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

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Abstract

With the emerging concepts of smart cities and intelligent transportation systems, accurate traffic sensing and prediction have become critically important to support urban management and traffic control. In recent years, the rapid uptake of the Internet of Vehicles and the rising pervasiveness of mobile services have produced unprecedented amounts of data to serve traffic sensing and prediction applications. However, it is significantly challenging to fulfill the computation demands by the big traffic data with ever-increasing complexity and diversity. Deep learning, with its powerful capabilities in representation learning and multi-level abstractions, has recently become the most effective approach in many intelligent sensing systems. In this paper, we present an up-to-date literature review on the most advanced research works in deep learning for intelligent traffic sensing and prediction.

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Notes

  1. Technical details and implementations of deep-learning models can be referred from https://github.com/rasbt/deeplearning-models.

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Acknowledgements

This research is supported by the NSF of China Projects: Grants no. 61872447, the Natural Science Foundation of Chongqing: Grant no. CSTC2018JCYJA1879, National Postdoctoral Program for Innovative Talents of China No. BX20190202, in part by China NSF grant No. 61702525, and China Scholarship Council: Grant no. 201603170125.

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Authors and Affiliations

  1. School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia

    Xiaochen Fan, Saeed Amirgholipour, Priyadarsi Nanda & Xiangjian He

  2. College of Computer Science, Chongqing University, Chongqing, China

    Chaocan Xiang & Xin He

  3. School of Software and BNRist, Tsinghua University, Beijing, China

    Liangyi Gong

  4. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yuben Qu

  5. School of Computer Science, Northwestern Polytechnical University, Xi’an, China

    Yue Xi

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  1. Xiaochen Fan
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  4. Xin He
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A Wireless sensor networks for traffic sensing and prediction

A Wireless sensor networks for traffic sensing and prediction

1.1 A.1 Wireless sensing technologies for urban traffic systems

Sensors are the fundamental elements in traffic sensing, and wireless sensor networks are widely used to satisfy the requirements of real-time and accurate traffic sensing (Xiao et al. 2019). A wireless sensor node usually consists of five critical functional modules as follows (Xu et al. 2014).

  • A sensing module for vehicle detection and data acquisition.

  • A wireless transceiver module for wireless data transmission.

  • A local data processing module for converting physicochemical signals into traffic values.

  • A memory module for storing sensing data and backup of system settings.

  • A power supply module that consistently provides energy for the sensor.

Table 5 Wireless sensing technologies for urban traffic systems
Full size table

We categorize wireless traffic sensors in Table 5 and introduce different traffic sensing technologies as follows. inductive loop sensors, as the most commonly used devices, are installed in the road surface to detect the presence of vehicles by the inducing currents from the vehicle. Similarly, magnetic sensors (including Magnetic sensors and Magnetic induction coil) can detect the presence of a vehicle through the anomaly in the magnetic field (Gong et al. 2018). Moreover, microwave radar sensors leverage antenna beams to detect the presence, passage, volume, lane occupancy, speed, or length of a vehicle by the reflected signals. Likewise, infrared sensors (either active or passive) detect the energy reflected by or emitted from vehicles, then convert the energy into electrical signals to further determine the presence of vehicles. Besides, laser radar sensors transmit power in the near-infrared spectrum and provide traffic measurements, such as vehicle presence, traffic volume, and traffic speed. Modern laser sensors can provide precise two-dimensional or three-dimensional image data of vehicles.

As another short-range sensing technique, RFIDs (Xiao et al. 2018a, b) have been utilized for fine-grained object detection. However, they are not feasible for the scenarios of large-scale traffic sensing, due to the constraints of communication scalability and the cost of RFID tags. Ultrasonic sensors work with pulse waveforms and can detect vehicle count, presence, and occupancy information. Furthermore, acoustic arrays are passive sensors that use signal processing algorithms to measure traffic volume and traffic speed in vehicular networks. For real-time traffic surveillance, video image sensors are the most pervasive devices of roadways that transmit television imagery to traffic operators. With the installed data processing modules, surveillance cameras can perform more advanced traffic sensing tasks, including plate recognition, driving behavior detection, and even driver facial recognition. Moreover, onboard GPS sensors can be categorized as indirect sensors that can provide city-wide trajectory data of vehicles. GPS trajectory can be utilized by speed inference models (Zhan et al. 2016) and traffic volume estimation models (Meng et al. 2017). Meanwhile, the tradeoff between incentive pricing and sensing quality on sensing data like GPS remains as a challenge, and various mechanisms have been proposed to address this issue (Qu et al. 2018; Xiang et al. 2016)

1.2 A.2 Wireless communication technologies for urban traffic systems

There are a number of wireless communication technologies that can support traffic data transmission under various requirements (e.g., transmitting distance, data volume). As shown in Table 6, we summarize the critical enabling transmission technologies for traffic sensing and prediction, including Bluetooth, ZigBee, Z-Wave, LoRaWAN, WiFi, WiMAX, LTE, and LTE-A.

Table 6 Wireless communication technologies for urban traffic systems
Full size table

To begin with, Bluetooth and ZigBee are more suitable for short-range communication between traffic sensors and road-side units, where Bluetooth is characterized for Peer-to-Peer (P2P) communications and ZigBee has higher scalability with lower transmission rate. In addition, Z-Wave has been applied for short-range communication of indoor traffic applications (Xiang et al. 2015), such as smart parking. Moreover, LoRaWAN can support wireless communication between gateways for long-range traffic monitoring scenarios (e.g., highways) and further secure bidirectional communication with moderate data load. Alternatively, WiFi with different configurations under IEEE 802.11 standards can be used for short-range, regional, and opportunistic traffic data transmission at intersections and business-intensive areas (Zhu et al. 2017; Fu et al. 2016a; Xiang et al. 2014). Moreover, WiMAX allows scalable data rates for long-range communication. Thereby, it is more desirable for video surveillance and image cameras in traffic sensing systems. At last, LTE and LTE-A are both under the 3GPP standard. Thus, they can provide portable mobile broadband connectivity across urban areas for traffic data transmission.

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Fan, X., Xiang, C., Gong, L. et al. Deep learning for intelligent traffic sensing and prediction: recent advances and future challenges. CCF Trans. Pervasive Comp. Interact. 2, 240–260 (2020). https://doi.org/10.1007/s42486-020-00039-x

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