Promoting Pedestrian Transportation for Reducing Air Pollution from Urban Transport

Increasing air pollution around the world causes many problems, especially in the field of health. Air pollution affects not only human health but also other living things health. The factors that cause air pollution the most are heating, industry, and transportation. Many countries in the world carry out various studies to reduce the effect of these factors on air pollution. Especially in the field of transportation, studies have been quite a lot in recent years. In this study, air pollution caused by transportation in Erzurum, Turkey has investigated. Emission amounts of NOX, PM10, and SO2 values have calculated according to the types of vehicles in the city. Then, the amount of emissions from transportation in the total sector has revealed. The transportation structure of the city has examined in general terms and the missing aspects in terms of pedestrian transportation have revealed. Finally, some solution proposals aiming to encourage the use of pedestrian transportation and micro mobility vehicles in order to reduce motor land vehicles are presented

An Estimation of Transport Energy Demand in Turkey via Artificial Neural Networks

The transportation sector accounts for nearly 19% of total energy consumption in Turkey, where energy demand increases rapidly depending on the economic and human population growth and the increasing number of motor vehicles. Hence, the estimation of future energy demand is of great importance to design, plan and use the transportation systems more efficiently, for which a reliable quantitative estimation is of primary concern. However, the estimation of transport energy demand is a complex task, since various model parameters are interacting with each other. In this study, artificial neural networks were used to estimate the energy demand in transportation sector in Turkey. Gross domestic product, oil prices, population, vehicle-km, ton-km and passenger-km were selected as parameters by considering the data for the period from 1975 to 2016. Seven models in total were created and analyzed. The best yielding model with the parameters of oil price, population and motor vehicle-km was determined to have the lowest error and the highest R2 values. This model was selected to estimate transport energy demand for the years 2020, 2023, 2025 and 2030.</p

Forecasting the Accident Frequency and Risk Factors: A Case Study of Erzurum, Turkey

Nowadays, life is intimately associated with transportation, generating several issues on it. Numerous works are available concerning accident prediction techniques depending on independent road and traffic features, while the mix parameters including time, geometry, traffic flow, and weather conditions are still rarely ever taken into consideration. This study aims to predict future accident frequency and the risk factors of traffic accidents. It utilizes the Generalized Linear Model (GLM) and Artificial Neural Networks (ANN) approaches to process and predict traffic data efficiently based on 21500 records of traffic accidents that occurred in Erzurum in Turkey from 2005 to 2019. The results of the comparative evaluation demonstrated that the ANN model outperformed the GLM model. The study revealed that the most effective variable was the number of horizontal curves. The annual average growth rates of accident occurrences based on the ANNꞌs method are predicted to be 11.22% until 2030

An Artificial Neural Network Model for Highway Accident Prediction: A Case Study of Erzurum, Turkey

This study presents an accident prediction model of Erzurum’s Highways in Turkey using artificial neural network (ANN) approaches. There are many ANN models for predicting the number of accidents on highways that were developed using 8 years with 7,780 complete accident reports of historical data (2005-2012). The best ANN model was chosen for this task and the model parameters included years, highway sections, section length (km), annual average daily traffic (AADT), the degree of horizontal curvature, the degree of vertical curvature, traffic accidents with heavy vehicles (percentage), and traffic accidents that occurred in summer (percentage). In the ANN model development, the sigmoid activation function was employed with Levenberg-Marquardt algorithm. The performance of the developed ANN model was evaluated by mean square error (MSE), the root mean square error (RMSE), and the coefficient of determination (R2). The model results indicate that the degree of vertical curvature is the most important parameter that affects the number of accidents on highways.</p

Examination of Aircraft Accidents That Occurred in the Last 20 Years in the World

Air transportation is a very preferred type of transportation for long-distance trips. This type of transportation has made great progress, especially in the last 20 years with the development of technology. Thanks to its fast and safe, passenger capacity is gradually increasing. Despite this situation, the mortality rate is quite high in the case of an aircraft accident. For this reason, hundreds of people can die in a single accident. In this study, aircraft accidents that occurred in the last 20 years in the world were examined. The data including the number of accidents, the number of deaths and the process of the flight where the accidents occurred were used. These data were analyzed using data mining algorithms such as multi-layer perceptron, k nearest neighborhood, Naive Bayes, J48 and regression methods. Accordingly, it was determined that the algorithm that gives the best results for error scale and performance analysis among five different algorithms is J48. Using this algorithm, the occurrence flight phase of aircraft accidents is classified in more detail. Thanks to this study, it has been revealed that choosing the J48 algorithm for the classification of similar data sets will give better results. Also, this study provides significant benefits in terms of getting to the center of the problems, as the stages of accidents are more detailed. Accordingly, it is possible to reduce accidents if policy makers carry out studies taking into account the stages in which accidents occur

Impact of Covid-19 on Public Transportation Usage and Ambient Air Quality in Turkey

COVID-19 caused by the SARS-CoV-2 virus is a global health concern due to the quick spread of the disease. In Turkey, the first confirmed COVID-19 case and death occurred on 11 and 15 March 2020, respectively. There is a lack of research on the impact of COVID-19 on public transportation mobility and the Air Quality Index (AQI) around the world. The objective of this research is to consider the impact of COVID-19 on public transportation usage and consequently the AQI level in Turkey. Data collection for the analysis of public transportation usage and the air quality status during pre-lockdown and lockdown was carried out using the public transportation applications Moovit and World’s Air Pollution. The results demonstrated that during the lockdown in Ankara and Istanbul, public transportation usage dramatically decreased by more than 80% by the end of March and did not change significantly until the end of May. As regards air quality, the results confirmed that air quality improved significantly during the lockdown. For Ankara and Istanbul, the improvement was estimated at about 9% and 47%, respectively

A Study on the Examination of the Geologic Structure in terms of Rail Transportation

966-970Rail systems have an important place among the types of transportation. Although it was preferred for intercity transportation in the past, but now-a-days it is frequently preferred in urban roads. Some rail system structures move from the ground surface, while others move under the ground. Various geotechnical researches have been carried out for rail systems moving under the ground. However, for the rail vehicles moving on the ground surface are generally placed on the highway route, ground parameters are not taken into consideration. This situation can cause serious rail system accidents. This study has been conducted in Turkey's Erzurum drilling planned light rail system in terms of soil properties, it was evaluated by survey results of drilling borehole, microtremor, and multichannel analysis of surface waves (MASW). According to the results of this study, a part of the light rail system (LRS) route was found to be insufficient in terms of ground safety. For this reason, improvement in the ground or revision of the route has been suggested

A Study on the Examination of the Geologic Structure in terms of Rail Transportation

Rail systems have an important place among the types of transportation. Although it was preferred for intercity transportation in the past, but now-a-days it is frequently preferred in urban roads. Some rail system structures move from the ground surface, while others move under the ground. Various geotechnical researches have been carried out for rail systems moving under the ground. However, for the rail vehicles moving on the ground surface are generally placed on the highway route, ground parameters are not taken into consideration. This situation can cause serious rail system accidents. This study has been conducted in Turkey's Erzurum drilling planned light rail system in terms of soil properties, it was evaluated by survey results of drilling borehole, microtremor, and multichannel analysis of surface waves (MASW). According to the results of this study, a part of the light rail system (LRS) route was found to be insufficient in terms of ground safety. For this reason, improvement in the ground or revision of the route has been suggested

Dünyada Son 20 Yılda Meydana Gelen Uçak Kazalarının İncelenmesi

Air transportation is a very preferred type of transportation for long-distance trips. This type of transportation has made great progress, especially in the last 20 years with the development of technology. Thanks to its fast and safe, passenger capacity is gradually increasing. Despite this situation, the mortality rate is quite high in the case of an aircraft accident. For this reason, hundreds of people can die in a single accident. In this study, aircraft accidents that occurred in the last 20 years in the world were examined. The data including the number of accidents, the number of deaths and the process of the flight where the accidents occurred were used. These data were analyzed using data mining algorithms such as multi-layer perceptron, k nearest neighborhood, Naive Bayes, J48 and regression methods. Accordingly, it was determined that the algorithm that gives the best results for error scale and performance analysis among five different algorithms is J48. Using this algorithm, the occurrence flight phase of aircraft accidents is classified in more detail. Thanks to this study, it has been revealed that choosing the J48 algorithm for the classification of similar data sets will give better results. Also, this study provides significant benefits in terms of getting to the center of the problems, as the stages of accidents are more detailed. Accordingly, it is possible to reduce accidents if policy makers carry out studies taking into account the stages in which accidents occur

Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7

Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and undermines traffic safety and comfort. To address this issue, the authors considered pedestrian crosswalks where vehicles, pedestrians, and micro-mobility vehicles collide at right angles in an urban road network. These road sections are areas where vulnerable people encounter vehicles perpendicularly. In order to prevent accidents in these areas, it is planned to introduce a warning system for vehicles and pedestrians. This procedure consists of multi-stage activities by sending warnings to drivers, disabled individuals, and pedestrians with phone addiction simultaneously. This collective autonomy is expected to reduce the number of accidents drastically. The aim of this paper is the automatic detection of a pedestrian crosswalk in an urban road network, designed from both pedestrian and vehicle perspectives. Faster R-CNN (R101-FPN and X101-FPN) and YOLOv7 network models were used in the analytical process of a dataset collected by the authors. Based on the detection performance comparison between both models, YOLOv7 accuracy was 98.6%, while the accuracy for Faster R-CNN was 98.29%. For the detection of different types of pedestrian crossings, YOLOv7 gave better prediction results than Faster R-CNN, although quite similar results were obtained