Mehran Fasihozaman Langerudi

Resolution of Activity Scheduling Conflicts: Reverse Pairwise Comparison of In-Home and Out-of-Home Activities

This paper proposes a new methodology for solving a pairwise comparison problem from a polychotomous classification. The proposed methodology reverses the pairwise coupling and employs the Bradley–Terry formulation for estimating pairwise comparison models. This methodology was implemented through relevant models in Agent-Based Dynamic Activity Planning and Scheduling (ADAPTS) and aims for solving pairwise activity conflicts. At one step of the framework, given the default algorithm, agents have to make a choice between in-home and out-of-home activities. At this time step, the reverse pairwise comparison models are called to investigate the agents’ resources and conditions for making the most probable decision about which activity to engage in. This work was conducted in conjunction with an earlier effort to incorporate in-home activities into ADAPTS, and the models proposed provide a behavioral decision-making process for this purpose.

Choice Set Imputation: Two-Step Weighted Stratified and Hazard-Based Approach

The problem of choice set formation for decision makers is an important subject in discrete choice modeling, especially when the choice set contains a large number of elemental alternatives. In general, the choice set of an individual could be a randomly sampled choice set; however, this is claimed to be a behaviorally unacceptable practice because of the fallacious assumption of individuals’ full knowledge of potential random choices. This issue brings up the need to devise methods to logically allocate credible choice alternatives for individuals. Although the use of these methods could be dependent on specific applications, this study attempted to identify the distinction between model estimation and prediction steps in the context of residential location choice modeling. From a theoretical point of view, the paper proposes a modified weighted stratified sampling approach that is an improved version of random sampling for model estimation. The approach is believed to be a better replicate of the universal choice set than other sampling methods, and it is capable of resulting in consistent estimates even with small sample sizes. The estimated model was applied in a simulation framework with a hazard-based imputed choice set approach for prediction.

Planning in-home activities in the ADAPTS activity-based model: a joint model of activity type and duration

ABSTRACT The role of in-home activities in the process of planning and scheduling of individuals’ daily activities has been traditionally ignored because of two reasons: (i) in-home activities are not directly involved with trips; and (ii) scarcity of data sources that provide required details on planning and scheduling of these activities. However, considering the interchangeable nature of out-of-home and in-home activities, and the significant effects that they have on each other, we argue that failing to incorporate in-home activities may result in overestimating frequency and duration of out-of-home activities, which may lead to inconsistent and unrealistic activity schedules. Recently, we have upgraded the ADAPTS activity-based model to account for planning and scheduling of both in-home and out-of-home activities. This paper aims to enhance the in-home activity planning module by modelling the type and duration of the in-home activities in a joint structure. To achieve this goal, using the American Time Use Survey data, we estimate joint discrete-continuous models, in which activity type and activity duration are estimated by a multinomial logit and a log-linear regression model, respectively. The joint structure of these two models is established using copula approach that captures the unobserved shared factors affecting the two activity attributes. The results indicate that the estimated joint models significantly outperform the independent models in terms of goodness-of-fit and prediction accuracy.

Individual trip rate transferability analysis based on a decision tree approach

ABSTRACT Transferring trip rates to areas without local survey data is a common practice which is typically performed in an ad hoc fashion using household-based cross-classification tables. This paper applies a rule-based decision tree method to develop individual-level trip generation models for eight different trip purposes as defined in the US National Household Travel Survey in addition to daily vehicle miles traveled. For each trip purpose, the models are obtained by finding the best fitted statistical distribution to each of the final decision tree clusters while considering the correlation between the trip rates for other trip purposes. The rule-based models are sensitive to changes in demographics. The performance of the models is then tested and validated in a transferability application to the Phoenix Metropolitan Region. These models can be employed in a disaggregate microsimulation framework to generate trips with different purposes at the individual or household level.