Transit oriented development among metro station areas in Shanghai, China: Variations, typology, optimization and implications for land use planning

Li, Zekun; Han, Zixuan; Jing, Xin; Luo, Xin; Su, Shiliang; Wang, Min

doi:10.1016/j.landusepol.2018.12.003

Cited by 94 publications

(39 citation statements)

References 46 publications

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“…Based on their nearness or similarity, the SOM can classify the objects of the system into clusters, for example categories or regions [38]. Compared to traditional clustering algorithms, the SOM has three main advantages: 1) prior knowledge is not required; 2) nonlinearity can be handled; and 3) excellent visualization is provided [39]. The neurons of the SOM are distributed into two layers: the input layer and the output layer by going through a training phase.…”

Section: Congestion Estimation Using An Artificial Neural Networkmentioning

confidence: 99%

Public Traffic Congestion Estimation Using an Artificial Neural Network

Wang

Dong

2020

IJGI

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Alleviating public traffic congestion is an efficient and effective way to improve the travel time reliability and quality of public transport services. The existing public network optimization models usually ignored the essential impact of public traffic congestion on the performance of public transport service. To address this problem, this study proposes a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs). The proposed methodology involves two steps: (1) Extracting three traffic indicators of the RSBs from smart card data and bus trajectory data; (2) The self-organizing map (SOM) is used to cluster and effectively recognize traffic patterns embedded in the RSBs. Furthermore, a congestion index for ranking the SOM clusters is developed to determine the congested RSBs. A case study using real-world datasets from a public transport system validates the proposed methodology. Based on the congested RSBs, an exploratory example of public transport network optimization is discussed and evaluated using a genetic algorithm. The clustering results showed that the SOM could suitably reflect the traffic characteristics and estimate traffic congestion of the RSBs. The results obtained in this study are expected to demonstrate the usefulness of the proposed methodology in sustainable public transport improvements. these network optimization models developed in the above-mentioned articles had been usually applied in uncongested road segments.Public traffic condition refers to the traffic volume of the road network and its dynamic spatial-temporal distribution, which can reflect the degree of congestion [8]. Recently, the traffic congestion impacts of bus operations on a road segment or a corridor have been investigated by researchers [9,10]. These congestion effects mainly include the effects of bus stop design, bus travel time, and bus priority options such as exclusive bus lanes or priority signals for buses [9]. Understanding these congestion impacts can help operators to identify the effectiveness of transport network optimization in relieving congested areas or congested routes. For example, under congested traffic conditions, it is difficult for buses to return to the driving lane, which leads to a longer travel time after picking-up/dropping off passengers at stops [11]. Furthermore, the bus travel time variation dominated by traffic congestion often results in unreliable service, which has negative impacts on both the operators and passengers [12]. Previous studies have pointed out that well-located stops have the potential to alleviate the impact of traffic congestion [3]. Therefore, it is critical to optimize PTN by considering the impact of traffic congestion in order to achieve a high level of public transport service and improve travel time reliability.In this study, we proposed a data-based methodology to estimate the traffic congestion of road segments between bus stops (RSBs) using a self-organizing feature map (SOM). The SOM was used to cluster and effective...

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Section: Congestion Estimation Using An Artificial Neural Networkmentioning

confidence: 99%

Public Traffic Congestion Estimation Using an Artificial Neural Network

Wang

Dong

2020

IJGI

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“…Given the transit system features mentioned above, the NSBM DEA model is suitable for evaluating transit efficiency. The mathematical expression for measuring transit efficiency is shown in Equation (8).…”

Section: Network Slacks-based Measure Deamentioning

confidence: 99%

“…Also, various studies established the TOD evaluation criteria to achieve a more efficient and sustainable transit environment and solve complex urban problems. Most previous research stated that efficient and sustainable transit must balance the social and economic aspects of the transportation environment [8][9][10]. It means that land use, population density, and residential environment should be closely examined in urban planning [9].…”

Section: Introductionmentioning

confidence: 99%

“…Transit efficiency was defined through the relationship between multiple inputs and outputs [34]. Transit efficiency refers to how well the transit system was introduced and managed with respect to the socio-economics, transit infrastructures, and transit trips of each station area [8]. Regarding the TOD concept, both transit design and efficiency must be considered to determine transit efficiency [16,17].…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis

et al. 2019

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The purpose of this research is to evaluate transit-oriented development (TOD) efficiency in Seoul using the network slacks-based measure data envelopment analysis (NSBM DEA) model. The smartcard data and socio-economic data are used to evaluate the transit efficiency of 352 subway station areas in Seoul. To measure the TOD efficiency, the two-stage network is designed with the transit design stage and the transit efficiency stage. The overall efficiency score of each station area is estimated through each score of the stage. The results of the efficiency evaluation by station area indicate that the overall efficiency score average is 0.349, with the transit design score and efficiency score estimated to be 0.453 and 0.245, respectively. The results indicate that the balance of each stage is crucial to achieve an efficient station in the concept of transit efficiency. With the efficiency scores of the 352 subway station areas, the TOD efficiency is also evaluated by the administrative units in Seoul. The results of district analysis reveal that the top 10 efficient administrative units are characterized by both residential and commercial land use. The results indicate that efficiency is found to be good in areas having both residential and commercial characteristics.

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“…TOD is acquainted with assistance both the public transport and to utilize the openings offered by such a system (Belzer and Autler 2002). A couple of assessments have prescribed that organized land-use plans around public transit stations can provoke an increase in ridership (Li et al 2019). TOD is not just any improvement close to transit; it improves territory capability so that individuals can walk, cycle, and use public transport.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Support Vector Machine Model for Classification of Transit-Oriented Development

Khare

Villuri

Chaurasia³

2021

Preprint

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Transit-oriented development (TOD) can invigorate sustainable development by conveying a more coordinated transit and surrounding land use. Given the dynamic nature of urban community development, urban planners find it hard to precisely respond to questions such as where TOD planning around the transit hubs can succeed in the city. The present study proposes a framework utilizing a support vector machine (SVM) to enhance the TODness prediction of an area to address this issue. An SVM model has successfully applied to 16 bus rapid transit station areas in Bhopal city, India, using the tenfold cross-validation resampling methods and thirteen predictor variables. The models performance was in good agreement with 93.75% precision, utilizing the sigmoid kernel function and the regularization parameter esteem equivalent to 4. This methodology could be used at any scale, and the outcomes could offer recommendations for more accurate urban planning, fortifying the relationship between TOD and spatial association. The study provides the basis for predicting better future TODness classification, which will help the urban planner for sustainable urban planning and policy making.

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Transit oriented development among metro station areas in Shanghai, China: Variations, typology, optimization and implications for land use planning

Cited by 94 publications

References 46 publications

Public Traffic Congestion Estimation Using an Artificial Neural Network

Public Traffic Congestion Estimation Using an Artificial Neural Network

Evaluating the Efficiency of Transit-Oriented Development Using Network Slacks-Based Data Envelopment Analysis

An Enhanced Support Vector Machine Model for Classification of Transit-Oriented Development

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