Feilong Wang

• Doctor of Philosophy
• PostDoc Position at The Hong Kong Polytechnic University

Big mobility data (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study 10 evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmar...

Big mobility datasets (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchma...

Emerging technologies drive the ongoing transformation of Intelligent Transportation Systems (ITS). This transformation has given rise to cybersecurity concerns, among which data poisoning attack emerges as a new threat as ITS increasingly relies on data. In data poisoning attacks, attackers inject malicious perturbations into datasets, potentially...

Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. With transportation infrast...

Traffic safety, reliability, and resilience are significantly influenced by environmental factors such as visibility, road surface, and weather conditions. Yet, current monitoring methods, including weather stations and onboard environmental sensors, often fall short due to their high costs, significant latency, and limited dissemination. This pape...

Existing tourism demand forecasting models mainly focus on forecasting demands of relatively long time spans at a single destination. These studies lack considering the evolution of demand patterns or presume fixed interaction structures among multiple destinations, limiting their applications during uncertain times when demands and their interacti...

Accurate and robust localization is crucial for supporting high-level driving automation and safety. Modern localization solutions rely on various sensors, among which GPS has been and will continue to be essential. However, GPS can be vulnerable to malicious attacks and GPS spoofing has been identified as a high threat. GPS spoofing injects false...

Understanding user behavior is crucial for the success of many emerging applications that aim to provide personalized services for target users, such as many patient-centered health apps and transportation apps. Models based on the random utility maximization (RUM) theory are widely used in learning and understanding behavioral preferences on the p...

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, the passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are g...

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are gener...

We develop a personalized system to modify individual travel behaviors by offering personalized incentives. Individual preferences are learned to provide personalized incentives so that the promoted alternative is likely accepted. Using knowledge from control theories and state estimation, we model travelers’ choice-making behaviors with the random...

People’s daily travels are structured and can be expressed as networks. Few studies explore how people organize their daily travels and which behavioral principles result in the choices of specific network types. In this study, we first reconstruct location networks and activity networks for numerous individuals from high-resolution mobile phone po...

Passively-generated data (e.g. mobile phone data) need to be processed to extract trips. Most existing trip extraction methods rely on data that are generated via a single positioning technology such as GPS or triangulation through cellular towers (or, single-sourced data), and methods to extract trips from data generated via multiple positioning t...

We develop a personalized control system to modify individual travel behaviors by offering personalized incentives. Individual preferences are learned to provide personalized incentives so that the promoted alternative is more likely to be accepted. The work described is based on the integration of two fields (controls and human behavior) that are...

With the explosion of the number of studies using big, passively-generated data for transportation analysis, this study focuses on understanding the properties of such data and how these properties affect our ability in deriving trip-related characteristics. Two big, passively solicited datasets were analyzed: a mobile phone data generated primaril...

The next-generation household travel surveys, the core data generation mechanism for supporting both short- and long-term transportation planning applications, are poised to be transformed. It is now increasingly recognized that passively-solicited big data, or large amount of data generated through various types of subscription services, will play...

Passively-generated mobile phone data is emerging as a potential data source for transportation research and applications. Despite the large amount of studies based on the mobile phone data, only a few have reported the properties of such data, and documented how they have processed the data. In this paper, we describe two types of common mobile ph...

We develop a personalized control system to modify individual travel behaviours by offering personalized incentives. Individual preferences are learned to maximize the probability of accepting the promoted alternative. The work described is based on the integration of two fields (controls and human behaviour) that are typically separate from each o...

Emerging datasets such as mobile phone and GPS data have now become a promising data source for many transportation planning applications, including origin-destination (OD) analyses, which serve as the basis for transportation investment and policy decisions. Generated from an entirely different process from the traditional household travel surveys...

As a dynamical complex system, traffic is characterized by a transition from free flow to congestions, which is mostly studied in highways. However, despite its importance in developing congestion mitigation strategies, the understanding of this common traffic phenomenon in a city-scale is still missing. An open question is how the traffic in the n...

Networks can be used to describe the interconnections among individuals, which play an important role in the spread of disease. Although the small-world effect has been found to have a significant impact on epidemics in single networks, the small-world effect on epidemics in interconnected networks has rarely been considered. Here, we study the sus...