2023
Yue, Yifan; Chen, Jun; Feng, Tao; Wang, Wei; Wang, Chunyang; Ma, Xinwei
In: Journal of Transportation Engineering, Part A: Systems, vol. 149, iss. 11, 2023.
Abstract | Links | BibTeX | Tags: Big data, classification algorithm, High-Speed Rail Station
@article{nokey,
title = {New Classification Scheme and Evolution Characteristics Analysis of High-Speed Railway Stations Using Large-Scale Mobile Phone Data: A Case Study in Jiangsu, China},
author = {Yue, Yifan and Chen, Jun and Feng, Tao and Wang, Wei and Wang, Chunyang and Ma, Xinwei},
url = {https://ascelibrary.org/doi/abs/10.1061/JTEPBS.TEENG-7855},
doi = {https://doi.org/10.1061/JTEPBS.TEENG-7855},
year = {2023},
date = {2023-09-06},
urldate = {2023-09-06},
journal = {Journal of Transportation Engineering, Part A: Systems},
volume = {149},
issue = {11},
abstract = {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.},
keywords = {Big data, classification algorithm, High-Speed Rail Station},
pubstate = {published},
tppubtype = {article}
}
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.
2022
Yu, Liang; Feng, Tao; Li, Tie; Cheng, Lei
Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations Journal Article
In: Urban Transit Rail, 2022.
Abstract | Links | BibTeX | Tags: Big data, Bike sharing, Machine learning, Transit
@article{nokey,
title = {Demand Prediction and Optimal Allocation of Shared Bikes Around Urban Rail Transit Stations},
author = {Liang Yu and Tao Feng and Tie Li and Lei Cheng},
url = {https://link.springer.com/content/pdf/10.1007/s40864-022-00183-w.pdf?pdf=button},
doi = {https://doi.org/10.1007/s40864-022-00183-w},
year = {2022},
date = {2022-12-13},
journal = {Urban Transit Rail},
abstract = {The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.},
keywords = {Big data, Bike sharing, Machine learning, Transit},
pubstate = {published},
tppubtype = {article}
}
The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system.
2021
Chen, Chao; Feng, Tao; Ding, Chuan; Yu, Bin; Yao, Baozhen
Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model Journal Article
In: JOURNAL OF TRANSPORT GEOGRAPHY, vol. 96, 2021, ISSN: 0966-6923.
Abstract | Links | BibTeX | Tags: Big data, Geography
@article{WOS:000703846700010,
title = {Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model},
author = {Chao Chen and Tao Feng and Chuan Ding and Bin Yu and Baozhen Yao},
url = {https://www.sciencedirect.com/science/article/pii/S0966692321002258},
doi = {10.1016/j.jtrangeo.2021.103172},
issn = {0966-6923},
year = {2021},
date = {2021-10-01},
urldate = {2021-10-01},
journal = {JOURNAL OF TRANSPORT GEOGRAPHY},
volume = {96},
abstract = {With the advance of intelligent transportation systems (ITSs) and data
acquisition systems (DASs), it becomes possible in recent to explore the
determinants of urban taxi ridership using multi-source heterogeneous
data. This paper aims to use floating car data, points-of-interests
(POIs) data and housing-price data to assess the influence of the built
environment on taxi ridership. Within a scale of 0.5 km grid, critical
indicators related to the economic aspect, intermodal connection, and
land use factors were obtained using the multi-source data in Shanghai.
To capture the spatial and temporal heterogeneity, Semi-parametric
Geographically Weighted Poisson Regression (SGWPR) models are built over
different time dimensions. It is found that SGWPR models result in
higher goodness-of-fit than the generalized linear models. More
importantly, the results show the impacts of built environment factors
on taxi demand are highly heterogeneous, positive or negative in
different city areas, reflected in the significant temporal variations
of the effects. Overall, these findings suggest that the built
environment factors have significant impacts on urban taxi demand, and
the spatial context should not be ignored. Findings in this paper are
expected to help better understand the relationship between urban taxi
demand and built environment factors, improving the service level of the
urban taxi system, and offering valuable insights into future urban and
transportation planning.},
keywords = {Big data, Geography},
pubstate = {published},
tppubtype = {article}
}
With the advance of intelligent transportation systems (ITSs) and data
acquisition systems (DASs), it becomes possible in recent to explore the
determinants of urban taxi ridership using multi-source heterogeneous
data. This paper aims to use floating car data, points-of-interests
(POIs) data and housing-price data to assess the influence of the built
environment on taxi ridership. Within a scale of 0.5 km grid, critical
indicators related to the economic aspect, intermodal connection, and
land use factors were obtained using the multi-source data in Shanghai.
To capture the spatial and temporal heterogeneity, Semi-parametric
Geographically Weighted Poisson Regression (SGWPR) models are built over
different time dimensions. It is found that SGWPR models result in
higher goodness-of-fit than the generalized linear models. More
importantly, the results show the impacts of built environment factors
on taxi demand are highly heterogeneous, positive or negative in
different city areas, reflected in the significant temporal variations
of the effects. Overall, these findings suggest that the built
environment factors have significant impacts on urban taxi demand, and
the spatial context should not be ignored. Findings in this paper are
expected to help better understand the relationship between urban taxi
demand and built environment factors, improving the service level of the
urban taxi system, and offering valuable insights into future urban and
transportation planning.
acquisition systems (DASs), it becomes possible in recent to explore the
determinants of urban taxi ridership using multi-source heterogeneous
data. This paper aims to use floating car data, points-of-interests
(POIs) data and housing-price data to assess the influence of the built
environment on taxi ridership. Within a scale of 0.5 km grid, critical
indicators related to the economic aspect, intermodal connection, and
land use factors were obtained using the multi-source data in Shanghai.
To capture the spatial and temporal heterogeneity, Semi-parametric
Geographically Weighted Poisson Regression (SGWPR) models are built over
different time dimensions. It is found that SGWPR models result in
higher goodness-of-fit than the generalized linear models. More
importantly, the results show the impacts of built environment factors
on taxi demand are highly heterogeneous, positive or negative in
different city areas, reflected in the significant temporal variations
of the effects. Overall, these findings suggest that the built
environment factors have significant impacts on urban taxi demand, and
the spatial context should not be ignored. Findings in this paper are
expected to help better understand the relationship between urban taxi
demand and built environment factors, improving the service level of the
urban taxi system, and offering valuable insights into future urban and
transportation planning.
2020
Dane, G.; Feng, T.; Luub, F.; Arentze, T.
Route choice decisions of E-bike users: Analysis of GPS tracking data in the Netherlands Book
2020, ISSN: 18632351.
Abstract | Links | BibTeX | Tags: Big data, E-bike, GPS, Route choice
@book{Dane2020b,
title = {Route choice decisions of E-bike users: Analysis of GPS tracking data in the Netherlands},
author = {G. Dane and T. Feng and F. Luub and T. Arentze},
doi = {10.1007/978-3-030-14745-7_7},
issn = {18632351},
year = {2020},
date = {2020-01-01},
journal = {Lecture Notes in Geoinformation and Cartography},
abstract = {Over the past years, the usage of electric bikes has emerged. E-bikes are suitable for short and medium distance trips. Therefore, the Dutch government promotes using e-bikes for daily commuting trips. However, the impact of increasing demand on the cycling infrastructure is unclear. Additionally, route choice models for e-bikes are limited. This paper estimates a route choice model for e-bike users in the Noord-Brabant region of The Netherlands. The data used are based on 17626 trips from 742 users including user profiles extracted from GPS data. In order to analyze the data, a mixed logit model is applied on the route choice of respondents with addition of the path-size attribute. Mixed logit model allows a panel data setup and enables the examination of preference heterogeneity around the mean of distance attribute. Moreover, the path-size attribute is included on the model to account for the overlap between alternatives. Socio-demographic characteristics and trip-related factors are found to be influencing on the route choice decisions of e-bike and bike users. There are differences on the significance of variables between e-bike and bike users.},
keywords = {Big data, E-bike, GPS, Route choice},
pubstate = {published},
tppubtype = {book}
}
Over the past years, the usage of electric bikes has emerged. E-bikes are suitable for short and medium distance trips. Therefore, the Dutch government promotes using e-bikes for daily commuting trips. However, the impact of increasing demand on the cycling infrastructure is unclear. Additionally, route choice models for e-bikes are limited. This paper estimates a route choice model for e-bike users in the Noord-Brabant region of The Netherlands. The data used are based on 17626 trips from 742 users including user profiles extracted from GPS data. In order to analyze the data, a mixed logit model is applied on the route choice of respondents with addition of the path-size attribute. Mixed logit model allows a panel data setup and enables the examination of preference heterogeneity around the mean of distance attribute. Moreover, the path-size attribute is included on the model to account for the overlap between alternatives. Socio-demographic characteristics and trip-related factors are found to be influencing on the route choice decisions of e-bike and bike users. There are differences on the significance of variables between e-bike and bike users.