Search results for: road condition classification

Authors: Rania Alshikhe, Vinita Jindal

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Digital innovation has played a crucial role in managing smart transportation. For this, big trajectory data collected from traveling vehicles, such as taxis through installed global positioning system (GPS)-enabled devices can be utilized. It offers an unprecedented opportunity to trace the movements of vehicles in fine spatiotemporal granularity. This paper aims to explore big trajectory data to measure the travel efficiency of road networks using the proposed statistical travel efficiency measure (STEM) across an entire city. Further, it identifies the cause of low travel efficiency by proposed least square approximation network-based causality exploration (LANCE). Finally, the resulting data analysis reveals the causes of low travel efficiency, along with the road segments that need to be optimized to improve the traffic conditions and thus minimize the average travel time from given point A to point B in the road network. Obtained results show that our proposed approach outperforms the baseline algorithms for measuring the travel efficiency of the road network.

Keywords: GPS trajectory, road network, taxi trips, digital map, big data, STEM, LANCE

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Authors: Muhammad Saqlain, Nazar Abbas Saqib, Muazzam A. Khan

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Heart Failure (HF) has become the major health problem in our society. The prevalence of HF has increased as the patient’s ages and it is the major cause of the high mortality rate in adults. A successful identification and progression of HF can be helpful to reduce the individual and social burden from this syndrome. In this study, we use a real data set of cardiac patients to propose a classification model for the identification and progression of HF. The data set has divided into three age groups, namely young, adult, and old and then each age group have further classified into four classes according to patient’s current physical condition. Contemporary Data Mining classification algorithms have been applied to each individual class of every age group to identify the HF. Decision Tree (DT) gives the highest accuracy of 90% and outperform all other algorithms. Our model accurately diagnoses different stages of HF for each age group and it can be very useful for the early prediction of HF.

Keywords: decision tree, heart failure, data mining, classification model

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Authors: Shuo Liu

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In Chinese text classification tasks, redundant words and phrases can interfere with the formation of extracted and analyzed text information, leading to a decrease in the accuracy of the classification model. To reduce irrelevant elements, extract and utilize text content information more efficiently and improve the accuracy of text classification models. In this paper, the text in the corpus is first extracted using the TextRank algorithm for abstraction, the words in the abstract are used as nodes to construct a text graph, and then the graph attention network (GAT) is used to complete the task of classifying the text. Testing on a Chinese dataset from the network, the classification accuracy was improved over the direct method of generating graph structures using text.

Keywords: Chinese natural language processing, text classification, abstract extraction, graph attention network

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Authors: K. S. Parlak, E. Turan

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The separation of marbles according to the pattern quality is a process made according to expert decision. The classification phase is the most critical part in terms of economic value. In this study, a self-learning system is proposed which performs the classification of marbles quickly and with high success. This system performs ten feature extraction by taking ten marble images from the camera. The marbles are classified by decision tree method using the obtained properties. The user forms the training set by training the system at the marble classification stage. The system evolves itself in every marble image that is classified. The aim of the proposed system is to minimize the error caused by the person performing the classification and achieve it quickly.

Keywords: decision tree, feature extraction, k-means clustering, marble classification

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Authors: Usama Ahmed

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Data mining is the process of analyze data which are used to predict helpful information. It is the field of research which solve various type of problem. In data mining, classification is an important technique to classify different kind of data. Diabetes is most common disease. This paper implements different classification technique using Waikato Environment for Knowledge Analysis (WEKA) on diabetes dataset and find which algorithm is suitable for working. The best classification algorithm based on diabetic data is Naïve Bayes. The accuracy of Naïve Bayes is 76.31% and take 0.06 seconds to build the model.

Keywords: data mining, classification, diabetes, WEKA

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Authors: Elbert S. Moyon, Edsel Matt O. Morales, Jaymer M. Jayoma, Kent C. Espejon, Jayson C. Dollete, Mark Phil B. Pacot

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This research paper focuses on the potential of using Web-GIS technology in prioritizing farm-to-market road projects by the Philippines’ Department of Agriculture (DA). The study aimed to explore the benefits of Web-GIS in addressing the limitations faced by the DA in terms of Farm to Market Road profiling and project prioritization, which include a lack of access to updated data, limited spatial analysis capabilities, and difficulties in sharing information between stakeholders. The research methodology involves a comprehensive literature review and a case study of a Web-GIS application developed for the DA, which was used to profile and prioritize farm-to-market road projects in the Philippines. The results showed that the Web-GIS technology provides the DA with an effective tool for analyzing and visualizing data, which can help in profiling and prioritizing road projects based on various criteria such as economic, social, and environmental impacts. The study also showed that Web-GIS technology could help in reducing the time and effort required for road project prioritization and improve communication between stakeholders.

Keywords: GIS, web application, farm-to-market road, FMR prioritization, Django, GeoServer

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Authors: M. Hijazi, A. Zeki, A. Ismail

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An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Pedro Pablo Ramírez Sánchez, Alassane Ballé Ndiaye, Roberto Rendeiro Martín-Cejas

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The aim of this paper is to analyse the different environmental policies adopted in Europe for car emissions, to comment on some of the possible perverse effects generated and point out these policies which are considered more efficient under the environmental perspective. This paper is focused on passenger cars as this category is the most significant in road transport. The utility of this research lies in this being the first step or basis to improve and optimise actual policies. The methodology applied in this paper refers to a comparative analysis from a practical and theoretical point of view of European environmental policies in road transport. This work describes an overview of the road transport industry in Europe pointing out some relevant aspects such as the contribution of road transport to total emissions and the vehicle fleet in Europe. Additionally, we propose a brief practice guide with the combined policies in order to optimise their aim.

Keywords: air quality, climate change, emission, environment, perverse effect, road transport, tax policy

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Authors: Ami Nkumbuye

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Background: Every year 1.3 million people die due to Road crashes, according to the Global status report. Road crashes remain the greatest killer aged between 15-29 years. Young people are paying an unacceptable price for their own safer mobility. 23,498 students attending class daily from home crossing the roads of 3 districts Kigali and Southern province is showing a similar trend with 40320 cross road daily. As most of them don't have any idea about the safety, they should have when they are crossing roads and traffic rules and signs as well. Despite the high number of mortality related to road crashes in Rwanda, we don't have any approved calendar to teach young people road safety as the most affected age group. Objective: The objective of this study was to identify the causes of road crashes and the outcome of victims after being involved in road crashes over a period of two years, from January 2020 to December 2021, in Huye district and Kigali City. Methods: A retrospective descriptive study with open questions and then data analysis, students were identified from 15 schools in Kigali City and Southern Province and through the Local Action Project supported by Global Youth Coalition for Road Safety and Youth for Road Safety (YOURS), students asked about the cause of road crashes through open and closed question and data analyzed. Result: There were 354 students from 15 schools: 198 males and 156 females. Their age ranged from 10 to 25 years. The commonest cause of road crashes among students attending schools daily was: high speed, lack of education on safe behavior on the road, drinking and driving, and poor road infrastructures, with 47%, 32%, 13% and 8 %, respectively. The hospital admission after road crashes for the victims was 32.3%. In most scenes where road crashes occur, students report that they didn't see any person who could provide post-crash care until the ambulance came, in some cases, resulted in bad outcomes for the victims after road crashes. Conclusion: This study revealed that high speed and lack of education n road safety are the major cause of road crashes among young people in Rwanda. If local Non-Governmental Organization and Decision makers work on these issues like never before, we can see a decrease in road crash among young people and adult as well. We would like to give a recommendation to two institutions: the first is the Rwanda National Police Traffic department to set 30km/m as the maximum speed limit in City and near schools. The second is for the Ministry of Education to put Road Safety and Post Crash Care curricula in both Primary and Secondary schools.

Keywords: road safety, post-crash care, young people, students

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Authors: Bhuyain Mobarok Hossain

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Image classification and localization research is currently an important strategy in the field of computer vision. The evolution and advancement of deep learning and convolutional neural networks (CNN) have greatly improved the capabilities of object detection and image-based classification. Target detection is important to research in the field of computer vision, especially in video surveillance systems. To solve this problem, we will be applying a convolutional neural network of multiple scales at multiple locations in the image in one sliding window. Most translation networks move away from the bounding box around the area of interest. In contrast to this architecture, we consider the problem to be a classification problem where each pixel of the image is a separate section. Image classification is the method of predicting an individual category or specifying by a shoal of data points. Image classification is a part of the classification problem, including any labels throughout the image. The image can be classified as a day or night shot. Or, likewise, images of cars and motorbikes will be automatically placed in their collection. The deep learning of image classification generally includes convolutional layers; the invention of it is referred to as a convolutional neural network (CNN).

Keywords: image classification, object detection, localization, particle filter

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Authors: Han Bai, Tong Zhang, Lemei Yu, Doudou Xie, Liang Zhao

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Road transition design on freeway tunnel entrance and exit is one vital factor in realizing smooth transition and improving traveling safety for vehicles. The goal of this research is to develop a horizontal road transition design tool that considers the transition technology of traffic capacity consistency to explore its accommodation mechanism. The influencing factors of capacity are synthesized and a modified capacity calculation model focusing on the influence of road width and lateral clearance is developed based on the VISSIM simulation to calculate the width of road transition sections. To keep the traffic capacity consistency, the right side of the transition section of the tunnel entrance and exit is divided into three parts: front arc, an intermediate transition section, and end arc; an optimization design on each transition part is conducted to improve the capacity stability and horizontal alignment transition. A case study on the Panlong Tunnel in Ji-Qing freeway illustrates the application of the tool.

Keywords: traffic safety, road transition, freeway tunnel, traffic capacity

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Authors: Zulkiflee Abd Latif, Sitinor Atikah Nordin, Alawi Sulaiman

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Different tree species possess different and various benefits. Arenga Pinnata tree species own several potential uses that is valuable for the economy and the country. Mapping vegetation using remote sensing technique involves various process, techniques and consideration. Using satellite imagery, this method enables the access of inaccessible area and with the availability of near infra-red band; it is useful in vegetation analysis, especially in identifying tree species. Pixel-based and object-based classification technique is used as a method in this study. Pixel-based classification technique used in this study divided into unsupervised and supervised classification. Object based classification technique becomes more popular another alternative method in classification process. Using spectral, texture, color and other information, to classify the target make object-based classification is a promising technique for classification. Classification of Arenga Pinnata trees is overlaid with elevation, slope and aspect, soil and river data and several other data to give information regarding the tree character and living environment. This paper will present the utilization of remote sensing technique in order to map Arenga Pinnata tree species

Keywords: Arenga Pinnata, pixel-based classification, object-based classification, remote sensing

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Authors: Ghada S. Moussa

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With the increase in population along with economic prosperity, an enormous increase in the number and types of vehicles on the roads occurred. This fact brings a growing need for efficiently yet effectively classifying vehicles into their corresponding categories, which play a crucial role in many areas of infrastructure planning and traffic management. This paper presents two vehicle-type classification approaches; 1) geometric-based and 2) appearance-based. The two classification approaches are used for two tasks: multi-class and intra-class vehicle classifications. For the evaluation purpose of the proposed classification approaches’ performance and the identification of the most effective yet efficient one, 10-fold cross-validation technique is used with a large dataset. The proposed approaches are distinguishable from previous research on vehicle classification in which: i) they consider both geometric and appearance attributes of vehicles, and ii) they perform remarkably well in both multi-class and intra-class vehicle classification. Experimental results exhibit promising potentials implementations of the proposed vehicle classification approaches into real-world applications.

Keywords: appearance attributes, geometric attributes, support vector machine, vehicle classification

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Authors: Stanislovas Buteliauskas, Aušrius Juozapavičius

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City road transport contributes significantly to climate change, and the ongoing world urbanization is only increasing the problem. The paper describes a city planning concept minimizing the number of vehicles on the roads while increasing overall mobility. This becomes possible by utilizing a recently invented two-level road junction with a unique property of serving both as an intersection of uninterrupted traffic and an easily accessible transport hub capable of accumulating private vehicles, and therefore becoming an especially effective park-and-ride solution, and a logistics or business center. Optimized layouts of city road infrastructure, living and work areas, and major roads are presented. The layouts are suitable both for the development of new cities as well as for the expansion of existing ones. Costs of the infrastructure and a positive impact on climate are evaluated in comparison to current city growth patterns.

Keywords: congestion, city infrastructure, park-and-ride, road junctions

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Authors: Ghada S. Moussa

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Vehicle classification is an important task in traffic surveillance and intelligent transportation systems. Classification of vehicle images is facing several problems such as: high intra-class vehicle variations, occlusion, shadow, illumination. These problems and others must be considered to develop a reliable vehicle classification system. In this study, a reliable multi-type vehicle classification system based on Bag-of-Words (BoW) paradigm is developed. Our proposed system used and compared four well-known classifiers; Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Decision Tree to classify vehicles into four categories: motorcycles, small, medium and large. Experiments on a large dataset show that our approach is efficient and reliable in classifying vehicles with accuracy of 95.7%. The SVM outperforms other classification algorithms in terms of both accuracy and robustness alongside considerable reduction in execution time. The innovativeness of developed system is it can serve as a framework for many vehicle classification systems.

Keywords: vehicle classification, bag-of-words technique, SVM classifier, LDA classifier, KNN classifier, decision tree classifier, SIFT algorithm

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Authors: Okyu Kwon, Dongho Kang, Byungsik Kim, Seungkwon Jung

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We propose a technique to measure the impact of loss of traffic function in a particular area to surrounding areas. The proposed method is applied to the city of Seoul, which is the capital of South Korea, with a population of about ten million. Based on the actual road network in Seoul, we construct an abstract road network between 1kmx1km grid cells. The link weight of the abstract road network is re-adjusted considering traffic volume measured at several survey points. On the modified abstract road network, we evaluate the traffic vulnerability by calculating a network measure of betweenness centrality (BC) for every single grid cells. This study analyzes traffic impacts caused by road dysfunction due to heavy rainfall in urban areas. We could see the change of the BC value in all other grid cells by calculating the BC value once again when the specific grid cell lost its traffic function, that is, when the node disappeared on the grid-based road network. The results show that it is appropriate to use the sum of the BC variation of other cells as the influence index of each lattice cell on traffic. This research was supported by a grant (2017-MOIS31-004) from Fundamental Technology Development Program for Extreme Disaster Response funded by Korean Ministry of Interior and Safety (MOIS).

Keywords: vulnerability, road network, beweenness centrality, heavy rainfall, road impact

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Authors: Khalid A. Alsaif, Mosaad A. Foda

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The primary objective of this study is to develop the first round of road traffic noise maps for Riyadh City using Geographical Information Systems (GIS) and software LimA 7810 predictor. The road traffic data were measured or estimated as accurate as possible in order to obtain reliable noise maps. Meanwhile, the attributes of the roads and buildings are automatically exported from GIS. The simulation results at some chosen locations are validated by actual field measurements, which are obtained by a system that consists of a sound level meter, a GPS receiver and a database to manage the measured data. The results show that the average error between the predicted and measured noise levels is below 3.0 dB.

Keywords: noise pollution, road traffic noise, LimA predictor, GIS

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Authors: Filippo Portera

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Usually standard algorithms employ a loss where each error is the mere absolute difference between the true value and the prediction, in case of a regression task. In the present, we present several error weighting schemes that are a generalization of the consolidated routine. We study both a binary classification model for Support Vextor Classification and a regression net for Multylayer Perceptron. Results proves that the error is never worse than the standard procedure and several times it is better.

Keywords: loss, binary-classification, MLP, weights, regression

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Authors: Hamid Reza Behnood, Esmaeel Ayati, Tom Brijs, Mohammadali Pirayesh Neghab

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Investment projects in road safety planning can benefit from an effectiveness evaluation regarding their expected safety outcomes. The objective of this study is to develop a decision support system (DSS) to support policymakers in taking the right choice in road safety planning based on the efficiency of previously implemented safety measures in a set of regions in Iran. The measures considered for each region in the study include performance indicators about (1) police operations, (2) treated black spots, (3) freeway and highway facility supplies, (4) speed control cameras, (5) emergency medical services, and (6) road lighting projects. To this end, inefficiency measure is calculated, defined by the proportion of fatality rates in relation to the combined measure of road safety performance indicators (i.e., road safety measures) which should be minimized. The relative inefficiency for each region is modeled by the Data Envelopment Analysis (DEA) technique. In a next step, a fuzzy decision-making system is constructed to convert the information obtained from the DEA analysis into a rule-based system that can be used by policy makers to evaluate the expected outcomes of certain alternative investment strategies in road safety.

Keywords: performance indicators, road safety, decision support system, data envelopment analysis, fuzzy reasoning

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Authors: Sezin Ekşioğlu

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Multilabel classification has a huge importance for several applications, it is also a challenging research topic. It is a kind of supervised learning that contains binary targets. The distance between multilabel and binary classification is having more than one class in multilabel classification problems. Features can belong to one class or many classes. There exists a wide range of applications for multi label prediction such as image labeling, text categorization, gene functionality. Even though features are classified in many classes, they may not always be properly classified. There are many ensemble methods for the classification. However, most of the researchers have been concerned about better multilabel methods. Especially little ones focus on both efficiency of classifiers and pairwise relationships at the same time in order to implement better multilabel classification. In this paper, we worked on modified ensemble methods by getting benefit from k-Nearest Neighbors and neural network structure to address issues within a beneficial way and to get better impacts from the multilabel classification. Publicly available datasets (yeast, emotion, scene and birds) are performed to demonstrate the developed algorithm efficiency and the technique is measured by accuracy, F1 score and hamming loss metrics. Our algorithm boosts benchmarks for each datasets with different metrics.

Keywords: multilabel, classification, neural network, KNN

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Authors: Sungho Kim, Jaechoul Shin, Yujin Baek

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In recent years, due to the environmental impacts and time factor, etc., various type of pavement deterioration is increasing rapidly such as crack, pothole, rutting and roughness degradation. The Ministry of Land, Infrastructure and Transport maintains regular pavement condition of the highway and the national highway using the pavement condition survey equipment and structural survey equipment in Korea. Local governments that maintain local roads, farm roads, etc. are difficult to maintain the pavement condition using the pavement condition survey equipment depending on economic conditions, skills shortages and local conditions such as narrow roads. This study presents a quantitative evaluation method of the pavement condition through the visual inspection to overcome these problems of roads managed by local governments. It is difficult to evaluate rutting and roughness with the naked eye. However, the condition of cracks can be evaluated with the naked eye. Linear cracks (m), area cracks (m²) and potholes (number, m²) were investigated with the naked eye every 100 meters for survey the cracks. In this paper, crack ratio was calculated using the results of the condition of cracks and pavement condition was evaluated by calculated crack ratio. The pavement condition survey equipment also investigated the pavement condition in the same section in order to evaluate the reliability of pavement condition evaluation by the calculated crack ratio. The pavement condition was evaluated through the SPI (Seoul Pavement Index) and calculated crack ratio using results of field survey. The results of a comparison between 'the SPI considering only crack ratio' and 'the SPI considering rutting and roughness either' using the equipment survey data showed a margin of error below 5% when the SPI is less than 5. The SPI 5 is considered the base point to determine whether to maintain the pavement condition. It showed that the pavement condition can be evaluated using only the crack ratio. According to the analysis results of the crack ratio between the visual inspection and the equipment survey, it has an average error of 1.86%(minimum 0.03%, maximum 9.58%). Economically, the visual inspection costs only 10% of the equipment survey and will also help the economy by creating new jobs. This paper advises that local governments maintain the pavement condition through the visual investigations. However, more research is needed to improve reliability. Acknowledgment: The author would like to thank the MOLIT (Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: asphalt pavement maintenance, crack ratio, evaluation of asphalt pavement condition, SPI (Seoul Pavement Index), visual investigation

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Authors: Sunita Singh, Rajani Srivastava

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For natural resource management and in other applications about earth observation revolutionary remote sensing technology plays a significant role. One of such application in monitoring and classification of crop types at spatial and temporal scale, as it provides latest, most precise and cost-effective information. Present study emphasizes the use of three different vegetation indices of Rapid Eye imagery on crop type classification. It also analyzed the effect of each indices on classification accuracy. Rapid Eye imagery is highly demanded and preferred for agricultural and forestry sectors as it has red-edge and NIR bands. The three indices used in this study were: the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) and all of these incorporated the Red Edge band. The study area is Varanasi district of Uttar Pradesh, India and Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Classification was performed with these three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 85% was obtained using three vegetation indices. The study concluded that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the Rapid Eye imagery can get satisfactory results of classification accuracy without original bands.

Keywords: GNDVI, NDRE, NDVI, rapid eye, vegetation indices

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Authors: Dimitriu Corneliu-Ioan, Gheorghe FrațIlă

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Romania has one of the highest rates of road deaths among European Union Member States, and there is a concern that the country will not meet its goal of "zero deaths" by 2050. The European Union also aims to halve the number of people seriously injured in road accidents by 2030. Therefore, there is a need to improve road infrastructure safety management in Romania. The aim of this study is to analyze road accident data through statistical methods to assess the current state of road infrastructure safety in Bucharest. The study also aims to identify trends and make forecasts regarding serious road accidents and their consequences. The objective is to provide insights that can help prioritize measures to increase road safety, particularly in urban areas. The research utilizes statistical analysis methods, including exploratory analysis and descriptive statistics. Databases from the Traffic Police and the Romanian Road Authority are analyzed using Excel. Road risks are compared with the main causes of road accidents to identify correlations. The study emphasizes the need for better quality and more diverse collection of road accident data for effective analysis in the field of road infrastructure engineering. The research findings highlight the importance of prioritizing measures to improve road safety in urban areas, where serious accidents and their consequences are more frequent. There is a correlation between the measures ordered by road safety auditors and the main causes of serious accidents in Bucharest. The study also reveals the significant social costs of road accidents, amounting to approximately 3% of GDP, emphasizing the need for collaboration between local and central administrations in allocating resources for road safety. This research contributes to a clearer understanding of the current road infrastructure safety situation in Romania. The findings provide critical insights that can aid decision-makers in allocating resources efficiently and institutionally cooperating to achieve sustainable road safety. The data used for this study are collected from the Traffic Police and the Romanian Road Authority. The data processing involves exploratory analysis and descriptive statistics using the Excel tool. The analysis allows for a better understanding of the factors contributing to the current road safety situation and helps inform managerial decisions to eliminate or reduce road risks. The study addresses the state of road infrastructure safety in Bucharest and analyzes the trends and forecasts regarding serious road accidents and their consequences. It studies the correlation between road safety measures and the main causes of serious accidents. To improve road safety, cooperation between local and central administrations towards joint financial efforts is important. This research highlights the need for statistical data processing methods to substantiate managerial decisions in road infrastructure management. It emphasizes the importance of improving the quality and diversity of road accident data collection. The research findings provide a critical perspective on the current road safety situation in Romania and offer insights to identify appropriate solutions to reduce the number of serious road accidents in the future.

Keywords: road death rate, strategic objective, serious road accidents, road safety, statistical analysis

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Authors: Kittichai Thanasupsin, Satis Sukniam

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Pavement marking is an essential task of road construction and maintenance. One of several benefits of pavement markings has been used to provide information about road alignment and road conditions ahead. In some cases, retro-reflectivity of road marking at night may not meet the standard. This degradation may be caused by internal factors such as the size of glass beads and the number of glass beads or external factors such as traffic volume, lane width, vehicle weight, and so on. This research aims to investigate the reflective efficiency of thermoplastic road marking with the glass beads. Ratios of glass beads, ranging from 359 to 553 grams per square meter on an asphaltic concrete, have been tested. The reflective efficiency data was collected at the beginning and at a specific time interval for a total of 8 months. It was found that the difference in glass beads quantity affects the rate of retro-reflectivity but does not affect the diffuse reflectivity. It was also found that other factors affect retro-reflectivity, such as duration, the position of road marking, traffic density, the quantity of glass beads, and dirt coating on top. The dirt coating on top is the most crucial factor that deteriorating retro-reflectivity.

Keywords: thermoplastic pavement marking, retro-reflectivity, diffuse reflectivity, asphalt concrete

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Authors: Sumit Roy

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A clear understanding of pedestrian behaviour at road crossing at intersections is needed for providing necessary infrastructure and also for enhancing pedestrian safety at any intersection. Pedestrian road crossing behaviour is studied at Motijheel and Kakrail intersections where Motijheel intersection is a controlled roundabout, and Kakrail intersection is a signalized intersection. Around 60 people at each intersection were interviewed for a questionnaire survey and video recording at different time of a day was done for observation at each intersection. In case of Motijeel intersection, we got pedestrian road crossings were much higher than Kakrail intersection. It is because the number of workplaces here is higher than Kakrail. From questionnaire survey, it is found that 80% of pedestrians crosses at intersection to avail buses and their loading and unloading locations are at intersection, whereas at Kakrail intersection only 25% pedestrian crosses the road for buses as buses do not slow down here. At Motijheel intersection 25 to 40% of pedestrians choose to jump over the barricade for crossing instead of using overbridge for saving time and labour. On the other hand, the pedestrians using overbridge told that they use overbridge for safety. Moreover, pedestrian crosses at the same pace for both red and green interval with vehicle movement in the range of 12.5 to 14.5 km/h and gaps between vehicle were more than 4 m. Here pedestrian crossing speed varies from 3.5 to 7.2 km/h. In Kakrail intersection the road crossing situation can be classified into 4 categories. In case of red time, pedestrians do not wait to cross the road, and crossing speed varies from 3.5 to 7.2 km/h. When vehicle speed varies from 5.4 to 7.4 km/h, and gaps between vehicle vary from 1.5 to 2 m, most of the pedestrians initially choose to wait and try to cross the road in group with crossing speed 2.7 to 3.5 km/h. When vehicle speed varies from 10.8 to 18 km/h, and gaps between vehicles varies from 2 to 3 m most of the people waits and cross the road in group with crossing speed 3.5 to 5.4 km/h. When vehicle speed varies from 25.2 to 32.4 km/h and gaps between vehicles vary from 4 to 6 m most of the pedestrians choose to wait until red time. In Kakrail intersection 87% of people said that they cross the road with risk and 60% of pedestrians told that it is risky to get on and off the bus at this intersection. Planned location of loading and unloading area for buses can improve the pedestrian road crossing behaviour at intersections.

Keywords: crossing speed, pedestrian behaviour, road crossing, use of overbridge

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Authors: Katherine Carl Payne, Moshe Dror

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The problem of minimum cost routing has been extensively explored in a variety of contexts. While there is a prevalence of routing applications based on least distance, time, and related attributes, exertion-based routing has remained relatively unexplored. In particular, the network structures traditionally used to construct minimum cost paths are not suited to representing exertion or finding paths of least exertion based on road gradient. In this paper, we introduce a topographical network or “topograph” that enables minimum cost routing based on the exertion metric on each arc in a given road network as it is related to changes in road gradient. We describe an algorithm for topograph construction and present the implementation of the topograph on a road network of the state of California with ~22 million nodes.

Keywords: topograph, RPE, routing, GIS

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Authors: Najam Aziz, Nasru Minallah, Ahmad Junaid, Kashaf Gul

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Landcover classification using automated classification techniques, while employing remotely sensed multi-spectral imagery, is one of the promising areas of research. Different land conditions at different time are captured through satellite and monitored by applying different classification algorithms in specific environment. In this paper, a SPOT-5 image provided by SUPARCO has been studied and classified in Environment for Visual Interpretation (ENVI), a tool widely used in remote sensing. Then, Artificial Neural Network (ANN) classification technique is used to detect the land cover changes in Abbottabad district. Obtained results are compared with a pixel based Distance classifier. The results show that ANN gives the better overall accuracy of 99.20% and Kappa coefficient value of 0.98 over the Mahalanobis Distance Classifier.

Keywords: landcover classification, artificial neural network, remote sensing, SPOT 5

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Authors: Tania Viju, Bimal P., Naseer M. A.

Abstract:

This study aims to develop a conceptual framework for the decision support system (DSS), that helps the decision-makers to dynamically choose appropriate safety measures for each identified accident blackspot. An accident blackspot is a segment of road where the frequency of accident occurrence is disproportionately greater than other sections on roadways. According to a report by the World Bank, India accounts for the highest, that is, eleven percent of the global death in road accidents with just one percent of the world’s vehicles. Hence in 2015, the Ministry of Road Transport and Highways of India gave prime importance to the rectification of accident blackspots. To enhance road traffic safety and reduce the traffic accident rate, effectively identifying and rectifying accident blackspots is of great importance. This study helps to understand and evaluate the existing methods in accident blackspot identification and prediction that are used around the world and their application in Indian roadways. The decision support system, with the help of IoT, ICT and smart systems, acts as a management and planning tool for the government for employing efficient and cost-effective rectification strategies. In order to develop a decision criterion, several factors in terms of quantitative as well as qualitative data that influence the safety conditions of the road are analyzed. Factors include past accident severity data, occurrence time, light, weather and road conditions, visibility, driver conditions, junction type, land use, road markings and signs, road geometry, etc. The framework conceptualizes decision-making by classifying blackspot stretches based on factors like accident occurrence time, different climatic and road conditions and suggesting mitigation measures based on these identified factors. The decision support system will help the public administration dynamically manage and plan the necessary safety interventions required to enhance the safety of the road network.

Keywords: decision support system, dynamic management, road accident blackspots, road safety

Procedia PDF Downloads 105

Authors: Ali Babaei

Abstract:

There is a worldwide growing tendency toward construction of infrastructures with the possibility of private sector participation instead of free exploitation of public infrastructures. The construction and development of roads through private sector participation is performed by different countries because of appropriate results and benefits such as compensation of public budget deficit in road construction and maintenance and responding to traffic growth (demand). Toll is the most definite form of budget provision in road development. There are two issues in the toll rate assignment: A. costing of transport, B. Cost allocation and distribution of cost between different types of vehicles as each vehicle pay its own share. There can be different goals in toll collection and its extent is variable according to the strategy of toll collection. Costing principles in different countries are based on inclusion of the whole transport and not peculiar to the toll roads. For example, fuel tax policy functions where the road network users pay transportation cost (not just users of toll road). Whereas transportation infrastructures in Iran are free, these methods are not applicable. In Iran, different toll freeways have built by public investment and government provides participation in the road construction through encouragement of financial institutions. In this paper, the existing policies about the toll roads are studied and then the appropriate method of costing and cost allocation to different vehicles is introduced.

Keywords: toll allocation, road pricing, transportation, financial and industrial systems

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Authors: S. Ananthakrishnan, U. H. Acharya

Abstract:

Whether shopping in a retail location or via the internet, consumers expect to receive their products intact. When products arrive damaged or over-packaged, the result can be customer dissatisfaction and increased cost for retailers and manufacturers. The packaging performance depends on both the transport situation and the packaging design. During transportation, the packaged products are subjected to the variation in vibration levels from transport vehicles that vary in frequency and acceleration while moving to their destinations. Spirits manufactured by this Company were being transported to various parts of the country by road. There were instances of package breaking and customer complaints. The vibration experienced on a straight road at some speed may not be same as the vibration experienced by the same vehicle on a curve at the same speed. This vibration may negatively affect the product or packing. Hence, it was necessary to conduct a physical road test to understand the effect of vibration in the packaged products. The field transit trial has to be done before the transportations, which results in high investment. The company management was interested in developing an in-house test environment which would adequately represent the transit conditions. With the objective to develop an in-house test condition that can accurately simulate the mechanical loading scenario prevailing during the storage, handling and transportation of the products a brainstorming was done with the concerned people to identify the critical factors affecting vibration rate. Position of corrugated box, the position of bottle and speed of vehicle were identified as factors affecting the vibration rate. Several packing scenarios were identified by Design of Experiment methodology and simulated in the in-house test facility. Each condition was observed for 30 minutes, which was equivalent to 1000 km. The achieved vibration level was considered as the response. The average achieved in the simulated experiments was near to the third quartile (Q3) of the actual data. Thus, we were able to address around three-fourth of the actual phenomenon. Most of the cases in transit could be reproduced. The recommended test condition could generate a vibration level ranging from 9g to 15g as against a maximum of only 7g that was being generated earlier. Thus, the Company was able to test the packaged cartons satisfactorily in the house itself before transporting to the destinations, assuring itself that the breakages of the bottles will not happen.

Keywords: ANOVA, Corrugated box, DOE, Quartile

Procedia PDF Downloads 94