7.2.1. Types of Trip

Some basic definitions are appropriate before we address the classification of trips in detail. We will attempt to clarify the meaning of journey, home based trip, non home based trip, trip production, trip attraction and trip generation.

This chunk introduces fundamental terms used in trip generation studies. A 'journey' is defined as the movement from one place (origin) to another (destination). A 'trip' is considered an outward journey and its return. If one of the locations (either origin or destination) is the traveler's home, it's termed a 'home based trip'. Conversely, trips that neither start nor end at home are called 'non home based trips.' 'Trip production' refers to the total number of trips originating from a particular area, while 'trip attraction' relates to the number of trips that end in that area. Finally, 'trip generation' encompasses the overall concept of calculating the total trip starts and ends in a given zone.

Think of a student going to school. The journey from their home to school illustrates a journey. If the student also returns home, this creates a trip. If the student decides to go to a friend's house instead of going home after school, that would be a non home based trip.

Trips can be classified by trip purpose, trip time of the day, and by person type.

This chunk explains that trips can be categorized in various ways. First, they can be classified based on their purpose; examples include trips for work, education, shopping, recreation, and others. Work and education trips are often referred to as 'mandatory trips' because they are usually necessary, whereas shopping and recreational trips are termed 'discretionary trips' because they are optional. The text states that approximately 80-85% of trips are home based, while non home based trips are less common and often not separately analyzed. Secondly, trips can be organized by the time they occur, typically divided into peak and off-peak trips. Finally, trips can also be classified based on the profile of the individual traveling, such as income level, vehicle ownership, and household size, as these attributes significantly influence travel behavior.

Imagine you're planning a day out. Your trip to your job would be a mandatory trip, while your visit to the mall for shopping is a discretionary trip. If you go during rush hour, that would be a peak time trip, but if you go in the afternoon when it's less crowded, that's an off-peak trip.

If either origin or destination of a trip is the home of the trip maker then such trips are called home based trips and the rest of the trips are called non home based trips.

This chunk differentiates between home-based and non home-based trips. Home-based trips are defined as those that either start or end at the traveler's home. This is significant because home based trips represent a large proportion of trip generation data. Understanding the dynamics of these trips helps in transportation planning and urban development, ensuring facilities cater to where most trips are originating. Non home-based trips, while less frequent, still play an important role in overall traffic patterns and should not be overlooked.

Consider a teacher who drives from her home to her school (a home-based trip) and then decides to meet a friend for coffee before returning home (the coffee shop visit was a non home-based trip). The trips show how many daily movements involve returning home versus those that don't.

The trips can be classified based on the purpose of the journey as trips for work, trips for education, trips for shopping, trips for recreation and other trips.

Here, the section identifies several purposes that can inform trip classification. Trips can be separated based on why they are undertaken. The most common categories include work trips (traveling to one’s job), education trips (attending school), shopping trips (visiting stores), and recreational trips (such as going to parks). This classification is essential, as trip generation models tend to be more accurate when they consider the trip's purpose separately, allowing for better predictions of traffic flows in urban areas.

Imagine a person’s daily routine: they drive to work, go to class in the evening, run errands on the way home, and visit a friend for leisure. Examining these trips through the lens of purpose can provide insights into traffic patterns and infrastructure needs.

The second way of classification is based on the time of the day when the trips are made. The broad classification is into peak trips and off peak trips.

This chunk discusses how the time of day influences traffic patterns. Trips can be categorized into 'peak' and 'off-peak' periods. Peak trips occur during busier travel times, such as early mornings or late afternoons, typically corresponding with work and school start and end times. In contrast, off-peak trips happen when there is less traffic, such as mid-morning or early afternoon. Understanding these patterns helps city planners anticipate congestion and strategically manage road capacity and public transport schedules.

Think of rush hour in a city; the roads are packed with commuters heading to work or school. Conversely, a leisurely Sunday drive in the afternoon would be considered an off-peak time, when traffic is lighter and movements smoother.

The third way of classification is based on the type of the individual who makes the trips. This is important since the travel behavior is highly influenced by the socio economic attribute of the traveler and are normally categorized based on the income level, vehicle ownership and household size.

Finally, this chunk emphasizes that the characteristics of the individuals making the trips can also lead to classification. Factors such as income level, vehicle ownership, and household size can all significantly alter travel behavior. For instance, higher income might correlate with more vehicle ownership, influencing the number of trips taken. By analyzing these variables, transportation models can better predict how different segments of the population engage with transport networks, leading to more effective public planning.

Imagine two different families: one with two cars and two working adults, and another with limited income and only one vehicle. The former family is likely to take more trips due to better access and resource availability, influencing traffic patterns significantly compared to the latter family, which may rely on more public transport or fewer trips overall.

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