Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities

Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for improving traffic prediction significantly and brings about new research problems. In order to lay the foundation for understanding the open research challenges in traffic prediction, this survey aims to provide a comprehensive overview of traffic prediction methodologies. Specifically, we focus on the recent advances and emerging research opportunities in Artificial Intelligence (AI)-based traffic prediction methods, due to their recent success and potential in traffic prediction, with an emphasis on multivariate traffic time series modeling. We first provide a list and explanation of the various data types and resources used in the literature. Next, the essential data preprocessing methods within the traffic prediction context are categorized, and the prediction methods and applications are subsequently summarized. Lastly, we present primary research challenges in traffic prediction and discuss some directions for future research.Comment: Published in Transportation Research Part C: Emerging Technologies (TR_C), Volume 145, 202

Explainable and Advisable Learning for Self-driving Vehicles

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-to-interpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers, etc., can understand what triggered a particular behavior. Explanations may be triggered by the neural controller, namely introspective explanations, or informed by the neural controller's output, namely rationalizations. Our work has focused on the challenge of generating introspective explanations of deep models for self-driving vehicles. In Chapter 3, we begin by exploring the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network's output (steering control). In the first stage, we use a visual attention model to train a convolution network end-to-end from images to steering angle. The attention model highlights image regions that potentially influence the network's output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network's behavior. In Chapter 4, we add an attention-based video-to-text model to produce textual explanations of model actions, e.g. "the car slows down because the road is wet". The attention maps of controller and explanation model are aligned so that explanations are grounded in the parts of the scene that mattered to the controller. We explore two approaches to attention alignment, strong- and weak-alignment. These explainable systems represent an externalization of tacit knowledge. The network's opaque reasoning is simplified to a situation-specific dependence on a visible object in the image. This makes them brittle and potentially unsafe in situations that do not match training data. In Chapter 5, we propose to address this issue by augmenting training data with natural language advice from a human. Advice includes guidance about what to do and where to attend. We present the first step toward advice-giving, where we train an end-to-end vehicle controller that accepts advice. The controller adapts the way it attends to the scene (visual attention) and the control (steering and speed). Further, in Chapter 6, we propose a new approach that learns vehicle control with the help of long-term (global) human advice. Specifically, our system learns to summarize its visual observations in natural language, predict an appropriate action response (e.g. "I see a pedestrian crossing, so I stop"), and predict the controls, accordingly

Air Quality Prediction in Smart Cities Using Machine Learning Technologies Based on Sensor Data: A Review

The influence of machine learning technologies is rapidly increasing and penetrating almost in every field, and air pollution prediction is not being excluded from those fields. This paper covers the revision of the studies related to air pollution prediction using machine learning algorithms based on sensor data in the context of smart cities. Using the most popular databases and executing the corresponding filtration, the most relevant papers were selected. After thorough reviewing those papers, the main features were extracted, which served as a base to link and compare them to each other. As a result, we can conclude that: (1) instead of using simple machine learning techniques, currently, the authors apply advanced and sophisticated techniques, (2) China was the leading country in terms of a case study, (3) Particulate matter with diameter equal to 2.5 micrometers was the main prediction target, (4) in 41% of the publications the authors carried out the prediction for the next day, (5) 66% of the studies used data had an hourly rate, (6) 49% of the papers used open data and since 2016 it had a tendency to increase, and (7) for efficient air quality prediction it is important to consider the external factors such as weather conditions, spatial characteristics, and temporal features

AQNet: 깊은 생성 모델을 이용한 대기 질의 시공간적 예측

학위논문(석사)--서울대학교 대학원 :공과대학 전기·정보공학부,2019. 8. Cha, Sang Kyun.With the increase of global economic activities and high energy demand, many countries have concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this thesis, we propose a deep generative model for spatio-temporal air quality prediction, entitled AQNet. Unlike previous work, our model transforms air quality index data into 2D frames (heat-map images) for effectively capturing spatial relations of air quality levels among different areas. It then combines the spatial representation with temporal features of critical factors such as meteorology and external air pollution sources. For prediction, the model first generates heat-map images of future air quality levels, then aggregates them into output values of corresponding areas. Based on the analyses of data, we also assessed the impacts of critical factors on air quality prediction. To evaluate the proposed method, we conducted experiments on two real-world air pollution datasets: Seoul dataset and China 1-year dataset. For Seoul dataset, our method showed a 15.2%, 8.2% improvement in mean absolute error score for long-term predictions of PM2.5 and PM10, respectively compared to baselines and state-of-the-art methods. Also, our method improved mean absolute error score of PM2.5 predictions by 20% compared to the previous state-of-the-art results on China dataset.세계 경제 활동과 에너지 수요가 증가함에 따라 많은 국가들이 대기 오염에 대한 우려를 제기하고 있다. 하지만 많은 요인들의 복잡한 상호 작용으로 인해 대기 질을 예측하는 것은 어려운 문제다. 본 논문에서는 AQNet이라는 이름의 시공간적 대기 질 예측을 위한 심층 생성 모델을 제안한다. 이전 연구와 달리 이 모델은 대기 질 지수 데이터를 2D 프레임(히트 맵 이미지)으로 변환하여 대기 품질 수준의 영역간 공간적 관계를 효과적으로 포착한다. 그런 다음 기상과 외부 대기 오염원과 같은 중요한 요소의 시간적 특징과 공간 표현을 결합한다. 예측 모델은 먼저 미래의 대기 품질 수준의 히트 맵 이미지를 생성한 다음 해당 영역의 출력 값으로 집계한다. 데이터 분석을 토대로 대기 오염 예측에 각 주요 요소들이 미치는 영향을 평가하였다. 제안된 방법을 평가하기 위해 실제 대기 오염 데이터 세트인 서울의 데이터 세트와 중국의 1년 데이터 세트를 실험했다. 본 논문에서 제안한 방법은 서울 데이터세트에서 수행된 PM2.5와 PM10의 장기 예측에 대해 이전의 SOTA 방법과 비교하여 MAE 점수가 각각 15.2%, 8.2% 향상되었다. 또한 중국 데이터 세트에 대한 이전 연구와 비교하여 PM2.5 예측의 MAE 점수를 20% 향상시켰다.Abstract i Contents ii List of Tables iv List of Figures v 1 INTRODUCTION 1 1.1 Air Pollution Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Overview of the Proposed Method . . . . . . . . . . . . . . . . . . . 2 1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 RELATED WORK 5 2.1 Spatio-Temporal Prediction . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Air Pollution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 OVERVIEW 8 3.1 System Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 3.2 User Interface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4 DATA MANAGEMENT 11 4.1 Real-time Data Collecting . . . . . . . . . . . . . . . . . . . . . . . 11 4.2 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 4.3 Spatial Transformation Function . . . . . . . . . . . . . . . . . . . . 13 4.3.1 District-based Interpolation . . . . . . . . . . . . . . . . . . 14 4.3.2 Geo-based Interpolation . . . . . . . . . . . . . . . . . . . . 15 5 Proposed Method 17 5.1 Data Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 5.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 5.3.1 Encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.3.2 Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.3.3 Training Algorithm . . . . . . . . . . . . . . . . . . . . . . . 26 6 EXPERIMENTS 28 6.1 Baselines and State-of-the-art methods . . . . . . . . . . . . . . . . . 28 6.2 Experimental Settings . . . . . . . . . . . . . . . . . . . . . . . . . . 29 6.2.1 Implementation details . . . . . . . . . . . . . . . . . . . . . 29 6.2.2 Evaluation Metric . . . . . . . . . . . . . . . . . . . . . . . . 30 6.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.3.1 Performance on Spatial Module Selection . . . . . . . . . . . 31 6.3.2 Comparison to Baselines and State-of-the-art Methods . . . . 33 6.3.3 Evaluation on China 1-year Dataset . . . . . . . . . . . . . . 36 6.3.4 Assessing the Impact of Critical Factors . . . . . . . . . . . . 37 7 CONCLUSION 41 Abstract (In Korean) 47 Acknowlegement 48Maste

Unleashing Realistic Air Quality Forecasting: Introducing the Ready-to-Use PurpleAirSF Dataset

Air quality forecasting has garnered significant attention recently, with data-driven models taking center stage due to advancements in machine learning and deep learning models. However, researchers face challenges with complex data acquisition and the lack of open-sourced datasets, hindering efficient model validation. This paper introduces PurpleAirSF, a comprehensive and easily accessible dataset collected from the PurpleAir network. With its high temporal resolution, various air quality measures, and diverse geographical coverage, this dataset serves as a useful tool for researchers aiming to develop novel forecasting models, study air pollution patterns, and investigate their impacts on health and the environment. We present a detailed account of the data collection and processing methods employed to build PurpleAirSF. Furthermore, we conduct preliminary experiments using both classic and modern spatio-temporal forecasting models, thereby establishing a benchmark for future air quality forecasting tasks.Comment: Accepted by ACM SIGSPATIAL 202