A Data-Driven Car-Following Model Based on the Random Forest

Shi, Huili; Wang, Tingli; Zhong, Fusheng; Wang, Hanqing; Han, Junyan; Wang, Xiaoyuan

doi:10.4236/wjet.2021.93033

Cited by 4 publications

(5 citation statements)

References 29 publications

(20 reference statements)

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Section: Literature Reviewmentioning

confidence: 99%

“…Some researchers have developed data-driven car-following models that can replace mathematical models. For instance, Shi et al developed a data-driven car-following model using random forest, and showed that the model could better predict car-following behavior than the GM model ( 18 ). Hao et al developed a data-driven car-following model based on rough set theory ( 19 ), and Zhang et al developed a model based on genetic algorithms ( 20 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Adewale,

Lee

2023

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicles (AVs) have moved from hype to reality as the penetration and acceptance rate continues to increase. As they are slowly integrated into traffic with human-driven vehicles (HDVs), it is necessary to predict the car-following behaviors of AVs and HDVs for better control of AV–HDV mixed traffic. This study extends a data-driven car-following model to incorporate drivers’ memory, and cooperation with the lead vehicle. The model predicts the following vehicle’s speed in AV–HDV mixed traffic. The effect of drivers’ cooperation on car-following behavior was modeled using prospect theory (PT), whereas the driver’s memory was incorporated using the memory cell of a long short-term memory (LSTM) neural network. This extended car-following model is called the “PT-LSTM model.” Real-world vehicle trajectories of HDVs and AVs in the Waymo AV Open Dataset were used to calibrate and validate the PT-LSTM model. The PT-LSTM model demonstrated higher accuracy compared with the LSTM model that did not consider drivers’ cooperation, the multiple layer perceptron model, Gipps’ model, and the intelligent driver model that incorporated PT. The importance of variables in different time steps in the PT-LSTM model was also evaluated using SHapley Additive exPlanations (SHAP). The SHAP results showed that AV followers were more likely to cooperate with the lead HDV, whereas HDV followers were more likely to cooperate with the lead AV than the lead HDV. Thus, this study underscores the importance of considering drivers’ memory and cooperation with the lead vehicle for the prediction of car-following behaviors in AV–HDV mixed traffic.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Adewale,

Lee

2023

Transportation Research Record: Journal of the Transportation R

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“…With the advancement of innovative technologies in an era of big data, there are other approaches to evaluating performance for providing critical information on transportation systems, for instance, modeling car-following behavior under multiple performance indicators [15] and utilizing connected vehicle data to assess the performance and operation of transportation infrastructures, such as interchanges [16] and pavement conditions [14] [17] [18].…”

Section: Performance Measurementmentioning

confidence: 99%

Making Data-Driven Transportation Decisions for Freight Operations

Abedi¹,

Codjoe²,

Thapa³

2023

JTTs

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Using Louisiana's Interstate system, this paper aims to demonstrate how data can be used to evaluate freight movement reliability, economy, and safety of truck freight operations to improve decision-making. Data mainly from the National Performance Management Research Data Set (NPMRDS) and the Louisiana Crash Database were used to analyze Truck Travel Time Reliability Index, commercial vehicle User Delay Costs, and commercial vehicle safety. The results indicate that while Louisiana's Interstate system remained reliable over the years, some segments were found to be unreliable, which were annually less than 12% of the state's Interstate system mileage. The User Delay Costs by commercial vehicles on these unreliable segments were, on average, 65.45% of the User Delay Cost by all vehicles on the Interstate highway system between 2016 and 2019, 53.10% between 2020 and 2021, and 70.36% in 2022, which are considerably high. These disproportionate ratios indicate the economic impact of the unreliability of the Interstate system on commercial vehicle operations. Additionally, though the annual crash frequencies remained relatively constant, an increasing proportion of commercial vehicles are involved in crashes, with segments (mileposts) that have high crash frequencies seeming to correspond with locations with recurring congestion on the Interstate highway system. The study highlights the potential of using data to identify areas that need improvement in transportation systems to support better decision-making.

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“…It can be seen from the above literature that the accuracy of driving behavior recognition varies widely, which is closely related to sensor data fusion and feature extraction. Some scholars also use the random forest model to identify driving behaviors such as car-following [50,51], lane change [8], and being followed [9] or to evaluate driving safety risks [10] and identify drivers [52,53], driving style [54], and driving posture [55]. Other researchers [56] combined random forest and other methods to construct a driving behavior recognition model.…”

Section: The Recognition Model Of Driving Behaviormentioning

confidence: 99%

“…Distracted driving [7] [49] Used for distracted driving behavior recognition Car-following [50] [51] Used for identification of following driving behavior Lane change [8] [56] Used for lane change driving behavior recognition Others [9,10] [52] [53][54][55] Used for acceleration and deceleration, turning, lane change, being followed, and driver, driving style, driving posture recognition 6 Journal of Advanced Transportation application fields and corresponding advantages and disadvantages is shown in Table 3.…”

Section: Types Of Driving Behavior Applications Advantages and Disadv...mentioning

confidence: 99%

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

Zhao

Zhong

Zhi-jun

et al. 2022

Journal of Advanced Transportation

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The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

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A Data-Driven Car-Following Model Based on the Random Forest

Cited by 4 publications

References 29 publications

Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Prediction of Car-Following Behavior of Autonomous Vehicle and Human-Driven Vehicle Based on Drivers’ Memory and Cooperation With Lead Vehicle

Making Data-Driven Transportation Decisions for Freight Operations

A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information

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