Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting

Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting the accident duration on arterial road networks in Sydney, based on operational requirements of incident clearance target which is less than 45 minutes. Using incident baseline information, we first deploy a classification method using various ensemble tree models in order to predict whether a new incident will be cleared in less than 45min or not. If the incident was classified as short-term, then various regression models are developed for predicting the actual incident duration in minutes by incorporating various traffic flow features. After outlier removal and intensive model hyper-parameter tuning through randomized search and cross-validation, we show that the extreme gradient boost approach outperformed all models, including the gradient-boosted decision-trees by almost 53%. Finally, we perform a feature importance evaluation for incident duration prediction and show that the best prediction results are obtained when leveraging the real-time traffic flow in vicinity road sections to the reported accident location

Prediction of home energy consumption based on gradient boosting regression tree

Abstract Energy consumption prediction of buildings has drawn attention in the related literature since it is very complex and affected by various factors. Hence, a challenging work is accurately estimating the energy consumption of buildings and improving its efficiency. Therefore, effective energy management and energy consumption forecasting are now becoming very important in advocating energy conservation. Many researchers work on saving energy and increasing the utilization rate of energy. Prior works about the energy consumption prediction combine software and hardware to provide reasonable suggestions for users based on the analyzed results. In this paper, an innovative energy consumption prediction model is established to simulate and predict the electrical energy consumption of buildings. In the proposed model, the energy consumption data is more accurately predicted by using the gradient boosting regression tree algorithm. By comparing the performance index Root Mean Square Error of different prediction models through experiments it is shown that the proposed model obtains lower values on different testing data. More detailed comparison with other existing models through experiments show that the proposed prediction model is superior to other models in energy consumption prediction

Predicting time to graduation at a large enrollment American university

The time it takes a student to graduate with a university degree is mitigated by a variety of factors such as their background, the academic performance at university, and their integration into the social communities of the university they attend. Different universities have different populations, student services, instruction styles, and degree programs, however, they all collect institutional data. This study presents data for 160,933 students attending a large American research university. The data includes performance, enrollment, demographics, and preparation features. Discrete time hazard models for the time-to-graduation are presented in the context of Tinto's Theory of Drop Out. Additionally, a novel machine learning method: gradient boosted trees, is applied and compared to the typical maximum likelihood method. We demonstrate that enrollment factors (such as changing a major) lead to greater increases in model predictive performance of when a student graduates than performance factors (such as grades) or preparation (such as high school GPA).Comment: 28 pages, 11 figure

Will higher traffic flow lead to more traffic conflicts? A crash surrogate metric based analysis

© 2017 Kuang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. In this paper, we aim to examine the relationship between traffic flow and potential conflict risks by using crash surrogate metrics. It has been widely recognized that one traffic flow corresponds to two distinct traffic states with different speeds and densities. In view of this, instead of simply aggregating traffic conditions with the same traffic volume, we represent potential conflict risks at a traffic flow fundamental diagram. Two crash surrogate metrics, namely, Aggregated Crash Index and Time to Collision, are used in this study to represent the potential conflict risks with respect to different traffic conditions. Furthermore, Beijing North Ring III and Next Generation SIMulation Interstate 80 datasets are utilized to carry out case studies. By using the proposed procedure, both datasets generate similar trends, which demonstrate the applicability of the proposed methodology and the transferability of our conclusions

Nonlinear relationship between the urban form and street vitality : a data informed approach involving twelve Chinese cities

The relationship between the urban form and urban vitality has been widely discussed over the past few decades. Nevertheless, due to the limitation of available fine-scale urban data and analytical methods, previous empirical studies have mainly focused on mesoscale urban morphological characteristics within the scope of a single city, while microscale analyses among different cities are rare. In addition, most researchers adopt traditional linear or pre-defined regression models while ignoring the nonlinear effects of urban form characteristics. To address these two gaps, this study examines multi-source big data to measure multidimensional urban form characteristics from both the top-down and human-scale perspectives. Taking the streets of 12 Chinese city old districts as samples, this study uses a gradient promotion decision tree model to examine the irregular association between the urban form and street vitality. The explanations for the model indicate that: (1) Compared with the functional and human-scale morphological characteristics, the mesoscale morphological characteristics, especially the street intersection density, block size, street density, building density, and floor area ratio are dominant contributors to street vitality; (2) There is a nonlinear relationship between some urban morphological characteristics and street vitality and their associations change drastically past some thresholds of urban form attributes

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen