Prediction of Average Speed Based on Relationships Between Neighbouring Roads Using K-NN and Neural Network

For decades, various algorithms to predict traffic flow have been developed to address traffic congestion. Traffic congestion or traffic jam occurs as a ripple effect from a road congestion in the neighbouring area. Previous research shows that there is a spatial correlation between traffic flow in neighbouring roads. Similar traffic pattern is observed between roads in a neighbouring area with respect to day and time. Currently, time series models and neural network models are widely applied to predict traffic flow and traffic congestion based on historical data. However, studies on relationships between road segments in a neighbouring area are still limited. It is important to investigate these relationships because they can assist drivers in avoiding roads which are impacted by road congestion. Also, the result can be used to improve the accuracy of prediction of traffic flow. Hence, this study investigates relationships of roads in a neighbouring area based on similarity of traffic condition. Traffic condition is influenced by number of vehicles and average speed of vehicles. In our study, clustering method is used to divide the speed of traffic into four (4) categories: very congested, congested, clear and very clear. We used k-means clustering method to cluster condition of traffic flow on road segments. Then, we applied the k-Nearest Neighbour (k-NN) method to classify the traffic condition in neighbouring roads. From the classification of traffic condition in neighbouring roads, we then determine the relationship between road segments. We presented the road with highest relationship on the map and used it as input factor to predict traffic speed of the road using neural network. Results show that combination of k-means and k-NN method produced better results than using both, correlation method and using the k-means method only.

The influence of emission sources on particulate matter pollution in adjacent areas

Particulates generated by anthropogenic activities are found in the ambient air in varying amounts, in a wide range of sizes and in a wide variety of chemical composition. The concentration of particulates at one site, as well as their composition, are influenced by their origin and dispersion factors. By chemical and dimensional analysis of particulates in an area, we can draw preliminary conclusions about the origin and level of pollution in that area. This paper presents the results obtained by concomitant monitoring of particulates emissions and the concentration of particulate matter in ambient air in the adjacent area of an industrial plant. The result of the measurements obtained and their correlation with the specific conditions during the monitoring period lead us to the conclusion that the concentration of particulate matter at the emission sources directly influences the concentrations of particulate matter in the neighbouring area.

Endogamia i zwyczaje małżeńskie na Kaszubach a kwestia tzw. genu kaszubskiego

One of the genetic disorders is LCHAD deficiency. It occurs relatively often in Kashubian population, which is among others attributed to endogamy. Before the second half of the 20th century the Kashubs majorly married within a single parish or a town, village. Also in this day and age, in Kashubia people most of all wed someone living in the neighbouring area. It stems from powerful family and group (ethnic) ties, the traditional family model, low level of education. The Kashubs’ close adherence to family values is best expressed in robust wedding ceremonies along with commonly celebrated Polterabend. This social gathering exhibiting some analogies with potlatch, serves as a kind of status marker for a particular family’s popularity; it confers prestige and indicates position within local community. Despite some anxiety brought about by the promulgation of knowledge about “Kashubian gene,” there is no visible tendency in Kashubian community to change the model of spouse selection.

TRAFFIC FLOW PREDICTION MODEL BASED ON NEIGHBOURING ROADS USING NEURAL NETWORK AND MULTIPLE REGRESSION

Monitoring and understanding traffic congestion seems difficult due to its complex nature. This is because the occurrence of traffic congestion is dynamic and interrelated and it depends on many factors. Traffic congestion can also propagate from one road to neighbouring roads. Recent research shows that there is a spatial correlation between neighbouring roads with different traffic flow pattern on weekdays and on weekends. Previously, prediction of traffic flow propagation was based on day and time during weekdays and on weekends. Results obtained from past studies show that further investigation is needed to reduce errors using a more efficient method. We observed from previous research that similarity of traffic condition on weekdays and weekends was not taken into account in predicting traffic flow propagation. Hence, our study is to create and evaluate a new prediction model for traffic flow propagation at neighbouring roads using similarity of traffic flow pattern on weekdays and weekends to achieve more accurate results. We exploit similarity of traffic flow pattern on weekdays and weekends by adding time cluster in our proposed model. Thus, our neural network model proposed high correlation road, time and day clusters as input factors in neural network model prediction. Our initial phase of the methodology involves investigation on correlation between neighbouring roads. This paper discusses the results of experiments we have conducted to determine relationship between roads in a neighbouring area and to determine input factors for our neural network traffic flow prediction model. To choose a particular road as a predicting factor, we calculated the distance between roads in neighbouring area to identify the nearest road. Then, we calculated correlation based on traffic condition (congestion) between roads in neighbouring area. The results were then used as input factors for prediction of traffic flow. We compared the results of the experiment using neural network without cluster parameters and multiple regression methods. We observed that neural network with time cluster parameter produced better results compared to neural network without parameter and multiple regression method in predicting average speed of vehicles on neighbouring roads.

Permukiman Gua di Sub-Cekungan Payakumbuh

Pre-historic men's life mainly relied on the availability of natural resources in the surrounding area. The settlements had inevitably to provide their needs of food and tools. Payakumbuh’s sub-basin, a strategic location for settlement, is a plain with a river in the middle that provides a place to shade and settle at its ‘Ngalau’ (caves and rock shelters). This location is also supported by the presence of hills and Sinamar River. Culturally, archaeological findings on the use of this site as a settlement are also found. This writing tries to describe the patterns of distribution and the use of caves at the Payakumbuh’s sub-basin. Archaeological landscape approach method is used to observe some physical and cultural aspects in that area. To provide further pictures, analyses on the neighbouring area are also done through the use of such softwares as Arc-View 3.2 and ArcGIS 9.3 with the extension of Network Analysis and Spatial Analysis.AbstrakKehidupan manusia pada masa prasejarah masih mengandalkan pada ketersediaan sumberdaya lingkungannya. Lokasi yang mereka jadikan sebagai lokasi permukiman harus menyediakan kebutuhan mereka akan makanan dan juga peralatannya. Sub-Cekungan Payakumbuh merupakan salah satu lokasi yang baik digunakan sebagai permukiman. Secara fisik, lokasi ini memiliki bentuklahan dataran dengan sungai yang mengalir pada bagian tengahnya, serta tersedianya lokasi berteduh dan bermukim di ‘Ngalau’ (gua dan ceruk). Lokasi ini didukung dengan bentangalam pedataran dengan bukit-bukit yang muncul di beberapa tempat dan juga didukung dengan keberadaan Sungai Sinamar. Secara budaya, pada lokasi ini juga telah ditemukan data arkeologi tentang pemanfaatanny sebagai lokasi permukiman. Pada tulisan ini akan membahas bagaimana pola sebaran dan pemanfaatan gua di Sub-Cekungan Payakumbuh. Untuk menjawab permasalahan tersebut digunakan model pendekatan arkeologi lansekap yang memperhatikan pada beberapa aspek fisik serta budaya pada lokasi tersebut. Untuk lebih menggambarkan hal tersebut digunakan juga analisis tetangga terdekat dengan bantuan software Arc-View 3.2 dan ArcGIS 9.3 dengan ekstensi Network Analysis, Buffer Wizard, dan Spasial Analysis.

The Study of Spatial Autocorrelation of the Land Consolidation in Lubelskie Voivodeship

In Poland land consolidation is carried out mainly in the southern part of the country. In three voivodships, Lublin Voivodship, Podkarpackie Voivodship and Lesser Poland Voivodship, in the years 2003–2014 there were numerous land consolidations, over 20,000 ha in each voivodship. That is above national average of land consolidation. In another three voivodships (Warmian-Masurian Voivodship, West Pomeranian Voivodship and The Kuyavian-Pomeranian Voivodship) there are no land consolidation, even though according to scientists from the Polish, every voivodship requires land consolidations processes. What is the reason for that situation? Why are so many land consolidations conducted in the area of several voivodships in Poland, and in other voivodships considerably less or not at all? It is known that the location of the implementation of agricultural land consolidations in a particular area is determined by numerous factors, inter alia the construction of line infrastructural projects i.e. motorways, faulty spatial structure found in a particular area, and farmers in Poland applying for the implementation of this project. It is also known that the neighbourhood of the implementation of these works is of significance. Situations are observed in which the appearance of one consolidation object contributes to the development of this measure in the neighbouring area. However, there is no empirical evidence to support this view. Therefore, the subject of considerations will be the investigation into the occurrence of spatial relationships between consolidation objects. Two variables were adopted for the analysis, namely the number and density of consolidations. In order to determine the relationships, spatial autocorrelation was applied.

Evaluation of the degree of healthiness of the pedunculate oak (Quercus robur L.) acorns in the Włoszczowa– Jędrzejów Nature Park and its neighbouring area

The aim of the study was to demonstrate the degree of healthiness of the pedunculate oak (Quercus robur) acorns found in the Włoszczowa-Jędrzejów Nature Park (abbreviation: W-JOChK) and in the neighbouring area. It was dealt with by making the analysis of health of the acorns (total 3,600). The research material included the samples of fallen down acorns, collected under the pedunculate oaks in Kurzelów (W-JOChK), as well as Żelisławice. The study was conducted from late September 2014 to early October 2015. The analysis of acorns demonstrated that over 50% of the acorns were damaged by insects. The ‘perpetrators’ of the damage proved to be Curculio glandium (Coleoptera: Curculionidae - acorn weevil) and Cydia splendana (Lepidoptera: Tortricidae - chestnut tortrix). It was stated that acorns from the pedunculate oak trees, which were found on the protected area, were twice less frequently inhabited by Curculio glandium than those originating from the trees in Żelisławice - near the industrial firm. The damages caused by Cydia splendana in both study sites were similar in number. The results indicate that the acorns of oaks in more polluted environment are probably more vulnerable to infestation by insects.