Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Fan, Xiaochen; Xiang, Chaocan; Gong, Liangyi; He, Xin; Qu, Yuben; Amirgholipour, Saeed; Yang, Xi; Nanda, Priyadarsi; He, Xiangjian

doi:10.1007/s42486-020-00039-x

Cited by 24 publications

(3 citation statements)

References 123 publications

(133 reference statements)

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“…DL plays a multifaceted role in transforming conventional ITS, encompassing tasks such as traffic flow predictions, incident detection, vehicle detection and classification, traffic sign recognition, autonomous driving, and more [64]- [66]. In recent years, DL has emerged as a distinctive and promising approach for addressing the complex challenges within ITS [67]- [70].…”

Section: How DL Can Transform Conventional Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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Intelligent Transportation Systems (ITS) operate within a highly intricate and dynamic environment characterized by complex spatial and temporal dynamics at various scales, further compounded by fluctuating conditions influenced by external factors such as social events, holidays, and weather conditions. Navigating the intricacies of modeling the intricate interaction among these elements, creating universal representations, and employing them to address transportation issues presents a significant endeavor. Yet, these intricacies comprise just one facet of the multifaceted trials confronting contemporary ITS. This paper offers an all-encompassing survey exploring Deep learning (DL) utilization in ITS, primarily focusing on practitioners' methodologies to address these multifaceted challenges. The emphasis lies on the architectural and problem-specific factors that guide the formulation of innovative solutions. In addition to shedding light on the state-of-the-art DL algorithms, we also explore potential applications of DL and large language models (LLMs) in ITS, including traffic flow prediction, vehicle detection and classification, road condition monitoring, traffic sign recognition, and autonomous vehicles. Besides, we identify several future challenges and research directions that can push the boundaries of ITS, including the critical aspects of explainability, transfer learning, hybrid models, privacy and security, and ultra-reliable low-latency communication. Our aim for this survey is to bridge the gap between the burgeoning DL and transportation communities. By doing so, we aim to facilitate a deeper comprehension of the challenges and possibilities within this field. We hope that this effort will inspire further exploration of fresh perspectives and issues, which, in turn, will play a pivotal role in shaping the future of transportation systems.

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Section: How DL Can Transform Conventional Itsmentioning

confidence: 99%

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Khalil,

Safelnasr,

Yemane

et al. 2024

IEEE Open J. Veh. Technol.

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“…The intricate nature of urban road networks can be easily modelled through graph structures and fed into a GCN model. Fan et al (2020) and Jiang and Luo (2022) surveyed an exhaustive list of literature for recent traffic-related research and identified that GCNs are at the frontier of deep learning-based traffic prediction research. Most of the literature in this domain has focused on analysing road-level traffic flow, primarily traffic volumes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Explainable spatially explicit geospatial artificial intelligence in urban analytics

Liu,

Zhang,

Biljecki

2023

Environment and Planning B: Urban Analytics and City Science

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Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph neural networks (GNNs) have become one of the most popular methods in recent years. However, along with the success of GNNs, the black box nature of AI models has led to various concerns (e.g. algorithmic bias and model misuse) regarding their adoption in urban analytics, particularly when studying socio-economics where high transparency is a crucial component of social justice. Therefore, the desire for increased model explainability and interpretability has attracted increasing research interest. This article proposes an explainable spatially explicit GeoAI-based analytical method that combines a graph convolutional network (GCN) and a graph-based explainable AI (XAI) method, called GNNExplainer. Here, we showcase the ability of our proposed method in two studies within urban analytics: traffic volume prediction and population estimation in the tasks of a node classification and a graph classification, respectively. For these tasks, we used Street View Imagery (SVI), a trending data source in urban analytics. We extracted semantic information from the images and assigned them as features of urban roads. The GCN first provided reasonable predictions related to these tasks by encoding roads as nodes and their connectivities and networks as graphs. The GNNExplainer then offered insights into how certain predictions are made. Through such a process, practical insights and conclusions can be derived from the urban phenomena studied here. In this paper we also set out a path for developing XAI in future urban studies.

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“…For example, New Jersey Department of Trasnportation (NJDOT) installs more than 600 cameras to monitor the traffic in the state. During the shift to the smart transportation system, IP-based and conventional cameras are used to not only detect but also mitigate traffic congestion on roads [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Smart Traffic Monitoring System using Computer Vision and Edge Computing

Liu

Shi

Kiani

et al. 2021

Preprint

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Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g. cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

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Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges

Cited by 24 publications

References 123 publications

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Advanced Learning Technologies for Intelligent Transportation Systems: Prospects and Challenges

Explainable spatially explicit geospatial artificial intelligence in urban analytics

Smart Traffic Monitoring System using Computer Vision and Edge Computing

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