<i>TrajCompressor</i>: An Online Map-matching-based Trajectory Compression Framework Leveraging Vehicle Heading Direction and Change

Chen, Chao; Ding, Yan; Xie, Xuefeng; Zhang, Shu; Wang, Zhu; Feng, Liang

doi:10.1109/tits.2019.2910591

Cited by 96 publications

(66 citation statements)

References 52 publications

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“…Rome for improvement remains. Researchers believe that map matching performance is influenced by a series factors, such as heading information [10], time information [11],driving speed [12], distant, historic data, azimuth of GPS [13], etc. Then different types of cost functions are defined.…”

Section: A Map Matchingmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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Bus arrival time prediction not only provides convenience for passengers, but also helps to improve the efficiency of intelligent transportation system. Unfortunately, the low precision of bus-mounted GPS system, lack of real-time traffic information and poor performance of prediction model lead to low estimation accuracy-greatly influence bus service performance. Hence, in this paper, a GPS calibration method is put forward, while projection rules of specific road shapes are discussed. Moreover, two traffic factors, travel factor and dwelling factor, are defined to express real-time traffic state. Then, considering both historic data and real-time traffic condition, a hybrid dynamic BAT prediction factor, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is defined. A LSTM training model is construct to realize BAT prediction. Experiment results demonstrate that our technique can provide a higher level of accuracy compared to methods based on traditional time-ofarrival techniques, especially in the accuracy of multi-stops BAT prediction. INDEX TERMS Bus arrival time prediction, LSTM model, GPS data calibration.

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Section: A Map Matchingmentioning

confidence: 99%

A Bus Arrival Time Prediction Method Based on Position Calibration and LSTM

et al. 2020

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“…(e ij * s fij − e sij ) , if e ij < e sij (9) The dissimilarity is considered for both overlapping pixels (e ij > e sij ) missing bits e ij < e sij using Equation (9). The ∇d value is nevertheless zero; rather, it is very small, so it can be discarded.…”

Section: A Level 1: Block Conversionmentioning

confidence: 99%

“…If this condition, i.e., ϕ ≥ ϕ s and δ ≥ δ s , is achieved, ∇d = 0 else the block consists of dissimilarity as computed using Equation (6) for the same pixels. If the number of blocks is different, then ∇d is estimated as in Equation (9). Here, ϕ s and δ s denote the entropy and correlation of the stored image.…”

Section: Level 3: Recurrent Analysismentioning

confidence: 99%

“…In Equation 12, the rate of variance is estimated for each of the N-blocks. The variable ∇d N represented in Equation 12is different from ∇d computed in Equations (6) and (9). Therefore, the following is adopted in recognizing the processed image with respect to the condition in Table 2.…”

Section: Level 4: Recognitionmentioning

confidence: 99%

“…A smart city environment exploits the complete development of the IoT and other reliable technologies such as Intelligent Transportation Systems (ITS) to improve end-user assistance and communication comfort [8], [9]. Real-time automated traffic management is a recognizable smart city application that improves road-safety and driving cautiousness for end users [10], [11].…”

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confidence: 99%

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CMRS: A Classifier Matrix Recognition System for Traffic Management and Analysis in a Smart City Environment

et al. 2019

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The application of the Internet of Things (IoT) in a smart city improves its efficiency in terms of communication and installation costs by scaling geographical distance through intelligent devices and digital information. Different applications in a smart city, including health care, road safety, industry and home automation, rely on the IoT. Considering the significance of the IoT in smart city road applications, this manuscript introduces a classifier matrix recognition system (CMRS) for improving realtime traffic optimization. This classifier matrix system performs an independent and matching analysis of the real-time traffic images and constructs a decision factor for deriving its conclusion. The conclusion is served as responding notifications through the connected IoT systems for the users employing roadsidecommunication-assisted applications. CMRS exploits the advantages of block classification and matrix operations for improving the correlation accuracy and similarity index. The experimental results indicate that the proposed CMRS improves correlation accuracy with a high similarity index and less processing time and dissimilarity rate. INDEX TERMS Block classification, image recognition, IoT, ITS, smart city, traffic management. I. INTRODUCTION The rapid development of the IoT is resulting in its services being used in different real-world applications. This communication platform is capable of connecting various elements, including humans, computers, machines, tiny sensors and other objects [1], [2]. More specifically, the connected devices are heterogeneous in nature, which facilitates pervasive access to resources, applications and services [3], [4]. The intelligence of the communicating devices in this paradigm provides anywhere and anytime access to resources the end user requests [5]. Because the interconnection of this platform incorporates heterogeneous devices, it supports various communication technologies ranging from device-todevice (D2D) to distributed cloud computing [6]. The independent operations of the devices are assisted via a wireless communication medium in which the protocol and technology are adopted appropriately. Distributed and concurrent The associate editor coordinating the review of this manuscript and approving it for publication was Chao Chen.

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