Travel Data Collection Research Articles

GPS-supported smartphone app-based integrated travel diary and time-use data collection: challenges and lessons learned

AbstractTravel behaviour and time-use data are two vital data sources for travel demand modelling. Travel behaviour is traditionally collected through household travel surveys, enhanced by using GPS-supported smartphone apps for passive location data collection. However, recruiting individuals willing to install these apps with sustained motivation to continue participation has been a critical challenge. This paper shares insights from a travel and time-use data collection procedure in Chicago and Sydney using the Fourstep app. Social media platforms were utilised as a solution to recruit participants in Chicago, where an international market research company failed to accomplish the task. This paper also discusses the challenges we faced and suggests ways to overcome them, offering valuable guidance to researchers in recruiting participants for smartphone application-based data collection. It also offers an analysis of travel, time-use, and travel-based multitasking behaviours based on the data collected from the Chicago and Sydney samples.

Travel Diary as a Method to Study Transport Behavior of the Population

The process of modeling the demand for transport services in urban environment starts with the collection of data on citizens’ transport behavior. Travel diary is the primary and most popular source of this sort of information. Features of three types of diaries are considered in the article — memory-based, semi-automated and automated. Based on the broad foreign empirical experience the author draws on the analysis of the current trend towards partially or even fully automated collection of travel data. The memory-based type of diary becomes obsolete nowadays. Its significant advantage and valuable element for studying urban mobility is the collection of subjective information about the transport behavior logic. Based on findings made by analyzing three types of travel diaries the author concludes that semi-automated diary is the optimal form of the diary. The main reason is its combination of both automated data collection on respondents’ travel choices and the possibility of its manual correction. Nevertheless, recommendations on which type of travel diary to choose depend on peculiarities and technical feasibilities of a particular project.

Inferring Trip Destination Purposes for Trip Records Collected through Smartphone Apps

Rapid developments in smartphones and Global Positioning System (GPS) technology have provided a new method for travel data collection. People’s travel trajectories can be passively collected through smartphones with built-in GPS sensors and processed to infer important attributes of travel behavior such as trip destination purposes. This paper examines the feasibility of using discrete choice models and tree-based machine learning models for trip purpose inference. Using smartphone GPS trajectories and land-use data that are open-sourced for academic research, four models are developed to classify trip destination purposes into one of thirteen categories. The models include multinomial logit, mixed logit, random forest, and gradient boosting decision tree. Because the data set is significantly unbalanced with more than 50% of the trips being home or work trips, a two-stage modeling process is applied to identify home and work trips first and then subsequently to classify trips for the remaining discretionary purposes. The results show that both discrete choice models and machine learning models can achieve more than 70% prediction accuracies for home trips and work trips, but less than 30% prediction accuracies for most of the discretionary trip purposes. The discrete choice models can accurately reproduce the market share distribution of the trip purposes, but the machine learning models fail to do so. The results imply that as purely data-driven models, machine learning models may not be the best solution for trip purpose inference because they lack the theoretical background provided by microeconomics and human psychology that are essential in explaining people’s travel and activity choices.

Advancing travel demand surveys using a new generation smartphone-based platform: Applications and users’ experience

Building on technological progress, travel survey methods have evolved to utilise smartphones for better data collection of individual-level travel data with less environmental impact. MobyApp platform is a tool that can replace paper-based travel demand surveys, advance online surveys, and improve the experience of participants. It is designed to collect detailed multi-day data on travel and activity patterns for use in urban and transport planning, modelling, and analysis. The objective of this paper is three-fold: 1.to present the capabilities of the state-of-the-art MobyApp that advances the travel demand survey methods, 2.to describe how the platform was utilised to host travel demand surveys within the HARMONY project in Turin Urban Area (IT); 3.to elaborate on the quality of the collected data for transport modelling purposes, and the experience of users using this survey tool.

Lessons learnt through gender-based travel data collection and related sexual harassment in Sub-Saharan Africa

Background and objectiveKey motivations for the study described in this paper were to gain insights into the differences in travel behaviour between males and females, as well as gender-based sexual harassment exposure in public transport in sub-Saharan Africa. The mobility and sexual harassment results, based on data collection in two African cities (Blantyre in Malawi and Lagos in Nigeria), is presented in this paper, to document the true extent of sexual harassment and ways in which sexual harassment practices affected women's mobility on the continent. The analysis aims to illuminate gender-based mobility differences and sexual harassment experiences, as well as provide some guiding principles towards addressing sexual harassment challenges. Methodand Data: This study developed a standardised gender-based travel and sexual harassment perception questionnaire that was applied via pen-and-paper, as well as technology (offline tablets) in the cities of Lagos (Nigeria) and Blantyre (Malawi). Overall, 1478 respondents participated. Approximately 58% of the sample in both cities were women. The data collected was analysed to highlight differences between male and female travel behaviour and differences in relation to exposure to sexual harassment whilst in the transport system. ResultsData revealed that women make more, shorter trips, while they carry a significantly higher sexual harassment burden. All parts of public transport trips are affected, including travelling to/from the system, while waiting for the vehicle and in the vehicle. The need for transfers increases the risk of crime and sexual harassment. This paper concludes that there is a need for improved policy frameworks, educational campaigns, for men and women, as well as the creation of safe reporting structures when sexual harassment does occur. Actions are needed in the two case study cities and beyond.

Travel data collection using a smart phone for the estimation of multimodal travel times of intra-city public transportation

Travel data collection using a smart phone for the estimation of multimodal travel times of intra-city public transportation

A comprehensive review of trip generation models based on land use characteristics

• Existing researches on the impact of land use on trip generation has been explored. • Advanced data collection techniques for travel data collection were investigated. • Numerous approaches to trip generation modelling have been discussed. To assess the impact of any proposed development, it is necessary to estimate the trips that are likely to be generated because of such development. In case of developing countries, land use pattern varies significantly and it becomes very challenging to model the trip rates. In view of this, an effort has been made to synthesize the available literature related to modelling of trip generation rates. This paper provides a comprehensive review of studies related to impact of land use on trip rates, data collection techniques and modelling approaches. Major challenges associated with modelling of trip generation includes the unavailability of standard reference databases for multimodal trip generation, consideration of land use evolvement, less explored machine learning modelling approaches, and the feasibility of using emerging data collection techniques in travel surveys. Finally, a framework is proposed for modelling multimodal trip generation rates considering the above identified issues.

Using genetic programming on GPS trajectories for travel mode detection

The widespread and increased use of smartphones, equipped with the global positioning system (GPS), has facilitated the automation of travel data collection. Most studies on travel mode detection that used GPS data have employed hand-crafted features that may not have the capabilities to detect all complex travel behaviours since their performance is highly dependent on the skills of domain experts and may limit the performance of classifiers. In this study, a genetic programming (GP) approach is proposed to select and construct features for GPS trajectories. GP increased the macro-average of the F1-score from 77.3 to 80.0 in feature construction when applied to the GeoLife dataset. It could transform the decision tree into a competitive classifier with support vector machines (SVMs) and neural networks that are both able to extract high-level features. Simplicity, interpretability, and a relatively lower risk of overfitting allow the proposed model to be readily used for passive travel data collection even on smartphones with limited computational capacities. The model is validated by a second dataset from Australia and New Zealand, which indicated that a decision tree with the GP constructed features as its input has a considerably higher transferability than SVMs and neural networks.

Built Environment Characteristics, Daily Travel, and Biometric Readings: Creation of an Experimental Tool Based on a Smartwatch Platform

Travel surveys can uncover information regarding travel behaviour, needs, and more. Collected information is utilised to make choices when reorganising or planning built environments. Over the years, methods for conducting travel surveys have changed from interviews and forms to automated travel diaries in order to monitor trips made by travellers. With the fast progression of technological advancements, new possibilities for operationalising such travel diaries can be implemented, changing from utilising mobile to wearable devices. Wearable devices are often equipped with sensors which collect continuous biometric data from sources that are not reachable from standard mobile devices. Data collected through wearable devices range from heart rate and blood pressure to temperature and perspiration. This advancement opens new possible layers of information in the collection of travel data. Such biometric data can be used to derive psychophysiological conditions related to cognitive load, which can uncover in-depth knowledge regarding stress and emotions. This paper aims to explore the possibilities of data analysis on the data collected through a software combining travel survey data, such as position and time, with heartrate, to gain knowledge of the implications of such data. The knowledge about the implications of spatial configurations can be used to create more accessible environments.

An inductive experimental approach to developing a web-based travel survey builder: developing guidelines to design an efficient web-survey platform

ABSTRACT The household travel survey (HTS) is the most widely used passenger travel data collection method, and web-based HTS is currently the most dominant survey mode. However, there is a lack of proper understanding on how much the web-based approach can be used without over-burdening respondents. This study investigates methods to improve web-based HTS data quality and to reduce response burdens. It presents the lessons learned from the development and field experiment of a web survey builder. A particular focus is on designing and testing a trip diary interface through usability tests. These tests include a mouse-movement tracking study, mock web-based HTS experiments with responsive designs, and the use of a route planner application programming interface (API). Results show that creating responsive designs for web-surveys based on screen size can significantly increase completion rates and improve the usability. Collecting detailed routes with a route planner API suggesting most likely routes does not significantly increase respondent fatigue. However, it significantly improves data quality. Household size and the age of the survey respondent are significant contributing factors to survey drop-off rates and respondent fatigue. The paper contributes to the literature on household travel surveys by providing evidence-based design guidelines for web-survey interfaces.

Paratransit Trip Data Collection System with Smartphone GPS and REST Web Service in Malang, Indonesia

Paratransit in Malang has its own irregular characteristics, same as in other cities in the world. It is tough to recognize the exact operational time, availability, and estimated travel time when using paratransit. The information about paratransit can be only obtained by making predictions due to its irregular characteristics. This research examined the importance of a system that can collect paratransit travel data as a basis for making predictions. Therefore, this research proposes the design of a paratransit travel data collection system by utilizing smartphone GPS and REST web service. To determine the position of the paratransit, the researchers used the concept of circle-based geofencing which is called pseudo-stop. The initial results obtained from the experiment indicated the influence of tracking intervals and radius of pseudo-stop on effectiveness and efficiency of the system. The shorter the tracking interval, the higher the tendency of the pseudo-stop radius value to decrease, resulting in decreased effectiveness and increased efficiency. Otherwise, the wider the tracking interval, the higher the tendency of the pseudo-stop radius value to be greater, resulting in increased effectiveness and decreased efficiency. In spite of periodically accessing location providers, the energy usage is quite low.

Paratransit Trip Data Collection System with Smartphone GPS and REST Web Service in Malang, Indonesia

Paratransit in Malang has its own irregular characteristics, same as in other cities in the world. It is tough to recognize the exact operational time, availability, and estimated travel time when using paratransit. The information about paratransit can be only obtained by making predictions due to its irregular characteristics. This research examined the importance of a system that can collect paratransit travel data as a basis for making predictions. Therefore, this research proposes the design of a paratransit travel data collection system by utilizing smartphone GPS and REST web service. To determine the position of the paratransit, the researchers used the concept of circle-based geofencing which is called pseudo-stop. The initial results obtained from the experiment indicated the influence of tracking intervals and radius of pseudo-stop on effectiveness and efficiency of the system. The shorter the tracking interval, the higher the tendency of the pseudo-stop radius value to decrease, resulting in decreased effectiveness and increased efficiency. Otherwise, the wider the tracking interval, the higher the tendency of the pseudo-stop radius value to be greater, resulting in increased effectiveness and decreased efficiency. In spite of periodically accessing location providers, the energy usage is quite low.

Analyzing Cross-country Pandemic Connectedness During COVID-19 Using a Spatial-Temporal Database: Network Analysis.

Communicable diseases including COVID-19 pose a major threat to public health worldwide. To curb the spread of communicable diseases effectively, timely surveillance and prediction of the risk of pandemics are essential. The aim of this study is to analyze free and publicly available data to construct useful travel data records for network statistics other than common descriptive statistics. This study describes analytical findings of time-series plots and spatial-temporal maps to illustrate or visualize pandemic connectedness. We analyzed data retrieved from the web-based Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, which contains up-to-date and comprehensive meta-information on civil flights from 193 national governments in accordance with the airport, country, city, latitude, and the longitude of flight origin and the destination. We used the database to visualize pandemic connectedness through the workflow of travel data collection, network construction, data aggregation, travel statistics calculation, and visualization with time-series plots and spatial-temporal maps. We observed similar patterns in the time-series plots of worldwide daily flights from January to early-March of 2019 and 2020. A sharp reduction in the number of daily flights recorded in mid-March 2020 was likely related to large-scale air travel restrictions owing to the COVID-19 pandemic. The levels of connectedness between places are strong indicators of the risk of a pandemic. Since the initial reports of COVID-19 cases worldwide, a high network density and reciprocity in early-March 2020 served as early signals of the COVID-19 pandemic and were associated with the rapid increase in COVID-19 cases in mid-March 2020. The spatial-temporal map of connectedness in Europe on March 13, 2020, shows the highest level of connectedness among European countries, which reflected severe outbreaks of COVID-19 in late March and early April of 2020. As a quality control measure, we used the aggregated numbers of international flights from April to October 2020 to compare the number of international flights officially reported by the International Civil Aviation Organization with the data collected from the Collaborative Arrangement for the Prevention and Management of Public Health Events in Civil Aviation dashboard, and we observed high consistency between the 2 data sets. The flexible design of the database provides users access to network connectedness at different periods, places, and spatial levels through various network statistics calculation methods in accordance with their needs. The analysis can facilitate early recognition of the risk of a current communicable disease pandemic and newly emerging communicable diseases in the future.

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.

Travel Behaviour of the Elderly in Planned and Unplanned Communities of Lagos Metropolis, Nigeria

Interests in the study of travel behaviour of the elderly have been growing in the last two decades and many of the works done are concentrated in the global north. Evidence from literature reveals that most of these studies focus more on the impact of individual and household socioeconomic attributes, urban form, travel attributes, and policy factors on the travel behaviour of the elderly, but less on the influence of neighbourhood planning. This paper examined the travel behaviour of old people in Festac town (planned) and Ketu (unplanned) community of Lagos metropolis, southwest Nigeria with a view to determining the influence of neighbourhood planning on travel pattern. The two neighbourhoods were carefully and purposively selected for collection of socio-economic and travel data through structured questionnaire that was administered on 155 randomly selected elderly respondents. The study revealed significant differences and some similarities in respondents’ socio-economic and travel characteristics. Respondents from the planned community have a higher daily mean trip rate and mean trip time than those in unplanned community. Residents of planned community also undertake higher work and social trips and rely on automobile more than those in unplanned community. Apart from the fact that the majority of respondents travel more during the off-peak period, frequent road congestion was reported as the top mobility challenge among respondents in the two neighbourhoods. The study concluded that neighbourhood planning affects travel behaviour and recommended a walkable neighbourhood concept and promotion of elderly friendly public transport system for the study area.

Travel Behaviour of the Elderly in Planned and Unplanned Communities of Lagos Metropolis, Nigeria

Interest in the study of travel behaviour of the elderly has been growing in the last two decades and many of the works done were concentrated in the global north. Evidence from literature reveals that most of these studies focus more on the impacts of individual and household socioeconomic attributes, urban form, travel attributes, and policy factors on the travel behaviour of the elderly, but less onthe influence of neighbourhood planning. This paper examined the travel behaviour of old people in Festac town (planned) and Ketu (unplanned) community of Lagos metropolis, southwest Nigeria to determine the influence of neighbourhood planning on travel pattern. The two neighbourhoods were carefully and purposively selected for collection of socio-economic and travel data through a structured questionnaire that was administered on 155 randomly selected elderly respondents. The study revealed significant differences and some similarities in respondents’ socio-economic and travel characteristics. Respondents from the planned community have a higher daily meantrip rate and mean trip time than those in unplanned community. Residents of the planned community also undertake higher work and social trips and rely on automobile more than those in unplanned community. Apart from the fact that the majority of respondents travel more during the off-peak period, frequent road congestion was reported as the top mobility challenge among respondents in the two neighbourhoods. The study concluded that neighbourhood planning affects travel behaviour and recommended a walkable neighbourhood concept and promotion of elderly-friendly public transport system for the study area.
 Keywords: Aging, community, elderly, neighbourhood planning, travel behaviour.

A Generic Future Mobility Sensing System for Travel Data Collection, Management, Fusion, and Visualization

In studies of human mobility, there is a need for a holistic system for collection of sensing data, management of data flows, fusion of multiple data sources, and visualization of integrated data to better understand travel behavior. We have designed and implemented a generic Future Mobility Sensing (FMS) system to serve these purposes. FMS harnesses various sensing technologies, heterogeneous multi-source data and analytical functionalities with three dedicated platforms, namely 1) the FMS Data Collection Platform, which intertwines sensing objects, machine learning algorithms and user verifications to collect high resolution, multi-day travel data; 2) the FMS Data Management Platform, which provides standardized APIs to access data stored in an interconnected data model; and 3) the FMS Data Fusion and Visualization Platform, which consolidates multi-source data to be interpreted and presented. Together, the three platforms form a mobility sensing flow to facilitate data-driven analysis and decision-making. With FMS, heterogeneous multi-source data are suitably integrated for analysis, and multi-dimensional knowledge is extracted and presented in intuitive and interactive analytical dashboards. The system is intended to be generic to support different requirements of mobility studies, such as travel surveys, as its function modules are reusable and can be customized to support a unified data collection, management, fusion and visualization process. This paper introduces the overall architecture of the FMS system and summarizes various applications that it can support. Specifically, we present a study of commercial vehicle parking in Singapore to demonstrate the capability of the system.

Perturbation Methods for Protection of Sensitive Location Data: Smartphone Travel Survey Case Study

Smartphone based travel data collection has become an important tool for the analysis of transportation systems. Interest in sharing travel survey data has gained popularity in recent years as “open data initiatives” by governments seek to allow the public to use these data, and hopefully to contribute their findings and analysis to the public sphere. The public release of such precise information, particularly location data such as place of residence, opens the risk of privacy violation. At the same time, in order for such data to be useful, as much spatial resolution as possible is desirable for utility in transportation applications and travel demand modeling. This paper evaluates the efficacy of two geographic random perturbation methods—geo-indistinguishability and donut geomask—in protecting the privacy of respondents whose residential location may be published. The study measures the performance of location privacy methods, preservation of utility, and randomness in the distribution of perturbation distances with varying parameters. It is found that both methods produce distributions of spatial perturbations that conform closely to common probability distributions and, as a result, that the original locations can be inferred with little information and a high degree of precision. It is also found that while achieved k -estimate anonymity increases linearly with desired anonymity for the donut geomask, geo-indistinguishability is highly dependent upon its privacy budget factor and is not very effective at assuring desired achieved k -estimate anonymity.

Development of Commute Mode Choice Model by Integrating Actively and Passively Collected Travel Data

Travel data collection, which is necessary for travel demand modeling, is always of great concern to modelers due to its huge cost and effort when a large sample is required to achieve satisfactory model precisions. In this paper, travel data collected based on a survey questionnaire and travelers’ active participation are called actively collected data (ACD). It is difficult to guarantee absolute randomness and unbiasedness in a sample when the ACD are collected due to self-selection issues. The aim of this study is to improve the model precision at low cost by using passively collected data (PCD), such as in-vehicle GPS data and transit smart card data, to release sample size restriction and reduce sampling bias of ACD in a commute mode choice model. In an empirical study, a multinomial-logit-based joint model is developed for commute mode choice by integrating ACD and PCD based on the choice-based sampling theory. A comprehensive set of explanatory variables are specified through data integration. Both simulation and empirical results show great improvement in coefficient precisions in the proposed joint model, relative to those in the ACD model and PCD model. In this study, ACD and PCD samples of Shanghai are integrated in the joint model so that several significantly influential level-of-service attributes are identified for auto, rail, and bus modes, and their impacts on commute mode choice probabilities are quantified. The findings can aid in better evaluating the program to improve the existing transit system.

Travel demand modelling, data collection and well-being

Travel demand modelling, data collection and well-being