Land use characteristics of Xi'an residential blocks based on pedestrian traffic system

Duan, Yaqiong; Lei, Kexin; Tong, Haiyan; Li, Bo; Wang, Wuyan; Hou, Quanhua

doi:10.1016/j.aej.2020.04.032

Cited by 10 publications

(9 citation statements)

References 16 publications

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“…In order to explore the reasons why the traffic level and built environment cannot achieve coordinated development, we Accessibility of workplace, CBD, or point of interest Distance to the city center 6800 and below (58) 6800 to 16000 (26) 16000 to 29000 (15) 29000 to 39000 (10) 39000 to 54000 (13) 54000 and above (8) Journal of Advanced Transportation calculated S j (i) and S j ′ (i) using formulas (4) and ( 5). e results are shown in Table 3.…”

Section: Analysis On the Reasons Of Non Dea Efficiencymentioning

confidence: 99%

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Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

Jiang

Jing

et al. 2021

Journal of Advanced Transportation

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The coordination relationship between urban built environment and transport system is an indispensable field in the study of urban planning. Recent research efforts in built environment and transport system have focused on the effects of built environment on travel behaviors, such as car ownership, choice of travel mode, and travel frequency. These travel behaviors will affect the traffic level. However, research studies on direct assessments of links between built environment and traffic level are still limited. This paper aims to fill this gap by modeling with data envelopment analysis based on Point of Interest (POI) data and floating car data collected in Jinan, China. It is found that the coordination relationship between built environment and traffic level is poor in Jinan. With regard to the built environment input index, the distance from the city center has the greatest influence on the coordination relationship. And for the built environment output index, bus stop influences the coordination relationship most significantly. This research can support the provision of quantitative basis for the formulation of governance priorities for traffic governance policies.

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Section: Analysis On the Reasons Of Non Dea Efficiencymentioning

confidence: 99%

“…In the above study, the land use mostly refers to the spatial distribution of various social activities, such as industrial areas, residential areas, and commercial areas [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

Jiang

Jing

et al. 2021

Journal of Advanced Transportation

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“…Researchers have identified a relation between land use change and traffic prediction. There are various models currently employed to model land use changes and traffic prediction [2,3]. However, there is a continuous necessity for new approaches to re-evaluate current models and improve them.…”

Section: Introductionmentioning

confidence: 99%

“…Detection of land use change is one of the key research areas of GIS. Although several techniques [2,4,5] can accurately detect land use changes from GIS data, there exist great challenges and opportunities to effectively organize and model such information from multi-temporal data.…”

Section: Introductionmentioning

confidence: 99%

“…Because of recent advantages in Geographic Information System (GIS)-based land use, traffic data, and aerial imagery, it has become an effective option that reveals characteristics of land use changes and predicts traffic volume. A wide variety of GIS-based methodologies have been proposed in the literature to analyze a city's land use changes and traffic estimations [2,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Azad

Xin

2021

IJGI

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Land use and transportation planning have a significant impact on the performance of cities’ traffic conditions and the quality of people’s lives. The changing characteristics of land use will affect and challenge how a city is able to manage, organize, and plan for new developments and transportation. These challenges can be better addressed with effective methods of monitoring and predicting, which can enable optimal efficiency in how a growing city like Calgary, Canada, can perform. Using ontology in land use planning is a new initiative currently being researched and explored. In this regard, ontology incorporates relationships between the various entities of land use. The aim of this study is to present Land Use Change Ontology (LUCO) with a deep neural network for traffic prediction. We present a Land Use Change Ontology (LUCO) approach, using expressions of how the semantics of land use changes relate to the integration of temporal land use information. This study examines the City of Calgary’s land use data from the years 2001, 2010, and 2015. In applying the LUCO approach to test data, experimental outcomes indicated that from 2001 to 2015 residential land use increased by 30% and open space decreased by 40%. Forecasting traffic is increasingly essential for successful traffic modelling, operations, and management. However, traditional means for predicting traffic flow have largely assumed restrictive model architectures that have not controlled for the amounts of land use change. Inspired by deep learning methods and effective data mining computing capabilities, this paper introduces the deep learning Recurrent Neural Network (RNN) to predict traffic while considering the impact of land use change. The RNN was successful in learning the features of traffic flow under various land use change situations. Experimental results indicated that, with the consideration of LUCO, the deep learning predictors had better accuracy when compared with other existing models. Success of our modeling approach indicates that cities could apply this modeling approach to make land use transportation planning more efficient.

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Impact of urban morphology on pedestrians: A review of urban approaches

elzeni

elmokadem²,

Badawy³

2022

Cities

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Land use characteristics of Xi'an residential blocks based on pedestrian traffic system

Cited by 10 publications

References 16 publications

Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

Analyzing the Coordination Relationship between Urban Built Environment and Traffic Level

Land Use Change Ontology and Traffic Prediction through Recurrent Neural Networks: A Case Study in Calgary, Canada

Impact of urban morphology on pedestrians: A review of urban approaches

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