Drivers' Phone Use Behavior at Red Traffic Signals

Jin, Sheng; Alagbe, Jeremie A; Ma, Dongfang

doi:10.1109/mits.2018.2889726

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Tang et al [9] introduced drivers' bounded rationality into the speed guidance model and demonstrated through simulation results that drivers' bounded rationality signifcantly impacts vehicle fuel consumption and emissions. Jin et al [10] studied drivers' behavior of using mobile phones at intersections, and the results show that using mobile phones has a signifcant negative impact on driving behavior. Furthermore, Liao et al [11] improved the traditional car-following model by taking into account drivers' driving habits, enhancing the model's safety and comfort.…”

Section: Introductionmentioning

confidence: 99%

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

Bai,

Jing,

Liu

et al. 2023

Journal of Advanced Transportation

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To investigate the heterogeneity of car-following behaviors across different vehicle combinations from the perspective of driver visual characteristics, the NGSIM dataset from I-80 and US-101 highways was selected and distinct car-following segments were extracted for analysis. Firstly, all the effective vehicle trajectories were extracted and categorized into different vehicle types based on their widths, resulting in four combination types of car-following segments. Visual angle and its change rate were introduced as variables representing driver visual characteristics. Additionally, one-way analysis of variance (ANOVA) was used to compare these variables with traditional ones. The driver’s visual characteristic variables were then incorporated to improve the full velocity difference (FVD) model. Genetic algorithms were employed to calibrate the model under different car-following types, revealing pronounced behavioral variations. After implementing the enhanced drivers’ visual angle (DVA) model, substantial reductions in calibration and validation errors were observed, with calibration errors decreasing by 51.93% and 42.22% and validation errors decreasing by 56.61% and 45.26%. This indicates the DVA model’s remarkable adaptability and stability. Lastly, through a sensitivity analysis of errors, the DVA model demonstrated greater robustness toward the improved error evaluation function. By integrating drivers’ visual characteristics, this study provides in-depth insights into heterogeneous car-following behaviors, enhancing our understanding of driver behaviors and micro-traffic simulation systems.

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Section: Introductionmentioning

confidence: 99%

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

Bai,

Jing,

Liu

et al. 2023

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

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A multimodal data-driven approach for driving risk assessment

Bai,

Jin,

Jing

et al. 2024

Transportation Research Part E: Logistics and Transportation Re

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The intersection of phones and traffic lights: analysis of usage and contributing factors

Alagbé

Cheng

Wang

et al. 2024

Proceedings of the Institution of Civil Engineers - Transport

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The use of mobile phones by drivers while stopped at red traffic lights represents a potential threat due to the cognitive residuals from recent phone use. This paper presents the results of a study that investigated the rates, strategies, and activities of phone use in this situation using both video-based naturalistic and online questionnaire data, and analyzed its significant influencing factors. The study found that about 37% of drivers used their phones at traffic lights, and the majority (85%) of phone use was initiated at intersections, suggesting the relevance of phone use to traffic lights. The questionnaire data revealed that the main purpose of phone use at red lights is social media (32%), followed by text messaging (19%) and map navigation (13%), with the phone ringing, boredom, and habit being the main reasons for engaging in such activities. The logistic regression model identified waiting time, time of day, driver's age, vehicle category, vehicle value, driver's license age, and the presence of passengers as predictors for phone use. Policymakers and driver educators can find useful information in this paper to enforce laws and educate drivers to ensure safety and efficiency at signalized intersections, thus avoiding potential accidents.

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Drivers' Phone Use Behavior at Red Traffic Signals

Cited by 4 publications

References 15 publications

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

Exploring Heterogeneity in Car-Following Behaviors Based on Driver Visual Characteristics: Modeling and Calibration

A multimodal data-driven approach for driving risk assessment

The intersection of phones and traffic lights: analysis of usage and contributing factors

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