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Applied Sciences
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Transport Geography, GIS and GPS
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Transport Geography, GIS and GPS

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 6619

Special Issue Editor

Prof. Dr. Chao Chen

E-Mail Website
Guest Editor
College of Computer Science, Chongqing University, Chongqing 400044, China
Interests: intelligent mobility; trajectory representation learning; taxi trajectory data compression and visualization; time and space trajectory mining and analysis; modeling of human movement behavior; intelligent unmanned system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the proliferation of GPS devices and wireless communication technologies in daily life, recent years have witnessed an increasing number of GPS-equipped vehicles, reporting their real-time moving locations to data centers continuously. On the other hand, due to convenience and speed, vehicles have become one of the most common transportation methods to move around the city. As a result, trajectory data that record where and when people move are now being gathered and readily available on a large scale, providing us a time-evolving view to understand how city transport takes place from a data-driven perspective. Nonetheless, a considerable gap still exists between data collection and consequent extraction of actionable insights when building smart cities. Such a gap poses fundamental challenges to how we can achieve such insights. To narrow this gap, advanced mathematical techniques are necessary. Such methods have particular technical challenges to overcome, which include algorithm effectiveness, computation speed, energy efficiency, user privacy, server security, and overall system scalability. This Special Issue aims to present state-of-the-art research achievements in addressing the above mentioned challenges in converting pervasive observation data to actionable insights, especially in the context of moving vehicles.

Prof. Dr. Chao Chen
Guest Editor

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. Applied Sciences 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.

Published Papers (3 papers)

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Research

23 pages, 6255 KiB  
Article
RegioRail—GNSS Train-Positioning System for Automatic Indications of Crisis Traffic Situations on Regional Rail Lines
by Jan Fikejz and Antonín Kavička
Appl. Sci. 2022, 12(12), 5797; https://doi.org/10.3390/app12125797 - 7 Jun 2022
Cited by 1 | Viewed by 1418
Abstract
The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. [...] Read more.
The identification of the position of rail vehicles plays a crucial role in the control of rail traffic. Available, up-to-date information on the position of vehicles allows us to efficiently deal with selected traffic situations where the position of vehicles is very important. The main objective of this article is to introduce (i) a concept of a solution for identification of the current position of rail vehicles based on the worldwide-recognized system of the GNSS with the use of an original railway network data model, and (ii) the use of this concept as supplementary support for the dispatcher control of rail traffic on regional lines. The solution was based on an original, multilayer rail network data model supporting (i) the identification of rail vehicle position and (ii) novel algorithms evaluating the mutual positions of several trains while detecting the selected crisis situation. In addition, original algorithms that enable automatic network model-building (on the database server level) directly from the official railway infrastructure database were developed. The verification of the proposed solutions (using rail traffic simulations) was focused on the evaluation of (i) the changing mutual positions (distances) of trains on the railway network, (ii) the detection of nonstandard or crisis traffic situations, and (iii) the results of the calculations of necessary braking distances of trains for stopping and collision avoidance. The above verification demonstrated the good applicability of the proposed solutions for the potential deployment within supplementary software support for real traffic control. The described concept of the supplementary support determined for railway traffic control (using the localization of trains by means of the GNSS) is intended mainly for regional, single-rail lines. This type of line is very often not sufficiently equipped with standard signaling and interlocking equipment to ensure the necessary traffic safety. Therefore, when deploying this support, the new algorithms for the automatic detection of critical traffic situations represent a significant potential contribution to increasing operational safety. Full article
(This article belongs to the Special Issue Transport Geography, GIS and GPS)
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15 pages, 1372 KiB  
Article
Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
by Junjie Zhou, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu and Zhijiang Shao
Appl. Sci. 2022, 12(11), 5340; https://doi.org/10.3390/app12115340 - 25 May 2022
Cited by 3 | Viewed by 1491
Abstract
With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction [...] Read more.
With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy. Full article
(This article belongs to the Special Issue Transport Geography, GIS and GPS)
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16 pages, 6790 KiB  
Article
Analyzing Multiscale Spatial Relationships between the House Price and Visual Environment Factors
by Xu Liao, Mingyu Deng and Hongyu Huang
Appl. Sci. 2022, 12(1), 213; https://doi.org/10.3390/app12010213 - 27 Dec 2021
Cited by 7 | Viewed by 2885
Abstract
House price is closely associated with the development of the national economy and people’s daily life. Understanding the spatial distribution characteristics and influencing factors of the house price is of great practical significance. Although a lot of attention has been paid to modeling [...] Read more.
House price is closely associated with the development of the national economy and people’s daily life. Understanding the spatial distribution characteristics and influencing factors of the house price is of great practical significance. Although a lot of attention has been paid to modeling the house price from structure and location attributes, limited work has considered the impact of visual attributes. Intuitively, a better visual environment may raise the surrounding house price. When aggregating multiple factors that influence house price, the multiscale geographically weighted regression (MGWR) provides a suitable solution. Specifically, the MGWR assigns each factor a bandwidth to model the spatial heterogeneity, e.g., a factor may have different influences at different places. In this paper, we introduce the visual environment factors into the MGWR method. In detail, we extract ten visual elements, e.g., sky, vegetation, road, from the Baidu street view (BSV) images, using a deep learning framework. We further define six visual environment factors to investigate their influence on house price. Based on the data from two representative Chinese cities, i.e., Beijing and Chongqing, we reveal the influence degree and spatial scale difference of six visual indexes on the house price in two cities. Results show that: (1) the influence intensity of our proposed six visual environment factors on the house price in different regions of the city can be identified, and the green view index (GVI) is the most important visual environmental factor; and (2) the influence of these view indexes changes significantly or even reversely depends on different areas. Full article
(This article belongs to the Special Issue Transport Geography, GIS and GPS)
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