A simultaneous two-dimensionally constraint disaggregate trip generation, distribution and mode choice model - Theory and application for a Swiss national model

The Swiss federal government has asked the IVT, ETH Zürich in collaboration with the TU Dresden and Emch+Berger, Zürich to estimate origin-destination matrices by mode and purpose for the year 2000. The zoning system employing about 3’000 zones of very uneven size required a solution algorithm which is fast, but also able to model generation, distribution and mode choice simultaneously, while addressing the different data availability for traffic within, destined for and passing through the country. The EVA algorithm developed by Lohse (1997) was adapted for this purpose. The key proper-ties of the algorithm are its disaggregate description of demand, its use of appropriate logit-type models for the demand distribution, while maintaining the known marginal distributions of the matrices generated. This last point is of particular importance in a large scale planning applica-tion such as the one at hand. The algorithm calculates trip production and attractions by zone using activity pairs. The 17 ac-tivity pairs distinguished are the combinations of two activities, such as home-work or work-leisure. The relevant daily rates are derived for each of the 17 activity pairs from the 2000 Swiss National Travel Survey (Bundesamt für Statistik and Bundesamt für Raumentwicklung, 2001). The zonal attractivity is defined separately for each trip purpose. In addition to the common variables, such as employment or population, detailed descriptions of education places, shop-ping or leisure facilities, overnight accommodations, shopping centres etc. are employed (see Tschopp, Keller and Axhausen, 2003 for the data). The combined destination and mode choice models estimated for the different traveller types and activity pairs are based on the Swiss National Travel survey (RP data), but incorporates re-sults from a prior SP study on mode and route choice (Vrtic and Axhausen, 2004). The different zone sizes and the different levels of data available required the formulation of new additional models for the transit traffic passing through Switzerland and the traffic originat-ing outside, respectively leaving the country The matching network models for public transport and road traffic were implemented using VISUM 9.0 of PTV AG, Karlsruhe. The timetable based assignment considers all scheduled train services plus the relevant interurban bus services, in particular in rural areas. The paper has three main parts: the first main part derives and describes for the first time the EVA algorithm in English, including the solution method used. The second part summarizes the results of choice model estimation using the generalised cost elasticities of demand by purpose and traveller type. The third part assesses the quality of the results. These assessments are based on two independently derived matrices, which are available for rail-travel from on board - counts and for commuters from the 2000 national census. In addition, we compare the assign-ment results with the available cross section counts. The conclusions discuss computing times, accuracy and issues for further research.

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

Closer to the total? Long-distance travel of French mobile phone users

Analyzing long-distance travel demand has become increasingly relevant because the share of traffic induced by journeys related to remote activities which are not part of daily life is growing. In today’s mobile world, such journeys are responsible for almost 50 percent of all traffic. Traditionally, surveys have been used to gather data needed to analyze travel demand. Due to the high response burden and memory issues, respondents are known to underreport their number of long-distance journeys. The question of the actual number of long-distance journeys therefore remains unanswered without additional data sources. This paper is the first to quantify the underreporting of long-distance tour frequencies in travel diaries. We took a sample of mobile phone billing data covering five months and compared the observed long-distance travel with the results of a national travel survey covering the same period and the same country. The comparison shows that most of the estimates of the number of missing tours by researchers have thus been too low. Our work suggests that the actual number of longdistance journeys is twice as high as that reported in surveys. Two different causes of underreporting were identified. Firstly, soft refusers travelled long distances but reported no long-distance tours. Secondly, respondents underestimated their number of long-distance tours. Consequently, there is a need to use alternative data sources in order to gain better estimates of long-distance travel demand

Income effects, cost damping and the value of time: theoretical properties embedded within practical travel choice models

Mackie et al. (Values of travel time savings in the UK. Report to Department for Transport. Institute for Transport Studies, University of Leeds & John Bates Services, Leeds and Abingdon, 2003) proposed an identity relating the value of time (VoT) for commute and leisure travel to income and travel cost, reporting the prevalence of ‘cost damping’ (i.e. the phenomenon where VoT increases as travel cost increases). This identity (or a variant thereof) has been adopted within official methods for estimating VoT in the UK, Switzerland and The Netherlands. The present paper shows that Mackie et al.’s identity: (i) implies linear preferences, not strictly convex preferences as reported by Mackie et al.; (ii) complies with homogeneity and symmetry by construction; (iii) complies with adding-up if and only if VoT is unit elastic with respect to income; (iv) complies with negativity if VoT is unit elastic or greater with respect to income; (v) violates both adding-up and negativity in the case of the 2003 UK national VoT study. We propose alternative identities which comply with adding-up and homogeneity by construction, and offer comparable fit to Mackie et al.’s identity on the UK VoT dataset. We also find that the imposition of adding-up and negativity on Mackie et al.’s identity, through appropriate constraint on model estimation, leads to an increase of around 20% in valuations from the 2003 UK dataset

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

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

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

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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