Performance Evaluation of New Energy Vehicles with Human-Machine Interaction in Intelligent Transportation Systems
DOI:
https://doi.org/10.13052/spee1048-5236.4042Keywords:
Energy vehicle, intelligent transport system, human-machine interaction, natural language processing, voice-over interfaceAbstract
An intelligent transport system is a sophisticated platform developed to provide new solutions related to transport administration, enabling people to be increasingly aware and make infrastructures secure, more synchronized, and more sophisticated utilization. Energy vehicles in intelligent transportation have additional positive benefits to society. However, handling mobility, connectivity, and communication infrastructure are significant challenges in integrating the energy vehicle in intelligent transport systems. This paper proposed an energy vehicle with a human interaction framework (EVHIF) to enhance the intelligent transportation system. The EVHIF provides human-machine interaction through a voice-over interface, and the natural language processing made the interface more flexible. The voice-over interaction framework adopts the advanced user interface, mobility problems and becomes more accessible. Static language program modifies the user voice interaction to the energy vehicles.
If correctly designed and executed, ITS can save a great deal of time, money, and even lives. ITS encompasses the information analysis and data transmission programs that enable the operation and maintenance of surface transportation modes to increase the safety and efficiency of conveying people and commodities. People, safety officers, and government also use its benefits.
Experimentation is done by focusing on the performance of EVHIF in an intelligent transportation system. Evaluation results prove that EVHIF is efficient at 98.56% and advisable for transportation systems with advanced interaction frameworks.
Downloads
References
Zhang, H., and Lu, X. (2020). Vehicle communication network in
intelligent transportation system based on internet of things. Computer
Communications, 160, 799–806.
Mahmoudi, R., Emrouznejad, A., Shetab-Boushehri, S. N., and Hejazi,
S. R. (2020). The origins, development, and future directions of
data envelopment analysis approach in transportation systems. Socio-
Economic Planning Sciences, 69, 100672.
Liu, B. H., and Nguyen, N. T. (2014, August). An efficient method for
sweep coverage with the minimum mobile sensor. In 2014 Tenth Inter-
national Conference on Intelligent Information Hiding and Multimedia
Signal Processing (pp. 289–292). IEEE.
P. Zhang et al.
Liu, B. H., Pham, V. T., and Nguyen, N. T. (2015, September). A
virtual backbone construction heuristic for maximizing the lifetime of
dual-radio wireless sensor networks. In 2015 International Conference
on Intelligent Information Hiding and Multimedia Signal Processing
(IIH-MSP) (pp. 64–67). IEEE.
Le, N. T., Wang, J. W., Wang, C. C., and Nguyen, T. N. (2019).
Automatic defect inspection for coated eyeglass based on symmetrized
energy analysis of color channels. Symmetry, 11(12), 1518.
Usman, N., Usman, S., Khan, F., Jan, M. A., Sajid, A., Alazab, M., and
Watters, P. (2021). Intelligent dynamic malware detection using machine
learning in IP reputation for forensics data analytics. Future Generation
Computer Systems, 118, 124–141.
Chen, J., Ramanathan, L., and Alazab, M. (2021). Holistic, big data
integrated artificial intelligent modeling to improve privacy and security
in data management of smart cities. Microprocessors and Microsystems,
, 103722.
Albahri, O. S., Zaidan, A. A., Salih, M. M., Zaidan, B. B., Khatari,
M. A., Ahmed, M. A., . . . and Alazab, M. (2021). Multidimensional
benchmarking of the active queue management methods of network
congestion control based on an extension of the fuzzy decision by opin-
ion score method. International Journal of Intelligent Systems, 36(2),
–831.
Appathurai, A., Manogaran, G., and Chilamkurti, N. (2019). Trusted
FPGA-based transport traffic inject, impersonate (I2) attacks beaconing
on the Internet of Vehicles. IET Networks, 8(3), 169–178.
Manogaran, G., Balasubramanian, V., Rawal, B. S., Saravanan, V.,
Montenegro-Marin, C. E., Ramachandran, V., and Kumar, P. M. (2020).
Multi-Variate Data Fusion Technique for Reducing Sensor Errors in
Intelligent Transportation Systems. IEEE Sensors Journal.
Yin, X., Li, J., Kadry, S. N., and Sanz-Prieto, I. (2021). Artificial
intelligence assisted intelligent planning framework for environmental
restoration of terrestrial ecosystems. Environmental Impact Assessment
Review, 86, 106493.
Bi, D., Kadry, S., and Kumar, P. M. (2020). Internet of things
assisted public security management platform for urban transportation
using hybridized cryptographic-integrated steganography. IET Intelli-
gent Transport Systems, 14(11), 1497–1506.
Performance Evaluation of New Energy Vehicles 359
Veningston, K., Kadry, S., Kalash, H. S., Balamurugan, B., and Sathi-
yaraj, R. (2020). Intelligent social network-based data modeling for
improving health care. Health and Technology, 10(1), 321–332.
Chen, W. H., Lin, W. I., Chang, S. H., and Mak, L. C. (2015). Exploring
relationships between the physiological and psychological condition of
seniors and their mobility and social activity. Transportation research
record, 2537(1), 103–110.
Chen, W. H., Chang, S. H., Kao, K. C., Lin, S. Y., and Lin, W. I. (2009).
Design and evaluation of bus information at bus stops and printed
information brochures for the elderly. Transportation Planning Journal,
(4), 355–380.
Chang, B., Pei, H. M., and Chang, J. R. (2011). Using the Rough Set
Theory to Investigate the Building Facilities for the Performing Arts
from the Performer’s Perspectives. In Intelligent Decision Technologies
(pp. 647–657). Springer, Berlin, Heidelberg.
Gupta, D., Rani, S., Ahmed, S. H., Garg, S., Piran, M. J., and Alrashoud,
M. (2021). ICN-Based Enhanced Cooperative Caching for Multime-
dia Streaming in Resource-Constrained Vehicular Environment. IEEE
Transactions on Intelligent Transportation Systems.
Zhou, B., Liu, A., Lau, V., Wen, J., Mumtaz, S., Bashir, A. K., and
Ahmed, S. H. (2020). Performance limits of visible light-based posi-
tioning for Internet-of-vehicles: Time-domain localization cooperation
gain. IEEE Transactions on Intelligent Transportation Systems.
Abbasi, M., Rafiee, M., Khosravi, M. R., Jolfaei, A., Menon, V. G., and
Koushyar, J. M. (2020). An efficient parallel genetic algorithm solution
for vehicle routing problems in cloud implementation of intelligent
transportation systems. Journal of Cloud Computing, 9(1), 6.
Jolfaei, A., and Kant, K. (2019, June). Privacy and security of con-
nected vehicles in the intelligent transportation system. In 2019 49th
Annual IEEE/IFIP International Conference on Dependable Systems
and Networks–Supplemental Volume (DSN-S) (pp. 9–10). IEEE.
Pathak, D. K., Thakur, L. S., and Rahman, S. (2019). Performance
evaluation framework for sustainable freight transportation systems.
International Journal of Production Research, 57(19), 6202–6222.
Brincat, A. A., Pacifici, F., Martinaglia, S., and Mazzola, F. (2019,
April). The internet of things for intelligent transportation systems in
real smart city scenarios. In 2019 IEEE 5th World Forum on Internet of
Things (WF-IoT) (pp. 128–132). IEEE.
P. Zhang et al.
Khattak, Z. H., Magalotti, M. J., and Fontaine, M. D. (2020). Oper-
ational performance evaluation of adaptive traffic control systems: A
Bayesian modeling approach using real-world GPS and private sec-
tor PROBE data. Journal of Intelligent Transportation Systems, 24(2),
–170.
Sobral, T., Galv ̃ao, T., and Borges, J. (2019). Visualization of urban
mobility data from intelligent transportation systems. Sensors, 19(2),
Ferdowsi, A., Challita, U., and Saad, W. (2019). Deep learn-
ing for reliable mobile edge analytics in intelligent transportation
systems: An overview. ieee vehicular technology magazine, 14(1),
–70.
Abbasi, M., Rafiee, M., Khosravi, M. R., Jolfaei, A., Menon, V. G., and
Koushyar, J. M. (2020). An efficient parallel genetic algorithm solution
for vehicle routing problems in cloud implementation of intelligent
transportation systems. Journal of cloud Computing, 9(1), 6.
Boukerche, A., Tao, Y., and Sun, P. (2020). Artificial intelligence-
based vehicular traffic flow prediction methods for supporting intelligent
transportation systems. Computer Networks, 182, 107484.
Janssen, C. P., Boyle, L. N., Kun, A. L., Ju, W., and Chuang, L. L.
(2019). A hidden markov framework to capture human-machine interac-
tion in automated vehicles. International Journal of Human-Computer
Interaction, 35(11), 947–955.
Zengeler, N., Kopinski, T., and Handmann, U. (2019). Hand ges-
ture recognition in automotive human-machine interaction using depth
cameras. Sensors, 19(1), 59.
Nalepka, P., Lamb, M., Kallen, R. W., Shockley, K., Chemero, A., Saltz-
man, E., and Richardson, M. J. (2019). Human social motor solutions for
human-machine interaction in dynamical task contexts. Proceedings of
the National Academy of Sciences, 116(4), 1437–1446.
Manogaran, G., Rawal, B. S., Saravanan, V., Kumar, P. M., Mart ́ınez, O.
S., Crespo, R. G., . . . and Krishnamoorthy, S. (2020). Blockchain based
integrated security measure for reliable service delegation in 6G com-
munication environment. Computer Communications, 161, 248–256.
Performance Evaluation of New Energy Vehicles 361
Zhu, S., Saravanan, V., and Muthu, B. (2020). Achieving data secu-
rity and privacy across healthcare applications using cyber security
mechanisms. The Electronic Library.
Baskar, S., Dhulipala, V. S., Shakeel, P. M., Sridhar, K. P., and Kumar, R.
(2020). Hybrid fuzzy based spearman rank correlation for cranial nerve
palsy detection in MIoT environment. Health and Technology, 10(1),
–270
https://aeeeuropeenergy.com/
