Taxi-hailing platforms:Inform or Assign drivers?

Online platforms for matching supply and demand, as part of the sharing economy, are becoming increasingly important in practice and have seen a steep increase in academic interest. Especially in the taxi/travel industry, platforms such as Uber, Lyft, and Didi Chuxing have become major players. Some of these platforms, including Didi Chuxing, operate two matching systems: Inform, where multiple drivers receive ride details and the first to respond is selected; and Assign, where the platform assigns the driver nearest to the customer. The Inform system allows drivers to select their destinations, but the Assign system minimizes driver-customer distances. This research is the first to explore: (i) how a platform should allocate customer requests to the two systems and set the maximum matching radius (i.e., customer-driver distance), with the objective to minimize the overall average waiting times for customers; and (ii) how taxi drivers select a system, depending on their varying degrees of preference for certain destinations. Using approximate queuing analysis, we derive the optimal decisions for the platform and drivers. These are applied to real-world data from Didi Chuxing, revealing the following managerial insights. The optimal radius is 1-3 kilometers, and is lower during rush hour. For most considered settings, it is optimal to allocate relatively few rides to the Inform system. Most interestingly, if destination selection becomes more important to the average driver, then the platform should not always allocate more requests to the Inform system. Although this may seem counterintuitive, allocating too many orders to that system would result in many drivers opting for it, leading to very high waiting times in the Assign system. (c) 2020 Elsevier Ltd. All rights reserved

Synergies between app-based car-related shared mobility services for the development of more profitable business models

Purpose: Emerging shared mobility services are an opportunity for cities to reduce the number of car single trips to both improve traffic congestion and the environment. Users of shared mobility services, such as carsharing, ridesharing and singular and shared ride-hailing services, often need to be customers of more than one service to cover all their transport needs, since few mobility providers offer more than one of these services from a single platform. On the other hand, providers offering these services separately do not optimize costly resources and activities, such as the vehicles or the technology. Hence, the aim of this paper is to find synergies between the different app-based car-related shared mobility services that foster the development of new business models, to increase the profitability of these services. Design/methodology/approach: The research approach is built on the literature of car-related shared mobility services business models, supported by the review of certain outstanding services websites, and face-to-face interviews with users and drivers of these transport services. The analysis is presented by means of the Business Model Canvas methodology. Findings: Based on the synergies found, this paper suggests a few different approaches for services to share some resources and activities. Originality/value: This study identifies the common features of carsharing, ridesharing and singular and shared ride-hailing services to develop more profitable business models, based on providing the services in aggregated form, or outsourcing activities and resources. In addition, the implications of these proposals are discussed as advantages and drawbacks from a business perspectivePeer ReviewedPostprint (published version

Balancing the Tradeoff between Profit and Fairness in Rideshare Platforms During High-Demand Hours

Rideshare platforms, when assigning requests to drivers, tend to maximize profit for the system and/or minimize waiting time for riders. Such platforms can exacerbate biases that drivers may have over certain types of requests. We consider the case of peak hours when the demand for rides is more than the supply of drivers. Drivers are well aware of their advantage during the peak hours and can choose to be selective about which rides to accept. Moreover, if in such a scenario, the assignment of requests to drivers (by the platform) is made only to maximize profit and/or minimize wait time for riders, requests of a certain type (e.g. from a non-popular pickup location, or to a non-popular drop-off location) might never be assigned to a driver. Such a system can be highly unfair to riders. However, increasing fairness might come at a cost of the overall profit made by the rideshare platform. To balance these conflicting goals, we present a flexible, non-adaptive algorithm, \lpalg, that allows the platform designer to control the profit and fairness of the system via parameters $\alpha$ and $\beta$ respectively. We model the matching problem as an online bipartite matching where the set of drivers is offline and requests arrive online. Upon the arrival of a request, we use \lpalg to assign it to a driver (the driver might then choose to accept or reject it) or reject the request. We formalize the measures of profit and fairness in our setting and show that by using \lpalg, the competitive ratios for profit and fairness measures would be no worse than $\alpha/e$ and $\beta/e$ respectively. Extensive experimental results on both real-world and synthetic datasets confirm the validity of our theoretical lower bounds. Additionally, they show that \lpalg under some choice of $(\alpha, \beta)$ can beat two natural heuristics, Greedy and Uniform, on \emph{both} fairness and profit

Essays on Information, Income, and the Sharing Economy

Many privately-owned items are somewhat non-rival in consumption, so there are often benefits to borrowing and lending underutilized goods and exchanging used goods. Although sharing is ubiquitous, it is understudied in economics. This dissertation seeks to help develop an economics of sharing. Chapter 1 presents a simple mathematical model of the “gains from sharing”, which connects the literatures on club goods, household economies, collective action, community governance, and decentralized cooperation. I argue that the level of sharing in society depends not just on technology but also on the norms that govern how people cooperate, on people’s preferences around privacy and independence, and on economies of scale in matching people with underutilized goods. Since institutions that facilitate new forms of sharing are still gaining users, experimenting with rules and etiquette, and developing tastes for peer-to-peer interactions, the level of sharing is likely to increase in the years to come. Chapter 2 investigates the current and potential value of sharing goods across households. Analyzing unique data from the online platform NeighborGoods, I find that the level of sharing among relatives, friends and neighbors makes informal borrowing and lending an important component of inter-household cooperation. The potential gains from sharing are even larger. My investigation of consumer expenditures reveals that the average household spends over $9,000 a year on goods that could, in principle, be shared across households. Given the large sums of money Americans spend on private vehicles, the greatest opportunities may be in increased ride-sharing and car-sharing. Finally, I address the relationship between income and sharing. Although traditional methods of sharing goods are disproportionately used by low-income people, I find that people of all incomes are equally likely to use new institutions for sharing goods, such as Craigslist, Airbnb, and Zipcar. This suggests that new forms of sharing may maintain their popularity as incomes rise in the long run. Chapter 3 studies the effect of Craigslist’s market for secondhand goods on solid waste generation. Economic theory suggests that falling transaction costs may increase incentives for owners to sell goods on secondhand markets and for buyers to purchase used goods instead of new goods. I use difference-in-difference methods to estimate Craigslist’s effect on waste by exploiting a natural experiment in how the platform expanded across California and Florida between 1996 and 2009. My results provide evidence that Craigslist led to substantial reductions in waste generation. This paper suggests that other online platforms may similarly generate economic as well as environmental benefits

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms

How to optimally dispatch orders to vehicles and how to tradeoff between immediate and future returns are fundamental questions for a typical ride-hailing platform. We model ride-hailing as a large-scale parallel ranking problem and study the joint decision-making task of order dispatching and fleet management in online ride-hailing platforms. This task brings unique challenges in the following four aspects. First, to facilitate a huge number of vehicles to act and learn efficiently and robustly, we treat each region cell as an agent and build a multi-agent reinforcement learning framework. Second, to coordinate the agents from different regions to achieve long-term benefits, we leverage the geographical hierarchy of the region grids to perform hierarchical reinforcement learning. Third, to deal with the heterogeneous and variant action space for joint order dispatching and fleet management, we design the action as the ranking weight vector to rank and select the specific order or the fleet management destination in a unified formulation. Fourth, to achieve the multi-scale ride-hailing platform, we conduct the decision-making process in a hierarchical way where a multi-head attention mechanism is utilized to incorporate the impacts of neighbor agents and capture the key agent in each scale. The whole novel framework is named as CoRide. Extensive experiments based on multiple cities real-world data as well as analytic synthetic data demonstrate that CoRide provides superior performance in terms of platform revenue and user experience in the task of city-wide hybrid order dispatching and fleet management over strong baselines.Comment: CIKM 201

Uber Effort: The Production of Worker Consent in Online Ride Sharing Platforms

The rise of the online gig economy alters ways of working. Mediated by algorithmically programmed mobile apps, platforms such as Uber and Lyft allow workers to work by driving and completing rides at any time or in any place that the drivers choose. This hybrid form of labor in an online gig economy which combines independent contract work with computer-mediated work differs from traditional manufacturing jobs in both its production activity and production relations. Through nine interviews with Lyft/Uber drivers, I found that workers’ consent, which was first articulated by Michael Burawoy in the context of the manufacturing economy, is still present in the work of the online gig economy in post-industrial capitalism. Workers willingly engage in the on-demand work not only to earn money but also to play a learning game motivated by the ambiguity of the management system, in which process they earn a sense of self-satisfaction and an illusion of autonomous control. This research points to the important role of technology in shaping contemporary labor process and suggests the potential mechanism which produces workers’ consent in technology-driven workplaces