Understand the Multi-Level Effects of the Built Environment on Trip-Chaining Behavior

2019 ◽  
Vol 2673 (4) ◽  
pp. 640-651
Author(s):  
Hao Pang ◽  
Ming Zhang
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Linear Models ◽  
Interactive Effect ◽  
Puget Sound ◽  
Residential Density ◽  
Vehicle Ownership ◽  
Trip Chain ◽  
Trip Chaining ◽  
Relationship Of

The debate on the effects of the built environment (BE) on travel behavior has been ongoing despite a large number of studies completed in the past three decades. This study aims to inform the debate by extending the BE–travel behavior investigation to the scope of trip-chaining. Specifically, the study conceptualized the contexture frame for the relationship of BE attributes and trip-chain travel behavior and estimated 2-level hierarchical linear models (HLM) of chained trip tours with travel survey data from the Puget Sound region. The results show that travelers who live in areas with better transit access, higher residential and non-residential density, and higher level of land use mixture generated low percentage of miles traveled by vehicle (PVMT) during their daily tours. Furthermore, considering the cross-level interactive effect, the study demonstrates that the impacts of the non-residential density at work location and the residential density at home location on PVMT are moderated by vehicle ownership.

Temporal-Spatial Microassignment and Sequencing of Travel Demand with Activity-Trip Chains

2003 ◽  
Vol 1831 (1) ◽  
pp. 89-97 ◽  
Author(s):  
Ahmed F. Abdelghany ◽  
Hani S. Mahmassani
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Travel Demand ◽  
User Equilibrium ◽  
Stochastic Dynamic ◽  
Arrival Times ◽  
Schedule Delay ◽  
Trip Chain ◽  
Trip Chaining ◽  
Distance Travel

A stochastic temporal–spatial microassignment and activity sequencing model for activity–trip chains is presented. In this model, trip-chain patterns are defined by the respective locations of destinations in the chain, preferred arrival times at these destinations, and the activity durations at the intermediate destinations; they are given as input to the model. A stochastic dynamic user equilibrium problem is formulated and solved for this purpose. In this problem, drivers simultaneously seek to determine their departure time, route choice, and sequence of their intermediate activities at the origin to minimize their perceived travel cost. This perceived cost is typically a function of the travel time and the schedule delay at the intermediate and final destinations. The model is presented through a study of the relative efficiency of carpooling and trip-chaining travel behavior in a network context. In that example, the performance of travelers who have the option to carpool and chain trips is compared with that of households with single-occupant and individual trip-based travel. Several measures of travel performance, including travel distance, travel time, and schedule delay, are considered for that comparison.

Analysis of Lifestyle Choices: Neighborhood Type, Travel Patterns, and Activity Participation

2002 ◽  
Vol 1807 (1) ◽  
pp. 119-128 ◽  
Author(s):  
Kevin J. Krizek ◽  
Paul Waddell
Keyword(s):  
Travel Behavior ◽  
Puget Sound ◽  
Residential Location ◽  
Three Dimensions ◽  
Activity Participation ◽  
Travel Patterns ◽  
Vehicle Ownership ◽  
Lifestyle Choices ◽  
Household Travel ◽  
Neighborhood Type

Activity-based travel modeling has begun to make significant progress toward a more behavioral framework for simulating household travel behavior. A significant challenge remains in the need to address the interaction of daily activity and travel patterns with longer-term household choices of vehicle ownership, residential location, and employment location. The choices often depend on one another and jointly define the lifestyle of the household. These choices are likely to evolve over the course of the life cycle as households are formed; as children are born, raised, and ultimately depart to form their own households; and as retirement and old age change patterns of residence, work, and travel. A framework is developed for analyzing household choices relating to three dimensions of lifestyle: travel patterns (including vehicle ownership), activity participation, and residential location (neighborhood type). With cluster analysis on data from the Puget Sound Transportation Panel, nine classifications of lifestyle are uncovered. These clusters demonstrate empirically how decisions of residential location reinforce and affect daily decisions related to travel patterns and activity participation. The applicability of these lifestyle clusters for land use transportation planning is discussed.

The Impact of Zoning and Built Environment Characteristics on Transit, Biking, and Walking

2019 ◽  
Vol 11 (1) ◽  
pp. 108-129
Author(s):  
Andrew G. Mueller ◽  
Daniel J. Trujillo
Keyword(s):  
Land Use ◽  
Built Environment ◽  
Discrete Choice ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Land Use Regulation ◽  
Policy Action ◽  
Development Goals ◽  
The Impact

This study furthers existing research on the link between the built environment and travel behavior, particularly mode choice (auto, transit, biking, walking). While researchers have studied built environment characteristics and their impact on mode choice, none have attempted to measure the impact of zoning on travel behavior. By testing the impact of land use regulation in the form of zoning restrictions on travel behavior, this study expands the literature by incorporating an additional variable that can be changed through public policy action and may help cities promote sustainable real estate development goals. Using a unique, high-resolution travel survey dataset from Denver, Colorado, we develop a multinomial discrete choice model that addresses unobserved travel preferences by incorporating sociodemographic, built environment, and land use restriction variables. The results suggest that zoning can be tailored by cities to encourage reductions in auto usage, furthering sustainability goals in transportation.

The built environment and vehicle ownership modeling: Evidence from 32 diverse regions in the U.S.

2021 ◽  
Vol 93 ◽  
pp. 103073
Author(s):  
Sadegh Sabouri ◽  
Guang Tian ◽  
Reid Ewing ◽  
Keunhyun Park ◽  
William Greene
Keyword(s):  
Built Environment ◽  
Vehicle Ownership ◽  
The U.S

Using Deep Learning to Understand Travel Demands in Different Urban Districts

2021 ◽  
pp. 036119812199458
Author(s):  
Shunhua Bai ◽  
Junfeng Jiao
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Prediction Accuracy ◽  
Travel Demand ◽  
Short Term Memory ◽  
Future Research ◽  
Demand Forecast ◽  
Time And Space ◽  
The University ◽  
The Impact

Travel demand forecast plays an important role in transportation planning. Classic models often predict people’s travel behavior based on the physical built environment in a linear fashion. Many scholars have tried to understand built environments’ predictive power on people’s travel behavior using big-data methods. However, few empirical studies have discussed how the impact might vary across time and space. To fill this research gap, this study used 2019 anonymous smartphone GPS data and built a long short-term memory (LSTM) recurrent neural network (RNN) to predict the daily travel demand to six destinations in Austin, Texas: downtown, the university, the airport, an inner-ring point-of-interest (POI) cluster, a suburban POI cluster, and an urban-fringe POI cluster. By comparing the prediction results, we found that: the model underestimated the traffic surge for the university in the fall semester and overestimated the demand for downtown on non-working days; the prediction accuracy for POI clusters was negatively related to their adjacency to downtown; and different POI clusters had cases of under- or overestimation on different occasions. This study reveals that the impact of destination attributes on people’s travel demand can vary across time and space because of their heterogeneous nature. Future research on travel behavior and built environment modeling should incorporate the temporal inconsistency to achieve better prediction accuracy.

Abstract 51: Ambulatory Blood Pressure Phenotype And Cardiovascular Risk In Youth: The Ship-ahoy Study

Hypertension ◽  
2021 ◽  
Vol 78 (Suppl_1) ◽  
Author(s):  
Joseph T Flynn ◽  
Philip Khoury ◽  
Joshua A Samuels ◽  
Marc B Lande ◽  
Kevin Meyers ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cardiac Function ◽  
Linear Models ◽  
Independent Predictor ◽  
Systolic Function ◽  
Left Ventricular ◽  
Cvd Risk ◽  
Oscillometric Device ◽  
Echocardiographic Parameters ◽  
Relationship Of

We investigated whether blood pressure (BP) phenotype based on clinic & 24-hour ambulatory BP (ABP) was associated with intermediate markers of cardiovascular disease (CVD) in 374 adolescents enrolled in a study of the relationship of BP to CV risk. Clinic BP was measured by auscultation and categorized using the 2017 AAP guideline. ABP was measured for 24 hours by an oscillometric device and analyzed using the adult ABP wake SBP cut-point (130 mmHg). This created 4 BP phenotype groups: normal BP (n=224), white coat hypertensive (n=48), ambulatory hypertensive (n=57) & masked hypertensive (n=45). Echocardiographic parameters & carotid-femoral pulse wave velocity (PWVcf) were measured to assess CVD risk. Left ventricular mass (LVM) was lowest in the normal BP group, whereas multiple measures of cardiac function and PWVcf were worse in the masked and ambulatory hypertensive groups: Generalized linear models adjusted for body mass index (BMI) were constructed to examine the associations between BP phenotype and the measured CVD variables. ABP phenotype was an independent predictor of LVM, diastolic and systolic function and PWVcf in the unadjusted model. ABP phenotype remained significantly associated with diastolic function (E/e’, e’/a’), systolic function (ejection fraction) and increased arterial stiffness (PWVcf) after adjustment for BMI percentile (all p<=0.05). We conclude that BP phenotype is an independent predictor of markers of increased CVD risk in adolescents, including impaired cardiac function and increased vascular stiffness. ABP monitoring has an important role in CVD risk assessment in youth.

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