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Enhancing GPS-Assisted Travel Data Collection Through Smartphones
... However, there are a number of real-world drawbacks to this kind of application, such as the requirement for high levels of user engagement and high implementation costs [16]. Largescale implementation of fig 8 GPS-assisted data collection techniques is hampered by these constraints [17]. ...
In our daily lives, mobile technology has grown omnipresent. It facilitates global device and Internet of Things (IoT) communication. Although 5G network technologies are a novel and inventive development, they also present security issues. The writers provide a broad outline of mobile technology components, such as GSM and 5G, with reference to security concerns. This paper also discusses the Network Equipment Security Assurance Scheme, which is an acceptable way to tackle the security challenges associated with these technologies.
Widely used data-collection methods fail to fully capture the lived experiences and un-met travel demands of marginalized populations and those at disproportionate risk of experiencing transportation disadvantage (TD). This study describes an interdisciplinary collaboration between social work, civil engineering, and computer science on the development of a transportation data collection tool, MyAmble. The theoretical framework of social exclusion was used as part of the study design in order to better understand the process of marginalization as a result of inability to participate in wider society, in part through lack of transportation. Thus, MyAmble augments the best practices in transportation daily diary research using features designed to characterize and quantify latent travel demand among individuals experiencing TD, as well as the subjective impact of lack of transportation on quality of life and well-being. MyAmble includes several innovations – a daily digital trip planning that captures latent demand, a text-messaging based qualitative interview tool, a self-guided travel history survey, and a challenge logger. We describe the design process and implementation that occurred during a five-day pilot test and present a case study. Finally, findings from the case study suggest that MyAmble was able to successfully capture planned, unplanned, and unserved trips as well as the lived experiences of TD.
Travel data collection has always been considered as a fundamental part of all transport planning studies, and there has been a continuous effort to employ new approaches in order to increase the accuracy of collected data and reflect the real behaviour of individuals. Many approaches have been utilized for performing travel surveys, but there are still significant areas for improvement, as they need considerable financial and human resources, have an insufficient time-coverage and mostly engage respondents considerably, which sometimes implies biased results. This paper introduces a research framework, which is focused on using innovative technology of smartphones to develop a practical and accurate travel survey approach and better understand the pattern of individual " s travel behaviour. The first section of this paper is devoted to reviewing previous studies and methods of GPS assisted travel surveys. Then after reviewing available data collection approaches, the appropriate approach to increase the accuracy of collected data and also reduce the burden on respondents has been employed. In the next step, ATLAS Project, an iPhone application which has been developed specifically for travel data collection is introduced and the procedure of designing, developing and employing of this application is explained.
This study addressed barriers associated with the use of Safety Analyst, a state-of-the-art tool that has been developed to assist during the entire Traffic Safety Management process but that is not widely used due to a number of challenges as described in this paper. As part of this study, a comprehensive database system and tools to provide data to multiple traffic safety applications, with a focus on Safety Analyst, were developed. A number of data management tools were developed to extract, collect, transform, integrate, and load the data. The system includes consistency-checking capabilities to ensure the adequate insertion and update of data into the database. This system focused on data from roadways, ramps, intersections, and traffic characteristics for Safety Analyst. To test the proposed system and tools, data from Clark County, which is the largest county in Nevada and includes the cities of Las Vegas, Henderson, Boulder City, and North Las Vegas, was used. The database and Safety Analyst together help identify the sites with the potential for safety improvements. Specifically, this study examined the results from two case studies. The first case study, which identified sites having a potential for safety improvements with respect to fatal and all injury crashes, included all roadway elements and used default and calibrated Safety Performance Functions (SPFs). The second case study identified sites having a potential for safety improvements with respect to fatal and all injury crashes, specifically regarding intersections; it used default and calibrated SPFs as well. Conclusions were developed for the calibration of safety performance functions and the classification of site subtypes. Guidelines were provided about the selection of a particular network screening type or performance measure for network screening.
The AASHTO software Safety Analyst is a state-of-the-art tool with significant capabilities and advanced analytical methods for comprehensive analysis and management of highway safety. However, this tool does not provide visualization capabilities. To address that limitation, this study proposes a visualization system for Safety Analyst that provides graphical displays, including location and color-coded information for each module. In addition, the system generates charts, which have various degrees of resolution and aggregation; tables; and a report summarizing safety performance measures. The system can use Google Maps or ESRI ArcGIS to generate the graphical displays. The advantage of using Google Maps is its simplicity; in contrast, the ArcGIS display provides additional modeling and computing capabilities of the GIS framework. All displays are very intuitive and can be customized according to user needs. Because the user can see the locations of every specific site, the displays facilitate analysis as well as the decision-making process. The visualization system interacts with Safety Analyst so that the user can access all tools and data throughout the entire modeling and analysis process.
The Future Mobility Survey (FMS) is a smartphone-based prompted-recall travel survey that aims to support data collection initiatives for transport-modeling purposes. This paper details the considerations that have gone into the survey's development, including the smartphone apps for iPhone and Android platforms, the online activity diary and user interface, and the background intelligence for processing collected data into activity locations and travel traces. The various trade-offs concerning user comprehension, resource use, and participant burden, including findings from usability tests and a pilot study, are discussed. Close attention should be paid to the simplicity of the user interaction, determinations of activity locations (such as the false positive and false negative trade-off in their automatic classification), and the clarity of interactions in the activity diary. The FMS system design and implementation provide pragmatic, useful insights into the development of similar platforms and approaches for travel and activity surveys.
Large-scale urban sensing data such as mobile phone traces are emerging as an important data source for urban modeling. This study represents a first step towards building a methodology whereby mobile phone data can be more usefully applied to transportation research. In this paper, we present techniques to extract useful mobility information from the mobile phone traces of millions of users to investigate individual mobility patterns within a metropolitan area. The mobile-phone-based mobility measures are compared to mobility measures computed using odometer readings from the annual safety inspections of all private vehicles in the region to check the validity of mobile phone data in characterizing individual mobility and to identify the differences between individual mobility and vehicular mobility. The empirical results can help us understand the intra-urban variation of mobility and the non-vehicular component of overall mobility. More importantly, this study suggests that mobile phone trace data represent a reasonable proxy for individual mobility and show enormous potential as an alternative and more frequently updatable data source and a compliment to the conventional travel surveys in mobility study.
This paper presents the state-of-the-art in the application and integration of GPS technology in a regional household travel survey. The Kansas City Regional Household Travel Survey is the ninth such study in the past eight years to include a GPS component. In addition to having the lowest recorded trip underreporting rate (10 percent) compared to these other studies, this project also included a small follow-up prompted recall survey. The prompted recall survey was invaluable in providing insight into what trip purposes are underreported and began to provide explanations for why trip underreporting occurs. The methods and results used in this study are summarized in this paper to provide information and guidance for designing a GPS component for the next nationwide household travel survey.
The acquisition of travel data is currently based on cost- and time-intensive questionnaires and yields mostly an incomplete picture due to limited coverage and inadequate updates. There is an urgent need for technologically supported data acquisition tools. This paper introduces a novel approach to developing a large-scale travel survey by intelligently employing data from smartphones. Based on signals of the embedded accelerometers and GPS reveivers, an ensemble of probabilistic classifiers is trained for automatically reconstructing the individual trips composing a tour, including the mode choice. In the region of Vienna, Austria, 266 hours of travel data were collected to train and evaluate the models. Using a set of 72 features, the best classification results are achieved for detecting walks (92%) and bike rides (98%). Railway modes were correctly identified in 80% of all cases, which is subject to further research. In case of GPS losses only accelerometer data are used, which still shows promising results. This allows the method to incorporate places where there is normally only a weak or no GPS signal. Future smartphone applications are intended to spread the tool among traffic users, while the effort for them should be kept to a minimum i.e. no manual entries or questionnaires are necessary. Due to the increasing popularity of smartphones, the tool has the potential to be used on a wide-spread basis.
Global Positioning System (GPS) technology can continuously monitor the time and location of vehicle usage. By recording and analyzing detailed vehicle activity data, researchers can analyze the safety and environmental implications of driver behavior and trip-making patterns. In 2000, NHTSA awarded the Georgia Institute of Technology a contract to equip 1,100 vehicles with a GPS-enhanced device to collect speed and location data. The objective was to acquire more accurate information on the role of excessive speed on crash frequency and severity. GPS technology allows the researcher to continuously measure driver speed, acceleration, and location. When merged with roadway characteristics within a geographic information system (GIS) environment, determinations of driver risk-taking behavior can be made. Second, continuous logging of GPS data allows researchers to capture high-resolution vehicle activity immediately before a crash event, reducing the potential error and bias introduced during determination of precrash speed estimates. Until May 1, 2000, the military degraded the position accuracy of GPS signals for commercial use, known as selective availability. For researchers, life without selective availability is a great improvement. Travel routes can clearly be discerned without the addition of differential correction units. The accuracy of speed, acceleration, and position data obtained from GPS signals for use in determining driver performance parameters without selective availability were tested. The test included four GPS packages, both corrected and uncorrected, simultaneously validated against a distance-measuring instrument. Equipment configuration, data collection methods, and sources of error are reported. The results suggested that noncorrected data can be used to obtain data within a reasonable range of the application requirements. Even without selective availability, GPS accuracy is still problematic in urban canyons and under heavy tree canopies. Although filtering for urban canyon outliers is labor intensive in a continuous monitoring situation, improvements in GIS hold promise for automation of this task.
Since the first Global Positioning System (GPS) studies in the mid-1990s, this method of surveying individual travel behavior has gained attention in transport research. Compared with classic travel survey methods, GPS studies offer researchers benefits of more accurate and reliable informa- tion. At the same time, the participants' burden is reduced substantially if the GPS data collection does not involve time-consuming questions. However, without additional information, such as modes and trip pur- poses, extensive postprocessing is required to derive data that can be used for analysis and model estimation. The corresponding procedures are an ongoing research issue. This paper describes a postprocessing procedure needing no input other than the most basic GPS raw data: three-dimensional positions and the corresponding time stamps. First, the data are thoroughly cleaned and smoothed. Second, trips and activities are determined. Third, the trips are segmented into single-mode stages, and the transport mode for each of the stages is identified. The procedure is applied to GPS records collected in the Swiss cities of Zurich, Winterthur, and Geneva. A total of 4,882 participants carried an on-person GPS receiver for an average of 6.65 days. The results are compared with the Swiss Microcensus 2005 to demonstrate that derived data are ready for further applications, such as discrete choice model estimations.
Travel surveys collect trip data such as origin, destination, mode, duration, distance and purpose of trips, as well as socioeconomic and demographic data for analysis. Transportation planners, policymakers, state departments of transportation, metropolitan planning organisations, industry professionals and academic researchers use survey data to better understand the current demand and performance of the transportation infrastructure, and to plan in preparation for future growth. Next-generation travel surveys will utilise global positioning systems (GPS) to collect trip data with minimal input from survey participants. Owing to their ubiquity, GPS-enabled mobile phones are developing into a promising survey tool. TRAC-IT is a mobile phone application that collects real-time GPS data and requires minimal input from the user for data such as trip purpose, mode and vehicle occupancy. To ease survey burden on participants and enable real-time, mode-specific location-based services, new techniques must be explored to derive more information directly from GPS data. As part of travel survey collection, TRAC-IT is able to passively determine trip mode using GPS-enabled mobile phones and neural networks. The mode detection technique presented in this article can be optimised using a critical point, pre-processing algorithm to reduce the size of required GPS datasets obtained from GPS-enabled mobile phones, thus reducing data collection costs while conserving precious mobile phone resources such as battery life.
In this paper, we commence by reviewing the recent history of household travel surveys. We note some of the problems that contemporary surveys are encountering throughout the world. We also review the data demands of current and emerging travel demand models, concluding that there are many new demands being placed on data, both in terms of the extent of the data required and the accuracy and completeness of the data. Noting that the standard method for conducting most household travel surveys is, and has been for some years, a diary, we briefly explore the evolution of the diary survey from the late 1970s to the present. In the next section of the paper, we explore a number of facets of potential future data collection. We include in this the use of GPS devices to measure travel, the potential of panel designs and some of the alternatives within panel designs, the development of continuous household travel surveys, especially in Australia, and the emerging capabilities in data fusion. Using some of these emerging methods for data collection and data simulation, we then propose a new paradigm for data collection that places the emphasis on a paid, national panel that is designed as a rotating, split panel, with the cross-sectional component conducted as a continuing survey. The basis of the panel data collection is proposed as GPS with demographic data, and the continuing national sample would also use GPS at its core. The potential to add in such specialised surveys as stated choice and process surveys is also noted as an advantage of the panel approach. We also explore briefly the notion that a special access panel or panels could be included as part of the design.
With the availability of Global Positioning System (GPS) receivers to capture vehicle location, it is now feasible to easily collect multiple days of travel data automatically. However, GPS-collected data are not ready for direct use in trip rate or route choice research until trip ends are identified within large GPS data streams. One common parameter used to divide trips is dwell time, the time a vehicle is stationary. Identifying trips is particularly challenging when there is trip chaining with brief stops, such as picking up and dropping off passengers. It is hard to distinguish these stops from those caused by traffic controls or congestion. Although the dwell time method is effective in many cases, it is not foolproof and recent research indicates use of additional logic improves trip dividing. While some studies incorporating more than dwell time to identify trip ends having been conducted, research including actual trip ends to evaluate the success of trip dividing methods used have been limited. In this research, 12 ten-day real-world GPS travel datasets were used to develop, calibrate and compare three methods to identify trip start points in the data stream. The true start and end points of each trip were identified in advance in the GPS data stream using a supplemental trip log completed by the participants so that the accuracy of each automated trip division method could be measured and compared. A heuristic model, which combines heading change, dwell time and distance between the GPS points and the road network, performs best, correctly identifying 94% of trip ends.
Improvements in vehicular tracking with Global Positioning Systems (GPSs) have fostered new analysis methods in transportation planning. Emerging geographical information systems have helped in developing new techniques in the collection and analysis of data specifically for travel demand forecasting. In 2002, more than 150 households in Laredo, Texas, participated in a GPS-enhanced household travel survey. Trip diary data were collected by means of a computer-assisted telephone interview (CATI), and GPS trip data were collected from survey participants’ vehicles. For trip purpose, a comparison of the two data sets yielded significant results. It was found that the number of trips in the GPS data was much greater than the number reported in the CATI data. Despite that, almost all home-based work (HBW) trips found in the GPS data were also found in the CATI data. That result differs sharply from the other trip purposes: home-based nonwork (HBNW) and non-home-based (NHB); for these two trip purposes, less than half the trips found in the GPS data were found in the CATI data. That result indicates the potential for serious deficiencies in the CATI process for collecting certain types of trips in the region of study. In additional, household size and household income were found to be significant factors affecting the reporting accuracy in the CATI data. Despite that, the CATI method of household trip data retrieval is still considered to be an effective and valuable tool.
An experiment that used an application of a pragmatic smartphone travel survey developed to minimize respondent burden while collecting primarily passive data between destinations is described; invited participants came from known population, Concordia University. Respondent burden was reduced by optimizing battery usage, requiring little from respondents apart from downloading and installing an app, completing a short survey, and allowing the app to run in their smartphones' background. The experiment showed that a surprisingly large number of people (892) contacted by e-mail were willing to participate in the study, with a resultant surprisingly large amount of data as well (4,154 respondent days). Moreover, the overall age distribution of the sample was found to be closer to the true population than a traditional origin-destination (O-D) survey capturing the same population. Differences in travel behavior results from the O-D survey appear plausible given what is known about both smartphone and traditional surveys. That respondents were not asked to validate their data reduced respondent burden, but some validated data are necessary to derive meaningful information from collected data. The collection of some less accurate data when GPS is not available is an important avenue to reduce the identification of missing trips. The authors view this experiment as a data point, among others, in attempts to understand the trade-offs involved in the development of smartphone applications. The authors hope it will contribute to the use of such applications on a larger scale in data collection initiatives.
It is clear that Global Positioning System (GPS) technologies have much to offer in the passive collection (or measurement) of detailed travel behavior data. When enhanced with cellular positioning (such as in A-GPS) and transmission capabilities, GPS logging devices are a powerful toll for collecting multi-day and multi-period survey data with little or no respondent burden. At the last Transport Survey Quality Conference at Kruger Park, Murakami reported on the benefits, barriers to use, data issues, and utility of technologies for travel surveys. Since then, the expanding use and exploration of technology applications, specifically GPS-based technologies, in travel surveys have furthered the understanding of the issues as well as resolutions. For example, the high costs of GPS studies are driven by equipment and deployment costs. These can be reduced significantly by using smaller sample sizes over longer time periods. The use of wireless or modem-based data transfer methods will also enable participation for extended periods of time.
The paper describes recent experience with the application of an innovative Global Positioning System (GPS)-assisted prompted recall (PR) method for a large-scale household travel survey (HTS) in Jerusalem, Israel. The survey was designed to support development of an advanced activity-based model (ABM). The requirements for an HTS to support an advanced ABM are discussed, and the corresponding decisions for survey methods are substantiated. Development of an advanced ABM requires individual records for the entire daily pattern without gaps, missing trips, overlaps, or other data inconsistencies found in a conventional HTS. A consistent record of joint activities and trips of multiple household members is essential. In addition, high levels of spatial and temporal resolution are required. The GPS-assisted PR survey has been identified as the most promising methodology for meeting these requirements. The experience of the first phase of the Jerusalem HTS in 2010 proved the feasibility of the GPS-PR method for all population sectors including specific Orthodox Jewish and Arab populations, which typically featured large household sizes. Various structural comparisons of trip and tour rates obtained during the first phase of the Jerusalem GPS-assisted HTS (3,000 households) with the non-GPS surveys previously implemented in Jerusalem and several metropolitan regions in the United States as well as comparisons between the GPS and non-GPS subsamples within the Jerusalem HTS were made. The results confirmed the ability of the GPS-PR approach to create full and consistent daily records of individual activity travel patterns and practically eliminate the underreporting issues that have plagued HTS.
Many studies have compared Global Positioning System (GPS) and diary records to estimate the amount of misreporting of travel that occurs in self-report diaries. This paper examines the comparison of GPS and diary records in detail to further the understanding of what leads to misreporting in diary surveys. The research is based on a recent comparative survey undertaken in the area of Melbourne, Victoria, Australia. The survey is analyzed in depth, and diary records are compared in detail with GPS records to check whether the two groups of data match. Departure and arrival times, trip lengths, and trip times are compared. Reasons for mismatches are suggested. The paper proposes some values as standards for similar analyses in the future.
Total words:3042(1TABLES and 10 FIGURES)x250=5,792words Paper submitted to Transportation Research Board 85 th Annual Meeting, January 2006 TRB 2006 Annual Meeting CD-ROM Paper revised from original submittal.
Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone’s positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a wide-spread basis and can complement existing travel survey methods.
With the advent of ubiquitous mobile sensing and self-tracking groups, travel demand researchers have a unique opportunity to combine these two developments to improve the state of the art of travel diary collection. While the use of mobile phones and the inference of travel diaries from GPS and sensor data allows for lower-cost, longer surveys, we show how the self-tracking movement can be leveraged to interest people in participating over a longer period of time. By compiling personalized feedback and statistics on participants’ travel habits during the survey, we can provide the participants with direct value in exchange for their data collection effort. Moreover, the feedback can be used to provide statistics that influence people’s awareness of the footprint of their transportation choices and their attitudes, with the goal of moving them toward more sustainable transportation behavior. We describe an experiment that we conducted with a small sample in which this approach was implemented. The participants allowed us to track their travel behavior over the course of two weeks, and they were given access to a website they were presented with their trip history, statistics and peer comparisons. By means of an attitudinal survey that we asked the participants to fill out before and after the tracking period, we determined that this led to a measurable change in people’s awareness of their transportation footprint and to a positive shift in their attitudes toward sustainable transportation.
Improvements in vehicular tracking with Global Positioning Systems (GPSs) have fostered new analysis methods in transportation planning. Emerging geographical information systems have helped in developing new techniques in the collection and analysis of data specifically for travel demand forecasting. In 2002, more than 150 households in Laredo, Texas, participated in a GPS-enhanced household travel survey. Trip diary data were collected by means of a computer-assisted telephone interview (CATI), and GPS trip data were collected from survey participants' vehicles. For trip purpose, a comparison of the two data sets yielded significant results. It was found that the number of trips in the GPS data was much greater than the number reported in the CATI data. Despite that, almost all home-based work (HBW) trips found in the GPS data were also found in the CATI data. That result differs sharply from the other trip purposes: home-based nonwork (HBNW) and non-home-based (NHB); for these two trip purposes, less than half the trips found in the GPS data were found in the CATI data. That result indicates the potential for serious deficiencies in the CATI process for collecting certain types of trips in the region of study. In additional, household size and household income were found to be significant factors affecting the reporting accuracy in the CATI data. Despite that, the CATI method of household trip data retrieval is still considered to be an effective and valuable tool.
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.
SUMMARY A generalization of the Kruskal-Wallis test, which extends Gehan's generalization of Wilcoxon's test, is proposed for testing
the equality of K continuous distribution functions when observations are subject to arbitrary right censorship. The distribution
of the censoring variables is allowed to differ for different populations. An alternative statistic is proposed for use when
the censoring distributions may be assumed equal. These statistics have asymptotic chi-squared distributions under their respective
null hypotheses, whether the censoring variables are regarded as random or as fixed numbers. Asymptotic power and efficiency
calculations are made and numerical examples provided.
Given C samples, with ni observations in the ith sample, a test of the hypothesis that the samples are from the same population may be made by ranking the observations from from 1 to Σni (giving each observation in a group of ties the mean of the ranks tied for), finding the C sums of ranks, and computing a statistic H. Under the stated hypothesis, H is distributed approximately as χ(C – 1), unless the samples are too small, in which case special approximations or exact tables are provided. One of the most important applications of the test is in detecting differences among the population means.** Based in part on research supported by the Office of Naval Research at the Statistical Research Center, University of Chicago.
Using GPS technology in the collection of household travel data has been gaining importance as the technology matures. This paper documents recent developments in the field of GPS travel surveying and ways in which GPS has been incorporated into or even replaced traditional household travel survey methods. A new household activity survey is presented which uses automated data reduction methods to determine activity and travel locations based on a series of heuristics developed from land-use data and travel characteristics. The algorithms are used in an internet-based prompted recall survey which utilizes advanced learning algorithms to reduce the burden placed on survey respondents. Initial results of a small pilot study are discussed and potential areas of future work are presented.
The combination of increasing challenges in administering household travel surveys and advances in global positioning systems (GPS)/geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extreme dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.
In the late 1990s, global positioning system (GPS) devices began to be used as a method for measuring personal travel. Early devices were for in-vehicle use only and derived their power from the accessory socket of the car. In the early 2000s, the first wearable devices appeared, using battery power from rechargeable batteries. The early wearable devices were heavy and ungainly, and success in having people use the devices was limited. In 2005, the Institute of Transport and Logistics Studies (ITLS) and NeveITS pioneered the use of a much smaller device with its own internal battery, similar in weight and dimensions to a mobile telephone. Subsequent to the initial deployment of this device, there have been further advances in the sensitivity of the antenna/receiver and we have developed with NeveITS a number of improvements to software. Most recently, another device called a Starnav, has been developed for ITLS in Taiwan, and offers further sophistication and user friendliness than the Neve devices. This paper describes these GPS devices and demonstrates the capability of these devices to provide detailed and accurate data on travel movements. We provide a brief description of the software we have developed and continue to improve for analysing the resulting data. The latest technologies for GPS devices indicate the potential to replace many conventional methods of data collection that are flawed because of known errors and inaccuracies.
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