Investigating the impacts of built environment on vehicle miles traveled and energy consumption: Differences between commuting and non-commuting trips

Ding, Chuan; Liu, Chao; Zhang, Yi; Yang, Jun; Wang, Yunpeng

doi:10.1016/j.cities.2017.05.005

Cited by 66 publications

(50 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, car ownership and use control policy needs to be developed based on local conditions. However, there are some similar results with prior studies conducted in western countries [16,[22][23][24]40]. It was found that residential density, land use mixture, bus stop density, and intersection density had significant effects on household car ownership.…”

Section: Discussionsupporting

confidence: 78%

“…Table 1 summarizes the existing studies. It shows that descriptive analysis and statistical models are most commonly used in the existing studies such as the negative binomial regression [1,32], the structural equation model [22,37,50,53,63,65], multilevel ordered probit model [23], the ordered logit model [36], the multinomial logit model [38], the ordinary least squares regression [38], and the logistic regression model [59,66]. The models can identify the influencing factors and measure the power of these factors simultaneously, which can help to understand the role played by built environment in influencing car ownership and use.…”

Section: Models For Built Environment and Car Dependencymentioning

confidence: 99%

“…The impacts of the built environment on car ownership and use have gained more and more attention due to its contribution to traffic congestion and environmental issues in developed countries [22][23][24]. However, there are several major gaps that need to be filled.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

View full text Add to dashboard Cite

Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

show abstract

Section: Discussionsupporting

confidence: 78%

Section: Models For Built Environment and Car Dependencymentioning

confidence: 99%

See 1 more Smart Citation

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

View full text Add to dashboard Cite

show abstract

“…The following socioeconomic characteristics and built environment features would impact car ownership, albeit to varied degrees: (1) income, e.g., household total income and average individual income (Dargay et al, 2007;Soltani, 2017); (2) population, e.g., the growth or decline of population (Ritter and Vance, 2013); (3) availability of competitive travel modes, e.g., ownership of e-bikes, motorcycles or taxis (Yang et al, 2017); (4) urban form, e.g., compact urban form or sprawling (Soltani, 2017;Li et al, 2010;Yang et al, 2017); (5) road network design; e.g., road density or intersection density (Yang et al, 2017); (6) access to services, e.g., access to public transportation and recreational facilities (Shen et al, 2016;Pan et al, 2013); (7) walking facilities, e.g., sidewalks and walking trails (Soltani, 2017); (8) safety, e.g., presence of heavy traffic and neighborhood crime-related safety (Sugiyama et al, 2009), and (9) urbanization, e.g., the difference between urban and rural residents (Kemperman and Timmerman, 2009;Lee et al, 2009). …”

Section: Literature Reviewmentioning

confidence: 99%

The Socioeconomic Characteristics, Urban Built Environment and Household Car Ownership in a Rapidly Growing City: Evidence from Zhongshan, China

Zhang

Liu³

et al. 2018

Journal of Asian Architecture and Building Engineering

Self Cite

View full text Add to dashboard Cite

Growth in car ownership has significant impacts on the use of urban space and management of urban environments, especially in rapidly developing countries such as China. However, among voluminous literature, few studies have investigated the correlates of household car ownership in the Chinese context, leading to the lack of effective measures to tackle rapid motorization. This study explored the impacts of household-level socioeconomic characteristics and neighborhood-level built environment on household car ownership, with data collected from 25,325 households in Zhongshan, China. The Zero-inflated Poission regression models detect that, all else being equal, living in a neighborhood with a compact and mixed urban form, better bus service and adjacency to CBD is associated with fewer household cars and higher probability to own zero cars. The models also suggest that household size and income, number of employed or students, and availability of competitive vehicles are significantly related to the number of cars in a household. The findings facilitate our understanding of the effects of socioeconomic characteristics and built environment on household car ownership and provide insights into an effective design of measures to slow down the rapid motorization in China.

show abstract

“…In the United States traffic congestion has caused enormous economic losses. For example, in 2013, urban Americans experienced an extra 6.8 billion hours of travel and 3.1 billion gallons of fuel consumed because of traffic congestion [4][5][6]. With the development of motor vehicle, researchers have recognized that automata vehicle is probably one of the most effective ways to tackle those problems [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment

Liu

et al. 2017

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

Autonomous vehicle is able to facilitate road safety and traffic efficiency and has become a promising trend of future development. With a focus on highways, existing literatures studied the feasibility of autonomous vehicle in continuous traffic flows and the controllability of cooperative driving. However, rare efforts have been made to investigate the traffic control strategies in autonomous vehicle environment on urban roads, especially in urban intersections. In autonomous vehicle environment, it is possible to achieve cooperative driving with V2V and V2I wireless communication. Without signal control, conflicted traffic flows could pass intersections through mutual cooperative, which is a remarkable improvement to existing traffic control methods. This paper established a cellular automata model with greedy algorithm for the traffic control of intersections in autonomous vehicle environment, with autonomous vehicle platoon as the optimization object. NetLogo multiagent simulation platform model was employed to simulate the proposed model. The simulation results are compared with the traffic control programs in conventional Synchro optimization. The findings suggest that, on the premises of ensuring traffic safety, the control strategy of the proposed model significantly reduces average delays and number of stops as well as increasing traffic capacity.

show abstract

Investigating the impacts of built environment on vehicle miles traveled and energy consumption: Differences between commuting and non-commuting trips

Cited by 66 publications

References 43 publications

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

The Socioeconomic Characteristics, Urban Built Environment and Household Car Ownership in a Rapidly Growing City: Evidence from Zhongshan, China

Traffic Control Models Based on Cellular Automata for At-Grade Intersections in Autonomous Vehicle Environment

Contact Info

Product

Resources

About