Research Output

@article{LI2025104160,

title = {What hinders car owners’ participation in private car sharing? Insights from a business perspective},

author = {Mengxia Li and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0969698924004569},

doi = {https://doi.org/10.1016/j.jretconser.2024.104160},

issn = {0969-6989},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Journal of Retailing and Consumer Services},

volume = {83},

pages = {104160},

abstract = {Private car sharing emerges as a viable solution due to flexibility, cost-effectiveness for users, and profit benefit for car owners. However, the number of shared private cars remains relatively low, and empirical evidence regarding car owners' sharing intention is scarce. This paper aims to identify the factors influencing car owners' participation decision in private car sharing from the perspective of business operators who provide various services, such as cleanliness, maintenance, insurance and inspection. In addition, we examine how latent factors like privacy protection, trust, social value, hygiene, orderliness, and utilitarian value impact car owners' willingness to participate in different business models. A mixed logit hybrid choice model, incorporating latent factors and random parameters to capture preference heterogeneity, was developed using data from a stated choice experiment conducted online in Hiroshima. The results indicate that car owners generally dislike the additional insurance and maintenance costs associated with business models, but free car inspections and cleaning services significantly increase their participation intention. Trust and social value positively influence adoption, while concerns about hygiene and orderliness negatively affect decisions. These insights can help private car-sharing enterprises enhance market penetration by addressing key behavioral factors.},

keywords = {Business model, Hybrid choice model, Private car sharing},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2025104338,

title = {Commuter choice of UAM-friendly neighborhoods},

author = {Ying Zhao and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0965856424003860},

doi = {https://doi.org/10.1016/j.tra.2024.104338},

issn = {0965-8564},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {192},

pages = {104338},

abstract = {Urban Air Mobility (UAM) which provides swift intra- and intercity transportation services has the potential to induce shifts in individuals’ commuting and residential decisions. It is anticipated that, in residential areas, UAM services would enhance accessibility for residents. An UAM-friendly neighborhood represents a novel, integrated neighborhood concept that provides the infrastructure and travel environment required to facilitate UAM services, thereby promoting sustainable neighborhood development and improving accessibility. To gain a deeper understanding of commuters’ choice behavior in UAM-friendly neighborhoods, we designed a stated choice experiment. Using data collected in Beijing city, we estimated a mixed logit model with interaction effects to identify the choice preferences of different people while capturing the unobserved preference heterogeneity. We found that individuals generally prefer to reside in such neighborhoods where the access distance to UAM vertiports is within one kilometer, the parking fee is either low (5 yuan/day) or free, the commuting time by UAT is 15 min, and drone window-docking delivery services are available. Households with high incomes (>400,000 yuan/year) and those owning a car are likely to adopt these novel neighborhoods. There is a varying degree of heterogeneity observed regarding residential location and distance to UAM vertiports among individuals in different age groups. Results of the elasticity analysis indicate that UAT commuting cost has the greatest impact on the likelihood of residing in UAM-friendly neighborhoods.},

keywords = {Interaction effect, Mixed logit model, Stated choice experiment, UAM-friendly Neighborhood, Urban air mobility, Urban Air Taxi},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1080/23249935.2024.2417896,

title = {A prospect theoretical choice model incorporating profitable and punctual uncertainties: an investigation in the participation of private car sharing},

author = {Mengxia Li and Tao Feng and Xin Dou and Yan Hu},

url = {https://doi.org/10.1080/23249935.2024.2417896},

doi = {10.1080/23249935.2024.2417896},

year  = {2024},

date = {2024-10-23},

urldate = {2024-10-23},

journal = {Transportmetrica A: Transport Science},

volume = {0},

number = {0},

pages = {1–26},

publisher = {Taylor & Francis},

keywords = {Car sharing, Choice behavior, Prospect theory},

pubstate = {published},

tppubtype = {article}

}

@article{LIU2024128175,

title = {ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction},

author = {Yutian Liu and Tao Feng and Soora Rasouli and Melvin Wong},

url = {https://www.sciencedirect.com/science/article/pii/S0925231224009469},

doi = {https://doi.org/10.1016/j.neucom.2024.128175},

issn = {0925-2312},

year  = {2024},

date = {2024-10-07},

urldate = {2024-01-01},

journal = {Neurocomputing},

volume = {601},

pages = {128175},

abstract = {Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicating the situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationships between nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlations of nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.},

keywords = {Adaptive mechanism, Spatiotemporal data, Traffic prediction},

pubstate = {published},

tppubtype = {article}

}

@article{doi:10.1177/23998083241249322,

title = {A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning},

author = {Bailing Zhang and Jing Kang and Tao Feng},

url = {https://doi.org/10.1177/23998083241249322},

doi = {10.1177/23998083241249322},

year  = {2024},

date = {2024-08-03},

journal = {Environment and Planning B: Urban Analytics and City Science},

volume = {0},

number = {0},

pages = {23998083241249322},

abstract = {The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HAN2024142192,

title = {Study on window-opening behavior of suburban sustainable life-pattern in residential buildings at Donglu town of Hezhou, China},

author = {Jie Han and Nan Mo and Jingyi Cai and Xinyue Li and Fuhao Xie and You Peng and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0959652624016408},

doi = {https://doi.org/10.1016/j.jclepro.2024.142192},

issn = {0959-6526},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Cleaner Production},

volume = {452},

pages = {142192},

abstract = {Window opening behavior is a crucial determinant of indoor thermal comfort and air quality. It plays an important role in accurate building performance simulations when it comes to regional contexts of climate. Few studies on window opening behavior have been carried out in suburban regions where window opening, as an adaptative adjustment, is more prevalent than in urban areas. To bridge this gap, this study based on questionnaire surveys and on-site measurements investigates the window opening behavior of suburban residents in bedrooms during the transitional season in Donglu town, China. The results indicate the duration of window opening exhibits a strong correlation with suburban residents' habits and occupations. The suburban residents tend to close their bedrooms' windows when leaving home, while they prefer opening them before going to sleep. A logistic regression model was developed for suburban household to predict the probability of window opening behavior. It has been evidenced that outdoor and indoor humidity, outdoor temperature, time periods and solar radiation intensity are strongly influencing on suburban residents' window opening and closing behaviors. In addition, due to the influence of continuous humid weather in transitional seasons, outdoor humidity is the principal factor that prompts residents to close their windows. Our findings contribute to further achieving novel knowledge on occupants’ interaction with suburban residential buildings.},

keywords = {Logistic regression, Suburban residential buildings, Transition season, Window opening behavior},

pubstate = {published},

tppubtype = {article}

}

@article{LIU2024103997,

title = {Electrification pathways for public transport systems},

author = {Kai Liu and Tao Feng and Toshiyuki Yamamoto and Ziqi Song},

url = {https://www.sciencedirect.com/science/article/pii/S1361920923003942},

doi = {https://doi.org/10.1016/j.trd.2023.103997},

issn = {1361-9209},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Transportation Research Part D: Transport and Environment},

volume = {126},

pages = {103997},

keywords = {Editorial note, Electric mobility},

pubstate = {published},

tppubtype = {article}

}

@article{LIU2024102078,

title = {RT-GCN: Gaussian-based spatiotemporal graph convolutional network for robust traffic prediction},

author = {Yutian Liu and Soora Rasouli and Melvin Wong and Tao Feng and Tianjin Huang},

url = {https://www.sciencedirect.com/science/article/pii/S1566253523003949},

doi = {https://doi.org/10.1016/j.inffus.2023.102078},

issn = {1566-2535},

year  = {2024},

date = {2024-01-01},

journal = {Information Fusion},

volume = {102},

pages = {102078},

abstract = {Traffic forecasting plays a critical role in intelligent transportation systems (ITS) in smart cities. Travelers as well as urban managers rely on reliable traffic information to make their decisions for route choice and traffic management. However, noisy or missing traffic data poses a problem for accurate and robust traffic forecasting. While data-driven models such as deep neural networks can achieve high prediction accuracy with complete datasets, sensor malfunctions, and environmental effects degrade the performance of such models, as these models rely heavily on precise traffic measurements for model training and estimation. Consequently, incomplete traffic data poses a challenge for robust model design that can make accurate traffic forecasts with noisy/missing data. This research proposes the Robust Spatiotemporal Graph Convolutional Network (RT-GCN), a traffic prediction model that handles noise perturbations and missing data using a Gaussian distributed node representation and a variance based attention mechanism. Through experiments conducted on four real-world traffic datasets using diverse noisy and missing scenarios, the proposed RT-GCN model has demonstrated its ability to handle noise perturbations and missing values and provide high accuracy prediction.},

keywords = {Gaussian distribution, Graph convolutional network, Missing data, Robustness, Traffic prediction},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2024129667,

title = {Modelling the dual dynamic traffic flow evolution with information perception differences between human-driven vehicles and connected autonomous vehicles},

author = {Guanfeng Wang and Hongfei Jia and Tao Feng and Jingjing Tian and Ruiyi Wu and Heyao Gao and Chao Liu},

url = {https://www.sciencedirect.com/science/article/pii/S0378437124001766},

doi = {https://doi.org/10.1016/j.physa.2024.129667},

issn = {0378-4371},

year  = {2024},

date = {2024-01-01},

journal = {Physica A: Statistical Mechanics and its Applications},

volume = {640},

pages = {129667},

abstract = {The introduction of connected autonomous vehicles (CAVs) potentially improves the link capacity and backward wave speed of traffic flow, while the advanced communication technology could well make it possible to allow CAV users to share their travel information. To bridge the knowledge gaps in the network evolution under mixed environment of human-driven vehicles (HVs) and CAVs, it is essential to explore multi-dimensional dynamic traffic assignment. An inertia-based multi-class dual dynamic traffic assignment (IMDDTA) model is proposed to capture the intraday and diurnal variations of the mixed traffic flow under the disequilibrium state simultaneously. Specifically, in this study we consider the inertia of HV users as well the information-sharing behaviour of CAV users respectively, characterized by different extensions of the multinomial logit (MNL) model. To demonstrate the properties of the model, two numerical case studies are conducted based on the Braess network and the Sioux Falls network. The results indicate an acceptable validity and applicability of the model and provide valuable insights on the evolution of traffic flow under mixed environment.},

keywords = {Connected autonomous vehicles, Dual dynamic traffic assignment, Inertial route choice behaviour, Information-sharing, Mixed traffic flow},

pubstate = {published},

tppubtype = {article}

}

@article{GU2024129666,

title = {A novel regional traffic control strategy for mixed traffic system with the construction of congestion warning communities},

author = {Xiaoning Gu and Chao Chen and Tao Feng and Baozhen Yao},

url = {https://www.sciencedirect.com/science/article/pii/S0378437124001754},

doi = {https://doi.org/10.1016/j.physa.2024.129666},

issn = {0378-4371},

year  = {2024},

date = {2024-01-01},

journal = {Physica A: Statistical Mechanics and its Applications},

volume = {639},

pages = {129666},

abstract = {Large-scale congestion can lead to traffic paralysis, which severely hampers the flow of vehicles and disrupts the normal functioning of urban traffic. Traffic optimization strategies can effectively improve the performance of road networks, but often ignore the impact of regional traffic conditions and equity. This paper presents a novel traffic strategy to solve regional traffic congestion in large cities, particularly focusing on mixed traffic scenarios of connected and non-connected vehicles. The proposed method involves monitoring the traffic condition of the congestion warning community and adjusting the internal access flow within each region. The problem is formulated as a Stackelberg game, with traffic policymakers and road users as the key players. The upper layer aims to control traffic access by issuing a community warning index, with the objective of minimizing congestion warning conditions within the community. This information is then disseminated to connected vehicles which utilize it to generate personalized route guidance, while non-connected vehicles remain unaffected. The lower-level objective is to allocate vehicles in the transportation network in a user-optimal manner. To solve the bi-level programming model, the paper introduces a variable neighborhood search approach based on graph theory. The Frank-Wolfe algorithm is used to solve the lower-level model, with a penalty function introduced to transform the constrained traffic assignment problem (TAP) into an unconstrained TAP. The proposed method is applied using the data of Beijing urban road network and a sensitivity analysis is conducted to examine the impacts of critical parameters, such as regional partitioning and mixed traffic proportion. The results show that the method exhibits improved optimization performance across different parameter settings, effectively utilizing idle links and contributing to a reduction in the occurrence of traffic warning regions.},

keywords = {Bi-level programming model, Congestion warning, Stackelberg game, Traffic access restriction, Urban traffic control},

pubstate = {published},

tppubtype = {article}

}

@article{land13040519,

title = {Effects of Urban Parks on Housing Prices in the Post-COVID-19 Pandemic Era in China},

author = {Xinxing Yang and Qiang Ye and You Peng and Shaobo Liu and Tao Feng},

url = {https://www.mdpi.com/2073-445X/13/4/519},

doi = {10.3390/land13040519},

issn = {2073-445X},

year  = {2024},

date = {2024-01-01},

journal = {Land},

volume = {13},

number = {4},

abstract = {Urban parks are important for improving the quality of living environments. Although the impact of parks on housing prices has been well documented, the effects of the COVID-19 pandemic remain vague. This paper analyzes the housing prices of neighborhoods around Meixi Lake park in Changsha, at the initial stage of the COVID-19 pandemic in June 2020 and the stable stage of the recovery period in June 2023, which demonstrates the impacts of urban parks on property pricing and housing choice based on residential transaction data. The results indicate that urban parks are given a high priority in determining people’s choices. In June 2020, the risk of epidemic transmission and noise interference lowered the price of property that is close to public parks and recreational facilities. However, good management and social services in residential areas increase housing prices. However, when the pandemic ended, the strong demand for outdoor activities led to a price rise in the properties near Meixi Lake park. People are most likely to choose houses in a neighborhood characterized by good educational facilities and a high-quality living environment. More specially, the houses with a short distance to parks and where residents can enjoy the view of a lake or mountains are preferable to any others. A residential area adjacent to a super large park paired with a small park is the most valuable consideration for property developers and housing consumers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WU2024103876,

title = {Spatial allocation of heavy commercial vehicles parking areas through geo-fencing},

author = {Jishi Wu and Tao Feng and Peng Jia and Gen Li},

url = {https://www.sciencedirect.com/science/article/pii/S0966692324000851},

doi = {https://doi.org/10.1016/j.jtrangeo.2024.103876},

issn = {0966-6923},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Transport Geography},

volume = {117},

pages = {103876},

abstract = {Inadequate parking planning for heavy commercial vehicles (HCV) exacerbates urban road congestion. As an effective means of parking management, geofencing that identifies the virtual boundary for geographic areas is essential to ensure these vehicles do not impede traffic and urban spaces. However, geofenced areas must be rationally designed to prevent mismatches between parking areas and real parking needs. This paper presents a data-driven approach that integrates the Spatial-temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) methods and a Gaussian mixture model for identifying and predicting potential parking areas for HCVs. Leveraging the HCV trajectory data and land use data in Shanghai, China, we characterize the spatial distribution of parking demand and create a probabilistic model to predict active HCV traffic patterns and the spatial confidence regions under varying land use conditions. The results show that clusters of HCV parking demand tend to congregate near ports, comprehensive transportation hubs, logistics centers, and commercial hubs. These clusters correspond to five distinct parking demand patterns (i.e., day-long HCV stops, morning peak time HCV stops, daytime HCV stops, afternoon peak time HCV stops, and nighttime HCV stops), each reflecting specific spatiotemporal characteristics. The geofenced spatial domain was found to be very sensitive to the timing of parking, emphasizing the importance of using advanced geofencing technologies. The methodological framework introduced in this study holds significant value for policymakers and HCV operators as it aids in determining parking at strategic levels, offering valuable insights and tools to enhance the effectiveness of parking management.},

keywords = {Commercial vehicles parking management, Gaussian mixture model, Geo-fenced parking area, ST-DBSCAN clustering},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2024104224,

title = {Heterogeneous choice of personalized Mobility-as-a-Service bundles and its impact on sustainable transportation},

author = {Boqing Wang and Min Yang and Tao Feng and Yuyuan Yang and Yalong Yuan},

url = {https://www.sciencedirect.com/science/article/pii/S1361920924001810},

doi = {https://doi.org/10.1016/j.trd.2024.104224},

issn = {1361-9209},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part D: Transport and Environment},

volume = {131},

pages = {104224},

abstract = {The Mobility-as-a-service (MaaS) framework has the potential to induce changes in individual travel behavior towards more sustainable transportation alternatives by integrating multimodal travel services. However, insights into the behavioral responses of specific groups of people to MaaS, thus promoting green travel, are currently lacking. This paper intends to investigate the intergroup heterogeneous preferences on the choice of MaaS bundles and evaluate their contributions to eco-friendly travel. A stated choice experiment is designed, incorporating personalized MaaS service bundles complemented by different green travel-oriented ticketing strategies. The experiment caters to different groups of users related to public transport, cars, and other modes of transport. The results indicate a substantial improvement in the goodness-of-fit of the behavioral model after user classification, revealing significant heterogeneity in preferences among users regarding MaaS bundle choices. Notably, the impact of sub-tickets on nudging toward green transportation is more pronounced than that of monthly and discount tickets.},

keywords = {Choice behavior, Heterogeneity, Mobility as a service (MaaS), Sustainable transportation, Travel bundle},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2024100843,

title = {Social demographics imputation based on similarity in multi-dimensional activity-travel pattern: A two-step approach},

author = {Bin Zhang and Soora Rasouli and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2214367X24001066},

doi = {https://doi.org/10.1016/j.tbs.2024.100843},

issn = {2214-367X},

year  = {2024},

date = {2024-01-01},

journal = {Travel Behaviour and Society},

volume = {37},

pages = {100843},

abstract = {In response to the absence of demographics in increasingly emerging big data sets, we propose a novel method for inferring the missing demographic information based on similarity in people’s daily multi-dimensional activity-travel patterns as well as the characteristics of the area they move about. Instead of using isolated activity-travel attributes to infer social demographic features, our proposed method first calculates the similarity of people’s multidimensional daily activities and travels as well as characteristics of their visiting locations, between those for whom the social demographics are to be imputed (target) and those with known demographics (base) using a polynomial function. The weights of the function are determined using the permutation feature importance method, and then dynamic time warping is used to align the multidimensional activity sequences of the base and target sample and measure their similarities. For each person in the target database, a matched list is created consisting of those with the most similar activity-travel sequences in the base sample. A support vector machine is then trained using the base sample as input to impute the demographics of the target sample. The proposed model is trained using a national travel survey and validated by applying it to a GPS dataset. The results show that the proposed method outperforms existing methods in predicting four selected demographics: gender, age, education level, and work status, with an accuracy range between 91% and 94% for the national dataset and 88% to 91% for the GPS data. This study highlights the importance of considering the multidimensional and sequential nature of peoples’ daily activity-travel patterns in the imputation of demographic features.},

keywords = {Daily activity travel, Demographics imputation, Dynamic time warping (DTW), Feature importance, Support vector machine (SVM)},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2024142988,

title = {Strategic integration of vertiport planning in multimodal transportation for urban air mobility: A case study in Beijing, China},

author = {Ying Zhao and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0959652624024375},

doi = {https://doi.org/10.1016/j.jclepro.2024.142988},

issn = {0959-6526},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Cleaner Production},

volume = {467},

pages = {142988},

abstract = {In pursuit of strategic sustainable development goals, this study seeks to analyze the spatial placement of vertiports for urban air mobility, positioning them as integral components within the multimodal transportation system. We propose an iterative weighted k-means clustering method that incorporates the value of travel time of different transportation modes to identify potential vertiport locations. A multi-objective optimization model that maximizes total air taxi ridership and minimizes total facility costs and travel distances to vertiports is developed considering the constraints of the distance from vertiports to existing mobility hubs. A case study is conducted using the data of Beijing city, incorporating the modes of taxis and metro. We found that 19 vertiports can guarantee a robust performance of urban air mobility service. Users of urban air mobility can benefit from substantial travel time savings of 80% (48 min on average). This study contributes to a deeper understanding of the optimal setting of urban air mobility facilities from the perspective of network synchronization, offering useful insights into policy decision-making for sustainable infrastructure planning.},

keywords = {Iterative weighted -means clustering, Multi-objective optimization, Multimodal transportation system, Urban air mobility, Value of travel time, Vertiport},

pubstate = {published},

tppubtype = {article}

}

@article{LI2024103337,

title = {Investigating urban mobility through multi-source public transportation data: A multiplex network perspective},

author = {Zhitao Li and Jinjun Tang and Tao Feng and Biao Liu and Junqiang Cao and Tianjian Yu and Yifeng Ji},

url = {https://www.sciencedirect.com/science/article/pii/S0143622824001425},

doi = {https://doi.org/10.1016/j.apgeog.2024.103337},

issn = {0143-6228},

year  = {2024},

date = {2024-01-01},

journal = {Applied Geography},

volume = {169},

pages = {103337},

abstract = {The integration of multi-source and diverse spatio-temporal travel data provides a comprehensive insight into urban mobility. Using data from Shenzhen's public transportation system, this study presents an analytical framework based on multiplex networks to examine variations in multi-mode public transportation usage (metro, bus, taxi, and shared bike) and their correlation with the built environment. This framework encompasses the analysis of network topological characteristics, centrality, and communities. The examination of network topological characteristics reveals that the multiplex transportation network exhibits high global accessibility and local connectivity. Network centrality analysis, focusing on weighted outdegree centrality, captures the patterns of public transportation ridership. Centrality modeling, employing the light gradient boosting machine, demonstrates a nonlinear relationship between ridership and the built environment. Factors including population density, residential land use percentage, entertainment service density, restaurant density, and metro station density consistently exhibit positive correlations with ridership across different times of the day. The community structure analysis, using consensus community detection, indicates that distinct urban areas exhibit clustering behavior based on public transportation demand patterns, forming distinct communities that closely align with the functional zoning of urban planning. These findings could provide valuable insights for the strategic planning of transportation services and the built environment.},

keywords = {Built environment, Community detection, Multiplex networks, Public transportation, Urban mobility},

pubstate = {published},

tppubtype = {article}

}

@article{TIAN2024104165,

title = {What to do with commuting time when driving autonomous vehicles? Results of a stated intention experiment},

author = {Zhihui Tian and Tao Feng and Harry J. P. Timmermans and Baozhen Yao},

url = {https://www.sciencedirect.com/science/article/pii/S0965856424002131},

doi = {https://doi.org/10.1016/j.tra.2024.104165},

issn = {0965-8564},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {187},

pages = {104165},

abstract = {Rapid improvements in autonomous driving technology and the availability of autonomous vehicles (AVs) are expected to change people’s habitual travel patterns. Fully autonomous vehicles (FAVs) do not need to be maneuvered by their users, implying users are allowed to participate in a number of non-driving in-vehicle activities (IVAs) when their FAV is bringing them to their destination. People can therefore use their travel time for working, relaxation, entertainment, communication and possibly other activities. Since FAVs provide a different environment than traditional travel modes, such as trains and busses, people’s preferences for conducting IVAs in FAV travel has become an emerging issue in transportation research. Understanding people’s preferences for conducting IVAs during FAV travel will generate important information for future vehicle interior design and the development of transportation policies. Hence, this paper presents the outcomes of a research study that aims at increasing our understanding of the intentions of individuals to conduct IVAs when travelling by FAV’s and the endogenous and exogenous factors and variables influencing these intentions. We designed an experiment and analyzed the response data using simultaneous equation modeling to examine the intentions to conduct IVAs during FAV travel and potential correlations that may exist across IVAs. The results show significant heterogeneity in IVA intentions and correlations between IVAs. Youngsters, high-education-level groups, and employed show a higher intention to engage in most IVAs. In addition, gender, household income, motion sickness, and license ownership affect people’s intentions. The estimated results suggest that the intentions to conduct IVAs depend on trip length. Moreover, the potential correlation between IVAs is confirmed. For example, respondents who have intentions to conduct to sleep show interest in eating or drinking and play games, but are not inclined to work with a computer. In contrast, respondents who intend to use social media during FAV travel are less likely to sleep when travelling by FAV.},

keywords = {Autonomous vehicles, In-vehicle activity, Simultaneous equation model},

pubstate = {published},

tppubtype = {article}

}

@article{LIU2024100874,

title = {Integrated travel path guidance for metro-bikeshare users considering system operational budget costs using smart card data},

author = {Yang Liu and Tao Feng and Zhuangbin Shi and Xinwei Ma and Mingwei He},

url = {https://www.sciencedirect.com/science/article/pii/S2214367X24001376},

doi = {https://doi.org/10.1016/j.tbs.2024.100874},

issn = {2214-367X},

year  = {2024},

date = {2024-01-01},

journal = {Travel Behaviour and Society},

volume = {37},

pages = {100874},

abstract = {Metro-bikeshare integration has emerged as a major sustainable mode of transportation for medium and long-distance travelers in various cities. To enhance the satisfaction of integrated metro-bikeshare users and improve the efficiency of urban multimodal transportation systems, this paper proposes integrated path guidance strategies for metro-bikeshare users, tailored to the diverse preferences of individuals. Using the actual smart card data collected from Nanjing, China, a path optimization model is developed to maximize integrated benefits within the metro-bikeshare multimodal network. These benefits include enhancing the overall travel utility of users, reducing the dispatching cost of shared bikes and realizing the load balance of passenger flow. The results show that an 8.89 % increase in total travel utility for all users though the optimization of travel path for 12.51 % of metro-bikeshare users, coupled with an average dispatching frequency of 1.18 times for each transfer node. Furthermore, tailored combined travel path optimization strategies are suggested for “first kilometer”, “last kilometer”, female, male, regular and non-regular users. These findings are helpful for governments and enterprises to formulate personalized path schemes and corresponding path guidance services for metro-bikeshare users.},

keywords = {Metro-bikeshare integration, Mobility as a service, Nanjing, Operational budget costs, Path optimization, Smart card data},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2024121851,

title = {A global comparative study on the impact of COVID-19 policy on atmospheric nitrogen dioxide (NO2): Evidence from remote sensing data in 2019–2022},

author = {Bailing Zhang and Junyi Zhang and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0301479724018371},

doi = {https://doi.org/10.1016/j.jenvman.2024.121851},

issn = {0301-4797},

year  = {2024},

date = {2024-01-01},

journal = {Journal of Environmental Management},

volume = {367},

pages = {121851},

abstract = {A significant body of research has documented the profound changes in global atmospheric conditions during the COVID-19 pandemic. However, there is still an inadequate comprehensive comparison and assessment of countries before, during, and after the pandemic. Variations in restriction policies, human behaviors, and national traits lead to significant differences in how restriction policies affect atmospheric pollution. This study focuses on NO2, a pollutant with high temporal sensitivity, and utilizes the Oxford COVID-19 policy stringency index along with demographic information. Through spatial-temporal mapping, we analyzed NO2 emission fluctuations and calculated the emission changes in each country. Drawing from this analysis, we explored the relationships among these factors and found that over the span of 2019–2022, across 193 countries, global NO2 emissions displayed a distinct trajectory: initially decreasing, subsequently rebounding, and eventually fluctuating. Most countries exhibited seasonal variations in NO2 emissions. Additionally, the study uncovered a correlation between the stringency of COVID-19 policies and the reduction in NO2 emissions: as policies became stricter, emissions significantly decreased in most countries. In contrast, in countries with lower population densities, stricter policies paradoxically led to an increase in emissions. These findings underscore the importance of considering demographic factors and geographical context in the formulation and implementation of environmental policies.},

keywords = {COVID-19 policy impact, Cross-national comparison, Global conflict, Global NO emissions, Global north-global south divide, Post-epidemic era},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2024100455,

title = {Global disparities in CO2 emissions from mobility sectors of diverse economies: A macroscopic exploration across 188 countries/regions},

author = {Bailing Zhang and Jing Kang and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2665972724001235},

doi = {https://doi.org/10.1016/j.indic.2024.100455},

issn = {2665-9727},

year  = {2024},

date = {2024-01-01},

journal = {Environmental and Sustainability Indicators},

volume = {23},

pages = {100455},

abstract = {Reducing CO2 emissions represents a global challenge, and the mobility sectors account for a considerable portion of total emissions, with marked disparities across diverse economies. Viewed from a macroscopic perspective, countries and regions around the world can be categorized in various ways. However, relying on a single or a few indicators often proves inadequate to meet the classification requirements for carbon reduction and sustainable development. In this study, employing machine learning and guided by 10 economic indicators, we classified 188 countries/regions into 6 identifiable clusters. Subsequently, by applying ratio analysis and random forest methodologies, we conducted a matrix-based analysis that elucidates the distinct emission characteristics of each mobility sector. Feature importance analysis revealed that for highly developed economies, the total population's contribution was significant, especially in domestic and international aviation, accounting for 50% and 25% of emissions, respectively. In contrast, for lower-middle-income countries and regions, while the total population still played a pivotal role, its influence was most pronounced in railway transportation, reaching 67%. For rapidly developing economies, domestic aviation emissions reached a peak influence of 61%. These insights emphasize the necessity for strategies tailored to the unique attributes of economic entities.},

keywords = {CO emissions, Global disparities, Macroscopic exploration, Mobility sectors, Qualitative study},

pubstate = {published},

tppubtype = {article}

}

@article{XU2024110467,

title = {Resilience analysis of metro stations integrating infrastructures and passengers},

author = {Peng-Cheng Xu and Qing-Chang Lu and Tao Feng and Jing Li and Gen Li and Xin Xu},

url = {https://www.sciencedirect.com/science/article/pii/S0951832024005398},

doi = {https://doi.org/10.1016/j.ress.2024.110467},

issn = {0951-8320},

year  = {2024},

date = {2024-01-01},

journal = {Reliability Engineering & System Safety},

volume = {252},

pages = {110467},

abstract = {Metro stations are important infrastructures ensuring people's daily commuting with tremendous travels every day, but susceptible to disruptions posing challenges in the resilience of the metro system. Previous studies mainly contributed to the resilience of the whole metro network, neglecting the resilience methodology and analysis of important stations. Integrating the infrastructure characteristics and the evolvement of passengers, a metro station resilience analysis methodology is developed. The approach is demonstrated with the stations of the metro system of Xi'an, China. Results show that station resilience varies differently across stations dominated by infrastructure types and queuing delay. Based on the comparison analysis under different infrastructure failures, failures of auto-ticket gates would have the highest impact on station resilience. When incidents on one infrastructure last for a period of time over 15 min, the resilience of station would be reduced by 9.8%, and dramatic resilience reductions could be observed if there are short-time incidents within 5 min on more than five infrastructures of a station. The findings would have practical significance for the resilience improvement of metro stations in infrastructure planning and passenger flow control against metro incidents.},

keywords = {Disruptions, Metro infrastructures, Passenger flow, Queuing model, Station resilience, Travel time},

pubstate = {published},

tppubtype = {article}

}

@article{JI2024112044,

title = {How do heat waves affect the relationship between built environment patches of different compactness and land surface temperature?},

author = {Yifeng Ji and You Peng and Hongyu Tang and Zhitao Li and Yiting Xia and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0360132324008862},

doi = {https://doi.org/10.1016/j.buildenv.2024.112044},

issn = {0360-1323},

year  = {2024},

date = {2024-01-01},

journal = {Building and Environment},

volume = {266},

pages = {112044},

abstract = {The compactness of the urban built environment significantly affects land surface temperature (LST), especially during heat waves (HW). However, the mechanisms by which the configuration of key building patches in built environments of varying compactness drives LST are unclear. This study proposes a new research framework combining local climate zones (LCZ), spatial pattern type (SPT) and landscape index (LI) to reveal the impacts of key building patches on LST. Taking Shenyang as an example, we utilized the geographically weighted regression (GWR) method to reveal the non-stationary relationship between building patches with different compactness and LST during heat and non-heat waves, and an optimal parameters-based geographical detectors model (OPGDM) to explore the mechanisms by which the configuration of key building patches drives LST. The results show that HW enhances the spatially non-stationary effects of different types of building patches on LST. The configuration of key building patches in the open built environment drives LST more strongly than those in the compact built environment. The relationship between LIs and LST in key building patches exhibits diverse characteristics during heat and non-heat waves, so differentiated configuration optimization strategies are required for built environments of different compactness. The interactions of patch configurations also require emphasis, especially the patch complexity. The research findings help to formulate urban planning strategies from a climate adaptation and mitigation perspective to cope with the increasing frequency of extreme heat events.},

keywords = {Built environment, Compactness, Heat waves, Non-stationary relationship, Optimal parameters-based geographical detectors model},

pubstate = {published},

tppubtype = {article}

}

@article{JI2024105869,

title = {Enhancing urban thermal comfort during heat waves: Exploring hierarchical optimization strategies through integration of network and patch morphology},

author = {Yifeng Ji and Zhitao Li and Yating Chang and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2210670724006930},

doi = {https://doi.org/10.1016/j.scs.2024.105869},

issn = {2210-6707},

year  = {2024},

date = {2024-01-01},

journal = {Sustainable Cities and Society},

volume = {115},

pages = {105869},

abstract = {Global warming increases the frequency of heat waves, worsening thermal comfort (TC) in cities. However, studies identifying key optimized areas and optimization mechanism of TC at an urban scale are lacking. This study combined network and patch morphology to construct a research framework to hierarchically optimize TC during heat waves. First, thermal discomfort (TD) and TC sources were identified based on morphological spatial pattern analysis and local climate zones. Then, a hierarchical TC-optimized network was constructed based on circuit theory. Finally, the morphological characteristics of TD and TC sources were quantified using landscape indices, and the mechanism by which the morphology affects TC were revealed using an optimal parameters-based geographical detector model. 20 TD sources, 20 TC sources, 40 TD corridors, and 38 TC corridors are identified. Different levels of sources and corridors have distinct spatial distribution characteristics. TD barrier points are found in the southeastern part of the study area, and TC barrier points are found in the western and central-southern parts of the study area. Different levels of TD and TC sources have different dominant landscape indices. Simultaneous considering the differentiated interactions of landscape indices at different levels of TD and TC sources can optimally enhance TC.},

keywords = {Heat waves, Hierarchical optimization strategies, Patch morphology, Thermal comfort optimized network, Urban thermal comfort},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2024104278,

title = {A novel data-driven approach for customizing destination choice set: A case study in the Netherlands},

author = {Bin Zhang and Soora Rasouli and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0965856424003264},

doi = {https://doi.org/10.1016/j.tra.2024.104278},

issn = {0965-8564},

year  = {2024},

date = {2024-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {190},

pages = {104278},

abstract = {Modeling the destination choice has been of great interest for travel behavior community as well as policymakers in understanding the demand for land use and transportation infrastructures at aggregate and disaggregate levels and possibly devising policies to balance the demand and supplies. One of the challenges underlying predictions of location choice is the large choice set. While traditionally many methods had been devised to limit the choice set size either on a rather ad hoc basis or based on space–time prism by removing the locations out of reach of the subjects, the current study takes a substantially different approach and proposes a data-driven method to customize the generation of the choice set. The proposition is that observing the mobility patterns of citizens for multiple weeks would enable us to limit the choice set, depending on how far the subjects travel (beyond or within the distance they travel for their most frequent activities) to conduct their various activities. More precisely, using longitudinal trajectory data, we first classify people into two subgroups: returners and explorers, based on the size of the area (around their k most visited locations: k-radius of gyration) they move during the observation period. The destination choice set for four types of activities is then customized for returners (and explorers) and is used in a sequence of decisions represented by decision trees for the prediction of their destinations. The models for the whole sample and each subgroup separately are compared. The results suggest that the accuracy of destination prediction improves substantially for all four selected activity types, especially for the returners whose choice sets are formed based on their radius of gyration.},

keywords = {Decision tree (DT), Destination choice, GPS data, Human mobility pattern, Longitudinal trajectory, Radius of gyration},

pubstate = {published},

tppubtype = {article}

}

@article{YU2024101230,

title = {Effects of COVID-19 on flex route transit utilization: An interrupted time series analysis},

author = {Jingcai Yu and Shunchao Wang and Bingtong Wang and Wenquan Li and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2210539524001329},

doi = {https://doi.org/10.1016/j.rtbm.2024.101230},

issn = {2210-5395},

year  = {2024},

date = {2024-01-01},

journal = {Research in Transportation Business & Management},

volume = {57},

pages = {101230},

abstract = {The COVID-19 outbreak has exerted serious effects on the transportation system, especially public transport. Flex route transit (FRT), one of public transport, has shown significant advantages in transporting passengers during COVID-19 due to its high flexibility and relatively large cabin spaces. This study aims to estimate the effects of the lockdown and first lockdown ease on FRT utilization through interrupted time series analysis. A survey was conducted in Beijing to collect data regarding the daily FRT utilization of individuals, spanning from December 2019 to May 2020. Results indicated that FRT utilization was immediately reduced during the lockdown, and a decreased trend was observed. After the first lockdown ease was rolled out, FRT utilization showed a rising level and trend. Private car owners showed a lower dependence on FRT than non-car owners. The level of income exerted varying effects in a wide range of periods. FRT utilization among the higher income group was more likely to take effect than the lower income group. The study yields various policy implications for FRT operations during COVID-19 or other pandemics.},

keywords = {COVID-19, Flex route transit, Interrupted time series analysis, Lockdown, Lockdown ease},

pubstate = {published},

tppubtype = {article}

}

@article{Liu2023,

title = {The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands},

author = {Yutian Liu and Tao Feng},

doi = {10.3390/app132111822},

issn = {2076-3417},

year  = {2023},

date = {2023-10-29},

journal = {Applied Sciences},

volume = {13},

number = {21},

publisher = {MDPI AG},

abstract = {<jats:p>The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.</jats:p>},

keywords = {Computer Science Applications, Fluid Flow and Transfer Processes, General Engineering, General Materials Science, Instrumentation, Process Chemistry and Technology},

pubstate = {published},

tppubtype = {article}

}

@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}

}

@article{PCOVID19202301,

title = {Assessing the Impacts of Stay-in-Place Policy of COVID-19 Pandemic during the Chinese Spring Festival: A Stated Preference Approach},

author = {Xiaofeng Pan and Tao Feng and Yanyi Chen},

url = {https://downloads.hindawi.com/journals/jat/2023/9662990.pdf},

doi = {https://doi.org/10.1155/2023/9662990},

year  = {2023},

date = {2023-01-17},

urldate = {2023-01-17},

journal = {Journal of Advanced Transportation},

volume = {2023},

number = {9662990},

abstract = {This paper aims to investigate Chinese people’s willingness to stay in the city where they work when the Spring Festival meets the COVID-19 pandemic. Specifically, a stated choice experiment about intercity travel including three homecoming trips (i.e., trips carried by conventional railway, high-speed railways, and private car) and the option “stay in place” was designed. Respondents were requested to choose the most preferred alternative in the context of the current situation of the COVID-19 pandemic and relevant policies. Based on the data collected from 800 respondents, a latent class mixed logit model was developed and estimated to capture the potential correlations within alternatives and respondents and the preference heterogeneity between respondents. Two latent classes were identified, one of which paid more attention to epidemic prevention policies while the other cared more about the characteristics of homecoming trips. Results show that people’s willingness to stay in the city of work is largely dependent on epidemic prevention policies in their hometowns and decisions of social network members.

},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WU2023103839,

title = {Spatiotemporal analysis of built environment restrained traffic carbon emissions and policy implications},

author = {Jishi Wu and Peng Jia and Tao Feng and Haijiang Li and Haibo Kuang},

url = {https://www.sciencedirect.com/science/article/pii/S1361920923002365},

doi = {https://doi.org/10.1016/j.trd.2023.103839},

issn = {1361-9209},

year  = {2023},

date = {2023-01-01},

journal = {Transportation Research Part D: Transport and Environment},

volume = {121},

pages = {103839},

abstract = {Urban environmental policies need to be rectified considering the spatioemporal variations of traffic emissions. However, knowledge to support such a decision-making process is insufficient. This study analyzes the spatiotemporal distributions of traffic emissions in the built environment and their potential nonlinear associations. Considering the recent innovations in machine learning, a tree-boosting algorithm combined with Gaussian process and random effects models (GPBoost) is applied using the big GPS taxi data from Dalian, China. The nonlinear relationships between built environment variables and traffic carbon (CO2) emissions are interpreted using the SHapley Additive ExPlanation (SHAP). It is found that the proposed GPBoost model that considers spatial heterogeneity enhances the overall predictive power compared to traditional machine learning models. Most of the built environment variables have a nonlinear relationship with traffic carbon emissions and the threshold effects vary over time, indicating the necessity of dynamic urban management.},

keywords = {Built environment, GPBoost, Machine learning, Nonlinear effects, SHapley Additive ExPlanation, Traffic carbon emissions},

pubstate = {published},

tppubtype = {article}

}

@article{YUE2023104640,

title = {Classification and Determinants of High-Speed Rail Stations Using Multi-Source Data: A Case Study in Jiangsu Province, China},

author = {Yifan Yue and Jun Chen and Tao Feng and Xinwei Ma and Wei Wang and Hua Bai},

url = {https://www.sciencedirect.com/science/article/pii/S2210670723002512},

doi = {https://doi.org/10.1016/j.scs.2023.104640},

issn = {2210-6707},

year  = {2023},

date = {2023-01-01},

journal = {Sustainable Cities and Society},

pages = {104640},

abstract = {High-speed rail (HSR) stations play a vital role in the HSR system. HSR stations not only facilitate the accessibility of interregional transportation but also stimulate population movements across various cities in China. HSR stations in different cities can vary greatly, and an efficient HSR system requires an in-depth understanding of the interrelations between the related influential factors and spatiotemporal passenger flow patterns of different HSR stations. This study adopts a new scheme for clustering HSR stations based on passengers' arrival and departure time series using mobile phone data in Jiangsu, China. To this end, 71 HSR stations are clustered into 3 classes and the spatiotemporal characteristics of passenger flow at different stations are compared. Finally, a geographically weighted multinomial logit model (GWMNL) is built to explore the influence of the built environment, socioeconomic indicators, and HSR station attributes on the classification results of HSR stations related to the time-varying characteristics of passenger flow. The model results show that the number of entertainment POIs, population, population density, area, GDP and building area are significantly associated with the classification results of HSR stations. Additionally, for HSR stations under the same classification result, these variables also have different effects on them in the geographical dimension. According to these findings, quantitative analysis of the linkages between the passenger flow patterns at different HSR stations and the impacting factors would offer implications for planners and policymakers in HSR station planning and associated urban development.},

keywords = {Built environment, Geographically Weighted Multinomial Logit Model, High-Speed Rail Station, Influential factors, Mobile Phone Data},

pubstate = {published},

tppubtype = {article}

}

@article{TIAN2023103763,

title = {Where to park an autonomous vehicle? Results of a stated choice experiment},

author = {Zhihui Tian and Tao Feng and Baozhen Yao and Yan Hu and Jing Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0965856423001830},

doi = {https://doi.org/10.1016/j.tra.2023.103763},

issn = {0965-8564},

year  = {2023},

date = {2023-01-01},

journal = {Transportation Research Part A: Policy and Practice},

volume = {175},

pages = {103763},

abstract = {The future innovation and growing popularity of autonomous vehicles have the potential to significantly impact the spatiotemporal distribution of parking demand. However, little knowledge is gained on how people will choose to park their autonomous cars. In principle, an autonomous vehicle is not necessarily parked close by like traditional vehicles leveraging the automated driving and parking capability, still, the decision made by people is important for policymakers in urban and transportation planning. This study attempts to gain useful insights to understand people’s parking location choices for autonomous vehicles. A stated choice experiment was designed, allowing people to choose a parking location for autonomous vehicles in varied contexts, including time windows, picking-up times, and the requirement for on-time arrival at the next activity. We found that similar to conventional cars people generally prefer cheaper and/or closer parking lots for autonomous vehicles. However, the distance between a parking lot and the activity location is relatively longer in the case of autonomous vehicles. The amount of time an autonomous vehicle spends in congestion while picking up the users influences the choice of parking locations. Moreover, substantial preference heterogeneity between individual people was found in the parking choice behavior. The maximum value of access time for autonomous cars is 34 $/h which is higher than the empirical value of walking time for conventional cars. Results of elasticity indicate that the influence of parking fees is larger than that of access time and congestion time.},

keywords = {Autonomous vehicles, Context effects, Parking location choice},

pubstate = {published},

tppubtype = {article}

}

@article{LYU2023103718,

title = {A multiscale spatial analysis of taxi ridership},

author = {Tao Lyu and Yuanqing Wang and Shujuan Ji and Tao Feng and Zhouhao Wu},

url = {https://www.sciencedirect.com/science/article/pii/S0966692323001904},

doi = {https://doi.org/10.1016/j.jtrangeo.2023.103718},

issn = {0966-6923},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Transport Geography},

volume = {113},

pages = {103718},

abstract = {Taxi plays a supplement role in sustainable development of urban public transport systems. However, the extent to which the built environment affects taxi ridership at various spatial scales deserves further exploration because understanding the true spatial heterogeneity across a varying scale could be valuable for both global and localized policy decision-makings. In this study, we attempt to analyze and discuss the spatial predictors of taxi ridership by utilizing an multiscale geographically weighted regression (MGWR) model and comparing the model's performance to that of ordinary least square (OLS) and geographically weighted regression (GWR) models. Using the taxi data of Xi'an city, we found that the MGWR model could explain 81.8% of the total taxi ridership fluctuations and allows localized and targeted policy makings to help taxi drivers search for passengers and to improve passengers' taxi-hailing experiences in specific districts.},

keywords = {Influencing factors, Multiscale geographically weighted regression, Scale effect, spatial heterogeneity, Taxi ridership},

pubstate = {published},

tppubtype = {article}

}

@article{WANG2023138077,

title = {An acceptability-based multi-objective traffic flow adjustment method for environmental sustainability and equity},

author = {Guanfeng Wang and Hongfei Jia and Tao Feng and Jingjing Tian and Mengxia Li and Luyao Wang},

url = {https://www.sciencedirect.com/science/article/pii/S0959652623022357},

doi = {https://doi.org/10.1016/j.jclepro.2023.138077},

issn = {0959-6526},

year  = {2023},

date = {2023-01-01},

journal = {Journal of Cleaner Production},

volume = {418},

pages = {138077},

abstract = {As a product of digital development, connected autonomous vehicles (CAVs) offer a unique prospective solution to alleviate the possible performance deterioration of road networks under the mixed environment of human-driven vehicles (HVs) and CAVs. In this paper, we propose a traffic flow adjustment method (TFAM) that treats CAVs as mobile regulators with the purpose to reshape traffic flow distribution on road networks by guiding rather than controlling CAVs. More specifically, we deploy subsidy nodes to briefly outline travel routes and achieve higher acceptability than traditional route-based control schemes. The TFAM is a multi-objective bi-level programming problem where the upper-level problem optimizes the network performance through regulating location and subsidy on the subsidy nodes. The lower-level problem is a dual dynamic traffic assignment (DDTA) model. Apart from the total travel time cost (TTTC), total emission cost (TEC) and network equity (NE) are also introduced as optimization objectives to highlight environmental sustainability and acceptability. To obtain the Pareto solution frontier, a meta-heuristic algorithm with an improved encoding process is proposed. Results of two numerical case studies demonstrate the effects of TFAM on traffic flow distribution and network performance, which yields valuable insights on the optimization of urban traffic systems.},

keywords = {Connected autonomous vehicles, Digital product, Emission reduction, Multi-objective bi-level programming, Subsidy nodes deploying scheme, Traffic flow adjustment method},

pubstate = {published},

tppubtype = {article}

}