2005
Yang, Z (Zhongzhen); Feng, T; Jia, Peng
Maximizing car ownership under constrains of environment sustainability in a city Journal Article
In: Journal of the Eastern Asia Society for Transportation Studies, vol. 6, iss. 2005, pp. 3077 – 3089, 2005, ISSN: 1881-1124.
@article{Yang2005,
title = {Maximizing car ownership under constrains of environment sustainability in a city},
author = {Z (Zhongzhen) Yang and T Feng and Peng Jia},
doi = {10.11175/easts.6.3077},
issn = {1881-1124},
year = {2005},
date = {2005-01-01},
journal = {Journal of the Eastern Asia Society for Transportation Studies},
volume = {6},
issue = {2005},
pages = {3077 – 3089},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2004
Yang, Z.; Feng, T.
A neural network based car ownership model Proceedings Article
In: 2004.
Abstract | Links | BibTeX | PlumX
@inproceedings{Yang2004,
title = {A neural network based car ownership model},
author = {Z. Yang and T. Feng},
doi = {10.1061/40730(144)117},
year = {2004},
date = {2004-01-01},
journal = {Proceedings of the International Conference on Applications of Advanced Technologies in Transportation Engineering},
abstract = {With the economic growth and the improvement of living standard, car ownership in China increases rapidly. It exerts enormous pressure on transportation services. It is necessary to forecast China car ownership for urban transport planning, transport infrastructure improvement and traffic management in terms of economic level, urban configuration, traffic situation and car-concerned policies. In this paper the main factors affecting car ownership are analyzed and a model to estimate car ownership in China city with the BP neural network technology is developed. The model can take the sudden effect of some external factors such as political and economic factors on car ownership into account.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
With the economic growth and the improvement of living standard, car ownership in China increases rapidly. It exerts enormous pressure on transportation services. It is necessary to forecast China car ownership for urban transport planning, transport infrastructure improvement and traffic management in terms of economic level, urban configuration, traffic situation and car-concerned policies. In this paper the main factors affecting car ownership are analyzed and a model to estimate car ownership in China city with the BP neural network technology is developed. The model can take the sudden effect of some external factors such as political and economic factors on car ownership into account.
0000
[No title] Bachelor Thesis
0000.
@bachelorthesis{nokey,
title = {[No title]},
abstract = {<script type="text/javascript" src="//cdn.plu.mx/widget-popup.js"></script>
<body >Effective management of the high-speed railways (HSR) system requires an in-depth understanding of the HSR stations in the network, e.g., the time-dependent volume distribution. The classification of HSR stations is the scientific basis for transport policymaking and land-use planning. Existing classification methods cannot meet the needs of temporal variation of passenger flow or the refined design and operation of HSR stations. This study adopts the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to classify HSR stations in different years. Using the data of Jiangsu Province, China, as an example, the time series of arrival and departure passenger flow at HSR stations are clustered via the DBSCAN algorithm, and the HSR stations are clustered into three classes. To determine the hierarchical structure of HSR stations representing the evolution of HSR networks, we use large-scale panel data obtained from mobile phone cellular data across years (July 1–14 from each of the years 2018, 2020, and 2021) to capture and analyze the spatial-temporal evolution characteristics of massive passenger flow at HSR stations. It is indicated that both HSR station hierarchy and passenger flow have the characteristics of spatial-temporal evolution across years, and the classification results are influenced by the geographical positions of cities and HSR layout. Accurate clustering of HSR stations via large-scale actual passenger flow data enables railway authorities and operators to identify critical nodes for efficient HSR network performance. The resulting classification would contribute to an in-depth understanding of the evolution characteristics of passenger flow in different years.
<a href="https://plu.mx/plum/a/-d7KiEYqRbyvIzyfgeUfkfQfAg5h3FsQZo4NcGJ3gOo" data-popup="right" data-size="large" class="plumx-plum-print-popup plum-bigben-theme" data-site="plum" data-hide-when-empty="true">General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators</a>
</body>},
keywords = {},
pubstate = {published},
tppubtype = {bachelorthesis}
}
<script type="text/javascript" src="//cdn.plu.mx/widget-popup.js"></script>
<body >Effective management of the high-speed railways (HSR) system requires an in-depth understanding of the HSR stations in the network, e.g., the time-dependent volume distribution. The classification of HSR stations is the scientific basis for transport policymaking and land-use planning. Existing classification methods cannot meet the needs of temporal variation of passenger flow or the refined design and operation of HSR stations. This study adopts the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to classify HSR stations in different years. Using the data of Jiangsu Province, China, as an example, the time series of arrival and departure passenger flow at HSR stations are clustered via the DBSCAN algorithm, and the HSR stations are clustered into three classes. To determine the hierarchical structure of HSR stations representing the evolution of HSR networks, we use large-scale panel data obtained from mobile phone cellular data across years (July 1–14 from each of the years 2018, 2020, and 2021) to capture and analyze the spatial-temporal evolution characteristics of massive passenger flow at HSR stations. It is indicated that both HSR station hierarchy and passenger flow have the characteristics of spatial-temporal evolution across years, and the classification results are influenced by the geographical positions of cities and HSR layout. Accurate clustering of HSR stations via large-scale actual passenger flow data enables railway authorities and operators to identify critical nodes for efficient HSR network performance. The resulting classification would contribute to an in-depth understanding of the evolution characteristics of passenger flow in different years.
<a href="https://plu.mx/plum/a/-d7KiEYqRbyvIzyfgeUfkfQfAg5h3FsQZo4NcGJ3gOo" data-popup="right" data-size="large" class="plumx-plum-print-popup plum-bigben-theme" data-site="plum" data-hide-when-empty="true">General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators</a>
</body>
<body >Effective management of the high-speed railways (HSR) system requires an in-depth understanding of the HSR stations in the network, e.g., the time-dependent volume distribution. The classification of HSR stations is the scientific basis for transport policymaking and land-use planning. Existing classification methods cannot meet the needs of temporal variation of passenger flow or the refined design and operation of HSR stations. This study adopts the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to classify HSR stations in different years. Using the data of Jiangsu Province, China, as an example, the time series of arrival and departure passenger flow at HSR stations are clustered via the DBSCAN algorithm, and the HSR stations are clustered into three classes. To determine the hierarchical structure of HSR stations representing the evolution of HSR networks, we use large-scale panel data obtained from mobile phone cellular data across years (July 1–14 from each of the years 2018, 2020, and 2021) to capture and analyze the spatial-temporal evolution characteristics of massive passenger flow at HSR stations. It is indicated that both HSR station hierarchy and passenger flow have the characteristics of spatial-temporal evolution across years, and the classification results are influenced by the geographical positions of cities and HSR layout. Accurate clustering of HSR stations via large-scale actual passenger flow data enables railway authorities and operators to identify critical nodes for efficient HSR network performance. The resulting classification would contribute to an in-depth understanding of the evolution characteristics of passenger flow in different years.
<a href="https://plu.mx/plum/a/-d7KiEYqRbyvIzyfgeUfkfQfAg5h3FsQZo4NcGJ3gOo" data-popup="right" data-size="large" class="plumx-plum-print-popup plum-bigben-theme" data-site="plum" data-hide-when-empty="true">General discussion of data quality challenges in social media metrics: Extensive comparison of four major altmetric data aggregators</a>
</body>
Liu, Yanyan; Li, Keping; Feng, Tao; Yan, Dongyang; Ma, Guangyi; Xu, Yuangxi
Novel Approach for Quantifying the Propagation of Subway Equipment Faults by Using Multimodal Networks Journal Article
In: Transportation Research Record, vol. 0, no. 0, pp. 03611981241249737, 0000.
Abstract | Links | BibTeX | PlumX
@article{doi:10.1177/03611981241249737,
title = {Novel Approach for Quantifying the Propagation of Subway Equipment Faults by Using Multimodal Networks},
author = {Yanyan Liu and Keping Li and Tao Feng and Dongyang Yan and Guangyi Ma and Yuangxi Xu},
url = {https://doi.org/10.1177/03611981241249737},
doi = {10.1177/03611981241249737},
journal = {Transportation Research Record},
volume = {0},
number = {0},
pages = {03611981241249737},
abstract = {In subway systems, equipment failures can lead to train stoppages, delays, or even accidents, severely affecting the safety of train operations. It is crucial for emergency handling to identify the vulnerable areas and explore propagation laws in the subway network. In this paper, based on multimodal networks, several indicators are systematically proposed to demonstrate the vulnerability of each subway station. These indicators are then applied in a gravitation model to quantify and study the propagation or spatiotemporal distribution of cascading effects caused by equipment failures. To verify the effectiveness of the proposed model, a case study is conducted using a data set of equipment faults in the Beijing subway. Quantification results show that the propagation of equipment faults between subway stations exhibits diagonal characteristics, with stations closer to the faulty equipment being more heavily affected. The results also indicate that gravitation values display a long-tail distribution and their highest proportion falls within a certain interval. Furthermore, this paper proposes several prevention measures in response to equipment fault propagation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In subway systems, equipment failures can lead to train stoppages, delays, or even accidents, severely affecting the safety of train operations. It is crucial for emergency handling to identify the vulnerable areas and explore propagation laws in the subway network. In this paper, based on multimodal networks, several indicators are systematically proposed to demonstrate the vulnerability of each subway station. These indicators are then applied in a gravitation model to quantify and study the propagation or spatiotemporal distribution of cascading effects caused by equipment failures. To verify the effectiveness of the proposed model, a case study is conducted using a data set of equipment faults in the Beijing subway. Quantification results show that the propagation of equipment faults between subway stations exhibits diagonal characteristics, with stations closer to the faulty equipment being more heavily affected. The results also indicate that gravitation values display a long-tail distribution and their highest proportion falls within a certain interval. Furthermore, this paper proposes several prevention measures in response to equipment fault propagation.