Activity-based Model Research Articles

Formalizing Sustainable Urban Mobility Management: An Innovative Approach with Digital Twin and Integrated Modeling

Background: Urban mobility management faces growing challenges that require the analysis and optimization of sustainable solutions. Digital twins (DTs) have emerged as innovative tools for this assessment, but their implementation requires standardized procedures and languages; Methods: As part of a broader methodology for continuous DT validation, this study focuses on the conceptual validation phase, presenting a conceptualization approach through formalization using Specification and Description Language (SDL), agnostic to simulation tools. The conceptual validation was achieved through stakeholder engagement in the Bolzano context, producing 41 SDL diagrams that define both elements common to different urban realities and specific local data collection procedures; Results: The feasibility of implementing this stakeholder-validated conceptualization was demonstrated using Simulation of Urban MObility (SUMO) for traffic simulation and optimization criteria calculation, and its framework SUMO Activity GenerAtion (SAGA) for generating an Activity-Based Modeling (ABM) mobility demand that can be improved through real sensor data; Conclusions: The SDL approach, through its graphical representation (SDL/GR), enables conceptual validation by enhancing stakeholder communication while defining a framework that, while adapting to the monitoring specificities of different urban realities, maintains a common and rigorous structure, independent of the chosen implementation tools and programming languages.

Week-long activity-based modelling: a review of the existing models and datasets and a comprehensive conceptual framework

ABSTRACT Activity-based travel demand models emerged mainly to fix the conceptual, statistical, and operational deficiencies of conventional trip-based models. This is done by microstimulating the activity scheduling behaviour of individuals/households instead of modelling the number of trips between the zones of an urban area. In the “Next Generation” of activity-based models (ABMs), researchers are making an effort to improve their capacity to replicate the travel-activity patterns of urban populations more realistically. Expanding the modelling time frame from a single day to an entire week is one of the essential aspects of the “Next Generation” of ABMs. Although there is still a long way to go before a comprehensive and operational week-long ABM can be developed, the literature on its different aspects, the theoretical and conceptual frameworks, and the efforts to collect multi-day travel-activity diaries are now at a stage that is worth a comprehensive and systematic review. Therefore, the current study is devoted to exploring the existing literature on multi-day activity-based modelling, categorising its elements in a systematic manner, searching for the research gaps in the existing models and proposing a comprehensive framework to fill those gaps.

Modeling evacuation activities amid compound hazards: Insights from hurricane Irma in Southeast Florida

Given the destructive nature of hurricanes in tropical regions, pre-disaster evacuation has emerged as a critical approach for hurricane preparedness. Nevertheless, the compounding effects of natural hazards and the outbreak of infectious diseases, such as Covid-19, significantly challenge hurricane evacuation management. To investigate emergency responses under compound hazards, this study develops an activity-based model to measure the evacuation behaviors of individuals, using Hurricane Irma as a case study. Four scenarios are designed, including a single hurricane hazard, Hurricane Irma compounded with a pandemic like Covid-19, Hurricane Irma compounded with flood damage to the transportation network, and a combination of all these hazards. The metropolis-hasting algorithm is utilized to generate a population with socioeconomic attributes, which is then allocated to census block groups covering Palm Beach, Broward, Miami-Dade, and Monroe Counties in Florida. Datasets from multiple sources are used to measure evacuation decisions, which are subsequently simulated using MATSim. The results highlight the potential impacts of compound hazards on transportation systems, including increased congestions in scenarios involving compounded hurricanes and floods, especially between 10 a.m. and 7p.m. Moreover, a higher proportion of socially vulnerable populations is observed in scenarios involving compounded hurricanes and pandemics, particularly in the Key West area. The developed model could be further applied to measure the indirect impacts of natural hazards on transportation systems.

Exploring impacts of electricity tariff on charging infrastructure planning: An activity-based approach

In the past decade, electric vehicles (EVs) have gained popularity for their efficiency and environmental benefits. Advances in battery technology and charging equipment have yielded long-range EVs and fast-charging. However, many major cities lack adequate charging infrastructure for daily EV use. This study addresses this gap by integrating activity-based modeling, charging behavior simulation, and charging infrastructure optimization. The research utilizes the POLARIS agent-based transportation model to accurately capture user activities, trip patterns, and traffic flows. Additionally, the study investigates the impact of fixed and spatiotemporal electricity rate distributions on optimal charging infrastructure deployment. The framework is applied to the Chicago regional area network and analyzed under various EV ownership scenarios. The results reveal significant impacts of the charging pricing strategy on user decision-making and charging demand distribution. There is also a need for consistent pricing policies in charging infrastructure planning and operational phases to avoid drops in service quality.

Predicting bacterial fitness in Mycobacterium tuberculosis with transcriptional regulatory network-informed interpretable machine learning.

Mycobacterium tuberculosis (Mtb) is the causative agent of tuberculosis disease, the greatest source of global mortality by a bacterial pathogen. Mtb adapts and responds to diverse stresses such as antibiotics by inducing transcriptional stress-response regulatory programs. Understanding how and when these mycobacterial regulatory programs are activated could enable novel treatment strategies for potentiating the efficacy of new and existing drugs. Here we sought to define and analyze Mtb regulatory programs that modulate bacterial fitness. We assembled a large Mtb RNA expression compendium and applied these to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity profiles. We utilized transcriptomic and functional genomics data to train an interpretable machine learning model that can predict Mtb fitness from transcription factor activity profiles. We demonstrated that this transcription factor activity-based model can successfully predict Mtb growth arrest and growth resumption under hypoxia and reaeration using only RNA-seq expression data as a starting point. These integrative network modeling and machine learning analyses thus enable the prediction of mycobacterial fitness under different environmental and genetic contexts. We envision these models can potentially inform the future design of prognostic assays and therapeutic intervention that can cripple Mtb growth and survival to cure tuberculosis disease.

Demand study of freight transportation via railway of four commodity groups: A case study on Costa Rica's Pacific Coast Route

Having railway mobility as an option among transportation modes is of prime importance for the freight logistics of a country and the overall performance of its commerce sector. This work presents a case study in Costa Rica, where freight transportation via railway was formerly available during the last century but has not been in full service since 1995. Specifically, this paper assesses the link connecting the capital city of San Jose to the port of Caldera on the Pacific Ocean coast, henceforth referred to as the “Pacific Coast Route”. Although government institutions and industries alike have stressed the importance of its reactivation in recent years, few efforts have been made to specifically study this route with a level of detail that enables them to understand the composition of freight markets.This study presents a novel demand estimation differentiated by four types of commodities developed to calculate the potential amount of goods that could flow through this corridor, measured in monetary terms and as volumes. Trends in the expected share were analyzed to identify variables that may improve the attractiveness of the railway service and the participation of clients along the route from the country's main economic activities which includes the following: agriculture & livestock, manufacturing, minerals, and forestry.The four-step transportation model was chosen for its suitability towards this case study; where available data sets were limited, the information was aggregated into yearly values, and the network was defined with few transportation zones. Activity-based models were not feasible in this case due to the lack of information on complete routes and volume flows. The model evaluated a first scenario given the present design of the route and a second scenario involving improvements in the alignment and the addition of a new segment.Regarding the opportunities of moving goods according to their type, in terms of volume, minerals (48 %) and agricultural (31 %) have the largest shares of participation in this region, followed by manufacturing (20 %) and forestry products (1 %). However, the manufacturing sector stands out in financial terms, accounting for 71 % of the total value of goods mobilized in the study area.It was found that the demand is most sensible for changes in the service fare, while the benefits of shorter travel times and a larger attraction area in scenario 2 were not significant. A service fee of $USD 2.35/ton in average is recommended to reach a 30 % modal shift towards the railway (following the European Commission's goals to reduce CO2 emissions from freight transportation in 2030).Finally, recommendations for public policies are provided to the entities in the country in the sectors of Agriculture, Health, Energy and Environment, Transportation, and Public Services. The suggestions aim to develop the new railway project with a holistic perspective, considering the inclusion of less profitable economic activities of the surrounding communities that require connectivity, compliance with environmental commitments, and design for resilience while aiming for a financially feasible business model.

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne’s zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

Correction: Agriesti et al. Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data. ISPRS Int. J. Geo-Inf. 2022, 11, 148

In the original publication [...]

Entrepreneurial Resilience During COVID-19: The Survival of Small Businesses Using Dynamic Capabilities Theory and Effectuation Theory

Objectives: This study explores how Bali's sport tourism stakeholders used dynamic capabilities to mitigate COVID-19's impacts. Given Bali's reliance on sport tourism, understanding recovery is crucial. The research aims to identify pandemic challenges, explore strategic recovery plans, and understand how entrepreneurs leverage dynamic capabilities for survival and growth. Theoretical Framework: The study integrates dynamic capabilities and effectuation theories with entrepreneurial resilience. Dynamic capabilities involve adapting, learning, and transforming activities to meet changing environments. Effectuation theory focuses on leveraging available means and making decisions based on emerging opportunities. Together, these theories offer a comprehensive understanding of adaptation strategies among Bali's sport tourism entrepreneurs. Method: Using a qualitative design, the research conducts semi-structured interviews with 10 small and medium-sized enterprises (SMEs) in Bali's sport tourism sector. Results and Discussion: Findings highlight resilience's role in fostering adaptability, motivation, and a learning mindset. An activity-based model for dynamic capabilities focuses on integration, learning, and transformation with employee and customer involvement. Leveraging dynamic capabilities, assessing opportunities with available means, and using innovative technologies are crucial for strategic decisions in uncertain environments. Research Implications: This study contributes to the entrepreneurship and strategic management literature. Integrating dynamic capacities, effectuation theories, and entrepreneurial resilience provides useful insights for academics, policymakers, and practitioners. The work offers practical solutions for resilience in dynamic contexts. Originality/Value: This study enhances understanding of how Bali’s sport tourism entrepreneurs adapt to a transformed entrepreneurial ecosystem. It serves as a valuable resource for developing resilient strategies for future disruptions.

Estimating and Implementing a Vehicle-Type Model in an Activity-Based Travel Model Framework

Characteristics of household vehicles can influence daily travel behavior and can vary greatly by vehicle type. Vehicle type is defined here as a combination of body type, fuel type, and age. Using data compiled primarily from the National Household Travel Survey, a multinomial logit model was developed to predict vehicle type based on characteristics of the household that owns the vehicle. The model was then implemented for the San Francisco Bay Area in the ActivitySim activity-based modeling framework and validated against observed data. The ActivitySim project’s goal is to create and maintain advanced, open-source, activity-based travel behavior modeling software, based on best software development practices, for distribution to the public, free of charge. A naïve 2030 scenario was analyzed to show model response to changes in the vehicle fleet.

Implementation of a realistic artificial data generator for crash data generation

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type – possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

Evaluating spatial inequity in last-mile delivery: a national analysis

PurposeDespite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.Design/methodology/approachThe model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).FindingsEvaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.Originality/valuePresents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

HIERARCHY OF MODERN THEORIES, METHODS AND APPROACHES TO TEACHING FOREIGN LANGUAGES

In today’s competence-based paradigm of language education, the priority direction is the development and formation of students’ not only productive foreign language speech skills and language skills that contribute to the creation of written and oral texts of various genres, but also the skills of interaction and mediation in the global digital space. It is possible to develop the above-mentioned skills using effective approaches, methods, technologies. Modern methodological literature presents an extremely wide variety of methodological models, pedagogical theories, specific technologies and approaches to teaching foreign languages. This article is devoted to the development of a general theoretical basis for the hierarchy of existing methods, approaches, methods of teaching foreign languages within the framework of a competent-based, communicatively oriented paradigm of modern education. The proposed hierarchy includes 4 levels, where the theoretical foundation of education is based on a certain psychological and pedagogical theory; the essence of learning activity corresponds to the general theoretical method, the principles of learning - to private methods, the learning process - to a system of tasks. Each level is responsible for a certain range of “didactic potential”. The essence of the lower level is the ways of implementing knowledge, and the upper one is the management of information transfer processes in the activity-based model of learning. This system is very important, because the hierarchically organized structure of methods allows instructors to understand how particular methods contribute to the achievement of the goals of teaching foreign languages. The article analyzes the psychological and pedagogical theories valid for the modern system of education - cognitivism, constructivism and connectivism. The typological characteristics are given for the classification of teaching methods, the main types of problem-oriented tasks are identified for the development of students’ foreign language skills.

A neural network approach for population synthesis

This work explores techniques and metrics applied to the process of population synthesis used in activity-based modeling for traffic and transport simulation. The paper presents a novel population synthesis approach based on applying artificial neural networks (ANNs) and evaluates the approach against techniques derived from iterative proportional fitting (IPF), Bayesian networks, and data sampling methods. The documented research also investigates the appropriateness of goodness-of-fit measures and the need to consider similarity measures in assessing technique effectiveness with a focus on measures derived from Jaccard similarity coefficient. We established that IPF techniques should be preferred when datasets with the required composition are available, targeting few output variables and in relatively large zones of 5% region size. However, in smaller zones with sparser datasets, or inadequate dataset composition, the proposed ANN technique and identified sampling method are favorable. The proposed ANN method shows suitability for the population synthesis problem compared with the examined methods, but further work is required to improve model fitting speed, explore mixture models of multiple ANNs, and apply data reduction techniques to reduce the observation–decision space. The research findings also established that comparing scenarios of varying sizes and variable numbers is challenging when employing specific goodness-of-fit measures. Furthermore, the mentioned similarity measures can reveal concerns regarding inconsistent archetypes and low-quality populations that can remain concealed when using error metrics.

Activity type detection of mobile phone data based on self-training: Application of the teacher–student cycling model

Incorporating mobile phone data, known for its high spatial and temporal resolution and extensive population coverage, into Activity-Based Models (ABM) for understanding individual travel and activity behaviors is a current research hotspot. However, applying them in ABM building is not straightforward because they miss key information — activity type. In this paper, we present an activity types detection method named the Teacher–Student Cycling model based on a self-training framework. Our model can combine travel survey data with extensive prior knowledge and mobile phone data. We introduce two different resolutions of mobile phone datasets to test the model performance. Our results show that our proposed model can achieve good performance on datasets of different resolutions. Our model, one of the semi-supervised learning models that uses a mixture of labeled and unlabeled knowledge performs better than the supervised learning model that uses labeled knowledge alone. In addition, our model improves the overall detection accuracy by 7% over the second-best semi-supervised learning model and improves the detection accuracy of secondary activities by up to 17%. Our model can be valuable in generating daily activity schedules for agent-based models.

Data-Driven Framework for Generating Travelers with Demographic-Activity-Travel Information

Transportation demand models and traffic simulations, particularly in the realm of innovative activity-based models, necessitate comprehensive demographic-activity-travel data. Although cell phone data holds the potential to provide detailed insights into activity-travel patterns, transportation planners often resort to aggregate data, constrained by limited socio-demographic attributes because of stringent privacy protection policies. This creates a significant challenge: how to reconstruct detailed traveler profiles from such aggregate data. In this paper, we propose a novel data-driven integration framework, DATG, based on improved Markov and XGBoost classification models, with which we are able to reconstruct travelers with diverse socio-demographic attributes as well as their one-day activity-travel information. We test our model on a real cell phone data set and validate the proposed model by relying on travel survey data. Our framework is able to provide fundamental data support for agent-based models, activity-based models, and transportation planning.

Missing Typical Weekdays in Travel Surveys: A Pseudo-Panel Approach to Explore Weekly Travel Patterns

Travel surveys generally rely on single-day travel diaries where respondents report their travel information for a typical weekday. However, the concept of a typical weekday does not represent the current reality, as travel behavior has been largely altered in the post-pandemic period. Besides, the conclusions based on analyzing single-day travel diaries lack the ability to capture daily variations in travel behavior. In response to these concerns, this research proposed a framework to expand single-day travel diaries into longitudinal multi-day travel data using a pseudo-panel approach. Leveraging the constructed longitudinal data, the study evaluated the determinants of people’s daily participation in work–school, routine, and discretionary activities. Fixed and random effects panel data estimation models were used for this purpose. Results showed that activity participation is largely attributed to vehicle ownership, income, education, driving license, and household structure. Noticeable daily trends were observed in work–school and discretionary activities. A negative association between transit pass ownership and activity participation was noticed, suggesting social exclusion faced by transit users. In addition, teleworkers were found to be relatively more engaged in discretionary activities. Suburban residents were found to travel longer to participate in activities compared to urban dwellers. The proposed research framework can support future activity-based modeling aspects, such as activity participation, scheduling, mode choice, shared travel, and destination choice models, specifically addressing the “typical weekday” barrier.

Inspiratory and sigh breathing rhythms depend on distinct cellular signalling mechanisms in the preBötzinger complex.

Breathing behaviour involves the generation of normal breaths (eupnoea) on a timescale of seconds and sigh breaths on the order of minutes. Both rhythms emerge in tandem from a single brainstem site, but whether and how a single cell population can generate two disparate rhythms remains unclear. We posit that recurrent synaptic excitation in concert with synaptic depression and cellular refractoriness gives rise to the eupnoea rhythm, whereas an intracellular calcium oscillation that is slower by orders of magnitude gives rise to the sigh rhythm. A mathematical model capturing these dynamics simultaneously generates eupnoea and sigh rhythms with disparate frequencies, which can be separately regulated by physiological parameters. We experimentally validated key model predictions regarding intracellular calcium signalling. All vertebrate brains feature a network oscillator that drives the breathing pump for regular respiration. However, in air-breathing mammals with compliant lungs susceptible to collapse, the breathing rhythmogenic network may have refashioned ubiquitous intracellular signalling systems to produce a second slower rhythm (for sighs) that prevents atelectasis without impeding eupnoea. KEY POINTS: A simplified activity-based model of the preBötC generates inspiratory and sigh rhythms from a single neuron population. Inspiration is attributable to a canonical excitatory network oscillator mechanism. Sigh emerges from intracellular calcium signalling. The model predicts that perturbations of calcium uptake and release across the endoplasmic reticulum counterintuitively accelerate and decelerate sigh rhythmicity, respectively, which was experimentally validated. Vertebrate evolution may have adapted existing intracellular signalling mechanisms to produce slow oscillations needed to optimize pulmonary function in mammals.

Individual-level activity-based modeling and indicators for assessing construction sites noise exposure in urban areas

Construction site noise significantly contributes to urban noise pollution. To accurately assess the noise exposure from urban construction sites, it is essential to consider the variability and dynamic nature of the urban context. This paper proposes an innovative approach using individual-level activity-based modeling to tackle the given challenge. The methodology introduced encompasses: the modeling of construction site noise emission sources, individual-level activity-based modeling of zone of influences, a modeling chain for assessing individual noise exposure levels, and a novel dedicated exposure indicator. To illustrate the relevance of our methodology, a case study is conducted in Champigny-sur-Marne, France; this study involves the generation of a synthetic population of 1,344,394 individuals distributed among 591,153 households, with 4,854,597 associated activities, using the Eqasim simulation framework. A traffic model is then established using the multi-agent transport simulation software MATSim. NoiseModelling software is employed to compute noise maps both with and without the construction site. Subsequently, the noise exposure for each individual is assessed throughout the day for both configurations. Thanks to this complete modeling chain, the proposed indicator quantifying the cumulative amount of time of exposure above a given threshold can be compute, with the case study result being estimated at more than 3,300 person.quarter-hours above 65 dB(A).

Faster Convergence of Integrated Activity-Based Models in Dynamic Multimodal Transit Assignment Using Macroscopic Road Congestion Estimation

This paper proposes an integrated framework of an activity-based behavior model and a multimodal transit assignment-simulation tool that captures road network congestion dynamics. The framework has two levels: the upper level is the demand-side activity-based model that decides individual travelers’ behavioral choices based on up-to-date information from the lower level; the lower level consists of both transit and road network estimation models on the supply side, whose inputs are trips from the upper level. The objective of this framework is to assess impacts of transit service policies, so the transit network is simulated with an agent-based multimodal hyperpath assignment model in each iteration, while the road network is mainly estimated by a macroscopic model of congestion (metamodel) instead of a simulation-based assignment model to accelerate execution time toward an equilibrated solution. Convergence under this framework is also defined from two aspects: individual choice behaviors and transit hyperpath assignment. One contribution of this paper is to incorporate the exogenous effects of road network dynamics into the integrated demand and transit assignment model, and to reduce the time to reach convergence with macroscopic modeling. This paper uses mode choice behavior as an example to demonstrate mathematical formulations and implementation procedures to reach two-level convergence. The framework is tested with the large-scale regional network of the Greater Chicago metropolitan area. The results suggest that the major advantage of the macroscopic road model is to accelerate convergence toward equilibrium when it is used to capture the traffic network congestion effects in this integrated mode choice-transit assignment framework.