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b'This episode\\u2019s guest is Associate Professor Jan-Dirk Schm\\xf6cker 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. \\n\\nIn 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. \\n\\nGraham and Jan-Dirk also discuss Jan-Dirk\\u2019s 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.\\n\\nTowards 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. \\n\\nFinally, Jan-Dirk discusses some of his work using Automatic Vehicle Location (AVL) data to understand passengers\\u2019 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.\\n \\nFind out more about Jan-Dirk and his work at:\\nHis biography on the Kyoto University website at: https://trans.kuciv.kyoto-u.ac.jp/its/Schmoecker.html\\nHis publications at:\\nGoogle Scholar: https://scholar.google.com.au/citations?user=DIFXh60AAAAJ&hl=en&oi=ao\\nResearchGate: https://www.researchgate.net/profile/Jan-Dirk-Schmoecker\\nWorld Transit Research: https://www.worldtransitresearch.info/do/search/?q=Jan-Dirk%20Schm%C3%B6cker%20&start=0&context=1060035&facet=\\nand a recent project on using crowdsourced data at: https://concert-japan-daruma.github.io/\\n\\nHave feedback? Find us on twitter and Instagram @transitpodcast or using #researchingtransit\\nMusic from this episode is from https://www.purple-planet.com'