Coordinated Multimodal Transportation Infrastructure Planning in Megacities: An Agent-AI-Discrete Network Design Problem Approach Based on Chat-GPT

The Discrete Network Design Problem (DNDP) has mainly been used in unimodal infrastructure planning in small cities. This is because it does not consider differences in traveler mode choice. To address the challenges of multimodal transportation infrastructure planning in megacities, this study proposes a new Agent-AI DNDP (AA-DNDP) framework. AA-DNDP extends the traditional DNDP by incorporating mode-choice functionality. In each simulation iteration, the Agent-AI calls Chat-GPT via API, using the attributes of the origin and destination as prompts to generate a mode choice. Based on the selected transportation mode, the Agent-AI records the associated travel time and path. By aggregating the travel times and paths across a large number of Agent-AI simulations, two key indicators are derived: average using time (AUT) and path usage frequency (PUF). These metrics are then used to evaluate the suitability of different infrastructure planning strategies. Using the DNDP framework, a large number of multimodal transportation infrastructure planning schemes for the megacity are generated. By analyzing variations in AUT and PUF across these schemes, the most suitable planning configuration is identified. Chongqing was selected as the empirical case with which to evaluate the effectiveness of the proposed AA-DNDP framework. The results demonstrate that AA-DNDP can effectively assess the rationality of existing transportation infrastructure planning and identify theoretically optimal configurations based on AUT and PUF. The case study further confirms that AA-DNDP enhances the evaluation of network efficiency and facilitates coordinated multimodal infrastructure planning, offering a more practical and data-informed approach to improving transportation systems in megacities.