Tom Bellemans

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.

Street characteristics and traffic factors determining road users' exposure to black carbon

Many studies nowadays make the effort of determining personal exposure rather than estimating exposure at the residential address only. While intra-urban air pollution can be modeled quite easily using interpolation methods, estimating exposure in transport is more challenging. The aim of this study is to investigate which factors determine black carbon (BC) concentrations in transport microenvironments. Therefore personal exposure measurements are carried out using portable aethalometers, trip diaries and GPS devices. More than 1500 trips, both by active modes and by motorized transport, are evaluated in Flanders, Belgium. GPS coordinates are assigned to road segments to allow BC concentrations to be linked with trip and road characteristics (trip duration, degree of urbanization, road type, traffic intensity, travel speed and road speed). Average BC concentrations on highways (10.7μg/m(3)) are comparable to concentrations on urban roads (9.6μg/m(3)), but levels are significantly higher than concentrations on rural roads (6.1μg/m(3)). Highways yield higher BC exposures for motorists compared to exposure on major roads and local roads. Overall BC concentrations are elevated at lower speeds (<30km/h) and at speeds above 80km/h, in accordance to vehicle emission functions. Driving on roads with low traffic intensities resulted in lower exposures than driving on roads with higher traffic intensities (from 5.6μg/m(3) for roads with less than 500veh/h, up to 12μg/m(3) for roads with over 2500veh/h). Traffic intensity proved to be the major explanatory variable for in-vehicle BC exposure, together with timing of the trip and urbanization. For cyclists and pedestrians the range in BC exposure is smaller and models are less predictive; for active modes exposure seems to be influenced by timing and degree of urbanization only.

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.

A Conceptual Design of an Agent-based Interaction Model for the Carpooling Application

Abstract Carpooling is an emerging alternative transportation mode that is eco-friendly and sustainable as it enables commuters to save time, travel resource, reduce emission and traffic congestion. The procedure of carpooling consists of a number of steps namely (i) create a motive to carpool, (ii) communicate this motive with other interested agents, (iii) negotiate a plan with the interested agents, (iv) execute the agreed plans and (v) provide a feedback to all concerned agents. The state-of-the-art research work on agent-based modeling is limited to a number of technical and empirical studies that are unable to handle the complex agent behavior in terms of coordination, communication and negotiations. In this paper we present a conceptual design of an agent-based model (ABM) for the carpooling application that serves as a proof of concept. Our agent-based model for the carpooling application is a computational model that is used for simulating the interactions of autonomous agents and to analyze the effects of change in factors related to the infrastructure, behavior and cost. In our agent-based carpooling application we use agent profiles and social networks to initiate our agent communication model and then employ a route matching algorithm and a utility function to trigger the negotiation process between agents. We plan to, as a part of the future work, develop a prototype of our agent-based carpooling application on the basis of the work presented in this paper. Furthermore, we also intend to carry out a validation study of our results with real data.

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.

Application of Geographically Weighted Regression Technique in Spatial Analysis of Fatal and Injury Crashes

AbstractGeneralized linear models (GLMs) are the most widely used models utilized in crash prediction studies. These models illustrate the relationships between the dependent and explanatory variables by estimating fixed global estimates. Since crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is examined by means of calculating Moran’s I measures for dependent and explanatory variables. The results indicate the necessity of considering spatial correlation when developing crash prediction models. The main objective of this research is to develop different zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear model (GWGLM) framework in order to explore the spatial variations in association between number of injury crashes (NOICs) (including fatal, severely, and slightly injured crashes) and other explanatory variables. Different exposure, network, and sociodemographic variabl...

An agent-based model to evaluate carpooling at large manufacturing plants

Abstract Carpooling is thought to be part of the solution to resolve traffic congestion in regions where large companies dominate the traffic situation because coordination and matching between commuters is more likely to be feasible in cases where most people work for a single employer. Moreover, carpooling is not very popular for commuting. In order for carpooling to be successful, an online service for matching commuter profiles is indispensable due to the large community involved. Such service is necessary but not sufficient because carpooling requires rerouting and activity rescheduling along with candidate matching. We advise to introduce services of this kind using a two step process: (1) an agentbased simulation is used to investigate opportunities and inhibitors and (2) online matching is made available. This paper describes the challenges to build the model and in particular investigates possibilities to derive the data required for commuter behavior modeling from big data (such as GSM, GPS and/or Bluetooth).

Exploiting graph-theoretic tools for matching in carpooling applications

An automatic service to match commuting trips has been designed. Candidate carpoolers register their personal profile and a set of periodically recurring trips. The Global CarPooling Matching Service shall advise registered candidates how to combine their commuting trips by carpooling. Planned periodic trips correspond to nodes in a graph; the edges are labeled with the probability for for success while negotiating to merge two planned trips by carpooling. The probability values are calculated by a learning mechanism using on one hand the registered person and trip characteristics and on the other hand the negotiation feedback. The probability values vary over time due to repetitive execution of the learning mechanism. As a consequence, the matcher needs to cope with a dynamically changing graph both with respect to topology and edge weights. In order to evaluate the matcher performance before deployment in the real world, it will be exercised using a large scale agent based model. This paper describes both the exercising model and the matcher.

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.