RT 48 – Jan-Dirk Schmöcker –Hyperpaths and the benefits of unreliability

Researching Transit · RT 49 – Associate Professor Jan-Dirk Schmöcker –Hyperpaths and the benefits of unreliability

Researching Transit – Episode 48

Published: June 2023

Keywords: Public transport, demand modelling, reliability, fares, activity patterns, big data, AVL, crowd sourcing

This episode’s guest is Associate Professor Jan-Dirk Schmöcker from the Department of Urban Management at Kyoto University in Japan. Jan-Dirk has been in Japan for 15 years, 12 of them at Kyoto University. He is part of the Intelligent Transport Systems (ITS) group at the university and has a background in transportation planning and assignment modelling.

In this episode Jan-Dirk explains his research about hyperpaths on the transit. These relate to the range of options available to transit riders, representing a set of paths through the network. Together with understand a strategy (such as boarding the first arriving bus), these hyperpaths can be used to better understand how passengers make choices. Jan-Dirk also describes some of his research into bus bunching and the choices passengers might have between boarding an overcrowded first bus or a second, relatively empty bus.

Graham and Jan-Dirk also discuss Jan-Dirk’s research into the benefits of unreliability. This relates to the way that users might learn other parts of the network when there are service disruptions, crowding or other problems that force them to seek an alternate route. This may result in users discovering better options to their usual route. In unreliable transportation systems travellers also arguably experience their environment more.

Towards the end of the episode Jan-Dirk describes some of his work on transit fares. This has included looking at trends in fare structures, with some cities moving towards flat-fare pricing (which is easier to understand) while others are moving towards pricing that better reflects the marginal cost each passenger imposes on the system. Jan-Dirk discusses how much of the focus has been on the spatial dimension: whether to have a flat-fare, or a zonal or other distance-based pricing structure. In contrast, there has been less attention paid to other aspects of fare policy-making, such as the impacts of discounts for frequent users and daily/weekly/monthly/yearly passes, special pricing for particular user groups, and the impact of transit operators obtaining significant amounts of their revenue from non-transit businesses.

Finally, Jan-Dirk discusses some of his work using Automatic Vehicle Location (AVL) data to understand passengers’ origins and destinations. He also discusses his current research into using big data and crowd sourcing to better understand travel and activity patterns. Among others he is using Google Popular Times data and Twitter data to understand how tourists are using public transport in Kyoto.

Find out more about Jan-Dirk and his work at: