The rapid advancement of Generative Artificial Intelligence (GenAI) in 2023 has catalyzed transformative shifts across various industries, including urban transportation planning. This study evaluates the applicability of Large Language Models (LLMs) in transportation decision-making, focusing on two hypotheses: (H1) out-of-the-box LLMs exhibit basic transportation knowledge and reasoning capabilities, enabling them to design and execute analytical workflows; and (H2) larger parameter models and fine-tuned models demonstrate superior accuracy and contextual understanding, outperforming smaller and general-purpose models. Using a three-level evaluation framework, we assessed GPT-4 and Phi-3-mini across (1) geospatial skills, (2) domain-specific transportation knowledge, and (3) real-world transport problem-solving in congestion pricing scenarios. Results confirm that while LLMs possess baseline geospatial and transportation reasoning abilities, their effectiveness varies by task complexity. GPT-4 outperformed Phi-3-mini across all evaluation levels, achieving 86% accuracy in GIS tasks, 81% in MATSim comprehension, and 91% in real-world transport decision support, while Phi-3-mini scored 43–72%. These findings highlight the advantages of larger models in structured decision-making tasks and their potential as analytical copilots for transportation planners. The study contributes to the ongoing scientific debate on the role of GenAI in transportation governance, reinforcing the need for fine-tuning and retrieval-augmented generation (RAG) to enhance LLM performance in structured analytics. Future research should explore newer LLMs, transport-specific fine-tuning, and hybrid AI architectures to improve AI-driven transportation planning and decision support.

Multiple Geographical Feature Label Placement (MGFLP) has been a fundamental problem in geographic information visualization for decades. Moreover, the nature of label positioning has proven to be an Nondeterministic polynomial-time hard (NP-hard) problem. Although advances in computer technology and robust approaches have addressed the problem of label positioning, the lengthy running time of MGFLP has not been a major focus of recent studies. Based on a hybrid of the fixed-position and sliding models, a Message Passing Interface (MPI) parallel genetic algorithm is proposed in the present study for MGFLP to label mixed types of geographical features. To evaluate the quality of label placement, a quality function is defined based on four quality metrics: label-feature conflict; label-label conflict; label association with the corresponding feature; label position priority for all three types of features. The experimental results show that the proposed algorithm outperforms the DDEGA, DDEGA-NM, and Parallel-MS in both label placement quality and computation time efficiency. Across three datasets, compared to Parallel-MS, running times decreased from 118.45 to 8.34, 45.98 to 3.51, and 20.01 to 0.43 min, with further reductions in label-label and label-feature conflicts.