Transportation Engineering最新文献

{"title":"Calibration of SUMO Microscopic Simulation for Heterogeneous Traffic Condition: The Case of the City of Khulna, Bangladesh","authors":"Md. Mynul Hossain Chowdhury,&nbsp;Tanmoy Chakraborty","doi":"10.1016/j.treng.2024.100281","DOIUrl":"10.1016/j.treng.2024.100281","url":null,"abstract":"<div><div>In the context of Bangladesh, Efficient modeling of vehicular traffic is a challenging task. Simulation is one of the approaches for traffic modeling. As cities become more urbanized, intelligent transportation systems (ITS) are becoming progressively more important to the development of future smart cities. The purpose of this research is to compare simulation data with field data to calibrate the SUMO simulation model tool. In this study, a microscopic traffic simulation model for some CBD areas of Khulna City has been calibrated. However, the focus area of Powerhouse is strategically located at the intersection of four key places in Khulna City, encompassing the central business district (CBD) and the city's railway station. During the peak hour, congestion frequently occurs at this crossroads. This research aims to provide alternative scenarios to reduce queue and travel time in the Powerhouse intersect. Types of observation data are flow, length of queue, and travel time that are observed during field survey. The calibration process is done by minimizing the root mean square error (RMSE) of queue, and travel time, and combining both of them between observation and calibrated model. After the calibration process, the sigma and tau values came out to be 0.3 and 1.4, respectively. By using this calibrated value, the travel time output may accurately reflect real-world scenarios, allowing for experimentation with various scenarios. This study presented two alternate scenarios aimed at enhancing the performance of Powerhouse. Alternate: 1 is more of a geometric modification with the installment of Channelization, it will reduce travel time substantially for each lane, nearly 10 % to 30% of its current time. Alternate: 2 is more of applying strict and fast regulations for the traffic which is Traffic light management, it is a more convenient option that appears. The results generated by utilizing SUMO can be reliable and helpful in developing and optimizing urban transportation systems in the future. It is essential to understand that traffic simulation with SUMO is merely a transportation decision-making and planning tool and must be linked with field observations and reliable data for suitable and efficient transportation solutions.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100281"},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the relationship between data sample size and traffic flow prediction accuracy","authors":"Jianhu Zheng ,&nbsp;Minghua Wang ,&nbsp;Mingfang Huang","doi":"10.1016/j.treng.2024.100279","DOIUrl":"10.1016/j.treng.2024.100279","url":null,"abstract":"<div><div>Efficiently extracting and analyzing large urban traffic data, accurately predicting traffic conditions, and improving urban traffic management require careful selection of an appropriate data sample size. The suitable size of data sample assumes paramount importance in fostering sustainable transportation development. This paper investigates the relationship between traffic flow prediction performance and data sample size, considering data sample missing rates, duration, and road segment coverage. Real traffic flow data from 13 road sections in Changsha, China, are analyzed using the Decision Tree, Support Vector Machine, Gaussian Process Regression and Artificial Neural Network models. Some key findings include: Lower data sample loss rates improve prediction accuracy by capturing traffic flow patterns effectively, while higher loss rates decrease accuracy; an optimal data sample duration of around 7 days balances prediction accuracy and data stability, with longer durations providing more historical data but risking complexity; Broader road segment coverage gives a more comprehensive traffic flow information, but excessive coverage introduces noise and impacts the improvement of prediction accuracy. The results highlight the significant impact of data sample size on prediction performance. Enhancing reliability can be achieved by reducing data loss, selecting suitable durations, and considering appropriate road segment coverage, supporting improved traffic management and route planning.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100279"},"PeriodicalIF":0.0,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142358991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive analysis for removing obstacles in electric mobility: Revolution into EV adoption","authors":"Sujit Kumar ,&nbsp;Jayant Giri ,&nbsp;Sasanka Sekhor Sharma ,&nbsp;Shruti R. Gunaga ,&nbsp;Manikanta G ,&nbsp;T. Sathish ,&nbsp;S.M. Mozammil Hasnain ,&nbsp;Rustem Zairov","doi":"10.1016/j.treng.2024.100277","DOIUrl":"10.1016/j.treng.2024.100277","url":null,"abstract":"<div><div>This study aims to get insights into the overall consumer opinion of electric vehicles (EVs) and the obstacles that hinder their broad adoption. This study seeks to uncover and comprehend the elements associated with consumer purchases through theme analysis, which offers a broader spectrum of expression compared to conventional survey methods. Additionally, it considers a factor as influence of emotions is often disregarded. This study is using electronic word-of-mouth (eWOM) as a primary data source, highlighting the study's relevance to the digital age. Individuals predominantly utilize online platforms to express their opinions and freely disseminate information, which identifies the discrepancies, both tangible and intangible between the features and benefits of EVs and the consumer's expectations. The system results shows that, the enhanced vehicle range can significantly minimize the public charging infrastructure reliance with range of anxiety and long recharge times. The inter connection between the obstacles shows the complexity of overcoming barriers to widespread EV adoption. This study has significance into the interconnections among these obstacles, which enlarge a detrimental cascade impact on the total adoption rate.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100277"},"PeriodicalIF":0.0,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of machine learning techniques for enhanced vehicle tracking and analysis","authors":"Seema Rani,&nbsp;Sandeep Dalal","doi":"10.1016/j.treng.2024.100271","DOIUrl":"10.1016/j.treng.2024.100271","url":null,"abstract":"<div><div>The past few years have seen a marvellous growth in technology and science. This rapid improvement has proven to be a blessing, making human life easier. Technological developments such as autonomous driving systems and electric cars have made it easier to travel in a dependable and economical manner, satisfying the increasing need for convenient and environmentally friendly travel. However, the increase in traffic has led to a surge in accidents and road casualties. Despite efforts to enhance automobile design and traffic control, there remains a significant need for implementing a system for vehicle tracking, accident detection, and notification. Delays in information and unfulfilled medical needs often result in the loss of lives following accidents. This study reviews and compares different automatic accident detection and notification systems that use accelerometers, vibration detectors, and GPS technology to notify registered contacts of an accident's location via SMS or email. The analysis that follows will specifically look at the benefits, drawbacks, and future uses of various technologies that are used in these systems. In this study, different machine learning-based methods for improving the accuracy of car tracking and cutting down on reaction times in accident situations will be looked at and compared. For testing their usefulness, we used deep learning models like CNN, SVM, and YOLOv3 on a number of different datasets. According to our data, these methods greatly enhance the accuracy of spotting, with YOLOv3 showing the best level of accuracy. Furthermore, the study talks about the pros, cons, and possible future uses of these technologies. It stresses the need for more research into improving model performance in different situations.</div></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142421044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk and vulnerability analysis of road network in landslide prone areas in Munnar region, India","authors":"P.N. Salini ,&nbsp;P. Rahul","doi":"10.1016/j.treng.2024.100275","DOIUrl":"10.1016/j.treng.2024.100275","url":null,"abstract":"<div><p>Catastrophes like landslides have the potential to impair critical transportation infrastructure, particularly road networks. The hilly regions in the state of Kerala in India are particularly prone to hazards and changing climate conditions. During the monsoon season, landslides are common in the Western Ghats, and the intensity of adverse impacts is more severe due to its densely populated regions. The study area of this research is in Idukki district of Kerala, where over 60 % of the land is prone to landslides. The monsoon rains bring with them a slew of disastrous landslides in the region. Most of the roadways in the study area are often disrupted due to landslides. Landslide risk assessment (LRA) is a crucial component of research on adverse impacts of landslide. The use of Geographical information System (GIS) and Multi Criteria Decision Analysis (MCDA) using Analytical Hierarchical Process (AHP) for susceptibility mapping and hazard risk assessment assists in the identification of disaster-prone areas. The data collected by field surveys were used to confirm the study results and the high-risk zones of the region were identified and the risk maps are prepared for the region as well as for the road network in the region. The risk of landslides may be described as the possibility of negative repercussions on road network thereby adversely impacting the inhabitants of the region. The landslide risk assessment of the road network in a hilly region is carried out which gives important insights for risk management and for future planning of resilient development in the region. This study enhances the knowledge for management of road network risk and vulnerabilities in intricate hilly region settings by developing a thorough vulnerability analysis framework. The research advances by giving transportation engineers a useful quantitative tool for identifying the risk and vulnerabilities of road network and directing design strategies and mitigation measures to lessen the possible negative effects of disruptive events like landslides on road infrastructure. The research findings could be an input for policy makers to plan for alternative resilient strategies for landslide risk management in road networks. The rational methodology adopted here could be replicated for carrying out risk and vulnerability assessment in other landslide prone areas.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100275"},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000496/pdfft?md5=aad41dc2c6448d4cb69d0bdf20512803&pid=1-s2.0-S2666691X24000496-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142270433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of a low-cost comprehensive pavement inspection system","authors":"Lizette Tello-Cifuentes,&nbsp;Sergio Acero,&nbsp;Johannio Marulanda,&nbsp;Peter Thomson,&nbsp;Jhon Jairo Barona","doi":"10.1016/j.treng.2024.100274","DOIUrl":"10.1016/j.treng.2024.100274","url":null,"abstract":"<div><p>Assessing the condition of roads is crucial to the maintenance and rehabilitation process as a country's progress is closely linked to its transport infrastructure. Therefore, it is essential to have well-maintained roads and to be able to control and monitor them properly. Technological advancements have transformed the way pavement inspections are carried out. This study presents an innovative approach that combines stereo cameras and a GPS module for efficient and accurate data collection. This integration of low-cost technologies provides a detailed three-dimensional view of pavements, complemented by accurate geospatial information. The experimental results showed that the 3D images of pavement damage had a relative volume measurement error of 0.80 %. Unlike traditional systems such as LIDAR and ground-penetrating radar, which involve more expensive technologies, the proposed method offers a cost-effective solution. This methodology not only simplifies the inspection process but also improves the planning and execution of road maintenance and repair activities. Its low cost makes it a viable option for various projects and applications in road infrastructure.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100274"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000484/pdfft?md5=295cfaef8475d947346c1686e4ab6aa2&pid=1-s2.0-S2666691X24000484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven bottleneck detection on Tehran highways","authors":"Hamid Mirzahossein ,&nbsp;Pedram Nobakht ,&nbsp;Iman Gholampour","doi":"10.1016/j.treng.2024.100273","DOIUrl":"10.1016/j.treng.2024.100273","url":null,"abstract":"<div><p>In metropolitan areas, traffic congestion has become a prevalent challenge due to rapid urbanization and increased vehicle usage, adversely impacting mobility, productivity, and quality of life. Identifying and mitigating persistent traffic bottlenecks is crucial for developing efficient transportation systems and guiding infrastructure planning decisions. This research proposes an innovative data-driven methodology to pinpoint recurrent traffic bottlenecks in Tehran's extensive highway network, addressing the limitations of traditional traffic monitoring methods. Through data mining and image processing techniques applied to 16 months of traffic flow maps from Google Maps, diverse information is extracted, including traffic nodes, congestion hotspots, and locations with the longest queue lengths. The image processing approach involves color-based segmentation, pixel-level analysis, and machine learning algorithms to determine congestion levels across the highway network. The identified bottlenecks are validated against ground truth data from CCTV cameras, demonstrating a remarkable 92 % correlation for key identified points. The proposed approach leverages the power of advanced analytics to comprehensively analyze all major highways, including areas lacking CCTV infrastructure. The robust validation process reinforces the reliability of this data-driven solution in capturing real-world traffic dynamics. As urban mobility challenges escalate globally, the integration of diverse data sources and cutting-edge techniques will be instrumental in guiding intelligent transportation planning and policy decisions.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100273"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000459/pdfft?md5=f5bf9175387801379aacf5f591916102&pid=1-s2.0-S2666691X24000459-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Travel time prediction for an intelligent transportation system based on a data-driven feature selection method considering temporal correlation","authors":"Amirreza Kandiri ,&nbsp;Ramin Ghiasi ,&nbsp;Maria Nogal ,&nbsp;Rui Teixeira","doi":"10.1016/j.treng.2024.100272","DOIUrl":"10.1016/j.treng.2024.100272","url":null,"abstract":"<div><p>Travel-time prediction is a critical component of Intelligent Transportation Systems (ITS), offering vital information for tasks such as accident detection, congestion management, and traffic flow optimisation. Accurate predictions are highly dependent on the selection of relevant features. In this study, a two-stage methodology is proposed which consists of two layers of Optimisation Algorithm and one Data-Driven method (OA2DD) to enhance the accuracy and efficiency of travel-time prediction. The first stage involves an offline process where interconnected optimisation algorithms are employed to identify the optimal set of features and determine the most effective machine learning model architecture. In the second stage, the real-time process utilises the optimised model to predict travel times using new data from previously unseen parts of the dataset. The proposed OA2DD method was applied to a case study on the M50 motorway in Dublin. Results show that OA2DD improves the convergence curve and reduces the number of selected features by up to 50 %, leading to a 56 % reduction in computational costs. Furthermore, using the selected features from OA2DD, reduced the prediction error by up to 29 % compared to the full feature set and other feature selection methods, demonstrating the method's effectiveness and robustness.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000472/pdfft?md5=555a3c2d6bba3db50492e76fdef9983c&pid=1-s2.0-S2666691X24000472-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An examination of high-speed aircraft – Part 2: Safety and reliability","authors":"Luke Pollock ,&nbsp;Graham Wild","doi":"10.1016/j.treng.2024.100269","DOIUrl":"10.1016/j.treng.2024.100269","url":null,"abstract":"<div><p>This paper presents a detailed accident investigation into incidents involving high-speed vehicles, particularly supersonic and hypersonic platforms. Examining challenges related to engine performance, structural integrity, and economic aspects in military aerospace, the study emphasizes the importance of real-time health monitoring systems. A key highlight is the exploration of how these systems support the autonomy of hypersonic vehicle. While recognizing challenges related to sensitive technologies and data, the paper outlines research directions encompassing human factors, simulation and training programs, and policy advocacy for integrating high-speed aircraft into existing airspaces. In summary, this research contributes valuable insights to the understanding of high-speed aviation accidents, with implications for improving safety and efficiency in the future and ultimately shows that high-speed aviation safety cannot be treated in the same manner as its subsonic counterpart.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"18 ","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000447/pdfft?md5=9d40898964861f0cc65d43c4c3fa2122&pid=1-s2.0-S2666691X24000447-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building interfaces between unmanned aircraft systems (UAS), air traffic controllers (ATCo), and the national airspace system (NAS): A software training platform","authors":"Euclides Carlos Pinto Neto,&nbsp;Derick Moreira Baum,&nbsp;Jorge Rady de Almeida Jr.,&nbsp;João Batista Camargo Jr.,&nbsp;Paulo Sergio Cugnasca","doi":"10.1016/j.treng.2024.100266","DOIUrl":"10.1016/j.treng.2024.100266","url":null,"abstract":"<div><p>Nowadays, the development of technologies that improve airspace operation in many aspects is essential since the importance of air transportation for society is increasing. The airspace, although, may become more complex considering the integration of these aircraft due to the issues regarding the social acceptance of autonomous systems (e.g., familiarity between Air Traffic Controller - ATCo - and Unmanned Aircraft System - UAS) and the uncertainty in terms of operation (e.g., hardware failures, software failures, interfaces failures, and misunderstanding of instructions). However, standard procedures (e.g., landing procedures) may not be followed in complex situations due to safety constraints (e.g., loss of minimum aircraft separation). As a result, ATCos play an essential role in maintaining appropriate levels of safety and efficiency by conducting aircraft using Vectoring Points (VPs). Hence, ATCos must be trained to deal with such challenging scenarios, especially in resource-constrained regions, e.g., in the final sector of the Terminal Control Area (TMA), where the aircraft are guided to the landing phase. The primary goal of this research is to propose a framework for training Air Traffic Controllers (ATCos) to deal with complex situations (e.g., considering many aircraft as well as severe weather conditions) in the final sector considering the UAS integration into the National Airspace System (NAS). This approach is divided into a set of modules for (1) proposing the training scenarios, (2) proposing solutions, and (3) evaluating the quality and feasibility of the solutions proposed. The aspects evaluated in the solutions provided for the proposed scenarios are ATCo workload and efficiency.</p></div>","PeriodicalId":34480,"journal":{"name":"Transportation Engineering","volume":"17 ","pages":"Article 100266"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666691X24000411/pdfft?md5=4833a1b87f72363d848eccdafad30596&pid=1-s2.0-S2666691X24000411-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}