Bruno Kochan

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

To facilitate the development of dynamic activity-based models for transport demand, the FEATHERS framework was developed. This framework suggests a four-stage development trajectory for a smooth transition from the four-step models toward static activity-based models in the short term and dynamic activity-based models in the long term. The development stages discussed in this paper range from an initial static activity-based model without traffic assignment to a dynamic activity-based model that incorporates rescheduling, learning effects, and traffic routing. To illustrate the FEATHERS framework, work that has been done on the development of static and dynamic activity-based models for Flanders (Belgium) and the Netherlands is discussed. First, the data collection is presented. Next, the four-stage activity-based model development trajectory is discussed in detail. The paper concludes with the presentation of the modular FEATHERS framework, which discusses the functionalities of the modules and how they accommodate the requirements imposed on the framework by each of the four stages.

Activity-Based Modeling to Predict Spatial and Temporal Power Demand of Electric Vehicles in Flanders, Belgium

Electric power demand for household-generated traffic was estimated as a function of time and space for the region of Flanders, Belgium. An activity-based model was used to predict traffic demand. Electric vehicle (EV) type and charger characteristics were determined on the basis of car ownership and on the assumption that the market shares of EV categories would be similar to the current ones for internal combustion engine vehicles published in government statistics. Charging opportunities at home and work locations were derived from the predicted schedules and the estimation of the possibility to charge at work. Simulations were run for several levels of EV market penetration and for specific ratios of battery-only electric vehicles (BEVs) to pluggable hybrid electric vehicles. A single car was used to drive all trips in a daily schedule. Most of the Flemish schedules could be driven entirely by a BEV even after the published range values were reduced to account for range anxiety and for the overestimated ranges resulting from tests in accordance with standards. The current overnight period for low-tariff electricity was found to be sufficiently long to allow for individual cost optimizing while minimizing the peaks for overall power demand.

Field Evaluation of Personal Digital Assistant Enabled by Global Positioning System: Impact on Quality of Activity and Diary Data

A custom tool, PARROTS [Personal Digital Assistant (PDA) system for Activity Registration and Recording of Travel Scheduling] was developed to collect both activity data and global positioning system (GPS) data. This tool is currently deployed in a survey carried out on 2,500 households in Flanders (Belgium). The GPS-enabled PDA data collection tool features default answers, predefined drop-down lists, and many other graphical design elements. Two types of data were collected using PARROTS: activity and travel diaries input by the respondents and location data logged by a GPS receiver. To judge the effect of the PARROTS tool on the quality of activity and travel diaries, a paper-and-pencil diary was designed and deployed as well, and various analyses were performed on both the paper-and-pencil and PDA data. For the collected GPS data, the data quality was investigated in terms of availability of location information in the logs. In addition to investigating data quality, the impact of using PDA technology on user response rates was examined and compared with response rates for the paper-and-pencil format. The PARROTS tool provided high-quality activity and travel diary data, and it enabled the collection of scheduling and rescheduling information that would be too burdensome to collect using paper-and-pencil surveys. Moreover, PARROTS was able to collect GPS-based location information, and it made the data readily available in electronic form, while keeping the burden for the respondents at an acceptable level.

Implementation and validation of a modeling framework to assess personal exposure to black carbon

Because people tend to move from one place to another during the day, their exposure to air pollution will be determined by the concentration at each location combined with the exposure encountered in transport. In order to estimate the exposure of individuals in a population more accurately, the activity-based modeling framework for Black Carbon exposure assessment, AB(2)C, was developed. An activity-based traffic model was applied to model the whereabouts of individual agents. Exposure to black carbon (BC) in different microenvironments is assessed with a land use regression model, combined with a fixed indoor/outdoor factor for exposure in indoor environments. To estimate exposure in transport, a separate model was used taking into account transport mode, timing of the trip and degree of urbanization. The modeling framework is validated using weeklong time-activity diaries and BC exposure as revealed from a personal monitoring campaign with 62 participants. For each participant in the monitoring campaign, a synthetic population of 100 model-agents per day was made up with all agents meeting similar preconditions as each real-life agent. When these model-agents pass through every stage of the modeling framework, it results in a distribution of potential exposures for each individual. The AB(2)C model estimates average personal exposure slightly more accurately compared to ambient concentrations as predicted for the home subzone; however the added value of a dynamic model lies in the potential for detecting short term peak exposures rather than modeling average exposures. The latter may bring new opportunities to epidemiologists: studying the effect of frequently repeated but short exposure peaks on long term exposure and health.

Integrated health impact assessment of travel behaviour: model exploration and application to a fuel price increase.

Transportation policy measures often aim to change travel behaviour towards more efficient transport. While these policy measures do not necessarily target health, these could have an indirect health effect. We evaluate the health impact of a policy resulting in an increase of car fuel prices by 20% on active travel, outdoor air pollution and risk of road traffic injury. An integrated modelling chain is proposed to evaluate the health impact of this policy measure. An activity-based transport model estimated movements of people, providing whereabouts and travelled kilometres. An emission- and dispersion model provided air quality levels (elemental carbon) and a road safety model provided the number of fatal and non-fatal traffic victims. We used kilometres travelled while walking or cycling to estimate the time in active travel. Differences in health effects between the current and fuel price scenario were expressed in Disability Adjusted Life Years (DALY). A 20% fuel price increase leads to an overall gain of 1650 (1010-2330) DALY. Prevented deaths lead to a total of 1450 (890-2040) Years Life Gained (YLG), with better air quality accounting for 530 (180-880) YLG, fewer road traffic injuries for 750 (590-910) YLG and active travel for 170 (120-250) YLG. Concerning morbidity, mostly road safety led to 200 (120-290) fewer Years Lived with Disability (YLD), while air quality improvement only had a minor effect on cardiovascular hospital admissions. Air quality improvement and increased active travel mainly had an impact at older age, while traffic safety mainly affected younger and middle-aged people. This modelling approach illustrates the feasibility of a comprehensive health impact assessment of changes in travel behaviour. Our results suggest that more is needed than a policy rising car fuel prices by 20% to achieve substantial health gains. While the activity-based model gives an answer on what the effect of a proposed policy is, the focus on health may make policy integration more tangible. The model can therefore add to identifying win-win situations for both transport and health.

Dynamic Activity-Travel Diary Data Collection Using a GPS-Enabled Personal Digital Assistant

Activity-based transportation models have set the standard for modeling travel demand for the last decade. It seems common practice nowadays to collect the date to estimate these activity-based transportation models by means of activity-travel diaries. This paper presents a general functional framework of an advanced activity-travel diary data collection application to be deployed on a GPS-enabled personal digital assistant (PDA). The different modules, which are the building blocks of the application, will be scruitinized as well.

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

Travel demand management (TDM) consists of a variety of policy measures that affect the transportation systems 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 activity-based 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 traveled (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%.

Activity based models for countrywide electric vehicle power demand calculation

Smart grid design depends on the availability of realistic data. In the near future, energy demand by electric vehicles will be a substantial component of the overall demand and peaks of required power could become critical in some regions. Transportation research has been using micro-simulation based activity-based models for traffic forecasting. The resulting trip length distribution allows to estimate to what extent internal combustion engine vehicles can be substituted by electric vehicles. Second, combining the results emerging from activity based models with assumptions on electric vehicles market share, allows to predict energy and power demand in time and space. Furthermore, smart grid management effects can be investigated using activity based models because generated schedules determine how charging periods can float in time. This paper presents results calculated for the Flanders region.

Application of Different Exposure Measures in Development of Planning-Level Zonal Crash Prediction Models

Assessment of the safety impacts of travel demand management (TDM) policies must be conducted proactively. Because TDM policies are typically implemented at an aggregate level, crash prediction models should also be developed at a similar level. The resolution of these models should better match that at which evaluations of TDM policies are performed. Therefore, in this study zonal crash prediction models were considered to establish an association between observed crashes and a set of predictor variables in each zone. This analysis was performed with the generalized linear modeling procedure and the assumption of a negative binomial error distribution. Different exposure, network, and sociodemographic variables for 2,200 traffic analysis zones were considered predictors of crashes in the study area of Flanders, Belgium. An activity-based transportation model framework was applied to produce exposure measurements for crash data that consisted of injury crashes recorded between 2004 and 2007. Network and sociodemographic characteristics were also collected. Different zonal crash prediction models were developed to predict the number of injury crashes, including crashes involving fatalities and severe and slight injuries. These models were classified into three groups: (a) flow-based models, (b) trip-based models, and (c) a combination of the two. The results showed considerable improvement of model performance when both trip-based and flow-based exposure variables were used simultaneously in the formulation of the model. The main purpose of this study was to develop a predictive tool that could be used at the planning level to evaluate the impacts of different TDM policies on traffic safety.

Validation of an Activity-Based Traffic Demand Model for Flanders Implemented in the FEATHERS Simulation Platform

In this chapter the different steps in the operationalization of an activity-based model for Flanders (Belgium) inside the ‘Forecasting Evolutionary Activity-Travel of Households and their Environmental RepercussionS’ (FEATHERS) framework are worked out. In order to run the activity-based model for the Flemish situation, several data layers inside the FEATHERS database system have to be prepared. To this end, activity-based schedule information, a synthetic population data set and environment information about the study area in terms of zoning system, land use and transportation system have to be processed. In a second part, the chapter discusses the validation of the modelling results. Based on the validation results, it is demonstrated that the presented activity-based model is able to realistically mimic the spatial and temporal dimension of transportation demand in Flanders, as well as the evolution of the state of the road network in both space and time.