Abstract
AbstractA number of emerging dynamic traffic analysis applications, such as regional or statewide traffic assignment, require a theoretically rigorous and computationally efficient model to describe the propagation and dissipation of system congestion with bottleneck capacity constraints. An open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. This paper describes its three major modeling components: (1) a light-weight dynamic network loading simulator that embeds Newell’s simplified kinematic wave model; (2) a mesoscopic agent-based DTA procedure to incorporate driver’s heterogeneity; and (3) an integrated traffic assignment and origin–destination demand calibration system that can iteratively adjust path flow volume and distribution to match the observed traffic counts. A number of real-world test cases are described to demonstrate the effectiveness and performance of the prop...
Highlights
This paper describes the internal functions of DTALite, an open-source, light-weight dynamic traffic assignment (DTA) software package
Motivated by a wide range of application needs, such as region-wide traffic analysis and route guidance provision, dynamic traffic assignment (DTA) models have been increasingly recognized as an important tool for assessing operational performance of those applications at multiple spatial resolutions
The advances of DTA in this aspect are built upon the capabilities of DTA models in describing the formation, propagation, and dissipation of traffic congestion in a transportation network
Summary
Motivated by a wide range of application needs, such as region-wide traffic analysis and route guidance provision, dynamic traffic assignment (DTA) models have been increasingly recognized as an important tool for assessing operational performance of those applications at multiple spatial resolutions (e.g. network, corridor, and individual segment levels). Given existing traffic states in the previous time interval t − ΔT, the DNL model follows a multi-step process for moving agents/vehicles along a given path between specific origin and destination nodes, subject to capacity constraints and time-dependent traffic states defined by shockwave propagation in Newell’s simplified kinematic wave model. The maximum inflow capacity capin(t) at the upstream end of a link is calculated using the difference of cumulative arrival flow counts at two consecutive time stamps t − ΔT and t, which is determined by the outflow capacity capout(t − BWTT) at the downstream end between time stamps t − BWTT − ΔT and t − BWTT.
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