Optimization of timetable supplement from a passenger based socio-economic point of view

This article discusses how to optimize the timetable supplement in timetables. The focus of this article will be on railways, but the principle will in theory apply to all transportation modes within the area of public transportation. When constructing timetables it is important to plan the right amount of timetable supplement. Too little timetable supplement will result in many delays, while too high timetable supplement will result in a (too) high planned travel time which will affect every departure whether or not the train is delayed. At present timetable supplement is chosen based on experience or estimates. Through an optimization process it is possible to find the optimal timetable supplement. A way to do so is by using the passenger delay model in a socio-economic analysis as done by (Thorhauge & Piester, 2010). In this process the train delays (or a simulation of the train delays) are needed. The overall process is (if the train delays are not already known): Simulation of train delays → modeling passenger delays → estimation of the socio-economic effects A case study of an upgrade of Sydbanen between Ringsted and Rødby has been conducted using the passenger delay model and the methods are described in this article. The case study has shown that the optimum timetable supplement is between 6-9 % depending on the scenario. By optimizing the timetable supplement it is possible to achieve a surplus of 250-500 mio. DKK during the evaluation period compared to the proposed timetable by the Danish Transport Authority (Trafikstyrelsen, 2008). Note however that none of the investigated scenarios are socio-economic viable even though the timetable and timetable supplement is optimized. Note that this paper is regarded as a sequel to the article “The usability of passenger delay models in socio-economic analysis” (Thorhauge, 2010). This article is based on the results of (Thorhauge & Piester, 2010)

The usability of passenger delay models in socio-economic analysis

The following paper discusses how a passenger delay model can be used in socio-economic calculations. At present passenger delays are often omitted in the modeling phase and therefore not included in the socio economic analysis. By using a passenger delay model passenger delays can be included in a cost-benefit analysis. Including passenger delays in the cost-benefit analysis will increase the level of details and thereby improve the accuracy of socio-economic analysis. In this paper the third generation passenger delay model is used. This model is the newest and most detailed passenger delay model created so far. When using a passenger delay model, the main problem is how to properly include the delays in a socio- economic analysis. This is due to the fact that passenger delays are not necessarily unambiguously. In general, delays can occur on different parts of a journey; while the passengers are waiting for the train (waiting time, first waiting time or even hidden waiting time) or while the passenger are sitting in the train (or bus). Furthermore a delay can also be negative, meaning that a passenger will arrive before planned (a so-called negative delay). This article proposes that a delay is defined solely by the difference between the scheduled and realized arrival time. The recommendations are listed as follows: The value of time for a delay is defined as done by the Danish Ministry of Transport (Trafikministeriet, 2003) no matter how or when the delay has occurred. The size of the delay is calculated solely by the difference between the planned and realized arrival time. A negative delay is defined as the value of time of hidden waiting time. A negative delay is calculated as the difference between the planned and realized arrival time and is considered a surplus in the cost benefit analysis. A delay will not be included if the passenger arrives at his or her final destination on time even though the passenger may have experienced a delay (or travelled along a different route than planned) during the journey. Note that this paper is regarded as a prequel to the article “Optimization of timetable supplement from a passenger based socio-economic point of view” (Thorhauge, 2010). This article is based on the results of (Thorhauge & Piester, 2010)

The usability of passenger delay models in socio-economic analysis

The following paper discusses how a passenger delay model can be used in socio-economic calculations. At present passenger delays are often omitted in the modeling phase and therefore not included in the socio economic analysis. By using a passenger delay model passenger delays can be included in a cost- benefit analysis. Including passenger delays in the cost-benefit analysis will increase the level of details and thereby improve the accuracy of socio-economic analysis. In this paper the third generation passenger delay model is used. This model is the newest and most detailed passenger delay model created so far. When using a passenger delay model, the main problem is how to properly include the delays in a socio- economic analysis. This is due to the fact that passenger delays are not necessarily unambiguously. In general, delays can occur on different parts of a journey; while the passengers are waiting for the train (waiting time, first waiting time or even hidden waiting time) or while the passenger are sitting in the train (or bus). Furthermore a delay can also be negative, meaning that a passenger will arrive before planned (a so-called negative delay). This article proposes that a delay is defined solely by the difference between the scheduled and realized arrival time. The recommendations are listed as follows: The value of time for a delay is defined as done by the Danish Ministry of Transport (Trafikministeriet, 2003) no matter how or when the delay has occurred. The size of the delay is calculated solely by the difference between the planned and realized arrival time. A negative delay is defined as the value of time of hidden waiting time. A negative delay is calculated as the difference between the planned and realized arrival time and is considered a surplus in the cost benefit analysis. A delay will not be included if the passenger arrives at his or her final destination on time even though the passenger may have experienced a delay (or travelled along a different route than planned) during the journey. Note that this paper is regarded as a prequel to the article “Optimization of timetable supplement from a passenger based socio-economic point of view” (Thorhauge, 2010). This article is based on the results of (Thorhauge & Piester, 2010)

Communication technologies in the study environment: institutional and personal media as a reflection of organisational structure

In this article, I will analyse and discuss two qualitative case studies concerning ICT in the study environment at the University of Copenhagen, Denmark. I will place special focus on the way in which organisational perspectives as well as technological affordances shape how communication technologies are integrated into organisational structures and practices on campus. This involves a comparison between course management systems on the one hand and students’ personal media (mobile phones, e-mails) on the other hand, with regard to how these are used on campus. On the basis of this analysis, I will argue that the ways in which these technologies are used reflects two different perspectives on the interplay between communication technology and organisational structure: organisational structure as an anticipation of communication patterns implied in course management system’s design and implementation as well as organisational structure as a product of the use of personal media

Building efficient stated choice design for departure time choices using the scheduling model: Theoretical considerations and practical implementations

Modelling departure time is an important step in forecasting traffic demand. The purpose of this research is to contribute to the data collection field when studying departure time choices. Differently from the majority of the previous studies we used an efficient stated preference (SP) experiment. The objective in an efficient design is to construct a stated preference experiment which minimizes the standard errors of the estimated parameter in the model. Thus, the benefits of using an efficient design are more robust parameter estimates and/or that it allows using smaller sample sizes. However, building experimental designs for the departure time is challenging for two main reasons: 1) interdependence among attributes, and 2) realism in the choice tasks. To ensure realism, we customized the choice task based on the trips described by each individual in a trip diary and on the departure time needed in order to be at work at their preferred arrival time. However, with efficient designs it is not possible to customize the SP for each individual, unless the real trips are known before optimizing the SP design. To overcome this challenge, six different designs were constructed based on predefined travel times (10, 20, 30, 40, 50, and 60 minutes). Respondents were presented with the design which was closest to their reported travel time in the trip diary. The design was simulated using almost 20.000 observations, and was adjusted until the design was stable and the prior parameters could be recuperated