DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration

Abstract

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.

Transportation network design for maximizing space–time accessibility

“…13, where the travel time indices (average travel time divided by free-flow travel time) of three sample corridors in Chicago area are listed based on the observed average travel time. The arterial street travel times are obtained from a dynamic traffic simulation (Zhou and Taylor, 2014) using time-dependent OD demand. We need to calculate time-dependent shortest path for 149,382 origin-activity location pairs at each iteration.…”

Seismic Resilience Assessment of the Western Macedonia Highway Network in Greece

“…Given the travel demands and the additional input of the traffic capacity of every network link, pre-earthquake traffic flows over the whole network are calculated according to [10]. It is noted that the OD matrix used herein, refers to travel demands during the typical hour of a normal day and thus appropriate scaling factors are applied to the results whenever daily traffic data are deemed.…”

A Dynamic Spatiotemporal Analysis Model for Traffic Incident Influence Prediction on Urban Road Networks

“…When a shockwave propagates to an intersection, different factors of shockwave speed are set in relation to the straight-going and turning roads. These factors are calibrated according to the amount of traffic flow to different destinations [18], and the effective green time at intersections [36]. We calibrated the straight factor and turning factor to be 1.1 and 0.3, respectively, for the road network of Shanghai.…”

“…These microscopic models focus on typical road structures, such as intersections, freeways, and rectangular grid networks, to achieve precise results. In contrast, macroscopic network traffic simulation models derived from the LWR kinematic wave theory [17], have recently been proposed to simulate and predict various traffic behaviors, including incidents on large road networks [18,19]. Despite the comprehensive results obtained using these methods, the long computation times have made these techniques non-optimal for implementation within TIM.…”