In the field of transportation, the organization of daily activity chains has become more stressed, because the fast execution of the numerous tasks is a primary aspect. In order to reach high performance in the organization of the tasks, the attributes of the demand points, the transportation network and the external circumstances, as the changing traffic situation also have to be taken into account.
The objective of the project is to develop theoretical model to organize and supervise the daily activity chains.
The aim is to improve the basically for logistic processes by using of TSP method and apply it for personal transportation purposes. The method offers a location based service, which results the optimal order of the tasks based on subjective parameters.
Further aim of the project was to examine traffic flow estimation and the possibilities of intelligent transportation control from different data acquisition technologies, especially mobile network data.
The first step of the implementation is the elaboration of a data model, which is important to the construction of the daily activity chain. The model contains a Passenger table, where data about the passengers are stored. Some personal parameters belong to all passengers, which can be found in the Preferences table. These could denote preference of the transportation mode, disabilities or other factors. All preferences can be set in the interval of 1-5, which defines, how much the passenger demands the given service.
The certain points are contained in the POI table, which describes the name of the point, its address, opening hours and other information. These POI-s can be classified into types (e.g. food, sport, institutes and other categories), which is important because usually the passengers do not search for specific shops, but activity types. Using these categories finding the demanded activity becomes easier.
The most important table is the activity, where the passengers can construct their daily activity chains. To each activity, which can be realized at the point (POI), belong time intervals. The duration, the time when passenger would demand the service (TD) and the processing time (TP), which is the duration of the service. The POI_ID field is optional, when filled, it is a fix point. Furthermore, the priority should be filled, which denotes the importance of the activity.
In the next step using the TSP-TW method the travel times can be calculated and optimized according to the activity chain. Then the order of the points to be visited can be defined. The exact elaboration and implementation of this algorithm was the next relevant step of the research.
The knowledge about the daily activities of inhabitants creates an important base for implementation of transport policy on local, regional and national level.