Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach

Ding, Chuan; Mishra, Sabyasachee; Lu, Guangquan; Yang, Jun; Liu, Chao

doi:10.1016/j.trd.2017.02.002

Cited by 60 publications

(40 citation statements)

References 33 publications

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“…The results indicate that both density and land use mix have significant influences on travel behavior. Ding et al [17] investigated the impacts of the built environment on car ownership and travel distance. The results prove that employment density has a significant influence on car ownership and travel distance, whereas the influence of residence density is only significant for car ownership.…”

Section: The Impacts Of Built Environment On Car Ownership and Usementioning

confidence: 99%

“…Therefore, studies on the determiners and how they influence the car ownership and use have gained wide interests among transportation researchers [7][8][9][10]. To address the challenges, it is widely acknowledged that a solution to reduce car dependency by promoting urban development patterns [11][12][13][14][15][16][17][18][19][20], in which built environment plays an important role [21]. Moreover, the built environment could change with rapid urbanization in China, which also provides a good opportunity for understanding the link between the built environment and car dependency.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: The Impacts Of Built Environment On Car Ownership and Usementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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 basic idea is to establish the fuzzy judgment matrix by using the transformation principle to describe the data boundary of the factors in fuzzy set. Through the multilayer numerical calculation based on the evaluation criteria and weights, we will determine the results of the evaluation object [20,21]. The structure of synthetic evaluation model is shown as Figure 4.…”

Section: Evaluation Model Based On Real-time Traffic Information Fusionmentioning

confidence: 99%

Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

Wang

Xiao

et al. 2017

Journal of Sensors

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With the development of connected vehicle (CV) and Vehicle to X (V2X) communication, more traffic data is being collected from the road network. In order to predict future traffic condition from connected vehicles' data in real-time, we present an online traffic condition evaluation model utilizing V2X communication. This model employs the Analytic Hierarchy Process (AHP) and the multilevel fuzzy set theory to fuse multiple sources of information for prediction. First, the contemporary vehicle data from the On Board Diagnostic (OBD) is fused with the static road data in the Road Side Unit (RSU). Then, the real-time traffic evaluation scores are calculated using the variable membership model. The real data collected by OBU in field test demonstrates the feasibility of the evaluation model. Compared with traditional evaluation systems, the proposed model can handle more types of data but demands less data transfer.

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“…Scholars have conducted much research on the optimization of bus schedule schemes but have rarely investigated the division of the operating time [8][9][10][11]. To evaluate the effectiveness of a bus schedule scheme, Patnaik et al [12] selected as indexes the numbers of passengers boarding and alighting the bus and the number of midway stops.…”

Section: Introductionmentioning

confidence: 99%

Operating Time Division for a Bus Route Based on the Recovery of GPS Data

Wang

Cao

2017

Journal of Sensors

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Bus travel time is an important source of data for time of day partition of the bus route. However, in practice, a bus driver may deliberately speed up or slow down on route so as to follow the predetermined timetable. The raw GPS data collected by the GPS device equipped on the bus, as a result, cannot reflect its real operating conditions. To address this concern, this study first develops a method to identify whether there is deliberate speed-up or slow-down movement of a bus. Building upon the relationships between the intersection delay, link travel time, and traffic flow, a recovery method is established for calculating the real bus travel time. Using the dwell time at each stop and the recovered travel time between each of them as the division indexes, a sequential clustering-based time of day partition method is proposed. The effectiveness of the developed method is demonstrated using the data of bus route 63 in Harbin, China. Results show that the partition method can help bus enterprises to design reasonable time of day intervals and significantly improve their level of service.

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Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach

Cited by 60 publications

References 33 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

Online Traffic Condition Evaluation Method for Connected Vehicles Based on Multisource Data Fusion

Operating Time Division for a Bus Route Based on the Recovery of GPS Data

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