuristics are proposed and compared as well. [ABSTRACT FROM AUTHOR]

New transportation technologies (e.g., electric autonomous vehicles [EAVs]) and operation paradigms (e.g., car sharing) are discussed, researched, and to a small degree also deployed in recent years in response to rising energy crises and aggravating traffic congestions. In this research, we present a station‐based car‐sharing service system that integrates both EAV technologies and car‐sharing operations. Based on the simulation model, a dynamic and time‐continuous optimization model seeking a near‐optimum design of charging station location and EAV deployment is developed. By discretizing the model, we proposed a Monte Carlo simulation model to evaluate the total system cost for a given location and vehicle deployment design. A heuristic approach based on the genetic algorithm is developed to solve the system design of station location and vehicle deployment. A numerical test in Yantai City, China, is conducted to illustrate the effectiveness of the proposed model and to draw managerial insights into how the key parameters affect the system design. [ABSTRACT FROM AUTHOR]

Matching riders and drivers in ridesharing considering conflicting objectives of diverse stakeholders is challenging. The objective of this research is to formulate and evaluate the performance of four ridesharing matching‐objectives (i.e. system‐wide minimisation of passengers' wait time, minimisation of VMT, minimisation of detour distance, maximisation of drivers' profit) considering interests of diverse mobility stakeholders (i.e. drivers, riders, matching agencies, government transportation agencies). A grid roadway network was used to compare the performance of the four matching‐objectives in serving a ridesharing demand scenario. Performance comparison of matching‐objectives revealed that a system‐wide VMT minimisation matching‐objective performed best with least sacrifices on the other three matching‐objectives from their respective best performance level. Also, system‐wide VMT minimisation was the best matching‐objective, when drivers' and government transportation agencies' expectations were prioritised. System‐wide drivers' profit maximisation matching‐objective provided the highest monetary incentives for drivers and riders in terms of maximising profit and travel cost savings, respectively. System‐wide minimisation of detour distance was found to be least flexible in providing shared rides. The findings of this research provide useful insights on ridesharing matching system modelling and performance evaluation based on different matching‐objectives and can be used in developing and implementing ridesharing service considering multiple stakeholders' concerns. [ABSTRACT FROM AUTHOR]

A transition to electric vehicles and renewable energy is currently underway but may not be rapid enough in order to reach ambitious climate change mitigation targets. Therefore, additional, preferably instantaneous, measures are needed for quick emission reductions, which is where material efficiency (ME) could constitute a promising solution. ME strategies include but are not limited to vehicle lightweighting through material substitution, increased recycling of materials, reuse and remanufacturing of vehicle components, vehicle downsizing (switching to a smaller vehicle), and more intensive use by means of increased vehicle occupancy through sharing practices. While recent analyses have focused on a narrow subset of ME strategies, we find striking differences in the overall potential of different measures to decrease vehicular carbon footprints. Downsizing and more intensive use offer the largest mitigation potential but strongly depend on consumer behavior and are highly sensitive to modeling assumptions. Combined, the analyzed strategies can achieve emission reductions of up to 57% over the life cycle of a single vehicle, which is comparable to up to 83% achieved through a shift to low‐carbon energy supply. ME can cut carbon footprints of already efficient vehicles charging renewable electricity by half again. This makes ME both an excellent short‐term solution for climate change mitigation targeting the light‐vehicle sector but also an important complementary strategy to the long‐term transition toward electric vehicles and renewable energy supply. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Many aspects of our lives are associated with places and the activities we perform on a daily basis. Most of them are recurrent and demand displacement of the individual between regular places like going to work, school or other important personal locations. To accomplish these recurrent daily activities, people tend to follow regular paths with similar temporal and spatial characteristics, especially because humans are frequently looking for uniformity to support their decisions and make their actions easier or even automatic. In this work, we propose a method for discovering common pathways across users' habits from human mobility data. By using a density‐based clustering algorithm, we identify the most preferable locations the users visit, we apply a Gaussian mixture model over these places to automatically separate among all traces, the trajectories that follow patterns in order to discover the representations of individual's habits. By using the longest common sub‐sequence algorithm, we search for the trajectories that are more similar over the set of users' habits trips by considering the distance that pairs of users or habits share on the same path. The proposed method is evaluated over two real‐world GPS datasets and the results show that the approach is able to detect the most important places in a user's life, detect the routine activities and identify common routes between users that have similar habits paving the way for research techniques in carpooling, recommendation and prediction systems. [ABSTRACT FROM AUTHOR]

In this article, we develop and analyze a driver dispatching system for a control center that aims to minimize passengers' waiting time. The system imposes allocation constraints that ensure a minimum number of drivers in different regions to manage operational risk. The data‐driven system is based on Rolling Time Horizon approach and utilizes knowledge learned from historical data. It incorporates a hybrid forecasting model and a heuristic algorithm to solve the off‐line problem in each iteration. We show that the NP‐hardness of the off‐line problem lies in allocation constraints. We test the performance of the system with a simulation study based on actual past taxi order data. The result suggests that the system markedly decreases the average waiting time and saves planning time in comparison with the request‐driven dispatching mode. The result also demonstrates that in nonextreme cases, the dispatching system finds an acceptable solution which approximately satisfies allocation constraints while guaranteeing a short increase in waiting time. [ABSTRACT FROM AUTHOR]

Heavy‐duty vehicles have the advantages of heavy load, long transportation distance and so on, and ride comfort is an important feature of heavy‐duty vehicles. Flexibility of the heavy‐duty vehicle body is an essential factor in ride comfort, and a three‐axle heavy‐duty vehicle is the main commercial vehicle in China. To comprehensively analyze flexibility of vehicle body, elastic natural characteristics of free‐free beam with equal cross sections are derived, and vibration energy of rigid–elastic synthesis of the beam is obtained. Because the vehicle body is regarded as a free‐free beam with equal cross section, a 10 degree‐of‐freedom rigid elastic synthesis mechanics model of the three‐axle heavy‐duty vehicle is established, and a mathematics model of this heavy‐duty vehicle is derived based on the model energy. The frequency domain analysis method for the rigid–elastic synthesis model of a three‐axle heavy‐duty vehicle is determined by Fourier transform. The formulas of vibration response for the three‐axle heavy‐duty vehicle are derived, and the calculation method for statistical properties of vibration response is given. In order to analyze the effect of body flexibility on ride comfort, the ride comfort of a three‐axle heavy‐duty vehicle is simulated according to both the rigid–elastic synthesis model and the rigid model of three‐axle heavy‐duty vehicle, and the simulation results are compared on a B‐grade road with a common speed of 70 km/h. The results show that rigid–elastic synthesis of the vehicle body has a greater effect on vertical accelerations of the vehicle body and a lesser effect on human vertical acceleration, working places of suspensions and relative dynamic loads of wheels under regular analysis. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

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Autonomous vehicles (AVs) are paving a way to reshape the operation and management of urban transportation systems by improving travel mobility. In this study, we propose to use AVs for solving the first‐mile (FM) problem that aims to transport passengers from their homes to metro stations. Passengers submit travel requests in advance and a fleet operator dispatches AVs and arranges ridesharing in a rolling horizon framework. The ridesharing is implemented by assigning one AV to serve several requests subject to the vehicle capacity, the maximum travel time, and the accessibility constraints. A mixed integer linear programming (MILP) model is formulated to determine the AV dispatch and ridesharing schemes for the minimum operational costs. Then, another MILP model with the objective of the maximum user satisfaction is formulated for the purpose of comparison. A cluster‐based solution method is designed to deal with the large‐scale FM problem. Extensive numerical experiments are conducted to demonstrate the application of the proposed method. Results show that ridesharing can reduce both required AV fleet size and vehicle traveled miles. The proposed solution method is able to solve the problem efficiently and fulfil the requirement of online computation. [ABSTRACT FROM AUTHOR]

The introduction and adoption of autonomous vehicles (AVs) will likely reshape the transportation system and many economic activities. The economic literature on technology adoption, based on studies in agriculture and other sectors, provides lessons on the diffusion of AVs and its social and economic impacts. We rely on the threshold model of diffusion, where heterogeneous agents make decisions pursuing their self-interests. Applications of the threshold model point to case studies of other technologies where one can gain information and make predictions about the future of AVs. We find that private ownership of AVs may prevail after a transition period, as was the case in other technologies like computers, tractors, and conventional vehicles. With technological progress, the cost of privately owning AVs may decline. Further, there will be an increase in vehicle miles traveled (VMT) per capita, there may be more vehicles on the road, and perhaps the transportation user-base will expand to include those currently facing limited mobility. Congestion is likely to depend on the tradeoff between the expansion of VMT and increased efficiency of AVs to communicate and help regulate traffic. Furthermore, differentiation of vehicles will increase as driving time becomes freed for other activities. These trends may lead to increased greenhouse gas emissions and expansion of the transportation sector. Finally, the technology will evolve and may result in complementary innovations needing to be addressed, including the "last 10 feet" problem. It is evident that the future of the transportation system governed by AVs is most likely not going to be sustainable. This necessitates the importance of developing and enforcing rigorous policies at the metropolitan level and TNC levels to ensure a sustainable evolution of the future of transportation mobility. [ABSTRACT FROM AUTHOR]

Having the means to study the impact of electrical vehicle (EV) recharge on the power distribution network is one key aspect needed to manage the development of this technology. Power distribution grid and EVs are strongly connected elements that require to be wisely integrated to avoid that the limitations of the distribution network may hinder vehicle diffusion or that rapid growth of recharge requirements may put the distribution network in critical situations. In this study, a data-driven methodology is presented that aims at obtaining power requirement models that can be used to foresee the behaviour of the grid. The key to this methodology is the observation of charging profiles of a fleet of EVs over one year. The data collected defines a scenario representative of a generic fleet of commercial or sharing vehicles. The data is progressively loaded onto an existing database infrastructure and processed to obtain charge distributions that are then simulated in small sample networks in order to test the methodology. Starting from these data, a stochastic model is proposed to forecast the behaviour during the day and used to simulate by means of Monte Carlo techniques the impact on the power grid. [ABSTRACT FROM AUTHOR]

This article examines how ride‐hailing companies like Uber have disrupted not only the mainstream taxi industry but also progressive efforts to remedy that industry's shortcomings. The article focuses on Alliance Taxi Cooperative (ATC), a failed taxi worker cooperative in Philadelphia. ATC was deeply committed to economic democracy, living wages, and outreach to underserved communities. Its ambitions were nevertheless thwarted by regulatory obstruction and market disruptions caused by Uber. Drawing on relational poverty theory and literature on solidarity economy and platform capitalism, I suggest that ATC's story offers more than a cautionary tale about the pitfalls facing small taxi start‐ups. It also illuminates a great deal about the exploitative conditions and discriminatory geographies of the taxi and ride‐hailing industry, the biases of regulatory agencies, and the complicated ways that platform capitalism is refiguring class dynamics. [ABSTRACT FROM AUTHOR]

The paper presents three novel solutions for driving OLED displays including Distributive‐Driving, Prime‐Bridge, and the combination thereof. Distributive‐Driving re‐arranges the source and gate channel numbers on the back plane without changing the OLED fine metal mask (FMM) pattern, thereby increasing charging time, reducing fan‐out area, etc. Prime‐Bridge enables the sharing of the driving circuit by multiple subpixels and thus, can reduce the total number of thin‐film transistors (TFTs) and capacitors as well as the gate line number without sacrificing the charging time of each subpixel and the frame rate. By combining Distributive‐Driving and Prime‐Bridge, the driving circuit area can be increased in both horizontal and vertical directions, which is good for high PPI backplane design and can reduce BOM cost with higher yield. [ABSTRACT FROM AUTHOR]

We examine competition among ridesharing platforms, where firms compete on both price and the wait time induced with idled drivers. We show that when consumers are the only agents who multihome, idleness is lower in duopoly than when consumers face a monopoly ridesharing platform. When drivers and consumers multihome, idleness further falls to zero as it involves costs for each platform that are appropriated, in part, by their rival. Interestingly, socially superior outcomes may involve monopoly or competition under various multihoming regimes, depending on the density of the city, and the relative costs of idleness versus consumer disutility of waiting. [ABSTRACT FROM AUTHOR]

Policies to encourage carpooling in high‐occupancy vehicle (HOV) lanes have been adopted in the United States to lower congestion and reduce air pollution. We analytically model highway congestion and other vehicle‐related externalities. Encouraging carpooling decreases total costs when congestion relief in mainline lanes outweighs increased HOV lane congestion. Importantly, entry of new drivers via induced demand can negate the benefits of increased carpooling. Using 10 years of traffic data from Los Angeles we estimate time and route‐specific marginal external costs. Because costs vary substantially across routes, hours, and days, current policies to promote carpooling will often increase social costs. [ABSTRACT FROM AUTHOR]

Ridesharing's phenomenal growth has been fueled by a combination of efficiency gains through technological innovation and disregard for current taxi regulations. The end effect has been the generation of significant consumer surplus, but at what social cost? This dialogue piece debates whether ridesharing services have been good or bad for Canadian cities and then reviews current regulatory status in Toronto and Vancouver. Key Messages: Ridesharing, and Uber specifically, has experienced phenomenal growth in the last five years, which has upended the taxi industry in Canadian cities.Zwick argues that ridesharing has been detrimental to Canadian cities because it compromises safety standards, increases precarious labour, and intensifies congestion.Spicer argues that ridesharing has been a positive for Canadian cities because it generates consumer surplus, creates new employment opportunities, and disrupts a broken market. [ABSTRACT FROM AUTHOR]

The advent of smart‐phone based, ride‐sharing applications has revolutionized the vehicle for hire market. Advocates point to the ease of use, lower prices, and shorter wait times compared to hailing a taxi or prearranging limousine service. Others argue that proper government oversight is necessary to protect ride‐share passengers from driver error or vehicle parts failures and violence from unlicensed strangers. Using U.S. county‐level data from 2007 through 2015, we investigate whether the introduction of the ride‐sharing service Uber is associated with changes in fatal vehicle crashes and crime. We find that Uber's entry lowers the rate of DUIs and fatal accidents. For some specifications, we also find declines in arrests for assault and disorderly conduct. Conversely, we observe an increase in vehicle thefts. [ABSTRACT FROM AUTHOR]

Taxi is certainly the most popular type of on-demand transportation service in urban areas because taxi-dispatching systems offer more and better services in terms of shorter wait times and passenger travel convenience. However, a shortage of taxicabs has always been critical in many urban contexts especially during peak hours, and taxi has great potential to maximize its efficiency by employing the shared-ride concept. There are recent successes in dynamic ride-sharing projects that are expected to bring substantial benefits arising from energy consumption and operation efficiency and thus, it is essential to develop advanced shared-taxi-dispatch algorithms and investigate the collective benefits of dynamic ride-sharing by maximizing occupancy and minimizing travel times in real-time. This article investigates how taxi services can be improved by proposing shared-taxi algorithms and what type of objective functions and constraints could be employed to prevent excessive passenger detours. Hybrid-simulated annealing (HSA) is applied to dynamically assign passenger requests efficiently. A series of simulations are conducted with two different taxi operation strategies. The simulation results reveal that allowing ride-sharing for taxicabs increases productivity over the various demand levels and HSA can be considered as a suitable solution to maximize the system efficiency of dynamic ride-sharing. [ABSTRACT FROM AUTHOR]

This paper addresses the effect of workers' spatial flexibility (commuting and migration) on their probability of being over-educated. The empirical analysis deals with two possible sources of misspecification: the endogeneity of migration and the omission of relevant job characteristics. It also controls for area and personal characteristics. Results show that commuting is positively correlated with the quality of the education-job match. However, analysis does not support the conventional wisdom that migration unambiguously reduces over-education. It seems fair to conclude that the link between migration and over-education remains unclear and that further research is needed in order to better ground policy prescriptions. [ABSTRACT FROM AUTHOR]

There is a need to further explore ways to use Advanced Traveler Information Systems (ATIS) to encourage transit and ridesharing. One mechanism is to provide convenient travel itinerary information, not just for one trip, but for a day's travel. The formulation should consider time constraints, activity needs, real transit service parameters and the actual street system. Basic algorithmic development is needed to spur the private sector interest in such a product by demonstrating its utility. The activity travel planner will aid its user in planning his daily itinerary by arranging the sequence of stops, suggesting and possibly selecting stop locations, providing transit route and schedule information, and suggesting travel routes. This paper develops a framework for solving the Travel Itinerary Planning Problem (TIPP) which is a variant of the Traveling Salesman Problem (TSP). Implementation of the solution algorithm would be used to develop and test a prototype for an activity and travel planner. [ABSTRACT FROM AUTHOR]