Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform

Bellemans, Tom; Kochan, Bruno; Janssens, Davy; Wets, Geert; Arentze, Theo; Timmermans, Harry

doi:10.3141/2175-13

Cited by 126 publications

(65 citation statements)

References 23 publications

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“…9 determine activity time scheduling. Some examples of simulations using such data can be found in (Janssens et al 2007;Bellemans et al 2010;Yang et al 2014). The results in Fig.…”

Section: Activity Patternsmentioning

confidence: 82%

Discovering urban activity patterns in cell phone data

et al. 2015

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Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.

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“…9 determine activity time scheduling. Some examples of simulations using such data can be found in (Janssens et al 2007;Bellemans et al 2010;Yang et al 2014). The results in Fig.…”

Section: Activity Patternsmentioning

confidence: 82%

Discovering urban activity patterns in cell phone data

et al. 2015

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“…These rules represent the scheduling process of activities and travel by the individuals (e.g. Arentze & Timmermans, 2004;Bellemans et al, 2010). Once established they can be used as the probabilistic basis for a micro-simulation process, in which complete daily activity-travel sequences for each individual in the whole region are synthesized, using Monte Carlo simulation methods.…”

Section: Activity-based Transportation Modelsmentioning

confidence: 99%

“…1 (e.g. Bellemans et al, 2010;Rasouli & Timmermans, 2013;Yagi & Mohammadian, 2010). The internal validation involves the comparison of the estimated results with expanded travel survey data which is not used in the training phase of the model but usually collected in the same survey period.…”

Section: Problem Statementmentioning

confidence: 99%

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

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel 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.

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“…Subsequently, amongst others, the location, transport mode and duration of the activity are determined, taking into account the attributes of the individual (Kochan et al 2008). Traffic demand is subsequently assigned to the road network in such a way that an equilibrium is established between transportation demand and supply (Bellemans et al 2010), which results in a time-dependent traffic state on the road network. In order to run, calibrate and validate the activity-based model, three major types of data are required (Kochan et al 2011); data describing the environment (e.g.…”

Section: Feathers Frameworkmentioning

confidence: 99%

“…It is necessary to indicate that the FEATHERS model (Bellemans et al 2010) models the transportation demand of a population who is aware of the state of the transportation network. Hence, the assumed travel times during the activity-travel planning phase are in correspondence with the travel times obtained after assigning the total traffic demand to the road network (this is achieved through iteration).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling

Pirdavani

Brijs

Bellemans

et al. 2013

Accident Analysis & Prevention

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Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system's effectiveness by changing travel behavior. The primary objective to implement such TDM strategies is not to improve traffic safety, although their impact on traffic safety should not be neglected. The main purpose of this study is to evaluate the traffic safety impact of conducting a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) in Flanders, Belgium. Since TDM strategies are usually conducted at an aggregate level, Crash Prediction Models (CPMs) should also be developed at a geographically aggregated level. Therefore Zonal Crash Prediction Models (ZCPMs) are considered to present the association between observed crashes in each zone and a set of predictor variables. To this end, an activitybased transportation model framework is applied to produce exposure metrics which will be used in prediction models. This allows us to conduct a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models unlike traditional models in which the impact of TDM strategies are assumed. The crash data used in this study consist of fatal and injury crashes observed between 2004 and 2007. The network and socio-demographic variables are also collected from other sources. In this study, different ZCPMs are developed to predict the Number of Injury Crashes (NOCs) (disaggregated by different severity levels and crash types) for both the null and the fuel-cost increase scenario. The results show a considerable traffic safety benefit of conducting the fuel-cost increase scenario apart from its impact on the reduction of the total Vehicle Kilometers Travelled (VKT). A 20% increase in fuel price is predicted to reduce the annual VKT by 5.02 billion (11.57% of the total annual VKT in Flanders), which causes the total NOCs to decline by 2.83%.

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Implementation Framework and Development Trajectory of FEATHERS Activity-Based Simulation Platform

Cited by 126 publications

References 23 publications

Discovering urban activity patterns in cell phone data

Discovering urban activity patterns in cell phone data

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

Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling

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