The integration of CPS, CPSS, and ITS: A focus on data

Wei, Guo; Zhang, Yi; Li, Li · 2015 · OpenAlex-citations

DOI: 10.1109/tst.2015.7173449

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Summary

This survey paper examines the integration of Cyber-Physical Systems (CPS) and Cyber-Physical-Social Systems (CPSS) with Intelligent Transportation Systems (ITS), with a specific focus on the role of data. The authors argue that transportation systems are complex cyber-physical entities where the synthesis of data from cyber, physical, and social sources is essential for extracting knowledge that cannot be derived from any single domain. The primary motivation is to address the challenges posed by the increasing volume, diversity, and spatial distribution of transportation data, aiming to improve traffic safety, efficiency, and energy conservation through advanced data-centric approaches. The paper outlines a five-layer hierarchical architecture for CPS-ITS: perception, communication, computing, control, and application. The perception layer utilizes distributed sensors, including on-board units, roadside units, and smartphones, to collect high-granularity data on vehicle dynamics, road conditions, and traveler behavior. The communication layer employs technologies such as DSRC, 3G/4G, V2V, and V2I to ensure rapid and reliable information dissemination. The computing layer leverages cloud computing platforms for distributed processing and storage of massive, multimodal data. The control layer uses this enriched data to implement flexible optimization algorithms, such as adaptive traffic signal control, surpassing traditional fixed methods. Finally, the application layer delivers traveler-oriented services, including advanced traffic management and driver assistance systems. The authors identify four key characteristics of data in CPS-ITS: real-time capability, distributed nature, diversity of formats, and the critical need for reliability and security. To manage these challenges, the paper highlights four emerging technologies: big data security and management techniques for handling redundant and heterogeneous data; spatial-temporal separation technologies to ensure timely transmission; deep data fusion methods combining inputs from cameras, GPS, and probe vehicles; and multi-dimensional data-driven control strategies for coordinated system optimization. Two case studies illustrate these concepts: vehicular-communication-based traffic control systems, which provide drivers with speed guidance to avoid stops and allow operators to optimize signal timing; and smart parking systems, which use sensors to monitor occupancy and guide drivers to available spaces, thereby reducing congestion and improving resource utilization. The significance of this work lies in its transition from CPS to CPSS, incorporating social big data to understand the "why" behind travel behaviors. By integrating social media feeds and web search records with trace data, researchers can infer travel purposes and predict patterns, particularly during crises. The authors conclude that while CPS and CPSS introduce complexities, they offer the potential for large-scale, distributed coordination in transportation. Future research must address the challenges of modeling context-aware travel activities and managing the "deluge" of data to build more responsive and precise intelligent transportation systems.

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• v2x connected vehicle