Modellering av järnvägstransporter: en översikt kring datakällor


Information on freight and passenger flows is vital for advancing knowledge on railway transport. In addition to providing a description of the current use of railway transportation, the flows of freight and passengers can be used in transport models to make transportation forecasts. Such forecasts can, for example, be used to evaluate infrastructure investments, or to analyze the impact of new restrictions or new policies. The transport statistics in Sweden are, however, quite aggregate, which makes forecasts and other types of estimations rather uncertain. There is therefore reason to describe the current modelling of railway transport, as well as its development and potential in combining different (and new) data sources to provide the information on railway transport that is requested.

Sampers and Samgods are the national models in Sweden for freight and passenger transports, respectively. Three types of data are used in Sampers: data describing travel behavior, information about the transport network and public transport services, as well as attraction measures for trips to the different geographical zones in the model. The output from the demand model are passenger flows for each transport mode and destination. Train passengers are distributed on the railway network based on current traffic flows, while anticipated timetables are used in the forecasts. This generates information on the number of trips for each railway link during a year (that is, between stations or nodes on railway lines), as well as the number of embarking and disembarking passengers on the railway stations. These results need validation, which inter alia is performed by regions within the Swedish Transport Administration, using passenger statistics.

Data inputs for Samgods are information on the transport network, cost data, regulations, production and consumption data and goods flows. The data on goods flows are gathered from foreign trade statistics and the national commodity flow survey. The production and consumption data are used together with the goods flows data to calculate the transported volume of 34 different commodity types between 464 zones (of which 290 are municipalities in Sweden). A separate tool called Bangods is used to generate rail freight transport on the different track sections comprising the railway network. This is based on rail traffic data, which however lacks information on the type of freight trains as well as commodity type and volume of goods per train. At present, the output for rail freight transport consists of goods flows during a year on the different track sections (about 300), segmented into different types of freight trains and 12 groups of commodities.

Transport modelling approaches are becoming more disaggregate. More specifically, there is a development towards activity-based models for passenger transport, while modelling of logistic choices at company level is becoming more common for freight transport. This development requires more disaggregate data, in both time and space. The rapid technological development has provided new opportunities in collecting disaggregate data, notably through low prices and improved performance of the technical units required to record and communicate information on the positioning of people, goods or vehicles. Examples on data sources are data from radionavigation-satellite services (Global Positioning System, GPS), cell phone data, Radio Frequency Identification (RFID) or other types of sensors (for train weight or number of embarking and disembarking passengers).

There are pros and cons with these different data sources in transport modelling; one type of data source cannot cover the information needs. A combination of data can therefore be a solution. For example, GPS data and cell phone data can be good complements, as the former have a higher level of detail while the latter have larger blind spots. Still, a general problem with the automatic collection of data is that only a sub-sample of the trips are captured; even though it generates more detailed information on the position and timing of trips compared to traditional travel surveys, it is still based on a sub-sample of the trips made, which can create a selection bias. Moreover, this type of data lacks information on socio-economic aspects and the purpose of the trip. However, information on work and home location can be derived from mobility patterns based on GPS or cell phone data, which in turn can be linked to socio-economic information (at least at an aggregate level). Nevertheless, information on passenger and freight flows can be useful in the calibration and validation of the transport models. For instance, train payload measurements can be used to derive information on the number of passengers on each train (potentially between all stations if the train has a weighing capability, or if enough stationary weight detectors are installed on the infrastructure). Moreover, even though the weight of a freight train does not reveal the commodity type, it can be used in the calibration of Samgods and provide a more accurate flow of goods on different trains and track sections. In other words, new technology and data collection methods provide a significant potential for generating more disaggregate data on transport flows.



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