Applying FEATHERS for Travel Demand Analysis: Model Considerations (original) (raw)
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Activity-Based Travel Demand Forecasting Using Micro-Simulation
Data Science and Simulation in Transportation Research, 2014
Activity-based models of travel demand employ in most cases a micro-simulation approach, thereby inevitably including a stochastic error that is caused by the statistical distributions of random components. As a result, running a transport micro-simulation model several times with the same input will generate different outputs. In order to take the variation of outputs in each model run into account, a common approach is 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 chapter, systematic experiments are carried out by using the FEATHERS, an activity-based micro-simulation modeling framework currently implemented for Flanders (Belgium). Six levels of geographic detail are taken into account, which are building block level, subzone level, zone level, superzone level, province level, and the whole Flanders. Three travel indices (i.e., the average daily number of activities per person, the average daily number of trips per person, and the average daily distance travelled per person), as well as their corresponding segmentations with respect to socio-demographic variables, transport mode alternatives, and activity types are calculated by running the model 100 times. The results show that application of the FEATHERS at a highly aggregated level only requires limited model runs. However, when a more disaggregated level is considered (the degree of the aggregation here not only refers to the
Research on Restrained Study Areas for Effective Activity-Based Travel Demand Forecasting
CICTP 2014, 2014
Recently, considerable attention has been devoted to studying the activitybased approach for transportation planning and forecasting. However, one of the practical limitations of applying most of the currently available activity-based models is their computation time. In this research, we investigated the possibility of restraining the size of the study area to reduce the computation time when applying an activity-based model. By introducing an accuracy level of the model, we proposed an iterative 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 with regard to two different transport modes: car as driver and public transport. Additionally, a validation analysis was conducted. All the experiments were carried out by using the FEATHERS (Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS) framework, an activitybased micro-simulation modeling framework currently implemented for the Flanders region of Belgium.
Investigating the Minimum Size of Study Area for an Activity-Based Travel Demand Forecasting Model
Mathematical Problems in Engineering, 2015
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.
Enhancing the Modelling of Travel Demand Using an Activity-Based Approach
2016
RESUME Cette these vise a enrichir le processus actuel de la modelisation de la demande de transport dans la Grande Region de Montreal (GRM) en utilisant une approche basee sur le modele d’activites TASHA (Travel Activity Scheduler for Household Agents). TASHA a ete developpe en se basant sur des donnees provenant des deplacements de l'enquete Transportation Tomorrow Survey (TTS) de 1996 pour la Grande Region de Toronto (GRT). Cette recherche vise a appliquer le modele TASHA dans le contexte montrealais en utilisant l’enquete de deplacements Origine-Destination (O-D) de 2003 et les donnees du recensement canadien de 2001. TASHA simule, pour un jour typique de la semaine, les horaires quotidiens d'activites (individuelle et combine) de l’ensemble des personnes dans la region. Cette etude vise a evaluer la transferabilite du modele TASHA a une autre region metropolitaine en comparant les caracteristiques des activites observees et simulees par TASHA (frequence d'activite, ...
Transportation Research Part A: Policy and Practice, 2007
Regional travel models in the United States are clearly evolving from conventional models towards a new generation of more behaviorally realistic activity-based models. The new generation of regional travel demand models is characterized by three features: (1) an activity-based platform, that implies that modeled travel be derived within a general framework of the daily activities undertaken by households and persons, (2) a tour-based structure of travel where the tour is used as the basic unit of modeling travel instead of the elemental trip, and (3) micro-simulation modeling techniques that are applied at the fully-disaggregate level of persons and households, which convert activity and travel related choices from fractionalprobability model outcomes into a series of discrete or ''crisp'' decisions.
A Semi-Deterministic Approach for Modelling of Urban Travel Demand
Proceedings of the International Symposium for Next Generation Infrastructure, 2014
This paper presents a methodology to construct travel related activity schedules for individuals in a synthetic population. The resulting list of activity schedules are designed as an input into a micro-simulator for urban transport dynamics analysis. The methodology involves two main steps. The first step generates a synthetic population based on census data sourced from the Australian Bureau of Statistics (ABS). The second step assigns individuals in the synthetic population activity schedules using Household Travel Survey (HTS) data related to the geographical area of interest (in this case, the Sydney Greater Metropolitan area). Each individual is assigned an ordered set of trips, travel purpose, travel mode, departure time and estimated trip time. The significance of the methodology is twofold in that it generates a synthetic population aligned with area demographics, as well as generating activity schedules that realistically represent how the population uses existing transport infrastructure. The methodology also preserves the inter-dependencies (in terms of the sequence, travel times and purpose of trips) of individual's daily trips, in contrast to many trip generators for transport micro-simulation purposes. A case study of Randwick area in southern Sydney is presented where the proposed methodology is applied. Case study data is validated against real world results and the scalability and applicability to other urban areas are discussed.
In all developed and developing countries of the world, the government transportation's policies aimed at controlling aggregate phenomena such as congestion, emissions and land use patterns. These are achieved through the provision of employer-based commute programs, single occupant vehicle regulation, road pricing, multimodal facilities and transit oriented land development. But these policies affect the aggregate phenomen indirectly through the behaviour of individuals. Furthermore, individuals adjust their behaviour in complex ways, motivated by a desire to achieve their activity objectives. This paper examines the activity based modeling framework for travel demand and behaviour, the concepts underlying the methods and modeling approaches. Finally, it identified three classes of model systems, which are econometric model systems, hybrid simulation systems and the theory of planned behaviour model, and also look at some examples in each class, considering how they work, and their particular strengths and weaknesses, and above all, looking at the big picture.
Geographical Extension of the Activity-based Modeling Framework FEATHERS
Procedia Computer Science, 2014
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 km 2. 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 km 2. 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.
An Activity-Based Microsimulation Model for Travel Demand Forecasting
This paper summarizes the initial formulation of a micro-simulation model for activity-based travel demand forecasting that integrates household activities, land use distributions, regional demographics and transportation networks in an explicitly time-dependent fashion. Intended to form the initial elements of an alternative to the conventional four-step transportation planning process, the prototype model incorporates an activity-based travel behavior model in a micro-simulation approach utilizing a geographic information system platform to manipulate survey, demographic, land use and network databases. An aggregate classification using travel diaries produces representative activity patterns that are implicitly specified in terms of temporal information, activity purpose and sequencing. The classification also provides probability distributions of activity dimensions such as purpose and duration. Additional households are sampled and, based on demographic, land use and network characteristics provided by the GIS, a target representative activity pattern is specified as are ambient activity densities. Activity characterists such as purpose and duration are drawn from the distributions associated with the target pattern: trips are sequentially simulated based on a Monte Carlo approach of potential activity-specific destinations within a range of travel times from the prior and the home locations. The nature of the simulation is such that the simulated pattern, while maintaining the general characteristics of the target representative pattern, reflects the activity distributions and network characteristics of the household being simulated. The resultant set of activity patterns may be aggregated for any defined spatial-temporal limits. The model provides an activity-based method for estimating dynamic, linked-trip, origin-destination demand matrices. Effectively replacing the generation and distribution components of the conventional process, the model represents a potentially important step toward the development of alternative transportation planning methods.