How do you use AI to improve your choice models?

1 What is AI and how does it relate to choice modeling?

AI is a broad term that refers to the ability of machines or systems to perform tasks that normally require human intelligence, such as learning, reasoning, and problem-solving. AI can be applied to various domains and disciplines, including transportation planning and choice modeling. AI can help you enhance your choice models by incorporating more complex and dynamic behaviors, capturing hidden patterns and relationships, and reducing data and model errors.

2 How can you use AI to improve your choice model structure?

One way to use AI to improve your choice model structure is to use machine learning techniques, such as neural networks, decision trees, or support vector machines, to identify the relevant attributes and alternatives that influence people's choices. These techniques can help you discover non-linear and interactive effects, account for heterogeneity and context, and avoid arbitrary assumptions and constraints. For example, you can use a neural network to model the utility function of each alternative as a function of multiple inputs, such as travel time, cost, mode, and socio-demographic characteristics.

3 How can you use AI to improve your choice model estimation?

Another way to use AI to improve your choice model estimation is to use optimization algorithms, such as genetic algorithms, simulated annealing, or particle swarm optimization, to find the optimal values of the model parameters that best fit the observed data. These algorithms can help you overcome the limitations of conventional methods, such as gradient-based or maximum likelihood methods, that may get stuck in local optima, require smooth and continuous functions, or have high computational costs. For example, you can use a genetic algorithm to search for the optimal values of the coefficients and thresholds of a nested logit model.

4 How can you use AI to improve your choice model validation?

A third way to use AI to improve your choice model validation is to use simulation techniques, such as agent-based modeling, cellular automata, or artificial neural networks, to generate synthetic data that mimic the real-world phenomena and test the performance and robustness of your choice model. These techniques can help you evaluate the sensitivity and reliability of your choice model, compare different scenarios and alternatives, and identify potential errors and biases. For example, you can use an agent-based model to simulate the behavior and interactions of individual travelers and their choices under different conditions and policies.

5 How can you use AI to improve your choice model application?

A final way to use AI to improve your choice model application is to use data mining techniques, such as clustering, classification, or association rules, to extract useful information and insights from your choice model results and data. These techniques can help you segment and profile your target population, identify the key factors and patterns that influence their choices, and provide recommendations and feedback for your planning and decision-making. For example, you can use a clustering technique to group your travelers into different segments based on their preferences and characteristics, and then use a classification technique to predict their mode choice for a given trip.

AI is a powerful tool that can help you improve your choice models in various ways. By using AI, you can enhance the structure, estimation, validation, and application of your choice models, and ultimately, improve your understanding and prediction of travel behavior and choice.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?