System Optimal Time-Dependent Path Assignment and Signal Timing in Traffic Network

Abdelfatah, Akmal; Mahmassani, Hani S.

doi:10.3141/1645-23

Cited by 44 publications

(21 citation statements)

References 11 publications

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“…Emergence of dynamic traffic assignment solves few of the limitations as mentioned in the context of deterministic static equilibrium. Abdelfatah and Mahmassani [4] formulated simulation based combined traffic assignment and signal optimization problem. However, the non linear nature of the formulation does not allow them to solve it analytically.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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

Aziz

Ukkusuri

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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This work presents two analytical models for the traffic signal control problem within dynamic traffic assignment framework. The bi-level formulation is based on a Stackelberg Duopoly game where the upper level objective is to determine optimal signal timing plans with the lower level representing a dynamic user equilibrium conditions. In addition, a single level system optimal model is proposed. Cell transmission model is adapted as the embedded traffic flow model in the form of a mathematical program. The single level program is solved and tested on a network containing multiple signalized intersections. It is found that the proposed model performs better than pre-timed signal timing plans.

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Section: Related Work and Backgroundmentioning

confidence: 99%

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

Aziz

Ukkusuri

2011

2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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“…Simulations' results show superior fairness compared to the pure system optimum. Moreover, a formulation and a centralized solution algorithm for the joint optimization of signal control and route guidance decisions was presented in [16].…”

Section: Related Workmentioning

confidence: 99%

Fair route guidance: Bridging system and user optimization

Lujak

Giordani

Ossowskí

2014

17th International IEEE Conference on Intelligent Transportation Systems (ITSC)

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In this paper we study the problem of the assignment of road paths to vehicles. If we assume available the real-time road network information, then (self-concerned) vehicles select paths in a way related to user optimization which results in Wardrop equilibrium. The latter, even though fair for the vehicles of the same Origin-Destination (O-D) pair, in general can be arbitrarily more costly than the system optimum. System optimization, on the other hand, can produce unfair assignments both for the vehicles of the same as of different O-D pairs. To surmount the performance issue of the user-in respect to the system-optimization while considering the fairness issues, we propose a MAS-based distributed optimization model for path assignment to vehicles from the same and different O-D pairs at two levels. On the upper level, the proposed model optimizes the overall O-D pairs' Nash Welfare with the fairness related constraints while on the lower level, for every O-D pair separately, paths are assigned to individual vehicles through the auction algorithm. We test the solution approach through simulation, compare it with the conventional user-and systemoptimization, and thus demonstrate that it results in fair and globally efficient path-vehicle assignments.

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“…Gartner and Stamatiadis (1997) presented the conceptual framework for the combined solution of DTA and signal control. Abdelfatah and Mahmassani (1998) proposed a solution algorithm for the combined dynamic system optimum and signal control. Chang and Sun (2004) proposed a dynamic method for controlling oversaturated traffic signal network using a bang-bang-like model for the oversaturated intersections and TRANSYT for the undersaturated intersections.…”

Section: Introductionmentioning

confidence: 99%