What is Demand Forecasting?
Public transport is the main carrier of transportation in large cities and is indispensable for millions of people in their daily lives. However, delivering public transport services effectively requires a complex planning and management process. At the heart of this process is demand forecasting. Demand forecasting is the process of accurately determining future passenger demand. This is done by analyzing information from various data sources and using various mathematical models.
Demand Forecasting Processes
Analysis of Historical Data: The first step in demand forecasting involves a detailed analysis of historical data. This data includes information such as passenger numbers, route preferences, and travel frequencies over the past years. This analysis helps us understand past trends and patterns.
Consideration of Demographic Factors: Demographic data includes factors such as the age distribution of the population, income level, job distribution. These factors can help us predict increased demand during certain hours or days.
Weather Conditions and Special Events: Weather conditions can affect demand, especially on rainy or snowy days. Special events, large mass events such as concerts, sporting events, or festivals can have significant impacts on demand.
Consideration of Seasonal Variables: Seasonal variables reflect the fact that demand can increase or decrease during certain periods, such as the summer and winter months.
Artificial Intelligence and Machine Learning: Artificial intelligence and machine learning are increasingly used in demand forecasting processes. These technologies help predict future demand by analyzing large data sets. Deep learning models in particular can improve the accuracy of forecasts.
Integration of Demand Forecasting and Route Planning
Demand forecasting provides an important way to make public transport services more effective and efficient. Here is how these two processes are integrated:
Optimized Routes: Demand forecasts identify which routes and times are busy. This information is used to optimize routes. More vehicles are allocated during peak periods and alternative routes are determined based on traffic congestion.
Dynamic Adjustment: Route planning is dynamically adjusted to respond to instant changes in demand. Especially thanks to smart mobile applications, the real-time demands of passengers are monitored, and routes are adjusted accordingly.
Increased Efficiency: Demand forecasting reduces the number of idle vehicles. This reduces fuel and labor costs while minimizing environmental impacts.
As a result, public transport demand forecasting is considered the heart of public transport services in cities. Accurately forecasting future passenger demand improves the accessibility of cities and contributes to making transportation more sustainable. Proper integration of these processes allows for continuous improvement of public transport services, which in turn helps cities achieve their sustainable transportation goals. This is why we must take demand forecasting, the heart of public transport planning, seriously and keep abreast of technological developments in this field.