Nadine Rieser-Schüssler

Trip Purpose Identification from GPS Tracks

Travel surveys are increasingly taking advantage of GPS data, which offer precise route and time observations and a potentially reduced response burden. In these surveys, travel diaries are usually constructed automatically where research on the employed procedures has been focused on mode identification. The goal of the research reported here was to improve trip purpose identification. The analysis used random forests, a machine-learning approach that had been successfully applied to mode identification. The analysis was based on GPS tracks and accelerometer data collected by 156 participants who took part in a 1-week travel survey in Switzerland that was completed in 2012. The results show that random forests provide robust trip purpose classification. For ensemble runs, the share of correct predictions was between 80% and 85%. Different setups of the classifier were possible and sometimes required by the application context. The training set and its input variables (feature set) of the classifier were defined in various ways. Four relevant setups were tested for this study.

Route choice sets for very high-resolution data

With the increasing use of GPS in transport surveys, analysts can not only choose from numerous new ways to model travel behaviour but also face several new challenges. For instance, information about chosen routes is now available with a high level of spatial and temporal accuracy. However, advanced post-processing is necessary to make this information usable for route choice modelling. Out of many related issues, this article focuses on the generation of choice sets for car trips extracted from GPS data. The aim is to generate choice sets for about 36,000 car trips made by 2434 persons living in and around Zurich, Switzerland, on the Swiss Navteq network, a very high-resolution network. This network resolution is essential for an accurate identification of chosen routes. However, it substantially increases the requirements for the choice set generation algorithm in regard to performance as well as choice set composition. This article presents a new route set generation based on shortest path search with link elimination. The proposed procedure combines a Breadth First Search with a topologically equivalent network reduction and ensures a high diversity between the routes, as well as computational feasibility for large-scale problems like the one described above. To demonstrate the usability of the algorithm, its performance and the resulting route sets are compared with those of a stochastic choice set generation algorithm.

Within-Day Replanning of Exceptional Events

Typical software used in the simulation of traffic behavior focuses on scenarios describing common situations, such as an ordinary working day without remarkable incidents. To simulate such scenarios, iterative approaches are used. They assume that the people simulated adapt to previous iterations’ results. Such iterative approaches produce meaningful results for various scenarios only when typical, repetitive situations are modeled. However, a scenario may also contain incidents that occur randomly, and thus substantially increase a models complexity. In such scenarios, an iterative approach would produce illogical and even erroneous results. Within-day replanning is an attempt to handle such scenarios. This paper describes problems that arise when an iterative simulation approach is applied to a scenario with exceptional events. The within-day replanning technique is introduced and implemented in the multiagent transport simulation framework, allowing simulated agents to replan the routes between their activities while they are traveling. By doing this, agents can take current traffic conditions into account, an important requirement for scenarios containing unpredictable incidents such as road accidents. The implementation capability is demonstrated by conducting experiments in which capacities of several arterial roads in the city center of Zurich, Switzerland, are reduced as the result of an exceptional event. It is demonstrated that agents affected by those events are able to reduce their travel times if they replan their routes by using within-day replanning.

Investigating the Influence of Environmentalism and Variety Seeking on Mode Choice

In the transport modeling community, there is a growing consensus that socioeconomic attributes alone will not suffice to characterize travelers and make forecasts about their travel behavior. Therefore, an increasing number of recent studies have integrated latent variables representing attitudes, perceptions, and preferences into choice models. Because it is impossible to measure latent variables directly, psychometric scales are used as indicators. For the study presented in this paper, psychometric scales for environmentalism and variety seeking were developed and tested in a mail-back survey that included a 1-day travel diary and a questionnaire about socioeconomic characteristics. A factor analysis was carried out to establish predominant attitudinal factors, which were then used as latent variables in a mode choice model. The results of the estimated choice models show that the three latent variables investigated—awareness of environmental problems, denial of environmental issues, and desire for variety in ones daily routine—influenced the mode choices of the study participants in different ways that may be attributable to other socioeconomic characteristics of the participants. This finding indicates that the scales developed for this study are suitable for capturing attitudes that are relevant to transport behavior research.

Self-tracing and reporting: state of the art in the capture of revealed behaviour

5 Choice context 101 Konstadinos G. Goulias and Ram M. Pendyala 6 Selftracing and reporting: state of the art in the capture of revealed behaviour 131 Nadine RieserSchüssler and Kay W. Axhausen 7 Stated choice experimental design theory: the who, the what and the why 152 John M. Rose and Michiel C.J. Bliemer 8 Bestworst scaling: theory and methods 178 T.N. Flynn and A.A.J. Marley 9 The discrete choice experiment approach to environmental contingent valuation 202 Richard T. Carson and Mikołaj Czajkowski 10 Real choices and hypothetical choices 236 Glenn W. Harrison