Abstract

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port. Glavni namen raziskave je preučevanje vpliva vključevanja makroekonomskih kazalnikov na natančnost napovedovanja modela za predikcijo pretovora kontejnerjev. V ta namen sta uporabljeni dinamična faktorska analiza in Avtoregresivni integracijski model časovnih vrst s premikajočim povprečjem in eksogeninimi vhodi (ARIMAX). Obeh metodologiji sta integrirani v okviru inovativne štiristopenjske hevristične procedure. V prvem koraku najprej izluščimo dinamične faktorje iz zunanjih makroekonomskih kazalcev, ki vplivajo na opazovan pretovor. Nato v drugem koraku tvorimo celo družino ARIMAX modelov različnih redov na podlagi prej identificiranih dinamičnih faktorjev. V tretjem koraku iz- vedemo diagnostično testiranje modela vključno z validacijo kakovosti prileganja napovedi danim realnim podatkom. V sklopu tretjega koraka preverimo številne statistične kriterije, kriterije ocene napake modela, informacijske kriterije, ter preverimo optimalnost strukture modela. V zadnjem koraku s posebno hevristiko izberemo najboljši model in ga preizkusimo na dejanskih podatkih pretovora kontejnerjev v Luki Koper. Rezultati kažejo, da z uporabo makroekonomskih kazalnikov v predikcijskem modelu lahko dosežemo bolj natančne napovedi pretovora kontejnerjev. Raziskava nas prepriča, da bi predlagani model lahko predstavljal koristno okrepitev obstoječega sistema za napovedovanje v opazovanem pristanišču.

Abstract

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies Forecasting bike availability is of great importance when turning the shared bike into a reliable, pleasant and uncomplicated mode of transport. Several approaches have been developed to forecast bike availability in station-based bike sharing systems. However, dockless bike sharing systems remain fairly unexplored in that sense, despite their rapid expansion over the world in recent years. To fill this gap, this thesis aims to develop a generally applicable methodology for bike availability forecasting in dockless bike sharing systems, that produces automated, fast and accurate forecasts. To balance speed and accuracy, an approach is taken in which the system area of a dockless bike sharing system is divided into spatially contiguous clusters that represent locations with the same temporal patterns in the historical data. Each cluster gets assigned a model point, for which an ARIMA(p,d,q) forecasting model is fitted to the deseasonalized data. Each individual forecast will inherit the structure and parameters of one of those pre-build models, rather than building a new model on its own. The proposed system was tested through a case study in San Francisco, California. The results showed that the proposed system outperforms simple baseline methods. However, they also highlighted the limited forecastability of dockless bike sharing data.

Abstract

Traffic-related injuries represent a global public health problem, and contribute largely to mortality and years lived with disability worldwide. Over the course of the last decades, improvements to road traffic safety and injury surveillance systems have resulted in a shift in focus from the prevention of motor vehicle accidents to the control of injury events involving vulnerable road users (VRUs), such as cyclists and moped riders. There have been calls for improvements to the evaluation of safety interventions due to methodological problems associated with the most commonly used study designs. The purpose of this licentiate thesis was to assess the strengths and limitations of the interrupted time series (ITS) design, which has gained some attention for its ability to provide valid effect estimates. Two national road safety interventions involving VRUs were selected as cases: the Swedish bicycle helmet law for children under the age 15, and the tightening of licensing rules for Class 1 mopeds. The empirical results suggest that both interventions were effective in improving the safety of VRUs. Unless other concurrent events affect the treatment population at the exact time of intervention, the effect estimates should be internally valid. One of the main limitations of the study design is the inability to identify why the interventions were successful, especially if they are complex and multifaceted. A lack of reliable exposure data can also pose a further threat to studies of interventions involving VRUs if the intervention can affect the exposure itself. It may also be difficult to generalize the exact effect estimates to other regions and populations. Future studies should consider the use of the ITS design to enhance the internal validity of before-after measurements. Traffic-related injuries represent a global public health problem, and contribute largely to mortality and years lived with disability. Over the course of the last decades, improvements to road traffic safety and injury surveillance systems have resulted in a shift in focus from motor vehicle accidents to injury events involving vulnerable road users (VRUs), such as cyclists and moped riders. There have been calls for improvements to the evaluation of safety interventions due to methodological problems associated with the most commonly used study designs. The purpose of this licentiate thesis was to assess the strengths and limitations of the interrupted time series (ITS) design, which has gained some attention for its ability to provide valid effect estimates while accounting for secular trends. Two national interventions involving VRUs were selected as cases: the Swedish bicycle helmet law for children under the age 15, and the tightening of licensing rules for Class 1 mopeds. The empirical results suggest that both interventions were effective. These results are discussed in the light of some methodological considerations regarding internal and external validity, data quality and the ability to fully understand key causal mechanisms behind complex interventions.