275 research outputs found
Income and distance elasticities of values of travel time savings: New Swiss results
This paper presents the findings of a study looking into the valuation of travel time savings (VTTS) in Switzerland, across modes as well as across purpose groups. The study makes several departures from the usual practice in VTTS studies, with the main one being a direct representation of the income and distance elasticity of the VTTS measures. Here, important gains in model performance and significantly different results are obtained through this approach. Additionally, the analysis shows that the estimation of robust coefficients for congested car travel time is hampered by the low share of congested time in the overall travel time, and the use of an additional rate-of-congestion coefficient, in addition to a generic car travel time coefficient, is preferable. Finally, the analysis demonstrates that the population
mean of the indicators calculated is quite different from the sample means and presents methods to calculate those, along with the associated variances. These variances are of great interest as they allow the generation of confidence intervals, which can be extremely useful in cost-benefit analyses
Capturing human activity spaces: New geometries
Activity space, defined as “the local areas within which people move or travel during the course of their activities during a specified time period”, is a measure of an individual’s spatial behavior which captures individual and environmental differences and offers an alternative approach to studying the spatial reach of travelers. The shape and area of activity space is a product of how it is conceptualized and measured. This paper enlarges the set of geometries which can be used to describe activity space. It tests four parametric geometries (ellipse, superellipse, Cassini oval, and bean curve), which are identified as those capturing a specific share of all locations visited, i.e. 95%, while minimizing the area covered. They are estimated for a number of long-duration data sets while distinguishing between trip purposes.
We present both a flexible, easily adaptable method for calculating activity spaces of different shapes and a qualitative comparison of the four above-mentioned shape types on the basis of the given surveys. We can thus demonstrate that the choice of an appropriate shape representing an individual’s activity space is highly dependent on the spatial distributions and frequencies of the locations visited by the person in the given time period
Modelling contact mode and frequency of interactions with social network members using the multiple discrete–continuous extreme value model
Communication patterns are an integral component of activity patterns and the travel induced by these activities. The present study aims to understand the determinants of the communication patterns (by the modes face-to-face, phone, e-mail and SMS) between people and their social network members. The aim is for this to eventually provide further insights into travel behaviour for social and leisure purposes. A social network perspective brings value to the study and modelling of activity patterns since leisure activities are influenced not only by traditional trip measures such as time and cost but also motivated extensively by the people involved in the activity. By using a multiple discrete-continuous extreme value model (Bhat 2005), we can investigate the means of communication chosen to interact with a given social network member (multiple discrete choices) and the frequency of interaction by each mode (treated as continuous) at the same time. The model also allows us to investigate satiation effects for different modes of communication. Our findings show that in spite of people having increasingly geographically widespread networks and more diverse communication technologies, a strong underlying preference for face-to-face contact remains. In contrast with some of the existing work, we show that travel-related variables at the ego level are less important than specific social determinants which can be considered while making use of social network data
Routine pattern discovery and anomaly detection in individual travel behavior
Discovering patterns and detecting anomalies in individual travel behavior is
a crucial problem in both research and practice. In this paper, we address this
problem by building a probabilistic framework to model individual
spatiotemporal travel behavior data (e.g., trip records and trajectory data).
We develop a two-dimensional latent Dirichlet allocation (LDA) model to
characterize the generative mechanism of spatiotemporal trip records of each
traveler. This model introduces two separate factor matrices for the spatial
dimension and the temporal dimension, respectively, and use a two-dimensional
core structure at the individual level to effectively model the joint
interactions and complex dependencies. This model can efficiently summarize
travel behavior patterns on both spatial and temporal dimensions from very
sparse trip sequences in an unsupervised way. In this way, complex travel
behavior can be modeled as a mixture of representative and interpretable
spatiotemporal patterns. By applying the trained model on future/unseen
spatiotemporal records of a traveler, we can detect her behavior anomalies by
scoring those observations using perplexity. We demonstrate the effectiveness
of the proposed modeling framework on a real-world license plate recognition
(LPR) data set. The results confirm the advantage of statistical learning
methods in modeling sparse individual travel behavior data. This type of
pattern discovery and anomaly detection applications can provide useful
insights for traffic monitoring, law enforcement, and individual travel
behavior profiling
Clustering daily patterns of human activities in the city
Data mining and statistical learning techniques are powerful analysis tools yet to be incorporated in the domain of urban studies and transportation research. In this work, we analyze an activity-based travel survey conducted in the Chicago metropolitan area over a demographic representative sample of its population. Detailed data on activities by time of day were collected from more than 30,000 individuals (and 10,552 households) who participated in a 1-day or 2-day survey implemented from January 2007 to February 2008. We examine this large-scale data in order to explore three critical issues: (1) the inherent daily activity structure of individuals in a metropolitan area, (2) the variation of individual daily activities—how they grow and fade over time, and (3) clusters of individual behaviors and the revelation of their related socio-demographic information. We find that the population can be clustered into 8 and 7 representative groups according to their activities during weekdays and weekends, respectively. Our results enrich the traditional divisions consisting of only three groups (workers, students and non-workers) and provide clusters based on activities of different time of day. The generated clusters combined with social demographic information provide a new perspective for urban and transportation planning as well as for emergency response and spreading dynamics, by addressing when, where, and how individuals interact with places in metropolitan areas.Massachusetts Institute of Technology. Dept. of Urban Studies and PlanningUnited States. Dept. of Transportation (Region One University Transportation Center)Singapore-MIT Alliance for Research and Technolog
Reliability in the German value of time study
The German Federal Ministry of Transport and Digital Infrastructure is currently preparing the 2015 Federal Transport Investment Plan. As part of this, it is updating the overall methodology of its cost-benefit analysis meaning values of both reliability (VOR) and travel time (VOT) for personal and business travel will be estimated. While the VOTs will replace a set of existing values, the VOR will be estimated for the first time as they are not incorporated in the standard appraisal yet. The data collection adopted a two-stage approach: first respondents reported about current trips (revealed preference), which were then systematically varied to be the basis for stated preference experiments. This paper presents the findings of estimating the VOR. In the SP experiments the reliability of the travel modes was presented with different formats. The final model formulation differs in the definition of reliability for private and public transport. For car trips, saving travel time is “worthier” to the respondents than reducing the variability. The calculated VOR for the mean expected unscheduled delay of public transport trips are slightly lower than the VOTs which means that the reliability is here less important to the respondents than relevant travel time saving. One minute of mean expected unscheduled delay and one minute of standard deviation are almost equivalent to one minute of travel time saving (reliability ratio). As this has been the first official estimation of the value of reliability and time for Germany, the values should be reconsidered and updated on a regular basis
Using Appropriate Prior Information to Eliminate Choice Sets with a Dominant Alternative from D-Efficient Designs
Exploring the potential of phone call data to characterize the relationship between social network and travel behavior
[EN] Social network contacts have significant influence on individual travel behavior. However, transport models rarely consider social interaction. One of the reasons is the difficulty to properly model social influence based on the limited data available. Non-conventional, passively collected data sources, such as Twitter, Facebook or mobile phones, provide large amounts of data containing both social interaction and spatiotemporal information. The analysis of such data opens an opportunity to better understand the influence of social networks on travel behavior. The main objective of this paper is to examine the relationship between travel behavior and social networks using mobile phone data. A huge dataset containing billions of registers has been used for this study. The paper analyzes the nature of co-location events and frequent locations shared by social network contacts, aiming not only to provide understanding on why users share certain locations, but also to quantify the degree in which the different types of locations are shared. Locations have been classified as frequent (home, work and other) and non-frequent. A novel approach to identify co-location events based on the intersection of users' mobility models has been proposed. Results show that other locations different from home and work are frequently associated to social interaction. Additionally, the importance of non-frequent locations in co-location events is shown. Finally, the potential application of the data analysis results to improve activity-based transport models and assess transport policies is discussed.The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. The research leading to these results has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no 318367 (EUNOIA project) and no 611307 (INSIGHT project). The work of ML has been funded under the PD/004/2013 project, from the Conselleria de Educacion, Cultura y Universidades of the Government of the Balearic Islands and from the European Social Fund through the Balearic Islands ESF operational program for 2013-2017.Picornell Tronch, M.; Ruiz Sánchez, T.; Lenormand, M.; Ramasco, JJ.; Dubernet, T.; Frías-Martínez, E. (2015). Exploring the potential of phone call data to characterize the relationship between social network and travel behavior. Transportation. 42(4):647-668. https://doi.org/10.1007/s11116-015-9594-1S647668424Ahas, R., Aasa, A., Silm, S., Tiru, M.: Daily rhythms of suburban commuters’ movements in the tallinn metropolitan area: case study with mobile positioning data. Transp. Res. 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Evidence for a Conserved Quantity in Human Mobility
Recent seminal works on human mobility have shown that individuals constantly exploit a small set of repeatedly visited locations. A concurrent study has emphasized the explorative nature of human behaviour, showing that the number of visited places grows steadily over time. How to reconcile these seemingly contradicting facts remains an open question. Here, we analyse high-resolution multi-year traces of ~40,000 individuals from 4 datasets and show that this tension vanishes when the long-term evolution of mobility patterns is considered. We reveal that mobility patterns evolve significantly yet smoothly, and that the number of familiar locations an individual visits at any point is a conserved quantity with a typical size of ~25. We use this finding to improve state-of-the-art modelling of human mobility. Furthermore, shifting the attention from aggregated quantities to individual behaviour, we show that the size of an individual’s set of preferred locations correlates with their number of social interactions. This result suggests a connection between the conserved quantity we identify, which as we show cannot be understood purely on the basis of time constraints, and the ‘Dunbar number’ describing a cognitive upper limit to an individual’s number of social relations. We anticipate that our work will spark further research linking the study of human mobility and the cognitive and behavioural sciences
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Locative Media and Sociability:Using Location-Based Social Networks to Coordinate Everyday Life
Foursquare was a mobile social networking application that enabled people to share location with friends in the form of “check-ins.” The visualization of surrounding known social connections as well as unknown others has the potential to impact how people coordinate social encounters and forge new social ties. While many studies have explored mobile phones and sociability, there is a lack of empirical research examining location-based social network’s (LSBNs) from a sociability perspective. Drawing on a dataset of original qualitative research with a range of Foursquare users, the paper examines the application in the context of social coordination and sociability in three ways. First, the paper explores if Foursquare is used to organize certain social encounters, and if so, why. Second, the paper examines the visualization of surrounding social connections and whether this leads to “serendipitous encounters.” Lastly, the paper examines whether the use of Foursquare
can produce new social relationships
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