Stochastic user equilibrium model based on trip chain analysis under multi-modal guidance

In order to evaluate the impact of multi-modal guidance information on the traffic assignment in the integrated transportation network, a trip-chain-based probit stochastic user equilibrium model was proposed to study the variations of travelers' choice of combined trip mode, trip-chain cost and trip-chain structure under the multi-modal guidance. A MSA algorithm with Monte-Carlo method embedded was presented to solve the model. In the model, travelers were divided into two groups by accepting multi-modal guidance information or not, and both groups make choices according to their own expected cost of trip chains. In the course of solution, hyper-network theory was introduced, and traffic flow assignment was carried out on the hyper-network. The results show that multi-modal guidance information helps to increase the mode split rate of public transport by 5.63%, and the network benefit reach 196.254. It can be confirmed that multi-modal guidance information contributes to encourage travelers to decrease the use of car, and reduce trip-chain cost and simplify trip-chain structure.