Online Relocating and Matching of Ride-Hailing Services: A Model-Based Modular Approach

This study proposes an innovative model-based modular approach (MMA) to dynamically optimize order matching and vehicle relocation in a ride-hailing platform. MMA utilizes a two-layer and modular modeling structure. The upper layer determines the spatial transfer patterns of vehicle flow within the system to maximize the total revenue of the current and future stages. With the guidance provided by the upper layer, the lower layer performs rapid vehicle-to-order matching and vehicle relocation. MMA is interpretable, and equipped with the customized and polynomial-time algorithm, which, as an online order-matching and vehicle-relocation algorithm, can scale past thousands of vehicles. We theoretically prove that the proposed algorithm can achieve the global optimum in stylized networks, while the numerical experiments based on both the toy network and realistic dataset demonstrate that MMA is capable of achieving superior systematic performance compared to batch matching and reinforcement-learning based methods. Moreover, its modular and lightweight modeling structure further enables it to achieve a high level of robustness against demand variation while maintaining a relatively low computational cost

A Convolutional Long Short-Term Memory Neural Network Based Prediction Model

In recent years, the market demand for online car-hailing service has expanded dramatically. To satisfy the daily travel needs, it is important to predict the supply and demand of online car-hailing in an accurate manner, and make active scheduling based on the predicted gap between supply and demand. This paper puts forward a novel supply and demand prediction model for online carhailing, which combines the merits of convolutional neural network (CNN) and long short-term memory (LSTM). The proposed model was named convolutional LSTM (C-LSTM). Next, the original data on online car-hailing were processed, and the key features that affect the supply and demand prediction were extracted. After that, the C-LSTM was optimized by the AdaBound algorithm during the training process. Finally, the superiority of the C-LSTM in predicting online car-hailing supply and demand was proved through contrastive experiments

Vehicle Dispatching and Routing of On-Demand Intercity Ride-Pooling Services: A Multi-Agent Hierarchical Reinforcement Learning Approach

The integrated development of city clusters has given rise to an increasing demand for intercity travel. Intercity ride-pooling service exhibits considerable potential in upgrading traditional intercity bus services by implementing demand-responsive enhancements. Nevertheless, its online operations suffer the inherent complexities due to the coupling of vehicle resource allocation among cities and pooled-ride vehicle routing. To tackle these challenges, this study proposes a two-level framework designed to facilitate online fleet management. Specifically, a novel multi-agent feudal reinforcement learning model is proposed at the upper level of the framework to cooperatively assign idle vehicles to different intercity lines, while the lower level updates the routes of vehicles using an adaptive large neighborhood search heuristic. Numerical studies based on the realistic dataset of Xiamen and its surrounding cities in China show that the proposed framework effectively mitigates the supply and demand imbalances, and achieves significant improvement in both the average daily system profit and order fulfillment ratio

Real-Time Optimization for Dynamic Ride-Sharing

Throughout the last decade, the advent of novel mobility services such as ride-hailing, car-sharing, and ride-sharing has shaped urban mobility. While these types of services offer flexible on-demand transportation for customers, they may also increase the load on the, already strained, road infrastructure and exacerbate traffic congestion problems. One potential way to remedy this problem is the increased usage of dynamic ride-sharing services. In this type of service, multiple customer trips are combined into share a vehicle simultaneously. This leads to more efficient vehicle utilization, reduced prices for customers, and less traffic congestion at the cost of slight delays compared to direct transportation in ride-hailing services. In this thesis, we consider the planning and operation of such dynamic ride-sharing services. We present a wider look at the planning context of dynamic ride-sharing and discuss planning problems on the strategical, tactical, and operational level. Subsequently, our focus is on two operational planning problems: dynamic vehicle routing, and idle vehicle repositioning. Regarding vehicle routing, we introduce the vehicle routing problem for dynamic ridesharing and present a solution procedure. Our algorithmic approach consists of two phases: a fast insertion heuristic, and a local search improvement phase. The former handles incoming trip requests and quickly assigns them to suitable vehicles while the latter is responsible for continuously improving the current routing plan. This way, we enable fast response times for customers while simultaneously effectively utilizing available computational resources. Concerning the idle vehicle repositioning problem, we propose a mathematical model that takes repositioning decisions and adequately reflects available vehicle resources as well as a forecast of the upcoming trip request demand. This model is embedded into a real-time planning algorithm that regularly re-optimizes the movement of idle vehicles. Through an adaptive parameter calculation process, our algorithm dynamically adapts to changes in the current system state. To evaluate our algorithms, we present a modular simulation-based evaluation framework. We envision that this framework may also be used by other researchers and developers. In this thesis, we perform computational evaluations on a variety of scenarios based on real-world data from Chengdu, New York City, and Hamburg. The computational results show that we are able to produce high-quality solutions in real-time, enabling the usage in high-demand settings. In addition, our algorithms perform robustly in a variety of settings and are quickly adapted to new application settings, such as the deployment in a new city

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

Real-Time Idle Vehicle Relocation and Dynamic Pricing in Centralized Ride-Hailing Systems

Dynamic pricing and idle vehicle relocation are important tools for addressing demand-supply imbalance that frequently arises in the ride-hailing markets. Although interrelated, pricing and relocation have largely been studied independently in the literature. Moreover, the current mainstream methodologies, optimization and reinforcement learning (RL), suffer from significant computational limitations. The optimization needs to be solved in real-time and often trades off model fidelity (hence solution quality) for computational efficiency. Reinforcement learning requires a large number of samples to be trained offline and often struggles to achieve full coordination among the fleet. This thesis expands the research horizon and addresses the limitations of existing approaches. Chapter 1 designs an optimization model for computing both pricing and relocation decisions. The model ensures reasonable waiting time for the riders by reducing or postponing the demand that is beyond the service capacity. The postponement is by giving out discounts to riders who are willing to wait longer in the system, thus leveling off the peak without pricing out riders. Experiments show that the model ensures short waiting time for the riders without compromising the benefits (revenue and total rides served) of the platform. The postponement helps serve more riders during mild imbalances when there are enough vehicles to serve postponed riders after the peak. Chapter 2 presents a machine learning framework to tackle the computational complexity of optimization-based approaches. Specifically, it replaces the optimization with an optimization-proxy: a machine learning model which predicts its optimal solutions. To tackle sparsity and high-dimensionality, the proxy first predicts the optimal solutions on the aggregated level and disaggregates the predictions via a polynomial-time transportation optimization. As a consequence, the typical NP-Hard optimization is reduced to a polynomial-time procedure of prediction and disaggregation. This allows the optimization model to be considered at higher fidelity since it can be solved and learned offline. Experiments show that the learning + optimization approach is computationally efficient and outperforms the original optimization due to its higher fidelity. Chapter 3 extends one step further from Chapter 2, refining the optimization-proxy by reinforcement learning (RL). Specifically, RL starts from the optimization-proxy and improves its performance by interacting with the system dynamics and capturing long-term effects that are beyond the capabilities of the optimization approach. In addition, RL becomes far easier to train starting from a good initial policy. This hybrid approach is computationally efficient in both online deployment and offline training stages, and outperforms optimization and RL by combining the strengths of both approaches. It is the first Reinforcement Learning from Expert Demonstration (RLED) framework applied to the pricing and relocation problems and one of the few RL models with a fully-centralized policy.Ph.D

Accuracy and Uncertainty in Traffic and Transit Ridership Forecasts

Investments of public dollars on highway and transit infrastructure are influenced by the anticipated demands for highways and public transportations or traffic and transit ridership forecasts. The purpose of this study is to understand the accuracy of road traffic forecasts and transit ridership forecasts, to identify the factors that affect their accuracy, and to develop a method to estimate the uncertainty inherent in those forecasts. In addition, this research investigates the pre-pandemic decline in transit ridership across the US metro areas since 2012 and its influence on the accuracy of transit forecasts. The sample of 1,291 road projects from the United States and Europe compiled for this research shows that measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Similarly for 164 large-scale transit projects, the observed ridership was about 24.6% lower than forecasts on average. The accuracy depends on the mode, length of the project, year the forecast was produced as well as socio-economic and demographic changes from the production to observation year. In addition, we have found evidence of recent changes in transit demand to be affecting the transit ridership forecast accuracy. From 2012 to 2018, bus ridership decreased by almost 15% and rail ridership decreased by about 4% on average across the metropolitan areas in the United States. This decline is unexpected, because it coincided with the period of economic and demographic growth: indicators typically associated with rising transit ridership. We found that the advent of new mobility options in ride hailing services, bike and scooter shares as well as declining gas prices and increasing transit fares have the highest impact on ridership decline. Adjusting the ridership forecasts for these factors in a hypothetical scenario saw an improved transit ridership forecast performance. Despite the advances in modeling techniques and the availability of rich travel data over the years, expecting perfect forecasts (where observations are equal to the forecasts), may not be prudent because of its forward-facing nature. Forecasts need to convey their inherent uncertainty so that planners and policymakers can take that into account when they are making any decision about a project. The existing methods to quantify the uncertainty rely on flawed assumptions regarding input variability and interaction and are significantly resource intensive. An alternate method is one that considers the uncertainty inherent in the travel demand models themselves based on empirical evidence. In this research, I have developed a tool to quantify the uncertainty in traffic and transit ridership forecasts through a retrospective evaluation of the forecast accuracy from the two largest available databases of traffic and transit ridership forecasts. The factors associated with the accuracy and the recent decline in transit ridership lead the formulation of quantile regression as a new method to quantify the uncertainty in forecasts. Together with a consideration of decision intervals or breakpoints where a project decision might change, such ranges can be used to quantify project risk and produce better forecasts

Knowledge Discovery and Data Mining for Shared Mobility and Connected and Automated Vehicle Applications

The rapid development of shared mobility and connected and automated vehicles (CAVs) has not only brought new intelligent transportation system (ITS) challenges with the new types of mobility, but also brought a huge opportunity to accelerate the connectivity and informatization of transportation systems, particularly when we consider all the new forms of data that is becoming available. The primary challenge is how to take advantage of the enormous amount of data to discover knowledge, build effective models, and develop impactful applications. With the theoretical and experimental progress being made over the last two decades, data mining and machine learning technologies have become key approaches for parsing data, understanding information, and making informed decisions, especially as the rise of deep learning algorithms bringing new levels of performance to the analysis of large datasets. The combination of data mining and ITS can greatly benefit research and advances in shared mobility and CAVs.This dissertation focuses on knowledge discovery and data mining for shared mobility and CAV applications. When considering big data associated with shared mobility operations and CAV research, data mining techniques can be customized with transportation knowledge to initially parse the data. Then machine learning methods can be used to model the parsed data to elicit hidden knowledge. Finally, the discovered knowledge and extracted information can help in the development of effective shared mobility and CAV applications to achieve the goals of a safer, faster, and more eco-friendly transportation systems.In this dissertation, there are four main sections that are addressed. First, new methodologies are introduced for extracting lane-level road features from rough crowdsourced GPS trajectories via data mining, which is subsequently used as the fundamental information for CAV applications. The proposed method results in decimeter level accuracy, which satisfies the positioning needs for many macroscopic and microscopic shared mobility and CAV applications. Second, macroscopic ride-hailing service big data has been analyzed for demand prediction, vehicle operation, and system efficiency monitoring. The proposed deep learning algorithms increase the ride-hailing demand prediction accuracy to 80% and can help the fleet dispatching system reduce 30% of vacant travel distance. Third, microscopic automated vehicle perception data has been analyzed for a real-time computer vision system that can be used for lane change behavior detection. The proposed deep learning design combines the residual neural network image input with time serious control data and reaches 95% of lane change behavior prediction accuracy. Last but not least, new ride sharing and CAV applications have been simulated in a behavior modeling framework to analyze the impact of mobility and energy consumption, which addresses key barriers by quantifying the transportation system-wide mobility, energy and behavior impacts from new mobility technologies using real-world data