A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries

Pinnow, Jack; Masoud, Mahmoud; Elhenawy, Mohammed; Glaser, Sébastien

doi:10.1016/j.aap.2021.106185

Cited by 17 publications

(5 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, this study investigates video-processed and observer-based surrogate indicators for analytical purposes. The potential benefits and drawbacks of surrogacy are discussed in relation to its prospective applications [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Zheng et al conducted a review focusing on the modeling methodologies of traffic conflict frequency and severity [13]. Pinnow et al discussed the kinematic SSMs, as well as whether these surrogates may be contextualized at different road geometries [14]. Wang et al reviewed the applications of SSMs' in CAV traffic stream safety modeling [15].…”

Section: Introductionmentioning

confidence: 99%

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Das

Samandar²,

Autry

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Surrogate safety measures (SSMs) are critical tools for evaluating the safety performance of mixed traffic. Crashes are rare events, and historical crash data is scarce for mixed traffic that includes autonomous and/ or connected vehicles. Recent safety review papers focus on traditional human-driven vehicles (TVs) and do not encompass advanced technology vehicles such as Autonomous Vehicles (AVs), Connected Vehicles (CVs), and Connected-Autonomous Vehicles (CAVs). This study examines the development, implementation, and shortcomings of SSMs, and SSM-based models used for mixed traffic safety evaluation. It reviews the current relevant literature and applies a case study analysis using a real-world mixed traffic dataset. The study summarizes the fundamental SSM guiding concepts, as well as their most significant metrics including threshold values employed in the past for SSMs and SSM-based models. Primary benefits and limitations of examined SSMs and SSM-based models are also underlined. This review reveals significant gaps in the literature that might guide future research paths in SSM-based mixed traffic safety assessment. Critical gaps include the absence of robust SSM threshold selection criteria, the suitability of current SSMs in mixed traffic research, microsimulation modeling that lacks proper calibration and validation, and the absence of a framework for selecting or combining multiple SSMs.INDEX TERMS Mixed traffic, surrogate safety measures, real-world mixed traffic dataset, autonomous vehicles, connected vehicles, connected-autonomous vehicles

show abstract

“…As a result of these disadvantages, the application of a real-time collision prediction model, although having good predictive accuracy, subsides. Recently, researchers worldwide have promoted the use of traffic conflicts to evaluate traffic safety ( 16 – 18 ). With the availability of high-resolution, detailed datasets such as Naturalistic Driving Studies (NDS), and vehicle trajectories (extracted using advanced sensors like radar sensors [ 19 , 20 ] and improved video processing techniques [ 21 , 22 ]), the application of traffic conflicts for crash causation and traffic safety assessment seems to be a more prudent, rational, and practical approach.…”

mentioning

confidence: 99%

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Patel

Gore

Easa

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The present study proposed a real-time traffic safety evaluation framework using macroscopic flow variables. To this end, open-access extended vehicle trajectories were employed. Rear-end traffic conflicts and macroscopic traffic flow variables were derived from the trajectory data and were integrated for real-time safety evaluation. The Proportion of Stopping distance ( PSD) accounts for all types of interactions (both safe and unsafe) in the traffic stream; therefore, the same was adopted to analyze the rear-end traffic conflicts. A macroscopic indicator termed “time spent in conflict ( TSC)” was derived to evaluate the rear-end traffic conflicts. Machine learning models, namely, Random Forest (RF), Support Vector Machines (SVM), and eXtreme Gradient Boosting (XGB), were employed to predict TSCs using macroscopic traffic flow variables. The results revealed that the TSC computed based on PSD exhibits a reliable and explainable relationship with the macroscopic traffic flow variables. TSC computed based on PSD revealed that intermediately congested traffic flow conditions are critical in traffic safety and can be attributed to complex traffic phenomena such as traffic hysteresis, traffic oscillations, and increased speed variance. Moreover, a stable relation between traffic safety and traffic flow was suggested for varying threshold values. Among different machine learning models, the RF model was observed as the best-fitted model to predict TSC based on macroscopic traffic variables. TSC quantifies the safety status of a given traffic flow condition, where a higher value of TSC for a particular traffic flow condition indicates that vehicles prevail in the conflicting scenario for a longer time and, therefore, reflect higher operational risk. The developed machine learning model can be employed to predict TSC (operational risk) in real time using the macroscopic traffic flow variables and, therefore, facilitate traffic safety monitoring.

show abstract

A review of naturalistic driving study surrogates and surrogate indicator viability within the context of different road geometries

Cited by 17 publications

References 100 publications

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Surrogate Safety Measures: Review and Assessment in Real-World Mixed Traditional and Autonomous Vehicle Platoons

Novel Traffic Conflict-Based Framework for Real-Time Traffic Safety Evaluation Under Heterogeneous and Weak Lane-Discipline Traffic

Contact Info

Product

Resources

About