TY - GEN
T1 - Enhancing ITS with Agile Optimization for Dynamic Ride-Sharing Mobility
AU - Jaiswal, Anmol
AU - Kumar, Sudhakar
AU - Singh, Sunil K.
AU - Manhas, Ayushi
AU - Gupta, Brij B.
AU - Hsu, Ching Hsien
AU - Chui, Kwok Tai
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - The rapid proliferation of Internet of Things (IoT) devices within intelligent transportation systems (ITS) has led to a massive influx of data, overwhelming existing cloud, fog, and edge computing models. This surge in data volume presents significant challenges, including increased latency, reduced scalability, and inefficiencies in real-time decision-making. Current methods often struggle to manage and process this data effectively, leading to delays and unreliable transportation services. This paper aims to tackle these challenges by introducing a novel approach that leverages agile optimization algorithms specifically designed for dynamic ride-sharing solutions. By integrating edge computing capabilities, these algorithms improve decision-making and data processing in real time significantly improving system responsiveness and efficiency. This approach minimizes latency, improves scalability, and delivers more reliable and effective transportation services. In addition, it explores future advancements and the potential integration of emerging technologies, such as 5G and artificial intelligence, to further bolster the capabilities of intelligent transportation systems. The goal of this research is to offer a complete solution to the pressing issues faced by modern ITS infrastructures.
AB - The rapid proliferation of Internet of Things (IoT) devices within intelligent transportation systems (ITS) has led to a massive influx of data, overwhelming existing cloud, fog, and edge computing models. This surge in data volume presents significant challenges, including increased latency, reduced scalability, and inefficiencies in real-time decision-making. Current methods often struggle to manage and process this data effectively, leading to delays and unreliable transportation services. This paper aims to tackle these challenges by introducing a novel approach that leverages agile optimization algorithms specifically designed for dynamic ride-sharing solutions. By integrating edge computing capabilities, these algorithms improve decision-making and data processing in real time significantly improving system responsiveness and efficiency. This approach minimizes latency, improves scalability, and delivers more reliable and effective transportation services. In addition, it explores future advancements and the potential integration of emerging technologies, such as 5G and artificial intelligence, to further bolster the capabilities of intelligent transportation systems. The goal of this research is to offer a complete solution to the pressing issues faced by modern ITS infrastructures.
KW - Agile Optimization
KW - Dynamic Ride-Sharing Mobility
KW - Edge Computing
KW - Intelligent Transportation Systems (ITS)
KW - IoT Analytics
UR - https://www.scopus.com/pages/publications/105011935331
U2 - 10.1007/978-981-96-6294-4_2
DO - 10.1007/978-981-96-6294-4_2
M3 - Conference contribution
AN - SCOPUS:105011935331
SN - 9789819662937
T3 - Communications in Computer and Information Science
SP - 18
EP - 30
BT - Ubi-Media Computing, Pervasive Systems, Algorithms and Networks - 13th International Conference, Ubi-Media 2025, and 17th International Symposium, I-SPAN 2025, Proceedings
A2 - Hui, Lin
A2 - Hsu, Ching-Hsien
A2 - Ruengittinun, Somchoke
T2 - 13th International Conference on Ubi-Media Computing, Ubi-Media 2025 and 17th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2025
Y2 - 19 January 2025 through 23 January 2025
ER -
