**1. Introduction**

In recent years, behaviorally oriented activity-based travel demand models (ABMs) have received much attention, and the significance of these models in the analysis of travel demand is well documented in the literature [1, 2]. These models are found to be consistent and realistic in several fundamental aspects. They possess some significant advantages over the simple aggregated trip-based travel demand models [3]. To achieve this, ABMs consider the linkage among activities and travel for an individual as well as different people within the same household and place more attention to the constraints of time and space. In other words, these models are capable of integrating both the activity, time, and spatial dimensions. The comprehensive advantages of activity-based models in comparison to the trip-based models have been discussed in previous papers [4–8]. Activity-based models are suitable for a wider variety of transportation policies involving individual decisions such as congestion pricing and ridesharing. More especially, enabling the

relationship between activity and behavioral pattern of trip making is one of the main reasons for the shift from the aggregate-level in trip based models to disaggregate-level provided by ABMs [9].

Activity-based travel demand models (ABMs) can be classified into two main groups: Utility maximization-based econometric models and rule-based computational process models (CPM). Utility maximization-based econometric models apply different econometric structures such as logit, probit, hazard-based, and ordered response models. While the logit models rely on different assumptions about the distribution of the error terms in the utility functions, hazard-based models use the duration of activity based on end-of-duration occurrence to generate activity schedules [10]. Rule-based computational process models apply different sets of condition-action rules and focus on the implementation of daily travel and ordering activities to mimic individuals' behavior when constructing schedules. In addition to the aforementioned models, other approaches can be employed either in combination with these models or separately to develop activity-based models. Examples include agent-based and time-space prism approaches. While an agent-based approach allows agents to learn, modify, and improve their interactions with other agents as well as their dynamic environment, time-space prisms are utilized to capture spatial and temporal constraints under which individuals construct the patterns of their activities and trips. **Figure 1** exhibits critical elements of ABM such as activity generation, activity scheduling, and mobility choices. It also provides a comparison among the notable existing travel demand models regarding their different elements. The development of activity-based travel demand models has been reviewed comprehensively in previous studies [10, 11]. **Table 1** provides a summary of the literature on the evolution of these models over time by introducing the notable existing developed models and highlighting their limitations.

Despite the existence of many models as listed in **Table 1**, ABM's abilities in reflecting behavioral realism are still limited [40]. The capability of ABM models

**29**

*Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability*

CARLA [13] BSP [14] MAGIC [15] GISICAS [16]

San Francisco SFCTA [18] New York NYMTC [19] Columbus MORPC [20] Sacramento SACOG [21, 22]

CEMDAP [23, 24] FAMOS [25] CT-RAMP [26]

ADAPTS [31–33] Feathers [34]

Feathers [34] MATSim [35] TRANSIMS [36] SimMobilitiy [37] POLARIS [38]

**Examples Model limitations**

PESASP [12] Consider only individual accessibility,

Portland METRO [17] • Assume that all decision-makers

ALBATROSS [27, 28] Focus more on scheduling and

underlying rules in decision-making [11] TASHA [29, 30]

ALBATROSS [27, 28] • High computational complexity

values

and constraints

for the models [39]

rather than household-level accessibility Some system features, like open hours and travel times, are considered fixed [11]

> are fully rational utility maximizers which are not realistic in

• Unable to reflect latent behavioral mechanisms in the decision

• No transparency in the mechanical process of agents interacting with other agents and environment which depends on the parameters'

• Requires well-defined conditions

• Non-reproducibility due to the non-streamlined process of calibrating and imputing parameters

practice [10]

processes [11]

sequencing of activities than the

in predicting individual travel movements can be evaluated from two perspectives of input (data) and output (applicability). Activity schedules are an essential input into the ABM model. From an input point of view, the necessity of deriving activity schedules from dynamic resources together with their challenges will be reviewed. From the applicability perspective, the application of ABM output in integration with dynamic traffic assignment (DTA) models, transferring to a new geographical context, and why and how it is applied in transport planning management will also be discussed. To this end, the first part of this paper will review the new real-time data resources revealing the pattern and traces of traveler's mobility at a large scale and over an extended period of time. The big data enables new ABM models to reflect mobility behavior on an unprecedented level of detail while collecting data over a longer period (e.g., more than one typical day) would improve the behavioral realism in trip making [41]. The second part of this paper looks into the applicability of ABM models. This part includes (i) gap investigation

*DOI: http://dx.doi.org/10.5772/intechopen.93827*

**ABM type + year of** 

Constraint-based models

Utility maximizationbased models 1978

Computational process

Agent-based modeling

models 2000

2004

**Table 1.**

*ABM evolution over time.*

**proposal**

1967

**Figure 1.**

*Components of activity-based travel demand models.*


*Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx.doi.org/10.5772/intechopen.93827*

#### **Table 1.**

*Models and Technologies for Smart, Sustainable and Safe Transportation Systems*

gate-level provided by ABMs [9].

models and highlighting their limitations.

*Components of activity-based travel demand models.*

relationship between activity and behavioral pattern of trip making is one of the main reasons for the shift from the aggregate-level in trip based models to disaggre-

Activity-based travel demand models (ABMs) can be classified into two main groups: Utility maximization-based econometric models and rule-based computational process models (CPM). Utility maximization-based econometric models apply different econometric structures such as logit, probit, hazard-based, and ordered response models. While the logit models rely on different assumptions about the distribution of the error terms in the utility functions, hazard-based models use the duration of activity based on end-of-duration occurrence to generate activity schedules [10]. Rule-based computational process models apply different sets of condition-action rules and focus on the implementation of daily travel and ordering activities to mimic individuals' behavior when constructing schedules. In addition to the aforementioned models, other approaches can be employed either in combination with these models or separately to develop activity-based models. Examples include agent-based and time-space prism approaches. While an agent-based approach allows agents to learn, modify, and improve their interactions with other agents as well as their dynamic environment, time-space prisms are utilized to capture spatial and temporal constraints under which individuals construct the patterns of their activities and trips. **Figure 1** exhibits critical elements of ABM such as activity generation, activity scheduling, and mobility choices. It also provides a comparison among the notable existing travel demand models regarding their different elements. The development of activity-based travel demand models has been reviewed comprehensively in previous studies [10, 11]. **Table 1** provides a summary of the literature on the evolution of these models over time by introducing the notable existing developed

Despite the existence of many models as listed in **Table 1**, ABM's abilities in reflecting behavioral realism are still limited [40]. The capability of ABM models

**28**

**Figure 1.**

*ABM evolution over time.*

in predicting individual travel movements can be evaluated from two perspectives of input (data) and output (applicability). Activity schedules are an essential input into the ABM model. From an input point of view, the necessity of deriving activity schedules from dynamic resources together with their challenges will be reviewed. From the applicability perspective, the application of ABM output in integration with dynamic traffic assignment (DTA) models, transferring to a new geographical context, and why and how it is applied in transport planning management will also be discussed. To this end, the first part of this paper will review the new real-time data resources revealing the pattern and traces of traveler's mobility at a large scale and over an extended period of time. The big data enables new ABM models to reflect mobility behavior on an unprecedented level of detail while collecting data over a longer period (e.g., more than one typical day) would improve the behavioral realism in trip making [41]. The second part of this paper looks into the applicability of ABM models. This part includes (i) gap investigation in enriching ABMs by integrating time-dependent OD matrices produced by ABMs with dynamic traffic assignment; (ii) investigation of ABMs' applicability in transferring from one region to another; and (iii) enriching the capability of ABMs by moving beyond the transportation domain to other such as environment and management strategies.

The remainder of the paper is organized as follows. Section 2 introduces new data sources such as mobile phone call data records, transit smart cards, and GPS data where the influence of new data sources on the planning of activities, formation, and analysis of the travel behavior of individuals will be investigated. This section also introduces activity-based travel demand models, which generates activity-travel schedules longer than a typical day. Section 3 describes the existing experiences in transferring utility-based and CPM activity-based travel demand models from one geographical area to another. This section also reviews the integration of ABM models with dynamic traffic assignment and other models such as air quality models. The possibility of using activity-based models in travel demand management strategies with a focus on car-sharing and telecommuting are considered as examples. The last section concludes the paper and identifies remaining challenges in the area of activity-based travel demand modeling.
