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Introduction to Civil Engineering
Published in P.K. Jayasree, K Balan, V Rani, Practical Civil Engineering, 2021
P.K. Jayasree, K Balan, V Rani
The following subbranches are covered under this discipline: Highway engineering: Highway engineering deals with highways, covering the topics of planning, designing, and construction. It also covers questions pertaining to geometrics, materials of the structural design of highways.Railway engineering: Railway engineering involves planning and construction of a surface railway, setting an alignment for the tunnels, devising the signaling systems of meeting the traffic objectives, construction of station buildings and yards.Waterways engineering: Waterways engineering deals with the transportation of people and goods in vehicles that float upon water. It deals with the construction and development of docks and harbors.Airport engineering: Airport engineering is related to constructing, developing, and maintaining various elements of airports like runways, taxiways, airport pavements, etc.Traffic engineering: Traffic engineering is that branch of engineering which deals with the initial planning, the design of geometry, and the operations of traffic related to streets, roadway and highway networks, terminals, etc., for the accomplishment of effective and convenient movement of people, as well as goods in a safe manner. It uses principles of engineering to analyze the problems of transportation by bearing in mind the psychological habits of the commuters and obtain the most optimal solution.
Revealing representative day-types in transport networks using traffic data clustering
Published in Journal of Intelligent Transportation Systems, 2023
Matej Cebecauer, Erik Jenelius, David Gundlegård, Wilco Burghout
Data-driven methods for Intelligent Transport Systems (ITS) are experiencing a significant boom in research and practice (Laña et al., 2021). Thanks to the expansion of cost-effective sensor networks on highways, public transport, urban streets, etc. as well as open-data initiatives that make traffic data sources widely accessible, huge flows of information and data are becoming the norm (Laña et al., 2021; Zhang et al., 2011). As ITS produce and require large amounts data, the field is becoming increasingly interdisciplinary, combining traffic engineering with machine learning, artificial intelligence, and data science (Zhu et al., 2018). Recently, data-driven ITS have been defined by Zhang et al. (2011) as ITS where data-driven methods and learning algorithms play a key role in optimizing the system performance. There remain many challenges as well as opportunities in data-driven ITS (Zhu et al., 2018) concerning big data and their utilization in strategic, tactical, and operational real-time management planning.
Operational performance evaluation of adaptive traffic control systems: A Bayesian modeling approach using real-world GPS and private sector PROBE data
Published in Journal of Intelligent Transportation Systems, 2020
Zulqarnain H. Khattak, Mark J. Magalotti, Michael D. Fontaine
Traffic congestion is one of the preeminent problems faced by the field of transportation and traffic engineering all across the world. Congestion can contribute to crashes and reduces economic productivity. Although congestion remains a problem, capacity expansion is often not economically feasible, especially in major cities. As a result, many transportation agencies have sought to optimize the performance of existing infrastructure through the use of intelligent transportation systems (ITS) (Khattak, Park, Hong, Boateng, & Smith, 2018).
Probabilistic traffic breakdown forecasting through Bayesian approximation using variational LSTMs
Published in Transportmetrica B: Transport Dynamics, 2023
Douglas Zechin, Helena Beatriz Bettella Cybis
Traffic engineering has received valuable contributions from technological advances like the internet of things and artificial intelligence. The intersection between these areas has led to emerging innovative fields and contributed to traditional areas, such as active traffic management (ATM), in which this study fits. Traffic forecasting improvements and anticipation of unwanted scenarios, such as congestion, accidents, and increased travel-time, have been enabled by using more robust methods (Li, Abdel-Aty, and Yuan 2020).