Transit Route Network Design for Low-Mobility Individuals Using a Hybrid Metaheuristic Approach

Abstract

This paper follows the previous effort of authors and builds the model of transit route network design for low-mobility individuals, proposing an appropriate solution methodology. Firstly, a desired objective, whose priority is to meet transit demands of low-mobility individuals followed by those of general public, is presented to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs. Then, a hybrid metaheuristic approach based on ant colony and genetic algorithms is formulated to solve the proposed model in accordance with current conditions (i.e., existing routes that may need to undergo configuration adjustments to different degrees). Finally, the case study of Wenling is presented to highlight the performance and benefits of the proposed model and solution methodology.

Design and optimisation of multimodal traffic strategy for low‐mobility individuals

“…The developed heuristic algorithm was developed in MATLAB software and ran on a PC with an Intel 32 GHz processor using Windows 10 operating system. The feasibility of the proposed strategy and its optimisation were practically verified on the multimodal traffic network of Wenling using the data obtained in the previous studies of the authors [1, 3, 31].…”

“…However, only a few of the existing multimodal traffic strategy optimisation models have been focused on the LMIs. Zhang et al [3] stated that a multi‐objective non‐linear mixed‐integer model could consider the benefits of the LMIs by adopting the weights related to the travel cost of different modes. Therefore, this paper forms a multi‐objective optimisation function to describe the optimisation model of the MTSLMI and sets reasonable weights according to the optimisation goal of this paper and weight analysis presented by Fan and Machemehl [29] and Zhang et al [3].…”

“…The traffic volumes of the walking, non‐vehicle‐based transportation and travelling by private car were obtained by assigning the actual multimodal travel demand matrixes (259 × 259) of different groups to different links [31]. These matrices were equal to the multimodal origin–destination trips of different groups divided by the proportion of respondents provided in the previous survey [1, 3]. Some of the variables and parameters' values obtained according to the actual‐state optimisation requirements by (6), (12)–(14) are given in Table 4.…”

“…older adults, LI group and individuals with disabilities) have different difficulties in the multimodal traffic; for instance, older adults need to face frequent transit transfers and long walking distance, LI group usually have long‐distance trips, while individuals with disabilities are unable to access barrier‐free constructions well. Therefore, after building a transit route network model for LMIs [3], the design and optimisation of the multimodal traffic strategy for the LMIs (MTSLMI) are introduced to form a series of papers related to the LMI travel.…”

Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review

“…This approach has been implemented in a populous city. Finally, Zhang et al (2020) puts forward a model of transit route network design for low-mobility individuals and proposes a solving methodology. The objective function prioritizes the transit demands of low-mobility individuals followed by those of general public, and aims to minimize the weighted sum of direct traveler, transfer, and unsatisfied demand costs.…”