An open access journal
Enhancing Transportation with Intelligent Solutions
Abstract
This paper explores the enhancement of transportation through intelligent solutions enabled by AI-driven engineering. Through case studies and research insights, it investigates how artificial intelligence is revolutionizing traditional transportation systems, including traffic management, logistics optimization, and autonomous vehicles. The study highlights the application of AI techniques such as reinforcement learning, computer vision, and natural language processing in improving safety, efficiency, and sustainability in transportation networks. Additionally, it discusses the integration of AI with smart sensors, predictive analytics, and blockchain technology to enable real-time traffic monitoring, route optimization, and secure transactions in logistics operations. The paper also addresses challenges such as data privacy, infrastructure compatibility, and regulatory frameworks in the adoption of AI-driven engineering solutions in transportation. It emphasizes the importance of public-private partnerships, data governance, and user acceptance in leveraging AI's potential to build smarter, more inclusive transportation systems for the future.
Share and Cite
Article Metrics
References
- Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 961-971).
- Bishop, R. J., & Choudhury, C. F. (2019). Artificial intelligence techniques for transportation. Journal of Transportation Engineering, Part B: Pavements, 145(1), 04018086.
- Hagenauer, J., Gehrig, S., Schindler, K., & Amann, N. (2018). Deep learning-based traffic sign detection and classification in the wild. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops (pp. 0-0).
- Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., ... & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 675-678).
- Mohan, P., Padmanabhan, V. N., & Ramjee, R. (2008). Nericell: Rich monitoring of road and traffic conditions using mobile smartphones. In Proceedings of the 6th ACM conference on Embedded network sensor systems (pp. 323-336).
- Tripathi, A., Misra, S., Lall, B., & Gami, R. (2019). Blockchain technology in transportation: A state-of-the-art review and framework. Transportation Research Part D: Transport and Environment, 87, 102508.
- Wang, H., Wang, H., & Li, X. (2019). Deep learning for traffic data imputation and prediction: A survey. IEEE Transactions on Intelligent Transportation Systems, 21(11), 4037-4056.