An objective of DynusT is aimed at integrating with travel demand models and microscopic simulation models, supporting application areas in which realistic traffic dynamic representation is needed for a large-scale regional or corridor network. Mesoscopic and microscopic models are complementary to each other, and with proper integration, both can jointly accomplish optimal modeling capabilities.
Traffic modeling softwares are generally categorized as macroscopic, mesoscopic, or microscopic models based on the resolution at which vehicle flows are simulated. Macroscopic models trade off the ability to model individual vehicles at small time intervals for the ability to model entire metropolitan areas at a single arregated time. These models produce static, highly aggregated outputs, such as those found in the route assignment step in the traditional four-step process. Microscopic models make the opposite trade as these models are able to model individual vehicle interactions at time intervals as small as one-tenth of a second, but are best utilized to model smaller networks, such as individual intersections, arterial corridors or freeway segments. Mesoscopic models such as DynusT occupy the space between the two models, with higher time resolutions of traffic representation than macroscopic models, but still low enough to model large regional networks.
In the concept of multi-resolution modeling (MRM), all three models are integrated to provide insight into the network at all levels. These models should not be mutually exclusive, but rather complementary to each other. The macroscopic traffic network, traditionally used by planning organizations and DOTs, provides the link-node network and origin-destination (O-D) demand matrix; the mesoscopic dynamic traffic assignment model uses the network and O-D matrices and produces a time-dependent, capacity-constrained region-wide estimation of traffic distributions. Lastly, the microscopic model provides detailed analyses in selected sub-areas. MRM addresses issues that can fall beyond the reach of macroscopic models (which tend to model large areas, but are static) and microscopic models (which are dynamic, but suited for modeling smaller, more defined areas). Th MRM approach may be of use in network or corridor-wide traffic flow patterns deviations due to significant changes in roadway configurations or certain corridor management strategies.
Macro-Meso-Micro Integration
In macro-micro integration, where a sub-area of a macroscopic network is converted to run in a microscopic traffic model, including the macro model's converted O-D matrices. Inherently, the O-D-based flows departing entering the boundaries of the sub-area are not capacity constrained. From the macroscopic model's perspective, this results in links with volume/capacity ratios exceeding 1.0; however, microscopic models are capacity-constrained and will have difficulty being modeled with such input from the macroscopic model. To compensate, the sub-area network must be manually adjusted and calibrated until the model is close to actual system dynamics, which has a negative impact on the predictive power of the model for future year cases. This basic problem is illustrated in the left figure.
By contrast, using a macro-meso-micro multi-resolution approach, the output from the macroscopic model is fed into the mesoscopic DTA model, which produces time-dependent flows based on capacity constraints. Such constraints produce realistic route diversions due to time-varying congestions throughout the network. That is the advantage to modeling with a mesoscopic model. Sub-areas can then be cut from the mesoscopic DTA model, converted to the micro model, and will then be more easily updated and calibrated to match the actual system dynamics. This approach is shown in the figure below.
MRM in DynusT
Mesoscopic and microscopic models are complementary to each other, and with proper integration, both can jointly accomplish optimal modeling capabilities. However, the capability to integrate mesoscopic and microscopic models is almost nonexistent. Translating the mesoscopic model and results to a microscopic model has been shown to be troublesome and time-consuming without a streamlined process. Microscopic models have proven to be difficult to calibrate and apply because of their richness in parameters and their dependency on large sets of fine-grained, accurate input data. Microscopic models also require large amounts of computational memory and efficiency, and therefore, large networks are both troublesome and time-consuming to create. Mesoscopic models on the other hand, have shown their ability to accurately model the dynamics of traffic demand in large scale networks, but lack the detailed resolution needed to analyze individual vehicle and lane interactions.
To facilitate this process, a tool has been developed that integrates DynusT with a microscopic simulation model (VISSIM) through the use of PTV America’s ® open COM interface. Historically, multiple scenarios were modeled in DynusT through the use DTA capabilities. DTA, a component of both Advanced Traveler Information Systems (ATIS) and Advanced Traffic Management Systems (ATMS) uses either historic or real-time data to estimate and predict network traffic conditions. DynusT allows the user to use either pre-trip or enroute information to model ATIS and ATMS strategies, and has the ability to simulate multiple user classifications (MUC). These MUC's can be further defined in terms of their responsiveness to available information. Once all components are determined, DTA is run in the form of User Equilibrium (UE) where vehicles are assigned on various paths until convergence is reached or vehicles cannot improve their travel time by switching routes. Output files are generated in DynusT and are used as the basis for conversion to a microscopic network.
Prior to the development of this tool, output data from DynusT (in the form of time-dependent shortest paths and flows) were then manually fed into VISSIM as model input parameters. Roadway networks had to be created manually in VISSIM, and all input parameters for calibration were individually fed into the microscopic model. The most tedious and time-consuming part was converting dynamic routes from DynusT to static routes in VISSIM. Since DynusT runs DTA, hourly traffic volumes are continuously changing over time. The main problem in this approach was determining how to import dynamic path files to the micro level without compromising routing or traffic volumes.
In collaboration with Texas Transportation Institutes's (TTI) Center for Intelligent International Transportation Research (CIITR) in El Paso, and PTV America,Inc., a DynusT to VISSIM Conversion (DVC) toolkit has been developed and is currently under extensive testing. The combination of DynusT and DVC allows a sub-area to be defined in DynusT and the sub-area along with the assigned time-varying routes and flows to be automatically and consistently imported to VISSIM. The new tool developed enables mesoscopic users to create microscopic models with high levels of resolution and detail without the lingering task of data transfer or network recreation. Paths from the mesoscopic level are exported as time-dependent static routes, allowing for a more realistic time-based distribution of traffic. The end result is a tool that reduces the time to convert a mesoscopic model to the microscopic level. The capability of this new tool also allows for detailed intersection-level analyses based on the network-wide traffic assignment results.
Generally, DynusT integrates with microscopic models in an offline fashion. Once the dynamic traffic assignment is completed on the entire network, the user would define the sub-area of interest and the assigned flows that go through the sub-area would be processed to create both the demand tables, and vehicle and path files that are consistent with these flows. As shown in the figure below, the vehicles with trajectories traversing through the sub-area in the original network will be kept in a consistent fashion in the sub-area data.
The next integration step is to create the microscopic network that corresponds to the sub-area. The conversion tool automatically translates the DynusT sub-area dataset (including network, routes and assigned flows) to VISUM/VISSIM format. This automatic process saves a significant amount of resources for the user and ensures the correctness and compatibility between the DynusT and VISSIM datasets. The integration of DynusT and VISSIM provides expanded dimensions of modeling capabilities for existing DynusT and VISSIM users to take advantages of the modeling strengths of both models. In the modeling framework shown below, DynusT can be used to perform the DTA analysis in a regional network, capturing the flow distributions resulted from major demand or supply side scenarios/alternatives. The assigned time-varying flows and routes can then be imported into VISSIM for a user-defined sub-area for the modeler to perform detailed operational strategy analysis.
The “feedback loop” between VISSIM and DynusT could be used as an important modeling task, and thus needs to be exercised with careful modeling judgment. If the modeler believes that the operational strategies being introduced and tested in VISSIM creates only localized impact that is not significantly enough to induce system-wide flow re-distribution, then the feedback to DynusT can be omitted. Otherwise, performing the feedback loop to update the DTA flow distributions may be desirable. It is also important to note that the traffic flow dynamics between DynusT and VISSIM need to be tuned to consistency before conducting integrated analysis. In other words, both VISSIM and DynusT need to simulate traffic and produce similar MoE results in order for the integrated framework to produce meaningful and comparable results. The consistency can be established primarily by calibrating both models using the field data. Another item to note is that once the modeler decides to perform the feedback loop, the strategies that are implemented in VISSIM also need to be implemented in DynusT to ensure the strategy consistency. For example, if a freeway interchange configuration is being changed in VISSIM, such a configuration needs to be also coded in DynusT to ensure the consistency in the network geometric configuration.
