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

An Overview of Carbon Footprint Mitigation Strategies. Machine Learning for Societal Improvement, Modernization, and Progress

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in each of these areas. The chapter highlights how various machine learning algorithms are used to increase the use of renewable energy, efficient transportation, and waste management systems to reduce the carbon footprint. The authors summarize the findings from the current research literature and conclude by providing a few future directions

User identification and community exploration via mining big personal data in online platforms

User-generated big data mining is vital important for large online platforms in terms of security, profits improvement, products recommendation and system management. Personal attributes recognition, user behavior prediction, user identification, and community detection are the most critical and interesting issues that remain as challenges in many real applications in terms of accuracy, efficiency and data security. For an online platform with tens of thousands of users, it is always vulnerable to malicious users who pose a threat to other innocent users and consume unnecessary resources, where accurate user identification is urgently required to prevent corresponding malicious attempts. Meanwhile, accurate prediction of user behavior will help large platforms provide satisfactory recommendations to users and efficiently allocate different amounts of resources to different users. In addition to individual identification, community exploration of large social networks that formed by online databases could also help managers gain knowledge of how a community evolves. And such large scale and diverse social networks can be used to validate network theories, which are previously developed from synthetic networks or small real networks. In this thesis, we study several specific cases to address some key challenges that remain in different types of large online platforms, such as user behavior prediction for cold-start users, privacy protection for user-generated data, and large scale and diverse social community analysis. In the first case, as an emerging business, online education has attracted tens of thousands users as it can provide diverse courses that can exactly satisfy whatever demands of the students. Due to the limitation of public school systems, many students pursue private supplementary tutoring for improving their academic performance. Similar to online shopping platform, online education system is also a user-product based service, where users usually have to select and purchase the courses that meet their demands. It is important to construct a course recommendation and user behavior prediction system based on user attributes or user-generated data. Item recommendation in current online shopping systems is usually based on the interactions between users and products, since most of the personal attributes are unnecessary for online shopping services, and users often provide false information during registration. Therefore, it is not possible to recommend items based on personal attributes by exploiting the similarity of attributes among users, such as education level, age, school, gender, etc. Different from most online shopping platforms, online education platforms have access to a large number of credible personal attributes since accurate personal information is important in education service, and user behaviors could be predicted with just user attribute. Moreover, previous works on learning individual attributes are based primarily on panel survey data, which ensures its credibility but lacks efficiency. Therefore, most works simply include hundreds or thousands of users in the study. With more than 200,000 anonymous K-12 students' 3-year learning data from one of the world's largest online extra-curricular education platforms, we uncover students' online learning behaviors and infer the impact of students' home location, family socioeconomic situation and attended school's reputation/rank on the students' private tutoring course participation and learning outcomes. Further analysis suggests that such impact may be largely attributed to the inequality of access to educational resources in different cities and the inequality in family socioeconomic status. Finally, we study the predictability of students' performance and behaviors using machine learning algorithms with different groups of features, showing students' online learning performance can be predicted based on personal attributes and user-generated data with MAE$<10\%$. As mentioned above, user attributes are usually fake information in most online platforms, and online platforms are usually vulnerable of malicious users. It is very important to identify the users or verify their attributes. Many researches have used user-generated mobile phone data (which includes sensitive information) to identify diverse user attributes, such as social economic status, ages, education level, professions, etc. Most of these approaches leverage original sensitive user data to build feature-rich models that take private information as input, such as exact locations, App usages and call detailed records. However, accessing users' mobile phone raw data may violate the more and more strict private data protection policies and regulations (e.g. GDPR). We observe that appropriate statistical methods can offer an effective means to eliminate private information and preserve personal characteristics, thus enabling the identification of the user attributes without privacy concern. Typically, identifying an unfamiliar caller's profession is important to protect citizens' personal safety and property. Due to limited data protection of various popular online services in some countries such as taxi hailing or takeouts ordering, many users nowadays encounter an increasing number of phone calls from strangers. The situation may be aggravated when criminals pretend to be such service delivery staff, bringing threats to the user individuals as well as the society. Additionally, more and more people suffer from excessive digital marketing and fraud phone calls because of personal information leakage. Therefore, a real time identification of unfamiliar caller is urgently needed. We explore the feasibility of user identification with privacy-preserved user-generated mobile, and we develop CPFinder, a system which implements automatic user identification callers on end devices. The system could mainly identify four categories of users: taxi drivers, delivery and takeouts staffs, telemarketers and fraudsters, and normal users (other professions). Our evaluation over an anonymized dataset of 1,282 users with a period of 3 months in Shanghai City shows that the CPFinder can achieve an accuracy of 75+\% for multi-class classification and 92.35+\% for binary classification. In addition to the mining of personal attributes and behaviors, the community mining of a large group of people based on online big data also attracts lots of attention due to the accessibility of large scale social network in online platforms. As one of the very important branch of social network, scientific collaboration network has been studied for decades as online big publication databases are easy to access and many user attribute are available. Academic collaborations become regular and the connections among researchers become closer due to the prosperity of globalized academic communications. It has been found that many computer science conferences are closed communities in terms of the acceptance of newcomers' papers, especially are the well-regarded conferences~\cite{cabot2018cs}. However, an in-depth study on the difference in the closeness and structural features of different conferences and what caused these differences is still missing. %Also, reviewing the strong and weak tie theories, there are multifaceted influences exerted by the combination of this two types of ties in different context. More analysis is needed to determine whether the network is closed or has other properties. We envision that social connections play an increasing role in the academic society and influence the paper selection process. The influences are not only restricted within visible links, but also extended to weak ties that connect two distanced node. Previous studies of coauthor networks did not adequately consider the central role of some authors in the publication venues, such as \ac{PC} chairs of the conferences. Such people could influence the evolutionary patterns of coauthor networks due to their authorities and trust for members to select accepted papers and their core positions in the community. Thus, in addition to the ratio of newcomers' papers it would be interesting if the PC chairs' relevant metrics could be quantified to measure the closure of a conference from the perspective of old authors' papers. Additionally, the analysis of the differences among different conferences in terms of the evolution of coauthor networks and degree of closeness may disclose the formation of closed communities. Therefore, we will introduce several different outcomes due to the various structural characteristics of several typical conferences. In this paper, using the DBLP dataset of computer science publications and a PC chair dataset, we show the evidence of the existence of strong and weak ties in coauthor networks and the PC chairs' influences are also confirmed to be related with the tie strength and network structural properties. Several PC chair relevant metrics based on coauthor networks are introduced to measure the closure and efficiency of a conference.2021-10-2

Urban Mobility Analytics: Understanding, Inference and Forecasting

Transport systems are the backbones of social and economic activities, which promote industry development and accelerate the process of urbanization. However, the contradiction between the pursuit of travel quality and unbalanced/inadequate development needs the rational construction and operation of transport systems. Owing to the evolution of a massive amount of multi-source data from transport systems, urban mobility analytics, including understanding, inference, and forecasting, support the management and control of transport, which attracts great attention in the long term and becomes more essential in smart transport research. In this thesis, we focus on inferring passenger demographics and predicting passenger demand by understanding travel patterns based on deep spatial-temporal learning algorithms. We first review the latest state-of-the-art deep learning methods for traffic understanding and attributes inference, traffic forecasting, and demand forecasting to form an overview of the current research progress. Second, we introduce the study public transport dataset collected from the Greater Sydney area and analyze the distributions and similarities of multiple transport modes. Third, we study the investigation of spatial and temporal features in order to infer traveler attributes by proposing a deep-based network with two modules (i.e., a Product-based Spatial-Temporal Module and an Auto-Encoder-based Compression Module). In addition, we study providing confidence interval-based passenger demand forecasting by proposing Probabilistic Graph Convolution Model to help relevant authorities and institutions to better accommodate demand uncertainty/variability. Then, to explore the relations in multimodal transport to boost the demand prediction performance, we propose two deep-based networks for knowledge adaptation between different transport modes by data sharing and model sharing, respectively. Finally, we provide promising directions for future works and conclude the thesis

Efficient kNN Search with occupation in large-scale on-demand ride-hailing

The intelligent ride-hailing systems, e.g., DiDi, Uber, have served as essential travel tools for customers, which foster plenty of studies for the location-based queries on road networks. Under the large demand of ride-hailing, the non-occupied vehicles might be insufficient for new-coming user requests. However, the occupied vehicles which are about to arrive their destinations could be the candidates to serve the requests close to their destinations. Consequently, in our work, we study the k Nearest Neighbor search for moving objects with occupation, notated as Approachable kNN (AkNN) Query, which to the best of our knowledge is the first study to consider the occupation of moving objects in relevant fields. In particular, we first propose a simple Dijkstra-based algorithm for the AkNN query. Then we improve the solution by developing a grid-based Destination-Oriented index, derived from GLAD [9], for the occupied and non-occupied moving objects. Accordingly, we propose an efficient grid-based expand-and-bound algorithm for the approachable kNN search and conduct extensive experiments on real-world data. The results demonstrate the effectiveness and efficiency of our proposed solutions

Long-Distance Recreational Travel Behavior and Implications of Autonomous Vehicles

Have you ever wondered how people travel long distances and how it could be affected by the emergence of autonomous vehicles (AVs)? This dissertation aims to answer those questions by studying the current behavior of long-distance recreational travelers and their preference in the age of AVs. This dissertation has four main goals. First, it seeks to develop a reliable way to measure people’s satisfaction with long-distance recreational trips and understand the similarities and differences between long- and short-distance travel satisfaction. Second, it looks at the connection between how people travel, how satisfied they are with their travel experiences, and how this relates to their overall satisfaction with their destination. Third, it explores how people feel about using AVs for long-distance travel and tries to understand what influences their decisions. Lastly, it looks at the impact of vehicle automation, the interior of AVs, and how people use their time during travel on their choices and preferences. The necessary data is gathered through a survey of 696 people who visited national parks in the US. The survey responses are analyzed to understand the research objectives, and some interesting insights are obtained. First, a survey instrument (i.e., a list of questions) is developed to accurately measure long-distance travelers’ satisfaction. The analysis discovers that the factors that affect satisfaction with long-distance travel differ from those that affect short-distance travel. Second, a strong link is established between people’s satisfaction with their travel experiences (on the way) and their overall tourism experience (at destination). Third, the study suggests people might travel more frequently and for longer distances with the introduction of AVs. This result means that we should not only focus on managing tourism destinations but also consider the impact on traffic and infrastructure leading to these destinations. Finally, the study finds that people are interested in using their travel time more productively in AVs, but we should be mindful of the negative consequences, such as increased energy consumption and space requirements. In conclusion, this dissertation sheds light on long-distance travel behavior and the potential changes that could come with using AVs. It emphasizes the importance of enjoying the journey, the impact on tourism, and the need for sustainable transportation. So, next time you plan a road trip, remember there’s more to consider than just getting to your destination

Albuquerque Morning Journal, 01-28-1911

https://digitalrepository.unm.edu/abq_mj_news/2919/thumbnail.jp

Situation Awareness for Smart Distribution Systems

In recent years, the global climate has become variable due to intensification of the greenhouse effect, and natural disasters are frequently occurring, which poses challenges to the situation awareness of intelligent distribution networks. Aside from the continuous grid connection of distributed generation, energy storage and new energy generation not only reduces the power supply pressure of distribution network to a certain extent but also brings new consumption pressure and load impact. Situation awareness is a technology based on the overall dynamic insight of environment and covering perception, understanding, and prediction. Such means have been widely used in security, intelligence, justice, intelligent transportation, and other fields and gradually become the research direction of digitization and informatization in the future. We hope this Special Issue represents a useful contribution. We present 10 interesting papers that cover a wide range of topics all focused on problems and solutions related to situation awareness for smart distribution systems. We sincerely hope the papers included in this Special Issue will inspire more researchers to further develop situation awareness for smart distribution systems. We strongly believe that there is a need for more work to be carried out, and we hope this issue provides a useful open-access platform for the dissemination of new ideas