FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To preserve feature map resolution, we propose a Hyper-Atrous combination to integrate atrous convolution in FCN and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count. We extensively evaluated the proposed method on different counting tasks with three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21 on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201

Grid Global Behavior Prediction

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach

COMPUTATIONAL MODELING OF CLIMATE ATTRIBUTES AND CONDITION DETERIORATION OF CONCRETE HIGHWAY PAVEMENTS

An efficient and safe road network secures the nation’s economy and prosperity by providing public mobility and freight transport. Maintenance and rehabilitation of the road network cost billions of dollars annually. Road and highway infrastructures performance in any country is impacted by load repetitions and it is further compromised by climate attributes and extreme weather events. Damages to roads and bridges are among the infrastructure failures that have occurred during these extreme events. If maintenance and rehabilitation are not done promptly, the damages to the road caused by heavy traffic and extreme climate may lead to life-threatening conditions for road users. A disruption in any one system affects the performance of others. For example, damages in road and bridge infrastructure will delay the recovery operation after a disaster. In 2018, a total of 331 natural disaster occurrences were reported worldwide, which resulted in 14,385 deaths. From 1900 to 2000, in 119 years, 14,854 natural disaster occurrences were reported which caused 32,651,605 deaths. Natural disaster occurrences like hurricanes, floods, droughts, landslides, etc. may be influenced by specific climate mechanisms like El Niño and Southern Oscillation (ENSO). Several climate attributes models were developed in this research employing Auto-Regressive Integrated Moving Average (ARIMA) methodology. The sea surface temperature data were analyzed and a prediction model was developed to predict future ENSO years. The model successfully predicted the 2018-2019 El Niño year. The model prediction showed that the next El Niño years will be 2021-22 and 2025-26. The model prediction also shows that the next La Niña year will be 2028-29. Global mean sea level (GMSL) data were analyzed and a prediction model was developed. The predicted annual rate of change in GMSL is 0.6 mm/year from 2013 to 2050. But a higher annual rate of change (1.4 mm/year) is predicted from 2031 to 2050. Northern hemisphere (Arctic) sea ice extent and southern hemisphere (Antarctic) sea ice extent data were investigated and two different models were developed. The model prediction shows that the total loss of northern hemisphere sea ice extent in 2050 will be 1.66 million km2. But the total gain of southern hemisphere sea ice extent will be 1.24 million km2. The net change of global sea ice extent will be -0.24 million km2, which indicates a loss of sea ice. The model predictions of the climate attributes can be used to understand and assess the future climate change in different climate zones worldwide. This understanding of climate changes and future predictions of climate attributes will help to develop climate adaptation strategies and better prepare the communities for extreme weather-related natural disaster occurrences. The condition deterioration progression of infrastructures, such as roads and bridges, is caused by load repetitions, as well as climate attributes and extreme weather. Pavements undergo maintenance and rehabilitations periodically to provide a smooth riding experience to the riders. Previous researches never considered maintenance and rehabilitation action history in the development of the condition deterioration model. This research considered the maintenance and rehabilitation history in the development and implementation of pavement condition deterioration models. The development of the IRI prediction model using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) considered the Long Term Pavement Performance (LTPP) climatic region, pavement structural properties, and traffic. The developed models are more objective, incorporate important input variables that are easily available, and are easy to implement in decision making. The concrete highway pavement IRI deterioration prediction models were developed and evaluated in this research for LTPP datasets of 1,482 for JPCP, 577 for JRCP, and 575 for CRCP. Comparatively, the AASHTO MEPDG performance equations were developed using fewer test sections. Three performance models were developed for output variable, IRI (outside wheel path) (m/km) for Jointed Plain Concrete Pavement (JPCP), Jointed Reinforced Concrete Pavement (JRCP), and Continuously Reinforced Concrete Pavement (CRCP). The input variables are similar for all the models. An in-depth study of M&R history collected from the LTPP database for all concrete pavement produced several CN_Code. The best models were found with the CN_Code developed based on the IRI value improvement and the type of M&R action and this variable is a continuous variable where number increment indicates the frequency of M&R action provided in the pavement section. The models’ final structure and accuracy statistics can be summarized as: JPCP (13-19-1; ANN R2 =0.94 and MLR R2 =0.49), JRCP (11-19-1; ANN R2 =0.95 and MLR R2 =0.58), and CRCP (14-19-1; ANN R2 =0.95 and MLR R2 =0.83). The ANN models show better accuracy in predicting pavement performance compare to the multiple regression models for all types of concrete pavements. The developed IRI prediction models can successfully characterize the behavior (i.e. the increase of IRI values with time and decrease of IRI value after maintenance and rehabilitation). The ANN models can be used to provide future M&R action by changing CN_Code frequency and the model successfully distinguishes the behavior of IRI (i.e. decrease of IRI after M&R action and increase of IRI with time as CESAL increases). The developed condition deterioration models for concrete highway pavement present a significant improvement on the models currently used in the mechanistic-empirical pavement design method. It is recommended to implement the pavement condition deterioration model developed in this research for life-cycle asset management and M&R programs

Wage shocks and consumption variability in Mexico during the 1990s

This paper presents evidence on the relationship between shocks to relative male wages, and changes in household consumption in Mexico during the 1990s decade, which is a period characterized by high volatility. Apart from performing analysis of this type for Mexico for the first time, the paper has mainly two contributions. The first is the use of alternative data sources to construct instrumental variables for wages. The second is to examine differences across four consumption categories: non-durable goods, durable goods, education and health. Our results for non-durable goods consumption reject the hypothesis that Mexican households are able to insure idiosyncratic risk. For the comparisons across consumption categories, the conclusion is that households in Mexico tend to react to temporary shocks by contracting the consumption of goods that represents longer run investment in human capital, which makes them more vulnerable in the future

An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM

The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose great challenges for effective COVID-19 case prediction. To address this challenge, we propose a novel hybrid model in which the interpretability of the Autoregressive model (AR) and the predictive power of the long short-term memory neural networks (LSTM) join forces. The proposed hybrid model is formalized as a neural network with an architecture that connects two composing model blocks, of which the relative contribution is decided data-adaptively in the training procedure. We demonstrate the favorable performance of the hybrid model over its two component models as well as other popular predictive models through comprehensive numerical studies on two data sources under multiple evaluation metrics. Specifically, in county-level data of 8 California counties, our hybrid model achieves 4.173% MAPE on average, outperforming the composing AR (5.629%) and LSTM (4.934%). In country-level datasets, our hybrid model outperforms the widely-used predictive models - AR, LSTM, SVM, Gradient Boosting, and Random Forest - in predicting COVID-19 cases in 8 countries around the world. In addition, we illustrate the interpretability of our proposed hybrid model, a key feature not shared by most black-box predictive models for COVID-19 cases. Our study provides a new and promising direction for building effective and interpretable data-driven models, which could have significant implications for public health policy making and control of the current and potential future pandemics