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

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

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

Semantic Annotation of GPS Traces: Activity Type Inference

peer reviewedDue to the rapid development of technology, larger data sets concerning activity travel behavior become available. These data sets often lack semantic interpretation. This implies that annotation in terms of activity type and transportation mode is necessary. This paper aims to infer activity types from GPS traces by developing a decision tree-based model. The model only considers activity start times and activity durations. Based on the decision tree classification, a probability distribution and a point prediction model were constructed. The probability matrix describes 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 selects the activity type that has 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 comprises 18 leaves. Consequently, 18 if-then rules were derived. An accuracy of 74% was achieved when training the tree. The accuracy of the model for the validation set, i.e. 72.5%, shows that overfitting is minimal. When applying the model to the test set, the accuracy was almost 76%. The models indicate the importance of time information in the semantic enrichment process. This study contributes to future data collection in that it enables researchers to directly infer activity types from activity start time and duration information obtained from GPS data. Because no location information is needed, this research can be easily and readily implemented to millions of individual agents

Modeling Route Choice of Car Travelers using an Activity-Based Segmentation

peer reviewedThe aim of this research is to identify the relationships between activity patterns and route choice decisions. The focus is turned to the relationship between the purpose of a trip and the road categories used for the relocation. 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 he primary road category traveled on and the corresponding activity-travel behavior a multinomial logit model is developed. The results point out that route choice is a function of multiple factors, not just travel time or distance. Crucial for modeling 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. This certainly suggests 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. A potential pathway for further investigating route choice decisions might lie in the roots of more psychological underpinnings

Reduction of the immunogenicity of a lentiviral vector using miRNA target sequences: Evaluation of the effect of injection site in a mouse model

status: publishe

Accident Information from six European Countries Based on Self-reports

A questionnaire survey has been conducted in Belgium, Denmark, Germany, Poland, Spain and Sweden in 2016­2017. Once every third month through one year respondents have received a link to an online questionnaire which asked them about information on any traffic accidents they might have experienced in the period. The questionnaire contains questions on various aspects related to the accidents that might contribute with costs as well as basic accident information such as means of transport and time of the accident. A special focus in the survey is on pedestrian single accidents, which are not normally considered traffic accidents. The survey finds that more than 80% of the pedestrian accidents that have been self-­reported are in fact single accidents, which illustrates the need for further investigation of the pedestrian single accidents as the number of these might be quite high. The study also provides knowledge of basic consequences of the pedestrian falls, for instance 16% result in medical treatment, 14% in one or more days of absence from work and 37% in property damage. The self-­reported traffic accidents have proved difficult to compare with official accident statistics, both due to different national guidelines on what constitutes a reportable accident and to the legal limitations on personal information which may be asked in the questionnaire; this eliminates the possibility of combining information with official accident records. However, based on the self-reports, it can be concluded that in 8% of the accidents the respondent have been in contact with the police

Assessment of Safety of VRUs Based on Self-Reporting of Accidents and Near-Accidents

This report describes a study on self-reporting of accidents and near-accidents that was carried out to gain more knowledge about the safety of vulnerable road users, i.e. pedestrians, cyclists and moped riders. In the study, the participants registered their accidents and near-accidents in monthly questionnaires for a period of nine months (01.09.2016 - 31.05.2017). The study was conducted in Belgium, Denmark, Spain and Sweden. In total, 2343 participants contributed to the study, mainly from Belgium and Denmark. Therefore, the results in this report are based on the Belgian and Danish data. The results of the study show that more than one third of the registered accidents are single accidents of cyclists and pedestrians. In most cases, the registered accidents are less severe than what is registered by the police or at the hospital. The results indicate that as few as 2-7% of the participants, who were involved in an accident, have been in contact with the police. Furthermore, only 9% have registered that they had received treatment at the hospital or emergency room. This study thus indicates that selfreporting is a useful tool for gaining knowledge about a larger share of accidents. By including near-accidents as well, the amount of data can be further increased