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Electronics
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Advanced Intelligent Transportation Systems and Automated Vehicles in Smart Cities
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Advanced Intelligent Transportation Systems and Automated Vehicles in Smart Cities

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 6698

Special Issue Editors

Dr. Yuankai Wu

E-Mail Website
Guest Editor
Department of Civil Engineering, McGill University, Montréal, QC H3A 0G4, Canada
Interests: deep learning; machine learning; intelligent transportation system; intelligent energy management
Prof. Dr. Yi Lin

E-Mail Website
Guest Editor
College of Computer Science, Sichuan University, Chengdu 61000, China
Interests: deep learning; intelligent transportation system; machine learning; speech recognition; air traffic control
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Zhou

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Wisconsin–Madison, 1415 Engineering Dr, Madison, WI 53706, USA
Interests: connected and automated vehicle control; intelligent transportation system; traffic flow theory
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the core of modern society, cities play an increasingly vital role through the rapid progress of urbanization and civilization. We are dreaming of smart cities that will provide a highly efficient, comfortable, economically sound, and quality life to everyone by integrating advanced technologies in transportation systems. The emerging intelligent transportation system (ITS) and automated vehicle technologies can revolutionize the way people commute in smart cities. With the help of an ITS, communities can decrease traffic congestion, improve energy distribution and even improve air quality. This Special Issue focuses on the most recent and relevant developments towards realizing such intelligent future transportation systems.

This Special Issue covers the most recent ITS techniques in various fields, such as big traffic data analysis, connected and autonomous vehicles, intelligent infrastructure, advanced traffic management and control, and smart transportation electrification. Both academic research and practical deployment of ITSs are welcomed. 

Dr. Yuankai Wu
Prof. Dr. Yi Lin
Dr. Yang Zhou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Intelligent Transportation Systems
  • Smart City
  • Autonomous Vehicle
  • Traffic Management and Control
  • Connected Vehicle
  • Big Data and Machine learning
  • Artificial Intelligence
  • Intelligent Infrastructure
  • Smart Transportation Electrification

Published Papers (3 papers)

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Research

21 pages, 5718 KiB  
Article
The Fast Detection of Abnormal ETC Data Based on an Improved DTW Algorithm
by Feng Guo, Fumin Zou, Sijie Luo, Lyuchao Liao, Jinshan Wu, Xiang Yu and Cheng Zhang
Electronics 2022, 11(13), 1981; https://doi.org/10.3390/electronics11131981 - 24 Jun 2022
Cited by 12 | Viewed by 1678
Abstract
As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential [...] Read more.
As one of the largest Internet of Things systems in the world, China’s expressway electronic toll collection (ETC) generates nearly one billion pieces of transaction data every day, recording the traffic trajectories of almost all vehicles on the expressway, which has great potential application value. However, there are inevitable missed transactions and false transactions in the expressway ETC system, which leads to certain false and missing rates in ETC data. In this work, a dynamic search step SegrDTW algorithm based on an improved DTW algorithm is proposed according to the characteristics of expressway ETC data with origin–destination (OD) data constraints and coupling between the gantry path and the vehicle trajectory. Through constructing the spatial window of segment retrieval, the spatial complexity of the DTW algorithm is effectively reduced, and the efficiency of the abnormal ETC data detection is greatly improved. In real traffic data experiments, the SegrDTW algorithm only needs 3.36 s to measure the abnormal events of a single set of OD path data for 10 days. Compared with the mainstream algorithms, the SegrDTW performs best. Therefore, the proposal provides a feasible method for the abnormal event detection of expressway ETC data in a province and even the whole country. Full article
(This article belongs to the Special Issue Advanced Intelligent Transportation Systems and Automated Vehicles in Smart Cities)
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20 pages, 2434 KiB  
Article
Expressway Speed Prediction Based on Electronic Toll Collection Data
by Fumin Zou, Qiang Ren, Junshan Tian, Feng Guo, Shibin Huang, Lyuchao Liao and Jinshan Wu
Electronics 2022, 11(10), 1613; https://doi.org/10.3390/electronics11101613 - 18 May 2022
Cited by 18 | Viewed by 1916
Abstract
Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete [...] Read more.
Expressway section speed can visually reflect the section operation condition, and accurate short time section speed prediction has a wide range of applications in path planning and traffic guidance. However, existing expressway speed prediction data have defects, such as sparse density and incomplete object challenges. Thus, this paper proposes a framework for a combined expressway traffic speed prediction model based on wavelet transform and spatial-temporal graph convolutional network (WSTGCN) of the Electronic Toll Collection (ETC) gantry transaction data. First, the framework pre-processes the ETC gantry transaction data to construct the section speeds. Then wavelet decomposition and single-branch reconstruction are performed on the section speed sequences, and the spatial features are captured by graph convolutional network (GCN) for each reconstructed single-branch sequence, and the temporal features are extracted by connecting the gated recurrent unit (GRU). The experiments use the ETC gantry transaction data of the expressway from Quanzhou to Xiamen. The results indicate that the WSTGCN model makes notable improvements compared to the model of the baseline for different prediction ranges. Full article
(This article belongs to the Special Issue Advanced Intelligent Transportation Systems and Automated Vehicles in Smart Cities)
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13 pages, 4320 KiB  
Article
Demand Forecasting of Online Car-Hailing with Combining LSTM + Attention Approaches
by Xiaofei Ye, Qiming Ye, Xingchen Yan, Tao Wang, Jun Chen and Song Li
Electronics 2021, 10(20), 2480; https://doi.org/10.3390/electronics10202480 - 12 Oct 2021
Cited by 11 | Viewed by 1753
Abstract
The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of [...] Read more.
The accurate prediction of online car-hailing demand plays an increasingly important role in real-time scheduling and dynamic pricing. Most studies have found that the demand of online car-hailing is highly correlated with both temporal and spatial distributions of journeys. However, the importance of temporal and spatial sequences is not distinguished in the context of seeking to improve prediction, when in actual fact different time series and space sequences have different impacts on the distribution of demand and supply for online car-hailing. In order to accurately predict the short-term demand of online car-hailing in different regions of a city, a combined attention-based LSTM (LSTM + Attention) model for forecasting was constructed by extracting temporal features, spatial features, and weather features. Significantly, an attention mechanism is used to distinguish the time series and space sequences of order data. The order data in Haikou city was collected as the training and testing datasets. Compared with other forecasting models (GBDT, BPNN, RNN, and single LSTM), the results show that the short-term demand forecasting model LSTM + Attention outperforms other models. The results verify that the proposed model can support advanced scheduling and dynamic pricing for online car-hailing. Full article
(This article belongs to the Special Issue Advanced Intelligent Transportation Systems and Automated Vehicles in Smart Cities)
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