Traffic assignment problem under tradable credit scheme in a bi-modal stochastic transportation network: A cumulative prospect theory approach

The traffic equilibrium assignment problem under tradable credit scheme(TCS) in a bi-modal stochastic transportation network is investigated in this paper. To describe traveler’s risk-taking behaviors under uncertainty, the cumulative prospect theory(CPT) is adopted. Travelers are assumed to choose the paths with the minimum perceived generalized path costs, consisting of time prospect value(PV) and monetary cost. At equilibrium with a given TCS, the endogenous reference points and credit price remain constant, and are consistent with the equilibrium flow pattern and the corresponding travel time distributions of road sub-network. To describe such an equilibrium state, the CPT-based stochastic user equilibrium(SUE) conditions can be formulated under TCS. An equivalent variational inequality(VI) model embedding a parameterized fixed point(FP) model is then established, with its properties analyzed theoretically. A heuristic solution algorithm is developed to solve the model, which contains two-layer iterations. The outer iteration is a bisection-based contraction method to find the equilibrium credit price, and the inner iteration is essentially the method of successive averages(MSA) to determine the corresponding CPT-based SUE network flow pattern. Numerical experiments are provided to validate the model and algorithm.

Reliability analysis of stochastic park-and-ride network

Park-and-ride （P＆R） facilities can alleviate the traffic burden in central urban areas by enabling car drivers to park at the perimeter of congested areas and continue their journeys with public transportation （e.g., metro and bus rapid transit）. Whether a P＆R scheme is successful depends on its attractiveness to car users. This paper presents anevaluation method for the reliability analysis of P＆R mode. Two indices, P＆R reliability and mode reliability, are in- troduced to represent the reliabilities of a transfer point and an entire trip, respectively. Then, a systematic reliability analysis is conducted for a stochastic P＆R network, where travelers can complete their journeys via two options： auto mode or P＆R mode. A variational inequality （VI） model is proposed and solved by a heuristic solution algorithm. Nu- merical results show that the P＆R facility reliability is significantly influenced by the capacity of parking facilities, the dispatching frequency of the connecting metro, and the metro fare. In addition, a higher level of total demand in the network has significant negative impacts on P＆R mode＇s attractiveness compared to auto mode.