marchev-science / case-public-transport-prediction

Case Study: Predicting Bus Arrival Times Using Public Transportation and Weather Data

Overview

This case study examines data provided for a public transport bus line that operates between four designated stops. The dataset includes information regarding the scheduled time of arrival at each stop, the actual time of arrivl at each stop, and the duration of stay at each stop, and corresponding weather data for these periods. The goal is to develop three prediction models capable of accurately predicting the arrival times of the bus at each of the three stops.

Data Description

The dataset consists of time-stamped records of bus arrivals and departures at four distinct stops, with information on the time taken to travel between these stops and the duration of stay at each stop. The data spans over a substantial time period, allowing for a robust analysis across various times of the day, week, and year. Additionally, the data provides weather conditions (temperature, humidity, precipitation, wind speed, visibility) for each corresponding time stamp, collected from a reliable weather data source.

Tasks

The task is to develop three predictive models with the objective of accurately forecasting bus arrival times at each of the three sequent stops (having the first one as a starting point). For this purpose, we will leverage the available data, including weather information, to generate predictive models. These models should take into account various factors, such as the time of day, the day of the week, the season of the year, and the weather conditions, as these can all impact bus transit times.

Methodology

Data Preprocessing

The first stage of the modeling process will involve cleaning and pre-processing the data. This will include handling missing values, dealing with outliers, and encoding categorical variables such as 'day of the week' or 'season'. Time-based features like 'hour of the day', 'day of the week', 'month', and 'season' will be extracted from the timestamp.

Feature Engineering

We'll generate new features that might influence the arrival times, such as the 'previous stop arrival delay' or the 'average delay for a specific time slot'. The weather data can also be transformed into more useful features like 'is_raining' (if precipitation > 0) or 'is_windy' (if wind speed exceeds a certain threshold).

Model Development

All three models will be evaluated on the basis of their prediction accuracy and computational efficiency. Techniques such as cross-validation and hyperparameter tuning will be used to optimize the model performances.

Model Evaluation

Models will be evaluated based on appropriate error metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), or Mean Absolute Percentage Error (MAPE). These metrics provide insights into the average error in predicting bus arrival times.

Expected Outcome

The expected outcome of this case study is the successful development of three predictive models that accurately forecast bus arrival times. The analysis should also provide insights into the significant factors that influence these transit times, and the role weather conditions play in public transportation efficiency. The final models, if successful, can be implemented in real-world applications, leading to improved scheduling and customer satisfaction for public transportation systems.

Further Steps

Once the models are developed and evaluated, we can conduct a residual analysis to ensure the assumptions of our models are met. We can also test the models on unseen data, if available, to ensure their robustness and reliability in real-world scenarios. It would also be interesting to see if incorporating additional data, like traffic information or special events, improves model performance.