9.4. Types of Modal Split Models

Traditionally, the objective of transportation planning was to forecast the growth in demand for car trips so that investment could be planned to meet the demand. When personal characteristics were thought to be the most important determinants of mode choice, attempts were made to apply modal-split models immediately after trip generation. Such a model is called trip-end modal split model. In this way different characteristics of the person could be preserved and used to estimate modal split. The modal split models of this time related the choice of mode only to features like income, residential density and car ownership. The advantage is that these models could be very accurate in the short run, if public transport is available and there is little congestion. Limitation is that they are insensitive to policy decisions eg: Improving public transport, restricting parking etc. would have no effect on modal split according to these trip-end models.

Trip-end modal split models focus on predicting how many people will choose a particular mode of transport based on personal characteristics. These characteristics can include things like income or whether a person owns a car. The model is applied right after trip generation, which means it's used to analyze travel demand before considering the actual patterns of transport availability. This approach can be very precise, especially in situations where public transportation is efficient and the roads are not congested. However, the downside is that it doesn't consider broader transport policies—for example, if a city improves its bus system or limits parking, these models won't be able to adapt their predictions to account for those changes.

Imagine you are trying to predict how many of your friends will drive to a concert based solely on their income and whether they have a car. You might find a good estimate this way. However, if the concert organizers decided to offer free shuttle buses from several spots around the city, your initial predictions would likely be off because you didn't account for this new factor.

This is the post-distribution model; that is modal split is applied after the distribution stage. This has the advantage that it is possible to include the characteristics of the journey and that of the alternative modes available to undertake them. It is also possible to include policy decisions. This is beneficial for long term modelling.

Trip-interchange modal split models apply modal split analysis after assessing how trips are distributed. This model allows for a more comprehensive understanding of travel patterns as it factors in the features of the journey itself, such as the purpose of the trip and the different transport options available. Unlike trip-end models, interchange models can be influenced by changes in transportation policy or infrastructure, making them more adaptable and useful for long-term planning.

Think of planning a route to a new job. If you only look at who has a car and their income level, you might miss out on the subway line that just opened up near your office. But if you consider all options—like the subway, buses, and bike paths—your model is more accurate and can better predict how many of your coworkers will take which route based on convenience and new travel policies.

Mode choice could be aggregate if they are based on zonal and inter-zonal information. They can be called disaggregate if they are based on household or individual data.

Aggregate models look at travel patterns across broader areas—like zones within a city—allowing planners to see general trends and predict demand on a larger scale. In contrast, disaggregate models delve into more specific data, focusing on individual or household behaviors. While aggregate models provide a useful overall picture, disaggregate models offer more granular insights that can reveal how different factors impact travel choices for individuals or specific households.

Imagine trying to decide whether to open a new grocery store in a city. An aggregate model might show that the entire city has a high population and demand for groceries. However, a disaggregate model could reveal that certain neighborhoods have residents who prefer local farmers' markets instead, giving you a clearer picture of where your store might struggle or thrive.

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