In the past few years, ridesharing has been becoming increasingly popular in urban areas worldwide for its low cost and environment friendliness. In a typical scenario, the ridesharing service provider matches drivers of private vehicles or taxis to those seeking local taxicab- like transportation. Much research attention has been drawn to the optimization of travel costs in shared rides. However, other important factors in ridesharing, such as the social comfort, trust issues and revenue, have not been fully considered in the existing works. Social-aware ridesharing, which makes use of social relations among drivers and riders to address safety issues, and dynamic pricing, which dynamically determines shared ride fares, are two active research directions with important business implications. In this dissertation, we take the first step to comprehensively investigate the social-aware ridesharing queries. First, we study the problem of the top-k social-aware taxi ridesharing query. In particular, upon receiving a user's trip request, the service ranks feasible taxis in a way that integrates detour in time and passengers' cohesion in social distance. We propose a new system framework to support such a social-aware taxi-sharing service. It provides two methods for selecting candidate taxis for a given trip request. The grid-based method quickly goes through available taxis and returns a relatively larger candidate set, whereas the edge-based method takes more time to obtain a smaller candidate set. Furthermore, we design techniques to speed up taxi route scheduling for a given trip request. We propose travel-time based bounds to rule out unqualified cases quickly, as well as algorithms to find feasible cases efficiently. We evaluate our proposals using a real taxi dataset from New York City. Experimental results demonstrate the efficiency and scalability of the proposed taxi recommendation solution in real-time social-aware ridesharing services. Second, we study the problem of efficient matching of offers and requests in social-aware ridesharing. We formulate a new problem, named Assignment of Requests to Offers (ARO), that aims to maximize the number of served riders while satisfying the social comfort constraints as well as spatial-temporal constraints. We prove that the ARO problem is NP- hard. We then propose an exact algorithm for a simplified ARO problem. We further propose three pruning strategies to efficiently narrow down the searching space and speed up the assignment processing. Based on these pruning strategies, we develop two novel heuristic algorithms, the request-oriented approach and offer-oriented approach, to tackle the ARO problem. We also study the dynamic ARO problem and present a novel algorithm to tackle this problem. Through extensive experiments, we demonstrate the efficiency and effectiveness of our proposed approaches on real-world datasets. Third, we study the top-k vehicle matching in social ridesharing. In the current ridesharing research, optimizing social cohesion and revenue at the same time has not been well studied. We present a new pricing scheme that better incentivizes drivers and riders to participate in ridesharing, and then propose a novel type of Price-aware Top-k Matching (PTkM) queries which retrieve the top-k vehicles for a rider's request by taking into account both social relations and revenue. We design an efficient algorithm with a set of powerful pruning techniques to tackle this problem. Moreover, we propose a novel index tailored to our problem to further speed up query processing. Extensive experimental results on real datasets show that our proposed algorithms achieve desirable performance for real-world deployment. The work of this thesis shows that the social-aware ridesharing query processing techniques are effective and efficient, which would facilitate ridesharing services in real world.

In this dissertation, the first two chapters seek to understand and quantify how different types of frictions shape individual and market outcomes. This strand of my current research studies questions in urban settings such as the role of ridesharing platforms in mitigating the search and match frictions prevalent in taxi markets and how information frictions can hinder the growth small and medium sized firms in developing countries. The final chapter of my dissertation studies the consequences of electoral accountability in democracies. This strand of research studies the role of electoral incentives in shaping the allocation and provision of effort by politicians. The first chapter of my dissertation, co-authored with Motaz Al-Chanati, studiesthe sources of efficiency gains in ridesharing markets. The key motivation arises from the fact that in many decentralized transportation markets, search and match frictions lead to inefficient outcomes. Ridesharing platforms, who act as intermediaries in traditional taxi markets, improve upon the status quo along two key dimensions: surge pricing and centralized matching. We study how and why these two features make the market more efficient; and explore how alternate pricing and matching rules can improve outcomes further. To this end, we develop a structural model of the ridesharing market with four components: (1) dynamically optimizing drivers who make entry, exit and search decisions; (2) stochastic demand; (3) surge pricing rule and (4) a matching technology. Relative to our benchmark model, surge pricing generates large gains for all agents; primarily during late nights. This is driven by the role surge plays in inducing drivers to enter the market. In contrast, centralized matching reduces match frictions and increases surplus for consumers, drivers, and the ridesharing platform, irrespective of the time of the day. We then show that a simple, more flexible pricing rule can generate even larger welfare gains for all agents. Our results highlight how and why centralized matching and surge pricing are able to make the market more efficient. We conclude by drawing policy implications for improving the competitiveness between taxis and ridesharing platforms. My second chapter, co-authored with Jonas Hjort and Golvine de Rochambeau, studies the role of information frictions amongst firms in developing countries. Evidence suggests that many firms in poor countries stagnate because they cannot access growth-conducive markets. We hypothesize that overlooked informational barriers distort market access. To investigate, we gave a random subset of medium-sized Liberian firms vouchers for a week-long program that exclusively teaches “sellership”: how to sell to corporations, governments, and other large buyers. Firms that participate win three times as many formal contracts a year later. The impact is heterogeneous: informational sales barriers bind for about a quarter of firms. Three years after training, these firms continue to win desirable contracts, are more likely to operate, and employ more workers. In my final chapter, I analyze how politicians in Canada allocate their time and effort when faced with competitive elections. In particular I study how well the so-called discipline effect work in democratic elections and how does it affect the allocation of time and resources of politicians. To do this, I present causal evidence of the effect of electoral vulnerability on subsequent performance of Canadian Members of Parliament along various dimensions. More specifically, I document a politician’s substitution of effort across different tasks in response to plausibly exogenous variation in electoral vulnerability. Using party opinion polls on the day before the election as an instrument, I estimate that more electorally vulnerable politicians substitute effort away from attending the parliament and instead spend more money in their constituency and more money in the following election campaign. These MPs spend more on salaries to their staff, travel to and from the constituency and advertising to constituents. I also find evidence that electorally vulnerable MPs find it harder to raise money for their next election but are compensated by transfers from the political party they belong to. This substitution of effort towards constituency and campaign activities is rationalized with a simple political economy model where politicians can influence a voter’s belief about their ability by exerting effort on more costly, but informative actions.

Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimate the extent to which ride-hailing services have contributed to the recent decline in public transit ridership. With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA) . The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a Summation of Probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, the walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, we performed a Time Value Analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data. / Master of Science / Existing literature on the relationship between ridehailing (RH) and transit services is limited to empirical studies that rely on self-reported answers and lack spatial and temporal contexts. To fill this gap, the research takes a novel approach that uses real-time geospatial analyzes. Using this approach, we estimated the extent to which ride-hailing services have contributed to the recent decline in public transit ridership. With source data on ridehailing trips in Chicago, Illinois, we computed the real-time transit-equivalent trip for the 7,949,902 ridehailing trips in June 2019; the sheer size of this sample is incomparable to the samples studied in existing literature. An existing Multinomial Nested Logit Model was used to determine the probability of a ridehailer selecting a transit alternative to serve the specific origin-destination pair, P(Transit|CTA) . The study found that 31% of RH trips are replaceable, 61% are not replaceable, and 8% lie within the buffer zone. We measured the robustness of this probability using a parametric sensitivity analysis, and performed a two-tailed t-test, with a 95% confidence interval. In combination with a Summation of Probabilities, the results indicate that the total travel time for a transit trip has the greatest influence on the probability of using transit, whereas the airport pass price has the least influence. Further, the walk time, number of stops in the origin and destination census tracts, and household income also have significant impacts on the probability of using transit. Lastly, we performed a Time Value Analysis to explore the cost and trip duration difference between RH trips and their transit-equivalent trips on the probability of switching to transit. The findings demonstrated that approximately 90% of RH trips taken had a transit-equivalent trip that was less expensive, but slower. The main contribution of this study is its thorough approach and fine-tuned series of real-time spatial analyzes that investigate the replaceability of RH trips for public transit. The results and discussion intend to provide perspective derived from real trips and encourage public transit agencies to look into possible opportunities to collaborate with ridehailing companies. Moreover, the methodologies introduced can be used by transit agencies to internally evaluate opportunities and redundancies in services. Lastly, we hope that this effort provides proof of the research benefits associated with the recording and release of ridehailing data.

Submitted in the partial fulfilment for the Degree of Master of Arts in Development Studies Faculty of Humanities University of the Witwatersrand, March 2017 / The precarious existence of Uber drivers operating within Johannesburg’s metropolitan area is the primary area of study in which this dissertation has undertaken. Driver precarity, defined in the study as the loss of labour market security in various forms, is argued to stem from Uber’s sharing economy-inspired business model. The analysis of Uber’s business model, substantively focuses on the service’s dynamic pricing model of fare price setting, the implementation of a ‘rating’ system in which to evaluate driver performance and the use of ‘independent contractor’ labour. It is argued that each of these three Uber business practices place drivers in a position of precarity in the realm of their income, employment, work and job security. The study mobilises a qualitative research methodology, enlisting the methods of unstructured interviews on eight active Uber drivers, four autoethnographical observations on real-time work behaviour and document analysis to generate data for analysis. The prevailing argument made regarding Uber’s precarity-creation, is aided through a consultation of Guy Standing’s theorisation on precarity (2011), with Harvey’s flexible Accumulation theory (1990), Foucault’s Panopticism thesis (1975) and Hochschild’s emotional labour theory (1983) broadening the scope of the analysis. / XL2018

A fundamental challenge in operations management is to dynamically match spatially diversified supply sources with random demand units. This dissertation tackles this challenge in two major areas: in supply chain management, a company procures from multiple, geographically differentiated suppliers to service stochastic demands based on dynamically evolving inventory conditions; in revenue management of ride-hailing systems, a platform uses operational and pricing levers to match strategic drivers with random, location and time-varying ride requests over geographically dispersed networks. The first part of this dissertation is devoted to finding the optimal procurement and inventory management strategies for a company facing two potential suppliers differentiated by their lead times, costs and capacities. We synthesize and generalize the existing literature by addressing a general model with the simultaneous presence of (a) orders subject to capacity limits, (b) fixed costs associated with inventory adjustments, and (c) possible salvage opportunities that enable bilateral adjustments of the inventory, both for finite and infinite horizon periodic review models. By identifying a novel, generalized convexity property, termed (C1K1, C2K2)-convexity, we are able to characterize the optimal single-source procurement strategy under the simultaneous treatment of all three complications above, which has remained an open challenge in stochastic inventory theory literature. To our knowledge, we recover almost all existing structural results as special cases of a unified analysis. We then generalize our results to dual-source settings and derive optimal policies under specific lead time restrictions. Based on these exact optimality results, we develop various heuristics and bounds to address settings with fully general lead times. The second part of this dissertation focuses on a ride-hailing platform's optimal control facing two major challenges: (a) significant demand imbalances across the network, and (b) stochastic demand shocks at hotspot locations. Towards the first major challenge, which is evidenced by our analysis of New York City taxi trip data, the dissertation shows how the platform's operational controls--including demand-side admission control and supply-side empty car repositioning--can improve system performance significantly. Counterintuitively, it is shown that the platform can improve the overall value through strategic rejection of demand in locations with ample supply capacity (driver queue). Responding to the second challenge, a demand shock of uncertain duration, we show how the platform can resort to surge pricing and dynamic spatial matching jointly, to enhance profits in an incentive compatible way for the drivers. Our results provide distinctive insights on the interplay among the relevant timescales of different phenomena, including rider patience, demand shock duration and drivers' traffic delay to the hotspot, and their impact on optimal platform operations.

Transportation analysts have monitored with interest the emergence of trip chaining, or multi-purpose trip making, which is becoming a common method of travel for many households. As of 2001, 61% of all working age adults trip chained. From a policy perspective, this warrants attention as these 61% of adults who trip chain generate 68% of average daily vehicle miles traveled (VMT). In addition, most trip chaining is accomplished by automobile and generally alone or with other family members. Trip chaining research has focused predominantly on travel by workers and findings suggest that one reason for its increase is that workers are scheduling non-work activities into their work commute, largely to support household needs (primarily childcare but also for shopping and personal business). Since the 1990s, significant federal funding has supported programs to improve air quality through reduced emissions. These include employer-based programs that seek to reduce VMT through ride sharing and the use of transit, along with incentives for doing so. The success of these programs is based on the flexibility of the commuter to change his/her work mode. As indicated above, however, trip chaining is typically associated with decreased flexibility and almost in direct conflict with programs that encourage alternative commute modes. This research identifies household, demographic, work, and activity setting factors that influence trip chaining in order to understand the related policy implications for employer-based programs that seek to reduce VMT through encouraging alternative commute modes. Using the 2001 National Household Travel Survey, a market segmentation identified trip chaining influencers. These were primarily the presence of children under the age of 16, worker status, more than one household adult, a high vehicle-to-worker ratio, and educational attainment above the high school level. The findings indicate that while between 30 and 42% of workers commute in the traditional manner, employer-based programs can achieve greater returns if increased focus is placed on improving employer amenities. In addition, further VMT reduction can be achieved through new programs that target the household instead of the employer, as evidenced by the TravelSmart program in Australia and SmartTrips program in Portland, OR. / text

Transportation Network Companies (TNC) are companies that use online-enabled platforms to connect passengers with drivers. In recent years, they have sparked controversy with the taxi industry, which accuses TNCs of operating unfairly. In my study, I look at taxi regulation, consumer transportation preferences, and costs and benefits of TNCs. I analyze data comparing three of these companies, Uber, Lyft, and Sidecar, with a traditional taxicab, and evaluate trends in taxi employment from the Bureau of Labor Statistics. I find that Transportation Network Companies generally have shorter wait times, cheaper prices, and increased convenience, aspects that appeal to consumer preferences. I also find that taxi driver employment tends to fluctuate with economic conditions, however cities that are more likely to use TNCs exhibit smaller growth. I predict that at current conditions, TNCs such as Uber and Lyft will overtake taxi services. Thus, the taxi industry must focus on increasing TNC regulation, creating innovative technology, and modifying its service to appeal to consumers.

This dissertation investigates cabdriving as a form of spatial work, involved in the production and reproduction of social space through three interrelated products: physical movement from place to place; the experience of movement, of connection made between places; and the articulation of these places, movements, and experiences with visions of society and the social. The particular forms of knowledge involved in this work, and the politics in which taxicabs are entangled, are explored through fieldwork conducted in two very different cities: Mexico City and San Francisco, California. The political context of cabdriving knowledge changes as new technologies are introduced into the cab to reframe the relationship between the interior of the cab (where passengers and drivers interact) and the exteriors (urban and informational spaces) through which it passes. In Mexico City, interviews with libre, base, and sitio cabdrivers about their knowledge and work strategies revealed three aspects of cabdriving as a rhythm analytical practice: 1) the points of confluence, i.e., the spatial pattern or method by which drivers link up with passengers; 2) the temporal and monetary patterns of constraint the occupation puts on drivers; and 3) the sense of the city which emerges, as this is described by drivers. Each form of taxicab has different patterns of movement, and different spatial and technological means of establishing contact with customers, which results in differing experiences and strategies elaborated by drivers. In San Francisco, interviews were conducted with taxi, limousine, and "ridesharing" drivers on the impact of smartphone-enabled "e-hailing" technology. The term allegorithm (the productive co-deployment of a socially relevant allegorical script and a software-mediated algorithm) is borrowed from gaming studies to describe how interfaces reframe the cab-riding experience. Of particular interest is the emergence of "ridesharing," or the overcab (a cab-riding experience which is superior to the experience of riding in a cab). The effectiveness of the overcab’s reframing project depends on the acceptance and performance by participants of the "overcab" narrative. There are indications that the transcendence of the overcab is fragile, and that cracks are developing in the experiences of both drivers and passengers, due to continuing tensions which the overcab has failed to resolve, or which have been introduced as part of its regulating mechanism.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Motivated by the imbalance between demand (i.e., passenger requests) and supply (i.e., available vehicles) in the ride-hailing market and severe traffic congestion faced by modern cities, this dissertation aims to improve the efficiency of the sharing economy by building an agent-based methodological framework for optimal decision-making of distributed agents (e.g., autonomous shared vehicles), including passenger-seeking and route choice. Furthermore, noticing that city planners can impact the behavior of agents via some operational measures such as congestion pricing and signal control, this dissertation investigates the overall bilevel problem that involves the decision-making process of both distributed agents (i.e., the lower level) and central city planners (i.e., the upper level). First of all, for the task of passenger-seeking, this dissertation proposes a model-based Markov decision process (MDP) approach to incorporate distinct features of e-hailing drivers. The modified MDP approach is found to outperform the baseline (i.e., the local hotspot strategy) in terms of both the rate of return and the utilization rate. Although the modified MDP approach is set up in the single-agent setting, we extend its applicability to multi-agent scenarios by a dynamic adjustment strategy of the order matching probability which is able to partially capture the competition among agents. Furthermore, noticing that the reward function is commonly assumed as some prior knowledge, this dissertation unveils the underlying reward function of the overall e-hailing driver population (i.e., 44,000 Didi drivers in Beijing) through an inverse reinforcement learning method, which paves the way for future research on discovering the underlying reward mechanism in a complex and dynamic ride-hailing market. To better incorporate the competition among agents, this dissertation develops a model-free mean-field multi-agent actor-critic algorithm for multi-driver passenger-seeking. A bilevel optimization model is then formulated with the upper level as a reward design mechanism and the lower level as a multi-agent system. We use the developed mean field multi-agent actor-critic algorithm to solve for the optimal passenger-seeking policies of distributed agents in the lower level and Bayesian optimization to solve for the optimal control of upper-level city planners. The bilevel optimization model is applied to a real-world large-scale multi-class taxi driver repositioning task with congestion pricing as the upper-level control. It is disclosed that the derived optimal toll charge can efficiently improve the objective of city planners. With agents knowingwhere to go (i.e., passenger-seeking), this dissertation then applies the bilevel optimization model to the research question of how to get there (i.e., route choice). Different from the task of passenger-seeking where the action space is always fixed-dimensional, the problem of variable action set emerges in the task of route choice. Therefore, a flow-dependent deep Q-learning algorithm is proposed to efficiently derive the optimal policies for multi-commodity multi-class agents. We demonstrate the effect of two countermeasures, namely tolling and signal control, on the behavior of travelers and show that the systematic objective of city planners can be optimized by a proper control.