Neural Network-Based Food Delivery Time Prediction System in Urban Areas

This project addresses the challenge of predicting urban food delivery times. Using three types of data—orders, restaurants, and delivery partners—through data preprocessing and feature engineering, it compares the performance differences between traditional machine learning models (such as random forests and gradient boosting trees) and neural network models to build a reliable delivery time prediction system. The aim is to enhance user experience, optimize platform scheduling strategies, and provide decision support for merchants.

In today's fast-paced urban life, food delivery has become an important part of people's daily lives. However, accurate prediction of delivery time has always been a problem plaguing delivery platforms and users. Factors such as traffic congestion, fluctuations in restaurant food preparation speed, and delivery riders' route choices all affect the actual delivery duration. Accurate delivery time prediction can not only improve user experience but also help platforms optimize scheduling strategies and enhance operational efficiency.

Core Dataset

The project uses three core data types to depict the delivery scenario:

• Order data: Includes basic information such as order creation time, estimated delivery distance, and order amount, reflecting the basic attributes of delivery tasks.
• Restaurant data: Covers information such as restaurant location, historical food preparation speed, and ratings, which affect the initial stage of delivery.
• Delivery partner data: Records dynamic information such as delivery riders' historical performance, current location, and number of concurrent orders, directly affecting delivery duration.

Data Preprocessing Strategy

• Time feature extraction: Decompose date and time into time slots (morning/noon/evening peaks), days of the week (workdays/weekends), and whether it is a holiday, etc., to capture periodic patterns.
• Outlier handling: Identify and process extreme values of delivery time to avoid misleading the model.
• Feature scaling and encoding: Standardize numerical features and one-hot encode categorical features to ensure fair contribution of features.

The project explores and compares two modeling approaches:

• Traditional machine learning models: Such as random forests and gradient boosting trees, which have fast training speed, strong interpretability, can automatically learn non-linear relationships, and are suitable for medium-scale data.
• Neural network models: Adopt a multi-layer perceptron structure, learn high-order combinations of features through hidden layers, have strong expressive ability, and are suitable for scenarios with large data volumes and complex feature interactions.

Through cross-validation, the prediction errors of different models are compared, and their respective advantages and limitations are analyzed to provide decision-making basis for actual deployment.

The value of accurate delivery time prediction for the food delivery ecosystem:

• User experience optimization: Establish reasonable delivery expectations, reduce anxiety, and improve satisfaction.
• Operational efficiency improvement: Platforms dynamically adjust order assignment strategies, balance regional delivery pressure, and improve overall efficiency.
• Merchant decision support: Restaurants optimize food preparation rhythm, reduce waiting time, and increase order turnover rate.

This project demonstrates the implementation path of machine learning in vertical fields: from business understanding, data collection, feature engineering to model selection and evaluation, it needs to be designed in combination with domain knowledge. Neural networks perform better in complex scenarios, but traditional models still have advantages in interpretability and deployment cost. In the future, more complex network structures (such as graph neural networks combined with spatio-temporal information) can be explored, or real-time traffic data can be introduced to further improve prediction accuracy.