THE CLO3 was comprised of this major topic:

Study Guide # 3: Introduction to Travel Demand Forecasting (Classic Four- Step Forecasting Model)

OVERVIEW

This guide will help you recognize the most important aspect of transportation planning and the important aspects of data collection.

LEARNING OBJECTIVES

At the end of this module, you should be able to:

1. Determine the appropriate survey design, household data collection, and data analysis for your proposed development.

LEARNING ACTIVITY

TRAVEL DEMAND MODELING

For this topic, I want you to read the Chapter 5 Travel Demand Modeling by Tom V. Mathew and K V Krishna Rao (see uploaded pdf file).

DATA COLLECTION

For this topic, I want you to read the Chapter 6 Data Collection by Tom V. Mathew and K V Krishna Rao (see uploaded pdf file) and accomplish the following:

 Prepare a map of your study area and identify the appropriate traffic survey for your approved development.

Module 3: TRAVEL DEMAND MODELING AND DEMAND COLLECTION

Modeling is an important part of any large-scale decision-making process in any system. There are large number of factors that affect the performance of the system. It is not possible for the human brain to keep track of all the player in system and their interactions and interrelationships. Therefore we resort to models which are simplified but complex enough to reproduce key relationships of reality. Modeling could be either physical, symbolic, or mathematical In physical model one would make a physical representation of reality. For example, model aircraft used in wind tunnel is an example of physical models. In the symbolic model, complex relations could be represented with the help of symbols. Drawing a time-space diagram of vehicle movement is a good example of symbolic models. Mathematical model is the most common type when one could represent highly complex relations with the help of variables, parameters, and equations. Newton’s equations of motion or Einstein’s equation E = mc^2, can be considered as examples of mathematical model. No model is a perfect representation of the reality. The important objective is that models seek to isolate key relationships, and not to replicate the entire structure. Transport modeling is the study of the behavior of individuals in making decisions regarding the provision and use of transport. Therefore, unlike other engineering models, transport modeling tools have evolved from many disciplines like economics, psychology, geography, sociology, and statistics.

Travel demand modeling aims to establish the spatial distribution of travel explicitly by means of an appropriate system of zones. Modeling of demand thus implies a procedure for predicting what travel decisions people would like to make given the generalized travel cost of each alternatives. The base decisions include the choice of destination, the choice of the mode, and the choice of the route. Although various modeling approaches are adopted, we will discuss only the classical transport model popularly known as four-stage model(FSM).

For another topic, the four-stage modeling, an important tool for forecasting future demand and performance of a transportation system, was developed for evaluating large-scale infrastructure projects. Therefore, the four-stage modeling is less suitable for the management and control of existing software. Since these models are applied to large systems, they require information about travelers of the area influenced by the system. Here the data requirement is very high, and may take years for the data collection, data analysis, and model development. In addition, meticulous planning and systematic approach are needed for accurate data collection and processing. This chapter covers three important aspects of data collection, namely, survey design, household data collection, and data analysis. Finally, a brief discussion of other important surveys is also presented.

Module 3: TRIP GENERATION

The decision to travel for a given purpose is called trip generation. These generated trips from each zone is then distributed to all other zones based on the choice of destination. This is called trip distribution which forms the second stage of travel demand modeling. There are a number of methods to distribute trips among destinations; and two such methods are growth factor model and gravity model. Growth factor model is a method which respond only to relative growth rates at origins and destinations and this is suitable for short-term trend extrapolation. In gravity model, we start from assumptions about trip making behavior and the way it is influenced by external factors. An important aspect of the use of gravity models is their calibration, that is the task of fixing their parameters so that the base year travel pattern is well represented by the model.

It is evident in this topic that I apply travel demand forecasting computation and analysis.

Module 3: MODAL SPLIT

The third stage in travel demand modeling is modal split. The trip matrix or O-D matrix obtained from the trip distribution is sliced into number of matrices representing each mode. First the significance and factors affecting mode choice problem will be discussed. Then a brief discussion on the classification of mode choice will be made. Two types of mode choice models will be discussed in detail. ie binary mode choice and multinomial mode choice. The chapter ends with some discussion on future topics in mode choice problem.

It is evident in this topic that I apply travel demand forecasting computation and analysis.

Module 3: TRIP ASSSIGNMENT

The process of allocating given set of trip interchanges to the specified transportation system is usually referred to as traffic assignment. The fundamental aim of the traffic assignment process is to reproduce on the transportation system, the pattern of vehicular movements which would be observed when the travel demand represented by the trip matrix, or matrices, to be assigned is satisfied. The major aims of traffic assignment procedures are:

1. To estimate the volume of traffic on the links of the network and obtain aggregate network measures.

2. To estimate inter zonal travel cost.

3. To analyze the travel pattern of each origin to destination(O-D) pair.

4. To identify congested links and to collect traffic data useful for the design of future junctions.

It is evident in this topic that I apply travel demand forecasting computation and analysis.

One concrete application of this third-course learning objective would be in terms of VOLUME STUDY. In particular, the Average Annual Daily Traffic (AADT). The terminology refers to the average of 24-hour counts collected every day of the year. In this particular work, we were tasked to determine the AADT on an urban road, particularly in Elias Angeles Street, Naga City that has the volume distribution characteristics shown in the tables that the class discussed. At first, it was quite challenging because I don't know how to find the date, but in the end, I was able to find out which is which and be able to realize that it's just easy to find the data given that there are many formulas to be familiarized.   

As I delved deeper into travel demand forecasting, I began to recognize the challenges inherent in the process. Uncertainties related to future changes in population, technology, and travel preferences make it challenging to accurately forecast travel demand. Additionally, the reliance on historical data and assumptions can introduce biases and limitations to the forecasts. It became evident that forecasting is not a perfect science but rather a useful tool that provides insights and guidance, while acknowledging the inherent uncertainties.

Moreover, I realized the need to consider the broader context and implications of travel demand forecasting. Forecasts not only shape transportation infrastructure but also influence land use decisions, environmental impacts, and social equity considerations. The interplay between travel demand and various socio-economic factors requires careful consideration to ensure that the forecasts support sustainable and inclusive development.