Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

Yang, Xiping; Lu, Shiwei; Zhao, Weifeng; Zhao, Zhiyuan

doi:10.3390/ijgi8110486

Cited by 13 publications

(15 citation statements)

References 46 publications

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“…Most of the analyzed networks presented a GlobEff of small value (<0.20). Some pieces of research use the GlobEff as a parameter to compare PTNs [77,78], and others apply it to identify hubs [79,80]. Consequently, the degree of a node Complexity is ranked by comparing the changes in PTN efficiency after eliminating the node.…”

Section: Global Efficiencymentioning

confidence: 99%

Modeling the Public Transport Networks: A Study of Their Efficiency

López

2021

Complexity

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The public transportation network (PTN) provides mobility and access to community resources, employment, medical care, infrastructures, and other resources in the city. This research studies the process of the formation of links among nodes in different real-world PTNs. We have found that this process may be appropriately explained by a generalized linear model (GLM) using local, global, and quasilocal similarity indexes as explanatory variables. In modeling, the response variable was described by a binomial probability density function, and the logit function was used as a link function. In the crossvalidation process, utilising a downsampling approach, both average accuracy and area under the receiver operating characteristic curve (AUC) metrics presented higher values than 0.99. The kappa parameter had magnitudes larger than 0.93 for most of the PTNs. In the final validation stage, recall and specificity metrics took the value 1. Accuracy and precision parameters were larger than 0.99 and 0.87, respectively, for the majority of PTNs. Only one of the PTNs required utilising a smoothed bootstrap approach in order to achieve better results. The similarity measures with the greatest influence on the model were determined. We also assessed the impact of link removal on the global efficiency of PTNs, considering several similarity indexes. Additionally, we find that most of the networks show low local and global efficiencies (≤0.20), as well as travel times with a relevant variability, exhibiting standard deviations larger than 790 seconds. Significant similarities exist between the cumulative probability distributions of the local efficiency in all PTNs. With respect to the centrality measures, the eigenvector centrality presented a strong correlation with the hub/authority centralities (>0.80), while the pagerank showed a moderate, high, or very high correlation with the degree in all PTNs, >0.50.

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Section: Global Efficiencymentioning

confidence: 99%

Modeling the Public Transport Networks: A Study of Their Efficiency

López

2021

Complexity

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“…Zhang et al constructed the evaluation framework which includes convenience, comfort, security, reliability, and facility level according to survey data [54]. Lots of studies analyzed transit network topology structure using complex network theory [55][56][57]. Sun [60].…”

Section: Evaluation Of Transit Systemmentioning

confidence: 99%

Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data

Zhang

Liu

Shi

et al. 2021

Journal of Advanced Transportation

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Operational efficiency and stability are two critical aspects to measure bus systems. Influenced by many stochastic factors, buses always suffer from delay and bunching. Traditional studies focus on a single route and lack research on the systematic evaluation of bus network. In this paper, we propose a data-driven framework to analyze the efficiency and stability based on small granularity GPS trajectory data from the perspective of entire bus network. The IC card data and route data are used to extract the boarding passenger number and topological structure, respectively. The results show that the average headway of stations follows a lognormal distribution. Moreover, the distribution of arrival efficiency of stations is inhomogeneous and a small number of stations have large values. In addition, the relationships among average headway of stations, boarding passenger number, bus number, and complex network indicators are revealed. It is found that the average headway of station is negatively correlated with other indicators, which implies that complex network connections and more passenger flows could weaken the efficiency of bus operations. This paper provides a way to evaluate the operational performance of bus networks and could give help for monitoring and optimizing the daily operation of bus systems.

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“…e Line-Station-based UBN model is conventionally used to examine the characteristics of bus stations, while ignoring the bus lines, for instance, identification of a bus station with an important transit function in the network [34]. In addition, these studies tend to use the P-space to establish the complex network [3].…”

Section: Two Types Of Urban Bus Network Modelmentioning

confidence: 99%

“…Additionally, improving the service capacity of UBNs has become an important part of the current construction of livable cities promoted by the Chinese government. In this respect, an in-depth topological and statistical analysis of UBNs is of fundamental importance for the evaluation of the capacity of current urban public services [3,4]. is has also become an indispensable part of urban planning.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, these data are typically not readily available due to business privacy, which poses another constraint for UBN research. With the expansion of urban transportation systems, UBNs have become incredibly large and complicated, and there are high demands for the automatic construction of UBN models through open big data [3,13]. erefore, it is necessary to establish a framework for open big data acquisition and processing in UBN research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Using Open Big Data to Build and Analyze Urban Bus Network Models within and across Administrations

Wei

Wang

et al. 2020

Complexity

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Urban bus networks play an important role, when the capacity of urban public services is evaluated. With recent advancements in Internet and Communication Technologies, there is an emerging interest in building an urban bus network model through open big data. This has rarely been investigated and exposes several challenges in the provision of transportation services in urban planning. On the one hand, it is necessary to combine bus stations based on spatial distance constraints due to their ambiguous definition in open big data; on the other hand, it is difficult and time-consuming to relocate and build new stations, but the optimization of bus lines is relatively easy to implement. This study aimed to develop an explicit methodological framework for building and analyzing two different types of urban bus network model using open big data. Thereafter, the framework was applied in two case studies in China, within a county-level administration and in a region including three county-level administrations. The key result shows that there was a shortage of urban bus services across these different administrations. This paper contributes to the body of research methodologies into public transport networks and to understanding the sharing of urban public services across administrations, improving the management of urban bus networks, and highlighting the importance of examining the characteristics of urban bus network in county-level administrations rather than just in large cities in China.

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Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China

Cited by 13 publications

References 46 publications

Modeling the Public Transport Networks: A Study of Their Efficiency

Modeling the Public Transport Networks: A Study of Their Efficiency

Analysis of Spatial-Temporal Characteristics of Operations in Public Transport Networks Based on Multisource Data

Using Open Big Data to Build and Analyze Urban Bus Network Models within and across Administrations

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