
Wei MaThe Hong Kong Polytechnic University | PolyU · Department of Civil and Environmental Engineering
Wei Ma
Doctor of Philosophy
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102
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1,102
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Introduction
Publications
Publications (102)
Current methods for detecting outliers in traffic streaming data often struggle to capture real-time dynamic changes in traffic conditions and differentiate between genuine changes and anomalies. This study proposes a novel approach to outlier detection in traffic streaming data that effectively addresses stochasticity and uncertainty in observatio...
Spatiotemporal traffic data (STTD) displays complex correlational structures. Extensive advanced techniques have been designed to capture these structures for effective forecasting. However, because STTD is often massive in scale, practitioners need to strike a balance between effectiveness and efficiency using computationally efficient models. An...
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel parad...
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment an...
Vision language models (VLMs) have demonstrated impressive performance across a wide range of downstream tasks. However, their proficiency in spatial reasoning remains limited, despite its crucial role in tasks involving navigation and interaction with physical environments. Specifically, much of the spatial reasoning in these tasks occurs in two-d...
This paper presents a Bayesian data fusion framework for estimating journey time variability that uses a mixture distribution model to classify feeding data into different traffic states. Different from most studies, the proposed framework offers a generalized statistical foundation for making full use of multiple traffic data sources to estimate t...
Accurate monitoring and sensing network-wide traffic conditions under uncertainty is vital for addressing urban transportation obstacles and promoting the evolution of intelligent transportation systems (ITS). Owing to fluctuations in traffic demand, traffic conditions exhibit stochastic variations by the time of day and day of the year. The joint...
The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these complexities in urban delivery demand management problems. One...
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal data. Existing imputation solutions mainly include low-rank models and deep learning models. The former assumes general structural priors but has limited model capacity. The latter possesses salient expressivity, but lacks prior...
In the geospatial domain, universal representation models are significantly less prevalent than their extensive use in natural language processing and computer vision. This discrepancy arises primarily from the high costs associated with the input of existing representation models, which often require street views and mobility data. To address this...
With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highlighting the importance of charging coordination. Existing literature largely overlooks the interacti...
The balance between model capacity and generalization has been a key focus of recent discussions in long-term time series forecasting. Two representative channel strategies are closely associated with model expressivity and robustness, including channel independence (CI) and channel dependence (CD). The former adopts individual channel treatment an...
Estimating traffic flow models, such as speed-density relationships, using data from multiple sources with different temporal resolutions is a prevalent challenge encountered in real-world scenarios. The resolution incompatibility is often intuitively addressed by averaging the high-resolution (HR) data to synchronize with the low-resolution (LR) d...
This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner . Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to da...
This is the conference version of our paper: Spatiotemporal Implicit Neural Representation as a Generalized Traffic Data Learner. Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to dat...
Spatiotemporal Traffic Data (STTD) measures the complex dynamical behaviors of the multiscale transportation system. Existing methods aim to reconstruct STTD using low-dimensional models. However, they are limited to data-specific dimensions or source-dependent patterns, restricting them from unifying representations. Here, we present a novel parad...
City-scale traffic data, such as traffic flow, speed, and density on every road segment, are the foundation of modern urban research. However, accessing such data on a city scale is challenging due to the limited number of sensors and privacy concerns. Consequently, most of the existing traffic datasets are typically limited to small, specific urba...
This paper presents a distribution-free reliability-based prediction approach for estimating journey time intervals with multi-source data using a two-stream deep learning framework. The prediction framework consists of a long short-term memory (LSTM) module for extracting temporal features and a convolutional neural network (CNN) module for extrac...
Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curbside utilization and disturb the mainline...
Missing data is a pervasive issue in both scientific and engineering tasks, especially for the modeling of spatiotemporal
data. This problem attracts many studies to contribute to machine learning solutions. Existing imputation solutions mainly include
low-rank models and deep learning models. On the one hand, low-rank models assume general structu...
The rapid advancements of Internet of Things (IoT) and Artificial Intelligence (AI) have catalyzed the development of adaptive traffic control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) models produce state-of-the-art performance and have great potential for practical applications. In the existing DRL-based AT...
Accurate traffic density plays a pivotal role in the Intelligent Transportation Systems (ITS). The current practice to obtain the traffic density is through specialized sensors. However, those sensors are placed in limited locations due to the cost of installation and maintenance. In most metropolitan areas, traffic surveillance cameras are widespr...
To provide real-time parking information, existing studies focus on predicting parking availability, which seems an indirect approach to saving drivers' cruising time. In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend a parking space for a driver. To this end, a learn-to-rank (LTR) based OPR m...
Effective traffic control methods have great potential in alleviating network congestion. Existing literature generally focuses on a single control approach, while few studies have explored the effectiveness of integrated and coordinated control approaches. This study considers two representative control approaches: ramp metering for freeways and p...
Recent years have witnessed the rise of ride-hailing mobility services thanks to ubiquitous emerging technologies. Curbside spaces, as a category of public infrastructure, are being used by private ride-hailing services to pick up and drop off passengers, in addition to deliveries and parking access. This becomes quite common in urban areas and has...
Real-time traffic prediction models play a pivotal role in smart mobility systems and have been widely used in route guidance, emerging mobility services, and advanced traffic management systems. With the availability of massive traffic data, neural network-based deep learning methods, especially graph convolutional networks (GCN) have demonstrated...
Freeway congestion usually occurs in on-ramp areas; however, it also occurs around off-ramp areas and can even spill back onto the main line, which is common on many congested urban freeways during peak hours. Nevertheless, traditional ramp metering strategies, one of the most successful approaches to reducing freeway congestion, have not considere...
Automatic Vehicle Identification (AVI) technology has been widely used for real-time path travel time estimation. For a study path equipped with AVI sensors at both ends, the difference between the timestamps of vehicles entering and leaving the path is AVI data. In urban areas, there can be several alternative routes and vehicle entry/exit points...
To provide real-time parking information, existing studies focus on predicting parking availability, which seems an indirect approach to saving drivers’ cruising time. In this paper, we first time propose an on-street parking recommendation (OPR) task to directly recommend parking spaces for a driver. To this end, a learn-to-rank (LTR) based OPR mo...
This paper proposes a model-based data-driven approach for simultaneously estimating network-wide traffic speeds and flows. In the proposed model, heterogeneous traffic data from multiple types of traffic detectors (point detectors and automatic vehicle identification detectors) on observed links are used. Fundamental diagrams of link speeds and fl...
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time...
Day-to-day traffic data has been widely used in transportation planning and management. However, with the emerging of new technologies, one conventional assumption, on which many models rely, that all the day-to-day observations on the network follow a single pattern appears to be questionable. To better understand network flow patterns and their r...
Due to the propagation of traffic congestion from upstream to downstream links and the uncertainty of path choice behaviours, travel times between different links, particularly adjacent links, are highly correlated in a typical period from day to day. To improve the estimation accuracy of both the mean and covariance of link travel times, a novel m...
The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method fo...
This paper investigates the multi-type traffic sensor location problem for simultaneous
estimation of origin-destination (OD) demands and link travel times while also
considering the two sources of spatial covariance effects on a road network with
uncertainties. The first source is the statistical correlation of the vehicular traffic
demands for di...
Parking plays an essential role in urban mobility systems across the globe, especially in metropolises. Hong Kong is a global financial center, international shipping hub, fast-growing tourism city, and major aviation hub, and it thus has a high demand for parking. As one of the initiatives for smart city development, the Hong Kong government has a...
This paper proposes a short-term rolling horizon framework for the within-day prediction of travel times on links with and without point detectors (referred to as observed and unobserved links respectively) along a selected path covered in the Hong Kong journey time indication system (JTIS). In Hong Kong JTIS, the number of point detectors on major...
Curb space is one of the busiest areas in urban road networks. Especially in recent years, the rapid increase of ride-hailing trips and commercial deliveries has induced massive pick-ups/drop-offs (PUDOs), which occupy the limited curb space that was designed and built decades ago. These PUDOs could jam curb utilization and disturb the mainline tra...
System-level decision making in transportation needs to understand day-to-day variation of network flows, which calls for accurate modeling and estimation of probabilistic dynamic travel demand on networks. Most existing studies estimate deterministic dynamic origin-destination (OD) demand, while the day-to-day variation of demand and flow is overl...
Accurate short-term traffic prediction plays a pivotal role in various smart mobility operation and management systems. Currently, most of the state-of-the-art prediction models are based on graph neural networks (GNNs), and the required training samples are proportional to the size of the traffic network. In many cities, the available amount of tr...
Accurate real-time traffic forecast is critical for intelligent transportation systems (ITS) and it serves as the cornerstone of various smart mobility applications. Though this research area is dominated by deep learning, recent studies indicate that the accuracy improvement by developing new model structures is becoming marginal. Instead, we envi...
Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This...
Advanced autonomous vehicles (AVs)-related technologies stimulate a significant increase in AVs’ sensing capabilities, which accelerates the dramatic development of the smart city, especially in intelligent infrastructure management. Conventional sensing mainly depends on fix-point devices with laborers, which is time consuming, inefficient, and la...
Accurate short-term traffic prediction plays a pivotal role in various smart mobility operation and management systems. Currently, most of the state-of-the-art prediction models are based on graph neural networks (Gnns), and the required training samples are proportional to the size of the traffic network. In many cities, the available amount of tr...
The rapid advancements of Internet of Things (IoT) and artificial intelligence (AI) have catalyzed the development of adaptive traffic signal control systems (ATCS) for smart cities. In particular, deep reinforcement learning (DRL) methods produce the state-of-the-art performance and have great potentials for practical applications. In the existing...
Accurate traffic state information plays a pivotal role in the Intelligent Transportation Systems (ITS), and it is an essential input to various smart mobility applications such as signal coordination and traffic flow prediction. The current practice to obtain the traffic state information is through specialized sensors such as loop detectors and s...
Urban rail transit (URT) system plays a dominating role in many megacities like Beijing and Hong Kong. Due to its important role and complex nature, it is always in great need for public agencies to better understand the performance of the URT system. This paper focuses on an essential and hard problem to estimate the network-wide link travel time...
Smart traffic control and management become an emerging application for Deep Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks. Different traffic control and management policies can be tested on the traffic simulation. Current DRL-based studies are mainly supported by the microscopic simulation software (e.g., SUMO...
This paper proposes a bi-objective reliable path-finding algorithm for routing battery electric vehicles on a road network, with vehicles’ energy consumption uncertainty and travel time uncertainty. A bi-objective stochastic optimization problem is proposed and formulated to simultaneously maximize energy consumption reliability (ECR) and travel ti...
Real-time traffic prediction models play a pivotal role in smart mobility systems and have been widely used in route guidance, emerging mobility services, and advanced traffic management systems. With the availability of massive traffic data, neural network-based deep learning methods, especially the graph convolutional networks (GCN) have demonstr...
The COVID-19 outbreak has necessitated a critical review of urban transportation and its role in society against the backdrop of an exogenous shock. This article extends the transportation literature regarding community responses to the COVID-19 pandemic and what lessons can be obtained from the case of Hong Kong in 2020. Individual behavior and co...
Short-term origin-destination (OD) flow prediction in urban rail transit (URT) plays a crucial role in smart and real-time URT operation and management. Different from other short-term traffic forecasting methods, the short-term OD flow prediction possesses three unique characteristics: 1) data availability: real-time OD flow is not available durin...
Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilize...