tracking daily travel; assessing discrepancies between gps-derived and self-reported travel patterns

Transportation Research Part C 48 (2014) 97–108

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Transportation Research Part C

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Tracking daily travel; Assessing discrepancies betweenGPS-derived and self-reported travel patterns

http://dx.doi.org/10.1016/j.trc.2014.08.0130968-090X/� 2014 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +1 949 824 0563; fax: +1 949 824 8566.E-mail address: [email protected] (D. Houston).

Douglas Houston a,⇑, Thuy T. Luong b, Marlon G. Boarnet c

a Department of Planning, Policy, and Design, 300 Social Ecology I, University of California, Irvine, CA 92697-7075, United Statesb Transportation Science Graduate Program, University of California, Irvine, CA, United Statesc Sol Price School of Public Policy, University of Southern California, Los Angeles, CA, United States

a r t i c l e i n f o

Article history:Received 23 October 2013Received in revised form 5 June 2014Accepted 18 August 2014

Keywords:Travel behaviorSelf-reportGlobal Positioning SystemsTravel survey data collection

a b s t r a c t

Global Positioning Systems (GPS) technologies have been used in conjunction with tradi-tional one- or two-day travel diaries to audit respondent reporting patterns, but we usedGPS-based monitoring to conduct the first assessment to our knowledge of travel reportingpatterns using a seven-day travel log instrument, which could reduce response burden andprovide multiple-day, policy-relevant information for evaluation studies. We foundsubstantial agreement between participant-reported daily travel patterns and GPS-derivedpatterns among 116 adult residents of a largely low-income and non-white transportationcorridor in urbanized Los Angeles in 2011–2013. For all modes, the average differencebetween daily GPS- and log-derived trip counts was only about 0.39 trips and the averagedifference between daily GPS- and log-derived walking duration was about �11.8 min. Wefound that the probability that a day would be associated with agreement or discrepanciesbetween these measurement tools varied by travel mode and participant socio-demographiccharacteristics. Future research is needed to investigate the potential and limitations of thisand other self-report instruments for a larger sample and a wider range of population groupsand travel patterns.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction and aims

Portable Global Positioning Systems (GPS) technology has been increasingly used in public health and travel behaviorstudies to assess physical activity and exposure to environmental hazards and amenities based on extended monitoringof activities and locations for periods often ranging from 6 to 14 days (Bohte and Maat, 2009; Chaix et al., 2013; Krennet al., 2011). GPS has also been increasingly used to supplement traditional travel surveys, which typically use a one-daytravel diary requiring participants to provide substantial information about trip purpose, mode, start and end time, andthe location of the trip origin and destination (Stopher and Greaves, 2007). Requiring such detailed trip-level informationcan present a substantial burden for respondents and could be associated with response fatigue and lower reporting accu-racy (Golob and Meurs, 1986). In fact, comparisons of self-reported travel patterns with GPS-derived patterns indicate thatparticipants do not report all GPS-identified trips in their travel diaries (rates of unreported trips range from 10% to 80%), thatparticipants consistently over-report trip duration by about 4.4 min, and that discrepancies in trip measures are associatedwith socio-demographic and travel-behavior characteristics (Wolf, 2004; Wolf et al., 2004).

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98 D. Houston et al. / Transportation Research Part C 48 (2014) 97–108

The current study contributes to the literature by using GPS-based monitoring to evaluate reporting accuracy based on aseven-day travel log which we designed to reduce participant reporting burden and to provide sufficient seven-day traveldata to evaluate the impact of a new light rail transit service. In contrast to a travel diary format which requires trip-leveldetails, the travel log is simpler, requires respondents to only count the total number, duration, and mode of trips for eachday, and enables the collection of data for longer periods than a typical one-day travel diary survey. Although our log formatdoes not generate the type of trip-level information needed to calibrate the travel demand models used by transportationplanning organizations, it does provide substantial day-level information which will be used in our future longitudinal eval-uation of whether a major transit investment has succeeded in achieving its policy goals of encouraging reduced vehicle tra-vel and increased transit usage and physical activity. Given expected day-to-day variation in travel over days of the week,obtaining data for seven days for each participant before and after an investment could be important in evaluation studiesbecause such multi-day monitoring could provide estimates of average daily travel patterns in both phases of data collectionwhich are more representative of a participant’s typical travel habits. In addition to providing the first assessment of travelreporting patterns for an alternative log-based self report instrument, the current study addresses additional gaps whichhave previously been identified in the literature (Bricka et al., 2012; Kelly et al., 2013) by expanding our understanding ofreporting accuracy for non-auto modes of travel and for residents of a largely low-income and non-white urbanneighborhood.

The remainder of this paper is organized into four sections. First, we provide a literature review on the use of GPS in traveland activity surveys. Second, we provide an overview of the Expo Line Study and describe our data and methods. Third, wepresent our empirical analysis of discrepancies between self-reported travel-log travel patterns and GPS-derived patternsand our assessment of socio-demographic and household factors associated with discrepancies. The last section discussesthe implications of our findings for travel behavior analysis, transportation planning, and infrastructure evaluation studies.

2. Background

2.1. Unreported travel

Over the past decade, GPS monitoring has increasingly been used to supplement household travel diary data collection. Itsrole has largely been to identify trips that participants did not record in their diaries, to inform improvements to surveymethods and travel diary instruments, and to inform the generation of factors used to correct travel parameters in regionaltransportation demand models (California Department of Transportation, 2002; Department for Transport, 2009; Murakamiand Wagner, 1999; Stopher et al., 2007; Wolf et al., 2003). Early applications in regional travel surveys used GPS deviceinstalled in household vehicles and only tracked in-vehicle travel (Bricka and Bhat, 2006; Murakami and Wagner, 1999),but recently regional surveys have begun to use wearable GPS devices to provide insights into trip-reporting patterns forother modes of travel (Bricka et al., 2012; California Department of Transportation, 2013; Draijer et al., 2000).

Comparisons of data from travel diaries and corresponding GPS monitoring in regional travel surveys reveal that the rateat which trips not reported in travel surveys are identified in GPS data varies substantially. For instance, the rate of under-reporting was 7% in a survey in Sydney (Stopher et al., 2007), 10% in a survey in Kansas City (Wolf et al., 2004), 18–40% in asurvey of four California counties (California Department of Transportation, 2002), 30% in Ohio (Pierce et al., 2003), and 81%in a survey in Laredo, Texas (Wolf, 2004). In the 2004 Kansas City survey, participants who were younger, male, had lowereducational attainment, or who made more or longer trips had higher rates of trip under-reporting (Bricka and Bhat, 2006).In the 2001 California survey, households with more vehicles, lower income, more workers, or with younger adults hadhigher rates of under-reporting of vehicle travel, and trips under 10 min in duration were less likely to be reported in diaries(Zmud and Wolf, 2003). In the 2005 Sydney survey, shorter trips (in time and distance), trips in the evening, and trips with ashorter time spent at the destination were less likely to be reported (Stopher et al., 2007). The reasons for under-reporting oftrips may include survey length and the burden of reporting details for each trip, not understanding or following surveyinstructions, and failure to remember trips and their details in cases when participants record them at the end of the obser-vation day (Stopher et al., 2007; Wolf et al., 2003).

In contrast, a recent review of eight studies indicates that trip durations were consistently over-reported on diaries com-pared to GPS-derived trip durations. The pooled estimate of the difference across all studies was 4.4 min. The over-reportrate ranged from 2.2 to 13.5 min. This pattern may occur if people round travel times to the nearest 5- or 15-min clock timewhen reporting the beginning and ending times of trips or the duration of a given trip (Rietveld, 2001). Also, if survey respon-dents perceive a given trip to be difficult or onerous they may over-estimate its duration and record it as taking longer(Stopher et al., 2007). Unfortunately, available studies do not provide insight into whether reporting trip duration variesby participant or travel characteristics.

2.2. Procedures for characterizing travel using GPS data

The above studies examining under- and over-reporting of travel assumed that GPS measurements are a valid and reliablestandard of comparison, but GPS data and post-processing procedures such as correction for signal loss and spatial positionalinaccuracies, the identification of travel periods, and the classification of trip mode and details have some limitations.

D. Houston et al. / Transportation Research Part C 48 (2014) 97–108 99

Missing data can occur when GPS devices do not receive an adequate signal from at least three positional satellites, whichhappens during periods underground, indoors, or outdoors with an obstructed view of the sky due to vegetation or tall build-ings (Chen et al., 2010; Elgethun et al., 2003; Krenn et al., 2011; Stopher et al., 2008; Wu et al., 2010). Even when data areobtained under such conditions, the positional accuracy of GPS point locations can range from about 7 to 100 meters com-pared to about 7–27 m under open-sky conditions (Duncan et al., 2013).

The assumptions used to classify travel periods and trips using GPS data could affect daily counts of trips and estimates oftheir durations. Previous research has developed and validated automated procedures to process continuous GPS data todetect periods spent at stationary locations and periods of travel by different modes (Cho et al., 2011; Wu et al., 2011). Asecond and important step is to define trips within the GPS data, and the period used to define the minimum dwell timebetween trips that constitutes an activity (usually between 90 and 120 s) can affect the number and duration of observedtrips (Department for Transport, 2009; Pierce et al., 2003; Schönfelder and Axhausen, 2010).

2.3. Research needs

Unlike most studies, which assumed that GPS measurements are a valid standard of comparison, Bricka et al. (2012)examined differences in trip reporting between self-reported diaries and GPS-monitoring with wearable GPS devices with-out making assumptions a priori about the accuracy of either source. The importance of this approach is supported by studiesthat indicate that some trips reported on diaries do not appear in GPS data, perhaps because participants in some cases forgotto carry devices at some point during the travel period (Forrest and Pearson, 2005; Kelly et al., 2013; Stopher and Shen, 2011;Wolf et al., 2003).

Although some studies provide valuable insights about the trip-level factors that may correspond with under- or over-reporting patterns using trip-level matched data, another useful approach is to analyze the total daily discrepancy betweenthe number of self-reported and GPS-derived trips (Kelly et al., 2013). Total trips and travel duration are important metricsfor policy makers evaluating the impact of transportation systems and compact, transit-oriented, mixed-use development ontravel patterns since they provide a direct measure of whether these policies are associated with their objectives: an overalldecrease in daily vehicle travel and an increase in daily travel by transit and/or non-motorized modes. Further research isneeded to examine reporting discrepancies with alternative measurement instruments such as daily travel logs, which donot ask for details of each trip, just total daily travel by mode (Kelly et al., 2013). Research is also needed to investigatereporting discrepancies for public transit and walking modes and to assess the association of socio-demographic and travelcharacteristics with travel-duration reporting. Previous studies have largely been based on one-day travel diaries but longerobservation periods are needed given the expected variation in daily travel (Golob and Meurs, 1986).

3. Data and methods

3.1. The Expo Line Study

Data for this study were obtained through a 7-day survey of residents of south Los Angeles, conducted in two phases, onebefore (September 2011–February 2012) and one after (September 2012–January 2013) the Expo LRT service began in mid-2012. The study area covers about 12 square miles (31 square kilometers) along the Exposition and Crenshaw corridors insouth Los Angeles, California (Fig. 1). The population of the study area was about 9% non-Hispanic white, 41% Hispanic, and43% African–American. About a fifth of residents lived in households with income below the federal poverty level, about athird were foreign-born, and about a quarter had an educational attainment of a bachelor’s degree or higher (US CensusBureau. 2005–2009 American Community Survey; US Census Bureau. 2010 Decennial Census Summary File 1).

Since the overall Expo Study used an experimental-control study design, the study area was divided into an ‘‘experimen-tal’’ area consisting of the area within 0.5 miles of any Expo station, within which we hypothesized that participants wouldlikely walk to access the Expo service, and ‘‘control areas’’ more than 0.5 miles from any Expo station with similar demo-graphic, land use, and transit service patterns, which we hypothesized were far enough away to be affected by the newLRT service. Since the focus of the current study is a comparison of travel patterns derived from our seven-day travel logto those derived from GPS monitoring, an examination of differences between the experimental and control groups is beyondthe scope of the present analysis. To identify potential participants in the first phase of data collection, we purchased a list ofall household addresses within the study area (27,275) from InfoUSA, a marketing information firm. We mailed each house-hold an invitation letter, and all households who indicated they were interested in participating (651) were invited into thestudy. A total of 279 households submitted a complete set of responses resulting in an overall response rate of 1.0%. Althoughlow, this response rate is comparable to the 1.4% response rate for the region’s sample from the 2010 to 2012 CaliforniaHousehold Travel Survey (defined as Los Angeles County and Ventura County) (California Department of Transportation,2013) and the 0.4% response rate for a 2012 survey of households near rail transit in Los Angeles County (Houston et al.,2014), both of which collected household travel data using a one-day travel survey instrument. A comparison of the char-acteristics of responding households to all households in the study area indicated that the composition of the sample didnot vary greatly by household and demographic characteristics available in the InfoUSA address information. Comparedto all households contacted, the study sample included a slightly lower percentage of households headed by a male (36%

Fig. 1. Expo Study area.


vs. 42%), households headed by a younger adult aged 18–39 (21% vs. 27%), and households with an annual income below$30,000 (33% vs. 38%), but these differences were not statistically significant. This suggests the final sample was represen-tative of the study area population based on observable characteristics.

At least one household member age 12 or older completed the household baseline survey, which included questionsabout household composition, demographics, and transportation resources, as well as a the seven-day travel log, whichrequired each participant to record his or her daily number of trips by travel mode (passenger vehicle, public transit, walking,

Fig. 2. Expo Study travel log.


and cycling) and the number of minutes walking and cycling (Fig. 2). In addition, one adult participant from each of 134 ofthese households participated in supplemental GPS-based location tracking and accelerometer-based monitoring for thesame seven-day period. We re-contacted these participants in phase 2 of the study, and 100 repeated the seven-day mon-itoring period using the same survey and mobile-tracking protocol. Only participants with at least one day of complete andvalid travel-log and GPS data were eligible for inclusion in the current analysis. For phase 1, 113 of the 134 participants metthis requirement, and for phase 2, 83 of the 100 participants met this requirement, resulting in a total of 1230 days for poten-tial inclusion in the analysis (additional selection criteria are described below).

Participants in the mobile-tracking group received a grocery gift card worth $30 as an incentive for completing phase 1 ofthe study, and this incentive was increased in value to $75 for phase 2 to encourage the same households to participateagain. Participants met with a project staff member before their data collection period for training on the use of equipmentand again afterwards to return materials and to confirm compliance with study procedures.

3.2. GPS data post-processing

Participant locations were recorded at 15-s intervals for the seven-day data collection period using a portable QSTAR GPSlogger device (model QT-1000x), which has a high level of spatial precision and sufficient battery life for 24 h of use betweencharges (Duncan et al., 2013). Since GPS monitoring produces a large amount of temporal and spatial data, substantial post-processing was needed to identify the locations and activity types associated with sequential GPS locations, to account formissing data associated with participant noncompliance or device errors, to address spatial positional inaccuracies associ-ated with satellite signal obstructions, to identify unique trips during travel periods, and to tabulate daily trip counts bymode and walking duration (Bricka et al., 2012; Cho et al., 2011; Elgethun et al., 2003; Rainham et al., 2008; Schuesslerand Axhausen, 2009).

3.2.1. Classification of locations and travel periodsWe conducted post-processing of GPS data using a previously validated classification procedure (Houston et al., 2013; Wu

et al., 2011) to identify periods during which participants were at a location (home, work, school, grocery, etc.) and periodsduring which they were traveling between locations. Periods at a location were identified by sequential GPS locations thatwere clustered within 20 m of a given location for longer than 2 min. Previous research suggests the criteria required to suc-cessfully identify stationary periods could vary by data collection methods and study area structural characteristics andbuilding heights (Gong et al., 2012). We developed our criteria based on sensitivity tests of different distance and durationthresholds and our final distance criteria corresponds with the range of positional errors of the GPS device used (Duncanet al., 2013; Wu et al., 2010). Periods not at locations were classified as vehicle travel if the speed between sequentialGPS points was at least 6 mph (9.7 kph). Both points with an initial classification and non-classified points were reviewedto correct classifications by using ArcGIS software (ESRI, Redlands, CA) to examine the proximity of sequential GPS locationsto roadways, sidewalks, transit routes and stops, parcel boundaries, and various land use designations. Points recordedimmediately after a vehicle travel period were also classified as an in-vehicle travel period if they appeared to correspondto a brief in-vehicle stop along a roadway or parking area during a longer travel sequence. Vehicle travel periods were re-classified as public-transit travel periods if a trip began at a transit stop, followed a transit route, and ended at a transit stop.Finally, GPS locations not classified in earlier steps as being at a location or as part of a vehicle or transit travel period werereviewed to confirm whether they corresponded to a walking travel period based on whether they occurred in sequencebetween locations, were at a typical walking pace (2–6 mph), and followed a reasonable path along roadways, sidewalks,or open space areas.

3.2.2. Signal loss and day selectionWe developed a set of diagnostic thresholds to assess periods of missing data in GPS data and to identify days with suf-

ficient information to be included in our analysis of discrepancies in day-level trip counts and walking travel duration. Peri-ods of missing data could occur due to participant noncompliance, device error, or satellite signal obstructions during indoorperiods within a steel and concrete structure, underground periods on the subway, or outdoor periods with obstructionssuch as tall buildings, vegetation, or geography. We classified days with periods of missing data based on the length ofthe gaps in the GPS data and by whether the location and travel status were the same or different before and after the gap.

First, we only included data from the 119 participants who provided complete responses about the demographic infor-mation used in the analysis (age, gender, race, educational attainment, household income, number of household cars, andemployment status), which corresponded with 1125 days of GPS data. Next, we eliminated 83 of these days from the analysissince they included a gap lasting more than 30 min that had a different location or travel status after the gap than before(Table 1), a pattern that suggests that the GPS device may have missed at least one change in travel mode or location. How-ever, we kept days with a gap lasting over 30 min that started and ended at the same location under the assumption that notrips or activities were unobserved during the period of missing data. We removed an additional 130 days with gaps lastingmore than 2 h and ending after 10:00 am, under the assumption that such a large daytime gap may have included unob-served activities. We also eliminated 32 days for which the participant did not leave the original location but the recordedduration was less than 12 h or ended by noon, as well as 3 days in which a participant flew on an airplane. Our final analysisdataset included daily travel log and GPS data for 892 days, representing travel patterns for 116 participants.

Table 1Periods of signal loss (N = 1125 days, 119 participants).

Criteria (min) Days HH Average daily lost period Days in which changed status after lostsignala

Days in which same status after lostsignala

Number % Number %

30–60 82 53 1.56 23 28.05 63 76.8361–120 102 57 1.11 35 34.31 69 67.65>121 202 70 1.14 73 36.14 142 70.30

a A given day could have periods in more than one gap category.


3.2.3. Trip detection and classificationWe developed a set of criteria to identify trip starting and ending points in the GPS data. The use of dwell-time thresholds

to detect these points is a common approach in the literature. A dwell-time threshold is the minimum length of time withoutmovement that is assumed to represent a valid stop between two trips (Pierce et al., 2003). It needs to be short enough tocapture quick passenger–drop drop-off or pickup stops but long enough to distinguish true trip ends from stops at trafficlights or delays (Bricka et al., 2012). Previous studies have used thresholds ranging from 45 s to 3 min (Doherty et al.,2001; Schönfelder and Axhausen, 2010), and several studies in California, England, and Belgium have used a 2-min threshold(Beckx et al., 2010; Department for Transport, 2009; Wolf et al., 2003).

We analyzed the implications of using four different dwell-time criteria to identify trips: 30, 60, 120, and 180 s (Table 2).As expected, the aggregate number of trips identified in the GPS data was lower with longer dwell-time thresholds, and thispattern was most pronounced for the count of vehicle trips, which exceeded the log-derived vehicle trip count by 27% with a30-s threshold but fell 2% short of the log-derived trip count with a 180-s threshold. Participants were asked to report publictransit trips with a bus or train transfer as one trip, and our GPS-based public-transit trip counts treat sequential bus tripslinked by a bus transfer as one public-transit trip to match. The GPS-derived transit trip counts were about a third lower thanlog-based counts regardless of the threshold used (Table 2). The log instructions asked participants to record only the num-ber of walking trips that lasted more than 5 min, and our GPS-based walk trip counts only include walking trips which lastedfour or more minutes since participant perception of a 5-min walk may vary. The GPS-derived walk trip counts were about17–19% lower than log-based counts, regardless of the threshold used.

3.3. Analytical approach

We analyze discrepancies in daily trip counts and in walking trip duration between self-reported and GPS-derived measuresbased on a 7-day travel log. We first examine discrepancies by mode to assess the influence of trip dwell time criteria on dailydifferences between GPS- and log-derived trip patterns and on the daily rate of ‘‘good’’ reporting, defined as the percentage ofdays for which GPS- and log-derived patterns were largely in agreement. We define days with good reporting for a given mea-sure as those for which the difference between GPS- and log-derived trip counts was ±1 trip or the difference between GPS- andlog-derived walking trip duration was ±10 min. Second, we conduct bivariate analysis of socio-demographic factors associatedwith discrepancies and good reporting. Third, we conduct multivariate logistic analysis of GPS- and log-derived trip patterns tobetter understand what factors are associated with the probability that a participant-day has a good reporting classification (±1trip or ±10 min), had more GPS-derived trips (>1 trip) or longer walking duration (>10 min), or had more log-derived trips (>1trip) or longer walking duration (>10 min). We assessed the sensitivity of results by specifying models with alternative clas-sifications of days with good trip reporting (defined as days with equal trip counts and days with ±2 trips) and days with goodwalking duration reporting (defined as days with equal duration and days with ±5 min).

4. Data analysis

4.1. Dwell time and discrepancy estimates

We found substantial agreement between daily travel patterns derived from GPS data and those from our trip log instru-ment (Table 3, Fig. 3). Although significant in most cases, the magnitude of the difference in mean total trips and mean trips

Table 2Aggregate GPS trip counts by dwell time and mode (N = 892 days, 116 participants).

Log trip counts GPS trip counts by dwell time

30 s 60 s 120 s 180 sTrip mode Total Total (% Diff.) Total (% Diff.) Total (% Diff.) Total (% Diff.)

Total 4522 5068 (12.1%) 4870 (7.7%) 4532 (0.2%) 4124 (�8.8%)Vehicle 3149 3996 (26.9%) 3797 (20.6%) 3466 (10.1%) 3074 (�2.4%)Transit 429 290 (�32.4%) 290 (�32.4%) 286 (�33.3%) 285 (�33.6%)Walking (>5 min) 944 782 (�17.2%) 782 (�17.2%) 780 (�17.4%) 765 (�19.0%)


by mode between the two measures was relatively small. For instance, the difference for total daily trips ranged from 0.61with a 30-s dwell-time threshold to �0.45 with a 180-s threshold. Likewise, the rate of good trip reporting (±1 trip) for alltrips ranged from 46% with 30-s threshold to 48% with a 180-s dwell time. The average discrepancy in daily vehicle tripsranged from 1.14 to �.10, and the rate of good vehicle-trip reporting ranged from 45% to 50%. On average, the logs had moredaily transit and walking trips reported than were identified from GPS data, but these modes tended to have higher rates ofgood reporting (51–54%) compared to vehicle trips. Participant logs reported on average about 10–12 more minutes of walk-ing than GPS-derived data, and only about a third of observation days agreed on daily walking duration (±10 min). We foundsubstantial variation in the difference in daily walking duration between GPS-based and participant-reported estimates(standard deviation = 43 min). Given that we found substantial agreement between estimates of reporting discrepancyacross dwell-time thresholds used, the remainder of our analysis uses the travel patterns identified with the 60-s threshold.

4.2. Bivariate analysis of factors associated with good reporting and discrepancies

We examined two types of reporting discrepancies: days for which the number of GPS-derived trips exceeded partici-pant-reported trips by more than one trip or for which the GPS-derived walking minutes exceeded participant-reported min-utes by more than 10 min, and days for which participant-reported trips and walking minutes were greater than GPS-derivedmeasures using the same criteria (Table 4). For total trips, the percentage of days with more GPS trips and the percentage ofdays with more log trips was about the same (28% vs. 25%), but this pattern did not extend to individual modes. A higherpercentage of days had more GPS-derived vehicle trips than log-reported vehicle trips (35% vs. 19%), but for transit and walk-ing a higher percentage of days had more log-reported trips than GPS-derived trips (35% vs. 12% and 31% vs. 17%, respec-tively). Almost half (49%) of the days had more log-reported walking minutes, and only about a fifth (19%) had moreGPS-derived walking minutes.

Compared to all days, the rate of good reporting was lower across all categories for participants who were African–Amer-ican, had lower educational attainment, had lower income, or had no household car. Rates were higher for male participantsand weekend days across all categories (Table 4). Older participants had a lower rate of good reporting for vehicle trips but ahigher rate of good reporting for transit and walking trips. The rate of good reporting for all trips combined was higher dur-ing the first three days of data collection (50%) compared to subsequent days of data collection (45% for days 4 and 5, and 44%for days 6 and 7). The rate of good reporting for transit trips was particularly lower on average during the final two days ofthe seven-day data collection period compared to the rate of good reporting for transit trips for all collection days (39% vs.

Table 3Mean daily discrepancies between GPS-derived measures and self-reported trip counts.

GPS-derived measures Log-derived measures Difference (GPS-log) Good reportingc

Dwell time and mode Participants Days Mean SD Mean SD Mean Sig.b SD Mean

a. Dwell time 30 sAll trips 116 892 5.68 4.51 5.07 3.80 0.61 * 4.1 0.46Vehicle tripsa 113 740 5.40 3.94 4.26 3.24 1.14 * 3.7 0.45Transit tripsa 39 142 2.04 1.92 3.02 2.32 �0.98 * 2.6 0.54Walking tripsa 93 450 1.74 2.32 2.10 1.93 �0.36 * 2.7 0.52Walk minutesa 93 450 21.47 33.26 33.38 36.42 �11.91 * 43.1 0.31

b. Dwell time 60 sAll trips 116 892 5.46 4.20 5.07 3.80 0.39 * 3.75 0.48Vehicle tripsa 112 735 5.17 3.57 4.28 3.23 0.88 * 3.25 0.46Transit tripsa 39 142 2.04 1.91 3.02 2.32 �0.98 * 2.62 0.53Walking tripsa 93 453 1.73 2.30 2.08 1.93 �0.36 * 2.66 0.51Walk minutesa 93 453 21.35 33.33 33.15 36.40 �11.80 * 43.09 0.32

c. Dwell time 120 sAll trips 116 892 5.08 3.86 5.07 3.80 0.01 3.53 0.48Vehicle tripsa 111 732 4.73 3.24 4.30 3.23 0.43 * 3.02 0.48Transit tripsa 39 142 2.01 1.89 3.02 2.32 �1.01 * 2.60 0.53Walking tripsa 93 457 1.71 2.22 2.07 1.93 �0.36 * 2.58 0.53Walk minutesa 93 457 22.15 33.98 32.86 36.37 �10.71 * 43.54 0.32

d. Dwell time 180 sAll trips 116 892 4.62 3.51 5.07 3.80 �0.45 * 3.37 0.48Vehicle tripsa 111 731 4.21 2.88 4.31 3.22 �0.10 2.86 0.50Transit tripsa 39 142 2.01 1.89 3.02 2.32 �1.01 * 2.59 0.53Walking tripsa 92 457 1.67 2.13 2.07 1.93 �0.39 * 2.51 0.53Walk minutesa 92 457 22.84 34.90 32.86 36.37 �10.02 * 44.49 0.31

a Results by mode are calculated including only days with at least one trip taken by that mode.b * Indicates the difference between the mean GPS-derived measure and the log-derived measure is significant (unpaired t-test, P < 0.05).c Indicates the daily difference between GPS- and log-derived measures was within ±1 trip or ±10 min.

-15

-5

5

15

0 5 10 15 20 25 30

All Trips

Car Trips

Walk Trips

Transit Trips

GPS-Derived Trip Counts

Diff

eren

ce in

GPS

-Der

ived

and

Log

-Der

ived

Trip

Cou

nts

GoodReportingRange

Fig. 3. Range of daily good trip reporting and discrepancies by mode. Note: Good trip reporting indicates the difference between GPS- and log-derivedmeasures were within ±1 trips.


53%), but this difference was not apparent for other travel modes. The rate of good reporting for vehicle trips, walking trips,and walk duration did not vary greatly by the day of data collection.

4.3. Multivariate analysis of factors associated with good reporting and discrepancies

For all trips, older participants, employed participants, those with a lower educational attainment, and those with lowerincome were associated with a lower probability of a good trip-reporting day and a higher probability of a day with moreGPS-derived trips (Table 5, Model Set 1). The significance and influence of these variables were consistent in alternativemodels we specified in order to assess the sensitivity of our method to categorizing good reporting. African–American par-ticipants were associated with a higher probability of a day with more log-derived trips, and participants with a householdcar were associated with a higher probability of a good trip-reporting day. Male and female participants were not statisticallydifferent in terms of good trip-reporting after controlling for other factors.

Most of these patterns held for models for reporting of vehicle trips, with some exceptions. In particular, neither age northe presence of a household car were significant factors in the probability that a day was categorized as a good vehicle-tripreporting day (Table 5, Model Set 2). Consistent with bivariate results, African–American participants, those with a lowereducational attainment, and those with lower income were associated with a lower probability that a day was categorizedas a good vehicle-trip reporting day; these patterns held in our models using alternative definitions of good reporting. Olderparticipants were associated with a higher likelihood that a day was categorized as a good transit-trip reporting day and alower likelihood of days with more log transit trips (Table 5, Model Set 3). The final two days of the seven-day data collectionperiod were associated with a lower rate of good reporting for transit trips.

African–American participants were associated with a lower probability of a good walking-trip reporting day and a higherprobability of days with more log walking trips (Table 5, Model Set 4). Employed participants were also associated with alower probability of a good walking-trip reporting day, but a higher probability of days with more GPS walking trips.Participants with a lower income were associated with a lower probability of a good walking-trip reporting day. Having alower educational attainment was associated with a lower likelihood of good reporting of daily walking duration and ahigher likelihood of more log-derived walking minutes (Table 5, Model Set 5). African–American participants were associ-ated with a lower probability of more GPS-derived walking minutes. The significance and influence of these variables ongood reporting of walking duration held in our sensitivity tests.

5. Discussion

This study is the first to our knowledge to use GPS-based monitoring to evaluate travel reporting patterns for a seven-daytravel log instrument. In contrast to typical audit studies of trip reporting, which evaluate information from a one-day travelsurvey, we evaluate an instrument that could help reduce participant response burden by not requiring participants to

Table 4Rates of reporting by socio-demographic group.

Characteristics All trips Vehicle tripsa Transit tripsa Walk tripsa Walk mina

a. Average percentage of days with good reportingb

Total Days 0.48 0.46 0.53 0.51 0.32Weekend 0.53 0.50 0.56 0.54 0.34Older (>50 years) 0.43 0.43 0.63 0.53 0.31Male 0.50 0.51 0.55 0.53 0.34African–American/Black 0.43 0.40 0.51 0.42 0.29Educational Attain. < BA Degree 0.41 0.38 0.47 0.44 0.26Household Income < 15,000/yr 0.32 0.33 0.49 0.37 0.26No Household Car 0.44 0.38 0.51 0.41 0.31Employed Full Time 0.45 0.46 0.64 0.50 0.35Phase 2 Data Collection Day 0.51 0.49 0.52 0.51 0.35Data Collection Day 1, 2 or 3 0.50 0.49 0.59 0.51 0.32Data Collection Day 4 or 5 0.45 0.40 0.53 0.53 0.33Data Collection Day 6 or 7 0.44 0.47 0.39 0.50 0.31

b. Average percentage of days with more GPS trips/minc


c. Average percentage of days with more log trips/mind


a Results by mode are calculated including only days with at least one trip taken by that mode.b Indicates the daily difference between GPS- and log-derived measures was within ±1 trip or ±10 min.c Indicates the daily difference between GPS- and log-derived measures was >1 or >10 min.d Indicates the daily difference between GPS- and log-derived measures was <�1 or <�10 min.


record trip-level details (start and end time, purpose, etc.) and could provide multiple-day, policy-relevant information fordecision makers. Although our daily log instrument does not generate sufficient trip-level data often needed to calibrateregional transportation models, it does offer an alternative method for collecting the longer-term, longitudinal travel infor-mation needed for evaluation studies. The need for program and policy evaluation has become increasingly apparent as theplanning sector has prioritized integrative approaches such as smart growth, new urbanism, and transit-oriented develop-ment geared towards encouraging decreased automobile use and increased physical activity which could be associated withreductions in rates of asthma, obesity and heart disease. Unfortunately, available insights on the influence of these local landuse and development policies on travel and activity behavior are based on cross-sectional studies and few longitudinal eval-uation studies exist to guide policy (Salon et al., 2012). We have demonstrated the usefulness and reliability of a seven-daytrip log instrument which can reduce the burden of reporting trip-level information on traditional travel surveys while pro-viding a tool which can be used in future evaluation studies to collect substantial multi-day, policy-relevant, day-level traveldata for all travel modes. Future research is needed to investigate the potential limitations of this and other self-reportinstruments and how the accuracy of reporting on such alternative self-reported instruments compares to the accuracy ofreporting on traditional travel diary instruments. Further research is also needed to assess whether a shorter data collectionperiod using a daily travel log instrument provides sufficient information on daily travel for policy evaluation.

Table 5Logistic regression results: Factors associated with the probability of being in a reporting category.

a. Models for all, passenger vehicle, and transit trips

Independent Variables Model Set 1. All Trips Model Set 2. Passenger Vehicle Trips Model Set 3. Transit TripsModel 1a Model 1b Model 1c Model 2a Model 2b Model 2c Model 3a Model 3b Model 3c

Good More GPS More Log Good More GPS More Log Good More GPS More LogRepor�ng Trips (>1) Trips (>1) Repor�ng Trips (>1) Trips (>1) Repor�ng Trips (>1) Trips (>1)

Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig.

Weekend (1/0) 0.23 -0.51 *** 0.20 0.22 -0.53 *** 0.37 * -0.01 0.63 -0.32

Age -0.01 ** 0.02 *** -0.01 0.00 0.01 ** -0.01 0.05 ** 0.03 -0.06 ***

Male (1/0) 0.00 -0.10 0.10 0.05 -0.13 0.11 0.24 -0.31 -0.11

African-American/Black (1/0) -0.20 -0.07 0.37 ** -0.40 ** 0.01 0.66 *** -0.53 -0.45 0.90

Educa�onal A�ain. < BA Degree (1/0) -0.31 ** 0.39 ** -0.01 -0.43 ** 0.67 *** -0.28 -0.54 0.45 0.42

Household Income < 15,000/yr (1/0) -0.92 *** 0.67 *** 0.38 * -0.53 ** 0.40 * 0.20 0.16 -0.29 0.03

No Household Car (1/0) 0.44 * -0.37 -0.10 0.20 0.39 -1.19 ** 0.15 0.06 -0.23

Employed Full Time (1/0) -0.43 ** 0.43 ** 0.10 -0.26 0.18 0.16 0.19 0.02 -0.16

Wave 2 (1/0) 0.30 ** -0.29 * -0.07 0.27 -0.09 -0.29 -0.08 -0.74 0.43

Data Collec�on Day 6 or 7 (1/0) -0.14 0.07 0.11 0.10 -0.12 0.02 -0.85 ** 0.90 0.43

Pseudo R-square (Max-Rescaled) 0.08 0.08 0.02 0.07 0.09 0.04 0.15 0.08 0.19

N 892 735 142

b. Models for walking trips

Independent Variables Model Set 4. Walk Trips Model Set 5. Walk Minutes

Model 4a Model 4b Model 4c Model 5a Model 5b Model 5cGood More GPS More Log Good More GPS More LogRepor�ng Trips (>1) Trips (>1) Repor�ng Min. (>1) Min. (>1)

Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig. Coeff. Sig.

Weekend (1/0) 0.09 -0.44 0.18 0.07 0.01 -0.06

Age 0.01 0.01 -0.02 ** 0.00 0.02 ** -0.01

Male (1/0) -0.03 0.11 -0.04 0.07 0.26 -0.24

African-American/Black (1/0) -0.90 *** -0.09 1.19 *** 0.00 -0.57 ** 0.38 *

Educa�onal A�ain. < BA Degree (1/0) -0.08 -0.10 0.16 -0.66 ** -0.28 0.74 ***

Household Income < 15,000/yr (1/0) -0.56 ** 0.50 0.26 -0.16 0.53 -0.19

No Household Car (1/0) -0.12 0.36 -0.12 0.35 0.01 -0.29 Employed Full Time (1/0) -0.42 * 0.52 0.14 0.10 0.19 -0.22

Phase 2 Data Collec�on Day (1/0) 0.12 -0.78 *** 0.39 * 0.22 -0.68 ** 0.22

Data Collec�on Day 6 or 7 (1/0) -0.09 -0.10 0.18 -0.04 -0.23 0.18

Pseudo R-square (Max-Rescaled) 0.10 0.06 0.12 0.04 0.08 0.08

N 453 453

Significance: * p < 0.1. ** p < 0.05. *** p < 0.01.Note: We assessed the sensitivity of results by specifying models with alternative classifications of days with good trip reporting (defined as (a) days with equal trip counts and (b) days with ±2 trips) and dayswith good walking duration reporting (defined as (a) days with equal duration and (b) days with ±5 min). We note the variables in each model which were also significant with the same sign in at least 1alternative model ( ) or both alternative models ( ).

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We found substantial agreement between participant-reported daily travel patterns and GPS-derived patterns. The aver-age difference between daily GPS- and log-based trip counts was only 0.39 when using a 60 s dwell time to define trips inGPS data. Using a 120-s dwell time, the two trip counts were essentially equal. GPS methods tended to identify slightly moredaily car trips, and log methods tended to identify slightly more daily transit and walking trips. Although not directly com-parable to our day-level results, previous trip-level audits have found that the rate at which trips are not reported on travelsurveys but are identified using GPS tracking ranged from 7% to 81% (California Department of Transportation, 2002; Pierceet al., 2003; Stopher et al., 2007; Wolf, 2004; Wolf et al., 2004).

We also found that the average difference between daily GPS- and log-derived walking duration was about �11.8 min.Although we are not aware of comparable published estimates of daily walking duration, a meta-analysis of previous studiesexamining trip duration across various travel modes found that participants tended to self-report between 2.2 and 13.5 moreminutes per trip than were identified through GPS tracking (Kelly et al., 2013). Such discrepancies may occur due to partic-ipants rounding travel times to the nearest 5- or 15-min clock time or because participant estimates of trip duration varydepending on the perceived difficulty of a given trip (Rietveld, 2001; Stopher et al., 2007).

Consistent with previous studies (Bricka and Bhat, 2006; Stopher et al., 2007; Wolf et al., 2003), we found that the like-lihood that GPS- and participant-based travel estimates were in strong agreement (±1 trip or ±10 min) or had discrepanciesvaried by participant socio-demographic characteristics. Although we found some variation by mode, participants who wereolder, had a lower educational attainment or household income, or were employed were associated with a lower likelihoodthat a day would have good trip reporting after controlling for other factors. Participants who had lower educational attain-ment were associated with a lower likelihood that a day would have good walking-duration reporting. Discrepancies couldreflect differences in participant understanding of or willingness to follow survey instructions, and suggest the need forpotential improvements to participant training and coordination. In cases in which a participant did not complete their tra-vel log during the day as they traveled, they may have had difficulty remembering trips by each mode or estimating walkingminutes at the end of the observation day. Although we hypothesized that recording daily travel information for seven dayscould have represented a burden for participants depending on their daily household or employment obligations, we onlyfound evidence of potential fatigue for reporting public transit usage. Participants had a lower likelihood of good transit tripreporting during the final two days of the seven-day data collection period.

Our study contributes to the literature by examining reporting discrepancies for a largely representative sample of house-holds in a low-income and non-white urban neighborhood, a geographic focus which largely remains understudied in thetravel behavior literature (Liu and Painter, 2012). Our findings, however, cannot be readily generalized to other neighbor-hoods within the region. Future research is needed to evaluate our trip log instrument for larger sample sizes and acrossa wider range of population groups and travel patterns. This will allow for an expanded understanding of how householdand socioeconomic patterns influence the likelihood of good reporting and, if future surveys contain sufficiently large samplesizes for subpopulations, could enable the development of correction factors which can be used to adjust results for reportingerror rates. This could enhance the ability of transportation planners to evaluate the impact of policies aimed at creatingmore compact, transit-oriented, mixed-use communities on vehicle travel and associated emissions. Although beyond thescope of the current analysis, our future research will contribute to this important next-step by drawing on the Expo Study’sbefore-after quasi-experimental design to assess the impact of this new LRT service on the behavior of nearby residents.

Acknowledgements

The research was supported by the California Air Resources Board, the Haynes Foundation, the Lincoln Institute of LandPolicy, the San Jose State Mineta Transportation Institute, the Southern California Association of Governments, the Universityof California Transportation Center, the University of California Multi-Campus Research Program on Sustainable Transporta-tion, and the University of Southern California Lusk Center for Real Estate. The authors are grateful to the study participantsand thank the research assistants who supported data collection and processing. We are particularly thankful for theresearch assistance and support of Gaby Abdel-Salam, Gavin Ferguson, Wei Li, Xiaoxia Shi, Steven Spears, and DongwooYang.

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tracking daily travel; assessing discrepancies between gps-derived and self-reported travel patterns

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