An estimation of total vehicle travel reduction in the case of telecommuting. Detailed analysis using an activity-based modeling approach

peer reviewedransportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the possible measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on peak-period trips. However it is less studied whether telecommuting also effectively and significantly reduces total vehicle travel. For this reason, a conventional modeling approach was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting would materialize in the Flanders area. In a second part, the paper also introduces the use of an activity-based modeling approach to evaluate the effect of telecommuting. By doing so, an operational activity-based framework is externally validated by means of another completely different model, both calibrated for the same application and study area

Uncertainty in forecasts of complex rule-based systems of travel demand: Comparative analysis of the Albatross/Feathers model system

peer reviewedThis paper documents the results of a comparative analysis of model uncertainty of the Albatross/Feathers model system for respectively the Rotterdam region, The Netherlands and Antwerp region, Belgium. The assessment concerned the calculation of the coefficient of variation for the daily distance travelled per person. The calculations are performed both at the aggregated level and the disaggregated level (e.g. disaggregation by certain socio-demographics). Results indicate that model uncertainty differs by socio-demographic groups. Results of a regression analysis also indicate that in both regions uncertainty in daily distance travelled per person is strongly correlated with the inverse square root of the relevant socio-demographic population and the complexity of the classification, measured in terms of the number of possible classes

Uncertainty in forecasts of complex rule-based systems of travel demand: Comparative analysis of the Albatross/Feathers model system

peer reviewedThis paper documents the results of a comparative analysis of model uncertainty of the Albatross/Feathers model system for respectively the Rotterdam region, The Netherlands and Antwerp region, Belgium. The assessment concerned the calculation of the coefficient of variation for the daily distance travelled per person. The calculations are performed both at the aggregated level and the disaggregated level (e.g. disaggregation by certain socio-demographics). Results indicate that model uncertainty differs by socio-demographic groups. Results of a regression analysis also indicate that in both regions uncertainty in daily distance travelled per person is strongly correlated with the inverse square root of the relevant socio-demographic population and the complexity of the classification, measured in terms of the number of possible classes

Optimizing copious activity type classes based on classi cation accuracy and entropy retention

Despite the advantages, big transport data are characterized by a considerable disadvantage as well. Personal and activity-travel information are often lacking, making it necessary to deduce this information with data mining techniques. However, some studies predict many unique activity type classes (ATCs), while others merge multiple activity types into larger ATCs. This action enhances the activity inference estimation, but destroys important activity information. Previous studies do not provide a strong justification for this practice. An objectively optimized set of ATCs, balancing model prediction accuracy and preserving activity information from the original data, becomes essential. Previous research developed a classification methodology in which the optimal set of ATCs was identified by analyzing all possible ATC combinations. However, this approach is practically impossible in a finite amount of time for e.g. the US National Household Travel Survey (NHTS) 2009 data set, which comprises 36 ATCs (home activity excluded), since there would be 3.82•1030 unique combinations (an exponential increase). The aim of this paper is to optimize which original ATCs should be grouped into a new class, and this for data sets for which it is impossible or impractical to simply calculate all ATC combinations. The proposed method defines an optimization parameter U (based on classification accuracy and information retention) which is maximized in an iterative local search algorithm. The optimal set of ATCs for the NHTS 2009 data set was determined. A comparison finds that this optimum is considerably better than many expert opinion activity type classification systems. Convergence was confirmed and large performance gains were found

Efficient long-range conduction in cable bacteria through nickel protein wires

Filamentous cable bacteria display long-range electron transport, generating electrical currents over centimeter distances through a highly ordered network of fibers embedded in their cell envelope. The conductivity of these periplasmic wires is exceptionally high for a biological material, but their chemical structure and underlying electron transport mechanism remain unresolved. Here, we combine high-resolution microscopy, spectroscopy, and chemical imaging on individual cable bacterium filaments to demonstrate that the periplasmic wires consist of a conductive protein core surrounded by an insulating protein shell layer. The core proteins contain a sulfur-ligated nickel cofactor, and conductivity decreases when nickel is oxidized or selectively removed. The involvement of nickel as the active metal in biological conduction is remarkable, and suggests a hitherto unknown form of electron transport that enables efficient conduction in centimeter-long protein structures

Assessment of the Effect of Micro-Simulation Error on Key Travel Indices: Evidence from the Activity-Based Model FEATHERS

peer reviewedCurrent transportation models often do not explicitly address the degree of uncertainty in travel forecasts. Of particular interest in activity-based travel demand models is the model uncertainty that is caused by the statistical distributions of random components, i.e. micro-simulation error. Therefore, the main objective of this paper is to assess the impact of micro-simulation error on two key travel indices, namely the average daily number of trips per person and the average daily distance traveled per person. The effect of micro-simulation error will be investigated by running the activity-based modeling framework FEATHERS 200 times using the same 10% fraction of the population. Results show that micro-simulation errors are limited especially when disaggregation is limited to two levels. Notwithstanding, results indicate that for more elaborate analyses a 10% fraction might not be sufficient. The size of micro-simulation error increases along with complexity. Moreover, more commonly used transport modes such as using the car as driver have a lower error rate. Further research should investigate the impact of the population fraction on the micro-simulation error rates. Besides, one could also investigate other aspects (e.g. the number of activities) involved in the activity-scheduling process

An estimation of total vehicle travel reduction in the case of telecommuting. Detailed analyses using an activity-based modeling approach.

peer reviewedTransportation Demand Management (TDM) is often referred to as a strategy adopted by transport planners with the goal to increase transport system efficiency. One of the potential measures that can be adopted in TDM is the implementation of telecommuting. A significant number of studies have been conducted in the past to evaluate the effect of telecommuting on the amount of peak-period trips. However it is less studied whether telecommuting also effectively and significantly reduces total vehicle travel in terms of kilometers traveled throughout the day. For this reason, a conventional modeling approach was adopted in this paper to calculate total kilometers of travel saved in the case telecommuting would materialize in the Flanders area. In a second part, this paper introduces the use of an activity-based modeling approach to evaluate the effect of telecommuting on a more detailed time scale. As the second approach provides a more disaggregate result, both models can be compared on the more aggregate level to validate whether they correspond

Evaluating the Road Safety Effects of a Fuel Cost Increase Measure by means of Zonal Crash Prediction Modeling

ABSTRACT 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%