Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

Choi, Seongjin; Lee, Donghoun; Kim, Sari; Tak, Sehyun

doi:10.1016/j.trc.2023.104049

Cited by 19 publications

(3 citation statements)

References 57 publications

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“…The most common ITS service, TSP (Traffic Signal Priority), is currently the focus of international attention. This benefits all road users and traffic participants but is primarily intended to increase the effectiveness of public transport, e.g., in North America [2], Europe [3,4], and Asia [5]. Initially, the process of prioritization was individually examined.…”

Section: Motivationmentioning

confidence: 99%

Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport

Otto,

Partzsch,

Holfeld

et al. 2023

Applied Sciences

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Looking ahead: transforming conventional public transport prioritization into C-ITS G5 services. The city of Frankfurt aims to digitize its public transport prioritization system in order to fulfill the requirements of future public transport communication standards and, moreover, to build on this very infrastructure for the development of imminent C-ITS services. Therefore, the communication systems of the mobility and transport provider VGF (Verkehrsgesellschaft Frankfurt am Main mbH) are being revised fundamentally by implementing new technologies for Car2X C-ITS G5 communication. The hardware components of the C-ITS system are strategically positioned with the help of a newly developed planning tool that identifies and determines the range of communication. For highly significant sites and locations of the hardware components, the calculated data are validated by utilizing measurements within a mobile setup. The operational stability and the development of previously unused potential are then carried out via the combination of the C-ITS services TSP (Traffic Signal Priority) and GLOSA (Green Light Optimized Speed Advisory). The overlay of the C-ITS services results in a high level of operational stability. As a result, potentials can be adequately employed through the sensible shifting of waiting times to the stops and a smooth flow of traffic through information on optimal speed and remaining times of the traffic light potentials. This paper presents a new methodology with which it is now possible to plan and evaluate C-ITS with regard to service distribution and radio propagation.

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Section: Motivationmentioning

confidence: 99%

Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport

Otto,

Partzsch,

Holfeld

et al. 2023

Applied Sciences

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show abstract

“…In contrast, C-ITS enables the real-time collection of various traffic information remotely through terminals and provides that information to other drivers, facilitating rapid and safe responses to sudden incidents [1,2]. By identifying potential hazards and preventing accidents in advance, C-ITS distinguishes itself from traditional ITS and is therefore an essential technology for creating a future autonomous driving environment [3][4][5][6]. In conventional ITS, the collection and provision of traffic information relies on specific locations where traffic information collection and dissemination devices were installed.…”

Section: C-its (Cooperative Intelligent Transportation Systemmentioning

confidence: 99%

Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea

Kang,

Tak,

Park

2023

Sustainability

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This paper analyzes the impact of C-ITS service on driving behavior, focusing on a pilot project in Daejeon–Sejong, South Korea. C-ITS, an advanced technology, enables bidirectional wireless communication between vehicles or infrastructure, allowing for real-time traffic data collection and dissemination. The study uses a unique analytical method, employing parallel processing techniques for variable extraction and a paired t-test to examine the short-term effects of C-ITS on driving behavior. Findings indicate a significant change in drivers’ behavior, particularly in average speed, hard braking rate, severe deceleration rate, speeding rate, and excessive speeding rate, towards safer trends after receiving C-ITS warning services. Reductions in hard braking and severe deceleration were immediate after C-ITS service initiation, while a decrease in excessive speeding was observed after four months. Further research is needed to identify other potential influencing variables and provide an unbiased evaluation of C-ITS effectiveness. The study’s implications highlight its role in promoting public acceptance of C-ITS-service-based cooperative autonomous driving strategies.

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“…The rapid advancement in automated vehicle (AV) technology is expected to provide substantial improvements in transportation efficiency and safety. Recent initiatives have aimed to explore AV-based mobility services, such as public transportation, freight transport, and shared-ride mobility-on-demand services [1][2][3]. A crucial step in deploying these services involves classifying the road links appropriate for AV operation given a mobility service area.…”

Section: Introductionmentioning

confidence: 99%

Driving Safety Area Classification for Automated Vehicles Based on Data Augmentation Using Generative Models

Lee

2024

Sustainability

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The integration of automated vehicles (AVs) into existing road networks for mobility services presents unique challenges, particularly in discerning the driving safety areas associated with the automation mode of AVs. The assessment of AV’s capability to safely operate in a specific road section is contingent upon the occurrence of disengagement events within that section, which are evaluated against a predefined operational design domain (ODD). However, the process of collecting comprehensive data for all roadway areas is constrained by limited resources. Moreover, challenges are posed in accurately classifying whether a new roadway section can be safely operated by AVs when relying on restricted datasets. This research proposes a novel framework aimed at enhancing the discriminative capability of given classifiers in identifying safe driving areas for AVs, leveraging cutting-edge data augmentation algorithms using generative models, including generative adversarial networks (GANs) and diffusion-based models. The proposed framework is validated using a field test dataset containing disengagement events from expressways in South Korea. Performance evaluations are conducted across various metrics to demonstrate the effectiveness of the data augmentation models. The evaluation study concludes that the proposed framework significantly enhances the discriminative performance of the classifiers, contributing valuable insights into safer AV deployment in diverse road conditions.

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Framework for Connected and Automated Bus Rapid Transit with Sectionalized Speed Guidance based on deep reinforcement learning: Field test in Sejong City

Cited by 19 publications

References 57 publications

Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport

Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport

Analyzing the Impact of C-ITS Services on Driving Behavior: A Case Study of the Daejeon–Sejong C-ITS Pilot Project in South Korea

Driving Safety Area Classification for Automated Vehicles Based on Data Augmentation Using Generative Models

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