An analytical framework for vehicular traffic signal control integrated with dynamic traffic assignment using cell transmission model

Author(s):  
H. M. Abdul Aziz ◽  
S. V. Ukkusuri
Author(s):  
W. Y. Szeto

The lagged cell-transmission model (L-CTM) is an enhanced version of the CTM. Both can be incorporated into a dynamic traffic assignment framework for offline transport planning and policy evaluation and online intelligent transportation system applications. In contrast to the CTM, the L-CTM adopts a nonconcave flow-density relation, which can be used to predict the existence of rather dense traffic in queues coasting toward the end of the queue or to help disprove the existence of this phenomenon. However, this study shows that the L-CTM can yield unrealistic densities, namely, negative densities and densities higher than theoretical jam density, the former of which has not been addressed in the literature. To cope with these unrealistic results, this study improves the L-CTM by introducing one more term in each sending and receiving function of the model. The improved model, the enhanced L-CTM (EL-CTM), is proved to yield nonnegative densities not greater than the jam density but can still allow the use of nonconcave density relations. The EL-CTM yields Lighthill-Whitham-Richards solutions when cell lengths and time intervals tend to zero and includes the CTM and the L-CTM as special cases. The EL-CTM is also shown to give more accurate solutions than the L-CTM (and hence also the CTM) does under a small increase in computation time. Hence the EL-CTM is believed to be more suitable for both online and offline applications in the future.


2019 ◽  
Author(s):  
Sai Kiran Mayakuntla ◽  
Ashish Verma

This paper develops node-based formulations for user equilibrium (UE) and system optimum (SO) dynamic traffic assignment (DTA) problems with departure time choice and route choices for general multiple origin-destination networks. Both the formulations are embedded with a new cell transmission model that satisfies the link-level First-In-First-Out (FIFO) principle. Because the formulations are node-based, the need for path enumeration is obviated, which results in considerable computational efficiency compared to the existing path-based models. While this advantage of node-based (or bush-based) models has been widely accepted in the literature of static traffic assignment, the formulations of dynamic traffic assignment models have mostly been path-based. The present work first describes a node-based cell transmission model that satisfies the link-level FIFO principle, which is fit within a DTA framework that facilitates efficient computation of UE and SO solutions. Further contributions of the work include the introduction of a backpropagation algorithm to efficiently compute marginal costs and complementarity formulations of the problems. Finally, numerical results are presented to demonstrate the performance of the proposed models using two standard test networks, along with a discussion of their convergence.


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