Relevant bibliographies by topics / HYBRID INTELLIGENT TRANSPORTATION

Academic literature on the topic 'HYBRID INTELLIGENT TRANSPORTATION'

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Published: 11 September 2023

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Journal articles on the topic "HYBRID INTELLIGENT TRANSPORTATION"

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Borodin, Andrey, Evgenia Prokofieva, Vitaly Panin, and Alexander Erofeev. "Hybrid Intelligent Systems of Cooperative Transportation Planning." Transportation Research Procedia 54 (2021): 92–103. http://dx.doi.org/10.1016/j.trpro.2021.02.052.

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Syedyusuff, Syedakbar, Ramesh Subramaniam, and Ramya Vijay. "Orthogonally Integrated Hybrid Antenna for Intelligent Transportation Systems." Applied Computational Electromagnetics Society 36, no. 5 (June 14, 2021): 519–25. http://dx.doi.org/10.47037/2020.aces.j.360505.

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The aim of this paper is to design an orthogonally integrated hybrid antenna to address 5G/Wi-Fi/C-V2X communication simultaneously in one device. The proposed antenna consists of three planar monopoles and a defected ground plane with a dimension of 55x30x1.2mm3. High Frequency Structure Simulator (HFSS) is employed to design the proposed antenna, which resonates at three distinct frequencies 2.45 GHz (Wi-Fi), 3.5 GHz (5G), and 5.9 GHz. Further, the prototype antenna is fabricated and experimentally validated in comparing with simulation results. The excellent agreement among the simulation and measured results shows that the designed antenna operates simultaneously at 5G/Wi-Fi/C-V2X frequency bands and the isolation effects between the elements is less than 15dB.
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LIU, Yuan, Yuhao WANG, Siyue CHEN, Xiao LI, and Zhengfa RAO. "A Hybrid MAC Mechanism for Multiple Load Intelligent Vehicle Transportation Network." International Journal on Smart Sensing and Intelligent Systems 4, no. 4 (2011): 662–74. http://dx.doi.org/10.21307/ijssis-2017-461.

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Alonso de Armiño, Carlos, Daniel Urda, Roberto Alcalde, Santiago García, and Álvaro Herrero. "An Intelligent Visualisation Tool to Analyse the Sustainability of Road Transportation." Sustainability 14, no. 2 (January 11, 2022): 777. http://dx.doi.org/10.3390/su14020777.

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Road transport is an integral part of economic activity and is therefore essential for its development. On the downside, it accounts for 30% of the world’s GHG emissions, almost a third of which correspond to the transport of freight in heavy goods vehicles by road. Additionally, means of transport are still evolving technically and are subject to ever more demanding regulations, which aim to reduce their emissions. In order to analyse the sustainability of this activity, this study proposes the application of novel Artificial Intelligence techniques (more specifically, Machine Learning). In this research, the use of Hybrid Unsupervised Exploratory Plots is broadened with new Exploratory Projection Pursuit techniques. These, together with clustering techniques, form an intelligent visualisation tool that allows knowledge to be obtained from a previously unknown dataset. The proposal is tested with a large dataset from the official survey for road transport in Spain, which was conducted over a period of 7 years. The results obtained are interesting and provide encouraging evidence for the use of this tool as a means of intelligent analysis on the subject of developments in the sustainability of road transportation.
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Xu, Wangtu, Yuan Li, Hui Wang, and Peifeng Hu. "Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints." International Journal of Computational Intelligence Systems 4, no. 6 (2011): 1195. http://dx.doi.org/10.2991/ijcis.2011.4.6.11.

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钱, 光宏. "Design of Hybrid Enhanced Intelligent Transportation System Based on Human in Loop." Open Journal of Transportation Technologies 08, no. 05 (2019): 321–29. http://dx.doi.org/10.12677/ojtt.2019.85039.

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Xu, Wangtu, Yuan Li, Hui Wang, and Peifeng (Patrick) Hu. "Hybrid Intelligent Algorithm for Determining Network Capacity with Transportation Time Reliability Constraints." International Journal of Computational Intelligence Systems 4, no. 6 (December 2011): 1195–203. http://dx.doi.org/10.1080/18756891.2011.9727868.

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Boukerche, Azzedine, Noura Aljeri, Kaouther Abrougui, and Yan Wang. "Towards a secure hybrid adaptive gateway discovery mechanism for intelligent transportation systems." Security and Communication Networks 9, no. 17 (August 30, 2016): 4027–47. http://dx.doi.org/10.1002/sec.1586.

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Daneshvar, Hasan, Sadegh Niroomand, Omid Boyer, and Abdollah Hadi-Vencheh. "Designing a hybrid intelligent transportation system for optimization of goods distribution network routing problem." Decision Making: Applications in Management and Engineering 6, no. 2 (September 3, 2023): 907–32. http://dx.doi.org/10.31181/dma622023899.

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Given that finding the right and appropriate route in the daytime and busy city with the occurred traffic limitations is a major problem that not only causes inefficient performance in distribution networks but also causes irreparable environmental damage to society. This study focuses on improving the routing of the goods distribution network using the intelligent transportation system. In this regard, first, the problem is modeled, and then an intelligent transportation system is combined with some meta-heuristic algorithms to solve it. In the proposed algorithm, we first use the clustering algorithm to cluster location of customers and then create sub-clusters based on the time window. The proposed routes are created by using the genetic and particle swarm optimization meta-heuristic algorithms as the static part of the approach, and if the traffic conditions change, the Vehicular Ad - hoc Network (Vanet), which is one of the sub-systems of the intelligent transportation system as the dynamic part of the approach checks the new traffic conditions and sends the new information to the proposed algorithms to recheck the route. The Aarhus-Denmark data set is selected due to having urban traffic information, meteorology, and urban areas. This is related to the City Pulse project. According to the obtained results, in terms of reducing the cost of transmission, including the cost of service delay and total cost of moving, the proposed method reached better solutions comparing to the meta-heuristic algorithms of literature.
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Li, Xu, Qimin Xu, Chingyao Chan, Bin Li, Wei Chen, and Xianghui Song. "A Hybrid Intelligent Multisensor Positioning Methodology for Reliable Vehicle Navigation." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/176947.

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With the rapid development of intelligent transportation systems worldwide, it becomes more important to realize accurate and reliable vehicle positioning in various environments whether GPS is available or not. This paper proposes a hybrid intelligent multisensor positioning methodology fusing the information from low-cost sensors including GPS, MEMS-based strapdown inertial navigation system (SINS) and electronic compass, and velocity constraint, which can achieve a significant performance improvement over the integration scheme only including GPS and MEMS-based SINS. First, the filter model of SINS aided by multiple sensors is presented in detail and then an improved Kalman filter with sequential measurement-update processing is developed to realize the filtering fusion. Further, a least square support vector machine- (LS SVM-) based intelligent module is designed and augmented with the improved KF to constitute the hybrid positioning system. In case of GPS outages, the LS SVM-based intelligent module trained recently is used to predict the position error to achieve more accurate positioning performance. Finally, the proposed hybrid positioning method is evaluated and compared with traditional methods through real field test data. The experimental results validate the feasibility and effectiveness of the proposed method.
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Dissertations / Theses on the topic "HYBRID INTELLIGENT TRANSPORTATION"

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Park, Jaeyong. "Safe Controller Design for Intelligent Transportation System Applications using Reachability Analysis." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366201401.

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Liu, Yiting. "Applications of wireless communication in traffic networks using a hierarchical hybrid system model." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1173283543.

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Andersson, David, and Simon Robertsson. "Intelligent Transportation Systems : Capturing the socio-economic value of uncertain and flexible investments." Thesis, Uppsala universitet, Industriell teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-322388.

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The aim of this study is to evaluate an alternative socio-economical valuation method (i.e., Hybrid Real Options, HRO) to the traditional benefit cost method (CBA) for the evaluation of investments within Intelligent Transportation Systems (ITS). The proposed alternative method will be evaluated by the use of a case study where it is applied and compared to the results of the traditional method. The case study evaluates the socio-economical effects of an investment in Variable Speed Limits along a section of the motorway E18. The results of the study shows that the choice of evaluation methods affects both the investment strategy and the estimated socio-economical benefits of the investment. Using the HRO method yields twice as high socio-economical benefits compared to the CBA method. The main reason for this being that HRO account for risk and uncertainties wheras CBA only accounts for the most probable outcome of the investment. The choice of method is a complex task that involves many stakeholders however a more critical approach to the choice of socio-economical evaluation method is advocated based on the results of this study.
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Xiao, Yan. "Hybrid Approaches to Estimating Freeway Travel Times Using Point Traffic Detector Data." FIU Digital Commons, 2011. http://digitalcommons.fiu.edu/etd/356.

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The accurate and reliable estimation of travel time based on point detector data is needed to support Intelligent Transportation System (ITS) applications. It has been found that the quality of travel time estimation is a function of the method used in the estimation and varies for different traffic conditions. In this study, two hybrid on-line travel time estimation models, and their corresponding off-line methods, were developed to achieve better estimation performance under various traffic conditions, including recurrent congestion and incidents. The first model combines the Mid-Point method, which is a speed-based method, with a traffic flow-based method. The second model integrates two speed-based methods: the Mid-Point method and the Minimum Speed method. In both models, the switch between travel time estimation methods is based on the congestion level and queue status automatically identified by clustering analysis. During incident conditions with rapidly changing queue lengths, shock wave analysis-based refinements are applied for on-line estimation to capture the fast queue propagation and recovery. Travel time estimates obtained from existing speed-based methods, traffic flow-based methods, and the models developed were tested using both simulation and real-world data. The results indicate that all tested methods performed at an acceptable level during periods of low congestion. However, their performances vary with an increase in congestion. Comparisons with other estimation methods also show that the developed hybrid models perform well in all cases. Further comparisons between the on-line and off-line travel time estimation methods reveal that off-line methods perform significantly better only during fast-changing congested conditions, such as during incidents. The impacts of major influential factors on the performance of travel time estimation, including data preprocessing procedures, detector errors, detector spacing, frequency of travel time updates to traveler information devices, travel time link length, and posted travel time range, were investigated in this study. The results show that these factors have more significant impacts on the estimation accuracy and reliability under congested conditions than during uncongested conditions. For the incident conditions, the estimation quality improves with the use of a short rolling period for data smoothing, more accurate detector data, and frequent travel time updates.
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Kurt, Arda. "Hybrid-State System Modelling for Control, Estimation and Prediction in Vehicular Autonomy." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1325181635.

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Valenti, Giammarco. "Cooperative ADAS and driving, bio-inspired and optimal solutions." Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/336890.

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Mobility is a topic of great interest in research and engineering since critical aspects such as safety, traffic efficiency, and environmental sustainability still represent wide open challenges for researchers and engineers. In this thesis, at first, we address the cooperative driving safety problem both from a centralized and decentralized perspective. Then we address the problem of optimal energy management of hybrid vehicles to improve environmental sustainability, and finally, we develop an intersection management systems for Connected Autonomous Vehicle to maximize the traffic efficiency at an intersection. To address the first two topics, we define a common framework. Both the cooperative safety and the energy management for Hybrid Electric Vehicle requires to model the driver behavior. In the first case, we are interested in evaluating the safety of the driver’s intentions, while in the second case, we are interested in predicting the future velocity profile to optimize energy management in a fixed time horizon. The framework is the Co-Driver, which is, in short, a bio-inspired agent able both to model and to imitate a human driver. It is based on a layered control structure based on the generation of atomic human-like longitudinal maneuvers that compete with each other like affordances. To address driving safety, the Co-Driver behaves like a safe driver, and its behavior is compared to the actual driver to understand if he/she is acting safely and providing warnings if not. In the energy management problem, the Co-Driver aims at imitating the driver to predict the future velocity. The Co-Driver generates a set of possible maneuvers and selects one of them, imitating the action selection process of the driver. At first, we address the problem of safety by developing and investigating a framework for Advanced Driving Assistance Systems (ADAS) built on the Co-Driver. We developed and investigated this framework in an innovative context of new intelligent road infrastructure, where vehicles and roads communicate. The infrastructure that allows the roads to interact with vehicles and the environment is the topic of a research project called SAFESTRIP. This project is about deploying innovative sensors and communication devices on the road that communicate with all vehicles. Including vehicles that are equipped with Vehicle-To-Everything (V2X) technology and vehicles that are not, using an interface (HMI) on smart-phones. Co-Driver-based ADAS systems exploit connections between vehicles and (smart) roads provided by SAFESTRIP to cover several safety-critical use cases: pedestrian protection, wrong-way vehicles on-ramps, work-zones on roads and intersections. The ADAS provide personalized warning messages that account for the adaptive driver behavior to maximize the acceptance of the system. The ability of the framework to predict human drivers’ intention is exploited in a second application to improve environmental sustainability. We employ it to feed with the estimated speed profile a novel online Model Predictive Control (MPC) approach for Hybrid Electric Vehicles, introducing a state-of-the-art electrochemical model of the battery. Such control aims at preserving battery life and fuel consumption through equivalent costs. We validated the approach with actual driving data used to simulate vehicles and the power-train dynamics. At last, we address the traffic efficiency problem in the context of autonomous vehicles crossing an intersection. We propose an intersection management system for Connected Autonomous Vehicles based on a bi-level optimization framework. The motion planning of the vehicle is provided by a simplified optimal control problem, while we formulate the intersection management problem (in terms of order and timing) as a Mixed Integer Non-Linear Programming. The latter approximates a linear problem with a powerful piecewise linearization technique. Therefore, thanks to this technique, we can bound the error and employ commercial solvers to solve the problem (fast enough). Finally, this framework is validated in simulation and compared with the "Fist-Arrived First-Served" approach to show the impact of the proposed algorithm.
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Šindelářová, Jana. "Inovační aspekty elektronickeho výběru mýtného v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-10475.

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This thesis deals with the innovation of information system (IS) as a decision problem. The main objective of this thesis is the construction and the application of a method to select an optimal innovation variant of IS in an organisation of any scale. The designed method is based on the conception of the innovation of IS in the methodology "Multidimensional Management and Development of Information System (MMDIS)". The proposed selection method (Method of Innovation Modules based on MMDIS - MIM) is intended to serve mainly for decision-making on a strategic level. The IS structure is mapped in accordance with the MMDIS methodology. Innovation aspects are derived from two approaches: innovation aspects based on the MMDIS principles ("module of principles" - e.g. integration, flexibility, standardisation, measurability) and innovation aspects based on the IS dimensions defined in MMDIS methodology ("module of dimensions" - e.g. software, hardware, processes and functions, finances). The proposal method enforces a systematic approach to evaluation of the innovation variants, ensuring that the innovation aspects (the principles and the dimensions) are aligned with key requirements. As an example, this method is applied to an existing electronic toll system operated in the Czech Republic. Basic innovation variants of the electronic toll system are described - the microwave, the satellite and the hybrid toll system. Case studies from the Czech Republic, Austria and Germany are used for support the proposed innovation variants, as well as evaluation of variants. The principles and the dimensions form a framework for description and evaluation of the telematics, of the electronic toll and of the innovation variants of the electronic toll. The variants are compared within the framework of refined principles and dimensions, the optimal variant is proposed. The used evaluation parameters are a result of an analysis of published studies and specific toll system technical designs. The Method of Innovation Modules was constructed on the basis of MMDIS methodology and demonstrated by application in an example scenario (electronic toll). The key result of the proposed method is the comprehensive structured map of available system innovation opportunities.
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Chiang, Yi-hsien, and 江益賢. "Control of Freeway Traffic Flow in Intelligent Transportation System: A Hybrid Control Approach." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/14513582738567706956.

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博士
國立成功大學
電機工程學系碩博士班
95
In this dissertation, topics in an ITS, including modeling, control problems of freeway traffic flow, and longitudinal vehicle control are investigated. In the modeling of freeway traffic, a hybrid model in a macroscopic level is proposed to describe the dynamics of each defined state and their particular transitions by exploiting the notions of shockwave, continuum theory, and car-following property. The augmented medium volume model within discretized Payne traffic flow model is investigated based on the shockwave theory. A new computational weighting in terms of the shockwave speed is addressed for better estimation of the traffic flow. In the control of freeway traffic flow, the controllers based on the centralized and decentralized schemes are devised by employing and sliding mode theories, respectively. A feedback linearization approach is used to formulate the design problem so as to make the flow control system less affected by nonlinearities. In the intelligent cruise control system, an adaptive semi-autonomous controller with a new spacing policy is devised to account for the brake limitation. A parameter design for a specified deceleration of the preceding vehicle is presented to adjust the intervehicle spacing to accommodate traveling circumstances such as weather/road conditions.
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Munthikodu, Sreejith. "Driving data pattern recognition for intelligent energy management of plug-in hybrid electric vehicles." Thesis, 2019. http://hdl.handle.net/1828/11052.

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This work focuses on the development and testing of new driving data pattern recognition intelligent system techniques to support driver adaptive, real-time optimal power control and energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). A novel, intelligent energy management approach that combines vehicle operation data acquisition, driving data clustering and pattern recognition, cluster prototype based power control and energy optimization, and real-time driving pattern recognition and optimal energy management has been introduced. The method integrates advanced machine learning techniques and global optimization methods form the driver adaptive optimal power control and energy management. Fuzzy C-Means clustering algorithm is used to identify the representative vehicle operation patterns from collected driving data. Dynamic Programming (DA) based off-line optimization is conducted to obtain the optimal control parameters for each of the identified driving patterns. Artificial Neural Networks (ANN) are trained to associate each of the identified operation patterns with the optimal energy management plan to support real-time optimal control. Implementation and advantages of the new method are demonstrated using the 2012 California household travel survey data, and driver-specific data collected from the city of Victoria, BC Canada.
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Shimoga, Muddappa Vinay Kumar. "Electrochemical model based condition monitoring of a Li-ion battery using fuzzy logic." Thesis, 2014. http://hdl.handle.net/1805/5588.

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Indiana University-Purdue University Indianapolis (IUPUI)
There is a strong urge for advanced diagnosis method, especially in high power battery packs and high energy density cell design applications, such as electric vehicle (EV) and hybrid electric vehicle segment, due to safety concerns. Accurate and robust diagnosis methods are required in order to optimize battery charge utilization and improve EV range. Battery faults cause significant model parameter variation affecting battery internal states and output. This work is focused on developing diagnosis method to reliably detect various faults inside lithium-ion cell using electrochemical model based observer and fuzzy logic algorithm, which is implementable in real-time. The internal states and outputs from battery plant model were compared against those from the electrochemical model based observer to generate the residuals. These residuals and states were further used in a fuzzy logic based residual evaluation algorithm in order to detect the battery faults. Simulation results show that the proposed methodology is able to detect various fault types including overcharge, over-discharge and aged battery quickly and reliably, thus providing an effective and accurate way of diagnosing li-ion battery faults.
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Books on the topic "HYBRID INTELLIGENT TRANSPORTATION"

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Himavathi, S., Jens Bo Holm-Nielsen, P. Sanjeevikumar, and Chitra A. Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles. Wiley & Sons, Incorporated, John, 2020.

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Himavathi, S., Jens Bo Holm-Nielsen, P. Sanjeevikumar, and Chitra A. Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles. Wiley & Sons, Incorporated, John, 2020.

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Himavathi, S., Jens Bo Holm-Nielsen, Chitra A, and Sanjeevikumar Padmanaban. Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles. Wiley & Sons, Limited, John, 2020.

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Himavathi, S., Jens Bo Holm-Nielsen, P. Sanjeevikumar, and Chitra A. Artificial Intelligent Techniques for Electric and Hybrid Electric Vehicles. Wiley & Sons, Limited, John, 2020.

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Book chapters on the topic "HYBRID INTELLIGENT TRANSPORTATION"

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Dastgoshade, Sohaib, and Ajith Abraham. "A New Bi-objective Classic Transportation Model Considering Social Justice." In Hybrid Intelligent Systems, 299–308. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_30.

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Saravanan, M., R. Devipriya, K. Sakthivel, J. G. Sujith, A. Saminathan, and S. Vijesh. "Optimized Load Balancing and Routing Using Machine Learning Approach in Intelligent Transportation Systems: A Survey." In Hybrid Intelligent Systems, 929–39. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27409-1_85.

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Singh, Gurwinder, Amarinder Singh, and Bikramjit Singh. "Incorporation of Additional Modules Within Particle Swarm Optimization for Feasibility Conditions of Multi-index Transportation Problem." In Hybrid Intelligent Systems, 780–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_76.

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Luo, Pan, Muyi Lin, Yong Chen, Li Zhao, and Bin Ma. "Energy Management Strategy for Hybrid Engineering Vehicles with Composite Energy Storage." In Green Intelligent Transportation Systems, 147–56. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0302-9_15.

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Saini, Prince Kumar, and Bhanu Pratap. "Robust control of hybrid maglev-based transportation system." In Intelligent Control for Modern Transportation Systems, 135–51. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003436089-8.

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Carballedo, Roberto, Eneko Osaba, Pablo Fernández, and Asier Perallos. "A New Evolutionary Hybrid Algorithm to Solve Demand Responsive Transportation Problems." In Advances in Intelligent and Soft Computing, 233–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19934-9_29.

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Pintea, Camelia-M., Petrica C. Pop, and Mara Hajdu-Macelaru. "Classical Hybrid Approaches on a Transportation Problem with Gas Emissions Constraints." In Advances in Intelligent Systems and Computing, 449–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-32922-7_46.

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Sysoev, Anton, Alexander Galkin, and Elena Khabibullina. "Hybrid Model of Controlling Traffic Flows Within Regional Intelligent Transportation System." In Lecture Notes in Networks and Systems, 528–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68476-1_49.

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Büchter, Hubert, and Sebastian Naumann. "A Hybrid Planning Method for Charging Infrastructure for Electrically Driven Buses in Public Transportation." In Advances in Intelligent Systems and Computing, 175–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43991-4_15.

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Bavandi, S., S. H. Nasseri, and C. Triki. "Optimal Decision Making in Fuzzy Stochastic Hybrid Uncertainty Environments and Their Application in Transportation Problems." In Advances in Intelligent Systems and Computing, 65–72. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2459-2_5.

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Conference papers on the topic "HYBRID INTELLIGENT TRANSPORTATION"

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Parulekar, Mayur, Dhaval Shroff, Viraj Padte, Harsh Nangalia, and Akash Metawala. "Vehicular localization and Intelligent Transportation Systems." In 2012 12th International Conference on Hybrid Intelligent Systems (HIS). IEEE, 2012. http://dx.doi.org/10.1109/his.2012.6421352.

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Yadav, Pankaj, Vikas, Vikash Kumar Saini, Ameena S. Al-Sumaiti, and Rajesh Kumar. "Intelligent Energy Management Strategies for Hybrid Electric Transportation." In 2023 IEEE IAS Global Conference on Renewable Energy and Hydrogen Technologies (GlobConHT). IEEE, 2023. http://dx.doi.org/10.1109/globconht56829.2023.10087631.

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Swarnamugi, M., and R. Chinnaiyan. "IoT Hybrid Computing Model for Intelligent Transportation System (ITS)." In 2018 Second International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2018. http://dx.doi.org/10.1109/iccmc.2018.8487843.

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Xuan, Liu, and Huang Sheng Guo. "Petri Net Simulation Algorithm of the Shortest Path in Transportation of Wartime." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.106.

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Wang, Chengcheng, Jia Wang, and Zhixin Chen. "Study on the Network Structure of the Regional Security Monitoring Center in Comprehensive Transportation Hubs." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.130.

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Zhang, Lei, Zhixin Chen, Jia Wang, and Xiaohong Yin. "An Integrated Simulation Platform for Railway Synthetic Monitor and Control System in Multiple Layer Transportation Hinge." In 2009 Ninth International Conference on Hybrid Intelligent Systems. IEEE, 2009. http://dx.doi.org/10.1109/his.2009.127.

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Ramkumar, P., R. Uma, S. Usha, and R. Valarmathi. "Real Time Path Planning using Intelligent Transportation System for Hybrid VANET." In 2020 International Conference on Power, Energy, Control and Transmission Systems (ICPECTS). IEEE, 2020. http://dx.doi.org/10.1109/icpects49113.2020.9337057.

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Zhao, Shengjian, Shu Lin, and Jungang Xu. "Time Series Traffic Prediction via Hybrid Neural Networks." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917383.

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Flade, Benedict, Simon Kohaut, and Julian Eggert. "Error Decomposition for Hybrid Localization Systems." In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021. http://dx.doi.org/10.1109/itsc48978.2021.9564415.

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Burghout, W., H. N. Koutsopoulos, and I. Andreasson. "A discrete-event mesoscopic traffic simulation model for hybrid traffic simulation." In 2006 IEEE Intelligent Transportation Systems Conference. IEEE, 2006. http://dx.doi.org/10.1109/itsc.2006.1707369.

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