ProZeit: An Automated Workflow of Optimizing the Geometric Design of Railway Alignment for Travel Time Saving
ISBN 978-85-88783-11-9
Authors
1Yang, J.; 2Nottbeck, A.; 3Murphy, C.; 4Jahnke, M.; 5Meng, L.; 6Freudenstein, S.
1LEHRSTUHL FÜR KARTOGRAPHIE, TECHNISCHE UNIVERSITÄT MÜNCHEN Email: jian.yang@tum.de
2LEHRSTUHL FÜR VERKEHRSWEGEBAU, TECHNISCHE UNIVERSITÄT MÜNCHE Email: alexander.nottbeck}@vwb.bv.tum.de
3LEHRSTUHL FÜR KARTOGRAPHIE, TECHNISCHE UNIVERSITÄT MÜNCHEN Email: christian.murphy@bv.tu-muenchen.de
4LEHRSTUHL FÜR KARTOGRAPHIE, TECHNISCHE UNIVERSITÄT MÜNCHEN Email: mathias.jahnke@tum.de
5LEHRSTUHL FÜR KARTOGRAPHIE, TECHNISCHE UNIVERSITÄT MÜNCHEN Email: liqiu.meng@bv.tum.de
6LEHRSTUHL FÜR VERKEHRSWEGEBAU, TECHNISCHE UNIVERSITÄT MÜNCHE Email: stephan.freudenstein@vwb.bv.tum.de
Abstract
Railway is an important means of regional passenger transportation and serves as a crucial infrastructure for the regional development as well. A successful timesaving of a scheduled railway route, e.g., 5~10 minutes, is of great interest in the railway industry, which could lead to an in-crease of the transportation capacity, a new railway station in a built-up area and enhancement of railway’s competiveness among different transportation modes. However, the complexity of the railway system (Pyrgidis & Kontaxi, 2011) and the non-open nature of the design of railway infrastructure prohibit local industry initiatives to evaluate the desired improvements on the local railway infrastructure. In this paper, we propose a novel workflow, ProZeit (Yang et al., 2013), to achieve a minute-level travel timesaving by optimizing the geometric design of the railway alignment. This is a joint effort from both the car-tographic department and the railway engineering department at the TU Munich. With ProZeit, we attempt to answer the following questions: 1) What data need to be collected? 2) Which level of construction is neces-sary to achieve a minute-level travel timesaving? 3) What constraints should be taken into account? and 4) How can the proposed solution be adequately conveyed using geo-visualization techniques? ProZeit comprises 6 steps, namely railway track surveying, track ele-ment detection, geo-context labelling, train performance simulation, maintenance site selection and geo-visualization. 1) The railway track surveying acquires the geometric parameters of the railway track, e.g., curvature, length of the gauge, super elevation (cant), odometer and the coordinates of each measurement, etc., which is done by Eurailscout using track surveying car SIM11. 2) With the track geometry data, a sin-gle railway track is modelled as a linear sequence of geometric ele-ments, namely straight line, circular curve and transition curves. The previous two enable further speed-up performance evaluation of the train after changing the geometric parameters. 3) Based on the geomet-ric representation of the existing railway alignment, geo-context infor-mation such as inhabited area, railway station, bridges, tunnels etc. are enriched manually by using Google Earth. Not only technical regulations are to be satisfied, but also the environmental impact of the increased travel speed of the train needs to be taken into account, e.g., for noise increase. 4) With concerns to both technical constraints and environ-mental impacts, the maximum allowable speed and its required change of geometric design are calculated. Then, individual contribution to the total timesaving of the changed geometric elements is simulated using a Train Performance Calculator (TPC). 5) Furthermore, all geometric elements are ranked based on the benefit of time reduction and the cost of the maintenance work, i.e., tamping. For specified timesaving, the minimum amount of the changed elements is selected. 6) In order to intuitively convey the proposed maintenance plan, the cost-benefit of all individual changes on track elements are visualized in Google Earth. The workflow has been successfully applied in the planning of in total 7 railway routes in Bayern, Germany and has received quite many posi-tive feedbacks from the industry partners. In the future development, we will focus on a) developing a more sophisticated model to manage multiple linear attributes along the railway alignment using linear refer-encing; b) using a robust filtering technique in the track element detec-tion which takes into account multiple measurements simultaneously, i.e., by Kalman filtering; and c) including vertical alignment for energy consumption estimations.