Sensitivity analysis of integrated activity-based model: using MATSim as an example

Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

“…The future work will be focused on developing an agent-based EV market model incorporating the utility function developed in this paper. In order to take into account both heterogeneity and spatial factors (e.g., neighbour effect), the EV market model needs to be coupled with a population synthesizer (Pritchard and Miller, 2012), a social network generator (Arentze et al, 2012), and an activity-based travel demand model (Horni et al, 2016;Zhuge et al, 2017). Specifically, population synthesizer is used to generate a synthetic population containing individuals and households, as well as their attributes (e.g., income and car ownership) (Pritchard and Miller, 2012;Zhuge et al, 2016a;, which can be used as the inputs of the MNL models to predict the weights of each factor; the social network generator is used to generate a population-wide social network (Arentze et al, 2012;, so that the three types of social influence can be quantified and the results can be further used as the inputs of the utility function; Activity-based travel demand model, which is used to simulate the daily travel of each individual in the population (Horni et al, M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT 2016), can be used to quantify the vehicle usage and environmental awareness (e.g., the total amount of vehicular emissions) by aggregating the micro-simulation results.…”

An agent-based spatiotemporal integrated approach to simulating in-home water and related energy use behaviour: A test case of Beijing, China

“…Activity-based travel demand model (Zhuge et al, 2019b), which is a typical bottom-up approach in transport studies, is used to simulate individual daily out-of-home activities (e.g., shopping) and the associated travels. See Rasouli and Timmermans (2014) for a recent review of the activity-based models.…”

“…The simulation of out-ofhome activities here is only used to generate time slots available for in-home activities (e.g., bathing), and the water and energy consumptions of out-of-home activities have not been included. MATSim (Multi-Agent Transport Simulation) (Horni et al, 2016), which is a typical activity-based travel demand model, is used here for the simulation. Water-Energy Use Behaviour Model (see Section 2.3): is used to simulate the waterenergy use behaviour of each individual in the synthetic population in second, with the appliance and time constraints (obtained from the appliance ownership and activity-based models above, respectively), resulting in spatial and temporal distributions of the water-Framework of the Agent-based Integrated Approach 2.2 Extended Activity-based Travel Demand Model (MATSim) for both In-Home and Out-of-Home ActivitiesGiven a synthetic population, an activity-based travel demand model essentially simulates how individual agents schedule their daily plans for a whole day(Zhuge et al, 2019b). A daily plan contains the information on how an agent performs its out-of-home activities (e.g., shopping and work)…”

Agent- and activity-based large-scale simulation of enroute travel, enroute refuelling and parking behaviours in Beijing, China

“…In many cases, activity-based model was also coupled with Dynamic Traffic Assignment (Peeta and Ziliaskopoulos, 2001), resulting in an integrated activity-based model able to simulate how people perform their daily activities and travel from one activity location to another through transport networks (e.g., road and public transport networks) (Zhuge et al, 2019c). The outputs of the integrated model include traffic/passenger flow and daily plans of each person which contain both activity and travel information.…”

“…However, Cadyts does not search for an optimal set of model parameters, and thus is not behaviourally sound. In response, this paper will try another Sensitivity Analysis (SA)-based calibration method, which is capable of searching for an optimal parameter combination in an efficient way, based on the results of parameter SA (Saltelli et al, 2008;Zhuge et al, 2019c).…”