Activity-based Model Research Articles

The Activity-based model ABIT: Modeling 24 hours, 7 days a week

The paper introduces the activity-based model ABIT as a novel approach to modeling travel demand. Traditional aggregate transport models are limited in their ability to assess certain transport policies, such as ride-pooling services and autonomous cars, due to their inability to accurately represent complex travel behaviors. ABIT generates weekly activity patterns for individuals, forming the basis for understanding habitual and incremental travel behavior. Unlike traditional models, ABIT can distinguish between day-to-day variations in travel behavior and more significant year-to-year changes. The model's base-year structure is described in detail, including steps for assigning habitual modes, mandatory activities, discretionary activities, subtours, duration, start times, destination choices, and vehicle allocation. The paper emphasizes the importance of habitual mode choice, especially in modeling commute modes. The results of ABIT show variations in activity frequency across different days of the week, with weekdays dominated by work and education activities, while weekends exhibit a higher proportion of discretionary activities. The paper acknowledges longer runtimes and random variations as potential limitations, suggesting caution in analyzing results at a fine- grained level.

Analyzing Transportation Equity in the D.C. Metropolitan Area Using Large-Scale Agent-Based Modeling and Simulation

Policymakers face the challenge of providing equitable infrastructure services to all city dwellers with limited budgets. Further, equity analysis based on the socioeconomic characteristics of travelers is not palatable for some. This paper proposes a novel framework for analyzing equity in terms of access to opportunity rather than equity of outcomes. We showcase the use of a large-scale, high-fidelity agent-based, activity-based transportation model to analyze equity in the Washington DC metropolitan region. Travel time between each traffic analysis zone and its nearest activity area is used as a measure of access-to-opportunities, thereby carrying out equity analysis without using the socioeconomic characteristics of the traveler. The results of this analysis are visualized in a GIS heatmap. Decision-makers can visually identify regions needing attention and focus their funding and efforts on improving infrastructure services in the specified regions. Decision makers may elect to increase the capacity of road segments or develop a new employment activity center in the area of interest. The paper contributes to the literature by analyzing equity of opportunities without considering individuals’ socioeconomic characteristics, applying a higher fidelity activity-based model to a large metropolitan area, and documenting a framework and the results of large-scale equity analysis of the Washington DC metropolitan region.

The potential for spatiotemporal population data sets for aviation noise studies

Aviation noise analysis has traditionally focused on modeling the noise from an average day of operations. We investigate the potential of moving from this static approach to a dynamic method of assessing aircraft noise experienced by people where they are as they move about the day and night. In this research conducted on behalf of the National Academy of Sciences Airport Cooperative Research Program, we evaluated population data sets that account for the movement of people between their daytime versus nighttime location and assessed their applicability for aviation noise studies. We provided guidance on using these spatiotemporal data sets for aviation noise studies using the Aviation Environmental Design Tool, including sample maps and analyses. We also discussed the limitations of spatiotemporal population data, namely they only account for a person's location by time of day and not whether they are engaged in noise-sensitive activities. A fuller picture of noise impact by location and time of day may be achieved by developing an activity-based model, which combines data on location and level of noise sensitivity. We outline the basic elements of such a model.

Generating and understanding human daily activity sequences using Time-Varying Markov Chain models

Human activity patterns have raised broad interests across fields and activity-based modeling has been applied to analyze travel behaviors, effects of policies, etc. In this paper, we examined daily activity sequences from large volumes of datasets collected in the Tokyo Metropolitan Area over thirty years. We show that, using a simple time-varying (inhomogeneous) Markov Chain model, activity sequences at the individual level can be generated with high accuracy. The predictive power of the model is comparable to neural network models when generating activity sequences for each administrative region. The results are surprising considering the huge number of potential activity sequences and the complicated process of activity planning. A fundamental relationship between time-varying Markov Chain models and Multinomial Logit models is recognized to understand the unexpected accuracy. A new mechanism is proposed on how people make daily activity plans from the perspective of cost and utility, where the probability of one plan being chosen is the same as the probability of it being generated by the Markov Chain model. From this perspective, the Markov process becomes a utility-based model. Furthermore, given the average time-varying transition probabilities over a large spatial and temporal scale, the Markov Chain model for any specific area is reconstructed with six parameters. The generation accuracy using the reconstructed model is examined. Moreover, the statistical characteristics of the costs assigned to the Markov Chain model are investigated.

Numerical Analysis of Error From Sampling of Alternatives in Logit-Based Demand Forecasting Models with Massive Choice Sets

Use of the multinomial logit (MNL) functional form is widespread in transportation and land use demand modeling. As the state-of-the-art evolves away from aggregate forecasting and toward activity-based models and microsimulation, MNL models are being asked to accommodate increasingly large choice sets. Different strategies exist to address the challenge of estimating models with very large choice sets, but perhaps none is as commonly employed as sampling of alternatives. The effect of sampling of alternatives on parameter estimation has received considerable attention in the scientific literature. Yet comparatively little quantitative research exists that examines the issues that arise when the same sampling strategies are applied in the context of forecasting. In this study we conducted an analysis of the effect of sampling of alternatives in discrete choice models for disaggregate location choice forecasting. First, a novel measure of forecast error was defined and then used to quantify the extent of the problem as a function of sample rate, model error, and size of the universe of alternatives. Finally, we explored the potential for strategic sampling techniques to reduce the error observed under simple random sampling. In general, we found that the proportion of aggregate demand that was misallocated owing to sampling of alternatives was actually reduced as the size of the universe of alternatives increased. Additionally, simple random sampling was shown to outperform importance sampling in most scenarios. These findings suggest that error resulting from random sampling of alternatives is less of a problem for logit-based microsimulation models than previously assumed.

A negotiation model of individual matching and zonal-based travel behavior in carpooling

AbstractCarpooling is a sustainable and ecologically acceptable transportation mode. Individuals commonly engage in coordination and negotiation processes to find matching partners and typically modify their schedules to enable cooperation. Mutual cooperation between carpooling individuals plays an important role in executing trips. Through cooperation, participants can achieve challenging agreements effectively in a repetitive manner. This paper presents a negotiation mechanism that can match individuals for carpooling using organization and agent-based concepts. It describes a matching model and a carpooling social network. It studies several aspects of multi-zonal individual behavior to identify groups of carpooling candidates. The carpooling social network is simulated on an ongoing basis for each of the following carpooling activities: interaction, negotiation, and trip execution. The interaction process enables communication between individuals within carpooling social groups in order to activate the negotiation process. During the negotiation process, participants typically modify their schedules to support cooperation by considering their personal preferences and constraints. Negotiation leads to matching of individuals based on trip start times, driver selection, detour duration, and carpool group pickup and dropoff sequences. Trip start times are established on travel, social, financial, and schedule-related factors. The carpoolers’ pickup and dropoff sequences that are feasible for an optimal carpool group are projected using specific scoring methods. Carpooling community candidates are recognized via outcomes projected using the FEATHERS activity–based model. The framework is implemented through the Janus multi-agent system.

Simulating intra-household interactions for in- and out-of-home activity scheduling

Various interactions, time arrangements, and constraints exist for individuals scheduling their day as a member of a household, which affect their in-home as well as out-of-home activity schedule. However, the existing activity-based models are mostly based on the individual decision-making process, which are limited in their demonstration of behaviour. We simulate multiple intra-household interaction dimensions within the same framework and capture the coordination of the activity scheduling decisions among all household members. Our approach adopts the Optimisation-based Activity Scheduling Integrating Simultaneous choice dimensions (OASIS) framework, which is at the level of isolated individuals and focuses on out-of-home activity schedules. We jointly simulate in- and out-of-home activities and incorporate interactions into the framework. Our framework contributes to the state-of-the-art in activity-based modelling by explicitly capturing multiple interactions within the same model, such as the allocation of the private vehicle to household members, dividing household maintenance responsibilities, escorting, joint activity participation, and sharing rides. We operationalise the model using time-use-survey data from the United Kingdom. The simulation results demonstrate the ability of the framework to capture complex intra-household interactions. We then demonstrate how these interactions can cause individuals to deviate from their schedules planned in isolation. This is a general framework applicable to different household compositions and available resources.

Freight activity-travel pattern generation (FAPG) as an enhancement of freight (trip) generation modelling: Methodology and case study

Trip-based models and activity-based models represent two extreme ends of the spectrum of travel demand models in data granularity requirement and ability to reflect the underlying motivation to travel. Modelling of representative freight activity-travel patterns (RFAPs) has the potential to serve as the bridge between these approaches. RFAP clusters represent homogeneous groups of establishments, where utility maximization models predict the probability that an establishment belongs to a particular cluster. However, it is still an open question how to define, interpret and model activity-travel patterns in the context of freight system. To answer this question, this study conducted a large-scale establishment-based freight survey (EBFS) in seven cities of India and resulted in a sample of 432 establishments and their 1613 shipment records. In the first part, this paper proposes a novel approach for identifying RFAPs based on the notion that “activities” that inspire trip-making for passenger is equivalent to “freight orders” in the case of establishments. The cluster analyses revealed the presence of three well separated main clusters and nine less separated nested clusters. Through interpretation and labelling of these RFAPs, freight travel market is categorized into useful segments. The results suggested that a priori industrial classification systems used in trip-based models are overly simplified representations of the complex structure of the travel patterns. In the last part, freight activity-travel pattern generation (FAPG) models are developed which predicts the probability that an establishment exhibits a particular RFAP. The FAPG models developed using these RFAPs could replace the traditional freight generation (FG) and freight trip generation (FTG) models due to its ability to convert the assigned activity-patterns to trips or tonnage. For example, FAPG model suggest that at an employment level of 120, there is a 56% probability that establishments will exhibit MDV-HFMH (medium duty vehicles - high frequency medium haul) pattern which, in turn, implies that FP = 1630 tons/year; shipment frequency, i.e., FTP = 8 trips/week; length of haul = 240.6 km and commercial vehicle type choice = MDV. Thus, FAPG models can present an enhanced representation of freight flows since both FG and FTG are jointly modeled in this approach. That is, the best features of both commodity-based modelling (i.e., ability to capture the fundamental mechanism that drives freight demand) and vehicle-based modelling (i.e., ability to capture freight traffic implications) are included in FAPG models. The study findings are expected to assist in identifying the variations in establishments’ preferences so that it is possible to identify the type of transport supply improvements that the establishments will respond to accurately, and thus prioritize the infrastructure investments. Moreover, the discussions on these findings are expected to improve the behavioral and spatial foundations of traditional freight models.

Supporting scheduling decisions by using genetic algorithm based on tourists’ preferences

As the tourism industry switches from mass to personalized solutions, tourists’ preferences should be considered. This study aims to optimize tourists’ activity chains based on their preferences and 10 realistic parameters. A method using activity-based modeling and genetic algorithm is developed. The method handles flexible touristic activities with time window restrictions. Then scenarios including private vehicles (PV) and public transport (PT) are created. The results show high priority for security and provide significant reduction in journey times, particularly in case of flexible activities. The outcomes confirm that the proposed method provides an optimized solution to generate personalized travel itineraries that enhance tourists’ travel experience. To evaluate the performance of the algorithm, Wilcoxon Signed-Ranks Test is applied. The statistical results confirm that the performance of the GA significantly differs in the various scenarios. Travelers can benefit from the proposed approach since it offers a solution to complex real-world scheduling problems. In addition, travel patterns can be derived thus supporting the work of planners and decision-makers in the tourism industry.

Activity‐based model based on multi‐day cellular data: Considering the lack of personal attributes and activity type

AbstractCellular data is a sequence of base station‐interaction data that records user ID, timestamp, location area code (LAC), and cell identity (CID). With long observation periods, the data allows traffic planners to analyze coarse‐granularity user travel behaviours at low costs. However, utilizing cellular data for urban planning is not an easy task as the data lacks user socioeconomic attributes due to privacy issues. The data is also challenging to recognize user activity types. This paper proposed an activity‐based model (ABM) with skeleton schedule constraints for multi‐day cellular data. The model first infers the activity pattern and home location. Then it predicts start time, duration, and locations separately for primary and secondary activities. Next, the model infers the travel mode and path considering user multi‐day travel behaviour, path non‐linear coefficient, and transfers. Finally, a time adjustment module is proposed to avoid time conflicts in consecutive activities. The proposed activity‐based model is validated at activity, travel, and path levels. Results show that the proposed model can effectively predict activities and has much higher stability than existing ABMs based on travel surveys.

OASIS: Optimisation-based Activity Scheduling with Integrated Simultaneous choice dimensions

Activity-based models offer the potential of a far deeper understanding of daily mobility behaviour than trip-based models. However, activity-based models used both in research and practice have often relied on applying sequential choice models between subsequent choices, oversimplifying the scheduling process. In this paper we introduce OASIS, an integrated framework to simulate activity schedules by considering all choice dimensions simultaneously. We present a methodology for the estimation of the parameters of an activity-based model from historic data, allowing for the generation of realistic and consistent daily mobility schedules. The estimation process has two main elements: (i) choice set generation, using the Metropolis-Hasting algorithm, and (ii) estimation of the maximum likelihood estimators of the parameters. We test our approach by estimating parameters of multiple utility specifications for a sample of individuals from a Swiss nationwide travel survey, and evaluating the output of the OASIS model against realised schedules from the data. The results demonstrate the ability of the new framework to simulate realistic distributions of activity schedules, and estimate stable and significant parameters from historic data that are consistent with behavioural theory. This work opens the way for future developments of activity-based models, where a great deal of constraints can be explicitly included in the modelling framework, and all choice dimensions are handled simultaneously.

An integration modeling framework for individual-scale daily mobility estimation

This paper presents a novel microsimulation framework (BayABM) that integrates population synthesis, Bayesian Networks (BNs), and activity-based models (activity-BM) to model individual heterogeneity in mobility. The population synthesis simulates a synthetic population with socioeconomic and household-level attributes. The Bayesian Network is adopted to learn and compute the probability distributions of the mobility attributes and infer the mobility that most likely arises at a given time. A hierarchical location model with a random forest is developed to refine the destination choice based on the probability obtained from BN. A total of six mobility attributes are simulated, i.e., travel frequency, travel purpose, travel destination location, travel mode, activity duration, and travel duration. The strengths of the proposed framework are: (1) it can model the individual mobility dynamics; (2) it can generate individual mobility for a large-scale population; and (3) it has broader coverage with better representativeness and less strict data requirement. The proposed model is tested with a Miami-Dade County, Florida case study. A total of 7,253,672 travels for 2,680,607 persons and 860,380 households across the county are generated from a public travel diary survey. Results show that individuals at 20–50 age and 0–20 age travel most frequently, with a total of 3,413,710 and 2,074,677 activities per day. The travel peak of a day occurs at 11:00 and 18:00. The results demonstrate the efficacy of the enhanced model, especially in large-scale applications.

Workplace Assignment to Workers in Synthetic Populations in Japan

In this article, we assign workplace attributes to each worker in each household in a synthetic population using multiple censuses conducted in Japan. The synthetic population is a set of artificial individual attributes for each resident that is synthesized according to census data. We have synthesized a set of the synthetic populations of Japan. We assign a workplace attribute to each worker to estimate daytime population distribution and develop activity-based models in agent-based or microsimulations. Although statistical information in a residential area or a working place is released by the government and some individual moving data are released by cellphone companies, it is hard to collect the information with home and workplace location of a worker with their family and working information. We employ origin–destination–industry (ODI) statistics to estimate workplaces for workers. Since some attributes in ODI statistics are not available for privacy reasons, we propose a workplace assignment method for all cities, towns, and villages using restricted ODI and OD statistics in Japan. We show how much difference there are between the number of workers using the complete ODI statistics and the number of workers by the proposed workplace assignment method. We show that 88.2% of workers in a city in Japan are assigned to correct cities as workplaces by our proposed method. We also show several maps of daytime population distributions by our proposed method. Synthetic populations with workplace attributes enable real-scale social simulations to design transport or business systems in times of peace or to estimate victims and plan recoveries in times of emergency, such as disasters or pandemics.

Estimating Path Travel Costs in Large-Scale Networks Using Machine-Learning Techniques

The integration of activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) is proposed as a prominent alternative to the traditional four-step approach to modeling transportation networks. In DTA, the least-generalized cost path-finding algorithm is executed to route travelers to their optimal paths, while ABM requires travel-cost skims of the activities, regardless of actual paths, to assess various options available to users such as destination and mode choices. These skims can be calculated either by rerunning the shortest-path-finding algorithm or storing the dynamic travel-cost skims generated at the DTA level. Having a large-scale network with multiple user classes, these methods require high memory storage and run time. Therefore, this study presents a heuristic algorithm utilizing machine-learning methods to predict origin–destination (OD) travel distances, times, and costs using historical vehicle trajectory data generated at the DTA level. This study uses an ellipse boundary around the ODs to truncate the network and extract the relevant vehicle sub-trajectories. Extracted sub-trajectories are clustered into with-toll and non-toll groups based on trip costs to estimate travel distance, time, and monetary cost accordingly. This approach can also be used in navigation systems to have a better estimate of arrival times. Numerical results for the greater Chicago network show promising performance in travel-cost measures estimation with improved solution times. To test the scalability of the proposed method for different OD pairs, the prediction of the best ellipse size is carried out using random forest and neural network methods.

Evaluating Segment-Level Active Mode Shift Potential Using Desire Lines

Understanding which active transportation facilities have a high potential for shifting car trips to active modes is a potent metric of success for funders seeking to make high impact transportation investments. This paper documents the development and validation of a traveler alignment analysis tool that evaluates the orientation, proximity, and magnitude of trips by distance in origin–destination (OD) desire line data to evaluate mode shift potential at the segment level. This alignment analysis operates by using line features created by a Utah statewide OD matrix of vehicle trips taken from the Replica Places activity-based modeling data platform. Additionally, the research tests the implementation of jittering, a previously proposed method for OD line disaggregation, and the impact on traveler alignment performance. To tune parameters related to proximity, angle, and trip distance, a sensitivity analysis was conducted comparing similar mode shift potential trips generated by the proposed Traveler Alignment tool and 25 StreetLight Data pass-through zone analyses. This sensitivity analysis used a mean absolute error metric to minimize estimation error and understand how altering parameters and applying jittering affected tool performance. A Pearson correlation test indicates a significant, positive linear relationship (p < 0.01) between Traveler Alignment tool outputs and results of the select-link analysis, indicating that this method produces reasonable results that may be used to evaluate different projects comparatively for their active mode shift potential.

A data fusion approach for ride-sourcing demand estimation: A discrete choice model with sampling and endogeneity corrections

Ride-sourcing services offered by companies like Uber and Didi have grown rapidly in the last decade. Understanding the demand for these services is essential for planning and managing modern transportation systems. Existing studies develop statistical models for ride-sourcing demand estimation at an aggregate level due to limited data availability. These models lack foundations in microeconomic theory, ignore competition of ride-sourcing with other travel modes, and cannot be seamlessly integrated into existing individual-level (disaggregate) activity-based models to evaluate system-level impacts of ride-sourcing services. In this paper, we present and apply an approach for estimating ride-sourcing demand at a disaggregate level using discrete choice models and multiple data sources. We first construct a sample of trip-based mode choices in Chicago, USA by enriching household travel survey with publicly available ride-sourcing and taxi trip records. We then formulate a multivariate extreme value-based discrete choice model with sampling and endogeneity corrections to account for the construction of the estimation sample from multiple data sources and endogeneity biases arising from supply-side constraints and surge pricing mechanisms in ride-sourcing systems. Our analysis of the constructed dataset reveals insights into the influence of various socio-economic, land use and built environment features on ride-sourcing demand. We also derive elasticities of ride-sourcing demand relative to travel cost and time. Finally, we illustrate how the developed model can be employed to quantify the welfare implications of ride-sourcing policies and regulations such as terminating certain types of services and introducing ride-sourcing taxes.

Activity-based model based on long short-term memory network and mobile phone signalling data

With the advent of big data era, activity-based model (ABM) has once again become hot topics in the traffic planning. Traffic big data can reflect individual travel patterns, making it possible to establish ABMs. However, current ABMs based on big data are not mature, especially in the individual trip forecasting. Therefore, this paper proposes an advanced ABM using Long Short-Term Memory (LSTM) networks and mobile phone signalling data. The model is skeleton scheduling which contains primary activity chaining and secondary activity nesting. Then a time-dynamic adjustment model is proposed to adjust time conflicts among consecutive activities. A field test is conducted in Chengdu. The KS values of work and leisure departure time reach 35.20 × 10−2 and 41.02 × 10−2 separately, and that for activity duration reach 44.91 × 10−2 and 54.65 × 10−2. The results show our model can effectively predict activities, and has better accuracy and stability than existing BN, DT, GRNN, RF and GRU.

A joint and simultaneous prediction framework of weekday and weekend daily-activity travel pattern using conditional dependency networks

Daily activity pattern (DAP) prediction models within the Activity-based Modelling paradigm are being currently developed without adequate consideration of the various interdependencies among activities within a multi-day planning horizon. We hereby propose a conditional dependency network structure based interdependent multilabel-multiclass classification framework for joint and simultaneous prediction of weekday and weekend DAP of an individual. The prime advantage of the proposed modelling framework is flexibility of application of any algorithm for parameter estimation. Random Forest Decision Tree (RFDT), eXtreme Gradient Boosting and Light Gradient Boosting Machine (LightGBM) as the base classifier and probabilistic and non-probabilistic inference approaches are explored for measuring their comparative performance to provide insights for future researchers. Several variables representing neighbourhood characteristics are also investigated as DAP determinants along with socio-economic characteristics of individuals for the first time.This model is estimated based on two-days (weekday and weekend) activity-travel diary of 1808 households (6521 individuals) in Bidhanangar Municipal Corporation, India. The non-probabilistic approach-based models are found to achieve higher accuracy (0.81–0.92) compared to probabilistic models (0.76 to 0.82). RFDT and LightGBM are found to be the best performers in the probabilistic and non-probabilistic frameworks respectively. External validation results show that all proposed multiday-interdependent models (80%-94%) perform better than independent models (64%-83%).This framework can be applied to other transportations planning problems like household interaction in activity generation, joint destination and mode choice. This is also one of the first attempts to investigate the determinants of DAPs of urban commuters in an emerging country like India.

Using The Activity-Based Models to Study the Effect of Increasing Fuel Prices on the Individuals’ Daily Movement Patterns

Using The Activity-Based Models to Study the Effect of Increasing Fuel Prices on the Individuals’ Daily Movement Patterns

Elderly's intention and use behavior of urban parks: Planned behavior perspective

An urban park is a vital environment that promotes the elderly's physical, social and psychological health. Numerous studies have been conducted to determine the influencing factors that could encourage the elderly's usage of urban parks. However, little attention has been paid to elderly's intention of using urban parks. This study applies the theory of planned behavior (TPB) to understand the relationship between elderly's intentions of using urban park and use behavior. According to the nature of the different activity types elderly people intended to do in urban parks, three sub-models are built based on the original TPB model, which are social, enjoyment and physical activity-based models. These models explore the influencing factors that may affect elderly's use of urban park under the three different intentions. This study employed focus group meetings and questionnaire surveys (N = 537) to collect data and applied structural equation modelling to test the proposed social, enjoyment and physical activity-based models. In addition, this study found that elderly's use behavior of public parks was significantly affected by their intention of using public parks. The use behavior was also directly associated with intrapersonal constraints (degree of liking) and structural constraint variables (available leisure time and physical accessibility). Meanwhile, use behavior is indirectly associated with intrapersonal constraint variables (elderly's perception to urban park) and interpersonal constraints (conflict of use with other groups) through the influence of their intentions of using public parks. Furthermore, physical accessibility may affect elderly's use behavior, but it has no effect on elderly's intention of using urban parks. The findings and proposed models provide theoretical and practical contributions to better understand elderly's use behavior of urban park to encourage healthy ageing in an urban dense city.