Agent-Based Microsimulation Approach for Design and Evaluation of Flexible Work Schedules

Abstract

The policy of a flexible work schedule has been practiced for years in order to stimulate the redistribution of departure time of commuters. However, its potential influence on travelers’ departure time shifts is infrequently seen in existing studies. This study extended an agent-based positive departure time choice model to gain perspective on travelers’ dynamic reaction toward the flexible work schedule policy. Unlike most rational behavior models, the positive model emphasizes the bounded rationality in people's actual behavior and allows for heterogeneity among travelers. Dynamic traffic assignment is integrated with this proposed model to build up a feedback loop between individual choice (demand side) and network performance (supply side). Scenarios of different percentages of the population with a flexible work schedule are analyzed. It is found that travelers with flexibility in their work schedules tend to depart for work later to avoid peak periods in the morning. The average travel time in the network will decrease by at most 22% when 60% of the travelers have flexible work schedules.

Dynamic Routing of Heterogeneous Users After Traffic Disruptions Under a Mixed Information Framework

“…Modeling of agent level departure time choice under uncertainty has demonstrated that under high uncertainty, agents do not easily converge on an optimized equilibrium (Xiong and Zhang, 2013b). Research has demonstrated that flexible work schedules allow departure times to vary more greatly which reduces congestion in a transportation network (Zhu et al, 2015). The authors found that congestion could be reduced even when a relatively small percentage (10-20%) of road users had flexible work schedules.…”

Calibrating supply parameters of large‐scale DTA models with surrogate‐based optimisation

“…To investigate the impact of the increasing travel demand on existing and planned transportation infrastructures, Maryland State Highway Administration (SHA) has entrusted the University of Maryland (UMD) research team to develop several mesoscopic DTA models. The previous models, which were applied for the land development analysis and corridor management applications [62,63], have covered the inter-county connector (MD200) area, Montgomery County area etc. These models have proven to be a valuable tool in analysing dynamic route changes and corresponding congestion pattern changes.…”

“…work zones, incidents, and variable message signs. Besides, DTALite's agent-oriented design meets the ultimate goal of this large-scale simulation model; that is, to integrate agent-based travel behaviour model into DTA to make the analysis sensitive to the departure time choice, mode choice, and en-route diverting for a plenty of analyses [62,63]. This paper focuses on the calibration stage of this large-scale mesoscopic DTA model, and the integration with an agent-based behaviour model will be ready in the near future.…”