Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Evaluating and Prioritizing the Effect of Human and Road Factors on Road Accidents and Providing Solutions (Case Study: Northern Roads of Ardabil Province) 2131 2148 230540 10.22119/ijte.2025.465608.1666 EN Yousef Sajed Department of Civil Engineering, Ard. C., Islamic Azad University, Ardabil, Iran 0000-0002-9561-9898 Navid Daneshfar Department of Civil Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran Seyed Hojjat Fatahi Chief of Traffic Police, Ardabil Provincial Police Headquarters, Ardabil, Iran Journal Article 2025 02 14 A combination of various human and road factors leads to accident occurrence. By studying drivers' behavior, changing and developing geometric factors of the road, securing, and control measures, number of accidents will change significantly. This research focuses on human and road factors. Based on analysis of accidents in the northern roads of Ardabil province, accidents have been evaluated and analyzed using R software and the main causes have been identified. Also, based on CMF and N predicted (accident number prediction) methods in the Highway Safety Manual (HSM), the impact of lane widening, correcting some geometric factors, and converting the two-lane two-way road into a separated four-lane road on accident numbers was examined. The results of the human factors study indicate that exceeding the safe speed limit, unauthorized overtaking and the driver's failure to pay attention to the front are the most important factors in the accidents of roadways, which are responsible for more than 90% of the accidents, and the modification measures of the road geometry (such as widening and converting from two-lane to four-lane) as well as, securing the route and intersections, have a significant effect on reducing accidents. Prediction models indicate that widening the route and providing an 11m width including asphalt shoulders, applying the above measures, and controlling the road with cameras will lead to a 40% reduction in accidents in the short and medium term, and converting the two-lane route into a four-lane one will result in a 34% reduction in accidents.

http://www.ijte.ir/article_230540_e26d22d911fef48f9702db04de2a06e2.pdf

Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Which Personality Types are More Prone to a Traffic Accident? 2149 2171 228632 10.22119/ijte.2025.486214.1679 EN Hassan Jafari Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran Mansour Hadji Hosseinlou Department of Civil Engineering, K. N. Toosi University of Technology, Tehran, Iran Mahdi Yazdanpanah Department of Engineering, Faculty of Civil Engineering, University of Garmsar, Garmsar, Iran 0000-0001-7411-4017 Journal Article 2024 11 20 Every 24 seconds, one person dies in a traffic accident. The most considerable rate of road traffic deaths occurs in low and middle-income countries. As a result, it is critical to pay special attention to road safety in these countries. Road safety may be influenced by human, road, and vehicle variables. Despite significant investments in automotive and road building, long-term benefits are not yet comparable with these expenses; as a result, it is required to investigate the sources of this inefficiency in other instances. Academics have traditionally paid less attention to human psychological factors, the primary element determining accidents. Due to the complexity of the human being, continual and broad research to establish its characteristics might always be beneficial in the long run for improving road safety. Numerous elements influence driver behaviour, including the driver’s personality, identity, responsibility, haste, risk-taking, information, fear, loyalty, and experience. <span style="font-size: 10.0pt; line-height: 115%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-bidi-language: FA;">As a result, this study analyzed five personality traits of certified individuals residing in Tehran, Iran, using the standard 60-item NEO Five-Factor Inventory questionnaire (NEO-FFI). The impacts of these factors on the number of accidents over the last five years were also evaluated. The study employed a structural equation model and found that Neuroticism has a direct and significant association with the number of accidents during the previous five years. Contrary to this, agreeableness and openness had a significant and inverse relationship with the number of traffic accidents.

http://www.ijte.ir/article_228632_72503e003be904c7899843ca7b4db9f7.pdf

Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Enhancing Short-Term Traffic Flow Forecasting by Hybrid Deep Learning Architectures and Attention Mechanisms (Case Study: High-Density Karaj-Chalous Road, Iran) 2173 2192 230541 10.22119/ijte.2025.500027.1683 EN Seyed Saber Naseralvai Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 0000-0002-5392-910X Soheil Rezashoar Department of Transportation Planning, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran 0009-0003-3175-1026 Akram Mazaheri Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran 0009-0000-1439-3915 Saman Shafaati Department of Civil Engineering, Shahid Bahonar University of Kerman, Kerman, Iran 0009-0004-7279-7511 Journal Article 2025 01 15 The main tool to mitigate congestion and improve travel experiences effectively in intelligent traffic management is to predict the accurate and timely short-term traffic flow on high-volume roads. We present the performances of different deep learning models, such as LSTM, GRU, CNN, their hybrids CNN-LSTM and CNN-GRU, and versions with an attention mechanism for one-hour-ahead traffic flow prediction on mountainous and high-density Karaj-Chalous Road. The input data include the traffic data from two traffic counters. The cited data were derived for a period ranging from 01/01/1401 to 01/01/1403. Besides, the synoptic meteorological data were acquired within three-hour intervals, while the models are compared based on various quantitative accuracy and error metrics. The results showed that the CNN-LSTM model was the best among the rest, with an R² value of 0.83, because it captured complex traffic patterns and temporal dependencies effectively. The other models ranked next were LSTM, GRU, CNN-LSTM-GRU, and CNN-GRU, with R2 values of 0.82, 0.81, 0.80, and 0.80, respectively. While the weakest models, CNN and CNN-MultiHead-Attention, yielded an R² of 0.60 and 0.62, respectively, this is due to a lack of consideration in these models regarding the nature of traffic data as a time series. Employing attention mechanisms improved prediction accuracy in some model architectures. This effect was highly varied based on the model structure itself. The results depict that deep, hybrid models with the integration of attention mechanisms can give more reliable and valuable forecasts to the intelligent transportation management systems for better travel planning and congestion reduction in similar roadways.

http://www.ijte.ir/article_230541_bccfec69c12f4875927673c36fb63cf4.pdf

Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Comparative Analysis of Packaging Requirements, Laws, and Regulations in Iran and Neighboring Countries 2193 2215 228633 10.22119/ijte.2025.490046.1695 EN Hamid Mirzahossein Associate Professor, Department of Transportation Planning, Faculty of Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran 0000-0003-1615-9553 Mohammad Reza Kamalinezhad Master’s Student in Transportation Engineering and Planning, Faculty of Engineering, Imam Khomeini International University (IKIU), Qazvin, Iran Journal Article 2025 04 30 <span style="font-size: 10.0pt; line-height: 115%; font-family: 'Times New Roman',serif; mso-ascii-theme-font: major-bidi; mso-hansi-theme-font: major-bidi; mso-bidi-theme-font: major-bidi; mso-bidi-language: FA;">Packaging plays a crucial role in supply chains, product marketing, and consumer attraction while ensuring quality preservation and market competitiveness. This study conducts a comparative analysis of packaging requirements, laws, and regulations in Iran and neighboring countries (Iraq, the UAE, Turkey, Azerbaijan, and Russia). Using a library-based and documentary research approach, data were collected from reliable sources, including national laws, regulatory standards, industry reports, and academic studies. The analysis evaluates eight key criteria: standards and regulations, packaging technologies, waste management, recycling, and cultural influences on consumer preferences. Both qualitative and quantitative methods were employed, with findings presented through comparative tables and visual charts. The study highlights Iran’s advancements in food safety and labeling but identifies gaps in recycling, waste management, and alignment with international standards. In contrast, the UAE and Turkey demonstrate strong compliance with global norms and technological advancements, serving as potential models for Iran. By adopting best practices from these countries, Iran can enhance its packaging industry, improving efficiency, sustainability, and global competitiveness. The findings underscore the need for greater collaboration with international organizations and investment in eco-friendly packaging solutions. Implementing these improvements would boost product safety, reduce waste, and strengthen Iran’s position in international markets.

http://www.ijte.ir/article_228633_c77eea69c68b12d49e82af4dc021c67a.pdf

Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Development of a Safety Indicator Model Using Braking Behavior at Urban Signalized Four-Leg Intersections 2217 2232 228634 10.22119/ijte.2025.523010.1696 EN Kamran Sarvari Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran 0009-0007-2428-0480 Amin Mirza Boroujerdian Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran 0000-0003-3939-2526 Journal Article 2025 05 12 With the growth of the urban population and the resulting increase in the number of vehicles, ensuring safety on urban roads has become inevitable. In this context, the safety assessment of signalized intersections—due to their significant traffic and safety advantages—has become a common approach in traffic management. Signalized intersections are considered critical components of urban service systems. As urban traffic volumes increase, the number of trips rises accordingly, leading to a higher likelihood of conflicts and crashes. This study investigates the geometric and traffic-related factors influencing the frequency of hard braking events at urban signalized intersections, aiming to provide a framework for evaluating the safety index of such locations. This study analyzes the geometric and traffic characteristics of seven urban signalized intersections. Using aerial videography at each site and processing the footage through image analysis software, the number of critical conflicts—based on the hard braking conflict index—was determined. A total of 92,586 conflict events related to this safety index were examined. The statistical analysis was performed using a multiple linear regression model. The results indicate that an increase of one unit in traffic volume leads to a 0.6% rise in critical conflicts based on the hard braking index. Furthermore, a one km/h increase in average exit speed from intersections is associated with approximately a 3% increase in critical conflict frequency.

http://www.ijte.ir/article_228634_05280dcc514c0b9f36937d85b2003683.pdf

Tarrahan Parseh Transportation Research Institute International Journal of Transportation Engineering 2322-259X 13 2 2025 10 01 Analysis of Digital Maturity Components in Rail Transportation Industry: A Text Mining Approach 2233 2252 226814 10.22119/ijte.2025.529316.1700 EN Samaneh Moradi Department of Industrial Management, Qa.C., Islamic Azad University, Qazvin, Iran 0009-0006-9062-5419 Mehrdad Hosseini Shakib Department of Industrial Management , Ka.C. , Islamic Azad University, Karaj, Iran 0000-0003-1243-2559 Ali Badizadeh Department of Industrial Management, Qa.C. , Islamic Azad University, Qazvin, Iran 0000-0003-3141-5391 Journal Article 2025 06 09 The assessment of digital maturity in rail transportation, a crucial infrastructure for sustainable development, has gained increasing importance. This research employs text mining to systematically analyze scientific literature exploring digital maturity components in the rail industry. 87 scientific articles published between 2016 and 2025 from reputable international databases were analyzed using advanced text mining techniques, including sentiment analysis, K-means clustering, LDA topic modeling, and n-gram algorithm in Python. Results identify five key areas: emerging technologies, digitalization challenges, cybersecurity, practical applications, and environmental sustainability as main transformation axes. Sentiment analysis reveals predominantly positive attitudes (65%) towards this transformation, with 15% negative and 20% neutral responses. The findings recommend rail industry leaders adopt an integrated approach to digital transformation management, emphasizing cybersecurity infrastructure, environmental sustainability, and digital skills development.

http://www.ijte.ir/article_226814_8820053ebb6d434015dc4141b232bb77.pdf