Modeling dynamics of heterogeneous traffic flow is central to the control and operations of today’s increasingly complex transportation systems. We develop a macroscopic heterogeneous traffic flow model. This model considers interplay of multiple vehicle classes, each of which is assumed to possess homogeneous car-following behavior and vehicle attributes. We propose the concepts of road capacity split and perceived equivalent density for each class to model both lateral and longitudinal cross-class interactions across neighboring cells. Rather than leveraging hydrodynamic analogies, it establishes pragmatic cross-class interaction rules aspired by capacity allocation and approximate inter-cell fluxes. This model generalizes the classical Cell Transmission Model (CTM) to three types of traffic regimes in general, i.e. free flow, semi-congestion, and full congestion regimes. This model replicates prominent empirical characteristics exhibited by mixed vehicular flow, including formation and spatio-temporal propagation of shockwaves, vehicle overtaking, as well as oscillatory waves. Those features are validated against numerical experiments and the NGSIM I-80 data. Realistic class-specific travel times can be computed from this model efficiently, which demonstrates the feasibility of applying this multi-class model to large-scale real-world networks.

Modeling heterogeneous traffic flow: A pragmatic approach

Traffic congestion is becoming the issues of the entire globe. This study aims to explore and review the data mining and machine learning technologies adopted in research and industry to attempt to overcome the direct and indirect traffic issues on humanity and societies. The study's methodology is to comprehensively review around 165 studies, criticize, and categorize all these studies into a chronological and understandable category. The study is focusing on the traffic management approaches that were depended on data mining and machine learning technologies to detect and predict the traffic only. This study has found that there is no standard traffic management approach that the community of traffic management has agreed on. This study is important to the traffic research communities, traffic software companies, and traffic government officials. It has a direct impact on drawing a clear path for new traffic management propositions. This study is one of the largest studies with respect to the size of its reviewed articles that were focused on data mining and machine learning. Additionally, this study will draw general attention to a new traffic management proposition approach.

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Intelligent Transportation and Control Systems Using Data Mining and Machine Learning Techniques: A Comprehensive Study

Heterogeneous traffic flow modelling using second-order macroscopic continuum model

With the development of automated driving and communication technologies, the connected automated vehicles (CAVs) gradually enter market and become more popular. At the same time, the mixed traffic composed of conventional vehicles and CAVs will gradually become a very common form of traffic. In fact, the driving process of CAV is a typical cyber physical process which couples tightly the cyber factor of traffic information with the physical components of the vehicles. In this paper, we present a mixed traffic model from the perspective of cyber physical fusion, the model focuses on the fact that the CAVs can obtain the information from multiple vehicles ahead and the drivers have a reaction delay in driving process. The stability condition of the proposed model is derived via linear stability analysis. Furthermore, we investigate the fuel consumption and CO2 emission using the model we propose under different penetration rates of CAVs. The results show that the stability of mixed traffic is related to driver’s reaction delay, the penetration rate of CAVs, and the information from multiple vehicles ahead that CAVs can obtain. Numerical simulations are conducted to verify the analytical results. The simulation results demonstrate that the model proposed in this paper can better reflect the real advantages of CAV in mixed traffic, and further show that the model in this paper is more realistic. More specifically, the information obtained from multiple vehicles ahead including conventional vehicle and CAV can improve the stability of mixed traffic and traffic efficiency to a greater extent, and the driver’s reaction delay will destabilize mixed traffic. Besides, when the penetration rate of CAVs is high, the fuel consumption and CO2 emission in mixed traffic can be greatly reduced.

Modeling and stability analysis of mixed traffic with conventional and connected automated vehicles from cyber physical perspective

Vehicular traffic has strong implication in the severity and degree of Urban Heat Island (UHI) effect in a city. It is crucial to map and monitor the spatio-temporal heat patterns from vehicular traffic in a city. Data observed from traffic counting stations are readily available for mapping the traffic-related heat across the stations. However, macroscopic models utilizing traffic counting data to estimate dynamic directional vehicular flows are rarely established. Our work proposes a simple and robust cell-transmission-model to simulate all the possible cell-based origin-destination trajectories of vehicular flows over time, based on the traffic counting stations. Result shows that the heat patterns have notable daily and weekly periodical circulation/pattern, and volumes of heat vary significantly in different grid cells. The findings suggest that vehicular flows in some places are the dominating influential factor that make the UHI phenomenon more remarkable.

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Understanding heat patterns produced by vehicular flows in urban areas.

Macroscopic traffic flow models are suited for large scale, network wide applications where the macro-characteristics of the flow are of prime interest. A clear understanding of the existing macro-level traffic flow models will help in modelling of varying traffic scenarios more accurately. Existing state-of-the-art reports on traffic flow models have not considered macro-level models exclusively. This paper gives a review of macroscopic modelling approaches used for traffic networks including recent research in the past decade. The modelling of the two main components of the network i.e. links and nodes are reviewed separately in two sections and solution procedures are discussed followed by a synthesis on the advantages and disadvantages of these models. This review should encourage efficient research in this area towards network level application of these models.

State-of-the art of macroscopic traffic flow modelling

Heterogeneous Traffic Flow Modelling Using Macroscopic Continuum Model

This paper discusses on the choice of a recently proposed multi-class, speed-area occupancy based, one-dimensional continuum model towards the measurement of network level traffic flow variables, n...

Multi-class AR model: comparison with microsimulation model for traffic flow variables at network level of interest and the two-dimensional formulation

Heterogeneous traffic as observed in several south Asian countries is characterized by complex interactions of widely varying vehicle types that do not follow lanes and with smaller vehicles filling any gaps available. This paper discusses on four main components of macroscopic simulation of traffic flow in the Indian traffic context. One of the prime component, namely the traffic flow model, is discussed in detail with respect to the continuum modelling approach.

Ranju Mohan

Papers

Heterogeneous traffic flow modelling using second-order macroscopic continuum model

State-of-the art of macroscopic traffic flow modelling

Heterogeneous Traffic Flow Modelling Using Macroscopic Continuum Model

Multi-class AR model: comparison with microsimulation model for traffic flow variables at network level of interest and the two-dimensional formulation

Numerical Study with Field Data for Macroscopic Continuum Modelling of Indian Traffic