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

Abstract

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.

Human-like autonomous car-following model with deep reinforcement learning

“…In the hidden layers, each neuron has a rectified linear unit (RLU) activation function that 14 transforms its input to its output signal. The RLU function computes as ( ) max(0, ) f x x = and has been shown to accelerate the convergence of network parameter optimization significantly (38).…”

Detection of Driving Capability Degradation for Human-Machine Cooperative Driving

“…In practical traffic, driver behavior shows the characteristic of a strong randomness, nonlinearity and personalization. Machine learning algorithms have been adopted to achieve a better accuracy and adaptability [28], such as the Gauss Mixed and Hidden Markov Model (GMM-HMM) [24], Artificial Neural Network for Nonlinear Autoregressive Exogenous Process (ANN-NARX) [29], Deep Believe Network (DBN) [30], etc. Compared to others, NARX is a nonlinear autoregressive exogenous process containing an input delay, which leads to an extra ability to describe the reaction time of the driver.…”

A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

“…Normally, a larger amount of training data leads to a larger value of BIC. From (16), the value of K should be selected, ensuring the minimum value of BIC, and so the value of K for passenger car, bus, and truck is set to 10, 5, and 8, respectively. With the selected K, the raw data and trained GMM are shown in Figure 4.…”

“…The other two models have a comparatively larger RMSE and RMSPE. The reason is that the CA model is designed to keep a safer distance [1] and IDM is designed to eliminate the difference between the real situation and the preferred [15,16]. Therefore, they ignore the influence of the type of leading vehicle on the driver car-following behavior.…”

“…After calibration, the field data evaluation results show that this model has a higher accuracy than the original Gipps' model. The Intelligent Driver Model (IDM) is one of the most widely used models, which attempts to eliminate the difference between the real situation and the preferred [15,16].…”