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Data-driven Traffic Flow Model

Goal: Proposing the data-driven versions of various traffic flow models

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Zhengbing He
added 2 research items
Car-following models are always of great interest of traffic engineers and researchers. In the age of mass data, this paper proposes a nonparametric car-following model driven by field data. Different from most of the existing car-following models, neither driver's behaviour parameters nor fundamental diagrams are assumed in the data-driven model. The model is proposed based on the simple k-nearest neighbour, which outputs the average of the most similar cases, i.e., the most likely driving behaviour under the current circumstance. The inputs and outputs are selected, and the determination of the only parameter k is introduced. Three simulation scenarios are conducted to test the model. The first scenario is to simulate platoons following real leaders, where traffic waves with constant speed and the detailed trajectories are observed to be consistent with the empirical data. Driver's rubbernecking behaviour and driving errors are simulated in the second and third scenarios, respectively. The time-space diagrams of the simulated trajectories are presented and explicitly analysed. It is demonstrated that the model is able to well replicate periodic traffic oscillations from the precursor stage to the decay stage. Without making any assumption, the fundamental diagrams for the simulated scenario coincide with the empirical fundamental diagrams. These all validate that the model can well reproduce the traffic characteristics contained by the field data. The nonparametric car-following model exhibits traffic dynamics in a simple and parsimonious manner.
Lane-changing (LC), which is one of the basic driving behavior, largely impacts on traffic efficiency and safety. Modeling an LC process is challenging due to the complexity and uncertainty of driving behavior. To address this issue, this paper proposes a data-driven LC model based on deep learning models. Deep belief network (DBN) and long short-term memory (LSTM) neural network are employed to model the LC process that is composed of LC decisions (LCD) and LC implementation (LCI). The empirical LC data provided by Next Generation Simulation project (NGSIM) is utilized to train and test the proposed DBN-based LCD model and LSTM-based LCI model. The results indicate that the proposed data-driven model is able to accurately predict the LC process of a vehicle. The sensitivity analysis shows that the most important factor associated with LCD is the relative position of the preceding vehicle in the target lane. This may be the first work that comprehensively models LC using deep learning approaches.
Zhengbing He
added a project goal
Proposing the data-driven versions of various traffic flow models