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A 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 proposed models under different network and data availability conditions. This paper describes the internal functions of DTALite, an open-source, light-weight dynamic traffic assignment (DTA) software package. DTALite can be used for large-scale transportation modeling applications to help planning/engineering organizations and public officials make transportation infrastructure investment decisions. Particularly important applications may include modeling the traffic impacts of work zones, proposed freeways/ highways, and tolling facilities. DTALite's route choice model also allows organizations to test the effects of multiple strategies to improve traffic operations and manage travel demand. One of the highlights of this paper is DTALite's support for multi-threaded processing, which significantly reduces model run-times. This could allow users to evaluate more alternative strategies to solve a problem within a limited amount of time, providing more information to help decision-makers find better solutions to difficult problems.

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