Time Headway Analysis to Determine  the Road Capacity

Suweda, I Wayan

doi:10.24843/spektran.2016.v04.i02.p09

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…From vehicle trajectory data, a driver's characteristics can be illustrated by the speed, acceleration, jerk, and some other relevant traffic indicators, such as time headway (THW) and time to collision (TTC) [31]. THW and TTC are critical safety indicators for car-following behavior [32]. THW measures the time passed before reaching the leading vehicle's position while running at current speed, while TTC is usually used for judging the moment to start braking and in the control of braking [33].…”

Section: Classification Of Driver Profilesmentioning

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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Section: Classification Of Driver Profilesmentioning

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

View full text Add to dashboard Cite

show abstract

“…The headway determines the passage possibilities of vehicles at the intersection or ramp points. Speed and headway information constitutes important information for traffic engineering and is required for designing highperformance transportation systems [1][2][3][4][5][6][7]. The speed characteristic of the vehicle also constitutes travel time.…”

Section: Introductionmentioning

confidence: 99%

AdaBoost algoritmasını kullanarak demiryolu trafik yönetimi için seyir süresinin tahmini

Akçay

Akgündoğdu

Tiryaki

2022

Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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While determining the travel time between stations, a number of design parameters such as waiting time, motion resistance, slope, curve, traction force, maximum speed, vehicle mass, and distance between two stations are taken into consideration. These parameters form the infrastructure of the system definition of the motion of the vehicle. Furthermore, while creating the speed profile, special attention should be paid to the travel time in order to ensure the defined headway for the line. In this study, the travel time value between stations for intracity metro stations was predicted using the adaptive boosting method, which is one of the machine learning methods, and compared with various well-known methods. The data used were applied to the proposed model with the cross-validation and random sampling hold-out methods, and the values of the coefficient of determination (R2) were calculated.

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“…Also, when roads are placed parallel to or near river plains, embankments may be eroded by the flow. In addition to physical consequences of embankment damage, flows may temporally or permanently affect traffic and the level of service of a road, decreasing their operational capacity (Suweda 2016;Contreras-Jara et al 2018;Argyroudis et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Development of fragility curves for road embankments exposed to perpendicular debris flows

Nieto

Chamorro

Echaveguren

et al. 2021

Geomatics, Natural Hazards and Risk

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Debris flows cause recurrent interruptions and permanent damages in rural road networks, representing significant economic losses. Embankments are road assets frequently exposed to debris flows, especially in mountainous areas. The potential risk of infrastructure exposed to debris flows can be assessed in terms of the probability of expected damage based on fragility curves. The aim of the study is to develop fragility curves for road embankments exposed to debris flows representative to the expected physical damage and capacity loss. Two conceptual models are proposed to describe the impact and erosion of debris flows that run perpendicularly to road embankments. Probabilistic models are then developed in terms of limit state functions and the simulation of potential scenarios. The resulting models are finally fit to log-normal distributions and compared to historical data. Headcut erosion has higher probabilities of occurrence compared to sliding failure caused by the impact of debris flow. Lower road embankments present higher probabilities of failure to the impact of debris flows, especially for dense flows. Whereas, longer interaction times of less dense flows increase the probability of headcut erosion. Two-lane roads may present 50% more probabilities of headcut erosion compared to multilane roads for similar debris flows intensity.

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Time Headway Analysis to Determine the Road Capacity

Cited by 6 publications

References 2 publications

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

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

AdaBoost algoritmasını kullanarak demiryolu trafik yönetimi için seyir süresinin tahmini

Development of fragility curves for road embankments exposed to perpendicular debris flows

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