On the Empty Miles of Ride-Sourcing Services: Theory, Observation and Countermeasures

The proliferation of smartphones in recent years has catalyzed the rapid growth of ride-sourcing services such as Uber, Lyft, and Didi Chuxing. Such on-demand e-hailing services significantly reduce the meeting frictions between drivers and riders and provide the platform with unprecedented flexibility and challenges in system management. A big issue that arises with service expansion is the empty miles produced by ride-sourcing vehicles. To overcome the physical and temporal frictions that separate drivers from customers and effectively reposition themselves towards desired destinations, ride-sourcing vehicles generate a significant number of vacant trips. These empty miles traveled result in inefficient use of the available fleet and increase traffic demand, posing substantial impacts on system operations. To tackle the issues, my dissertation is dedicated to deepening our understanding of the formation and the externalities of empty miles, and then proposing countermeasures to bolster system performance. There are two essential and interdependent contributors to empty miles generated by ride-sourcing vehicles: cruising in search of customers and deadheading to pick them up, which are markedly dictated by forces from riders, drivers, the platform, and policies imposed by regulators. In this dissertation, we structure our study of this complex process along three primary axes, respectively centered on the strategies of a platform, the behaviors of drivers, and the concerns of government agencies. In each axis, theoretical models are established to help understand the underlying physics and identify the trade-offs and potential issues that drive behind the empty miles. Massive data from Didi Chuxing, a dominant ride-sourcing company in China, are leveraged to evidence the presence of matters discussed in reality. Countermeasures are then investigated to strengthen management upon the empty miles, balance the interests of different stakeholders, and improve the system performance. Although this dissertation scopes out ride-sourcing services, the models, analyses, and solutions can be readily adapted to address related issues in other types of shared-use mobility services.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163209/1/xzt_1.pd

Behavior of taxi customers in hailing vacant taxis: a nested logit model for policy analysis

This study models and examines the taxi customers' preferences for hailing vacant taxis on streets. A stated preference survey was conducted to randomly select and interview 1242 taxi customers at taxi stands and pedestrians on streets, who had experiences of taking taxis recently, about their choices under different given hypothetical scenarios. In total, 4968 observations were collected and used for developing the discrete choice models for the analysis. To account for the potential correlations among alternatives, two nested logit models are developed, calibrated, and compared with a standard multinomial logit model in the investigation. The results of likelihood ratio test demonstrate that one of the developed nested logit models is better than the standard multinomial logit model to describe the search behavior of taxi customers. The model results also show that the walking time to and the waiting time at the location for hailing taxis, the extra travel time to the destination because of local circulation for finding a way from the pickup location heading to a passenger's destination, as well as the taxi customers' perceptions for walking to and waiting at taxi stands were found as significant factors to influence their decisions. In addition, the results of market segmentation analysis illustrate the variations in taxi-search strategies of taxi customers in different districts and regions. Some policy implications on introducing more taxi stands and improving the utilization rates of taxi stands are also discussed. We believe that the proposed models, findings, and discussion are useful for developing micro-simulation models to evaluate the performance of road traffic networks with taxi services and developing simulation-based optimization models to answer policy questions related to taxi services. Copyright © 2015 John Wiley & Sons, Ltd.postprin

A DATA-DRIVEN OPTIMIZATION METHOD FOR TAXI DISPATCHING PROBLEM

Taxi service has become one of the most important means of transportation in the world. Optimization of the taxi service can significantly reduce transportation costs, idle driving times, waiting times, and increase service quality. However, optimization of the taxi service due to its specific characteristics is a cumbersome task. In this research, we studied the taxi dispatching problem and proposed a mathematical programming machine learning-based approach to optimize the network. We presented a data-driven optimization methodology by combining machine learning techniques, that incorporate historical time-series data to forecast future demand, and mathematical programming. Specifically, Support Vector Regression and K-Nearest Neighbor are adopted to learn the passenger demand patterns based on time-series data. Then a MIP model is built to minimize total idle driving distance concerning balancing the supply-demand ratio in different regions. Moreover, we aimed at balancing supply according to the demand in different regions (nodes) of a city in order to increase service efficiency and to minimize the total ideal driving distance. We proposed a method that utilizes historical GPS data to build demand models and applies prediction technologies to determine optimal locations for vacant taxis considering anticipated future demand. From a system-level perspective, we compute optimal dispatch solutions for reaching a globally balanced supply-demand ratio with the least associated cruising distance under practical constraints. We implemented our approach to a real-world case study from New York City to demonstrate its efficiency and effectiveness

Modeling and Optimizing Routing Decisions for Travelers and On-demand Service Providers

This thesis investigates the dynamic routing decisions for individual travelers and on-demand service providers (e.g., regular taxis, Uber, Lyft, etc). For individual travelers, this thesis models and predicts route choice at two time-scales: the day-to-day and within-day. For day-to-day route choice, methodological development and empirical evidences are presented to understand the roles of learning, inertia and real-time travel information on route choices in a highly disrupted network based on data from a laboratory competitive route choice game. The learning of routing policies instead of simple paths is modeled when real-time travel information is available, where a routing policy is defined as a contingency plan that maps realized traffic conditions to path choices. Using data from a competitive laboratory experiment, prediction performance is then measured in terms of both one-step and full trajectory predictions. For within day route choice, a recursive logit model is formulated in a stochastic time-dependent (STD) network without sampling any choice sets. A decomposition algorithm is then proposed so that the model can be estimated in reasonable time. Estimation and prediction results of the proposed model are presented using a data set collected from a subnetwork of Stockholm, Sweden. Taxis and ride-sourcing vehicles play an important role in providing on-demand mobility in an urban transportation system. Unlike individual travelers, they do not have a clear destination when there\u27s no passenger on board. The optimal routing of a vacant taxi is formulated as a Markov Decision Process (MDP) problem to maximize long-term profit over the full working period. Two approaches are proposed to solve the problem. One is the model-based approach where a model of the state transitions of the environment is obtained from queuing-theory based passenger arrival and competing taxi distribution processes. An enhanced value iteration for solving the MDP problem is then proposed making use of efficient matrix operations. The other is the model-free Reinforcement Learning (RL) approach, which learns the best policy directly from observed trajectory data. Both approaches are implemented and tested in a mega city transportation network with reasonable running time, and a systematic comparison of the two approaches is also provided

Multi-attribute taxi logistics optimization

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2006.Includes bibliographical references (leaves 102-103).According to U.S. government surveys, 12% of Americans used taxi service in the previous month' and spent about $3.7 billion a year for cab fare.2 Taxi service is one of the major modes of public transportation. Despite providing services 24 hours a day, driving relentlessly with an empty taxicab in search of passengers and answering dispatch calls instantaneously, taxi service is ranked the most unsatisfactory mode of transportation by the public. Charging higher fares than other major modes of transportation and averaging 10 to 12 hours work day, taxi drivers have a difficult time to earn a sustainable income.Approximately half of all the taxi mileage is paid mileage; this means a significant portion of a taxi's time and fuel is spent on non-revenue generating activities, i.e. without passengers. Current taxi allocation is inefficient. The number of taxis and the geographical service areas which they serve are heavily regulated in most cities. With limited competition and strict regulations, taxi service suffers with customers having to endure long wait times and inferior services. The current taxi systems in most U.S. cities may be greatly improved from their current state.(cont.) This thesis investigates the factors of inefficiency in the current taxi system, reviews previous taxi efficiency studies, and suggests possible solutions. After extensive literature reviews and field research, a computer simulation model has been built in the MATLAB environment. This computer model tests various attributes that affect logistic optimizations for taxi services. In particular, the effect of taxi fleet size, the quantity of hotspots, and the concentrations of customers at hotspots are analyzed in detail using the model. The metric of interest includes the customers' wait time, taxi revenue, and costs of operations. Results from the computer simulation experiments, field research, and literature review are analyzed and synthesized. Possible solutions are proposed as part of this thesis.by Sonny Li.S.M

Harnessing Big Data for the Sharing Economy in Smart Cities

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

A survey of urban drive-by sensing: An optimization perspective

Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table