A brief review: traffic conflict techniques and the challenges of the studies in Indonesia

Kusumastutie, Naomi Srie; Rusmandani, Pipit

doi:10.1051/matecconf/201927003004

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…For example, in studies on interventions in black spots, the observation of traffic conflicts can be used as supplement data along with observations of road and traffic conditions. The traffic conflict technique shows promise as a complementary indicator to accident data, and it has also been applied in several studies in Indonesia [71].…”

Section: Discussionmentioning

confidence: 99%

A review of accident data for traffic safety studies in Indonesia

Kusumastutie,

Patria,

Kusrohmaniah

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Accident data is a crucial indicator of traffic safety. This study investigates the use of accident data in traffic safety studies in Indonesia. The analysis was conducted on studies that met the eligibility criteria, which included using traffic accident data in Indonesia and articles published in Indonesian and English language journals or proceedings. The search was conducted on four databases: Garuda, Neliti, Google Scholar, and Scopus. The final selection resulted in 50 articles. The data analysis indicates the need for improvement in the number of studies, the utilization of data elements in investigations, supplement data, and data analysis techniques. In addition to providing recommendations to optimize the use of traffic accident data in future studies, this study also emphasizes the importance of improving the accuracy of traffic accident data.

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Section: Discussionmentioning

confidence: 99%

A review of accident data for traffic safety studies in Indonesia

Kusumastutie,

Patria,

Kusrohmaniah

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…Moreover, in some other related works, the traffic conflicts technique (TCT) using surrogate safety measures based on vehicle trajectories has been proposed to assess traffic safety [24]. ese indicators can be used for predicting potential collisions [15,25,26]. Nevertheless, most of the literature addresses road traffic safety without considering driving profiles.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Driver Behavior and Intervehicular Collision: A Data-Based Traffic Modeling and Simulation Approach

Zhao

Farhi

Christoforou

et al. 2022

Journal of Advanced Transportation

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The emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. The numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers. We then replicate car collision occurrences by varying the percentages of these three driver profiles in the simulated environment, allowing us to establish a relationship between driver profiles and car collision occurrences. We also investigate the severity of car collisions and classify them with respect to the driver profiles of the cars involved in the collisions. Our approach of replicating car collision occurrences in numerical simulations will facilitate the testing and validation of ICVs in the future, especially regarding the testing of ICV functionalities in dealing with traffic accidents.

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