Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area

Abstract

In their daily activity planning, travelers always considers time and space constraints such as working or education hours and distances to facilities that can restrict the location and time-of-day choices of other activities. In the field of population synthesis, current demand models lack dynamic consistency and often fail to capture the angle of activity choices at different times of the day. This article presents a method for synthetic population generation with a focus on activity-time choice. Activity-time choice consists mainly in the activity’s starting time and its duration, and we consider daily planning with some mandatory home-based activity: the chain of other subsequent activities a traveler can participate in depends on their possible end-time and duration as well as the travel distance from one another and opening hours of commodities. We are interested in a suburban area with sparse data available on population, where a discrete choice model based on utilities cannot be implemented due to the lack of microeconomic data. Our method applies activity-hours distributions extracted from the public census, with a limited corpus, to draw the time of a potential next activity based on the end-time of the previous one, predicted travel times, and the successor activities the agent wants to participate in during the day. We show that our method is able to construct plannings for 126k agents over five municipalities, with chains of activity made of work, education, shopping, leisure, restaurant and kindergarten, which fit adequately real-world time distributions.

Modeling Visit Potential to Predict Hotspots of a Future District

“…The transportation simulation is an activity-based model with a dynamic traffic assignment; during the day an agent participates in different activities, with predetermined location and purpose, and the choice of departure time and mode of transport is estimated relative to traffic condition, personal routing behavior, and the individual possibility to adapt their hours [44]. In this study, agents with leisure and shopping trip purposes in their daily planning are proposed an alternate location inside the new district to conduct these specific activities, and we look for what factors can be determinant in a decision that triggers a change of location that favors visitation of the new district.…”

“…The current inhabitant of a 15 min catchment area including five cities around the district and future worker originating from outside this area of influence shown in yellow in Figure 1. The methodology to generate activity plans for the population within the catchment area can be found in [44]. This population of 126,000 agents encompasses residents, externals (workers and students), and other potential visitors to the LaVallée district.…”

“…The travel supply is made of the road network and facilities location. We build daily activity planning for a set of agents (a synthetic population), and calculate the temporal evolution of planned trips; that is to say that the predicted path of people during a typical day as well as their mode of travel [44].…”

“…It is open-source and widely used to implement large-scale activity-based modeling. Therefore, the method to build the daily planning (chain of activities) of each agent as well as the location and time of departure is described in [44,47]. Trip purposes included in this study are work, education, shopping, leisure (including restaurant), and kindergarten.…”

“…There are two aspects that need to be validated in the proposed method: the trip simulation over a day, with the daily planning of agents, and the urban hotspots resulting from the Visit Potential Model. The transportation simulation accuracy was previously evaluated in [44,48]. It provides result coherent with the travel census of the department of interest [43] in term of activity and travel mode.…”