The presentation focuses on optimizing the port placement and vehicle management for the Tsuku-Chari shared bicycle system in Tsukuba City, aiming to improve revenue structure and eliminate deficits.
- The Tsuku-Chari shared bicycle project is currently operating under a deficit.
- The financial reports from fiscal years 2021 to 2024 indicate consistent losses, with an annual deficit of approximately 763,095 yen (~2,000 yen/day).
- The goal of the study is to eliminate this deficit through cost-efficient operational improvements.
- The main expenses come from contract fees, including system management and bicycle redistribution.
- Despite increasing revenues, the project remains in deficit, mainly due to:
- High costs of bicycle reallocation.
- Port relocation and installation expenses.
- Costs associated with adding new bicycles.
- The project plans a full-scale operation starting from October 2024, with an expansion of ports and bicycles, requiring sustainable financial strategies.
- Instead of adding new ports, the strategy suggests relocating existing ports at zero cost whenever possible.
- Minimize installation costs by using low-cost or existing infrastructure.
- Reduce operational expenses by minimizing unnecessary bicycle movements.
- Optimize the frequency of redistribution to lower costs while maintaining usability.
- Zero-cost measures, such as port relocations and optimized bicycle reallocation, are prioritized to reduce operational expenses and eliminate financial losses.
- The study aims to find a balance between service availability and cost efficiency.
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Port Density: We incorporated variables related to port density within specific buffers (e.g., the number of ports within certain distances). This variable, added by ZHENG, plays a crucial role in understanding the concentration of share-cycle stations and user access points.
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Weather Data: Weather data was integrated into the model, an improvement led by Inaba-san, to account for external factors affecting bicycle usage patterns.
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Excess-Zero Problem: Watanabe-san addressed the issue of excess zeros in the data, optimizing the model to handle cases where the demand for bicycles was zero at certain locations or times.
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Human Flow Data: Additional variables, such as human flow within a 500-meter mesh, were included by ZHENG to enrich the explanatory power of the model.
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Reanalysis with XGBoost: Li reanalyzed the dataset using XGBoost, improving the prediction accuracy by better capturing nonlinear relationships between the explanatory variables.
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Transformer Model Construction: Li also worked on building a Transformer-based model as one of the candidate models to further improve the predictive power.
- Port Data Organization: We organized variables related to port locations, such as land use type, distance to the nearest train station or bus stop, population data, and human flow data. This process ensures a more structured approach to predicting where share-cycle stations should be placed.
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Bicycle Data: Data on the number of available bicycles (collected from 2023.08 to 2024.10) was obtained from Tanaka-san, forming the basis for analyzing current usage patterns and vehicle distribution.
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Bicycle Usage Data: Inaba-san worked on organizing the data related to bicycle usage frequency, a key factor in understanding station demand and usage trends.
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Vehicle Data Processing: ZHENG contributed by analyzing the availability of bicycles at different times, focusing on empty-port scenarios and understanding the factors behind them.
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Potential Demand Analysis: Li prepared to analyze latent demand, with daily potential demand data being processed. ZHENG visualized the current empty port situations, making it easier to interpret usage trends.
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Demand vs. Supply Analysis: We identified the gaps between daily potential demand and the number of available bicycles, aiming to highlight mismatches between the number of vehicles and the actual demand.
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Scenario Proposals:
- Scenario 1: Adjust the number of redistributed bicycles based on the demand.
- Scenario 2: Propose the relocation of certain share-cycle stations.
- Scenario 3: Adjust the frequency of vehicle redistribution operations.
- Scenario 4: Add more share-cycle ports, bicycles, and racks to meet increasing demand.
The integration of AI technologies like XGBoost, Catboost and Transformer-based models plays a central role in addressing complex urban mobility challenges. Through advanced data analysis, these models can predict user demand patterns, optimize vehicle redistribution strategies, and ensure that share-cycle stations are strategically positioned throughout Tsukuba. This data-driven approach not only enhances the efficiency of the share-cycle system but also significantly improves user accessibility and satisfaction.
To support this initiative, automated data scraping is used to collect real-time information from relevant sources such as share-cycle usage data, traffic conditions, and user behaviors. By employing a custom test.py file, we can automate the data collection process from official websites and APIs, ensuring that the most up-to-date information is always available for AI model training and evaluation. This method allows for continuous monitoring and adjustment of AI predictions based on real-time trends, leading to more responsive and adaptive urban infrastructure planning.
This project exemplifies the application of AI in real-world settings, where data-driven solutions directly contribute to enhancing public transportation systems and creating smarter, more efficient urban environments. The success of these technologies in Tsukuba's share-cycle program highlights the potential for similar approaches to revolutionize mobility in other cities.
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