GenAI to Optimise Train Network Operations

Understanding passenger behaviour and optimising operations are essential for providing superior service and boosting profitability. GenAI offers powerful tools for simulating and analysing user journeys, revealing invaluable insights that can drive effective marketing strategies and efficient train network operations. This blog explores how GenAI's capabilities can transform the way we manage train networks, with a focus on the geographical distribution of users and purchasing behaviours.

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How GenAI simulates user journeys

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GenAI utilises advanced machine learning algorithms to simulate user journeys and analyse large datasets efficiently. Here's how it works:

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1. Data collection: First we aggregate data from various sources, including ticket sales, travel history, and user demographics.

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2. Journey simulation: By simulating potential journeys based on collected data, GenAI can predict future travel behaviours and identify trends.

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3. Pattern recognition: The AI system recognises patterns in the data, such as peak travel times, common routes, and frequent destinations.

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4. Predictive analysis: GenAI's predictive capabilities allow for forecasting demand, enabling train operators to optimise schedules, manage capacity, and enhance overall service.

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Use case

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Reinforcement Learning (RL) is a subset of machine learning where an agent learns to make decisions by taking actions in an environment to maximise a cumulative reward. RL plays a pivotal role in strengthening the capabilities of GenAI by providing a robust framework for learning through interaction and optimising decisions over time.

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In the context of simulating user journeys, RL is a powerful tool to model and predict passenger behaviours in a train network, enabling operators to optimise service and marketing strategies effectively.

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To simulate user journeys using RL, we need to define the following components:

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1. Environment: The train network, including stations, routes, and schedules.

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2. State: The current context of the user, including location, time, ticket type, and travel history.

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3. Actions: Possible choices the user can make, such as selecting a train route, buying a specific ticket type, or travelling at a particular time.

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4. Reward: Feedback based on the outcome of the action, such as reduced travel time, cost savings, or increased comfort.

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Example scenario: Commuter journey optimisation

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Let's consider an example where we use RL to simulate and optimise the journey of a commuter, who travels from a suburban area to the city centre.

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1. Define the environment:

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• The train network with nodes representing stations and edges representing routes.

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• Schedules and capacities of trains on different routes.

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2. Set the initial state:

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• User's current location (suburban station).

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• Time of day (e.g., 7:00 AM).

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• User's historical travel data and ticket preferences.

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3. Action space:

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• Choice of train routes.

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• Decision to buy a single ticket, return ticket, or season pass.

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• Time of travel (current time or delayed departure).

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4. Reward function:

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• Positive rewards for actions leading to reduced travel time, lower cost, or increased convenience.

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• Negative rewards for delays, higher costs, or overcrowding.

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Simulation process

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1. Initialisation:

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• The user is at the suburban station at 7:00 AM, needing to reach the city centre by 8:00 AM.

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2. Agent decision:

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• The RL agent evaluates possible actions (e.g., taking the express train, choosing a less crowded but slower train etc.).

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3. Action execution:

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• The user selects a train route based on the RL agent's policy.

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4. Reward feedback:

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• User's travel experience is evaluated: Did he arrive on time? Was the train crowded? How much did the ticket cost?

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• The reward is calculated based on these factors.

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5. Policy update:

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• The RL agent updates its policy to favour actions that resulted in higher rewards, learning to make better decisions over time.

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Benefits of using RL for journey simulation

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• RL can adapt to changing conditions in real-time, such as delays or changes in passenger volume.

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• By learning individual user preferences, RL can provide personalised journey recommendations that enhance user satisfaction.

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• RL can help in optimising train schedules, route planning, and resource allocation based on predicted user behaviour.

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• Insights gained from RL can inform targeted marketing campaigns, such as promotions for specific ticket types or travel times.

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Applying GenAI to user locations

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Geospatial data analysis is crucial for train operators to understand where their users live and travel. GenAI can be effectively applied to map user locations, providing insights that drive operational efficiency and targeted marketing strategies.

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By analysing where users live, operators can predict travel patterns and peak times, enabling better resource allocation and scheduling. The maps below provide a visual representation of the busiest train stations on a Monday morning and a Friday morning. The maps use geohashing, a method for encoding geographic coordinates into a short string of letters and digits, to highlight the stations with the highest demand across different regions.

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Identifying areas with high demand helps train operators in planning new routes, increasing train frequency, and improving station facilities in high-demand areas.

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The second map illustrates the types of tickets purchased by users in different regions. This map categorises ticket purchases, such as 5-day travel tickets, flexible tickets, anytime return tickets, and young traveller passes, providing a granular view of consumer preferences.

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By visualising ticket type distribution, train operators can identify which products are most popular in specific regions. For example, areas with a high concentration of 5-day travel tickets holders might indicate a commuter-heavy population. Also, this analysis provides insights for targeted marketing campaigns. Promotions can be tailored to encourage the purchase of less popular ticket types or to provide special offers for frequent travellers.

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Applications in marketing strategies

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The insights derived from GenAI analysis can significantly enhance marketing efforts:

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1. Personalised offers: Tailor promotions and discounts based on user preferences and travel habits. For instance, offer discounts on season passes to frequent single-ticket purchasers.

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2. Regional campaigns: Develop marketing campaigns that resonate with specific regions, such as promoting weekend getaways in areas with a high number of weekend travellers.

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3. Customer engagement: Use insights to engage with customers through personalised communication, improving customer loyalty and satisfaction.

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Enhancing train network operations

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Operational efficiency can be greatly improved by leveraging GenAI insights:

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1. Resource allocation: Allocate resources such as trains and staff based on predicted demand, ensuring optimal service during peak times.

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2. Route planning: Introduce new routes or modify existing ones based on user density and travel patterns, making the network more responsive to passenger needs.

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3. Capacity management: Manage train capacity effectively by understanding and anticipating passenger volume, reducing overcrowding and enhancing the travel experience.

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Conclusion

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GenAI's ability to simulate and analyse user journeys offers a transformative approach to managing train networks and creating marketing strategies. By identifying user's behaviour, train operators can make data-driven decisions that enhance service quality, operational efficiency, and customer satisfaction. GenAI is not just about keeping up with technology; it's about revolutionising the way we understand and adjust to the needs of train users.