Synthesis of first practices and operational research approaches in activity-based travel demand modeling

Davidson, William A.; Donnelly, Rick; Vovsha, Peter; Freedman, Joel; Ruegg, Steve; Hicks, Jim; Castiglione, Joe; Picado, Rosella

doi:10.1016/j.tra.2006.09.003

Cited by 97 publications

(73 citation statements)

References 8 publications

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“…In this modeling paradigm, travel is analyzed through daily patterns of activity behavior related to and derived from the context of land-use and transportation network as well as personal characteristics such as social-economic background, lifestyles and needs of individuals (e.g. Bhat & Koppelman, 1999;Davidson et al, 2007;Fan & Khattak, 2012;Lemp et al, 2007;Wegener, 2013). All the above information, complemented with a training set of household travel surveys which document the full daily activity-travel sequences of a small sample of individuals during one or a few days, is analyzed and translated into heuristic decision making strategy rules.…”

Section: Activity-based Transportation Modelsmentioning

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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Section: Activity-based Transportation Modelsmentioning

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

View full text Add to dashboard Cite

show abstract

“…This is why activity-based models have been taken into consideration. The main difference between four-step models and activity-based transportation models is that the latter try to predict interdependencies between several facets of activity profiles (Davidson et al 2007). Hence, activity-based models are designed to keep the linkages between the travel decisions of individual members of a single household.…”

Section: Impact Of Fuel-related Cost On Traffic Demandmentioning

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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“…On an international level, activity-based models have become the norm to model travel behaviour (Davidson et al, 2007). The most important characteristic of these models is that the travel behaviour of persons or families is a product of the activities that they wish or have to perform, procuring a more realistic description and a better understanding of people's travel behaviour (Cirillo et al, 2012;Flötteröd et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

et al. 2013

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The aim of this research is to identify the relationship between activity patterns and route choice decisions. The focus is twofold: on the one hand, the relationship between the purpose of a trip and the road categories used for the relocation is investigated; on the other hand, the relationship between the purpose of a trip and the deviation from the shortest path is studied. The data for this study were collected in 2006 and 2007 in Flanders, the Dutch speaking and northern part of Belgium. To estimate the relationship between the primary road category travelled on and the corresponding activity-travel behaviour a multinomial logit model is developed. To estimate the relationship between the deviation from the shortest path and the corresponding activity-travel behaviour a Tobit model is developed. The results of the first model point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modelling route choices or in general for traffic assignment procedures is the conclusion that activity patterns have a clear influence on the road category primarily driven on. Particularly, it was shown that the likelihood of taking primarily through roads is highest for work trips and lowest for leisure trips. The second model shows a significant relationship between the deviation from the shortest path and the purpose of the trip. Furthermore, next to trip-related attributes (trip distance), also socio-demographic variables and geographical differences play an important role. These results certainly suggest that traffic assignment procedures should be developed that explicitly take into account an activity-based segmentation. In addition, it was shown that route choices were similar during peak and off-peak periods. This is an indication that car drivers are not necessarily utility maximizers, or that classical utility functions in the context of route choices are omitting important explanatory variables.

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Synthesis of first practices and operational research approaches in activity-based travel demand modeling

Cited by 97 publications

References 8 publications

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

Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data

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