Peter SCHNECK

Biographical note

Since February 2015, Peter Schneck takes charge as CEO of Trapeze Switzerland GmbH. In this position, Peter Schneck bears the responsibility for the entire Trapeze ITS and ticketing portfolio in Europe as well as selected growth markets. This comprises Trapeze operations in Neuhausen, Bad Honnef, Wroclaw, Berlin, Darmstadt, Loughton, Johannesburg and Riyadh. Peter Schneck holds a degree as Executive MBA and has accumulated extensive experience as managing director in the fields of light manufacturing and plant construction as well as in software development. He was in charge of the international new customer business at the global Apcoa Group for four years. At the same time, he was the managing director of Apcoa Parking headquartered in Stuttgart, the Group’s company with the highest revenue. Before this, he was with the German system provider Scheidt & Bachmann for more than 12 years.

Presentation: How can history-based predictions improve service quality?

In the internet age, passengers expect transport companies to predict departure times with a very high level of accuracy. Prediction based on historical data can help to considerably improve the prediction quality of trip announcements. Prediction based on historical data supplies top quality data. More accurate arrival time predictions are achieved by extending the prediction service in the control system. Current data sources are taken into account in the prediction, in addition to vehicle’s current timetable adherence. This helps particularly with hindrances caused by congestion, accidents, route closures or weather-related delays. The model is based on measured values in the AVLC which register all vehicle travel times. These times are then saved in a central online database and included in the prediction. To obtain highly accurate predictions, all measured travel times are broken down into a 30-minute time grid to take account of the varying traffic situations in the course of a day. At the same time, long-term statistics are also generated, categorised according to days of the week, weekends and public holidays. The observed travel times are used to weight these values so that the predictions can be constantly adapted. There is also an additional compensation possibility for early times or delays. Prediction based on historical data is bringing about a considerable improvement in prediction quality. This solutions support clearly the attractiveness of public transport. In general terms, it can be said that prediction based on historical data generates a great added value for all transport companies and the passengers.