Alternative travel behaviour data sources

As discussed in the last section, considerable methodological and theoretical progress has been achieved in the spatial-temporal investigation of travel behaviour. A persistent challenge in this field of studies, however, relates to seeking reliable travel behaviour data sources.

In travel behaviour research, survey-based methods (e.g., questionnaire survey, telephone-based survey) have long been the major method for collecting travel behaviour data, well-known survey datasets including the 1971 Uppsala data, Sweden (Hanson and Huff, 1988), the 1973 Reading data, England (Pas and Koppelman, 1987), and the 1999 Mobidrive data, Germany (Susilo and Kitamura, 2005). Despite the prevalence of the survey-based method, it has also been recognised that travel survey data inherently suffer from certain methodological limitations that may critically compromise the data quality (Pas, 1985; Noland and Polak, 2002; Richardson et al., 1996; Greaves, 2006; Clarke et al., 1981).

One-day record of travel survey has been commonly criticised for its assumption that people’s travel behaviour is highly routinised on a day-to-day basis (Hanson and Huff, 1988;

30 Huff and Hanson, 1986). Multi-day survey (or travel diary survey), while partially addressing such issues, also creates its own hazards stemming from the survey process. The fact that multi-day survey data require days to weeks of self-administered recording of travel details apparently places a non-trivial burden (psychologically or physiologically) on the participants, that further causes non-responses either at the stage of recruiting participants (i.e., not participating at all) or during the survey process (e.g., dropping from the survey, or providing incomplete responses) (Clarke et al., 1981; Pas, 1985). The commonly low response rate of travel surveys (e.g., 10-20%) speaks for this point. In such cases, the resulting samples are likely subject to ‘non-response bias’, compromising the generalisability of the samples collected (Clarke et al., 1981; Pas, 1985; Richardson et al., 1996).

Additionally, travel information collected faces the issue of inaccuracy, since participants may provide inaccurate information (location or time) or omit certain trips due to reasons such as fatigue over multi-days of completing the survey, or considering a trip insignificant (Clarke et al., 1981; Richardson et al., 1996). These issues apparently also have the potential of compromising the results of spatial-temporal analysis of travel behaviour. Last, the costs of a survey can easily be substantial, since it usually involves the employment and training of a number of surveyors and the deployment of facilities (telephones, post) in order to approach a large number of participants to do the survey. The high costs of travel surveys posit barriers for the renewal of travel behaviour data in a timely manner, and therefore resulting in the use of dated travel behaviour data (Bagchi and White, 2005).

Given the aforementioned drawbacks of travel survey data, it becomes compelling to search for alternative data sources that have the capacity to offer more accurate and reliable travel behaviour information with less costs for data collection and a lower burden for the participants.

Census data has been applied as an alternative data source to survey data for investigating travel behaviour patterns from a time-series perspective (Milthorpe and Raimond, 1998;

Shuttleworth et al., 2000; Cosgrove, 2011). Census data is normally collected at a regular time interval (e.g., every five years) for analysing and publishing socio-economic and demographic characteristics of the population of a relatively large geographic area (e.g., a country) (United Nations, 2008). With regards to the travel behaviour of a population, many censuses also collect information concerning the population’s travel patterns for work trips (e.g., modal share, number of people travelling between employment and residential

31 locations), due to the primary interest of governments in work trips because of the traffic burdens generated during peak hours (Mees et al., 2008; Senior, 2009). Compared to travel survey data, census data is considered to be superior in providing travel information with a much more comprehensive population and geographic coverage (Mees et al., 2008;

Rickwood and Glazebrook, 2009). As such, it to a degree avoids the issue of generalisability of sample of the travel survey data.

A number of studies have drawn on census data to investigate the travel patterns of country-wide or city-wide populations in relation to other socio-demographic variables (e.g., employment-residence ratio), e.g., Giuliano and Small (1993), Shuttleworth et al (2000), Weber and Sultana (2007), Mees et al (2008), Senior (2009), Li et al (2012). In terms of spatial-temporal analysis of travel behaviour, the strength of census data is that it allows the investigation of travel behaviour spatial patterns at various aggregated levels, given that census data are usually provided on a variety of geographic units (e.g., postcode area, local government area). Related to the aggregated nature, census data has some critical limitations as well. First, the observation of individual or household travel behaviour is not possible for census data. Additionally, the trip information of census data usually focuses on work-based trips on the census date while omitting other types of trips such as shopping and recreation on weekends. Finally, the common collection interval of several years (e.g., five years) renders the census unsuitable for more continuous analysis of travel behaviour (e.g., day-to-day).

Apart from the census data, the emergence of ‘big data’ has received growing attention in transport studies in the past two decades. ‘Big data’ are usually characterised by large quantities of information, high velocity of data collection and high resolution of data details (Boyd and Crawford, 2012; Kitchin, 2013; Miller, 2010). The ‘big data’ collected by location-aware systems, such as Global Positioning System (GPS) and automatic data collection (ADC) within the UPT context are worth mentioning here.

Rather recently, location-aware systems, including GPS and personal handy-phone system (PHS), have been increasingly applied in providing enhanced trip information for spatial-temporal travel behaviour studies (Wolf et al., 2001; Ohmori et al., 2000; Asakura and Hato, 2004; Stopher et al., 2007). Facilitated by location-positioning satellites, a location-aware instrument (e.g., a GPS tracker, or a cellular phone) can continuously track and record the spatial location (i.e., coordinates) and the associated temporal stamps of its carrier (e.g., a survey participant) at a small interval (e.g., 15 seconds) over time (e.g.,

32 several days to weeks) (Ohmori et al., 2000; Asakura and Hato, 2004). Hence, the trip information collected by such an approach (location-tracking data) is usually considered of high spatial and temporal resolution. It has been shown that location-tracking data surpass the traditional survey data in reducing the issues of omitting certain trips and reporting inaccurate geographic and time information associated with the trips of the participants (Bohte and Maat, 2009; Stopher et al., 2007). Additionally, considering the automatic manner of location-aware systems in collecting trip data, the burden for the participants in the survey process is arguably much smaller compared to the traditional survey method (Bohte and Maat, 2009).

A growing body of studies has exploited the utility of GPS and other location-tracking datasets such as PHS-based data and revealed detailed spatial-temporal dynamics of individual and vehicular travel behaviour, e.g., Wolf et al (2003), Stopher et al (2003), Demissie et al (2013), Guo et al (2012). A critical limitation of location-tracking data, however, is that certain important trip information, including travel mode and purposes are not recorded by location-aware instruments (Asakura and Hato, 2004; Bohte and Maat, 2009). An examination of existing literature shows that methods (e.g., additional survey) have been developed to deal with such issues (Bohte and Maat, 2009). Furthermore, location-aware systems, while able to record detailed trip information of individuals, have been essentially applied as a survey tool with relatively small sample sizes ranging from dozens to a few thousand participants (or subjects) in the existing literature. Large scale application of location-aware systems in travel behaviour studies appears to be rather limited (Bohte and Maat, 2009). This might be attributed to that the fact that large-scale application of location-aware instruments is not easily fulfilled from both an expenditure and feasibility point of view.

Last, with the growing prevalence of the ADC systems in the UPT context globally, the resulting data has received particular research attention in investigating UPT passenger travel behaviour (Bagchi and White, 2005; Pelletier et al., 2011; Chu, 2004). ADC systems, in particular automatic fare collection (AFC) have been introduced in the UPT context with the initial aim of improving the efficiency of UPT system operation, such as automating ticketing processing. Given the case of AFC implementation with an exhaustive network-coverage, the trip information (e.g., passengers’ boarding and alighting location and time) collected is typically characterised by very large quantity (e.g., hundreds of thousands of data entries on daily basis) and relatively high spatial-temporal resolution.

Given such characteristics, AFC-based data to a degree encompass the strengths of both

33 census and GPS data in providing relatively detailed spatial-temporal trip information with a sample size that approximates to an entire population (e.g., UPT passengers of a city).

Considering that BRT has formed an integral component of UPT infrastructure across more than 180 cities worldwide, AFC-based data, if available, are arguably more suited for investigating the tangible aspect of BRT system dynamics compared to other location-tracking data, e.g., GPS data. Bearing this point in mind, the next section provides a more detailed review concerning the application of AFC data as well as the implications and challenges for investigating BRT usage with such datasets.