A stochastic mesoscopic cell-transmission model for operational analysis of large-scale transportation networks

The cell transmission model (CTM), developed by Daganzo in 1994 was not fully exploited as an operations model for analysis of large-scale traffic networks. Because of its macroscopic/mesoscopic features, CTM offers calibration and computational advantages over microscopic models. This study presents a series of enhancements to the original form of CTM. These enhancements show potential to increase the model's accuracy and realism of traffic flow representation. For example, topological enhancements and modifications to the flow advancing equation are introduced to allow variable cell lengths and non-discrete movements of vehicles between cells. In addition, implementation of lane-changing behavioral logics and algorithmic enhancements to model vehicle flows at network junctions demonstrate potential in modeling realistic non-homogeneous traffic streams in CTM. A calibration exercise was conducted to account for randomness in driving behavior using vehicle trajectory data. This proves the models potential in modeling stochastic variations of real-life networks. A sample freeway network of I-10 corridor in Baton Rouge was used to evaluate and compare the performance of the improved version of CTM versus CORSIM. The simulation results showed comparable performance of both platforms in terms of link occupancy (density) and total network travel time and demonstrate the potential of employing CTM in traffic operations applications.