Personalized route planning is becoming increasingly crucial in complex multimodal transportation systems. This study aims to provide personalized route recommendations by combining Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) mechanism. By integrating a multi-source urban mobility database—comprising real-time transit information, user profiles, and contextual road conditions—the research facilitates context-aware and preference-sensitive navigation. The objective is to precisely capture individual travel behavior and improve personal mobility through tailored, context-sensitive route planning.

Speeding has been a great concern around the world due to the occurrence and severity of road crashes. To evaluate the effectiveness of different penalty and camera-based enforcement strategies in curbing traffic offences by professional drivers, a stated preference survey approach is employed.

In the past decades, research confirms that shifting people from private cars to more sustainable modes delivers huge benefits for the health and prosperity of cities and their citizens. However, recent studies suggest a modal shift from shared travel to private cars during COVID-19 or post pandemic world. These findings may not be applicable to public transport-oriented cities with very low rates of private car ownership.

Due to the outbreak of COVID-19, substantial changes have been introduced to air travel, such as health control measures at airports. Back to the mid-1990s, air travel used to be very easy. Then, the 9/11 terrorist attack happened, which has caused long-lasting changes to the air travel experience. The history of airport security suggests that security attacks often prompted new security measures, similar to the COVID-19 security policies. It is likely that airports may introduce health screening procedures as routine operations based on the lesson learnt during the COVID-19 pandemic.

Quite often, newly introduced mobility services achieve significantly less impact than assumed. One of the main reasons for this frequent underperformance is that in many cases users of new technologies and services do not behave as expected. Therefore, it is important to consider human Factor perspective in digitized mobility development, i.e., passenger acceptance and behavior analysis can help improve the mobility product and service development, which is extremely important for minimizing undesired or even opposite effects of innovations.