Day-to-day traffic user equilibrium model considering influence of intelligent highways and advanced traveler information systems

Abstract

To explore the influence of intelligent highways and advanced traveler information systems (ATIS) on path choice behavior, a day-to-day (DTD) traffic flow evolution model with information from intelligent highways and ATIS is proposed, whereby the network reliability and experiential learning theory are introduced into the decision process for the travelers’ route choice. The intelligent highway serves all the travelers who drive on it, whereas ATIS serves vehicles equipped with information systems. Travelers who drive on intelligent highways or vehicles equipped with ATIS determine their trip routes based on real-time traffic information, whereas other travelers use both the road network conditions from the previous day and historical travel experience to choose a route. Both roadway capacity degradation and travel demand fluctuations are considered to demonstrate the uncertainties in the network. The theory of traffic network flow is developed to build a DTD model considering information from intelligent highway and ATIS. The fixed point theorem is adopted to investigate the equivalence, existence and stability of the proposed DTD model. Numerical examples illustrate that using a high confidence level and weight parameter for the traffic flow reduces the stability of the proposed model. The traffic flow reaches a steady state as travelers’ routes shift with repetitive learning of road conditions. The proposed model can be used to formulate scientific traffic organization and diversion schemes during road expansion or reconstruction.

Keywords

day-to-day model / intelligent highway / advanced traveler information systems / uncertainty

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