Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior

Sangster, John; Rakha, Hesham A.; Du, Jianhe

doi:10.3141/2390-03

Cited by 59 publications

(29 citation statements)

References 22 publications

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“…Also, the mean gap distance increases along with the increase of speed. This is in line with what was concluded in [29], [33][34][35][36][37]. Time gap shows almost no correlation with the host vehicle speed.…”

Section: Comparison Of Ttc and Time Gap In Various Relative Speed supporting

confidence: 91%

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Tong

Selpi

2020

IEEE Trans. Intell. Transport. Syst.

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Understanding car-following behaviour in different countries is essential for the design and development of autonomous driving and further development of active safety systems that can function well worldwide, in particular in mixed traffic conditions. However, very few studies exist that compare car-following behaviours in different countries based on real driving data. This paper analyses the similarities and differences of drivers' car-following behaviour, in terms of time gap, gap distance, and time to collision, using both China and Sweden datasets from real road driving studies, in a bid to identify how these indicators affect drivers' speed control in car-following situations. Results indicate that the highest frequency of gap distance is observed in the same value-range in both datasets, while the highest frequency of time gap in the Sweden dataset is found at a lower value-range than the corresponding value-range in the China dataset. For both datasets, time gap is observed to be a more reliable indicator for car-following analysis than gap distance, since it is less sensitive to speed variations. Furthermore, time to collision (TTC) in the low travel speed ranges (v<50 km/h) tends to be steady in comparison with TTC at other speed ranges, so is the time gap in the high speed ranges (v>90 km/h). Therefore, time gap is recommended as the safety indicator for car-following analysis in high speed conditions, while a combination of time gap and TTC is recommended for low speed conditions, especially on urban roads.

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Section: Comparison Of Ttc and Time Gap In Various Relative Speed supporting

confidence: 91%

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Tong

Selpi

2020

IEEE Trans. Intell. Transport. Syst.

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“…l Maximum acceleration: the maximum acceleration rate a driver adopted during his/her all car-following events (Sangster et al, 2013). l Comfortable deceleration: the maximum deceleration rate a driver adopted during his/her all car-following events (Sangster et al, 2013).…”

Section: Estimating Idm Parameter By Empirical Observationmentioning

confidence: 99%

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Zhu

Wang

Tarko

et al. 2018

Transportation Research Part C: Emerging Technologies

213

105

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Although car-following behavior is the core component of microscopic traffic simulation, intelligent transportation systems, and advanced driver assistance systems, the adequacy of the existing car-following models for Chinese drivers has not been investigated with real-world data yet. To address this gap, five representative car-following models were calibrated and evaluated for Shanghai drivers, using 2,100 urban-expressway car-following periods extracted from the 161,055 km of driving data collected in the Shanghai Naturalistic Driving Study (SH-NDS). The models were calibrated for each of the 42 subject drivers, and their capabilities of predicting the drivers' car-following behavior were evaluated.The results show that the intelligent driver model (IDM) has good transferability to model traffic situations not presented in calibration, and it performs best among the evaluated models. Compared to the Wiedemann 99 model used by VISSIM ® , the IDM is easier to calibrate and demonstrates a better and more stable performance. These advantages justify its suitability for microscopic traffic simulation tools in Shanghai and likely in other regions of China. Additionally, considerable behavioral differences among different drivers were found, which demonstrates a need for archetypes of a variety of drivers to build a traffic mix in simulation. By comparing calibrated and observed values of the IDM parameters, this study found that 1) interpretable calibrated model parameters are linked with corresponding observable parameters in real world, but they are not necessarily numerically equivalent; and 2) parameters that can be measured in reality also need to be calibrated if better trajectory reproducing capability are to be achieved.

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“…The RPA model, which integrates a user-specified steady-state speed-spacing relationship (Van Aerde model), collision avoidance constraints, and vehicle acceleration constraints, was validated against empirical data [35]. In order to ensure realistic vehicle accelerations, the model uses the driver throttle input together with a vehicle dynamics model that estimates the maximum vehicle acceleration level.…”

Section: B Experimental Validationmentioning

confidence: 99%

Development and Testing of a 3G/LTE Adaptive Data Collection System in Vehicular Networks

Drira

Ahn

Rakha

et al. 2016

IEEE Trans. Intell. Transport. Syst.

Self Cite

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Vehicular ad hoc networks (VANETs) are a special case of mobile ad hoc networks (MANETs). The distinctive characteristics of VANETs include high-speed vehicular nodes and significant variability in node density. Collecting data from VANETs is important in monitoring, controlling, and managing road traffic. However, efficient collection of the needed data is challenging because vehicles are continuously moving and generating a significant amount of events and data. The focus of this paper is on vehicle data collection using 3G/LTE. Initially, a comparison of proactive and reactive data collection schemes is conducted using simulation. The results show that proactive schemes produce the lowest delay and bandwidth usage but the highest loss ratio. To efficiently use the available bandwidth, an adaptive data collection scheme is developed and described. This adaptive data collection scheme is based on a proactive scheme using variable polling periods depending on the vehicle positions in the network and travel time to provide accurate traffic and travel time information to the Traffic Management Center (TMC). Simulation results, using taxi traces in Qatar, show that the proposed algorithm consumes an acceptable amount of megabytes (≈31 MB) per month when the basic polling interval is set to 10 s. Furthermore, traffic simulation results demonstrate that the proposed scheme has a minimum impact on delay and travel time estimates (relative error less than 2.5%), but can produce significant degradations in fuel consumption and emission estimates if computations are made at the TMC (relative error greater than 28% and 65%, respectively). These errors can be eliminated if computations are done on the vehicle.Index Terms-Mobile communication, data collection, automotive applications, intelligent transportation systems, wireless networks, traffic information, data aggregation, travel time estimation, vehicle emission estimates. 1524-9050

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Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior

Cited by 59 publications

References 22 publications

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Comparison of Car-Following Behavior in Terms of Safety Indicators Between China and Sweden

Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

Development and Testing of a 3G/LTE Adaptive Data Collection System in Vehicular Networks

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