Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

A Simple Baseline for Travel Time Estimation using Large-Scale Trip Data

The increased availability of large-scale trajectory data around the world provides rich information for the study of urban dynamics. For example, New York City Taxi Limousine Commission regularly releases source-destination information about trips in the taxis they regulate. Taxi data provide information about traffic patterns, and thus enable the study of urban flow -- what will traffic between two locations look like at a certain date and time in the future? Existing big data methods try to outdo each other in terms of complexity and algorithmic sophistication. In the spirit of "big data beats algorithms", we present a very simple baseline which outperforms state-of-the-art approaches, including Bing Maps and Baidu Maps (whose APIs permit large scale experimentation). Such a travel time estimation baseline has several important uses, such as navigation (fast travel time estimates can serve as approximate heuristics for A search variants for path finding) and trip planning (which uses operating hours for popular destinations along with travel time estimates to create an itinerary).Comment: 12 page

Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics

The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns. The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%

Offline reconstruction of missing vehicle trajectory data from 3D LIDAR

LIDAR has become an important part of many autonomous vehicles with its advantages on distance measurement and obstacle detection. LIDAR produces point clouds which have important information about surrounding environment. In this paper, we collected trajectory data on a two lane urban road using a Velodyne VLP-16 Lidar. Due to dynamic nature of data collection and limited range of the sensor, some of these trajectories have missing points or gaps. In this paper, we propose a novel method for recovery of missing vehicle trajectory data points using microscopic traffic flow models. While short gaps (less than 5 seconds) can be recovered with simple linear regression, and longer gaps are recovered with the proposed method that makes use of car following models calibrated by assigning weights to known points based on proximity to the gaps. Newell's, Pipes, IDM and Gipps' car following models are calibrated and tested with the ground truth trajectory data from LIDAR and NGSIM I-80 dataset. Gipps' calibrated model yielded the best result

프로브 차량 자료를 이용한 도시교통 네트워크의 속도 추정 순환형 신경망 모형

학위논문(박사)--서울대학교 대학원 :공과대학 건설환경공학부,2020. 2. 고승영.Urban traffic flows are characterized by complexity. Due to this complexity, limitations arise when using models that have commonly been using to estimate the speed of arterial road networks. This study analyzes the characteristics of the speed data collected by the probe vehicle method in links on the urban traffic flow, presents the limitations of existing models, and develops a modified recurrent neural network model as a solution to these limitations. In order to complement the limitations of existing models, this study focused on the interrupted flow characteristics of urban traffic. Through data analysis, we verified the separation of platoons and high-frequency transitions as phenomena in interrupted flow. Using these phenomena, this study presents a two-step model using the characteristics of each platoon and the selected dropout method that applies traffic conditions separately. In addition, we have developed an active imputation method to deal with frequent missing data in data collection effectively. The developed model not only showed high accuracy on average, but it also improved the accuracy of certain states, which is the limitation of the existing models, increased the correlation between the estimated value and the estimated target value, and properly learned the periodicity of the data.도시교통류는 복잡성을 내재하고 있다. 이 복잡성으로 인해, 일반적으로 지역간 간선 도로 네트워크의 속도를 추정하던 모형들을 사용할 경우 여러가지 한계점이 발생하게 된다. 본 연구는 도시교통류 상의 링크에서 프로브 차량 방식으로 수집된 속도자료의 특성을 분석하고, 기존 모형의 한계점을 제시하고, 이러한 한계점에 대한 해법으로서 변형된 순환형 신경망 모형을 개발하였다. 모형 개발에 있어, 기존 모형의 한계점을 보완하기 위해, 본 연구에서는 도시교통류의 단속류적 특징에 주목하였다. 자료 분석을 통해, 본 연구에서는 단속류에서 나타나는 현상으로서 차량군의 분리와 높은 빈도의 전이상태 발생을 확인하였다. 해당 현상들을 이용하여, 본 연구에서는 각 차량군의 특징을 이용한 이용한 2단계 모형과, 교통 상태를 분리하여 적용하는 선택적 드롭아웃 방식을 제시하였다. 추가적으로, 자료의 수집에 있어 빈발하는 결측 데이터를 효과적으로 다루기 위한 능동적 대체 방식을 개발하였다. 개발 모형은 평균적으로 높은 정확도를 보일 뿐 아니라, 기존 모형들의 한계점인 특정 상황에 대한 정확도를 제고하고 추정값과 추정 대상값의 상관관계를 높이며, 자료의 주기성을 적절하게 학습할 수 있었다.Chapter 1. Introduction 1 1.1. Study Background and Purpose 1 1.2. Research Scope and Procedure 8 Chapter 2. Literature Review 11 2.1. Data Estimation 11 2.2. Traffic State Handling 17 2.3. Originality of This Study 20 Chapter 3. Data Collection and Analysis 22 3.1. Terminology 22 3.2. Data Collection 23 3.3. Data Analysis 26 Chapter 4. Model Development 54 4.1. Basic Concept of the Model 54 4.2. Model Development 58 Chapter 5. Result and Findings 72 5.1. Estimation Accuracy of Developed Models 72 5.2. Correlation Analysis of Developed Model 77 5.3. Periodicity Analysis for Developed Models 81 5.4. Accuracy Analysis by Traffic State 86 5.5. Summary of the Result 92 Chapter 6. Conclusion 94 6.1. Summary 94 6.2. Limitation of the Study 95 6.3. Applications and Future Research 96 Appendix 98 Bibliography 119Docto

Multi-Sensor Data Fusion for Travel Time Estimation

The importance of travel time estimation has increased due to the central role it plays in a number of emerging intelligent transport systems and services including Advanced Traveller Information Systems (ATIS), Urban Traffic Control (UTC), Dynamic Route Guidance (DRG), Active Traffic Management (ATM), and network performance monitoring. Along with the emerging of new sensor technologies, the much greater volumes of near real time data provided by these new sensor systems create opportunities for significant improvement in travel time estimation. Data fusion as a recent technique leads to a promising solution to this problem. This thesis presents the development and testing of new methods of multi-sensor data fusion for the accurate, reliable and robust estimation of travel time. This thesis reviews the state-of-art data fusion approaches and its application in transport domain, and discusses both of opportunities and challenging of applying data fusion into travel time estimation in a heterogeneous real time data environment. For a particular England highway scenario where ILDs and ANPR data are largely available, a simple but practical fusion method is proposed to estimate the travel time based on a novel relationship between space-mean-speed and time-mean-speed. In developing a general fusion framework which is able to fuse ILDs, GPS and ANPR data, the Kalman filter is identified as the most appropriate fundamental fusion technique upon which to construct the required framework. This is based both on the ability of the Kalman filter to flexibly accommodate well-established traffic flow models which describe the internal physical relation between the observed variables and objective estimates and on its ability to integrate and propagate in a consistent fashion the uncertainty associated with different data sources. Although the standard linear Kalman filter has been used for multi-sensor travel time estimation in the previous research, the novelty of this research is to develop a nonlinear Kalman filter (EKF and UKF) fusion framework which improves the estimation performance over those methods based on the linear Kalman filter. This proposed framework is validated by both of simulation and real-world scenarios, and is demonstrated the effectiveness of estimating travel time by fusing multi-sensor sources

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance