Better for Everyone: An Approach to Multimodal Network Design Considering Equity

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• 作者：Rosalia Camporeale^a ; ^{rosalia.camporeale@poliba.it} ; Leonardo Caggiani^a ; Achille Fonzone^b ; Michele Ottomanelli^a
• 关键词：Equitable transit ; Multimodal Network Design problem ; Fuzzy optimisation ; Multi criteria decision making
• 刊名：Transportation Research Procedia
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：19
• 期：Complete
• 页码：303-315
• 全文大小：699 K
• 卷排序：19

文摘

We propose a formulation of the Network Design Problem (NDP) to support transport planners in dealing with multimodal networks in contexts characterised by different (and sometimes conflicting) interests and limited resources in a transparent way. We expect that the implementation of the method can increase the acceptability of transport schemes.The proposed formulation expands the scope of traditional NDP approaches: firstly, it takes public transit into account alongside private transport. Then, it considers the relevance of equity among other planning goals, enabling the achievement of solutions with a fair distribution of transport impacts (benefits and costs) among the users. Finally, it proposes the conjoint use of fuzzy and rigid goals and constraints to improve the quality of the solutions.Equityis defined as the mode-specific relative variation of the overall mobility between Origin-Destination (OD) pairs. We propose two specifications ofthe equitable NDP: one uses a crisp approach, with objective function equal to the overall network cost. The other is a fuzzy maximisation of the level of satisfaction generated by a certain network configuration. The level of satisfaction depends on the extent at which a given solution achieves private and public transport equity and overall network cost targets.We illustrate the approach in the case of a signal time planning problem in a small network. The evaluation of the performance of crisp and fuzzy optimisation shows that the former approach providesbetter solutions to private transport and vice versa. We propose that, when using fuzzy optimisation, the decision maker should evaluate a set of nearly-optimal solutions selected on the basis of Pareto optimality.