Modelling Rail-Based Park and Ride with Environmental Constraints in a Multimodal Transport Network. (4th October 2018)

• Record Type: Journal Article
• Authors: Chen, Xinyuan; Kim, Inhi
• Other Names: Wang David Z. W. Guest Editor.
• Is Part Of: Journal of advanced transportation. Volume 2018(2018)
• Publication Date: 2018-10-04
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2018/2310905
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Environmental sustainability is a significant aspect in the sustainable development of modern urban cities, especially in the road transport system. As traffic demands increase, public transport requires more promotion to accommodate the increasing travel demands while maintaining the environmental quality. Public transport, however, is less attractive in vast suburb areas mainly due to its longer travel distance and waiting time. Therefore, this paper proposes a rail-based Park-and-Ride (RPR) scheme to promote public transport in the multimodal transport network. To remedy the heterogeneous distribution of vehicle pollutants in the network, regulations in environmental sensitive districts are required and studied in this paper. To quantitatively evaluate and analyse this joint RPR and environmental regulation strategy in multimodal transport systems, this paper develops an environmental constrained combined modal split and traffic assignment (EC-CMSTA) model. The proposed formulation adopts the concept of fix-point to reformulate the nonlinear complementarity conditions associated with the combined modal split and user equilibrium conditions, which is subsequently incorporated into a VI formulated nonlinear complementarity conditions associated with environmental constraints. The proposed VI formulation can handle a general constraint structure, which enhances the modelling adaptability and flexibility. The strictly monotone and Lipschitz continuity properties of this model are rigorously proved, giving rise to efficient algorithms for the model. A customized projection based self-adaptive gradient projection (SAGP) algorithm is then developed. Numerical studies demonstrate that the EC-MSTA model could enhance the behavioural modelling of network users' travel decisions and assist in quantitatively evaluating the effectiveness of RPR schemes and environmental regulations.