Research Output

@article{HU2026101141,

title = {Crowdshipping with private autonomous vehicles: Exploring the propensity of car owners},

author = {Yan Hu and Ying Zhao and Xiaodong Li and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2214367X25001590},

doi = {https://doi.org/10.1016/j.tbs.2025.101141},

issn = {2214-367X},

year  = {2026},

date = {2026-01-01},

journal = {Travel Behaviour and Society},

volume = {42},

pages = {101141},

abstract = {As e-commerce and on-demand mobility services continue to experience rapid growth, the demand for parcel delivery has increased, prompting the exploration of innovative solutions like crowdshipping to address transportation challenges. Despite its potential, the current crowdshipping delivery mode grapples with efficiency issues and contributes to heightened traffic congestion. The emergence of autonomous vehicles presents a promising prospect for a new and potentially more efficient delivery mode. Upon completing passenger drop-off at their destinations, autonomous vehicles face the challenge of finding parking spaces or cruising in proximity while waiting for the next call, escalating the pressure on parking and traffic. Integrating idle autonomous vehicles into crowdshipping, however, provides an effective means to optimize resources, reduce the dependence on parking infrastructure, and generate revenue for car owners. In this study, we aim to analyze and understand the behavior and decision-making mechanism of autonomous vehicle owners regarding the adoption of crowdshipping. A stated choice experiment was designed focusing on people’s adoption behavior when traveling in various contexts related to travel purpose, duration, revenue, platform fee, and waiting time. Findings derived from a mixed logit model reveal notable variations in preferences among car owners. Revenue, platform fee, waiting time, and social influence all significantly influence the crowdshipping decision. Moreover, individuals with a college education, employed, and higher income groups showed a greater propensity for adopting crowdshipping. These findings provide valuable insights for crowdshipping operators, urban planners, and other stakeholders involved.},

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}

@article{JI2026108251,

title = {How urban sprawl patterns shape the thermal environment during hot summers: An empirical analysis of 338 Chinese cities},

author = {Yifeng Ji and Zhitao Li and Ying Liu and Hongyu Tang and Hao Sun and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0195925525004482},

doi = {https://doi.org/10.1016/j.eiar.2025.108251},

issn = {0195-9255},

year  = {2026},

date = {2026-01-01},

journal = {Environmental Impact Assessment Review},

volume = {117},

pages = {108251},

abstract = {With accelerating global warming trends, urban sprawl has emerged as a key driver of thermal environment changes in urban areas. However, limited attention has been given to how sprawl patterns shape the thermal environment at the city level. This study sets out to reveal the heterogeneous relationship between the spatial patterns of urban sprawl and thermal environment by analyzing 338 Chinese cities during hot summers from 1990 to 2020. Multi-source data were employed to assess the spatial arrangement and temporal dynamics of four urban sprawl patterns (centering, clustering, fragmentation, and complexity) alongside thermal conditions. A geographically weighted regression model revealed spatially varying associations between sprawl patterns and thermal environment. Results indicate a northward gradient of thermal deterioration, with the most severe warming in Northeast and Northwest China, while some southern tropical cities exhibit localized cooling. Sprawl patterns vary across regions and exhibit dual heterogeneity in their thermal impacts. Fragmentation demonstrates cooling effects in the southeastern coast, eastern Northwest, and northeastern Southwest China. Clustering improves thermal conditions in the southeastern coast, Bohai Rim, and northeastern and northwestern China, but intensifies heat stress in the southwest. Complexity worsens the thermal environment in the Yangtze River Delta and the junction of eastern Northwest and northeastern Southwest China, while slightly mitigating the thermal environment in northern cities. Centering shows limited impact, with minor improvements observed in southeastern coastal cities. Based on these findings, seven optimization zones have been identified with tailored thermal-mitigation strategies, providing evidence to guide regionally adaptive planning for climate-resilient urban development.},

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pubstate = {published},

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}

@article{QI2026101159,

title = {Modeling the effects of additional crowd-sourced delivery service on transportation mode choice in mobility-as-a-service},

author = {Qiang Qi and Soora Rasouli and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2214367X25001772},

doi = {https://doi.org/10.1016/j.tbs.2025.101159},

issn = {2214-367X},

year  = {2026},

date = {2026-01-01},

journal = {Travel Behaviour and Society},

volume = {42},

pages = {101159},

abstract = {This paper investigates the impact of people’s decision to be a crowd-shipper within Mobility-as-a-Service (MaaS) on their transportation mode choice. Leveraging the unique characteristics of MaaS as a comprehensive mobility service platform, we propose embedding Crowd-Sourced Delivery (CSD) as an additional service. This integrated service not only allows travelers to choose a transportation mode but also provides the option to accept (or decline) parcel delivery tasks during their journeys. Given the aim of MaaS to promote the use of sustainable transportation modes and replacement of car-based transport, we designed a stated choice experiment to capture respondents’ choices of transportation modes with and without the additional CSD service. This approach enables us to explore whether being a crowd-shipper has an impact on transportation mode choice. A random parameter mixed logit model was estimated to identify the effects of various factors. The results reveal that car-based transport is generally preferred for delivering parcels. Despite this preference, which contradicts the aim of MaaS, several factors can encourage the use of sustainable transportation modes, including public and active transport, within integrated CSD and MaaS. The parcels not being fragile, less strict delivery time window, and higher monetary incentives for using sustainable transport in CSD are among those factors. Additionally, travel contexts such as travel distance and weather, as well as the age of respondents, also influence the transportation mode choice decision once a parcel delivery task is accepted. These insights offer valuable information for policymakers and service operators to promote the use of sustainable transportation modes within CSD.},

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@article{ZHOU2025104426,

title = {Active travel and the built environment: A life-course perspective},

author = {Ting Zhou and Tao Feng and Astrid Kemperman},

url = {https://www.sciencedirect.com/science/article/pii/S0966692325003175},

doi = {https://doi.org/10.1016/j.jtrangeo.2025.104426},

issn = {0966-6923},

year  = {2025},

date = {2025-01-01},

journal = {Journal of Transport Geography},

volume = {129},

pages = {104426},

abstract = {Active travel has many health, environmental, economic, and social benefits, yet it is not clear how life events and factors from the built environment influence active travel over time. To address this gap and provide causal and dynamic evidence, this study used a 5-year longitudinal dataset from the Netherlands Mobility Panel and applied a Dynamic Bayesian Network approach to explore the temporal relationships between life events, built environment factors and active travel frequency, including frequency of conventional bike, e-bike, and walking. Results show that life events play a much more important role in influencing active travel frequency than the built environment factors. In addition, owning an e-bike has the greatest impact on cycling frequency, impeding conventional bike use and increasing e-bike use. Furthermore, childbirth has different temporal effects on the frequency of all three types of active travel. Moreover, workplace change negatively impacts e-bike use both concurrently and over time. These findings provide empirical evidence for urban planning and policymaking to promote active travel.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{WU2025103833,

title = {Revealing the built environment impacts on curbside freight parking demand using a deep generalized additive modeling framework},

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

url = {https://www.sciencedirect.com/science/article/pii/S0967070X25003762},

doi = {https://doi.org/10.1016/j.tranpol.2025.103833},

issn = {0967-070X},

year  = {2025},

date = {2025-01-01},

journal = {Transport Policy},

volume = {174},

pages = {103833},

abstract = {The increasing intensity of urban activity has heightened conflicts between commercial vehicles curbside parking and limited road space. However, limited knowledge exists on how the urban built environment affects curbside freight parking, which restricts effective policymaking. This study investigates the spatial patterns of curbside freight parking demand using large-scale commercial vehicle trajectory data and quantifies the impacts of the built environment using an interpretable generalized additive neural network (IGANN) model. Unlike black-box models, IGANN achieves superior interpretability, reliability, and predictive accuracy. Results show that spatial heterogeneity in freight curbside parking demand is pronounced in high-density cities, where intensive development amplifies parking needs and worsens congestion. These findings highlight the importance of adopting spatially differentiated development strategies to balance freight operations with competing curb users.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HU2025103797,

title = {Context-dependent uncertainty-aware parking choices of autonomous car owners},

author = {Yan Hu and Ying Zhao and Xiaodong Li and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0967070X25003403},

doi = {https://doi.org/10.1016/j.tranpol.2025.103797},

issn = {0967-070X},

year  = {2025},

date = {2025-01-01},

journal = {Transport Policy},

pages = {103797},

abstract = {As autonomous vehicle (AV) technology advances, effectively managing parking behavior has become increasingly critical for urban parking systems, traffic congestion, and transportation sustainability. This study aims to investigate AV owners’ parking choices under various activity-travel contexts, uncertainty conditions, and parking attributes. A stated choice experiment was designed, and a mixed logit cumulative prospect theoretical model was developed to analyze parking decisions under uncertain waiting times underlying different parking modes. The findings indicate that individuals exhibit risk preferences in response to waiting time uncertainty. When waiting times are shorter than expected, they show risk aversion with diminishing sensitivity to further reductions. Conversely, longer-than-expected waits lead to risk-seeking behavior, heightened sensitivity, and biases in probability perception. These risk preferences significantly influence individuals' parking choices, particularly in prolonged, uncertain waiting scenarios. Additionally, travel context factors (e.g., purpose, activity duration, and idle time) and parking attributes (e.g., cost, revenue, and social influence) significantly influence AV owners' parking decisions, along with socio-demographic characteristics (e.g., age, education, and car ownership). The findings provide valuable insights for urban planners, policymakers, and car-sharing platforms, suggesting flexible pricing strategies and the promotion of shared mobility services to enhance parking efficiency and support sustainable transportation development.},

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pubstate = {published},

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}

@article{ZHAO2025103784,

title = {Assessment of passengers’ safety and risk attitudes on integrated urban air mobility and airline services},

author = {Ying Zhao and Yan Hu and Tao Feng and Anming Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0967070X25003270},

doi = {https://doi.org/10.1016/j.tranpol.2025.103784},

issn = {0967-070X},

year  = {2025},

date = {2025-01-01},

journal = {Transport Policy},

volume = {172},

pages = {103784},

abstract = {This study introduces the concept of Air Mobility as a Service (AMaaS) by integrating Urban Air Taxi (UAT) services into the Mobility as a Service (MaaS) framework, aiming to enabling seamless multimodal transportation. The objective is to investigate commuter preferences for adopting multimodal UAT services. A stated choice experiment was designed to capture joint choice of UAT-based alternatives and subscription schemes, alongside attitudinal measures assessing the influence of safety and risk perceptions on adoption behavior. Using data collected in Beijing, a hybrid choice model with latent variables was estimated. Results show that subscription-based schemes, particularly sustainable options like Bike + UAT and PT + UAT, are generally preferred over pay-as-you-go alternatives. Government support and discounts significantly increase adoption likelihood. Safety perceptions also play a critical role. Specifically, perceived UAT safety encourages adoption, while safety consciousness, and perceived UAT risks hinder the use of these services. Individuals with higher safety consciousness are less likely to use pay-as-you-go options, and those perceiving UAT as risky are less inclined to use subscription schemes, particularly Taxi + UAT. These findings provide valuable insights for policymakers and UAT service providers in designing effective policies and marketing strategies to promote UAT adoption.},

keywords = {},

pubstate = {published},

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}

@article{WANG2025104623,

title = {The impacts of carbon emission regulatory policies on demand-responsive transit operation: a strategy to avoid service equity loss},

author = {Jiangbo Wang and Zhongyu Sun and Kai Liu and Jianjun Wu and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0965856425002514},

doi = {https://doi.org/10.1016/j.tra.2025.104623},

issn = {0965-8564},

year  = {2025},

date = {2025-01-01},

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

volume = {200},

pages = {104623},

abstract = {As one of the primary contributors to carbon emissions, road transportation systems are expected to be included in carbon emission management frameworks in the near future. Demand-responsive transit (DRT) will face a new constraint: carbon emission costs. Currently, no widely established framework exists for carbon emission management in transportation systems. Carbon tax and carbon trading are two commonly implemented carbon regulatory policies worldwide, and both are expected to be extended to the transportation sector. However, the effectiveness of these policies in incentivizing carbon reductions in DRT systems and the potential challenges they may pose remain uncertain. To fill this gap, this paper developed route planning models for the Open Capacitated Low-carbon Vehicle Routing Problem (OCLCVRP) and designed a series of experiments using standard benchmark instances to explore the impacts of carbon tax and carbon trading policies on the operation of DRT systems. The results indicate that although both carbon tax and carbon trading policies are effective in reducing carbon emissions, they lead to the loss of a fundamental attribute of DRT systems—service equity. As carbon prices rise, this loss of equity becomes more pronounced, ultimately increasing a higher failure rates of DRT systems and undermining the effectiveness of carbon emission regulatory policies. To mitigate this issue, this paper proposed a dynamic carbon quota-based carbon trading scheme. Through a case study, the proposed dynamic carbon quota proves to be effective for the public transportation sector in reducing carbon emissions while ensuring service equity under the carbon trading scheme.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2025960,

title = {Passengers’ vertiport choices for integrated urban air mobility and airline services},

author = {Ying Zhao and Yan Hu and Tao Feng and Anming Zhang},

url = {https://www.sciencedirect.com/science/article/pii/S0967070X25002811},

doi = {https://doi.org/10.1016/j.tranpol.2025.07.028},

issn = {0967-070X},

year  = {2025},

date = {2025-01-01},

journal = {Transport Policy},

volume = {171},

pages = {960-969},

abstract = {Vertiports are essential hubs for Urban Air Mobility (UAM) airport shuttles, however, passenger choice behavior in vertiports remains underexplored. In this paper, we conducted a stated choice experiment in Beijing and applied a mixed logit model with interaction effects to examine passengers’ heterogeneous choice preferences in the context of synchronized UAM and airline services. Results show passengers generally prefer small, nearby vertiports, integrated with public transport, and equipped with multiple facilities and services. Preferences vary notably among middle-income groups (100,000–200,000 CNY/year), and passengers with helicopter experience prefer less proximate vertiports due to noise and privacy concerns. Elasticity analyses underscore the critical influence of access distance, providing valuable insights for policymakers and UAM service providers in infrastructure planning and development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LI2025105252,

title = {Activity-aware urban area embedding with contrastive learning for intelligent transportation systems applications},

author = {Gen Li and Tao Feng and Dan He and Li Yan and Jiwon Kim},

url = {https://www.sciencedirect.com/science/article/pii/S0968090X25002566},

doi = {https://doi.org/10.1016/j.trc.2025.105252},

issn = {0968-090X},

year  = {2025},

date = {2025-01-01},

journal = {Transportation Research Part C: Emerging Technologies},

volume = {178},

pages = {105252},

abstract = {Embedding is a machine learning technique that represents data entities as continuous vector representations, capturing the underlying semantic relationships between them. Urban area embedding applies this concept to urban regions, representing each area as a vector that encapsulates its key characteristics. These embeddings enable models to better understand the relationships between different urban areas, facilitating applications such as traffic management, urban planning, and resource allocation. In this paper, we propose a comprehensive framework called AUAEC (Activity-aware Urban Area Embedding with Contrastive Learning) that integrates diverse open datasets including Location-Based Social Network (LBSN) check-ins, taxi flow data, and Points of Interest (POI) to produce enriched and context-aware region embeddings. To capture both mobility patterns and activity-aware semantics of LBSN users, we apply spatial interpolation based on road network, coupled with activity vector construction to represent user daily activity and movement patterns. To refine these embeddings into comprehensive urban regional representations, the AUAEC incorporates two complementary contrastive learning strategies: View-wise Contrastive Learning, which aligns representations across multiple data views, and Activity-aware Contrastive Learning, which captures inter-region relationships based on activity-aware semantics. The resulting embeddings are evaluated across four critical ITS tasks including land use distribution classification, traffic incident prediction, public transport delay prediction and traffic volume prediction using real-world data. Our approach demonstrates promising results, outperforming state-of-the-art solutions and highlighting the superiority of AUAEC in providing robust, contextual representations of urban areas for ITS and urban planning applications.},

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pubstate = {published},

tppubtype = {article}

}

@article{ZHOU2025106146,

title = {Non-linear associations between the built environment and outdoor activity duration: An application of gradient boosting decision trees},

author = {Ting Zhou and Tao Feng and Astrid Kemperman},

url = {https://www.sciencedirect.com/science/article/pii/S0264275125004470},

doi = {https://doi.org/10.1016/j.cities.2025.106146},

issn = {0264-2751},

year  = {2025},

date = {2025-01-01},

journal = {Cities},

volume = {165},

pages = {106146},

abstract = {Outdoor activities benefit human physical and mental health. However, how the built environment influences the duration of location-based outdoor activities is unclear. To address this gap, we measured the built environment using different methods including Geographic Information Systems and a semantic segmentation technique with Google Street View images and associated these independent variables with outdoor activity duration by employing a gradient boosting decision tree model. We found neighborhood characteristics play a much more important role in influencing outdoor activity duration than location characteristics (features about a specific area where outdoor activities take place). Although all seventeen variables had a non-linear and threshold impact, distance to the nearest public transport station is the most significant predictor, followed by residential density, distance to the nearest school, and street connectivity. These findings allow urban planners and policymakers to give detailed advice on planning and designing built environments that better promote outdoor activities.},

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pubstate = {published},

tppubtype = {article}

}

@article{LIU2025100192,

title = {SAFER-predictor: Sparse adversarial training framework for robust traffic prediction under missing and noisy data},

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

url = {https://www.sciencedirect.com/science/article/pii/S2772424725000320},

doi = {https://doi.org/10.1016/j.commtr.2025.100192},

issn = {2772-4247},

year  = {2025},

date = {2025-01-01},

journal = {Communications in Transportation Research},

volume = {5},

pages = {100192},

abstract = {Accurate traffic flow forecasting is essential for developing intelligent transportation systems (ITSs) to reduce congestion, optimize road management, and improve safety. While data-driven traffic prediction approaches have shown high accuracy, they rely heavily on precise measurements, making them vulnerable to perturbed environmental factors, like sensor malfunctions, data storage issues, and adverse weather conditions. To overcome the limitation, we propose SAFER-Predictor, a novel sparse adversarial training (Sparse AT) framework for enhancing the reliability of deep learning based spatiotemporal traffic prediction models. Sparse AT extends traditional adversarial training (AT) through a two-phase process: pre-training and fine-tuning. In the pre-training phase, the model is optimized to capture normal traffic patterns, enhancing predictive performance by understanding standard dynamics without external disruptions. In the fine-tuning phase, the focus shifts to strengthening robustness against corrupted inputs by employing an iterative min-max strategy during AT, optimizing performance for worst-case scenarios. Furthermore, we derive theoretical formulations that establish an upper bound on the model's prediction error following Sparse AT under certain noise levels. Experimental results indicate that incorporating Sparse AT into the representative traffic flow prediction models improves stability and ensures high accuracy under various perturbation scenarios.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{HU2025104520,

title = {Examining the temporary use behavior of autonomous vehicles under uncertainty: A stated preference analysis},

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

url = {https://www.sciencedirect.com/science/article/pii/S096585642500148X},

doi = {https://doi.org/10.1016/j.tra.2025.104520},

issn = {0965-8564},

year  = {2025},

date = {2025-01-01},

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

volume = {198},

pages = {104520},

abstract = {The rapid advancement of autonomous vehicles (AVs) is reshaping traditional parking, posing challenges for urban and transportation planning. This study investigates AV owners’ parking choice behavior to inform policy decisions. A stated choice experiment was designed to evaluate three parking modes including regular parking, cruising, and car-sharing, integrating dual uncertainties in parking fees and sharing revenues, an aspect largely overlooked in prior research. Unlike previous studies focusing solely on parking versus cruising decisions, we incorporate car-sharing as an additional behavioral option for AVs users upon reaching their destinations. Using a random parameter mixed logit-cumulative prospect theory model, we analyze AV owners’ risk attitudes and parking decisions under uncertainty. Results indicate different risk attitudes toward parking mode choice and varied preferences concerning the uncertain parking fee and revenue. Additionally, socio-demographic characteristics, travel purposes, waiting times, social influences, and congestion charges significantly impact parking choice behaviors. The findings provide valuable insights for urban planning, transportation policy, and car-sharing platform development.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{JI2025106445,

title = {Stratified strategies for enhancing thermal comfort through multidimensional compactness optimization in urban built-up areas during heatwaves},

author = {Yifeng Ji and Ying Liu and Hongyu Tang and Zhitao Li and Yihang Bai and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S221067072500321X},

doi = {https://doi.org/10.1016/j.scs.2025.106445},

issn = {2210-6707},

year  = {2025},

date = {2025-01-01},

journal = {Sustainable Cities and Society},

volume = {127},

pages = {106445},

abstract = {Thermal comfort (TC) in built-up areas with varying levels of compactness is unevenly affected during heatwaves (HW). However, identifying zones that should prioritize compactness optimization to effectively enhance TC is often overlooked. This study constructed a research framework for enhancing TC through stratified planning strategies by identifying key compactness-optimized areas and patterns during HW. Taking the built-up area of Shenyang, China, as an example, the compactness index containing spatial, functional and socio-economic dimensions and the TC index were first constructed based on multi-source data. Afterwards, different types of compactness-optimized areas, dominant compactness in different regions, and trade-off and synergy patterns among various dimensions of compactness were revealed using a geographically weighted regression model (GWRM) and local bivariate spatial autocorrelation analysis. The results show that compactness decreases from the center to the periphery of the built-up area, while TC follows the opposite trend. A total of 10 types of compactness-optimized areas are identified, including 8 key types covering 40.472 % of the built-up area. Based on the trade-offs and synergies between different compactness dimensions, 8 optimization patterns are revealed, with synergistic optimization across all three dimensions representing the largest share (71.256 %). Furthermore, 4 optimization categories with different priorities are identified, each exhibiting distinct spatial patterns and targeted optimization strategies. These findings support hierarchical resource allocation and strategic intervention to enhance thermal comfort and promote climate-resilient cities.},

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pubstate = {published},

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}

@article{Zhang14052025,

title = {Transportation mode choice prediction using a new multi-class association rule model},

author = {Jiajia Zhang and Tao Feng and Harry J. P. Timmermans and Qing Dai and Zhengkui Lin and},

url = {https://doi.org/10.1080/23249935.2025.2497897},

doi = {10.1080/23249935.2025.2497897},

year  = {2025},

date = {2025-01-01},

journal = {Transportmetrica A: Transport Science},

volume = {0},

number = {0},

pages = {1–54},

publisher = {Taylor & Francis},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LAI2025103423,

title = {Grid-level assessment on spatial equity in access to urban public facilities by vulnerable groups based on the multi-source data},

author = {Wenbo Lai and Dahu Lin and Zhuowen Li and You Peng and Weiqiang Zhou and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0197397525001390},

doi = {https://doi.org/10.1016/j.habitatint.2025.103423},

issn = {0197-3975},

year  = {2025},

date = {2025-01-01},

journal = {Habitat International},

volume = {161},

pages = {103423},

abstract = {In the context of promoting human-centered sustainable cities and communities, achieving spatial equity in the development of urban public facilities (UPF) plays an essential role and faces a significant challenge. Although the regional and social disparity of UPF's distribution is well documented, researchers seldom consider the spatial distribution disparities between multiple types of UPF and vulnerable groups at a fine scale. This study built a comprehensive evaluation framework with a fine-grained model, which is comprised of grid-based partitioning, multi-source data crawling, cumulative opportunity model, and spatial statistical analysis, to analyze spatial equity in Guangzhou, China. The accessibility to UPF and the social vulnerability index (SVI) are measured utilizing the cumulative opportunity measure and principal component analysis (PCA). This study, therefore, analyzed the imbalance between the distribution of UPF and vulnerable groups. Moreover, the study accurately identified spatial inequity areas using the Gini coefficient and bivariate Moran's I. The results show that numerous areas within the study area have limited opportunities to access UPF. Nevertheless, the formation of multiple urban centers during urbanization has increased the diversity of accessible types of UPF within the community life unit. In addition, due to the aging population in the old city center and the complex population structure of urban villages, the areas in the old town often become high-value clusters spatially with SVI. In particular, due to planning problems caused by urban villages, these areas are often prone to become spatial inequity areas characterized by "high SVI - low comprehensive accessibility." This evaluation framework is user-friendly and suitable for application in other cities, helping to identify spatial inequity areas that require planning intervention and priority.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{@DOUXIN202501,

title = {Investigating the Impact of the COVID-19 Pandemic on Travel Mode Choice Behaviour – A Stated Preference Case in Wuhan, China},

author = {Xin Dou and Xiaofeng Pan and Tao Feng},

url = {https://hrcak.srce.hr/en/327643},

doi = {doi.org/10.7307/ptt.v37i1.736},

year  = {2025},

date = {2025-01-01},

journal = {Promet - Traffic&Transportation},

volume = {37},

issue = {1},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZHANG2025104416,

title = {The battery-swapping revolution: Exploring user preferences in electric micro-mobility sector},

author = {Fan Zhang and Huitao Lv and Chenchen Kuai and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S0965856425000448},

doi = {https://doi.org/10.1016/j.tra.2025.104416},

issn = {0965-8564},

year  = {2025},

date = {2025-01-01},

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

volume = {194},

pages = {104416},

abstract = {Battery swapping services (BSS) offer innovative solutions to address the challenges of charging, safety, and battery management for electric micro-mobility vehicles (EMVs), such as electric bicycles and mopeds. However, the preferences and willingness to pay (WTP) for BSS among EMV users remain uncertain. This study examines consumer preferences for battery-swapping versus rechargeable EMVs within the Chinese market, identifying key factors that influence user choices. We employ a Stated Preference (SP) approach combined with a hybrid choice model (HCM) to assess both the economic viability and psychological impacts, including risk perception and social influence. Key findings demonstrate a higher willingness to pay for battery-swapping EMVs due to reduced charging time and enhanced safety features. Specifically, consumers are willing to pay an additional $2.86 for each minute reduction in facility accessibility time and an additional $27.52 for the safety features of battery-swapping EMVs. Existing users of traditional EMVs show a strong preference for BSS, willing to pay an additional $97.86. Frequent riders, who are more cost-sensitive, prefer rechargeable EMVs and are willing to reduce their expenditure by $47.95. In contrast, long-distance riders value battery endurance and are willing to pay an additional $55.96 for battery-swapping EMVs. These findings deepen our understanding of consumer behavior and provide valuable insights for policymakers and manufacturers aiming to optimize EMV adoption strategies.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2025106997,

title = {Optimization of passenger flow control and customized bus bridging in urban rail transit network},

author = {Qingqing Zhao and Jinjin Tang and Chao Li and Qiuhan Dong and Tao Feng and Xingwei Yang},

url = {https://www.sciencedirect.com/science/article/pii/S0305054825000255},

doi = {https://doi.org/10.1016/j.cor.2025.106997},

issn = {0305-0548},

year  = {2025},

date = {2025-01-01},

journal = {Computers & Operations Research},

pages = {106997},

abstract = {When dealing with large passenger flows in urban rail transit, developing reasonable passenger flow control plans is a crucial aspect of operational management. When many passengers wait outside the stations, customized buses can serve as an effective way to alleviate passenger pressure. To obtain reasonable passenger flow control and customized bus connection plans, we propose an optimization method for passenger flow control and customized bus connections. Firstly, we construct a two-stage model. The first-stage model formulates the passenger flow control plan to minimize the number of waiting passengers inside and outside the station considering factors such as passenger arrivals and urban rail transit train departures and arrivals. The second-stage model develops the customized bus plan based on a time–space-state network. According to the characteristics of the model, the CPLEX solver is used to solve the first-stage model, and a Lagrange relaxation-based solution method is designed to solve the second-stage model. To verify the effectiveness of the model, a small-scale case study including two lines and five stations is designed, and the results show that the proposed method can achieve effective passenger flow control and bus connection plans. Additionally, actual data from the Chengdu Metro is used to further validate the feasibility of the method. The results indicate that the passenger flow control and bus bridging plans can effectively reduce passenger waiting time. The proposed method can effectively alleviate passenger pressure, ensure passenger travel safety, and further promote the coordinated development of public transportation},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LAN2025105754,

title = {Role of remote working center in a hybrid work culture},

author = {Jieyuan Lan and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S026427512500054X},

doi = {https://doi.org/10.1016/j.cities.2025.105754},

issn = {0264-2751},

year  = {2025},

date = {2025-01-01},

journal = {Cities},

volume = {159},

pages = {105754},

abstract = {Remote Working Centers (RWCs) have gained popularity as alternative workspaces. However, to what extent are teleworkers willing to use RWCs in comparison to home and office considering transportation effects remains underexplored. This study investigates teleworkers' joint decisions on work location and transportation mode under different contexts. Using stated preference data collected in Tokyo Metropolis, Japan, a random parameter error component model was estimated, capturing the unobserved heterogeneity among teleworkers. The results indicate a preference for working in RWCs when tasks have a higher work intensity, involve teamwork, last between 4 and 6 h or longer. In general, the intention to travel to RWCs decreases with longer travel times. Females and high-income individuals are more likely to use RWCs. In accessing to RWCs, low-income individuals prefer active travel modes, while high-income individuals opt for public transportation or private cars. These findings provide insights for urban planners in shaping policies to promote the use of RWCs in a hybrid work culture.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{ZHAO2025100990,

title = {Exploring the integration of urban air mobility into Mobility-as-a-Service: A stated preference analysis of commuters},

author = {Ying Zhao and Yan Hu and Tao Feng},

url = {https://www.sciencedirect.com/science/article/pii/S2214367X25000080},

doi = {https://doi.org/10.1016/j.tbs.2025.100990},

issn = {2214-367X},

year  = {2025},

date = {2025-01-01},

journal = {Travel Behaviour and Society},

volume = {39},

pages = {100990},

abstract = {This study introduces the concept of air mobility as a Service (AMaaS), integrating urban air mobility (UAM) into the framework of Mobility-as-a-Service (MaaS), with the goal of establishing highly synchronized mobility services. To facilitate the successful deployment of Urban Air Taxi (UAT) services, we aim to investigate people’s preferences for multimodal air taxi services. A stated choice experiment was designed incorporating pay-as-you-go options for various multimodal UAT services and subscriptions to ride-based discounts. Using data collected in Beijing, China, a random parameter error component model was estimated to identify preference heterogeneity among different individuals and potential correlations between alternatives. Our findings indicate a general preference for subscription schemes over pay-as-you-go options across all UAT services. The choice within AMaaS is significantly influenced by the various attributes of UAT alternatives and incentive measures, e.g., stronger government support and/or price discounts increase the probability of using AMaaS. Workers aged 44 or above, high-income groups, car owners, regular car commuters, individuals in managerial positions, and those having helicopter experience are more inclined to commute via multimodal UATs. These findings provide valuable insights for policy decision-making in the planning of UAM, especially when integrated into MaaS.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{XIA2025126242,

title = {Integration management of vessel transportation in complex waterway network of port clusters: A column generation-based solution approach},

author = {Zicheng Xia and Qi Tian and Tao Feng and Zijian Guo and Yun Peng and Ying Jiang and Wenyuan Wang},

url = {https://www.sciencedirect.com/science/article/pii/S0957417424031099},

doi = {https://doi.org/10.1016/j.eswa.2024.126242},

issn = {0957-4174},

year  = {2025},

date = {2025-01-01},

journal = {Expert Systems with Applications},

volume = {268},

pages = {126242},

abstract = {The integration of regional ports will heighten the complexity of maritime traffic in port waters, thus necessitating a unified management approach for this intricate traffic flow. Maritime Bureau implements the integration of multi-port vessel traffic organization based on the advance information provided. The staff manually arranged and organized plans, thereby eliminating planning conflicts during the actual implementation process. Inefficiency and limitation of manual scheduling result in frequent delays and cannot guarantee vessel safety. In this paper, the multi-port waterway network vessel traffic management problem (MPWN-VTM) under multi-rule constraints, e.g., navigation rules, speed control, priority weights of vessels, and tidal time windows requirements, in an actual waterway network is investigated. A mixed-integer linear program (MILP) is proposed to improve scheduling efficiency. The problem is formulated into a more compact set-partitioning formulation and a column generation (CG) algorithm is developed to solve this problem. Several acceleration strategies are used to improve the performance of CG and obtain the optimal or near-optimal solutions. Numerical experiments with the real port network demonstrate the efficiency and applicability of the algorithm by comparing it with practical port scheduling strategy, general heuristic algorithm, and branch-and-cut. The empirical result indicates that the proposed MILP can successfully handle the actual daily scheduling plan for up to 300 vessels within 15 min, with an optimality gap of no more than 5%. This work can be extended to provide intelligent support for potential regional port integration works by promoting the development of traffic management systems under multi-port vessel traffic organization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {},

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 = {},

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 = {},

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 = {},

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{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 = {},

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 = {},

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 = {},

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 = {},

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 = {},

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{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 = {},

pubstate = {published},

tppubtype = {article}

}