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See detailSemantic Annotation of Global Positioning System Traces: Activity Type Inference
Reumers, Sofie; Liu, Feng; Janssens, Davy et al

in Transportation Research Record: Journal of the Transportation Research Board (2013), 2383

Because of the rapid development of technology, larger data sets on activity travel behavior have become available. These data sets often lack semantic interpretation. This lack of interpretation implies ... [more ▼]

Because of the rapid development of technology, larger data sets on activity travel behavior have become available. These data sets often lack semantic interpretation. This lack of interpretation implies that annotation of activity type and transportation mode is necessary. This paper aims to infer activity types from Global Positioning System (GPS) traces by developing a decision tree-based model. The model considers only activity start times and activity durations. On the basis of the decision tree classification, a probability distribution and a point prediction model were constructed. The probability matrix described the probability of each activity type for each class (i.e., combination of activity start time and activity duration). In each class, the point prediction model selected the activity type that had the highest probability. Two types of data were collected in 2006 and 2007 in Flanders, Belgium (i.e., activity travel data and GPS data). The optimal classification tree constructed contained 18 leaves. Consequently, 18 if-then rules were derived. An accuracy of 74% was achieved when the tree was trained. The accuracy of the model for the validation set (72.5%) showed that overfitting was minimal. When the model was applied to the test set, the accuracy was almost 76%. The models indicated the importance of time information in the semantic enrichment process. This study contributes to future data collection in that it enables researchers to infer activity types directly from activity start time and duration information obtained from GPS data. Because no location information is needed, this research can be easily and readily applied to millions of individual agents. [less ▲]

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See detailIdentifying the Determinants of Light Rail Mode Choice for Medium- and Long-Distance Trips: Results from a Stated Preference Study
Creemers, Lieve; Cools, Mario ULg; Tormans, Hans et al

in Transportation Research Record: Journal of the Transportation Research Board (2012), 2275

he introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of ... [more ▼]

he introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium- and long-distance trips (10 to 40 km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system-specific factors (i.e., travel cost, in-vehicle travel time, transit punctuality, waiting time, access and egress time, transfers, and availability of seats) as well as the travelers' personal traits were analyzed by using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey conducted in Flanders. The modeling results are in line with literature: most transport system-specific factors as well as socioeconomic variables, attitudinal factors, perceptions, and the frequency of using public transport contribute significantly to the preference for light rail transit. In particular, the results indicate that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access-egress time. Seat availability appeared to play a more important role than did transfers in deciding to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful. [less ▲]

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See detailChanges in Travel Behavior in Response to Weather Conditions: Do Type of Weather and Trip Purpose Matter
Cools, Mario ULg; Moons, Elke; Creemers, Lieve et al

in Transportation Research Record: Journal of the Transportation Research Board (2010), 2157

Weather can influence travel demand, traffic flow, and traffic safety. A hypothesis—the type of weather determined the likelihood of a change in travel behavior, and changes in travel behavior because of ... [more ▼]

Weather can influence travel demand, traffic flow, and traffic safety. A hypothesis—the type of weather determined the likelihood of a change in travel behavior, and changes in travel behavior because of weather conditions depended on trip purpose—was assayed. A stated adaptation study was conducted in Flanders (the Dutch-speaking region of Belgium). A survey, completed by 586 respondents, was administered both on the Internet and as a traditional paper-and-pencil questionnaire. To ensure optimal correspondence between the survey sample composition and the Flemish population, observations in the sample were weighted. To test the main hypotheses, Pearson chi-square independence tests were performed. Results from both the descriptive analysis and the independence tests confirm that the type of weather matters and that changes in travel behavior in response to these weather conditions are highly dependent on trip purpose. This dependence of behavioral adjustments on trip purpose provides policy makers with a deeper understanding of how weather conditions affect traffic. Further generalizations of the findings are possible by shifting the scope toward revealed travel behavior. Triangulation of both stated and revealed travel behavior on the one hand and traffic intensities on the other hand is a key challenge for further research. [less ▲]

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See detailAssessing the Impact of Public Holidays on Travel Time Expenditure: Differentiation by Trip Motive
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2010), 2157

The impact of public holidays on the underlying reasons for travel behavior, namely, the activities people perform and the trips made, is seldom investigated. Therefore, the effect of holidays on travel ... [more ▼]

The impact of public holidays on the underlying reasons for travel behavior, namely, the activities people perform and the trips made, is seldom investigated. Therefore, the effect of holidays on travel time expenditure in Flanders, differentiated by trip motive, is examined. The data used for the analysis stem from a household travel survey carried out in 2000. The zero-inflated Poisson regression approach is used; it explicitly takes into account the inherent contrast between travelers and nontravelers. The zero-inflated Poisson regression models yield findings that are harmonious with international literature: socio-demographic variables, temporal effects, and transportation preferences contribute significantly to unraveling the variability of travel behavior. In particular, it is shown that the effect of public holidays on daily travel behavior cannot be ignored. Triangulation of quantitative and qualitative techniques is a solid basis for insight into the underpinnings of travel behavior. [less ▲]

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See detailCalibrating Activity-Based Models with External Origin-Destination Information: Overview Of Different Possibilities
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2010), 2175

Many practitioners question the advantages of activity-based models over conventional four-step models in regard to replication of traffic counts. This paper highlights a framework that actively links ... [more ▼]

Many practitioners question the advantages of activity-based models over conventional four-step models in regard to replication of traffic counts. This paper highlights a framework that actively links travel demand models in general and activity-based models in particular with traffic counts. Two approaches are presented that calibrate activity-based models with traffic count—an indirect and a direct approach. The indirect approach tries to incorporate findings based on the analysis of traffic counts into the components of the activity-based models. The direct approach calibrates the parameters of the travel demand model in such a way that the model replicates the observed traffic counts (quasi-) perfectly. A practical example is provided to illustrate the direct approach. The study area for this practical example is Hasselt, Belgium, a city of about 70,000 residents, and its surrounding municipalities. The practical examples revealed not a single roadway to success in calibrating activity-based models, but different options exist in fine-tuning the activity-based model. It is important to recognize some open issues and avenues for further research. First, it is not always appropriate to assume that traffic counts are completely correct. Setting up some belief structure might increase the responsiveness of the activity-based model. In addition, the origin-destination matrix calibration that optimizes the correspondence between estimated and observed screen-line counts could negatively affect the correspondence to other measures, such as vehicle miles traveled. To conclude, the formulation of a multiobjective calibration method is a key challenge for further research. [less ▲]

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See detailAssessing the Quality of Origin-Destination Matrices Derived from Activity Travel Surveys: Results from a Monte Carlo Experiment
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2010), 2183

To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on ... [more ▼]

To support policy makers combating travel-related externalities, quality data are required for the design and management of transportation systems and policies. To this end, much money has been spent on collecting household- and person-based data. The main objective of this paper is to assess the quality of origin-destination (O-D) matrices derived from household activity travel surveys. To this purpose, a Monte Carlo experiment is set up to estimate the precision of O-D matrices given different sampling rates. The Belgian 2001 census data, containing work- and school-related travel information for all 10,296,350 residents, are used for the experiment. For different sampling rates, 2,000 random stratified samples are drawn. For each sample, three O-D matrices are composed: one at the municipality level, one at the district level, and one at the provincial level. The correspondence between the samples and the population is assessed by using the mean absolute percentage error (MAPE) and a censored version of the MAPE (MCAPE). The results show that no accurate O-D matrices can be derived directly from these surveys. Only when half of the population is queried is an acceptable O-D matrix obtained at the provincial level. Therefore, use of additional information to grasp better the behavioral realism underlying destination choices and collection of information about particular O-D pairs by means of vehicle intercept surveys are recommended. In addition, results suggest using the MCAPE next to traditional criteria to examine dissimilarities between different O-D matrices. An important avenue for further research is the investigation of the effect of sampling proportions on travel demand model outcomes. [less ▲]

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See detailRoad Pricing as an Impetus for Environment-Friendly Travel Behavior: Results from a Stated Adaptation Experiment
Janssens, Davy; Cools, Mario ULg; Moons, Elke et al

in Transportation Research Record: Journal of the Transportation Research Board (2009), 2115

An important policy instrument for governments to modify travel behavior and manage the increasing travel demand is the introduction of a congestion pricing system. In this study, the influence of a ... [more ▼]

An important policy instrument for governments to modify travel behavior and manage the increasing travel demand is the introduction of a congestion pricing system. In this study, the influence of a detailed classification of activities is examined to assess likely traveler response to congestion pricing scenarios. Despite the fact that most studies do not differentiate between activity categories, the value of time and in general the space–time properties and constraints of different types of activities vary widely. For this reason, it is of importance to provide sufficient detail and sensitivity in assessing the impact of congestion pricing scenarios. In addition, a first assessment of travelers’ possible multifaceted adaptation patterns is presented. For these purposes, a stated adaptation study was conducted in Flanders, the Dutch-speaking region of Belgium. The experiment was conducted through an interactive stated adaptation survey. In the stated adaptation experiment, respondents could indicate their responses to the congestion pricing scenario. The most prevalent conclusion is that the activity type significantly predetermines the willingness to express a more environment-friendly behavior (i.e., reducing the number of trips, reducing the total distance traveled, switching to more environment-friendly modes). Also, the willingness to show ecological activity-travel behavior (e.g., carpooling and using public transport) in a nonpricing situation is a major differentiator of future behavior in a congestion pricing scenario. [less ▲]

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See detailInvestigating the Variability in Daily Traffic Counts Trough Use of ARIMAX and SARIMAX Models: Assessing the Effect of Holidays on Two Site Locations
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2009), 2136

In this paper, daily traffic counts are explained and forecast by different modeling philosophies: an approach using autoregressive integrated moving average (ARIMA) models with explanatory variables (i.e ... [more ▼]

In this paper, daily traffic counts are explained and forecast by different modeling philosophies: an approach using autoregressive integrated moving average (ARIMA) models with explanatory variables (i.e., the ARIMAX model) and approaches using a seasonal autoregressive integrated moving average (SARIMA) model as well as a SARIMA model with explanatory variables (i.e., the SARIMAX model). Special emphasis is placed on the investigation of seasonality in daily traffic data and on the identification and comparison of holiday effects at different sites. To get insight into prior cyclic patterns in the daily traffic counts, spectral analysis provides the required framework to highlight periodicities in the data. The analyses use data from single inductive loop detectors, which were collected in 2003, 2004, and 2005. Four traffic count locations are investigated in this study: an upstream and a downstream traffic count site on a highway used extensively by commuters, and an upstream and a downstream traffic count site on a highway typically used for leisure travel. The different modeling techniques show that weekly cycles appear to determine the variation in daily traffic counts. The comparison between seasonal and holiday effects at different site locations reveals that both the ARIMAX and the SARIMAX modeling approaches are valid frameworks for identifying and quantifying possible influencing effects. The techniques yield the insight that holidays have a noticeable impact on highways extensively used by commuters, while having a more ambiguous impact on highways typically used for leisure travel. Future research challenges are the modeling of daily traffic counts on secondary roads and the simultaneous modeling of underlying reasons for travel and revealed traffic patterns. [less ▲]

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See detailInvestigating Effect of Holidays on Daily Traffic Counts: Time Series Approach
Cools, Mario ULg; Moons, Elke; Wets, Geert

in Transportation Research Record: Journal of the Transportation Research Board (2007), 2019

In this paper, different modeling philosophies are explored in order to explain and forecast daily traffic counts. The main objectives of this study are the analysis of the impact of holidays on daily ... [more ▼]

In this paper, different modeling philosophies are explored in order to explain and forecast daily traffic counts. The main objectives of this study are the analysis of the impact of holidays on daily traffic, and the forecasting of future traffic counts. Data coming from single inductive loop detectors, collected in 2003, 2004 and 2005, were used for the analysis. The different models that were investigated showed that the variation in daily traffic counts could be explained by weekly cycles. The Box-Tiao modeling approach was applied to quantify the effect of holidays on daily traffic. The results showed that traffic counts were significantly lower for holiday periods. When the different modeling techniques were compared with respect to forecasting with a large forecast horizon, Box-Tiao modeling clearly outperformed the other modeling strategies. Simultaneous modeling of both the underlying reasons of travel, and revealed traffic patterns, certainly is a challenge for further research. [less ▲]

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