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Advances in Intelligent Transportation Systems
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Advances in Intelligent Transportation Systems

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 November 2024 | Viewed by 5711

Special Issue Editors

Dr. Seoungbum Kim

E-Mail Website
Guest Editor
Department of Urban Engineering, Engineering Research Institute, Gyeongsang National University, Jinju-si 52828, Republic of Korea
Interests: transportation operation and management; traffic safety and accident analysis; transportation infrastructure design; transportation planning; ITSs (intelligent transportation systems)
Dr. Joyoung Lee

E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: connected and automated vehicles; intersection control and traffic operations; transportation data analytics and deep learning

Special Issue Information

Dear Colleagues,

Intelligent transportation systems encompass a wide range of technologies aimed at improving the efficiency, safety, and sustainability of transportation. In particular, with the evolution of communication technology and innovative improvements in computation power, the data that can be collected and transmitted from ITSs are being subdivided into individual vehicle units, and as a result, it is possible to present alternatives to contemporary transportation problems that could not be solved with conventional ITSs. Therefore, the objective of this Special Issue is to discuss novel ITS alternatives to solve problems occurring in various transportation fields and new applications using big data collected from various ITS sensors. Topics of interest include, but are not limited to, the following:

  • Cooperative intelligence transportation systems;
  • Vehicle sensor data-based analysis;
  • ITS applications of emerging technologies in traffic operation, road maintenance, road safety, and environment;
  • Automated highway systems;
  • Application of AI and machine learning for ITS;
  • Real-time applications of ITS.

Dr. Seoungbum Kim
Dr. Joyoung Lee
Guest Editors

Manuscript Submission Information

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Keywords

  • cooperative intelligence transportation systems
  • vehicle sensor data-based analysis
  • ITS applications of emerging technologies in traffic operation, road maintenance, road safety, and environment
  • automated highway systems
  • application of AI and machine learning for ITS
  • real-time applications of ITS

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Published Papers (7 papers)

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Research

16 pages, 634 KiB  
Article
LGTCN: A Spatial–Temporal Traffic Flow Prediction Model Based on Local–Global Feature Fusion Temporal Convolutional Network
by Wei Ye, Haoxuan Kuang, Kunxiang Deng, Dongran Zhang and Jun Li
Appl. Sci. 2024, 14(19), 8847; https://doi.org/10.3390/app14198847 - 1 Oct 2024
Abstract
High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flows can propagate backwards on road networks when modeling spatial relationships, [...] Read more.
High-precision traffic flow prediction facilitates intelligent traffic control and refined management decisions. Previous research has built a variety of exquisite models with good prediction results. However, they ignore the reality that traffic flows can propagate backwards on road networks when modeling spatial relationships, as well as associations between distant nodes. In addition, more effective model components for modeling temporal relationships remain to be developed. To address the above challenges, we propose a local–global features fusion temporal convolutional network (LGTCN) for spatio-temporal traffic flow prediction, which incorporates a bidirectional graph convolutional network, probabilistic sparse self-attention, and a multichannel temporal convolutional network. To extract the bidirectional propagation relationship of traffic flow on the road network, we improve the traditional graph convolutional network so that information can be propagated in multiple directions. In addition, in spatial global dimensions, we propose probabilistic sparse self-attention to effectively perceive global data correlations and reduce the computational complexity caused by the finite perspective graph. Furthermore, we develop a multichannel temporal convolutional network. It not only retains the temporal learning capability of temporal convolutional networks, but also corresponds each channel to a node, and it realizes the interaction of node features through output interoperation. Extensive experiments on four open access benchmark traffic flow datasets demonstrate the effectiveness of our model. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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12 pages, 4088 KiB  
Article
Factors Affecting Rear-End Collisions in Underground Road Junctions Using VISSIM
by Zion Park, Gunwoo Lee, Choongheon Yang and Jin-Kak Lee
Appl. Sci. 2024, 14(18), 8509; https://doi.org/10.3390/app14188509 - 21 Sep 2024
Abstract
Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high [...] Read more.
Due to urban overcrowding, available land is limited and traffic congestion has increased. Underground roads are being built to mitigate traffic congestion as an alternative. Studies associated with underground roads are needed because these roads are dark and closed and have a high risk of accidents compared to surface roads. In particular, there is limited study on junctions that connect two or more underground roads. In this study, an underground road network including junctions was constructed to analyze the factors behind rear-end collisions at underground road connections. To reflect the driving behavior on underground roads, the scenario analysis was conducted by applying the speed distribution of underground roads in Korea. The results of the analysis showed that variables such as acceleration standard deviation and lateral position standard deviation are crucial for accidents on underground roads. Thus, this study can be used as a basis for traffic management and safety improvement in the operation of underground road junctions in the future. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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18 pages, 7541 KiB  
Article
A Backpropagation-Based Algorithm to Optimize Trip Assignment Probability for Long-Term High-Speed Railway Demand Forecasting in Korea
by Ho-Chan Kwak
Appl. Sci. 2024, 14(17), 7880; https://doi.org/10.3390/app14177880 - 4 Sep 2024
Viewed by 156
Abstract
In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for [...] Read more.
In Korea, decisions for high-speed railway (HSR) construction are made based on long-term demand forecasting. A calibration process that simulates current trip patterns is an important step in long-term demand forecasting. However, a trial-and-error approach based on iterative parameter adjustment is used for calibration, resulting in time inefficiency. In addition, the all-or-nothing-based optimal strategy algorithm (OSA) used in HSR trip assignment has limited accuracy because it assigns all trips from a zone with multiple accessible stations to only one station. Therefore, this study aimed to develop a backpropagation-based algorithm to optimize trip assignment probability from a zone to multiple accessible HSR stations. In this algorithm, the difference between the estimated volume calculated from the trip assignment probability and observed volumes was defined as loss, and the trip assignment probability was optimized by repeatedly updating in the direction of the reduced loss. The error rate of the backpropagation-based algorithm was compared with that of the OSA using KTDB data; the backpropagation-based algorithm had lower errors than the OSA for most major HSR stations. It was especially superior when applied to areas with multiple HSR stations, such as the Seoul metropolitan area. This algorithm will improve the accuracy and time efficiency of long-term HSR demand forecasting. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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14 pages, 1938 KiB  
Article
The Environmental Benefits of an Automatic Idling Control System of Connected and Autonomous Vehicles (CAVs)
by Hoe Kyoung Kim
Appl. Sci. 2024, 14(11), 4338; https://doi.org/10.3390/app14114338 - 21 May 2024
Viewed by 667
Abstract
The transportation sector is regarded as the main culprit in greenhouse gas emission in the urban network, particularly idling vehicles waiting at signalized intersections. Although autonomous vehicles can be a promising technology to tackle vehicle idling, their environmental benefits receive little attention compared [...] Read more.
The transportation sector is regarded as the main culprit in greenhouse gas emission in the urban network, particularly idling vehicles waiting at signalized intersections. Although autonomous vehicles can be a promising technology to tackle vehicle idling, their environmental benefits receive little attention compared with their safety and mobility issues. This study investigated the environmental benefits of autonomous vehicles equipped with an automatic idling control function based on the queue discharge time and traffic signal information transmitted from the traffic signal controller via V2I communication using microscopic mobility and emission simulation models, VISSIM and MOVES, in Haeundae-gu in Busan, Korea. This study found that the function contributes to a significant reduction in CO2 emissions by 23.6% for all-inclusive emission and 94.3% for idling emission, respectively. Moreover, total reduced idling time accounts for 47.6% of the total travel time and 94.3% of the total idling time, respectively. Consequently, the autonomous vehicles equipped with automatic vehicle idling control function under C-ITS can play an important role in reducing greenhouse gas emissions and fuel consumption as well in the urban network. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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13 pages, 3638 KiB  
Article
Evaluating the Impact of V2V Warning Information on Driving Behavior Modification Using Empirical Connected Vehicle Data
by Hoseon Kim, Jieun Ko, Aram Jung and Seoungbum Kim
Appl. Sci. 2024, 14(6), 2625; https://doi.org/10.3390/app14062625 - 21 Mar 2024
Viewed by 670
Abstract
A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with [...] Read more.
A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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19 pages, 4363 KiB  
Article
A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity
by Baixi Shi, Zihan Wang, Jianqiang Yan, Qi Yang and Nanxi Yang
Appl. Sci. 2024, 14(5), 1949; https://doi.org/10.3390/app14051949 - 27 Feb 2024
Viewed by 950
Abstract
Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal [...] Read more.
Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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18 pages, 5224 KiB  
Article
A Multi-Agent Driving-Simulation Approach for Characterizing Hazardous Vehicle Interactions between Autonomous Vehicles and Manual Vehicles
by Aram Jung, Young Jo, Cheol Oh, Jaehong Park and Dukgeun Yun
Appl. Sci. 2024, 14(4), 1468; https://doi.org/10.3390/app14041468 - 11 Feb 2024
Viewed by 816
Abstract
The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs [...] Read more.
The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety. Full article
(This article belongs to the Special Issue Advances in Intelligent Transportation Systems)
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