Modeling Traffic Flow Dynamic And Stochastic Evolutions

Stochastic traffic flow modelling is the application foundation of Intelligent Trans-portation Systems (ITS), traffic engineering, traffic management and control and so on.It is of a certain significance to enrich the modern traffic flow theory. Road traffic flow iswith complex, dynamic and stochastic characteristics. The new generation of ITS bringsforward higher requirements to the traditional static and deterministic theory, so that itno longer satisfies the demands of dynamic and stochastic modelling. This dissertationinvestigates multiple heterogeneous data to establish stochastic traffic flow models basedon vehicle trajectory information, reveals the underlying mechanism of complexity andstochastic evolutions, and overcomes the deficiency of traditional models that added ran-dom disturbance terms to determinant functions. The main contents and results are asfollows:(1) Data mining: we first analyze the characteristics of empirical traffic measure-ments to reveal the underlying mechanism of complexity, dynamic and stochastic evo-lutions quantitatively and qualitatively. This work lays the foundation to the stochastictraffic flow modelling. By using Eulerian measurements (e.g. inductive loop data offreeways I80-W in Berkeley and G6-S in Beijing) and Lagrangian measurements (e.g.vehicular trajectories of NGSIM dataset Highway101), we study shifted lognormal dis-tributions of headway/spacing/velocity, disturbances of congested platoons (jam queues)and time-frequency properties of traffic oscillations.(2) Microscopic connection: based on the joint probability distributions of head-way/velocity and spacing/velocity, we propose a Markov model for road traffic by incor-porating the connection between empirical distributions and microscopic car-followingmodels. Applying the transition probability matrix to describe random choices of drivers,the results show that the stochastic model more veritably reflects the dynamic evolutioncharacteristics of traffic flow.(3) Macroscopic connection: we propose a stochastic FD model through the con-nection between headway/spacing distributions and the macroscopic fundamental dia-gram (FD). We also study the stochastic Newell condition, wide scattering features andprobabilistic boundaries in flow-density plot, which compensate for the lack of analyticalderivation of the meta-stable2D region. (4) On-ramp/off-ramp bottleneck modeling: we propose a traffic flow breakdownprobability model based on headway/spacing distributions. It assumes the stochastic anddynamic road capacity. We study the capacity of highway on-ramp bottlenecks and themechanism of traffic breakdown phenomena, analyze the formation, propagation and dis-sipation of bottleneck congestions, and use the spatial-temporal queueing model to derivephase transition conditions of5traffic jam patterns. This work is beneficial to take mea-sures in active traffic management (ATM), obtain optimal control strategies to preventrecurrent congestions and improve reliability.This dissertation relies on Lagrangian and Eulerian measurements to study jointdistributions of headway/spacing/velocity, proposes a series of stochastic traffic flowmodels to reveal the potential impact factors and the formation mechanism of stochasticcharacteristics. This study assists in establishing urban ATM systems.