An analysis of the factors influencing differences in survey-reported and GPS-recorded trips

Recent advances in global positioning systems (GPS) technology have resulted in a transition in household travel survey methods to test the use of GPS units to record travel details, followed by the application of an algorithm to both identify trips and impute trip purpose, typically supplemented with some level of respondent confirmation via prompted-recall surveys. As the research community evaluates this new approach to potentially replace the traditional survey-reported collection method, it is important to consider how well the GPS-recorded and algorithm-imputed details capture trip details and whether the traditional survey-reported collection method may be preferred with regards to some types of travel. This paper considers two measures of travel intensity (survey-reported and GPS-recorded) for two trip purposes (work and non-work) as dependent variables in a joint ordered response model. The empirical analysis uses a sample from the full-study of the 2009 Indianapolis regional household travel survey. Individuals in this sample provided diary details about their travel survey day as well as carried wearable GPS units for the same 24-h period. The empirical results provide important insights regarding differences in measures of travel intensities related to the two different data collection modes (diary and GPS). The results suggest that more research is needed in the development of workplace identification algorithms, that GPS should continue to be used alongside rather than in lieu of the traditional diary approach, and that assignment of individuals to the GPS or diary survey approach should consider demographics and other characteristics.     

Deriving and validating trip purposes and travel modes for multi-day GPS-based travel surveys: A large-scale application in the Netherlands

In the past few decades, travel patterns have become more complex and policy makers demand more detailed information. As a result, conventional data collection methods seem no longer adequate to satisfy all data needs. Travel researchers around the world are currently experimenting with different Global Positioning System (GPS)-based data collection methods. An overview of the literature shows the potential of these methods, especially when algorithms that include spatial data are used to derive trip characteristics from the GPS logs. This article presents an innovative method that combines GPS logs, Geographic Information System (GIS) technology and an interactive web-based validation application. In particular, this approach concentrates on the issue of deriving and validating trip purposes and travel modes, as well as allowing for reliable multi-day data collection. In 2007, this method was used in practice in a large-scale study conducted in the Netherlands. In total, 1104 respondents successfully participated in the one-week survey. The project demonstrated that GPS-based methods now provide reliable multi-day data. In comparison with data from the Dutch Travel Survey, travel mode and trip purpose shares were almost equal while more trips per tour were recorded, which indicates the ability of collecting trips that are missed by paper diary methods.     

Assessing the accuracy of the Sydney Household Travel Survey with GPS

Over the past few years, GPS has been used in a number of surveys in the US to assess the accuracy of household travel surveys. The results have been somewhat alarming in that most of these exercises have shown that the standard trip-based CATI survey conducted in the US under-reports travel by about 20–25%. It was decided to use GPS to assess the accuracy of the Sydney Household Travel Survey, a continuous survey conducted by face-to-face interviewing. The procedure used was for the interviewers to recruit households for the household travel survey in the normal manner, and then, if the household met certain criteria, to endeavour to recruit the household to also undertake a GPS survey. A small sample of about 50 households was obtained, and GPS devices successfully retrieved that measured data on the same day as the travel diary was completed. In addition, participants in the GPS survey completed a prompted recall survey a week or two later, using maps and tabulations of travel obtained from the GPS devices, to identify mode, purpose and occupancy for trips measured by the GPS, and also to check for accuracy in defining trip ends and total number of trips. Based on the analysis of the GPS compared to the diary results, it was found that respondents under-reported their travel by about 7%, which is much less than in the US CATI results. Respondents were also found to under-report travel distances and over-report travel times. There was also a high incidence of non-reporting for VKT.

Combining web and face-to-face in travel surveys: comparability challenges?

Response rates for household travel surveys are tending to fall, and it seems unlikely that this trend will be reversed in the future. In recent years, travel data collection methods have evolved in order to obtain reliable data that are sufficiently detailed to feed increasingly complex models, and in order to integrate new technologies into survey protocols (Internet, GPS??). Combining different media is an obvious low-cost way of improving data quality as it increases the overall response rate. But the question of the comparability of data over time and between different survey modes remains unresolved. This paper makes a comparative analysis between the travel behaviours of web-based survey respondents and respondents to a face-to-face interview. The data were obtained from the 2006 Lyon conurbation household travel survey. Our analysis shows that the Internet respondents reported fewer trips per day than the face-to-face respondents (3.00 vs. 4.04 daily trips), and that the differences between the two groups varied according to the travel mode and trip purpose. While part of this difference can be explained by socioeconomic disparities (the Internet respondents had a specific profile) we cannot exclude the possibility of under-reporting due to the web medium.     

Effect of land use on decisions of shopping tour generation: A case study of three traditional neighborhoods in WA

This study investigates the relationship between land use and shopping tour generation using an activity-based shopping model that captures the effects of land use patterns on household decisions of shopping tour frequency, tour scheduling and mode choice. The model was calibrated using travel data collected in three traditional neighborhoods located in the Puget Sound region, WA, and shopping travel patterns across seven common household structures were analyzed. The results reveal that land use patterns have virtually no impact on overall shopping tour frequency. However, land use does seem to be associated with decisions about the type of shopping tours undertaken. For example, households with poorer accessibility tend to make fewer one-stop shopping tours, and are more likely to combine shoppingtrips with other trips to form multi-stop shopping tours as a means of compensating for locational deficiencies. Finally, we also found that traditional neighborhood residents who live closer to the neighborhood commercial street, and thus, have greater accessibility, are more inclined to use non-auto modes for one-stop shopping tours.     

Identifying travel mode with GPS data

Travel mode identification is an essential step in travel information detection with global positioning system (GPS) survey data. This paper presents a hybrid procedure for mode identification using large-scale GPS survey data collected in Beijing in 2010. In a first step, subway trips were detected by applying a GPS/geographic information system (GIS) algorithm and a multinomial logit model. A comparison of the identification results reveals that the GPS/GIS method provides higher accuracy. Then, the modes of walking, bicycle, car and bus were determined using a nested logit model. The combined success rate of the hybrid procedure was 86%. These findings can be used to identify travel modes based on GPS survey data, which will significantly improve the efficiency and accuracy of travel surveys and data analysis. By providing crucial travel information, the results also contribute to modeling and analyzing travel behaviors and are readily applicable to a wide range of transportation practices.     

A Trip Reconstruction Tool for GPS-based Personal Travel Surveys

ABSTRACT

This article reports on the development of a trip reconstruction software tool for use in GPS-based personal travel surveys. Specifically, the tool enables the automatic processing of GPS traces of individual survey respondents in order to identify the road links traveled and modes used by each respondent for individual trips. Identifying the links is based on a conventional GIS-based map-matching algorithm and identifying the modes is a rule-based algorithm using attributes of four modes (walk, bicycle, bus and passenger-car). The tool was evaluated using GPS travel data collected for the study and a multi-modal transportation network model of downtown Toronto. The results show that the tool correctly detected about 79% of all links traveled and 92% of all trip modes.     

Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review

Accurate measurement of travel behaviour is vital for transport planning, modelling, public health epidemiology, and assessing the impact of travel interventions. Self-reported diaries and questionnaires are traditionally used as measurement tools; advances in Global Positioning Systems (GPS) technology allow for comparison. This review aimed to identify and report about studies comparing self-reported and GPS-measured journey durations. We systematically searched, appraised, and analysed published and unpublished articles from electronic databases, reference lists, bibliographies, and websites up to December 2012. Included studies used GPS and self-report to investigate trip duration. The average trip duration from each measure was compared and an aggregated, pooled estimate of the difference, weighted by number of trips, was calculated. We found 12 results from eight eligible studies. All studies showed self-reported journey times were greater than GPS-measured times. The difference between self-report and GPS times ranged from over-reporting of +2.2 to +13.5 minutes per journey. The aggregated, pooled estimate of the difference, weighted by number of trips, was over-report of +4.4 minutes (+28.6%). Studies comparing self-reported and GPS-measured journey duration have shown self-reported to be consistently over-reported across the study sample. Our findings suggest that when using self-reported journey behaviour, the journey durations should be treated as an over-estimation.     

The impacts of built environment on home-based work and non-work trips: An empirical study from Iran

This paper aims to explore the impact of built environment attributes in the scale of one quarter-mile buffers on individuals’ travel behaviors in the metropolitan of Shiraz, Iran. In order to develop this topic, the present research is developed through the analysis of a dataset collected from residents of 22 neighborhoods with variety of land use features. Using household survey on daily activities, this study investigates home-based work and non-work (HBW and HBN) trips. Structural equation models are utilized to examine the relationships between land use attributes and travel behavior while taking into account socio-economic characteristics as the residential self-selection. Results from models indicate that individuals residing in areas with high residential and job density, and shorter distance to sub-centers are more interested in using transit and non-motorized modes. Moreover, residents of neighborhoods with mixed land uses tend to travel less by car and more by transit and non-motorized modes to non-work destinations. Nevertheless, the influences of design measurements such as street density and internal connectivity are mixed in our models. Although higher internal connectivity leads to more transit and non-motorized trips in HBW model, the impacts of design measurements on individuals travel behavior in HBN model are significantly in contrast with research hypothesis. Our study also shows the importance of individuals’ self-selection impacts on travel behaviors; individuals with special socio-demographic attributes live in the neighborhoods with regard to their transportation patterns. The findings of this paper reveal that the effects of built environment attributes on travel behavior in origins of trips do not exactly correspond with the expected predictions, when it comes in practice in a various study context. This study displays the necessity of regarding local conditions of urban areas and the inherent differences between travel destinations in integrating land use and transportation planning.     

Can using global positioning system (GPS) improve trip reporting?

In the United States, information about daily travel patterns is generally captured using self-reported information using a written diary and telephone retrieval (or mail-back of diary forms). Problems with these methods include lack of reporting for short trips, poor data quality on travel start and end times, total trip times and destination locations.This project combined a hand-held computer (Personal Digital Assistant or PDA) with a Global Positioning System (GPS) receiver to capture vehicle-based, daily travel information. The vehicle driver uses a menu to enter variables such as trip purpose and vehicle occupancy, but other data such as date, start time, end time, and vehicle position (latitude and longitude) are collected automatically at frequent intervals. The field test was conducted in Lexington, Kentucky in fall, 1996, with 100 households to use the equipment for six days. Respondents also completed a telephone survey for one day of travel (attempted for day 5).The field test was a test of equipment and willingness of the general public to participate, rather than to obtain a statistically valid travel behavior dataset for the Lexington area. One improvement to the hardware would be for the equipment to turn on automatically. There are limitations to the dataset and analyses that are discussed where appropriate. Although the dataset is small, this paper compares the results of the machine-recorded trips to self-reported trips captured by telephone interview.Self-reported distances are much longer than distances recorded by the PDA/GPS. A recalled distance of 10 miles was, on average, only 6.5 miles when the GPS points are matched to a positionally accurate base file. Similarly, recalled times generally exceed median measured values, but the differences are much smaller than for distances. Respondents reported that data entry of 1 min at the beginning of each trip over the six-day survey period was not burdensome.Recommendations for improving the hardware and software for conducting other travel surveys using GPS, and improving the utility of travel data collected using GPS are provided. One of the benefits of incorporating a GPS device into the survey process was the ability to collect information on route choice and travel speed. However, this paper does not address these topics.     

Impact of interviewing by proxy in travel survey conducted by telephone

Telephone‐interview surveys are a very efficient way of conducting large‐scale travel surveys. Recent advancements in computer technology have made it possible to improve upon the quality of data collected by telephone surveys through computerization of the entire sample‐control process, and through the direct recording of the collected data into a computer. Notwithstanding these technological advancements, potential sources of bias still exist, including the reliance on an adult member of the household to report the travel information of other household members. Travel data collected in a recent telephone interview survey in the Toronto region is used to examine this issue. The statistical tool used in the research was the Analysis of Variance (ANOVA) technique as implemented within the general linear model framework in SAS. The study‐results indicate that reliance on informants to provide travel information for non‐informant members of their respective households led to the underreporting of some categories of trips. These underreported trip categories were primarily segments of home‐based discretionary trips, and non home‐based trips. Since these latter two categories of trips are made primarily outside the morning peak period, estimated factors to adjust for their underreporting were time‐period sensitive. Further, the number of vehicles available to the household, gender, and driver license status respectively were also found to be strongly associated with the underreporting of trips and thus were important considerations in the determination of adjustment factors. Work and school trips were found not to be underreported, a not surprising result giving the almost daily repetitiveness of trips made for these purposes and hence the ability of the informant to provide relatively more precise information on them.     

Time use in travel surveys and time use surveys – Two sides of the same coin?

An in-depth understanding of travel behaviour determinants, including the relationship to non-travel activities, is the foundation for modelling and policy making. National Travel Surveys (NTS) and time use surveys (TUS) are two major data sources for travel behaviour and activity participation. The aim of this paper is to systematically compare both survey types regarding travel activities and non-travel activities. The analyses are based on the German National Travel Survey and the German National Time Use Survey from 2002.The number of trips and daily travel time for mobile respondents were computed as the main travel estimates. The number of trips per person is higher in the German TUS when changes in location without a trip are included. Location changes without a trip are consecutive non-trip activities with different locations but without a trip in-between. The daily travel time is consistently higher in the German TUS. The main reason for this difference is the 10-min interval used. Differences in travel estimates between the German TUS and NTS result from several interaction effects. Activity time in NTS is comparable with TUS for subsistence activities.Our analyses confirm that both survey types have advantages and disadvantages. TUS provide reliable travel estimates. The number of trips even seems preferable to NTS if missed trips are properly identified and considered. Daily travel times are somewhat exaggerated due to the 10-min interval. The fixed time interval is the most important limitation of TUS data. The result is that trip times in TUS do not represent actual trip times very well and should be treated with caution.We can use NTS activity data for subsistence activities between the first trip and the last trip. This can potentially benefit activity-based approaches since most activities before the first trip and after the last trip are typical home-based activities which are rarely substituted by out-of-home activities.     

Car use,commuting and urban form in a rapidly growing city: evidence from Beijing

The links between urban form and travel behaviour have been widely studied in the field of travel demand management. However, the existing literature is dominated by case studies from the developed countries. A study of a city in a developing and industrialising country can add some fresh evidence to the debate on the impacts of urban form on travel patterns. Using household survey data from Beijing, this paper finds that aspects of urban form have significant effects on workers' car use and the duration of travel by car in journeys to work, while controlling for socio-economic factors and households’ preferences related to residential location. The sprawling patterns of land development play a negative role in reducing motorised commuting trips and shortening vehicle hours travelled in the current processes of rapid urban expansion and motorisation. Since urban sprawl is greatly influenced by growing market forces, the findings in this paper reveal the negative effects of regulation–liberation of land development management on travel behaviour modification.     

When is big data big enough? Implications of using GPS-based surveys for travel demand analysis

A number of studies in the last decade have argued that Global Positioning Systems (GPS) based survey offer the potential to replace traditional travel diary surveys. GPS-based surveys impose lower respondent burden, offer greater spatiotemporal precision and incur fewer monetary costs. However, GPS-based surveys do not collect certain key inputs required for the estimation of travel demand models, such as the travel mode(s) taken or the trip purpose, relying instead on data-processing procedures to infer this information. This study assesses the impact that errors in inference can have on travel demand models estimated using data from GPS-based surveys and proposes ways in which these errors can be controlled for during both data collection and model estimation. We use simulated datasets to compare performance across different sample sizes, inference accuracies, model complexities and estimation methods. Findings from the simulated datasets are corroborated with real data collected from individuals living in the San Francisco Bay Area, United States. Results indicate that the benefits of using GPS-based surveys will vary significantly, depending upon the sample size of the data, the accuracy of the inference algorithm and the desired complexity of the travel demand model specification. In many cases, gains in the volume of data that can potentially be retrieved using GPS devices are found to be offset by the loss in quality caused by inaccuracies in inference. This study makes the argument that passively collected GPS-based surveys may never entirely replace surveys that require active interaction with study participants.     

Trip distribution for limited destinations: a case study for grocery shopping trips in the Netherlands

In this paper, we introduce a new trip distribution model for destinations that are not homogeneously distributed. The model is a gravity model in which the spatial configuration of destinations is incorporated in the modeling process. The performance was tested on a survey with reported grocery shopping trips in the Dutch city of Almelo. The results show that the new model outperforms the traditional gravity model. It is also superior to the intervening opportunities model, because the distribution can be described as a function of travel costs, without increasing the computational time. In this study, the distribution was described by a simple function of Euclidean distance, which provides a good fit to the survey data. The slope of the distribution is quite steep. This shows that most trips are made to nearby supermarkets. However, a significant fraction of trips, mainly made by car, still goes to supermarkets further away. We argue that modeling of these trips by the new method will improve traffic flow predictions.     

Travel,transport and energy implications of university-related student travel: A case study approach

This study highlighted significant cultural differences and complexity in travel behaviour associated with travel to university across the UK and Ireland. This paper examines university travel behaviours and the implications for emissions, across the 2012–2013 academic year, based on responses from 1049 students across 17 universities in Ireland and the UK. Surveys were analysed to examine the trips of students both during term time and when accessing the universities each year. The data analysis in this paper examines three aspects of the transport implications of travel to and from university. Firstly the journey between university and term time address (or permanent address if the respondent does not have a separate term time address), secondly the journey between the university area and a separate permanent address where relevant; and thirdly implications for emissions resulting from university-related travel.The study found that student car users were more likely to be female, older students, or studying part time; male students were more likely to use active modes. The study indicated interesting differences between students living in different parts of the UK and Ireland. For example, it was found that there was a higher level of car dependence amongst Northern Irish students compared to other areas; and a greater variability in travel distances in Scotland and Northern Ireland. In England, car use was more pronounced when students travelled from their permanent address to term time address, and, as in Ireland, there was evidence of more car sharing on such trips. Public transport usage was more pronounced amongst Scottish students. The effect of these transport choices on emissions is significant and demonstrates the importance of education related trips to the development of a transport policy response. The analysis shows that annual emissions are highest for regular travel to and from university when a student has a permanent address rather than a separate term time and permanent address.     

The impact of “free” public transport: The case of Brussels

Public transport subsidies play an important role in the present Belgian mobility policy. The introduction of “free” bus transport in Hasselt in 1997 was an important event. Later, the Flemish government in cooperation with the regional public transport company elaborated the so-called “third-payer system” for target groups. The price of public transport is not paid by the user or the provider, but partly or completely by a “third party”. In how far these measures contribute to a more sustainable mobility system has caused much debate.In the academic year 2003–2004, a “free public transport” initiative was introduced for the students of Flemish colleges and universities in Brussels. These students had the opportunity to obtain a refunded annual season ticket on Brussels public transport. Brussels was selected for the case study, because in the same city there is a group of students that benefits from the measure, and another group (students from French speaking universities and colleges) that does not. In order to examine the effects of this measure, we conducted a survey among the students to examine their present travel behaviour (number of trips, motives, modal choice …) and the changes with the travel behaviour of the previous year. In addition we compare the current travel behaviour between the students benefiting from the measure, and those who do not.     

Dockless bike-sharing systems: what are the implications?

ABSTRACT

The emergence of dockless bike-sharing services has revolutionised bike-sharing markets in recent years, and the dramatic growth of shared bike fleets in China, as well as their rapid expansion throughout the world, exceeds prior expectations. An understanding of the impacts of these new dockless bike-sharing systems is of vital importance for system operations, transportation and urban planning research. This paper provides a first overview of the emerging literature on implications of dockless bike-sharing systems for users' travel behaviour, user experience, and relevant social impacts of dockless bike-sharing systems. Our review suggests that the dockless design of bike-sharing systems significantly improves users' experiences at the end of their bike trips. Individuals can instantly switch to a dockless shared bike without the responsibility of returning it back to a designated dock. Additionally, the high flexibility and efficiency of dockless bike-sharing often makes the bike-sharing systems' integration with public transit even tighter than that of traditional public bikes, providing an efficient option for first/last-mile trips. The GPS tracking device embedded in each dockless shared bike enables the unprecedented collection of large-scale riding trajectory data, which allow scholars to analyse people's travel behaviour in new ways. Although many studies have investigated travel satisfaction amongst cyclists, there is a lack of knowledge of the satisfaction with bikeshare trips, including both station-based and dockless bikeshare systems. The availability and usage rates of dockless bike-sharing systems implies that they may seriously impact on individuals' subjective well-being by influencing their satisfaction with their travel experiences, health and social participation, which requires further exploration. The impact of dockless bike-sharing on users' access to services and social activities and the related decreases in social exclusion are also relevant issues about which knowledge is lacking. With the increases in popularity of dockless shared bikes in some cities, issues related to the equity and access and the implications for social exclusion and inequality are also raised.     

Encouraging environmentally sustainable holiday travel

Holiday travel behavior, individual characteristics of holiday travelers and strategies to change holiday travel behavior are the subjects of this article. From the environmental perspective, the journey to the destinations is the most critical aspect of traveling. Based on a 2003 survey of 1991 German inhabitants, the kilometers traveled and the choice of transportation mode for holiday purposes have been quantified. According to the number of trips and kilometers traveled, four travel groups have been identified. The groups vary according to socio-demographics, psychological factors, number of holiday trips, and travel mode choice. Persons who traveled to more distant destinations also traveled more often and used air travel for more than 60% of their trips. For the other groups, car travel was more important. Correlating the four travel groups with greenhouse gas emissions reveals that the smallest group—the long-haul travelers—was responsible for 80% of the emissions of the whole sample. Income, education, and openness to change were main indicators of individual greenhouse gas emissions. Target group oriented strategies to reduce the environmental impact of holiday mobility are discussed against the background of 84 in-depth interviews conducted with selected representatives of the first survey.