Failure of classical traffic flow theories: a critical review

Abstract

We explain that the fundamental empirical basis for automatic driving, reliable control and optimization of traffic and transportation networks is the set of empirical features of traffic breakdown at a road bottleneck. We show why generally accepted traffic and transportation theories and models are not consistent with this empirical fundament of traffic science. In particular, these classical traffic theories are as follows: (i) the Lighthill-Whitham-Richards (LWR) theory and traffic flow models in the framework of the LWR theory (for example, Daganzo's cell transmission model) that explain traffic breakdown through a fundamental diagram of traffic flow, (ii) General Motors (GM) class of traffic-flow models that explain traffic breakdown through traffic flow instability due to a driver reaction time (for example, the following well-known models belong to the GM model class: Gipps's model, Payne's model, Newell's optimal velocity (OV) model, Wiedemann's model (VISSIM traffic simulation tool), Bando et al. OV model, Treiber's Intelligent Driver Model, Krauß model (SUMO tool), the Aw-Rascle model), (iii) the classical understanding of stochastic highway capacity, and (iv) Wardrop's principles for dynamic control, assignment, and optimization of traffic and transportation networks.In turn, this can explain why dynamics network optimization and control approaches based on these classical traffic flow theories failed by their applications in the real world. We discuss why rather that the assumption about the existence of stochastic highway capacity, at any time instant there should be the infinite number of highway capacities within a range of the flow rate between a minimum capacity and a maximum capacity as assumed in three-phase theory introduced by the author. Because the assumption about the infinite number of highway capacities is consistent with the set of the fundamental empirical features of traffic breakdown at highway bottlenecks, this can be considered a theoretical fundament for the development of reliable automatic driving, control and optimization of vehicular traffic and transportation networks. We discuss briefly some features of the three-phase theory explaining the empirical fundament of transportation science.

Exploring background risk factors for fatigue crashes involving truck drivers on regional roadway networks: a case control study in Jiangxi and Shaanxi, China

“…Third, there is neither “breathalyzer” nor other direct and reliable tool that an investigating police officer can use to determine whether some level of fatigue was a major or contributory factor in a crash, and thus police findings during the original accident report may underestimate the level of fatigue through the “I-know-it-when-I-see-it” approach in identifying fatigue as a cause 1 ; thus, establishing a national monitoring system will help improve uniformity in data collection and analysis between provinces, especially in rural areas. Although traffic flow characteristics have been found to have significant effects on the occurrence of crashes in many previous researches (Kerner 2013 , 2015 , 2016 ), they have not been considered here due to the incomplete or missing messages in TADS, so further works should pay close attention to the association between traffic flow conditions and probability of truck crash occurrence. All these suggestions and future studies might be helpful for promulgating potential policy initiatives and promoting effective strategies to increase overall safety performance on roads in China.…”

Microscopic theory of traffic-flow instability governing traffic breakdown at highway bottlenecks: Growing wave of increase in speed in synchronized flow

“…This is because rather than an F→J transition of the models of the GM model class, in all real field traffic data traffic breakdown is a phase transition from a metastable free flow to synchronized flow (F→S transition) [27,[29][30][31][32][33][34][35][36][37][38][39].…”

Evaluation, Classification, and Influential Factors Analysis of Traffic Congestion in Chinese Cities Using the Online Map Data

“…Over the past 50 years, a lots of studies focused on distinguishing congestion state on certain road links. Researchers have developed a huge number of traffic flow models (see, e.g., reviews [1][2][3]), including well-known Macroscopic Fundamental Diagram (MFD) [4,5], and Kerner's three-phase traffic theory [6][7][8][9][10] (see papers of Rehborn et al [11,12] about the development of Kerner's theory) is used to distinguish and describe congestion patterns on road links. In practice, level of service (LOS) is applied in the US HCM 2000 using letters A through F to describe it, with A being the best and F being the worst [13].…”