Spatio-Temporal Analysis of Highway Congestion and Accidents: A Case Study of Interstate 285 in Georgia

Road traffic crashes threaten thousands of drivers every day and significant efforts have been put forth to reduce the number of traffic crashes and their impact. Traffic congestion could be both a result of and a contributing factor to traffic crashes. The aim of this study is to investigate spatio-temporal traffic congestion and crash patterns to gain a better understanding of the causation of congestion and accidents, and their interaction. The Interstate 285 (I-285) in Georgia was used as a case study. With the aid of Geographic Information Systems (GIS), spatial clustering and densities of accidents were performed by following Anselin Local Moran’s I method of spatial auto correlation. The results indicated that the location of high-high accident clusters was in the northern half of the I-285 for all crash types. Additionally, geometric and traffic-related variables were correlated with accidents using logistic regression. The results showed that road segments involving merging, diverging, or weaving lanes had a positive correlation with the number of accidents. Specifically, the merging segments exhibited the highest crash frequency, followed by weaving and diverging segments. On the other hand, the road curvature did not play a significant role in crash occurrence, which is likely due to the gentleness of the road curvatures along the I-285 loop. However, the impact of acceleration on crash frequency remained inconclusive. It appeared that a lower average traffic speed correlated with a higher crash frequency, which may be due to a slow-down condition prior to crash occurrence

Traffic incident duration prediction via a deep learning framework for text description encoding

Predicting the traffic incident duration is a hard problem to solve due to the stochastic nature of incident occurrence in space and time, a lack of information at the beginning of a reported traffic disruption, and lack of advanced methods in transport engineering to derive insights from past accidents. This paper proposes a new fusion framework for predicting the incident duration from limited information by using an integration of machine learning with traffic flow/speed and incident description as features, encoded via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN sentiment classifier). The paper constructs a cross-disciplinary modelling approach in transport and data science. The approach improves the incident duration prediction accuracy over the top-performing ML models applied to baseline incident reports. Results show that our proposed method can improve the accuracy by $60\%$ when compared to standard linear or support vector regression models, and a further $7\%$ improvement with respect to the hybrid deep learning auto-encoded GBDT model which seems to outperform all other models. The application area is the city of San Francisco, rich in both traffic incident logs (Countrywide Traffic Accident Data set) and past historical traffic congestion information (5-minute precision measurements from Caltrans Performance Measurement System)

Towards Universality in Automatic Freeway Incident Detection: A Calibration-Free Algorithm

Freeway automatic incident detection (AID) algorithms have been extensively investigated over the last forty years. A myriad of algorithms, covering a broad range of types in terms of complexity, data requirements, and efficiency have been published in the literature. However, a 2007 nationwide survey concluded that the implementation of AID algorithms in traffic management centers is still very limited. There are a few reasons for this discrepancy between the state-of-the-art and the state-of the-practice. First, current AID algorithms yield unacceptably high rates of false alarm when implemented in real-world. Second, the complexities involved in algorithm calibration require levels of efforts and diligence that may overburden Traffic Management Center (TMC) personnel. The main objective of this research was to develop a self-learning, transferable algorithm that requires no calibration. The dynamic thresholds of the proposed algorithm are based on historical data of traffic, thus accounting for variations of traffic throughout the day. Therefore, the novel approach is able to recognize recurrent congestion, thus greatly reducing the incidence of false alarms. In addition, the proposed method requires no human-intervention, which certainly encourages its implementation. The presented model was evaluated in a newly developed incident database, which contained forty incidents. The model performed better than the California, Minnesota, and Standard Normal Deviation algorithms

TAP: A Comprehensive Data Repository for Traffic Accident Prediction in Road Networks

Road safety is a major global public health concern. Effective traffic crash prediction can play a critical role in reducing road traffic accidents. However, Existing machine learning approaches tend to focus on predicting traffic accidents in isolation, without considering the potential relationships between different accident locations within road networks. To incorporate graph structure information, graph-based approaches such as Graph Neural Networks (GNNs) can be naturally applied. However, applying GNNs to the accident prediction problem faces challenges due to the lack of suitable graph-structured traffic accident datasets. To bridge this gap, we have constructed a real-world graph-based Traffic Accident Prediction (TAP) data repository, along with two representative tasks: accident occurrence prediction and accident severity prediction. With nationwide coverage, real-world network topology, and rich geospatial features, this data repository can be used for a variety of traffic-related tasks. We further comprehensively evaluate eleven state-of-the-art GNN variants and two non-graph-based machine learning methods using the created datasets. Significantly facilitated by the proposed data, we develop a novel Traffic Accident Vulnerability Estimation via Linkage (TRAVEL) model, which is designed to capture angular and directional information from road networks. We demonstrate that the proposed model consistently outperforms the baselines. The data and code are available on GitHub (https://github.com/baixianghuang/travel).Comment: 10 pages, 5 figure

DESIGNING AN INDUCTIVE SENSOR FOR ROAD TRAFFIC MONITORING SYSTEMS AND CONTROL

The purpose of this study is to design an inductive sensor,which detect a vehicle on the road. The main objectives are to design an inductive sensor using an enameled copper wire and interface it to an electronics circuit. The analyses of experiments will mainly the important part of this project. Then, a demonstration will be held to demonstrate the sensing process using a working model. This sensor can change some work from manual to automatically. Examples of situation that can implement this sensor is to control the barrier automatically on the main gates on the roads, to monitor traffic on a narrow curved portion of the road and to count the number of vehicles from a particular point per unit time. At present, there are a lot of sensors available in the market that uses inductive sensor. Many methods can be used in detecting the presence of vehicle and a complete circuit of inductive sensor has also been developed. The result from these methods will assist in the future work of this project

Interpretable Machine Learning을 활용한 구간단속시스템 설치에 따른 인명피해사고 감소 효과 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 건설환경공학부, 2020. 8. 김동규.In this study, a prediction model for casualty crash occurrence was developed considering whether to install SSES and the effect of SSES installation was quantified by dividing it into direct and indirect effects through the analysis of mediation effect. Also, it was recommended what needs to be considered in selecting the candidate sites for SSES installation. For this, crash prediction model was developed by using the machine learning for binary classification based on whether or not casualty crash occurred and the effects of SSES installation were analyzed based on crashes and speed-related variables. Especially, the IML methodology was applied that considered the predictive performance as well as the interpretability of the forecast results as important. When developing the IML which consisted of black-box and interpretable model, KNN, RF, and SVM were reviewed as black-box model, and DT and BLR were reviewed as interpretable model. In the model development, the hyper-parameters that could be set in each methodology were optimized through k-fold cross validation. The SVM with a polynomial kernel trick was selected as black-box model and the BLR was selected as interpretable model to predict the probability of casualty crash occurrence. For the developed IML model, the evaluation was conducted through comparison with the typical BLR from the perspective of the PDR framework. The evaluation confirmed that the results of the IML were more excellent than the typical BLR in terms of predictive accuracy, descriptive accuracy, and relevancy from a human in the loop. Using the result of IML's model development, the effect on SSES installation were quantified based on the probability equation of casualty crash occurrence. The equation is the logistic function that consists of SSES, SOR, SV, TVL, HVR, and CR. The result of analysis confirmed that the SSES installation reduced the probability of casualty crash occurrence by about 28%. In addition, the analysis of mediation effects on the variables affected by installing SSES was conducted to quantify the direct and indirect effects on the probability of reducing the casualty crashes caused by the SSES installation. The proportion of indirect effects through reducing the ratio of exceeding the speed limit (SOR) was about 30% and the proportion of indirect effects through reduction of speed variance (SV) was not statistically significant at the 95% confidence level. Finally, the probability equation of casualty crash occurrence developed in this study was applied to the sections of Yeongdong Expressway to compare the crash risk section with the actual crash data to examine the applicability of the development model. The analysis result verified that the equation was reasonable. Therefore, it may be considered to select dangerous sites based on casualty crash and speeding firstly, and then to install SSES at the section where traffic volume (TVL), heavy vehicle ratio (HVR), and curve ratio (CR) are higher than the other sections.본 연구에서는 구간단속시스템(Section Speed Enforcement System, SSES) 설치 효과를 정량화하기 위해 인명피해사고 예측모형을 개발하고, 매개효과 분석을 통해 SSES 설치에 대한 직접효과와 간접효과를 구분하여 정량화하였다. 또한, 개발한 예측모형에 대한 고속도로에서의 적용 가능성을 검토하고, SSES 설치 대상지 선정 시 고려해야할 사항을 제안하였다. 모형 개발에는 인명피해사고 발생 여부를 종속변수로 하는 이진분류형 기계학습을 활용하였으며, 기계학습 중에서는 모형의 예측 성능과 더불어 예측 결과에 대한 해석력을 중요하게 고려하는 인터프리터블 머신 러닝(Interpretable Machine Learning, IML) 방법론을 적용하였다. IML은 블랙박스 모델과 인터프리터블 모델로 구성되며, 본 연구에서는 블랙박스 모델로 KNN, RF 및 SVM을, 인터프리터블 모델로 DT와 BLR을 검토하였다. 모형 개발 시에는 각 기법에서 튜닝이 가능한 하이퍼 파라미터에 대하여 교차검증 과정을 거쳐 최적화하였다. 블랙박스 모델은 폴리노미얼 커널 트릭을 활용한 SVM을, 인터프리터블 모델은 BLR을 적용하여 인명피해사고 발생 확률을 예측하는 모형을 개발하였다. 개발된 IML 모델에 대해서는 PDR(Predictive accuracy, Descriptive accuracy and Relevancy) 프레임워크 관점에서 (typical) BLR 모델과 비교 평가를 진행하였다. 평가 결과 예측 정확도, 해석 정확도 및 인간의 이해관점에서의 적합성 등에서 모두 IML 모델이 우수함을 확인하였다. 또한, 본 연구에서 개발된 IML 모델 기반의 인명피해사고 발생 확률식은 SSES, SOR, SV, TVL, HVR 및 CR의 독립변수로 구성되었으며, 이 확률식을 기반으로 SSES 설치에 대한 효과를 정량화하였다. 정량화 분석 결과, SSES 설치로 인해 약 28% 정도의 인명피해사고 발생 확률이 감소함을 확인할 수 있었다. 또한, 모형 개발에 활용된 변수 중 SSES 설치로 인해 영향을 받는 변수들(SOR 및 SV)에 대한 매개효과 분석을 통해 SSES 설치로 인한 인명피해사고 감소 확률을 직접효과와 간접효과를 구분하여 제시하였다. 분석 결과, SSES와 제한속도 초과비율(SOR)의 관계에서 있어서는 약 30%가 간접효과이고, SSES와 속도분산(SV)의 관계에 있어서는 매개효과가 통계적으로 유의하지 않음을 확인할 수 있었다. 마지막으로 영동고속도로를 대상으로 인명피해사고 발생 확률식 기반의 예측 위험구간과 실제 인명사고 다발 구간에 대한 비교 분석을 통해 연구 결과의 활용 가능성을 확인하였다. 또한, SSES 설치 대상지 선정 시에는 사고 및 속도 분석을 통한 위험구간을 선별한 후 교통량(TVL)이 많은 곳, 통과차량 중 중차량 비율(HVR)이 높은 곳 및 구간 내 곡선비율(CR)이 높은 곳을 우선적으로 검토하는 것을 제안하였다.1. Introduction 1 1.1. Background of research 1 1.2. Objective of research 4 1.3. Research Flow 6 2. Literature Review 11 2.1. Research related to SSES 11 2.1.1. Effectiveness of SSES 11 2.1.2. Installation criteria of SSES 15 2.2. Machine learning about transportation 17 2.2.1. Machine learning algorithm 17 2.2.2. Machine learning algorithm about transportation 19 2.3. Crash prediction model 23 2.3.1. Frequency of crashes 23 2.3.2. Severity of crash 26 2.4. Interpretable Machine Learning (IML) 31 2.4.1. Introduction 31 2.4.2. Application of IML 33 3. Model Specification 37 3.1. Analysis of SSES effectiveness 37 3.1.1. Crashes analysis 37 3.1.2. Speed analysis 39 3.2. Data collection & pre-analysis 40 3.2.1. Data collection 40 3.2.2. Basic statistics of variables 42 3.3. Response variable selection 50 3.4. Model selection 52 3.4.1. Binary classification 52 3.4.2. Accuracy vs. Interpretability 53 3.4.3. Overview of IML 54 3.4.4. Process of model specification 57 4. Model development 59 4.1. Black-box and interpretable model 59 4.1.1. Consists of IML 59 4.1.2. Black-box model 60 4.1.3. Interpretable model 68 4.2. Model development 72 4.2.1. Procedure 72 4.2.2. Measures of effectiveness 74 4.2.3. K-fold cross validation 76 4.3. Result of model development 78 4.3.1. Result of black-box model 78 4.3.2. Result of interpretable model 85 5. Evaluation & Application 91 5.1. Evaluation 91 5.1.1. The PDR framework for IML 91 5.1.2. Predictive accuracy 93 5.1.3. Descriptive accuracy 94 5.1.4. Relevancy 99 5.2. Impact of Casualty Crash Reduction 102 5.2.1. Quantification of the effectiveness 102 5.2.2. Mediation effect analysis 106 5.3. Application for the Korean expressway 118 6. Conclusion 121 6.1. Summary and Findings 121 6.2. Further Research 125Docto

고속도로 속도예측을 위한 기계학습 접근법

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 건설환경공학부, 2018. 2. 고승영.Prediction of freeway traffic speed can be used for predictive traffic management to improve the quality of the intelligent transportation system. The data-driven prediction is widely used due to its predictive capability. Recently, the non - parametric method using machine learning shows excellent predictive capability. In these methods, the feature extraction or selection is used to mitigate the overfitting and reflect the congestion mechanism. Although this nonparametric approach can be used as advanced traveler information system due to its excellent capability, it cannot provide any information on the congestion mechanism. Lack of information makes it difficult to establish a strategy for use in operational management. This study proposes a highway speed prediction model based on machine learning approach with a feature selection that provides both high predictive performance and interpretation of traffic flow characteristics. To do this, a supervised feature selection is applied using principal component analysis (PCA) based variable grouping and ordering and support vector machine (SVM) based variable selection. Varimax rotation is also applied to obtain the simple structure. In the variable ranking, the variables in the PC are ranked by using the nonlinear correlation coefficient which implies the predictive capability in the machine learning model. The cross-correlation coefficients were used in this study. With this grouped and ranked variables, the variables are selected by the forward selection method. The machine learning regression model in this study is SVM regression which has excellent generalization performance and low computational cost. Empirical data evaluation was implemented based on the several month's data of Kyungbu freeway in Korea and the interstate (I-880) freeway in the United States. Comparing other approaches, the proposed feature selection approach well captured the characteristics of traffic flow among spatiotemporal variables. In particular, the feature selection performance is somewhat better than that of the artificial neural network feature extraction model, stacked auto-encoder, and the ensemble learning model, random forest. The vector space of the PCA is transformed into the traffic phase diagram between two spatiotemporal variables to obtain the implication of proposed approach in traffic engineering area. Based on the traffic phase interpretation, principal components with some loading of dependent variable can explain the propagation of traffic state. The proposed approach captures the propagation of traffic state well according to prediction step. The proposed approach would be used to establish strategies for avoiding congestion or preventing rear-end accidents because it has advantages in the multi-step prediction on congested areas and in identifying the congestion mechanism.Chapter 1. Introduction 1 1.1 Background 1 1.2 Objective and Scope 4 Chapter 2. Literature Review 8 2.1 Traffic Flow Based Model 8 2.2 Data Based Model 11 2.3 Review Result and Study Direction 18 Chapter 3. Method 20 3.1 Principal Component Analysis (PCA) 20 3.2 PCA Based Supervised Feature Selection 29 3.3 Comparison Models 40 Chapter 4. Empirical Data Evaluation 45 4.1 Evaluation Strategy 45 4.2 Case 1: Korean Freeway 47 4.3 Case 2: Interstate Freeway 59 4.4 Comparison of Predictive Capability 68 Chapter 5. Implication for Traffic Analysis 74 5.1 Traffic Phase of Principal Components 74 5.2 Comparison of Selected Variables 94 Chapter 6. Conclusions 103 Reference 106Docto

Implementation and development of traffic speed and flow prediction through Artificial Neural Networks

In this work we introduce the most recent techniques to predict traffic flow and speed. This work is composed of the following sections: an introduction, a state of the art, and conclusions sections. In the introduction section we see the importance of being able to predict the traffic conditions for speed and flow; we set our hypothesis that is that using the Levenberg-Marquardt training algorithm we’ll be able to find a global minimum for the problem of predicting traffic conditions; we also specify our general objective that is developing efficient algorithms for the traffic prediction for its variables speed and flow; as well as establishing that our scientifical novelty is using the Levenberg-Marquardt as Artificial Neural Network training algorithm. In the state of the art section we present the summarized contents of the most outstanding research papers about traffic prediction. We continue with the presentation of an approach that uses two statistical algorithms for traffic prediction. This information cover spatial impact, and accidents, and construction events. Additionally, it compares the results of outstanding research done with Artificial Neural Networks, and tatistical Methods, to their own statistical method that consists of two statistical methods embedded into only one by using a threshold used to determine which statistical method should be used and when, depending on the road conditions. We also present the results of traffic speed prediction by data mining techniques and a comparative to Artificial Neural Networks. Additionally, we introduce a section that analyze the problem of traffic prediction but only with Artificial Neural Networks done by the company SIEMENS in Germany. We introduce the Los Angeles Department of Transportation (LA DOT) infrastructure used to obtain the speed and flow measurements as well as the set of programs develop by the author of this thesis to retrieve the information from LA DOT, to process this information and calculate the prediction of flow and speed for a specific sensor. We continue with the problem of traffic prediction. In the process of predicting traffic flow, and speed we made our first approach using a Nonlinear Autoregressive Neural Network with External Input in MATLAB and we obtained promising results. However, this Artificial Neural Network does not have the ability to predict multiple outputs, and we transformed it to a Feedforward Neural Network also from MATLAB. The results obtained are impressive because they reduce dramatically the traffic prediction errors. In order to validate our results with the ones in the international research community we use the data from 95 days which is equivalent to 3 months, which is the commonly reported amount of time studied in this kind of problems. We also present an optimization process for the Feedforward Neural Network for both problems speed and flow prediction. Finally, we present a numerical sensibility analysis in order to determine how robust is our Artificial Neural Network. To close this thesis we present our conclusions as a success of using the Levenberg-Marquardt algorithm to train an Artificial Neural Network for the problem of traffic prediction, and we set up the possibilities of exploring the recently found result by using the learning techniques of deep learning.Consejo Nacional de Ciencia y TecnologíaContinental Guadalajara Services S.A. de C.

Designing the next generation intelligent transportation sensor system using big data driven machine learning techniques

Accurate traffic data collection is essential for supporting advanced traffic management system operations. This study investigated a large-scale data-driven sequential traffic sensor health monitoring (TSHM) module that can be used to monitor sensor health conditions over large traffic networks. Our proposed module consists of three sequential steps for detecting different types of abnormal sensor issues. The first step detects sensors with abnormally high missing data rates, while the second step uses clustering anomaly detection to detect sensors reporting abnormal records. The final step introduces a novel Bayesian changepoint modeling technique to detect sensors reporting abnormal traffic data fluctuations by assuming a constant vehicle length distribution based on average effective vehicle length (AEVL). Our proposed method is then compared with two benchmark algorithms to show its efficacy. Results obtained by applying our method to the statewide traffic sensor data of Iowa show it can successfully detect different classes of sensor issues. This demonstrates that sequential TSHM modules can help transportation agencies determine traffic sensors’ exact problems, thereby enabling them to take the required corrective steps. The second research objective will focus on the traffic data imputation after we discard the anomaly/missing data collected from failure traffic sensors. Sufficient high-quality traffic data are a crucial component of various Intelligent Transportation System (ITS) applications and research related to congestion prediction, speed prediction, incident detection, and other traffic operation tasks. Nonetheless, missing traffic data are a common issue in sensor data which is inevitable due to several reasons, such as malfunctioning, poor maintenance or calibration, and intermittent communications. Such missing data issues often make data analysis and decision-making complicated and challenging. In this study, we have developed a generative adversarial network (GAN) based traffic sensor data imputation framework (TSDIGAN) to efficiently reconstruct the missing data by generating realistic synthetic data. In recent years, GANs have shown impressive success in image data generation. However, generating traffic data by taking advantage of GAN based modeling is a challenging task, since traffic data have strong time dependency. To address this problem, we propose a novel time-dependent encoding method called the Gramian Angular Summation Field (GASF) that converts the problem of traffic time-series data generation into that of image generation. We have evaluated and tested our proposed model using the benchmark dataset provided by Caltrans Performance Management Systems (PeMS). This study shows that the proposed model can significantly improve the traffic data imputation accuracy in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) compared to state-of-the-art models on the benchmark dataset. Further, the model achieves reasonably high accuracy in imputation tasks even under a very high missing data rate (\u3e50%), which shows the robustness and efficiency of the proposed model. Besides the loop and radar sensors, traffic cameras have shown great ability to provide insightful traffic information using the image and video processing techniques. Therefore, the third and final part of this work aimed to introduce an end to end real-time cloud-enabled traffic video analysis (IVA) framework to support the development of the future smart city. As Artificial intelligence (AI) growing rapidly, Computer vision (CV) techniques are expected to significantly improve the development of intelligent transportation systems (ITS), which are anticipated to be a key component of future Smart City (SC) frameworks. Powered by computer vision techniques, the converting of existing traffic cameras into connected ``smart sensors called intelligent video analysis (IVA) systems has shown the great capability of producing insightful data to support ITS applications. However, developing such IVA systems for large-scale, real-time application deserves further study, as the current research efforts are focused more on model effectiveness instead of model efficiency. Therefore, we have introduced a real-time, large-scale, cloud-enabled traffic video analysis framework using NVIDIA DeepStream, which is a streaming analysis toolkit for AI-based video and image analysis. In this study, we have evaluated the technical and economic feasibility of our proposed framework to help traffic agency to build IVA systems more efficiently. Our study shows that the daily operating cost for our proposed framework on Google Cloud Platform (GCP) is less than $0.14 per camera, and that, compared with manual inspections, our framework achieves an average vehicle-counting accuracy of 83.7% on sunny days