Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method

This study explores the possibility of employing social media data to infer the longitudinal travel behavior. The geo-tagged social media data show some unique features including location-aggregated features, distance-separated features, and Gaussian distributed features. Compared to conventional household travel survey, social media data is less expensive, easier to obtain and the most importantly can monitor the individual’s longitudinal travel behavior features over a much longer observation period. This paper proposes a sequential model-based clustering method to group the high-resolution Twitter locations and extract the Twitter displacements. Further, this study details the unique features of displacements extracted from Twitter including the demographics of Twitter user, as well as the advantages and limitations. The results are even compared with those from traditional household travel survey, showing promises in using displacement distribution, length, duration and start time to infer individual’s travel behavior. On this basis, one can also see the potential of employing social media to infer longitudinal travel behavior, as well as a large quantity of short-distance Twitter displacements. The results will supplement the traditional travel survey and support travel behavior modeling in a metropolitan area.     

A structural equation model of activity participation and travel behavior using longitudinal data

Abstract

This paper presents a dynamic structural equation model (SEM) that explicitly addresses complicated causal relationships among socio-demographics, activity participation, and travel behavior. The model assumes that activity participation and travel patterns in the current year are affected by those in previous years. Using the longitudinal dataset collected from Puget sound transportation panel ‘wave 3’ and ‘wave 4,’ these assumptions are tested with suggested SEMs. Within each wave, the model is structured to have a three-level causal relationship that describes interactions among endogenous variables under time-budget constraints. The resulting coefficients representing the activity durations indicate that people tend to allocate their time according to the importance and the obligation of the activity level. Results from the dynamic SEM confirm the fact that people's current activity and travel behavior do have effects on those in the future. The resulting model also shows that activity participation and travel behavior in ‘wave 3’ are closely related to those in ‘wave 4.’ These explicit explanations of relationships among variables could provide important perspectives in the activity-based approach which becomes recognized as a better analytical tool for the transportation planning and policy making process.     

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa,Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.     

The impacts of household structure on the individual stochastic travel and out‐of‐home activity time budgets

The amount of time individuals and households spend in travelling and in out‐of‐door activities can be seen as a result of complex daily interactions between household members, influenced by opportunities and constraints, which vary from day to day. Extending the deterministic concept of travel time budget to a stochastic term and applying a stochastic frontier model to a dataset from the 2004 UK National Travel Survey, this study examines the hidden stochastic limit and the variations of the individual and household travel time and out‐of‐home activity duration—concepts associated with travel time budget. The results show that most individuals may not have reached the limit of their ability to travel and may still be able to spend further time in travel activities. The analysis of the model outcomes and distribution tests show that among a range of employment statuses, only full‐time workers' out‐of‐home time expenditure has reached its limit. Also observed is the effect of having children in the household: Children reduce the flexibility of hidden constraints of adult household members' out‐of‐home time, thus reducing their ability to be further engaged with out‐of‐home activities. Even when out‐of‐home trips are taken into account in the analysis, the model shows that the dependent children's in‐home responsibility reduces the ability of an individual to travel to and to be engaged with out‐of‐home activities. This study also suggests that, compared with the individual travel time spent, the individual out‐of‐home time expenditure may perform as a better budget indicator in drawing the constraints of individual space–time prisms. Copyright © 2013 John Wiley & Sons, Ltd.     

Incorporating the extended theory of planned behavior in a school travel mode choice model: a case study of Shaoxing,China

Travel to and from school can have social, economic, and environmental implications for students and their parents. Therefore, understanding school travel mode choice behavior is essential to find policy-oriented approaches to optimizing school travel mode share. Recent research suggests that psychological factors of parents play a significant role in school travel mode choice behavior and the Multiple Indicators and Multiple Causes (MIMIC) model has been used to test the effect of psychological constructs on mode choice behavior. However, little research has used a systematic framework of behavioral theory to organize these psychological factors and investigate their internal relationships. This paper proposes an extended theory of planned behavior (ETPB) to delve into the psychological factors caused by the effects of adults’ cognition and behavioral habits and explores the factors’ relationship paradigm. A theoretical framework of travel mode choice behavior for students in China is constructed. We established the MIMIC model that accommodates latent variables from ETPB. We found that not all the psychological latent variables have significant effects on school travel mode choice behavior, but habit can play an essential role. The results provide theoretical support for demand policies for school travel.     

High-speed rail's potential for the reduction of carbon dioxide emissions from short haul aviation: a longitudinal study of modal substitution from an energy generation and renewable energy perspective

Abstract

This paper quantifies and evaluates, utilising a ‘bottom-up’ approach, the effect on CO₂ emissions of a modal shift from short-haul air travel to high-speed rail (HSR), based on projected passenger movements, between Sydney and Melbourne, Australia during the period 2010–2030. To date, peer-reviewed studies assessing the CO₂ emissions from these competing modes of high-speed transportation have been restricted principally to a cross-sectional assessment, with a Eurocentric bias. This present comparative study seeks to address a gap in the literature by assessing, longitudinally, the CO₂ emissions associated with the proposed operation of HSR against the ‘business-as-usual’ air scenario between Sydney and Melbourne. Under the assumed 50/50 modal shift, and the Australian government's current renewable electricity target, an annual reduction in CO₂ emissions of approximately 14% could be achieved when compared with a ‘business-as-usual’ air scenario. This percentage reduction represents a 62 kt reduction in base year, 2010, and a 114 kt reduction in the final year, 2030. In total, the overall reduction achieved by such a modal shift, under the assumed conditions, during the period 2010–2030, equates to approximately 1.87 Mt of CO₂. Importantly, if the electrical energy supply for HSR operations was further ‘decarbonised’, then it follows that a greater emission reduction would be achieved.     

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.     

Evaluation of a cellular phone-based system for measurements of traffic speeds and travel times: A case study from Israel

The purpose of this paper is to examine the performance of a new operational system for measuring traffic speeds and travel times which is based on information from a cellular phone service provider. Cellular measurements are compared with those obtained by dual magnetic loop detectors. The comparison uses data for a busy 14 km freeway with 10 interchanges, in both directions, during January–March of 2005. The dataset contains 1 284 587 valid loop detector speed measurements and 440 331 valid measurements from the cellular system, each measurement referring to a 5 min interval. During one week in this period, 25 floating car measurements were conducted as additional comparison observations. The analyses include visual, graphical, and statistical techniques; focusing in particular on comparisons of speed patterns in the time–space domain. The main finding is that there is a good match between the two measurement methods, indicating that the cellular phone-based system can be useful for various practical applications such as advanced traveler information systems and evaluating system performance for modeling and planning.     

Environmental impacts of transformative land use and transport developments in the Greater Beijing Region: Insights from a new dynamic spatial equilibrium model

This paper reports the insights into environmental impacts of the ongoing transformative land use and transport developments in Greater Beijing, from a new suite of dynamic land use, spatial equilibrium and strategic transport models that is calibrated for medium to long term land use and transport predictions. The model tests are focused on urban passenger travel demand and associated emissions within the municipality of Beijing, accounting for Beijing’s land use and transport interactions with Tianjin, Hebei and beyond. The findings suggests that background trends of urbanization, economic growth and income rises will continue to be very powerful drivers for urban passenger travel demand across all main modes of transport beyond 2030. In order to achieve the dual policy aims for a moderately affluent and equitable nation and reducing the absolute levels of urban transport emissions by 2030, road charging and careful micro-level coordination between land use, built form and public transport provision may need to be considered together for policy implementation in the near future.