Operationalizing the neighborhood effects of the built environment on travel behavior

2020 ◽  
Vol 82 ◽  
pp. 102561 ◽  
Author(s):  
Steven R. Gehrke ◽  
Liming Wang
Keyword(s):  
Built Environment ◽  
Neighborhood Effects ◽  
Travel Behavior

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.

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.

Frontiers in Land Use and Travel Research

2012 ◽  
pp. 633-664
Author(s):  
Marlon G. Boarnet
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Built Environment ◽  
Travel Behavior ◽  
Standard Approach ◽  
Hypothesis Tests ◽  
Key Issues

This article examines research concerning land use and travel behavior in relation to urban planning. It summarizes the standard approach to studying land use and travel behavior, and identifies the key issues that should be the focus of planning research going forward. The analysis reveals that the literature on land use and travel behavior has so far focused almost exclusively on hypothesis tests regarding the association between the built environment and travel, and on the magnitude of the associations.

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.

Assessing the impact of the built environment on travel behavior: a case study of Buffalo, New York

2011 ◽  
Vol 38 (4) ◽  
pp. 663-678 ◽  
Author(s):  
Andrew J. Tracy ◽  
Peng Su ◽  
Adel W. Sadek ◽  
Qian Wang
Keyword(s):  
New York ◽  
Built Environment ◽  
Travel Behavior ◽  
The Impact
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