In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to determine these factors’ impact on injury severity outcomes. Also, the proposed RNN model was compared with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models to understand its advantages and limitations. The results of the comparative analyses showed that the RNN model outperformed the MLP and BLR models. The validation accuracy of the RNN model was 71.77%, whereas the MLP and BLR models achieved 65.48% and 58.30% respectively. The findings of this study indicate that the RNN model, in deep learning frameworks, can be a promising tool for predicting the injury severity of traffic accidents.

An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia.

Land-cover maps provide essential data for a wide range of practical and small-scale applications. A number of data sources appropriate for land-cover extraction are available. Among these, images captured using unmanned aerial vehicles (UAVs) are low cost, have very high resolution, and can be acquired at any time with few restrictions. Over the past two decades, various classification techniques have been developed to extract land-cover features from UAV images, and object-based image analysis (OBIA) is the preferred technique based on the recent literature. This study presents a novel method that integrates the fuzzy unordered rule induction algorithm (FURIA) into OBIA to achieve accurate land-cover extraction from UAV images. The images were segmented using a multiresolution segmentation algorithm with an optimized scale parameter. The scale parameter was optimized using a novel approach that integrated feature space optimization into the plateau objective function. During the classification stage, significant features were selected via random forest, and rule sets were developed using FURIA. For comparison, result of the proposed approach was compared with those of decision tree (DT) rules and the Support Vector Machine (SVM) classification method. The results of this study indicate that the proposed method outperforms DT and SVM with an overall accuracy of 91.23%. A transferability evaluation showed that FURIA achieved accurate classification results on different UAV image subsets captured at different times. The findings suggest that fuzzy rules are more appropriate than conventional crisp rules for land-cover extraction from UAV images.

Accurate information on accidents and on the relevant factors that affect them is critical for establishing the relationship between accident frequency and explanatory factors. In this study, we present a simplified method to extract road geometric features accurately from very high-resolution laser scanning data to analyze accident frequency on the North-South Expressway in Malaysia. Using expressway geometric features (i.e. horizontal and vertical alignments) extracted from laser scanning data and accident histories, this research first developed an APM based on geometric regression and a geographic information system (GIS). Then, an elasticity analysis was conducted to investigate the relationship between accident occurrence and road geometric design features. Results of the case study showed that the length of the road segments (mean = 0.014, elasticity = 0.122), the number of vertical curves in a road section (mean = 4.797, elasticity = 0.999), and the presence of a horizontal curve in a road segment (mean = 2.746, elasticity = 0.877), the average distance to the nearest access point (mean = -0.001, elasticity = −0.035), and AADT (mean = 3.01, elasticity = 0.881) determined accident occurrence, all at a significance level of 5%. This study shows that laser scanning systems can provide an easy and efficient method to collect transportation data, particularly those for accident analysis.

In the last decade, object-based image analysis (OBIA) has been extensively recognized as an effective classification method for very high spatial resolution images or integrated data from different sources. In this study, a two-stage optimization strategy for fuzzy object-based analysis using airborne LiDAR was proposed for urban road extraction. The method optimizes the two basic steps of OBIA, namely, segmentation and classification, to realize accurate land cover mapping and urban road extraction. This objective was achieved by selecting the optimum scale parameter to maximize class separability and the optimum shape and compactness parameters to optimize the final image segments. Class separability was maximized using the Bhattacharyya distance algorithm, whereas image segmentation was optimized using the Taguchi method. The proposed fuzzy rules were created based on integrated data and expert knowledge. Spectral, spatial, and texture features were used under fuzzy rules by implementing the particle swarm optimization technique. The proposed fuzzy rules were easy to implement and were transferable to other areas. An overall accuracy of 82% and a kappa index of agreement (KIA) of 0.79 were achieved on the studied area when results were compared with reference objects created via manual digitization in a geographic information system. The accuracy of road extraction using the developed fuzzy rules was 0.76 (producer), 0.85 (user), and 0.72 (KIA). Meanwhile, overall accuracy was decreased by approximately 6% when the rules were applied on a test site. A KIA of 0.70 was achieved on the test site using the same rules without any changes. The accuracy of the extracted urban roads from the test site was 0.72 (KIA), which decreased to approximately 0.16. Spatial information (i.e., elongation) and intensity from LiDAR were the most interesting properties for urban road extraction. The proposed method can be applied to a wide range of real applications through remote sensing by transferring object-based rules to other areas using optimization techniques.

This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006–2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r² = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r² = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated.