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Applied Sciences
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Advancements in Intelligent Transportation Systems and Traffic Analysis
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Advancements in Intelligent Transportation Systems and Traffic Analysis

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

Deadline for manuscript submissions: 20 April 2025 | Viewed by 7110

Special Issue Editor

Prof. Dr. Jian Zhang

E-Mail Website
Guest Editor
1. School of Transportation, Southeast University, Nanjing 211189, China
2. Engineering College, Tibet University, Tibet 850000, China
Interests: intelligent scheduling for public transit (analysis, modeling and simulation); traffic information system (data platform system design, highway traffic operation)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of intelligent transportation systems and traffic analysis has made remarkable progress in recent years and these advances have attracted attention on a global scale. As one of the main bodies of the intelligent transportation system, the development of intelligent vehicles is also progressing. Pure visual perception and vehicle-road collaborative autonomous driving has gradually enhanced the function of current vehicles. From an industry perspective, these advances demonstrate the huge potential of intelligent transportation systems and traffic analysis, and how they can play a key role in improving traffic efficiency, safety and environmental friendliness.

This Special Issue seeks to propose innovative control and analysis methods based on the new generation of intelligent transportation systems, as well as articles focusing on the latest advances in urban transportation planning, data mining and vehicle engineering, that address the most relevant challenges facing current and future intelligent transportation systems.

Prof. Dr. Jian Zhang
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.

Keywords

  • Internet of Vehicles
  • ITS
  • intelligent and connected vehicles
  • cooperative vehicle infrastructure system
  • traffic analysis

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Published Papers (5 papers)

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Research

17 pages, 18630 KiB  
Article
Investigating a Toolchain from Trajectory Recording to Resimulation
by Florian Lüttner, Malte Kracht, Corinna Köpke, Annette Schmitt, Mirjam Fehling-Kaschek, Alexander Stolz and Alexander Reiterer
Appl. Sci. 2024, 14(22), 10682; https://doi.org/10.3390/app142210682 - 19 Nov 2024
Viewed by 479
Abstract
The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of [...] Read more.
The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of these systems requires a substantial amount of testing kilometers, with estimates suggesting that around 2.1 billion kilometers would be necessary to ensure that each situation pertinent to the driving function is encountered at least once with a probability of 50%. This paper advances the microscopic simulation of traffic scenarios beyond linear patterns, utilizing the open-source environment openPASS. It addresses the research question of whether existing microscopic simulations are able to realistically represent non-linear traffic scenarios. A comprehensive toolchain integrates simulation with video recordings and laser scans. The study compares recorded traffic flow data with simulations at a T-junction, assessing the realism of vehicle models and trajectory representation. Three scenarios are analyzed, considering vehicles and pedestrians. The 3D geometry of the scene was captured with a laser scanner, enabling the mapping of recorded video data onto a geo-referenced environment. Object trajectories were extracted using an ’Regions with Convolutional Neural Networks features’ object detector. While openPASS simulated vehicle and pedestrian behaviors effectively, limitations in trajectory variability and reaction times were observed. These findings highlight the need for more realistic behavior models. This research emphasizes the necessity for improvements to accommodate complex driving behaviors and pedestrian dynamics. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis)
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21 pages, 7788 KiB  
Article
Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
by Ligang Yuan, Jing Liu and Haiyan Chen
Appl. Sci. 2024, 14(21), 9968; https://doi.org/10.3390/app14219968 - 31 Oct 2024
Viewed by 612
Abstract
The taxi-out time of an airport scene can be categorized into the unimpeded taxi-out time and the additional taxi-out time. Usually, additional taxi-out time is used as a key index to monitor taxi-out performance, and its accurate prediction plays an important role in [...] Read more.
The taxi-out time of an airport scene can be categorized into the unimpeded taxi-out time and the additional taxi-out time. Usually, additional taxi-out time is used as a key index to monitor taxi-out performance, and its accurate prediction plays an important role in optimizing the allocation of time slots at an airport and improving scene operation efficiency. Taking Shanghai Pudong International Airport as the research object, we first analyze its layout and construct the origin–destination pairs (ODPs) based on the stand groups and runways. Then, we develop a multiple linear regression model based on the arrival and departure flows to calculate the unimpeded taxi-out times for all ODPs. The actual taxi-out time is then subtracted from the unimpeded taxi-out time to obtain the historical additional taxi-out time of each flight. We propose three new flow features related to the structure: the corridor departure flow, the corridor arrival flow, and the departure flow proportion of ODPs, based on which we construct a dataset for training the prediction model. We then propose an additional taxi-out time prediction model based on the nutcracker optimization algorithm (NOA) and XGBoost and run comparison experiments on the operation data of our target airport. The results show that the optimized prediction model we proposed has the best performance compared with the traditional XGBoost model and other commonly used prediction models, and the proposed structure-related features have high correlations with additional taxi-out time. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis)
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13 pages, 5419 KiB  
Article
Study of Tunnel Vehicle GNSS/INS/OD Combination Position Based on Lateral Distance Measurement and Lane Line Constraint
by Hongbin Zhang and Xu Zhang
Appl. Sci. 2024, 14(10), 4309; https://doi.org/10.3390/app14104309 - 19 May 2024
Cited by 1 | Viewed by 1439
Abstract
The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD [...] Read more.
The high-precision dynamic positioning of highway vehicles is the foundation and prerequisite for achieving intelligent connected transportation. To address the shortcomings of the GNSS/INS combination and GNSS/INS/OD combination in tunnel vehicle positioning, this paper proposes a tunnel vehicle positioning method for the GNSS/INS/OD combination based on lateral distance measurements and lane constraints. Firstly, a lateral distance measurement of vehicles inside the tunnel is conducted based on laser radar point cloud data. Secondly, map matching positioning is performed based on lateral distance measurements, odometer, and lane markings. Experimental results demonstrate that, for a 4.6 km tunnel, the average absolute error in the lateral positioning is 0.294 m, and the longitudinal positioning error is no more than 0.6 m, which can effectively meet practical operational requirements. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis)
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18 pages, 8457 KiB  
Article
Hidden Markov Model-Based Dynamic Hard Shoulders Running Strategy in Hybrid Network Environments
by Jinqiang Yao, Yu Qian, Zhanyu Feng, Jian Zhang, Hongbin Zhang, Tianyi Chen and Shaoyin Meng
Appl. Sci. 2024, 14(8), 3145; https://doi.org/10.3390/app14083145 - 9 Apr 2024
Cited by 2 | Viewed by 1159
Abstract
With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics [...] Read more.
With the development of vehicle-road network technologies, the future traffic flow will appear in the form of hybrid network traffic flow for a long time. Due to the change in traffic characteristics, the current hard shoulder running strategy based on traditional traffic characteristics cannot effectively serve the hybrid network traffic flow scenario, and will even lead to the further deterioration of traffic congestion. In order to propose a hard shoulder running strategy suitable for a hybrid network environment, a traffic breakdown prediction method based on a hidden Markov model was established. Secondly, the characteristics of traffic breakdown in a hybrid network environment were analyzed. Finally, based on the traffic breakdown characteristics in a hybrid network environment, a dynamic hard shoulder running method based on the hidden Markov model was proposed. The effectiveness of HMMD-HSR was verified by simulation and comparison with HMM-HSR, LMD-HSR, and N-HSR. The simulation results show that the HMMD-HSR proposed in this paper can improve operation efficiency and reduce travel time in a congested expressway. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis)
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21 pages, 2533 KiB  
Article
A Comparative Study of IEEE 802.11bd and IEEE 802.11p on the Data Dissemination Properties in Dynamic Traffic Scenarios
by Shanzheng Xue, Siyuan Gong and Xinyi Li
Appl. Sci. 2024, 14(5), 2099; https://doi.org/10.3390/app14052099 - 2 Mar 2024
Cited by 2 | Viewed by 2423
Abstract
With the rapid deployment of intelligent transportation systems in real-life applications, both dedicated short-range communications (DSRC) and cellular Vehicle-to-Everything (C-V2X), utilized to enable V2X communication, are undergoing extensive development to meet the quality of service (QoS) demands of advanced vehicular applications and scenarios. [...] Read more.
With the rapid deployment of intelligent transportation systems in real-life applications, both dedicated short-range communications (DSRC) and cellular Vehicle-to-Everything (C-V2X), utilized to enable V2X communication, are undergoing extensive development to meet the quality of service (QoS) demands of advanced vehicular applications and scenarios. Compared to C-V2X, which lacks fully validated effective reliability, DSRC has undergone extensive field testing worldwide, ensuring its practicality. IEEE 802.11bd, as the next-generation V2X (NGV) standard within DSRC, is expected to greatly exceed the performance of its predecessor, 802.11p. However, existing studies mention that the ambient traffic environment will influence the performance of V2X due to the cyber-physical properties of V2X. To fully assess the advancements of NGV, this study presents a comparative analysis of IEEE 802.11bd and IEEE 802.11p, focusing on dynamic traffic conditions. Specifically, the technical advancements of the IEEE 802.11bd standard are first theoretically examined, emphasizing significant enhancements in aspects like modulation and coding schemes, coding rates, and channel coding. Subsequently, these critical technical enhancements are implemented in Veins, a simulation framework for the Internet of Vehicles (IoV), encompassing large-scale dynamic traffic scenarios. The simulation results indicate that the IEEE 802.11bd standard significantly enhances the data transfer rate compared to IEEE 802.11p, achieving a stable twofold increase. Furthermore, the data transmission latency is reduced by over half compared to IEEE 802.11p, while the data transmission reliability experiences a noteworthy 20% enhancement. Notably, the enhanced data transmission mode of the IEEE 802.11bd standard requires an increased signal-to-noise ratio (SNR). Additionally, this research evaluates the data dissemination properties in the IoV and finds that the traffic volume has a limited impact on the data propagation speed. Full article
(This article belongs to the Special Issue Advancements in Intelligent Transportation Systems and Traffic Analysis)
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