Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability [chapter]

Atousa Tajaddini, Geoffrey Rose, Kara M. Kockelman, Hai L. Vu
2020 Transportation Systems for Smart, Sustainable, Inclusive and Secure Cities [Working Title]  
Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more
more » ... te, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM's abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making. 2 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 agentbased 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 in predicting individual travel movements can be evaluated from two perspectives Figure 1. Components of activity-based travel demand models. 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 ABM type + year of proposal Examples Model limitations Constraint-based models 1967 PESASP [12] Consider only individual accessibility, rather than household-level accessibility Some system features, like open hours and travel times, are considered fixed [11] CARLA [13] BSP [14] MAGIC [15] GISICAS [16] Utility maximizationbased models 1978 Portland METRO [17] • Assume that all decision-makers are fully rational utility maximizers which are not realistic in practice [10] • Unable to reflect latent behavioral mechanisms in the decision processes [11] San Francisco SFCTA [18] New York NYMTC [19] Columbus MORPC [20] Sacramento SACOG [21, 22] CEMDAP [23, 24] FAMOS [25] CT-RAMP [26] Computational process models 2000 ALBATROSS [27, 28] Focus more on scheduling and sequencing of activities than the underlying rules in decision-making [11] TASHA [29, 30] ADAPTS [31-33] Feathers [34] Agent-based modeling 2004 ALBATROSS [27, 28] • High computational complexity Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx.doi.org/10.5772/intechopen.93827 Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx.doi.org/10.5772/intechopen.93827 Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability DOI: http://dx. Recent Progress in
doi:10.5772/intechopen.93827 fatcat:7a7ygjcwgbh5he53dyd7gtelfu