Abstract

Train dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hours separately. The peak-hour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off-peak-hour dwell times are estimated using a non-parametric regression model, in particular, the k-nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%-88.5% during peak hours and 80.1% during off-peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible.

Abstract

WOS: 000415149800005 Air Traffic Management (ATM) research generally focuses on achieving a safer, more effective and economical air traffic system. The current airspace system has become increasingly strained as the demand for air travel has steadily grown. Innovative, proactive and multi-disciplinary approaches to research are needed to solve flight congestion and delays as a consequence of this rapid growth. As a result of this growth, air traffic flow becomes more complex, especially in Terminal Airspaces (TMA) where climb and descent manoeuvres of departing and arriving flights take place around airports. As air traffic demand exceeds the capacity in a TMA, the resultant congestion leads to delays that spread all over the system. Therefore, the reduction of delays is critical for airspace designers to increase customer satisfaction and the perception of service quality. Numerous studies have been conducted to reduce delays within TMAs. This research focuses on defining the causes of delays quantitatively through statistical analysis. The first step was to create a fast-time simulation model of sample airspace for collecting delay data. After building up this model using the SIMMOD fast-time ATM simulation tool, simulation experiments were run to produce various traffic scenarios and to generate traffic delay data. The number of airports, entry points, fixes and flight operations in airspace and the probability of wide-body aircraft were considered as independent variables. The correlations between the considered variables were analysed, and the total delay data was modelled using a linear regression model. The findings of regression model present a statistical approach for airspace designers and air traffic flow planners. Anadolu University Scientific Research Projects Commission [1203F052] This study was supported by Anadolu University Scientific Research Projects Commission under grant no: 1203F052.