Статті в журналах: "HYBRID INTELLIGENT TRANSPORTATION"

1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Shiripour, Saber, Nezam Mahdavi-Amiri, and Iraj Mahdavi. "A transportation network model with intelligent probabilistic travel times and two hybrid algorithms." Transportation Letters 9, no. 2 (June 9, 2016): 90–122. http://dx.doi.org/10.1080/19427867.2016.1187893.

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12

Ho, Eric Pengkuan, Steven I‐jy Chien, and Ching‐Jung Ting. "A hybrid modeling method for the planning and evaluation of intelligent transportation systems." Transportation Planning and Technology 24, no. 1 (December 2000): 1–23. http://dx.doi.org/10.1080/03081060008717658.

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13

Chang, Victor, G. P. Sunitha, S. M. Dilip Kumar, S. Raghavendra, and N. N. Srinidhi. "Hybrid energy-efficient and QoS-aware algorithm for intelligent transportation system in IoT." International Journal of Grid and Utility Computing 11, no. 6 (2020): 815. http://dx.doi.org/10.1504/ijguc.2020.10032054.

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14

Srinidhi, N. N., G. P. Sunitha, S. Raghavendra, S. M. Dilip Kumar, and Victor Chang. "Hybrid energy-efficient and QoS-aware algorithm for intelligent transportation system in IoT." International Journal of Grid and Utility Computing 11, no. 6 (2020): 815. http://dx.doi.org/10.1504/ijguc.2020.110897.

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15

Barenji, Ali Vatankhah, W. M. Wang, Zhi Li, and David A. Guerra-Zubiaga. "Intelligent E-commerce logistics platform using hybrid agent based approach." Transportation Research Part E: Logistics and Transportation Review 126 (June 2019): 15–31. http://dx.doi.org/10.1016/j.tre.2019.04.002.

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16

Gabbar, Hossam A. "Resiliency Analysis of Hybrid Energy Systems within Interconnected Infrastructures." Energies 14, no. 22 (November 10, 2021): 7499. http://dx.doi.org/10.3390/en14227499.

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Анотація:

There are world tendencies to implement interconnected infrastructures of energy-water-waste-transportation-food-health-social systems to enhance the overall performance in normal and emergency situations where there are multiple interactions among them with possible conversions and improved efficiencies. Hybrid energy systems are core elements within interconnected infrastructures with possible conversions among electricity, thermal, gas, hydrogen, waste, and transportation networks. This could be improved with storage systems and intelligent control systems. It is important to study resiliency of hybrid energy systems within interconnected infrastructures to ensure reduced risks and improved performance. This paper presents framework for the analysis of resiliency layers as related to protection layers. Case study of hybrid energy system as integrated with water, waste, and transportation infrastructures is presented where different resiliency and protection layers are assessed. Performance measures are modeled and evaluated for possible interconnection scenarios with internal and external factors that led to resiliency demands. Resiliency layers could trigger protection layers under certain conditions, which are evaluated to achieve high performance hybrid energy systems within interconnected infrastructures. The proposed approach will support urban, small, and remote communities to achieve high performance interconnected infrastructures for normal and emergency situations.

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17

Indulal, S., S. Ushakumari, and P. S. Chandramohanan Nair. "Real time analysis of an intelligent torque controller for a hybrid bicycle." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 166. http://dx.doi.org/10.14419/ijet.v7i2.21.11860.

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Most of the means of transportation is based on Internal Combustion engines, since they are fast and furious means of transportation. But bicycles are also relevant nowadays since they are the ideal means of the short commutation and which also helps in improving the human health by serving as a work out machine. But in our busy life bicycles are not preferred due to the uneven terrains. Electric bikes are the solution for this issue. Pedal assist sensor (PAS) based hybrid bicycle are also available, which will intermittently turn on and control the speed of electric drive based on the pedal crank speed. Thus there the electric drive assistance will be provided based on the speed of the pedal cranking. The real assistance should be provided when our torque requirement is needed. This paper deal with a novel sensor which sends the effort required at the pedal by the rider and intelligently control the electric drive so as to meet the required torque. The advantage of this controller is that the rider need to give only the same effort at the pedal irrespective of the terrain variations for a constant speed ride. A Fuzzy Logic Controller (FLC) is proposed here. The performance of the controller is simulated and analysed with the experimental results to prove the efficacy of the proposed technique.

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18

Srivastava, Praveen Ranjan, Zuopeng (Justin) Zhang, Prajwal Eachempati, and Hongbo Lyu. "An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach." Journal of Advanced Transportation 2021 (March 1, 2021): 1–22. http://dx.doi.org/10.1155/2021/6624129.

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The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters’ customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings.

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19

Hou, Shengyan, Hai Yin, Yan Ma, and Jinwu Gao. "Energy Management Strategy of Hybrid Electric Vehicle Based on ECMS in Intelligent Transportation Environment." IFAC-PapersOnLine 54, no. 10 (2021): 157–62. http://dx.doi.org/10.1016/j.ifacol.2021.10.157.

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20

Diteleva, A., V. Sleptsov, S. Savilkin, S. Matsykin, and A. Granko. "Hybrid capacitor based on carbon matrix for intelligent electric energy storage and transportation system." Journal of Physics: Conference Series 1925, no. 1 (May 1, 2021): 012083. http://dx.doi.org/10.1088/1742-6596/1925/1/012083.

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21

Ning, Zheng, Chen Tao, Lin Fei, and Xu Haitao. "A Hybrid Heuristic Algorithm for the Intelligent Transportation Scheduling Problem of the BRT System." Journal of Intelligent Systems 24, no. 4 (December 1, 2015): 437–48. http://dx.doi.org/10.1515/jisys-2014-0134.

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AbstractThis work proposes a hybrid heuristic algorithm to solve the bus rapid transit (BRT) intelligent scheduling problem, which is a combination of the genetic algorithm, simulated annealing algorithm, and fitness scaling method. The simulated annealing algorithm can increase the local search ability of the genetic algorithm, so as to accelerate its convergence speed. Fitness scaling can reduce the differences between individuals in the early stage of the algorithm, to prevent the genetic algorithm from falling into a local optimum through increasing the diversity of the population. It can also increase the selection probability of outstanding individuals, and speed up the convergence at the late stage of the algorithm, by increasing the differences between individuals. Using real operational data of BRT Line 1 in a city of Zhejiang province, the new scheduling scheme can be obtained through algorithm simulation. The passengers’ total waiting time in a single way will be reduced by 40 h on average under the same operating cost compared with the original schedule scheme in a day.

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22

Wang, Huan, and Haichuan Zhang. "A Hybrid Method of Vehicle Detection based on Computer Vision for Intelligent Transportation System." International Journal of Multimedia and Ubiquitous Engineering 9, no. 6 (June 30, 2014): 105–18. http://dx.doi.org/10.14257/ijmue.2014.9.6.11.

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23

Kazemi, Hadi, Yaser P. Fallah, Andrew Nix, and Scott Wayne. "Predictive AECMS by Utilization of Intelligent Transportation Systems for Hybrid Electric Vehicle Powertrain Control." IEEE Transactions on Intelligent Vehicles 2, no. 2 (June 2017): 75–84. http://dx.doi.org/10.1109/tiv.2017.2716839.

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24

Erskine, Samuel Kofi, and Khaled M. Elleithy. "Real-Time Detection of DoS Attacks in IEEE 802.11p Using Fog Computing for a Secure Intelligent Vehicular Network." Electronics 8, no. 7 (July 11, 2019): 776. http://dx.doi.org/10.3390/electronics8070776.

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The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.

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25

Fang, Bo, Ming Feng, Cheng Qiu, and Ximing Zhang. "Rail Multi Vehicle Scheduling Method for Intermediate Depot of Steel Plant Based on Improved Genetic Algorithm." Scientific Programming 2022 (April 22, 2022): 1–11. http://dx.doi.org/10.1155/2022/4971638.

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Appropriate transportation network plays a significant role in the economic development of country. The rising transport demand increases the congestion in railway networks, and thus, they become more interdependent and more complex to operate. A ground railway system is established in the reservoir area of steel plants to ensure smooth running of multi vehicle logistics transportation. In recent years, the development of the ground rail multi vehicle logistics system in the reservoir area of steel plants has been increased. However, such logistic systems need an intelligent dispatching control system to avoid the possibility of safety hazards, to follow optimal tracks and to effectively manage logistics operation. In this paper, an improved hybrid genetic algorithm is proposed to realize the decision making and control of multi vehicle scheduling. Task assignment to multiple vehicles and multi-stage subparent vehicle scheduling is performed based on the information obtained from the concerned subsection. The self-learning hybrid algorithm works on the data extracted from an improved population and suggests an optimized solution. The individual behavior and optimization process are updated by self-learning, which ensures the effectiveness of iterative evolution. The selection, cross mutation, and self-learning expert base operation methods of the hybrid genetic algorithm are optimized. The proposed system is evaluated by taking the multi vehicle logistics system of cold rolling intermediate steel depot as a case study. The improved algorithm is implemented in MATLAB and is compared with the traditional genetic and particle swarm optimization algorithms. Results of the analysis prove that the hybrid genetic algorithm of self-learning knowledge expert base is effective in solving the multi vehicle logistics scheduling optimization problem. With the inclusion of the self-learning knowledge expert base genetic algorithm, the convergence trend of the proposed algorithm is enhanced; the traditional genetic algorithm converges after 140 iterations, while in the proposed algorithm, it is reduced to 100 iterations. The evaluation reveals that the proposed algorithm is speedier than the traditional genetic algorithm (GA) and particle swam optimization (PSO); the average solution time of the proposed algorithm is 119.1 while that of GA, PSO is 137.4, 131.4, respectively. The proposed algorithm is applicable to improve the operation and efficiency of the logistics transportation system. The approach is beneficial in intelligent control of metallurgical production. The proposed algorithm has practical significance to be followed in the development of intelligent metallurgical production and logistics transportation.

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26

Asaithambi, Suriya Priya R., Ramanathan Venkatraman, and Sitalakshmi Venkatraman. "MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems." Big Data and Cognitive Computing 4, no. 3 (July 9, 2020): 17. http://dx.doi.org/10.3390/bdcc4030017.

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Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

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27

Pop, Mădălin-Dorin, Octavian Proștean, Tudor-Mihai David, and Gabriela Proștean. "Hybrid Solution Combining Kalman Filtering with Takagi–Sugeno Fuzzy Inference System for Online Car-Following Model Calibration." Sensors 20, no. 19 (September 27, 2020): 5539. http://dx.doi.org/10.3390/s20195539.

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Nowadays, the intelligent transportation concept has become one of the most important research fields. All of us depend on mobility, even when we talk about people, provide services, or move goods. Researchers have tried to create and test different transportation models that can optimize traffic flow through road networks and, implicitly, reduce travel times. To validate these new models, the necessity of having a calibration process defined has emerged. Calibration is mandatory in the modeling process because it ensures the achievement of a model closer to the real system. The purpose of this paper is to propose a new multidisciplinary approach combining microscopic traffic modeling theory with intelligent control systems concepts like fuzzy inference in the traffic model calibration. The chosen Takagi–Sugeno fuzzy inference system proves its adaptive capacity for real-time systems. This concept will be applied to the specific microscopic car-following model parameters in combination with a Kalman filter. The results will demonstrate how the microscopic traffic model parameters can adapt based on real data to prove the model validity.

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28

Tayyaba Sahar, Hayl Khadami, and Muhammad Rauf. "Efficient Detection and Recognition of Traffic Lights for Autonomous Vehicles Using CNN." Sukkur IBA Journal of Emerging Technologies 5, no. 2 (February 3, 2023): 49–56. http://dx.doi.org/10.30537/sjet.v5i2.1181.

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Smart city infrastructure and Intelligent Transportation Systems (ITS) need modern traffic monitoring and driver assistance systems such as autonomous traffic signal detection. ITS is a dominant research area among several fields in the domain of artificial intelligence. Traffic signal detection is a key module of autonomous vehicles where accuracy and inference time are amongst the most significant parameters. In this regard, the aim of this study is to detect traffic signals focusing to enhance accuracy and real-time performance. The results and discussion enclose a comparative performance of a CNN-based algorithm YOLO V3 and a handcrafted technique that gives insight for enhanced detection and inference in day and night light. It is important to consider that real-world objects are associated with complex backgrounds, occlusion, climate conditions, and light exposure that deteriorate the performance of sensitive intelligent applications. This study provides a direction to propose a hybrid technique for TLD not only in the daytime but also in night light.

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Kaur, Lakhveer, and Rajbhupinder Kaur. "Review: An Enhancement of Road Scenes Captured Images Using HDCP and Color Discriptor." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 2, no. 6 (June 30, 2015): 07–12. http://dx.doi.org/10.53555/nncse.v2i6.442.

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The visibility of images of outdoor road scenes will generally become degraded when captured during inclement weather conditions. Drivers often turn on the headlights of their vehicles and streetlights are often activated, resulting in localized light sources in images capturing road scenes in these conditions. Additionally, sandstorms are also weather events that are commonly encountered when driving in some regions. A novel and effective haze removal approach to remedy problems caused by localized light sources and color shifts, which thereby achieves superior restoration results for single hazy images. The Road image degradation can cause problems for intelligent transportation systems such as traveling vehicle data recorders and traffic surveillance systems, which must operate under a wide range of weather conditions. The objective of this work is to implement the Road Scenes Captured by Intelligent Transportation Systems using Hybrid technique. To enhance the images using different filters and enhancement techniques.

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30

Wang, Pengyu, Jinke Li, Yuanbin Yu, Xiaoyong Xiong, Shijie Zhao, and Wangsheng Shen. "Energy management of plug-in hybrid electric vehicle based on trip characteristic prediction." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 234, no. 8 (March 19, 2020): 2239–59. http://dx.doi.org/10.1177/0954407020904464.

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In the research regarding plug-in hybrid electric vehicle energy management strategies, the use of global positioning system and intelligent transportation system information to optimize control strategy will be the future trend, and this is relatively scarce in the existing researches. Therefore, an adaptive energy management strategy of plug-in hybrid electric vehicle based on trip characteristic prediction was investigated in this paper, and the main achievement is to suggest a way to determine the reference state of charge for control strategy using global positioning system or intelligent transportation system information. First, given the historical driving data of a driver by global positioning system, the important location points of the commuting routes were discovered. Second, a Markov trajectory prediction model based on the key points was established to predict and identify the driving routes. As such, the trip characteristics, such as information of mileage and driving cycles, were collected. Then, five typical driving cycles were extracted. According to the trip characteristic information, the optimal battery state of charge consumption regulation of plug-in hybrid electric vehicle was realized using a dynamic programming algorithm. This algorithm was applied to the research of state of charge trajectory planning algorithm. Moreover, an adaptive equivalent consumption minimization strategy based on state of charge planning trajectory was developed. The comparison of different control strategies proved that the developed strategy uses battery power reasonably and reduces fuel consumption of the vehicle.

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31

Chen, Ta-Cheng, Yuan-Yong Hsu, An-Chen Lee, and Shiang-Yu Wang. "GA BASED HYBRID FUZZY RULE OPTIMIZATION APPROACH FOR ELEVATOR GROUP CONTROL SYSTEM." Transactions of the Canadian Society for Mechanical Engineering 37, no. 3 (September 2013): 937–47. http://dx.doi.org/10.1139/tcsme-2013-0080.

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Elevators are the essential transportation tools in high buildings so that elevator group control system (EGCS) is developed to dynamically layout the schedule of elevators in a group. In this study, a fuzzy rule based intelligent EGCS optimized by genetic algorithm has been proposed where the rules with the corresponding parameters are generated optimally so as to maximize service quality. The experimental results show that the performance of our approach is superior to these of traditional approaches in the literature.

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32

Zhang, Yue, Christos G. Cassandras, Wei Li, and Pieter J. Mosterman. "A Discrete-Event and Hybrid Simulation Framework Based on SimEvents for Intelligent Transportation System Analysis." IFAC-PapersOnLine 51, no. 7 (2018): 323–28. http://dx.doi.org/10.1016/j.ifacol.2018.06.320.

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33

Xie, Jiaming, and Yi-King Choi. "Hybrid traffic prediction scheme for intelligent transportation systems based on historical and real-time data." International Journal of Distributed Sensor Networks 13, no. 11 (November 2017): 155014771774500. http://dx.doi.org/10.1177/1550147717745009.

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34

K.C. Lo, Steven, and Huan-Chao Keh. "Embedding a Multi-agents Collaboration Mechanism into the Hybrid Middleware of an Intelligent Transportation System." Information Technology Journal 10, no. 6 (May 15, 2011): 1113–25. http://dx.doi.org/10.3923/itj.2011.1113.1125.

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35

Guchhait, Arghya, Maji B, and Kandar D. "A hybrid V2V system for collision-free high-speed internet access in intelligent transportation system." Transactions on Emerging Telecommunications Technologies 29, no. 3 (February 19, 2018): e3282. http://dx.doi.org/10.1002/ett.3282.

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36

Zhang, Kaisa, Gang Chuai, Jinxi Zhang, Xiangyu Chen, Zhiwei Si, and Saidiwaerdi Maimaiti. "DIC-ST: A Hybrid Prediction Framework Based on Causal Structure Learning for Cellular Traffic and Its Application in Urban Computing." Remote Sensing 14, no. 6 (March 16, 2022): 1439. http://dx.doi.org/10.3390/rs14061439.

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The development of technology has strongly affected regional urbanization. With development of mobile communication technology, intelligent devices have become increasingly widely used in people’s lives. The application of big data in urban computing is multidimensional; it has been involved in different fields, such as urban planning, network optimization, intelligent transportation, energy consumption and so on. Data analysis becomes particularly important for wireless networks. In this paper, a method for analyzing cellular traffic data was proposed. Firstly, a method to extract trend components, periodic components and essential components from complex traffic time series was proposed. Secondly, we introduced causality data mining. Different from traditional time series causality analysis, the depth of causal mining was increased. We conducted causality verification on different components of time series and the results showed that the causal relationship between base stations is different in trend component, periodic component and essential component in urban wireless network. This is crucial for urban planning and network management. Thirdly, DIC-ST: a spatial temporal time series prediction based on decomposition and integration system with causal structure learning was proposed by combining GCN. Final results showed that the proposed method significantly improves the accuracy of cellular traffic prediction. At the same time, this method can play a crucial role for urban computing in network management, intelligent transportation, base station siting and energy consumption when combined with remote sensing map information.

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37

Ajayi, Olasupo O., Antoine B. Bagula, Hloniphani C. Maluleke, and Isaac A. Odun-Ayo. "Transport Inequalities and the Adoption of Intelligent Transportation Systems in Africa: A Research Landscape." Sustainability 13, no. 22 (November 22, 2021): 12891. http://dx.doi.org/10.3390/su132212891.

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Intelligent Transportation Systems (ITS), also known as Smart Transportation, is an infusion of information and communication technologies into transportation. ITS are a key component of smart cities, which have seen rapid global development in the last few decades. This has in turn translated to an increase in the deployment and adoption of ITS, particularly in countries in the Western world. Unfortunately, this is not the case with the developing countries of Africa and Asia, where dilapidated road infrastructure, poorly maintained public/mass transit vehicles and poverty are major concerns. However, the impact of Westernization and “imported technologies” cannot be overlooked; thus, despite the aforementioned challenges, ITS have found their way into African cities. In this paper, a systematic review was performed to determine the state of the art of ITS in Africa. The output of this systematic review was then fed into a hybrid multi-criteria model to analyse the research landscape, identify connections between published works and reveal research gaps and inequalities in African ITS. African peculiarities inhibiting the widespread implementation of ITS were then discussed, followed by the development of a conceptual architecture for an integrated ITS for African cities.

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38

Zhou, Wei, Wei Wang, Xuedong Hua, and Yi Zhang. "Real-Time Traffic Flow Forecasting via a Novel Method Combining Periodic-Trend Decomposition." Sustainability 12, no. 15 (July 22, 2020): 5891. http://dx.doi.org/10.3390/su12155891.

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Accurate and timely traffic flow forecasting is a critical task of the intelligent transportation system (ITS). The predicted results offer the necessary information to support the decisions of administrators and travelers. To investigate trend and periodic characteristics of traffic flow and develop a more accurate prediction, a novel method combining periodic-trend decomposition (PTD) is proposed in this paper. This hybrid method is based on the principle of “decomposition first and forecasting last”. The well-designed PTD approach can decompose the original traffic flow into three components, including trend, periodicity, and remainder. The periodicity is a strict period function and predicted by cycling, while the trend and remainder are predicted by modelling. To demonstrate the universal applicability of the hybrid method, four prevalent models are separately combined with PTD to establish hybrid models. Traffic volume data are collected from the Minnesota Department of Transportation (Mn/DOT) and used to conduct experiments. Empirical results show that the mean absolute error (MAE), mean absolute percentage error (MAPE), and mean square error (MSE) of hybrid models are averagely reduced by 17%, 17%, and 29% more than individual models, respectively. In addition, the hybrid method is robust for a multi-step prediction. These findings indicate that the proposed method combining PTD is promising for traffic flow forecasting.

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39

Karballaeezadeh, Nader, Farah Zaremotekhases, Shahaboddin Shamshirband, Amir Mosavi, Narjes Nabipour, Peter Csiba, and Annamária R. Várkonyi-Kóczy. "Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems." Energies 13, no. 7 (April 4, 2020): 1718. http://dx.doi.org/10.3390/en13071718.

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Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.

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40

Hernández-Jiménez, Roberto, Cesar Cardenas, and David Muñoz Rodríguez. "Modeling and Solution of the Routing Problem in Vehicular Delay-Tolerant Networks: A Dual, Deep Learning Perspective." Applied Sciences 9, no. 23 (December 3, 2019): 5254. http://dx.doi.org/10.3390/app9235254.

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The exponential growth of cities has brought important challenges such as waste management, pollution and overpopulation, and the administration of transportation. To mitigate these problems, the idea of the smart city was born, seeking to provide robust solutions integrating sensors and electronics, information technologies, and communication networks. More particularly, to face transportation challenges, intelligent transportation systems are a vital component in this quest, helped by vehicular communication networks, which offer a communication framework for vehicles, road infrastructure, and pedestrians. The extreme conditions of vehicular environments, nonetheless, make communication between nodes that may be moving at very high speeds very difficult to achieve, so non-deterministic approaches are necessary to maximize the chances of packet delivery. In this paper, we address this problem using artificial intelligence from a hybrid perspective, focusing on both the best next message to replicate and the best next hop in its path. Furthermore, we propose a deep learning–based router (DLR+), a router with a prioritized type of message scheduler and a routing algorithm based on deep learning. Simulations done to assess the router performance show important gains in terms of network overhead and hop count, while maintaining an acceptable packet delivery ratio and delivery delays, with respect to other popular routing protocols in vehicular networks.

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41

Banach, Marzena. "Use of Intelligent hybrid solutions in sustainable public transportation system for the net of small cities." Zarządzanie Publiczne, no. 3 (47) (2019): 205–23. http://dx.doi.org/10.4467/20843968zp.19.016.10692.

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42

Fan, Qi, Wei Wang, Xiaojian Hu, Xuedong Hua, and Zhuyun Liu. "Space-Time Hybrid Model for Short-Time Travel Speed Prediction." Discrete Dynamics in Nature and Society 2018 (2018): 1–9. http://dx.doi.org/10.1155/2018/7696592.

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Short-time traffic speed forecasting is a significant issue for developing Intelligent Transportation Systems applications, and accurate speed forecasting results are necessary inputs for Intelligent Traffic Security Information System (ITSIS) and advanced traffic management systems (ATMS). This paper presents a hybrid model for travel speed based on temporal and spatial characteristics analysis and data fusion. This proposed methodology predicts speed by dividing the data into three parts: a periodic trend estimated by Fourier series, a residual part modeled by the ARIMA model, and the possible events affected by upstream or downstream traffic conditions. The aim of this study is to improve the accuracy of the prediction by modeling time and space variation of speed, and the forecast results could simultaneously reflect the periodic variation of traffic speed and emergencies. This information could provide decision-makers with a basis for developing traffic management measures. To achieve the research objective, one year of speed data was collected in Twin Cities Metro, Minnesota. The experimental results demonstrate that the proposed method can be used to explore the periodic characteristics of speed data and show abilities in increasing the accuracy of travel speed prediction.

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43

Qing, Li, Tao Yongqin, Han Yongguo, and Zhang Qingming. "The Forecast and the Optimization Control of the Complex Traffic Flow Based on the Hybrid Immune Intelligent Algorithm." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 245–51. http://dx.doi.org/10.2174/1874129001408010245.

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Transportation system has time-varying, coupling and nonlinear dynamic characteristics. Traffic flow forecast is one of the key technologies of traffic guidance. It is very difficult to accurately forecast them effectively. This paper has analyzed the complexity and the evaluation index of urban transportation network and has proposed the forecasting model of the hybrid algorithm based on chaos immune knowledge. First of all, the chaos knowledge is introduced into the topology structure of immune network, so as to obtain the matching predictive values and knowledge base. Secondly, this algorithm can dynamically control and adjusted the regional search speed and can fuse the information obtained by the chaos and immune algorithm, in order to realize the short-term traffic flow forecast. Finally, the simulation experiment shows that the traffic flow forecasting error obtained by the method is small, feasible and effective and can better meet the needs of the traffic guidance system.

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44

Wang, Zhanzhong, Ruijuan Chu, Minghang Zhang, Xiaochao Wang, and Siliang Luan. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning." Sustainability 12, no. 20 (October 9, 2020): 8298. http://dx.doi.org/10.3390/su12208298.

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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.

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45

Rahman, Imran, Pandian M. Vasant, Balbir Singh Mahinder Singh, and M. Abdullah-Al-Wadud. "Swarm Intelligence-Based Smart Energy Allocation Strategy for Charging Stations of Plug-In Hybrid Electric Vehicles." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/620425.

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Recent researches towards the use of green technologies to reduce pollution and higher penetration of renewable energy sources in the transportation sector have been gaining popularity. In this wake, extensive participation of plug-in hybrid electric vehicles (PHEVs) requires adequate charging allocation strategy using a combination of smart grid systems and smart charging infrastructures. Daytime charging stations will be needed for daily usage of PHEVs due to the limited all-electric range. Intelligent energy management is an important issue which has already drawn much attention of researchers. Most of these works require formulation of mathematical models with extensive use of computational intelligence-based optimization techniques to solve many technical problems. In this paper, gravitational search algorithm (GSA) has been applied and compared with another member of swarm family, particle swarm optimization (PSO), considering constraints such as energy price, remaining battery capacity, and remaining charging time. Simulation results obtained for maximizing the highly nonlinear objective function evaluate the performance of both techniques in terms of best fitness.

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46

Yang, Chao, Mingjun Zha, Weida Wang, Kaijia Liu, and Changle Xiang. "Efficient energy management strategy for hybrid electric vehicles/plug-in hybrid electric vehicles: review and recent advances under intelligent transportation system." IET Intelligent Transport Systems 14, no. 7 (July 1, 2020): 702–11. http://dx.doi.org/10.1049/iet-its.2019.0606.

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47

Kamble, Shridevi Jeevan, and Manjunath R. Kounte. "Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning." Transport and Telecommunication Journal 23, no. 4 (November 1, 2022): 293–310. http://dx.doi.org/10.2478/ttj-2022-0024.

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Abstract Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

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48

Zhou, Zhongxin, Minghu Ha, Hao Hu, and Hongguang Ma. "Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation." Sustainability 13, no. 3 (January 26, 2021): 1262. http://dx.doi.org/10.3390/su13031262.

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How to reduce the accidents of hazardous materials has become an important and urgent research topic in the safety management of hazardous materials. In this study, we focus on the half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. The goal is to determine the vehicle allocation and the optimal route with minimum risk and cost for hazardous materials transportation. A novel transportation risk model is presented considering the variation of vehicle loading, vehicle types, and hazardous materials category. In order to balance the transportation risk and the transportation cost, we propose a bi-objective mixed integer programming model. A hybrid intelligent algorithm is developed based on the ε-constraint method and genetic algorithm to obtain the Pareto optimal solutions. Numerical experiments are provided to demonstrate the effectiveness of the proposed model. Compared with the close multi-depot heterogeneous vehicle routing problem, the average risk and cost obtained by the proposed bi-objective mixed integer programming model can be reduced by 3.99% and 2.01%, respectively. In addition, compared with the half open multi-depot homogeneous vehicle routing problem, the cost is significantly reduced with the acceptable risk.

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49

Wu, Jianqing, Qiang Wu, Jun Shen, and Chen Cai. "Towards Attention-Based Convolutional Long Short-Term Memory for Travel Time Prediction of Bus Journeys." Sensors 20, no. 12 (June 12, 2020): 3354. http://dx.doi.org/10.3390/s20123354.

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Travel time prediction is critical for advanced traveler information systems (ATISs), which provides valuable information for enhancing the efficiency and effectiveness of the urban transportation systems. However, in the area of bus trips, existing studies have focused on directly using the structured data to predict travel time for a single bus trip. For state-of-the-art public transportation information systems, a bus journey generally has multiple bus trips. Additionally, due to the lack of study on data fusion, it is even inadequate for the development of underlying intelligent transportation systems. In this paper, we propose a novel framework for a hybrid data-driven travel time prediction model for bus journeys based on open data. We explore a convolutional long short-term memory (ConvLSTM) model with a self-attention mechanism that accurately predicts the running time of each segment of the trips and the waiting time at each station. The model is more robust to capture long-range dependence in time series data as well.

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Sidharthan, Vishnu P., Yashwant Kashyap, and Panagiotis Kosmopoulos. "Adaptive-Energy-Sharing-Based Energy Management Strategy of Hybrid Sources in Electric Vehicles." Energies 16, no. 3 (January 22, 2023): 1214. http://dx.doi.org/10.3390/en16031214.

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The energy utilization of the transportation industry is increasing tremendously. The battery is one of the primary energy sources for a green and clean mode of transportation, but variations in driving profiles (NYCC, Artemis Urban, WLTP class-1) and higher C-rates affect the battery performance and lifespan of battery electric vehicles (BEVs). Hence, as a singular power source, batteries have difficulty in tackling these issues in BEVs, highlighting the significance of hybrid-source electric vehicles (HSEVs). The supercapacitor (SC) and photovoltaic panels (PVs) are the auxiliary power sources coupled with the battery in the proposed hybrid electric three-wheeler (3W). However, energy management strategies (EMS) are critical to ensure optimal and safe power allocation in HSEVs. A novel adaptive Intelligent Hybrid Source Energy Management Strategy (IHSEMS) is proposed to perform energy management in hybrid sources. The IHSEMS optimizes the power sources using an absolute energy-sharing algorithm to meet the required motor power demand using the fuzzy logic controller. Techno-economic assessment wass conducted to analyze the effectiveness of the IHSEMS. Based on the comprehensive discussion, the proposed strategy reduces peak battery power by 50.20% compared to BEVs. It also reduces the battery capacity loss by 48.1 %, 44%, and 24%, and reduces total operation cost by 60%, 43.9%, and 23.68% compared with standard BEVs, state machine control (SMC), and frequency decoupling strategy (FDS), respectively.

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