STRC. Where do you want to go today? - More observations on daily mobility. Stefan Schönfelder, IVT / ETHZ Uta Samaga, Technische Universität Dresden - PDF

Where do you want to go today? - More observations on daily mobility Stefan Schönfelder, IVT / ETHZ Uta Samaga, Technische Universität Dresden STRC 03 Conference Paper Updated 24/03/2003 Session Mobility

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Where do you want to go today? - More observations on daily mobility Stefan Schönfelder, IVT / ETHZ Uta Samaga, Technische Universität Dresden STRC 03 Conference Paper Updated 24/03/2003 Session Mobility STRC 3 rd Swiss Transport Research Conference Monte Verità / Ascona, March 19-21, 2003 Where do you want to go today? More observations on daily mobility Stefan Schönfelder IVT / ETHZ Zürich Phone: Fax: Uta Samaga Technische Universität Dresden Dresden Abstract The exploration of the Mobidrive six-week travel diary data over the last few years has deepened the understanding of the dynamics, the regularity and the variability in daily travel. The success of the Mobidrive project with the implementation of a longitudinal travel survey and the subsequent data analysis, motivated to search for mobility data which covers even longer periods of observation eventually even capturing the seasonal variations of individual travel demand. At present, such an innovative long-term travel behaviour data source based on Global Positioning Systems (GPS) information is jointly processed and analysed at the IVT (ETHZ) and ROSO / DMA (EPFL). The Borlänge (Sweden) GPS data covers a monitoring period for privately used cars of up to two years. Travel behaviour and time use research has never had the opportunity to track individuals for longer than some weeks in order to find out about the systematic and the spontaneous part of behaviour. This paper gives an overview of the current status of the GPS data post-processing work and reports on the suitability of GPS travel data for the analysis of individual activity spaces. Keywords Activity spaces GPS data travel behaviour 3 rd Swiss Transport Research Conference STRC 03 Monte Verità I 1. The description and measuring of daily mobility Transport planning and policy have a notable interest in the mobility patterns of persons and households. Understanding the structures of individual mobility and their connections with the organisation of daily life and the inner-household interaction better allows to design and adjust measures to influence travel behaviour according to the current transport policy priorities. This may include for example the necessary reduction of travel demand in congested areas by land use reorganisation or measures to promote walking or cycling. For various reasons, the sensitivity of travel behaviour towards supply change (pull strategies) as well as demand orientated measures (push strategies) has long been tested by collecting and analysing data bases on cross-sectional surveys, only. As a consequence, mobility patterns observed on single days have often been interpreted as optimal decisions of the traveller and as a state of behavioural equilibrium which is assumed to exist for any point of time and any situation. Travel behaviour research has consistently questioned this working hypothesis (see e.g. Huff and Hanson, 1986; Jones and Clarke, 1988), as daily mobility contains a significant amount of variability and flexibility over time. The stability within travel as well as the deviations from the predominantly routinised structure of daily life can only be revealed by exploring individual panel data, i.e. observations of behaviour of single persons over a prolonged period. Individual travel data covering periods substantially longer than one week are rare as data collection by means of travel diaries is costly and includes the risk of loosing data precision by fatigue or no-response effects. The Mobidrive project (see Axhausen, Zimmermann, Schönfelder, Rindsfüser and Haupt, 2002) successfully showed that these fears were in the main unjustified. With the implementation of a continuous six-week travel diary as core of the project, a current data set of long-term individual travel behaviour is now available for analysis. The extensive investigation of the data during the last years has led to the development and the adoption of a range of analysis and modelling approaches for daily travel and the stability as well as variability within it (see Zimmermann, Axhausen, Beckmann, Düsterwald, Fraschini, Haupt, König, Kübel, Rindsfüser, Schlich, Schönfelder, Simma and Wehmeier, 2001 for an overview of results). A part of the Mobidrive results focusing on locational choice in travel was presented at the 1 st STRC (Schönfelder, 2001). 1 A current analysis stream which is adding to the spatial aspect of the Mobidrive data exploration is the visualisation and measuring of human activity spaces (Schönfelder and Axhausen, 2002a; b; Schönfelder and Axhausen, 2003). The identification of revealed individual activity spaces based on a longitudinal observation of trip making and activity performance will increase transport planning s ability to realistically define choice set for destination choice. Furthermore, it allows us to understand the mechanisms of clustering activities around the important pegs of daily life such as home or work. The Mobidrive success in data collection and analysis has increased researchers interest in accessing long-term travel data bases which eventually cover even longer periods of observation. One technical possibility is the collection of travel behaviour data by in-vehicle or on person Global Positioning System (GPS) devices. This innovative data collection methodology is promising especially in the field of route choice analysis where exact choice data over prolonged periods is quasi non-existent 1. The Institute of Transport Planning and Systems jointly with colleagues of the Department of Mathematics at the EPFL is currently exploring the potentials of a vehicle based GPS log data source from Sweden for the purpose of travel behaviour research (Schönfelder, Axhausen, Antille and Bierlaire, 2002). The fully automatically collected data matches the research direction of the Mobidrive work mentioned above as it contains movement information for single travellers for up to two years. This paper gives an overview about both, the recent methodological approaches to capture human activity spaces based on longitudinal travel data and the initial results of the data postprocessing steps for the GPS log data. The remainder of the paper is organised as follows: First, a short conceptual introduction into the activity space concept is given in order to outline our underlying research interest. The subsequent chapter reports on the structure of the Borlänge data and the current stage of the necessary data post-processing. Then, a recently developed approach to visualise and to measure activity spaces is applied to the enhanced GPS based travel data. Finally, some notes on the analysis so far and an outlook to the future work conclude the paper. 1 The Danish / European Union research project AKTA / PROGRESS ( uses a large GPS data set to evaluate road pricing schemes and to detect related changes in route and destination choice. 2 2. Activity spaces: Background, planning implications and new methodological approaches of measuring A micro-geographical concept which captures the spatial extent of daily mobility patterns is the activity space. The activity space concept which was developed in parallel with a range of related approaches to describe individual perception, knowledge and actual usage of space in the 1960s and 1970s (see Golledge and Stimson, 1997 for a discussion) aims to represent the spatial unit which contains the places frequented by an individual over a period of time. Activity spaces are geometric indicators of the observed or realised daily travel patterns (see also Axhausen, 2002). This is stressed here as related concepts such as the action space (e.g. Horton and Reynolds, 1971), the awareness space (e.g. Brown and Moore, 1970), the perceptual space (e.g. Dürr, 1979), mental maps (e.g. Lynch, 1984) or space-time prisms (e.g. Lenntorp, 1976) describe the individual potentials of travel based on spatial knowledge, mobility resources, the objective supply of opportunities etc. Activity spaces are defined here as a two-dimensional form which is constituted by the spatial distribution of those locations a traveller has personal experience (contact) with. The geometry, size and inherent structure of activity spaces are determined by three determinants (Golledge and Stimson, 1997): Home: The position of the traveller s home location, the duration of residence, the supply of activity locations in the vicinity of home and the resulting neighbourhood travel Regular activities: Mobility to and from frequently visited activity locations such as work or school Travel between and around the pegs: Movements between the centres of daily life travel Figure 1 shows a schematic representations of both, the basic concept and a revealed activity space based on the six-week Mobidrive travel data. 3 Figure 1 Simplified activity space representation: Generalisation and actually observed pattern in Karlsruhe (Mobidrive) * a) Friend s place Work place Home Business place Second shop Sports First shop Locations Travel b) a) Adopted from Maier, Paesler, Ruppert and Schaffer (1977) 57 b) Schönfelder and Axhausen, 2003 * Dots show observed activity locations of one respondents over six weeks of reporting In a wider sense, the activity space comprises those locations of which a traveller has personal experience, as well as those of which the traveller has second hand experiences through family, friends, books, films or other media (the knowledge space) (see e.g. Horton and Reynolds, 1971; Dürr 1979 or Goldenberg, Libai and Muller, 2001). In the following, though, activity space refers only to the first set of locations, those which a traveller has personally visited. 4 For land use as well as transport planning and policy, there exists a range of benefits from a deeper knowledge of individual activity spaces (see e.g. Dangschat Droth, Friedrichs, Heuwinkel and Kiehl, 1980), such as better conclusions on the quality and acceptance of the local infrastructure in urban sub-areas, the frequency of usage and the effectiveness of recent infrastructure improvements concerning the change in travellers behaviour. This may lead to a more demand-tailored planning instead of a focus on pre-defined standards and reference values. better assessments of user groups behaviour and elasticities as well as their affection from general and particular planning measures (e.g. impact opening times, effects of modified infrastructure schemes on time budgets, potentials effects of growing centralisation of facilities) improvement of transport modelling techniques, especially by the more realistic design of choice sets for location and route choice. 2.1 Development of approaches based on the Mobidrive work Empirical work on revealed activity spaces, i.e. the physical mapping or enumeration of the places visited by individuals, is rare. Where such work has been done concerning a geometrical representation of personal activity spaces or even the measurement of their sizes, the focus was mostly on travel potentials or opportunities. This was often inspired by the conceptual approaches of space-time geography which puts spatial movement into a context of individual and societal and constraints (Hägerstrand, 1974; Chapin, 1974). Only few studies concentrated on the detailed measurement of individual activity spaces (e.g. Dijst, 1999) 2. The Mobidrive longitudinal data structure which gives us a detailed insight into people s daily travel behaviour has opened up the opportunity for methodological developments and empirical work. As the Mobidrive data set contains exact locational data by comprehensive geocoding of most of the reported trips (approximately made by approximately 320 respondents), the analysis of the variability in spatial behaviour over time is now possible. The precise locational data was obtained by geocoding the trip destination addresses of all main study trips. The addresses including home and workplace locations were transformed into Gauss-Krüger coordinates in a WGS 84 (World Geodetic System) geodetic reference system. 2 It should be noted that there is a range of studies of spatial behaviour and activity spaces on the aggregate level of sociodemographic groups or zones. Those studies use cross-sectional travel or time-use data. 5 The methodological development to capture human activity spaces lead to three measures (Figure 2) (see Schönfelder and Axhausen, 2002a; b; Schönfelder and Axhausen, 2003): A two-dimensional confidence ellipse (interval) around a suitably chosen centre point. The activity space measured by kernel densities where again information about the locations visited is used. The third approach is based on the idea of minimum spanning tree (network), i.e. the length of the minimum distance routes between the locations visited. The three approaches to describe the structure and the size of individual activity spaces are models of human behaviour and therefore simplifications of environmental perception and actual decision processes. Nevertheless, they already proved to be powerful for both, visualisation and measuring in initial applications to revealed data. It could be shown that the measures are flexible and allow the researchers to chose the input parameters according to the particular analysis interest, such as the interactions between activity location supply and destination choice the implementation of the measurement is possible within common GIS software packages. the visualisation of examples is straightforward and enables practitioners to gain insight into the travellers mobility routines. the measurement results are consistent with earlier findings on locational choice, such as the positive relationship of the amount of travel and the number of unique locations visited. 6 Figure 2 Measuring activity spaces: Overview of concepts developed within the Mobidrive framework a) Confidence ellipses Basic approach: Probability; smallest possible area in which a defined share of all visited locations is situated Measure: Size of area (plus direction of main axis) Special feature/quality: Shows dispersion of visited locations Dots show location and intensity of observed activity locations of one respondent b) Kernel densities Basic approach: Density surface; based on the proximity of activity locations Measures: a) Area covered exceeding a certain threshold value, b) Volume (sum of all kernel densities calculated) Special feature/quality: Represents local clusters / sub-centres within individual activity space c) Minimum spanning trees (networks) Basic approach: Smallest possible geometry based on all observed origin-destination relations Measure: a) Length of tree, b) Size of buffered area around the tree indicating potential knowledge spaces Special feature/quality: Suitable indicator for the perception of urban space and networks 7 3. The Borlänge GPS data an innovative data processing experiment The visualisation and measuring of revealed activity spaces requires the collection of longterm individual travel data which allows the enumeration of places visited over time. From a methodological point of view, the collection of such data by ordinary travel diary methods remains costly in terms of survey expenses but especially in terms of the burden for the respondents. Travel behaviour research recently raised the question of more suitable survey designs not the least based on the experiences with the Mobidrive data collection and analysis (see Massot, Madre and Armoogum, 2000; Schlich and Schönfelder, 2001). 3.1 GPS data and travel behaviour a short review As an additional data collection approach, the use of GPS data for travel behaviour research has been discussed and tested in transport research since mid of the 1990s (see Wolf, Guensler and Bachman, 2001 or Schönfelder et al., 2002 for an overview of studies). The general technical approach used in recent feasibility studies is the combination of mobile GPS data loggers and a Geographical Information System (GIS) (see Draijer, Kalfs and Perdok, 2000 for an example). Principally, vehicles or in fewer cases pedestrians or cyclists are equipped with an on-board / on-body data collection system consisting of a GPS receiver, a data storage device with a GIS for mapping all movements and a mobile power supply. For each trip (irrespective of the mode), the respondents switch on the system independently which starts data transmission to the computer (storage) in short intervals of e.g. 10 seconds. After data collection (i.e. the tracking of the travellers), the highly-exact spatial and temporal information is transferred to a conventional PC for processing. The existing data collection approaches may be categorised as follows (Lee-Gosselin, 2002; Wolf, 2003): GPS based data collection as enhancement of traditional travel diaries In most of the feasibility studies, the portable or in-vehicle GPS / GIS device acts as a supplementary means of collecting exact time, location, route choice data. Hence, the technique substitutes the respective parts of the ordinary travel diary survey and reduces the reporting tasks of the survey respondents. The remaining trip related information, such as trip purpose, number of people travelling together or activity ex- 8 penses are collected separately either by means of ordinary travel diary forms or electronic data collection devices such as Personal Digital Assistants (PDA). Passive monitoring Within a passive monitoring framework, the travellers are observed automatically without providing any additional information on their trip making (no driver-device interaction). Most of the studies which use passive monitoring are traffic safety driven. The focus of the analysis here is the style of driving respectively the behavioural reaction of the drivers towards external conditions the rationale for the drive and the activity related to the movement are of a minor interest. 3.2 Borlänge Rättfart the background Transport psychologists from the universities of Dalarna and Uppsala (Sweden) kindly provided the Institute of Transport Planning and Systems (IVT) with the GPS data set Rätt Fart which nicely matches the data requirement for activity space analysis. The traffic safety project Rätt Fart (Right Speed) 3, based in the Middle-Swedish town of Borlänge, is one of the sub-projects of the Swedish National Road Administration initiative approach Intelligent Speed Adaptation (ISA). ISA aims to influence car and truck drivers behaviour by in-vehicle information (see Vägverket, 2000a). The ISA sub-projects are designed to analyse the responses of drivers, ways of integrating interactive technologies into vehicles and the effect of intelligent speed adaptation systems on road safety and the environment. Rätt Fart in Borlänge had its focus on provision of information for the drivers using GPS devices. The study was conducted from 1999 to 2001 with about 300 private and commercial cars which were equipped with GPS and speed adaptation systems over the period of up to 2 years. Each drive s characteristics such as speed, acceleration, actual time, position etc. was stored internally for analysis in logs every second respectively every tenth second depending to the road link used (see below). The GPS receiver itself did not transmit any signal of its own which prevented external sources to get access to the car s location or other information. The data logs from the vehicles as well as supplementary data concerning the acceptance of the ISA device are analysed right now in terms of traffic safety by researchers of the two Swedish universities. 3 See 9 The movement file of which one half with about private car trips is ready for analysis provides accurate trip-specific information such as times, positions, speeds and path choices. The area for detailed monitoring was limited, though, to the town of Borlänge plus some surrounding re
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