Activity-based Microsimulation Research Articles

Project-based agent/activity microsimulation of travel demand: theory, experience & future prospects

This paper describes an agent- and activity-based microsimulation framework for modelling daily travel demand that addresses many of the weaknesses of conventional tour-based models currently in common use. The framework has been successfully applied in operational planning use in numerous Canadian cities and its international transferability has been demonstrated.

Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future.

Coupling Epidemiological Model within an Activity-based Travel Modelling System for Halifax, Canada

This study combines an epidemiological model (Susceptible-Infected-Recovered, S-I-R) with the Shorter-term Decisions Simulator (SDS), an activity-based microsimulation model developed for Halifax, Canada. The decision support tool assesses infected individuals’ travel behavior during the COVID-19 pandemic. The results of the S-I-R model reveal the rate of infection, p=0.39, the rate of recovery, q=0.34, and the reproduction number, R0=1.15 in Halifax region. In terms of activity participation, most infected individuals are found to participate in two activities (53%) followed by three activities (25%). Infected individuals share a similar type of proportion for work, shopping, and recreational type of activity (23%, 20% and 19%, respectively). In response to travel distance and travel duration for the infected individuals, the age group of 30 to 40 shows a higher density than others. This paper’s findings will help policymakers to develop policy interventions to deal with ongoing, emerging COVID-19 public health crisis.

Evaluating the Traffic and Emissions Impacts of Congestion Pricing in New York City

Traffic congestion is a major challenge in metropolitan areas due to economic and negative health impacts. Several strategies have been tested all around the globe to relieve traffic congestion and minimize transportation externalities. Congestion pricing is among the most cited strategies with the potential to manage the travel demand. This study aims to investigate potential travel behavior changes in response to cordon pricing in Manhattan, New York. Several pricing schemes with variable cordon charging fees are designed and examined using an activity-based microsimulation travel demand model. The findings demonstrate a decreasing trend in the total number of trips interacting with the central business district (CBD) as the price goes up, except for intrazonal trips. We also analyze a set of other performance measures, such as Vehicle-Hours of Delay, Vehicle-Miles Traveled, and vehicle emissions. While the results show considerable growth in transit ridership (6%), single-occupant vehicles and taxis trips destined to the CBD reduced by 30% and 40%, respectively, under the $20 pricing scheme. The aggregated value of delay for all vehicles was also reduced by 32%. Our findings suggest that cordon pricing can positively ameliorate transportation network performance and consequently, improve air quality by reducing particular matter inventory by up to 17.5%. The results might facilitate public acceptance of cordon pricing strategies for the case study of NYC. More broadly, this study provides a robust framework for decision-makers across the US for further analysis on the subject.

Evaluating Automated Demand Responsive Transit Using Microsimulation

Recent advancements in automated vehicle technology and the concurrent emergence of ride-hailing services have focused increasing attention on Automated Mobility-on-Demand (AMOD; a system of shared driverless taxis) as a potential solution for sustainable future urban mobility. However, the impacts of an unrestricted deployment of AMOD are as yet uncertain and likely to be context-specific; evidence with existing on-demand services suggests that they may lead to the cannibalization of mass-transit and increased traffic congestion. In this context, automated demand-responsive transit (also termed microtransit), which provides similar on-demand services (stop-to-stop or curbside) through higher capacity vehicles, may prove to be a promising substitute and/or complement. In this study, we evaluate the performance of such an automated demand response transit system (hereafter AMOD minibus) through agent-based simulations of the Singapore network. Towards this end, we extend SimMobility (an agent- and activity-based microsimulation laboratory) with the capability of modeling an AMOD minibus service including demand, supply and their interactions. On the demand side, we use an activity-based model system that draws on data from a stated-preferences survey conducted in Singapore. On the supply side, an insertion heuristic is applied to dynamically perform both the assignment of requests to vehicles and vehicle routing. Scenario simulations on the Singapore network (with an area-wide deployment of the AMOD services) indicate the potential benefits of an automated demand responsive transit service for local circulation, which can result in a reduction of Vehicle Kilometres Traveled of up to 50% (compared to the AMOD shared taxis) whilst satisfying the same demand, with a modest increase in average travel times.

Uncertainty analysis of an activity-based microsimulation model for Singapore

Transport models inherit uncertainties due to a variety of assumptions, inputs and their structural properties. With the increase in complexity of modern travel demand models, the uncertainty analysis becomes more important and it becomes a non-trivial procedure that requires a careful consideration for its investigation. This paper analyses the model uncertainty of the activity-based microsimulation (ABM) travel demand model including specification and parameter uncertainty. The ABM model predicts the entire day activity-travel schedule and was developed and calibrated using a variety of datasets from Singapore. The model is computationally heavy as it includes 22 sub-models, which follows multinomial and nested logit structure for different activity-travel decisions and in overall includes 817 parameters. The model specification uncertainty addressed in the study include simulation error and sample uncertainty, both measured by means of simulation techniques under various scenarios with running the entire model. The parameter uncertainty is estimated based on the simulation with sampling from a parametric multivariate distribution preserving the correlations between the sampled variables. The parameter uncertainty includes a sensitivity-based screening of the sub-models to identify the major contributors, followed by simulation runs of the entire model for the most influential parameters. The results showed that the order of magnitude of all considered kinds of uncertainty strongly depends on how frequently the alternative is predicted in the choice process. The parameter uncertainty is higher than the sampling uncertainty, and the sampling uncertainty is comparable with the simulation uncertainty. Moreover, this is the first study to compare the order of magnitude of the simulation, sampling and parameter uncertainties of an ABM model. The suggested method can be used to analyse both input and parameter uncertainties in computationally heavy models that have a hierarchical structure consisting of smaller sub-models. The uncertainty calculated for the model outcomes in this study will allow practitioners to choose a strategy for dealing with it.

Multi-level urban models: Integration across space, time and policies

Urban and regional models have been developed for different policy fields at different levels of spatial and temporal resolution. But it has become apparent that policies interact across space and time and need to be modelled together. The first urban and regional models were aggregate in space and comparative-static in time. More recently, new data sources and computing techniques have stimulated ever more disaggregation in space and time culminating in agent-based, activity-based microsimulation despite its significant even larger data needs, computing requirements and theoretical problems. This paper argues for models that are instead multi-level and multi-scale in space, time and subsystems. This paper starts with a brief history of urban models and the experience of the authors with the highly integrated urban microsimulation model ILUMASS. Based on this experience, it discusses the benefits and pitfalls of microsimulation and proposes a three-level model system of spatial development, ranging from the European to the local level. The paper closes with new challenges for urban models posed by climate change, energy scarcity, new social problems and new technologies and argues that they make multi-level, multi-scale models even more important and illustrates this by ongoing work with the multi-level model for cities in the Ruhr.

Enhanced Synthetic Population Generator That Accommodates Control Variables at Multiple Geographic Resolutions

Microsimulation models that simulate travel demand at the level of individual travelers have been gaining increasing interest among practitioners. Transportation planning agencies across the country are steadily migrating to activity-based microsimulation models, which provide considerable flexibility when testing policy scenarios. Generating a synthetic population is the first step in the application of any activity-based model system and has therefore been a topic of extensive research in the activity-based modeling arena. Several researchers have developed population synthesizers that can generate synthetic populations and can match household- and person-level constraints at a specified geographic resolution (e.g., a census block group). However, although information for some control variables may be available at the specified spatial resolution, information for other control variables may be available only at a more aggregate spatial resolution. Ignoring control variables at different levels of spatial resolution could result in the generation of a synthetic population that would not be representative of the underlying population. However, there has been limited progress in the development of synthetic population generators that are capable of accommodating control variables at multiple spatial resolutions. This paper proposes a robust approach to control for constraints at multiple geographic resolutions when generating a synthetic population. The method is an extension of the iterative proportional updating algorithm previously proposed and implemented by the authors. A case study demonstrating the efficacy of the enhanced algorithm is presented.

Activity-Based Travel Demand Modeling Framework FEATHERS: Sensitivity Analysis with Decision Trees

The decision tree technique is increasingly applied in activity-based travel demand modeling. It has the strength of representing the full complexity of interactions between different variables. However, this complexity often hinders an interpretation of the relative effects of these variables on the activity travel choice. In this study, a sensitivity analysis is performed on decision trees in FEATHERS, an activity-based microsimulation modeling framework, with the purpose of quantitatively measuring the relative effect of input variables involved in the given decision trees on the choice variable. The local and global sensitivity analysis approaches are investigated: ( a) a one-at-a-time approach that predicts the choice frequency distribution by varying selected input variables one after another and keeping all other variables as observed and ( b) the improved Sobol method, which evaluates the effect of an input variable while all other variables are varied as well. With the application of these two approaches to two representative decision trees concerning work-related activity (i.e., commute trip) choice and transport mode choice for work-related activities in the FEATHERS framework, consistent results concerning the key input variables for these two decision trees are derived, and some extra insights are gained from each of the approaches.

Activity-based Modeling and Microsimulation of Emergency Evacuations

Most existing emergency evacuation studies assume evacuees to evacuate from their residence locations. However, depending on the time of day, people's movements are constrained not only by their workplaces, but also the necessity of picking up family members. Family member interactions, a typical activity during an evacuation, can greatly affect the evacuation process. Activity-based modeling has been applied to estimate daily traffic demand widely. However, only limited research has been reported to incorporate the activity component in examining evacuation processes, particularly for mass evacuations at a micro-scale. Under this context, this study aims to analyze the activity-based travel pattern and its impact on emergency evacuations in the case of a hypothetical emergency evacuation of Galveston Island, Texas. In the study, one typical type of daily trip, picking up school-age children, is considered. All households with school-age children are assumed to pick up their children first in an evacuation. Trip chains are defined to represent the movements from workplaces to schools and then to destinations. This study employs agent-based microsimulation techniques to model the evacuation process at the individual-driver level. The simulation results suggest that the overall evacuation time may not be significantly affected when the trips of picking-up school-age children are considered in the case of Galveston Island. However, the average travel and delay time of individual vehicles may increase dramatically, which suggests the occurrence of considerable congestions during the evacuation. The findings demonstrated the importance of considering activity-based trips in evacuations.

Travel Demand Forecasting Using Activity-Based Modeling Framework FEATHERS: An Extension

Activity-based travel demand modeling is the approach with most relation and need for intelligent solutions as it directly aims at reproducing human decision making in daily life. Therefore, the way to implement the selected intelligent solution plays an important role in the successful application of the models. FEATHERS (the Forecasting 16 Evolutionary Activity-Travel of Households and their Environmental RepercussionS) is an activity-based microsimulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. For the sake of more detailed travel demand forecasting, we investigated in this study the extension of the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems was compared. The extended modeling system was further applied to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders.

Investigating micro-simulation error in activity-based travel demand forecasting: a case study of the FEATHERS framework

Activity-based models of travel demand have received considerable attention in transportation planning and forecasting in recent years. However, in most cases they use a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a consequence, running a transport micro-simulation model several times with the same input will generate different outputs, which baffles practitioners in applying such a model and in interpreting the results. A common approach is therefore to run the model multiple times and to use the average value of the results. The question then becomes: what is the minimum number of model runs required to reach a stable result? In this paper, systematic experiments are carried out using Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS (FEATHERS), an activity-based micro-simulation modelling framework currently implemented for the Flanders region of Belgium. Six levels of geographic detail are taken into account. Three travel indices – average daily activities per person, average daily trips per person and average daily distance travelled per person, as well as their corresponding segmentations – are calculated by running the model 100 times. The results show that the more disaggregated the level, the larger the number of model runs is needed to ensure confidence. Furthermore, based on the time-dependent origin-destination table derived from the model output, traffic assignment is performed by loading it onto the Flemish road network, and the total vehicle kilometres travelled in the whole Flanders are subsequently computed. The stable results at the Flanders level provides model users with confidence that application of FEATHERS at an aggregated level requires only limited model runs.

Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model

Nowadays, considerable attention has been paid to the activity-based approach for transportation planning and forecasting by both researchers and practitioners. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time, especially when large amount of population and detailed geographical unit level are taken into account. In this research, we investigated the possibility of restraining the size of the study area in order to reduce the computation time when applying an activity-based model, as it is often the case that only a small territory rather than the whole region is the focus of a specific study. By introducing an accuracy level of the model, we proposed in this research an iteration approach to determine the minimum size of the study area required for a target territory. In the application, we investigated the required minimum size of the study area surrounding each of the 327 municipalities in Flanders, Belgium, with regard to two different transport modes, that is, car as driver and public transport. Afterwards, a validation analysis and a case study were conducted. All the experiments were carried out by using the FEATHERS, an activity-based microsimulation modeling framework currently implemented for the Flanders region of Belgium.

Building a validation measure for activity-based transportation models based on mobile phone data

Activity-based micro-simulation transportation models typically predict 24-h activity-travel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure.Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models.By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

Reverification of an Activity-Based Microsimulation of Travel Demand during a Transition in the Transportation Environment in Jakarta, Indonesia

Following the analyses of household auto–motorcycle ownership and joint mode and destination choice models presented at previous TRB annual meetings, this paper offers the results of reverification of an activity-based microsimulation of travel demand for the Jakarta, Indonesia, metropolitan area that was developed from 2002 surveys. Comprehensive household and household member data from a large-scale commuter travel survey in 2010 were used as inputs to the microsimulation. This paper first presents the overall microsimulation processes and model validation, along with several activity scheduling decision rules and intrahousehold interaction rules, including joint tour–activity generation and household maintenance tour allocation. For model reverification, the microsimulation outcomes of the new base year of 2010 were investigated both internally and externally. The internal investigation involved comparison with the detailed activity-related data from a person-tracking survey that was able to catch all the trips made by the respondents carrying a GPS logger. The external investigation involved the comparison with the observed traffic counts on two major screen lines in the region. The paper also discusses the results and secondary or emerging effects and trends caused by the changing transportation environment. Overall, the activity-based microsimulation model developed from the 2002 surveys provides only travel estimates of limited accuracy. Thus, the authors’ further effort includes reestablishment of comprehensive activity-based models resulting from the latest abundant and accurate travel data source so as to make a powerful, practical decision-making tool useful for policy applications not only in Jakarta but also in other metropolitan regions.

Determining Electric Vehicle Charging Point Locations Considering Drivers’ Daily Activities

In this paper the daily temporal and spatial behavior of electric vehicles (EVs) is modelled using an activity-based (ActBM) micro-simulation model for Flanders region (Belgium). Assuming that all EVs are completely charged at the beginning of the day, this mobility model is used to determine the percentage of Flemish vehicles that cannot cover their programmed daily trips and need to be recharged during the day.Assuming a variable electricity price, an optimization algorithm determines when and where EVs can be recharged at minimum cost for their owners. This optimization takes into account the individual mobility constraint for each vehicle, as they can only be charged when the car is stopped and the owner is performing an activity.From this information, the aggregated electric demand for Flanders is obtained, identifying the most overloaded areas at the critical hours.Finally it is also analyzed what activities EV owners are underway during their recharging period. From this analysis, different actions for public charging point deployment in different areas and for different activities are proposed.

Geographical Extension of the Activity-based Modeling Framework FEATHERS

FEATHERS is an activity-based micro-simulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. In this study, we investigated the transferability of applying the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure in order to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems were compared. The results indicated the validity and also the necessity of this extension.

A household-level activity pattern generation model with an application for Southern California

This paper develops and estimates a multiple discrete continuous extreme value model of household activity generation that jointly predicts the activity participation decisions of all individuals in a household by activity purpose and the precise combination of individuals participating. The model is estimated on a sample obtained from the post census regional household travel survey conducted by the South California Association of Governments in the year 2000. A host of household, individual, and residential neighborhood accessibility measures are used as explanatory variables. The results reveal that, in addition to household and individual demographics, the built environment of the home zone also impacts the activity participation levels and durations of households. A validation exercise is undertaken to evaluate the ability of the proposed model to predict participation levels and durations. In addition to providing richness in behavioral detail, the model can be easily embedded in an activity-based microsimulation framework and is computationally efficient as it obviates the need for several hierarchical sub-models typically used in extant activity-based systems to generate activity patterns.

An Activity-based Carpooling Microsimulation Using Ontology

This study aims to show an ability of Ontology, which is a formal explicit description of concepts in a domain of interest, in an activity-based microsimulation. Thus, an agent-based carpooling application using ontology techniques is presented as a case study with a focus on three functions of the Ontology. First, Ontology facilitates integrating between heterogeneous databases by defining the relationship between their concepts. Second, Ontology verifies the compatibility and consistency between the different angles to combine varied models in a common structure by providing shared knowledge between different domains modelling with the definition of objects and concepts. Lastly, Ontology is useful for modelling agent communication by means of making explicit the parsed message between agents with the shared knowledge. This paper introduces related studies and basic knowledge about using methodologies, and supports an example of using Ontology in an agent-based carpooling simulation.

Estimation of Carsharing Demand Using an Activity-Based Microsimulation Approach: Model Discussion and Some Results

ABSTRACT Carsharing is a system that allows individuals to use a car from a fleet on an hourly basis. The growing popularity of carsharing is reflected by a continuous increase in the number of users worldwide. However, the estimation of travel demand for this mode has only sporadically been addressed by researchers and not in a completely satisfactory way. The work reported in this article introduces a new methodology to estimate travel demand for carsharing: activity-based microsimulation. An existing open source software, MATSim, has been enhanced to allow modeling carsharing. This article reports the modeling approach and describes the implementation of the carsharing mode in the system. Some results, based on a simulation scenario with about 160,000 agents representing the urban area of Zurich, Switzerland, are presented and validated against customer data of Mobility Switzerland, the dominant carsharing operator active in the study area.