Travel Behavior Data Research Articles

Does bicycle network level of traffic stress (LTS) explain bicycle travel behavior? Mixed results from an Oregon case study

This paper draws on census data, mode choice, and regional household travel survey data to investigate the relationship between the varying levels of traffic stress (LTS) routes and bicycle travel behavior. Specifically, does bicycle level of traffic stress explain bicycle behavior enough to warrant its use given the limitations? Bicycle level of traffic stress is a new system of bicycle infrastructure classification for cities that cannot afford existing alternatives such as bicycle level of service which is more data-intensive. The LTS criteria lacks many of the input variables found significant in predicting both route choice and mode choice, including several that are required for its more expensive alternatives. The authors select the Salem-Keizer metropolitan area, a case study representative of the kinds of small and medium-sized communities that may prefer to use the LTS to evaluate cycling infrastructure given its lower input costs. The results validate LTS on travel behavior data from the Oregon Household Activity Survey (OHAS), but not mode choice data from the American Community Survey (ACS). These results suggest the LTS criteria may not be useful for cities looking to prioritize infrastructure improvements for specifically increasing commuter cycling. That said, results suggest the system provides a valid measure of a household's propensity to cycle. Further research on a broader cross-section of communities can clarify these mixed results.

Testing the Reliability of a Smartphone-Based Travel Survey: An Experiment in Seoul

With programmable applications that utilize sensors, such as global positioning systems and accelerometers, smartphones provide an unprecedented opportunity to collect behavioral data in an unobtrusive and cost-effective manner. This paper assesses the relative accuracy and reliability of the Future Mobility Sensing (FMS), a smartphone-based prompted-recall travel survey. We compared the data extracted from FMS with the data collected from the Korea Passenger Trip Survey (PTS), a traditional self-reported, paper-based travel survey. In total, 46 undergraduate students completed the PTS for seven consecutive days, while also carrying their smartphones with the activated FMS applications for the same time span. After completing the PTS, the participants validated their FMS data on the web-based prompted recall surveys. We then matched the validated FMS data with the PTS-based records. The FMS turns out to be superior in detecting short trips, which are usually under-reported in self-reported travel surveys. The reported PTS travel times are longer than for the FMS, suggesting that participants tend to overestimate their travel time in the PTS. This study contributes to the ongoing development of smartphone-based travel behavior data collecting methods.

The climate impact of Norwegians’ travel behavior

Transportation is the largest sector in Norway in terms of greenhouse gas emissions. The mitigation potential of this sector can be better understood by investigating the climate impact of disaggregated data on travel behavior. Here, we use travel behavior data for Norwegians travelling domestically and abroad in 2009 to explore mitigation potential in Norwegian transportation. The climate impact of this aggregated data is calculated by including the impact of all relevant long-lived greenhouse gases and short-lived climate forcers. The climate responses have been compared by using a range of emission metrics (both global temperature change and integrated radiative forcing, both pulse and sustained emissions for time horizons up to 100years). For most choices, the total climate impact is dominated about equally by air (55±20%) and car transport (36±19%), with air transport having a slightly stronger impact for a majority of the cases. The highest income quintile causes a climate impact that is 240% larger than the lowest income quintile. The few trips longer than 100km contribute to 68% of the impact. In addition, we analyze what the effect would be from several proposed mitigation policy targets in Norway and find that policies focused on technology have larger impacts than those on travel behavior.

Handling Intrahousehold Correlations in Modeling Travel: Comparison of Hierarchical Models and Marginal Models

In this study, the necessity for treating intrahousehold correlation was investigated by analyzing two travel behavior indexes, travel time and travel distance, for three important travel motivations (commuting, shopping, and leisure). Data stemming from the 2010 Belgian National Household Travel Survey were used in the analysis. Two model approaches that accommodated intrahousehold correlation were compared, namely, the generalized linear mixed model (GLMM) and the generalized estimating equation (GEE) model. Both model approaches showed that high levels of intrahousehold correlation were present, and therefore the use of models that took into account intrahousehold correlation was strongly recommended. Results indicated that this requirement was the most urgent for noncommuting trips. Moreover, the results showed that the GLMM and the GEE model yielded comparable estimates in the case of normally distributed data. Furthermore, evidence was provided that the more the estimates of the intrahousehold correlation provided by the two approaches differed, the less the homogeneity of the parameters was ensured. In this regard, if one has to choose between the GLMM and the GEE model, the negative consequences of choosing an inappropriate covariance model in the case of GLMM especially favor the selection of the GEE model. Further research is needed to compare the two approaches in the context of nonnormally distributed travel behavior data.

Exploring the Potential of Mobile Phone Data in Travel Pattern Analysis

To support increasingly complex planning activities, many agencies are facing the challenges of obtaining highly nuanced travel behavior data while managing shrinking financial resources. Recent advancements in smartphones and GPS technologies present new opportunities to track travelers’ trips. Many studies have applied GPS-based data to planning and demand analysis, but cell phone (mobile phone) GPS data have not received much attention. Google location history (GLH) data provide an opportunity to explore the potential of cell phone GPS data. This paper presents the findings of a study that used GLH data, including the data-processing algorithm used to derive travel information, and their potential applications to understanding travel patterns. For the pilot study, GLH data were obtained from 25 participants over a 1-month period. The data showed that GLH provides a sufficient amount of high-resolution data that can be used to study people’s movement without a burden on the respondent. The algorithms developed in this study worked well with the pilot data. However, because of the limitations of the pilot data as a result of the sample size and sample representation, conclusions cannot be drawn from the results of the analysis conducted in this study. Nevertheless, this pilot study shows the potential of mobile phone GPS data as a supplement or complement to conventional data. Given the high rate of penetration of smartphones and the low respondent burden, these data could facilitate the investigation of various issues, such as the reason for less frequent long-distance travel, daily variations in travel behavior, and human mobility patterns on a large spatiotemporal scale.

Travel Model Blind Spots: Importance of Understanding Special Markets Related to Visitors

In many small and medium-sized communities, the need to reflect the unique characteristics of travel by special markets is an important consideration. Visitor travel is a special market for many communities that enjoy the benefits of being a destination for visitors from outside the region. Visitor travel can affect the transportation system in a community as well as benefiting the economy. Visitor travel is often not modeled explicitly because of the cost associated with collecting behaviorally rich survey data to support model development. This paper presents a low-cost option for collecting travel behavior data for special markets through the administration of a small-sample intercept survey of persons staying in recreational vehicle (RV) campgrounds in the French Broad River region of North Carolina. Analysis of the survey data showed that travel by these surveyed RV households was different from both retired and seasonal part-time resident households, captured in the region’s household travel survey. Development of a special market model to address travel by RV visitors in the region is also discussed.

Analysis of consumer response to fuel price fluctuations applying sample selection model to GPS panel data: Dynamics in individuals’ car use

The effects of fuel price increases on people’s car use have been widely discussed during the last few decades in travel behavior research. It is well recognized that fuel price has significant effects on driving distance and driving efficiency. However, most of this research assumed that these effects are invariant across individuals and weather conditions. Moreover, intrinsic variability in people’s preferences has not been given much attention due to the difficulty of collecting the necessary data. In this paper, we collected detailed travel behavior data of 276 respondents in the Netherlands, spanning a time period between one week and three months using GPS logs. These GPS data were fused with weather data, allowing us to estimate both exogenous (such as weather and fuel price) and endogenous effects (inertia and activity plans) on individual’s car use behavior. To further understand the effects of fuel price on the environment, we estimated the effects of fuel price fluctuation on CO2 emissions by car. The results show a significant degree of inertia in car use behavior in response to increased fuel prices. Weather and fuel price showed significant effects on individual’s car using behavior. Moreover, fuel price shows two-week lagged effects on individual’s travel duration by car.

Field Evaluation of the Smartphone-based Travel Behaviour Data Collection App “SmartMo”

This paper outlines an innovative approach to the evaluation of a self-administered smartphone-based survey for the collection of travel behaviour data. For this approach, a traditional travel survey is modified to match mobile devices. The smartphone application “SmartMo” is designed in a multi-stage iterative development process. It is then implemented and evaluated through a number of field tests involving 97 participants. Results of the field evaluation will be discussed including the technical performance (e.g. secure data transfer and data management, energy consumption, map-matching), usability (e.g. comprehensibility, handling, joy of use) as well as user acceptance (e.g. willingness to participate, data protection and privacy). A brief overview of the SmartMo data collection system will also be provided.

Development of location-based services for recommending departure stations to park and ride users

More and more commuters are beginning to favour public transportation. Fast and convenient park and ride (PnR) services provided by public transportation authorities are the result of changes of household demographics and household, increasing fuel prices and a focus on environmental sustainability. However, lack of parking spaces in PnR facilities creates a major bottleneck to this service. The aim of this research is to develop a location-based service (LBS) application to help PnR users choose the best train station to use to reach their destination using a multicriteria decision making model. A fuzzy logic method is used to estimate parking availability when a user is estimated to arrive at a PnR facility. Two surveys are conducted to collect traffic flow, travel behaviour and service quality data at four selected Perth Western Australia train stations. With the proposed approach and survey data, a prototype of LBS application, Station Finder, was developed using the Android SDK 4.0 and Google API 16. This application is a useful and practical tool to save travel cost and time of PnR users’.

South Moravian Mobility Study Basic Findings and Comparison with German Data

Transport planning in the Czech Republic faces the problem of a lack of data on travel behaviour. The paper deals with the usability of foreign travel behaviour data for transport modelling in the Czech Republic. The authors present their findings on the example of a comparison between the results from the data analysis of a travel demand survey in the South Moravian region, which was performed in 2013 on the sample of 1000 households and a German survey Mobilität in Deutschland.

The SMS–GPS-Trip method: A new method for collecting trip information in travel behavior research

This article presents a new method for collecting travel behavior data, based on a combination of GPS tracking and SMS technology, coined the SMS–GPS-Trip method. The state-of-the-art method for collecting data for activity based traffic models is a combination of travel diaries and GPS tracking data, an approach which is not well suited for capturing data on experiences surrounding trips. Currently increasing research is being done on how to incorporate such data in traffic models, and there is therefore a need for a method, which is suited to collect such data. The new method presented in this article builds on ideas from experience sampling methods (ESM) and it is well suited specifically for collecting such experience data. Given the use of SMS technology, this method makes it possible to reach a wide range of respondents. The usefulness of the new method is proven on a theoretical level and illustrated in practice through a case study.

Investigating the Link between Transit Oriented Development and Sustainable Travel Behavior in Brisbane: A Case-Control Study

The purpose of this research is to empirically test the prevailing view that transit oriented development enhances the use of more sustainable modes of transport using Brisbane, Australia as a case. Transit oriented development has been adopted as a new policy tool to reduce car-based travel worldwide. Despite being a billion dollar investment, the impacts of transit oriented development on promoting sustainable travel behavior is not conclusive. The research uses a case-control approach to empirically investigate this relationship based on travel behavior data collected from 88 individuals living in two contrasting neighborhoods in Brisbane: Kelvin Grove Urban Village – a transit oriented development, and Annerley – a traditional suburb (non-transit oriented development). A comparative investigation of travel behavior was subsequently conducted using distance travelled by modes and purposes between the neighborhoods. Results show that the availability of opportunity and services located within the transit oriented development reduces the car use by 5% and increases the use of active transport by 4%. The findings in this research support the implementation of TOD policies in Brisbane.

The Use of Smartphone Applications in the Collection of Travel Behaviour Data

The MOVE project deals with the collection and analysis of crowd behaviour data. The main goals of the project are to collect data through the use of mobile phones and to develop new technologies to process and mine the collected data for crowd behaviour analysis. This paper describes the different steps in the development of tracking applications for smartphones that make use of advanced data mining. The results on data collection, analysis, and reporting have led to the development and operation of an advanced urban data monitoring system.

Evaluation of travel behaviors in order to prepare Travel Plans for residential areas and schools of Gyor

The municipality of Gyor participated in the last five years in two Intelligent Energy Europe projects (Pro Motion project, Travel Plan plus project), which aim was to deliver transport-sector energy savings by creating a new approach to site-based mobility management across Europe. During the development of the two projects there has been worked out two Local Travel Plans, one for the residential area Menfocsanak, in the city, Gyor and one for the educational institutions of Gyor. The first one is a so called ‘area travel plan’ and the second one is a so-called ‘work travel plan’. The aims of these studies were to reduce CO2 emission caused by private car users and to promote sustainable mobility choices for the residents of the area and for students. In order to solve the future mobility challenges in a sustainable way it is necessary to analyze the existing travel demand and modal-split caused by target groups (residents and students).It is important to develop Local Travel Plans for residential areas and schools, based on quantitative travel behavior data, which can be used for modeling of current and future transport to provide optimized solutions for daily mobility. It is very important to collect data from various sources according to the needed detail of a Travel Plan.

Study of Emission Reduction: Benefits of Urban Rail Transit

As many cities in China have been planning, establishing and operating urban rail transit (URT), it is necessary to study URT's impacts on emission reduction. In this paper, the environmental benefit strategy was addressed through the real operating data and travel behavior data of Beijing's URT, using the skeleton planning method to estimate HC, CO, NOX emission reductions. Moreover, the study calculates the direct economic benefits of URT in terms of fuel consumption savings and air pollution control cost savings. The results show that URT operation can not only encourage the use of URT, but also reduces mobile source emissions substantially, thus creating substantial environmental benefits. Our results show that the URT of Beijing could reduce 1036.733 ton of HC, 85.827 ton of CO, 326.295 ton of NOx, which leads to over 8.56 billion Yuan savings every year. More specifically, the findings of this paper have insightful implications to planning, construction, operation and assessment of URT.

Transportation mode recognition using GPS and accelerometer data

Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.

Analysis of the Relationship between Urban Characters and Physical Activity Levels Based on the Travel Behavior Data

本研究は、健康との関連が指摘される交通身体活動量の基礎的な傾向を、利用交通手段や個人属性、さらには都市特性との関連から明らかにするため、最初に、2005年実施の全国都市交通特性調査の交通行動データを用いて、個人の交通身体活動量と利用交通手段及び個人属性との関連を分析した。次いで、1987年、1992年実施の全国都市パーソントリップ調査を加えた3年次を対象に、移動時の身体活動量の経年変化を把握し、1987年から2005年にかけて約2割減少していることを明らかにするとともに、都市規模や都市圏内での都市の位置づけといった観点から、身体活動量の差異を検証した。最後に、都市単位での基本的な特性として、市街化区域人口密度と世帯あたり自動車保有台数に着目した上で、これらの都市特性と移動時の身体活動量との関連を経年的に分析した。

Exploring a Method of Estimating Vehicle Miles of Travel

This research attempts to develop a statistical model that can analyze the impact of land use on vehicle use. By analyzing travel behavior data from 2009 National Household Travel Survey (NHTS) data, a sequential modeling approach with five sub?models, called the SCAG NHTS Model, was developed to estimate the land use-travel behavior relationship, particularly for household Vehicle Miles of Travel (VMT). The SCAG NHTS Model was developed using the parcel database on socioeconomic and land use data. The test for model validity and sensitivity has shown that the SCAG NHTS Model produces reasonable and accountable results and is sensitive to land use characteristics. The SCAG NHTS Model also produced model results similar to SCAG’s trip?based model. After running the model with SCAG 2012 Regional Transportation Plan scenario data, the SCAG NHTS Model estimates household VMT, which is 2.4 % lower than the trip?based model.

Foreign living experience as a predictor of domestic travel behavior

Travel behavior researchers have long been interested in various factors influencing how individuals make transportation decisions. Research has shown that foreign born residents and recent immigrants to the US exhibit significantly different travel behavior than native born populations in the same regions. This paper expands on that existing research by examining the travel behavior data of 662 students from Brigham Young University (52.1% of whom have lived outside the United States for more than 60 consecutive days). First the paper identifies if an individual’s experience living abroad results in a change in travel behavior upon their return to the United States; second, if the foreign location where the experience was gained influences any behavioral change, and if so which regions of the world produce the greatest influence?; and third, if the duration of an individual’s residence abroad affects their travel behavior and transportation perceptions upon returning to the United States. The analysis revealed that individuals who have lived abroad do exhibit a significant change in travel behavior upon returning to the United States, and the continent where the individual lived does significantly affect their change in travel behavior. However it appears that this change in behavior upon their return to the United States may be a regression to the mean, manifested by individuals adopting travel behavior similar to the transportation culture of their domestic environment. The location and duration of foreign experience does not have a significant impact on the current travel behavior of the individuals represented in this sample.

Development and Implementation of an Adapted Evacuation Planning Methodology in the Framework of Emergency Management and Disaster Response: A Case Study Using TransCAD

The nature of natural disasters is often unpredictable, so it is extremely important that sufficient planning be done to evaluate the preparedness, the system response, and the ability of transportation infrastructures to handle evacuation traffic. This article presents an adapted evacuation planning methodology that essentially incorporates the traditional four-step planning model and dynamic traffic assignment. The authors utilize the transportation network in Western Massachusetts as an example to test the effectiveness of the proposed methodology using two with-notice hurricane evacuation scenarios. The analysis is performed by using the off-the-shelf computer-based planning software package TransCAD to assist the four-step transportation planning process. By altering inputs and using dynamic traffic assignment, TransCAD can be used to predict how the transportation system behaves during an evacuation. Two scenarios are used as the basis of the evacuation modeling. A production-attraction model is presented to replicate the behavior of evacuees. The production-attraction model results are compared with a series of reports, and the model falls within the recommendations of these reports. Several different evacuation speeds are examined for two different study areas. The Results section shows the outputs that can be garnered from the program such as the clearance time, critical locations, V/C ratios, and location-to-location travel times. Finally, recommendations are made on how these results can be used to aid planners. It is worth mentioning that the proposed evacuation modeling methodology can be extended and implemented in other places with minor modifications of network topology and localized travel behavior data.