Urban planning field trip: application of AI in route planning

Artificial Intelligence (AI) has revolutionised various fields, including urban planning and design. In this study, we focused on AI’s role in optimising travel routes. The AI tools used included Google Maps API, OpenStreetMap, and custom algorithms developed for route optimisation. These tools were essential in generating efficient and effective routes for our field trip.

The students were divided into 18 groups of three, with each group randomly assigned one of six route themes: Go for tasty dishes, Famous tourism spots, Leisure in parks, Suzhou Garden sightseeing, Shopping paradise, and Heritage sites exploration. These themes were chosen to address different urban planning challenges and to test the versatility of AI in route planning.

In the morning, the students attended a session led by Professor Hyung-Chul Chung and Xiaohan Yu, where they learned about using AI for travel route planning. The students then actively discussed on AI applications, route optimisation, and the use of travel logs and questionnaires with the instructors. 

Before starting the field study, students completed questionnaires to document their planned routes. During the field trip, they used multiple modes of transportation - walking, buses, bicycles, subways, and taxis - to evaluate the routes from different perspectives. They kept logs detailing traffic conditions, connectivity between destinations, accessibility, and issues encountered along the way.

The field trip was conducted in two sessions: one during off-peak hours and the other during peak hours. This structure enabled students to compare and evaluate the differences in route feasibility under varying traffic conditions. Detailed observations and data were collected during both sessions, which were later analysed to understand the impact of traffic conditions on route efficiency.

Each transportation mode used during the field trip was evaluated for its effectiveness in the context of the AI-generated routes. Walking provided a firsthand experience of the urban environment, while buses, bicycles, subways, and taxis offered different perspectives on route connectivity and accessibility. The advantages and disadvantages of each mode were discussed in relation to the AI-generated routes.

During the field trip, several interesting findings and challenges were encountered. For example, one group discovered that the AI-generated scenic route passed through a construction zone, highlighting the need for real-time updates in route planning. These case studies and anecdotes provided deeper insights into the effectiveness of AI in route planning.

Despite the successes, several challenges and limitations were identified during the field trip and data analysis. Issues with AI route accuracy, data collection limitations, and unexpected traffic events were among the key challenges faced. These findings will guide future improvements in AI route planning applications.

The AI-generated routes were compared with traditional route planning methods. The advantages of AI in terms of efficiency, accuracy, and adaptability to changing conditions were evident. However, traditional methods still offered valuable insights into local knowledge and human intuition in route selection.

In the following days, each student invited five additional participants to complete the same steps as mentioned above. Finally, each group compiled a report based on their field trip findings and the survey data provided by all additional participants.

Based on the field trip experience, recommendations for future studies include integrating real-time traffic data and developing more sophisticated AI algorithms. These improvements will enhance the accuracy and reliability of AI in route planning applications.