GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

Guo, Jingqiu; Liu, Yangzexi; Yang, Qun; Wang, Yibing

doi:10.1080/23249935.2020.1745927

Cited by 56 publications

(26 citation statements)

References 36 publications

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“…However, GPS can provide a vehicle guidance facility for the user to drive towards vacant parking lots. From GPS data, many systems can forecast parking lot occupancy and road traffic congestion using CNN or DL algorithms [ 95 ]. The accuracy of GPS depends on the number of receivers it has.…”

Section: Approaches To Smart Parking Systemmentioning

confidence: 99%

Smart parking systems: comprehensive review based on various aspects

Fahim

Hasan

Chowdhury

2021

Heliyon

121

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Parking allocation has become a major problem in modern cities for which numerous smart parking systems (SPS) have been developed. This paper aims to provide comprehensive study, comparison and extensive analysis of SPSs in terms of technological approach, sensors utilized, networking technologies, user interface, computational approaches, and service provided. Moreover, the paper fills up the research gap by providing a clear insight into the suitability of SPSs in various environmental conditions and highlights their advantages/disadvantages. The extensive comparison among multiple aspects of SPSs would enable researchers, designers, and policymakers to identify the best suited SPS and understand the current trends in this sector.

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Section: Approaches To Smart Parking Systemmentioning

confidence: 99%

Smart parking systems: comprehensive review based on various aspects

Fahim

Hasan

Chowdhury

2021

Heliyon

121

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“…Long short-term memory (LSTM) is an advanced form of RNN that uses memory cells for long chains of sequential data inputs (35,36). The onedimensional convolutional neural network (1D CNN) has been applied in image classification (37) and traffic speed prediction (38)(39)(40).…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

Shabarek

Chien

Hadri³

2020

Transportation Research Record

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The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

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“…The application of big data has served as a basic strategic digital resource in smart cities. Many researchers have analyzed the trajectory GPS data of transportation vehicles in order to mine the hidden information behind the data to reflect the urban operation status and define temporal and spatial change rules [1], in addition to use in traffic congestion status analysis [2][3][4][5][6][7], crowd movement distribution [8][9][10], traffic travel recommendation [11,12], and road planning [13,14], urban hotspot discovery [15][16][17][18], and so on. Such research results are directly applied to the construction of a smart city to elucidate more reasonable urban road planning and a more reasonable dispersion of vehicle flow and human flow.…”

Section: Introductionmentioning

confidence: 99%

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

et al. 2021

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With the development of cities, urban congestion is nearly an unavoidable problem for almost every large-scale city. Road planning is an effective means to alleviate urban congestion, which is a classical non-deterministic polynomial time (NP) hard problem, and has become an important research hotspot in recent years. A K-means clustering algorithm is an iterative clustering analysis algorithm that has been regarded as an effective means to solve urban road planning problems by scholars for the past several decades; however, it is very difficult to determine the number of clusters and sensitively initialize the center cluster. In order to solve these problems, a novel K-means clustering algorithm based on a noise algorithm is developed to capture urban hotspots in this paper. The noise algorithm is employed to randomly enhance the attribution of data points and output results of clustering by adding noise judgment in order to automatically obtain the number of clusters for the given data and initialize the center cluster. Four unsupervised evaluation indexes, namely, DB, PBM, SC, and SSE, are directly used to evaluate and analyze the clustering results, and a nonparametric Wilcoxon statistical analysis method is employed to verify the distribution states and differences between clustering results. Finally, five taxi GPS datasets from Aracaju (Brazil), San Francisco (USA), Rome (Italy), Chongqing (China), and Beijing (China) are selected to test and verify the effectiveness of the proposed noise K-means clustering algorithm by comparing the algorithm with fuzzy C-means, K-means, and K-means plus approaches. The compared experiment results show that the noise algorithm can reasonably obtain the number of clusters and initialize the center cluster, and the proposed noise K-means clustering algorithm demonstrates better clustering performance and accurately obtains clustering results, as well as effectively capturing urban hotspots.

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GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

Cited by 56 publications

References 36 publications

Smart parking systems: comprehensive review based on various aspects

Smart parking systems: comprehensive review based on various aspects

Deep Learning Framework for Freeway Speed Prediction in Adverse Weather

A Novel K-Means Clustering Algorithm with a Noise Algorithm for Capturing Urban Hotspots

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