4.7 Article

Modeling heterogeneous traffic flow: A pragmatic approach

Journal

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
Volume 99, Issue -, Pages 183-204

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.trb.2017.01.011

Keywords

Heterogeneous traffic flow; Multi-modal; Multi-class; LWR; Data driven; NGSIM; Fundamental diagram

Funding

  1. Carnegie Mellon University's Traffic21 Research Institute and Technologies for Safe and Efficient Transportation, a National USDOT University Transportation Center for Safety (T-SET UTC) - US Department of Transportation
  2. Div Of Civil, Mechanical, & Manufact Inn
  3. Directorate For Engineering [1558887] Funding Source: National Science Foundation

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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 1-80 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. (C) 2017 Elsevier Ltd. All rights reserved.

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