The rapid increase in the cyber-physical nature of transportation, availability of GPS data, mobile applications, and effective communication technologies have led to the emergence of On-Demand Transit (ODT) systems. In September 2018, the City of Belleville in Canada started an on-demand public transit pilot project, where the late-night fixed-route (RT 11) was substituted with the ODT providing a real-time ride-hailing service. We present an in-depth analysis of the spatio-temporal demand and supply, level of service, and origin and destination patterns of Belleville ODT users, based on the data collected from September 2018 till May 2019. The independent and combined effects of the demographic characteristics (population density, working-age, and median income) on the ODT trip production and attraction levels were studied using GIS and the K-means machine learning clustering algorithm. The results indicate that ODT trips demand is highest for 11:00 pm-11:45 pm during the weekdays and 8:00 pm-8:30 pm during the weekends. We expect this to be the result of users returning home from work or shopping. Results showed that 39% of the trips were found to have a waiting time of smaller than 15 minutes, while 28% of trips had a waiting time of 15-30 minutes. The dissemination areas with higher population density, lower median income, or higher working-age percentages tend to have higher ODT trip attraction levels, except for the dissemination areas that have highly attractive places like commercial areas.

Spatio-Temporal Analysis of On Demand Transit: A Case Study of Belleville, Canada

Information is one of the most important thing in our lives, while humans is naturally impatient when searching for information from the internet. Users want to get the right answer instantaneously with minimal effort. News headlines can be used to categorize news types, as appropriate. The appropriate type of news can make it easier for us to choose the particular topic we want. Similarity in a title can be used to clustering news based on news title. From those reason this dataset research contain the title of online news site. TFIDF used as Document Preprocessing method, K-Means as clustering method, and elbow method used to optimize number of cluster. Purity method applied to evaluate news title clustering as internal evaluation. SSE (Sum Square Error) of each cluster are calculate and compared to optimize number of cluster in the elbow method, the result of those comparison evaluate using internal evaluation called purity, purity value is conformity between cluster and ideal cluster. From the calculation of elbow method, the most optimal number of cluster are 8 cluster, there is 0.228 point between 7cluster and 8 cluster SSE value so the elbow form are made. Purity evaluation method generates value 0.514 in the number of cluster are 8, this is the highest value and the one closest to one rather than the other number of cluster which mean the most ideal. The conclusion is the elbow method can be used to optimize number of cluster on K-Mean clustering method.

The Determination of Cluster Number at k-Mean Using Elbow Method and Purity Evaluation on Headline News

Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the stability of benches and slopes in open-pit mines, and impact underground water, railway, highway, and puzzling for neighboring communities. Therefore, controlling, accurate prediction, and mitigating blast-induced PPV are necessary. This study contributed a new computational model in predicting blast-induced PPV for the science community and practical engineering with high accuracy level. In this study, a novel hybrid artificial intelligence model based on the hierarchical k-means clustering algorithm (HKM) and artificial neural network (ANN), namely a HKM–ANN model, was considered and proposed for predicting blast-caused PPV in open-pit mines. Accordingly, input data were first clustered by the HKM algorithm, and then, the ANN models were developed based on the obtained clusters. For this aim, 185 blasting events were collected and analyzed. A hybrid model based on fuzzy c-means clustering (FCM) and support vector regression (SVR), i.e., FCM–SVR model, which was proposed by previous authors was also applied for comparison of results with our proposed HKM–ANN model. In addition, an empirical method, several ANN and SVR models (without clustering), FCM–ANN, and HKM–SVR were developed for comparison purposes. For measuring the performance of the improved models, coefficient determination (R2), root-mean-square error, and variance account for were used as the performance indicators. The results show that the HKM algorithm played a significant role in improving the accuracy of the ANN models. The proposed HKM–ANN model was the most superior model in estimating PPV caused by blasting operations in this study.

Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network

Blasting is an essential task in open-pit mines for rock fragmentation. However, its dangerous side effects need to be accurately estimated and controlled, especially ground vibration as measured in the form of peak particle velocity (PPV). The accuracy for estimating blast-induced PPV can be improved by hybrid artificial intelligence approach. In this study, a new hybrid model was developed based on Hierarchical K-means clustering (HKM) and Cubist algorithm (CA), code name HKM-CA model. The HKM clustering hybrid technique was used to separate data according to their characteristics. Subsequently, the Cubist model was trained and developed on the clusters generated by HKM. Empirical technique, the benchmark algorithms [random forest (RF), support vector machine (SVM), classification and regression tree (CART)], and single CA model were also established for benchmarking the HKM-CA model. Root-mean-square error (RMSE), determination coefficient (R2), and mean absolute error (MAE) were the key indicators used for evaluating the model performance. The results revealed that the proposed HKM-CA model was a powerful tool for improving the accuracy of the CA model. Specifically, the HKM-CA model yielded a superior result with an RMSE of 0.475, R2 of 0.995, and MAE of 0.373 in comparison to other models. The proposed HKM-CA model has the potential to be used for predicting blast-induced PPV on-site to control undesirable effects on the surrounding environment.

A new soft computing model for estimating and controlling blast-produced ground vibration based on Hierarchical K-means clustering and Cubist algorithms

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps.

consist of hundreds of thousands of small and cost effective sensor nodes. Sensor nodes are used to sense the environmental or physiological parameters like temperature, pressure, etc. For the connectivity of the sensor nodes, they use wireless transceiver to send and receive the inter-node signals. Sensor nodes, because connect their selves wirelessly, use routing process to route the packet to make them reach from source to destination. These sensor nodes run on batteries and they carry a limited battery life. Clustering is the process of creating virtual sub-groups of the sensor nodes, which helps the sensor nodes to lower routing computations and to lower the size routing data. There is a wide space available for the research on energy efficient clustering algorithms for the WSNs. LEACH, PEGASIS and HEED are the popular energy efficient clustering protocols for WSNs. In this research, we are working on the development of a hybrid model using LEACH based energy efficient and K-means based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters automatically. In the proposed method, finding an optimum "k" value is performed by Elbow method and clustering is done by k-means algorithm, hence routing protocol LEACH which is a traditional energy efficient protocol takes the work ahead of sending data from the cluster heads to the base station. The results of simulation show that at the end of some certain part of running the proposed algorithm, at some point the marginal gain will drop dramatically and gives an angle in the graph. The correct "k" i.e. number of clusters is chosen at this point, hence the "elbow criterion".

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Purnima Bholowalia

Papers

EBK-Means: A Clustering Technique based on Elbow Method and K-Means in WSN