Urban road capacities are influenced by various factors, such as traffic accidents, vehicle breakdowns, traffic signal failures, road construction and weather variations. This paper takes into account the uncertainty of road conditions and analyzes the choice behaviors of travelers who are risk averse based on stochastic capacity. The level of risk aversion in this study is closely related to stochastic link capacities, which represent the degree of sensitivity to changes in capacity. Both sensitivity and awareness affect the behavior of travelers. All types of travelers choose the route that minimizes their travel disutility. To address this issue, we adopt a stochastic equilibrium assignment model and utilize the Method of Successive Averages (MSA) algorithm to solve it. A 9-node network is presented to illustrate the proposed model. Simulation results show that the impact of sensitivity differs with changes in awareness. The best results are achieved when sensitivity and awareness are combined, and excessive sensitivity is detrimental to travelers.
In order to ensure the effective use of funds, the government must carry out reasonable traffic planning, in which traffic network design is one of the core contents of traffic planning. The traditional transportation planning has unreasonable factors due to deterministic assumptions. This paper assumes that the demand is a random variable, and then considers the time factor. With the cost recovery and link update as the constraint conditions, the real option is used to solve the problem of the flexibility value of the optimization strategy. The optimization model under uncertainty considering the time factor is given, and the real option is solved by using the LSM method. Genetic algorithm with Monte Carlo is used for network design. The example analysis shows that time factor has a significant impact on network construction decision-making, and real options can effectively describe the flexibility of network construction decision-making.
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