We propose a framework to find optimal price-based policies to regulate markets characterized by oligopolistic competition and in which consumers make a discrete choice among a finite set of alternatives. The framework accommodates general discrete choice models available in the literature in order to capture heterogeneous consumer behavior. In our work, consumers are utility maximizers and are modeled according to random utility theory. Suppliers are modeled as profit maximizers, according to the traditional microeconomic treatment. Market competition is modeled as a non-cooperative game, for which an approximate equilibrium solution is sought. Finally, the regulator can affect the behavior of all other agents by giving subsidies or imposing taxes to consumers. In transport markets, economic instruments might target specific alternatives, to reduce externalities such as congestion or emissions, or specific segments of the population, to achieve social welfare objectives. In public policy, different agents have different individual or social objectives, possibly conflicting, which must be taken into account within a social welfare function. We present a mixed integer optimization model to find optimal policies subject to supplier profit maximization and consumer utility maximization constraints. Then, we propose a model-based heuristic approach based on the fixed-point iteration algorithm that finds an approximate equilibrium solution for the market. Numerical experiments on an intercity travel case study show how the regulator can optimize its decisions under different scenarios.
为全面掌握交通运输与区域社会经济发展国内外研究现状,关键技术以及主要学术成果、结论。本文从Web of Science数据库和CNKI数据库中选取近20年的文献,借助文献可视化软件VOSviewer对所有文献进行分析。结果表明美国、德国、法国在此方面研究起步较早;研究热点主要围绕交通运输与区域经济的协同发展、产业结构、产业集聚、耦合协调模型等方面展开。未来研究应结合大数据分析,聚焦具体区域交通运输与社会经济间耦合协调度,促进交通运输与区域经济协调发展。 相似文献