As many of you know, DILAX has been holding conferences for national and international customers for many years. Beginning in 2013, we created a completely new, future-oriented concept for our events: the DILAX Congress. The follow-up event took place this year.
In mid-October, about 70 international participants attended the second DILAX Congress in Berlin. The highlight of the event were two hands-on workshops focused on our software DavisWeb Mobile and our customers’ case study presentations. These presentations illustrated the diverse use cases for the implementation of DILAX systems – from traffic planning and revenue sharing to optimizing traffic control and possibilities to influence human behavior.
Seat Management & Passenger Counting Systems
“In public transport, it is becoming increasingly important to manage passenger flow on platforms before and during boarding, as this is essential to increase overall efficiency, reduce passenger exchange times and improve the passengers’ travel experience”, says Jerzy Berg, Project Manager of PesaDART at PESA Bydgoszcz S.A., one of the largest railway and tram manufacturers in central Europe.
Hamburger Hochbahn AG, a longstanding user of DILAX counting systems – is increasing the number of vehicles in its fleet that are equipped with an automatic passenger counting (APC) system to 20%. This year, DILAX is fitting new APC systems to another 70+ buses and 10 more underground trains operated by HOCHBAHN. Further vehicles will be added next year.
Hamburger Hochbahn AG is the second-largest local public transport operator in Germany. With four underground train lines and more than 100 bus lines, HOCHBAHN delivers top-quality transportation services in Hamburg and the surrounding area.
Valid passenger numbers in relation to planned services are the basis for evaluating and analyzing the data captured by any automatic passenger counting (APC) system. Imprecise allocation of GPS coordinates to scheduled stops or variations from the regular service can cause difficulties during data analysis, which users previously found hard to account for.