but what transit agencies can do is to prepare to respond to failure in a timely and effective manner. To this end, in-formation about daily travel demand patterns are crucial to develop efficient failure response strategies. To the extent of urban computing, smart card data offers us the opportu

Abstract: In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help

to have extensive and longitudinal data on ridership; second, it must be possible to isolate the weather effect from other causes of variation. In this project, we try to address both issues with the help of continuous smart card data from Gatineau and Montréal (Québec) public transit networks. Results

Investigating congestion in train rapid transit systems (RTS) in today’s urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent

Abstract—Contactless smart card systems have gained univer-sal prevalence in modern metros. In addition to its original goal of ticketing, the large amount of transaction data collected by the smart card system can be utilized for many operational and management purposes. This paper investigates

from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variabil-ity of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore’s quadriennial Household Interview Travel

I would like to thank Prof. Dr. Torsten Braun, head of the Computer Network and Distributed Systems group (RVS), for supervising this work and for his insightful advises. Prof. Dr. Torsten Braun encouraged and motivated me to publish my research results and he provided me the opportunity to present the work on various conferences, for which I thank him. I would also like to thank Prof. Dr. Roger Wattenhofer, responsible for the Koreferat of this work. Also, Prof. Dr. Oscar Nierstrasz who was willing to be the co-examinator of this work deserves many thanks. Many thanks go to my colleagues of the RVS group and of the IAM for our various interesting discussions about all kinds of topics and for making the institute a very pleasant and friendly place to work at. Special thanks go to David Steiner, Marc Steinemann, Matthias Scheidegger, Florian Baumgartner, Ruy De Oliveira, and Attila Weyland. There are many students who worked with me and helped a lot in developing and implementing. Among them I especially thankful to Thomas Bernoulli,

Weh ve been developing a mobile passenger support system for public transport. Passengers can maketh'D travel plans and purchrc necessary tickets by accessing databases via th system. After startingth travel, a mobile terminal chE ks th travel schxxz] of its user by accessing several

The emphasis of this research effort was on using AVL and APC dynamic data to develop a bus travel time model capable of providing real-time information on bus arrival and departure times to passengers (via traveler information services) and to transit controllers for the application of proactive

relationship between public transit and private cars and vice versa. Travel time data of sections of the study routes were collected using GPS and this information was used for representing the present traffic situation. Linear regression models were developed to predict travel times of modes, one with respect