Built environment impact on travel behavior in the context of China : evidence from transit-oriented development areas in Shenzhen

2015 ◽  
Author(s):  
Li Lin
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Transit Oriented Development ◽  
Environment Impact

scholarly journals Is Built Environment Associated with Travel Mode Choice in Developing Cities? Evidence from Hanoi

2020 ◽  
Vol 12 (14) ◽  
pp. 5773
Author(s):  
Thi Mai Chi Nguyen ◽  
Hironori Kato ◽  
Le Binh Phan
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Control Strategies ◽  
Multinomial Logit Model ◽  
Spatial Aggregation ◽  
Policy Implications ◽  
Transit Oriented Development ◽  
Travel Mode ◽  
Aggregation Effect ◽  
Household Travel

This paper examines the association between the built environment (BE) and travel behavior in Hanoi, Vietnam. A multinomial logit model is used to analyze individuals’ choice of travel mode from a dataset collected via a questionnaire-based household travel survey in 2016 and the geospatial data of BE variables; the dataset contains 762 responses from local residents in ten districts of the Hanoi Metropolitan Area about their daily travel episodes. It also examines a spatial aggregation effect by comparing model performances among four buffering distances and ward-zones. The results showed that (1) a higher population density around an individual’s home is associated with more bus use and less motorbike and car use; (2) mixed land use around the home, average tax revenue near the home, and bus frequency at the workplace have positive relationships with bus ridership; (3) senior people, students, or unskilled laborers tend to use the bus; (4) the spatial aggregation bias significantly affects the estimation results; and (5) new immigrants tend to choose to reside in areas designed for automobile users. Finally, there are several policy implications for transit-oriented development (TOD) in Hanoi, including: (1) parking regulations and/or control strategies should be jointly incorporated into the Hanoi’s TOD policy; (2) Hanoi’s TOD policy should be carefully designed in terms of its scope of development site and type; and (3) a polycentric structure strategy only may not be sufficient for increasing public transit ridership.

scholarly journals How Urban Form Characteristics at Both Trip Ends Influence Mode Choice: Evidence from TOD vs. Non-TOD Zones of the Washington, D.C. Metropolitan Area

2019 ◽  
Vol 11 (12) ◽  
pp. 3403
Author(s):  
Arefeh Nasri ◽  
Lei Zhang
Keyword(s):  
Built Environment ◽  
Travel Behavior ◽  
Urban Form ◽  
Mode Choice ◽  
Discrete Choice Models ◽  
Sustainable Transportation ◽  
Lower Probability ◽  
Transit Oriented Development ◽  
Travel Mode ◽  
Travel Mode Choice

Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.

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.

scholarly journals Sustainable Streetscape and Built Environment Designs around BRT Stations: A Stated Choice Experiment Using 3D Visualizations

2021 ◽  
Vol 13 (12) ◽  
pp. 6594
Author(s):  
Ahmad Adeel ◽  
Bruno Notteboom ◽  
Ansar Yasar ◽  
Kris Scheerlinck ◽  
Jeroen Stevens
Keyword(s):  
Built Environment ◽  
Mass Transit ◽  
Local Context ◽  
Transit Oriented Development ◽  
Commercial Building ◽  
Related Factors ◽  
Housing Projects ◽  
The Road ◽  
Development Goals ◽  
Set Up

The incompatibility between the microscale-built environment designs around mass transit stations and stakeholders’ preferences causes dissatisfaction and inconvenience. The lack of a pedestrian-friendly environment, uncontrolled development patterns, traffic and parking issues make the street life vulnerable and unattractive for users, and affect the mass transit usage. How to design the streetscapes around mass transit stations to provide a user-friendly street environment is a crucial question to achieve sustainable transit-oriented development goals. To recognize the specific attributes of streetscape environment relevant in local context of BRT Lahore, this paper presents the results of a visual preference experiment in which nine attributes of built environment were systematically varied across choice sets. Multinomial logit models were set up to identify the preferences of three target groups: BRT users, commercial building users and residents at different locations. The research indicates that not only the road-related factors (bike lane and sidewalk widths, crossings facilities, street greenery) have a significant influence on people’s preference but also that building heights, and the typology of buildings and housing projects around BRT corridor have shaped these preferences. When planning and designing urban design projects around mass transit projects, these significant attributes should be considered.

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.

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