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With the advancement of urbanization, urban agglomerations have become a main carrier of China's development. As the ultimate stage of urban agglomeration, the realization of urban agglomeration integration (UAI) plays a dominant role in regional prosperity. Appropriate evaluation of UAI could monitor the integration level and guide its coordinated development. However, little attention has been paid to UAI evaluation research. To this end, this study proposes a comprehensive evaluation model to investigate UAI via the entropy method blended with TOPSIS. Furthermore, utilizing grey relational analysis (GRA), critical dimensions of UAI are also identified to provide concrete evidence. Finally, the proposed model is applied to evaluate the UAI of the Pearl River Delta (PRD), and the main results are as follows: (1) The development trend of the overall UAI in the PRD has increased in fluctuation during the research period, achieving a "good" degree; (2) Except for the policy measures integration (0.882), the integration values for other dimensions are in the range of 0.6 to 0.8 in 2020, reflecting a fairly high level; (3) The infrastructure integration has a major effect on UAI. These findings are favorable in providing references for understanding the current situation of UAI in China, which are of considerable practical significance. [ABSTRACT FROM AUTHOR]

Generated by internal synergy relationships in project portfolio (PP), project portfolio synergy benefits (PPSBs) are essential for enterprises to achieve their strategic objectives. However, few studies have been conducted on measuring PPSBs, which hinders managers from making rational use of PP synergy relationships to realize and improve PPSBs. This research deals with this issue by constructing a PPSB measurement model. First, the PPSB measurement model elements, including PP synergy, PPSB measurement criteria, and PPSB influencing factors, are identified. At this stage, to integrate PP synergy into the measurement model, a new method that emphasizes project similarity based on the project niche is proposed to quantify it. Then, a system dynamics model is developed by quantifying the causal relationships within these elements to measure PPSBs. Finally, the proposed model is demonstrated and validated with a numerical example. Results show that this model can help managers to measure and optimize PPSBs. To our knowledge, this proposed model is the first to realize the measurement of PPSBs, enriching the literature on project portfolio management and providing managers with a tool for enhancing the PPSBs following the organization strategy. [ABSTRACT FROM AUTHOR]

Service provider portfolio selection (SPPS) can be a major challenge for organizations to achieve project success. Hence, organizations need to decide on which service provider portfolio (SPP) is appropriate for project management (PM). However, there has been limited research on how to select a SPP in PM. To address this research gap, we establish a novel model for SPPS based on a BP neural network integrated with entropy-AHP from the perspective of the comprehensive economic benefit. This model employs a BP neural network due to its robustness and memory and nonlinear mapping abilities. Furthermore, we implement the proposed model for a construction project to verify the effectiveness. Our results indicate that the model performs well with a prediction accuracy of 97%. Moreover, the model is confirmed to be robust as it still achieves high prediction accuracy when the input data are disturbed randomly. [ABSTRACT FROM AUTHOR]

Assessing project portfolio risk (PPR) is essential for organizations to grasp the overall risk levels of project portfolios (PPs) and realize PPR mitigation. However, current research is inadequate to effectively assess PPR, which brings challenges to managing PPR. In this context, the purpose of this study is to develop a PPR assessment model via an enhanced backpropagation neural network (BPNN). First, PPR assessment criteria considering project interdependencies are determined. Second, fuzzy logic is used to obtain original data for assessment criteria. Principal component analysis (PCA) is then employed to reduce the dimensionality of assessment criteria and derive the input and output of BPNN. Third, an improved genetic algorithm (IGA) is designed to optimize the initial weights and thresholds of BPNN. On this basis, the PCA-IGA-BPNN assessment model is constructed, followed by training and testing, possessing a test accuracy of 98.6%. Finally, comparison experiments are conducted from both internal and external perspectives. For internal comparison, the proposed model yields less mean absolute percentage error (MAPE), mean square error (MSE), and root mean square error (RMSE) than PCA-GA-BPNN, IGA-BPNN, PCA-BPNN and BPNN and offers the largest convergence speed (γ). As for external comparison, the presented model produces lower MAPE, MSE, and RMSE than Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN) and has the largest coefficient of determination (R 2). Results indicate that the established model performs more satisfactorily in assessing PPR. This research enriches PPR assessment methods and provides managers with a useful tool to evaluate PPR. • A hybrid project portfolio risk assessment model based on an enhanced BPNN is established. • GA is improved by adaptively adjusting the probability of crossover and mutation operations. • PCA and improved GA are integrated with BPNN to enhance its evaluation performance. • The effectiveness and superiority of the proposed model are validated through experiments. [ABSTRACT FROM AUTHOR]

Project portfolio networks (PPNs) are becoming increasingly interdependent at multiple levels to better realize enterprises' strategic objectives. However, interdependencies between PPN components increase the potential for risk cascading propagation and amplify the impact of certain risks into the collapse of the entire PPN. To maintain PPN robustness and controllability, it is necessary to study the risk cascading propagation process in PPN. First, the PPN is constructed based on a scale-free network generation algorithm. Second, a risk cascading propagation model of the PPN is developed using a load capacity model. Four different mixed strategies are proposed to explore the effect of risk cascading propagation on the PPN, and a numerical simulation is applied to examine the robustness and controllability of the PPN under different strategies. The results show that the nonlinear load capacity model with the initial residual capacity redistribution principle provides optimal robustness against risk cascading propagation. When the management capacity redundancy reaches a certain value, PPN has better risk cascading propagation controllability, regardless of the mixed strategy. These results provide a basis for managers to protect against risk cascading propagation and improve PPN robustness and controllability in a specific context. The proposed model can also enrich project portfolios and risk management theory. • The risk cascading propagation model of project portfolio network is proposed. • Different risk triggering scenarios are considered in the proposed model. • The definition of component load, including initial, risk and extra loads, is given. • Different mixed strategies are developed to maintain the network characteristics. [ABSTRACT FROM AUTHOR]

Due to dynamic changes in both internal and external environments, the risk evolution of the project portfolio (PP) becomes extremely complicated, thereby increasing the difficulties of effective risk response. In particular, the real-time influence of risk interactions on the evolution of project portfolio risk (PPR) often goes unnoticed. Meanwhile, risk contagiousness is completely ignored in risk response. To tackle this challenge, this study proposes a PPR evolution and response (PPRER) model by improving the Barrat–Barthelemy–Vespignani (BBV) model and by introducing the evolutionary dynamics method into the PPR response research. The feasibility and applicability of the proposed model are verified through a numerical illustration. Computational results demonstrate that the proposed model can simulate the evolution process of PPRs under the influence of their interactions and give a snapshot of the real-time interactive relationship between PPRs. Based on the obtained results, decision-makers can take effective risk responses by identifying critical strategy intrusion nodes at any time in the evolution process. [ABSTRACT FROM AUTHOR]

Urban rail transit (URT) is under accelerated construction worldwide, which exerts important effects on the ridership of existing stations. However, few studies evaluated the determinants of station ridership considering the development of URT system. By employing the spatial difference-in-differences (SDID) model, this paper evaluates the impacts of a new line on the ridership of existing stations and analyzes its spatial heterogeneity. Combined with a geographically weighted regression (GWR) model, an interaction analysis is performed to understand the spatial variations of the impacts of the new line and surrounding land use on the ridership of existing stations. The results show that the operation of a new line can effectively share the ridership of existing stations and has spatial spillover effects. For different lines and different areas, the impacts can be heterogeneous. Additionally, the coordinated development of the new line and residential land accelerates the ridership growth of existing stations. Conversely, business development around the new line can attenuate the ridership of existing stations. The findings of this paper can help to make proactive planning of URT and provide policy implications for the integrated development of URT and surrounding land use. • Evaluates the impacts of a new urban rail transit line on the ridership of existing stations. • Explores the interaction effects of the new line and surrounding land use on the station ridership. • The operation of the new line can share the ridership of existing stations and has spatial spillover effects. • The interaction effects vary across the space. • Provides policy implications for the proactive planning of urban rail transit systems. [ABSTRACT FROM AUTHOR]

This paper considers a prefabricated construction supply chain (PCSC) consisting of a project contractor who shoulders the on-site assembly task and orders prefabs from a prefabricated factory. To mitigate the heavy double handling costs associated with early and late delivery of prefabs, the project contractor requires the right quantity and exact type of prefabs are carried to construction sites according to its assembly schedule. This is known as just-in-time purchasing (JITP). However, JITP may increase the pressure on the prefabricated factory to hold excessive inventory or to compress production time. For these reasons, the prefabricated factory may be reluctant to switch to JITP. To initiate the operation of JITP and establish a win-win outcome, side-payment contracts including a delivery-time dependent subsidy and two constant transfer terms are designed as coordinate schemes. Employing Stackelberg, we explore participants' optimal decisions. The results show that the JITP yields higher profit for PCSC, and the proposed contracts are capable of achieving a win-win coordination. In particular, the constant transfer cost term is relatively equitable to participants, while the constant cost-sharing transfer term outperforms the constant transfer cost term under a high double handling cost. Moreover, the prefabricated factory earns more profit when the double handling cost for early or late delivery is high. Some managerial implications are also obtained and help to strengthen cooperation among participants and promote the sustainable development of the PCSC. • The effect of just-in-time purchasing on double handling costs is investigated by mathematical model. • Information sharing between participants under just-in-time purchasing is considered and quantified. • The different contract combinations are employed to provide participants with various coordination schemes. • Just-in-time purchasing yields higher profits for the prefabricated construction supply chain. • Side-payment contracts can resolve profit conflicts between participants arising from just-in-time purchasing. [ABSTRACT FROM AUTHOR]

This paper considers a prefabricated construction supply chain (PCSC) consisting of a project contractor who shoulders the on-site assembly task and orders prefabs from a prefabricated factory. To mitigate the heavy double handling costs associated with early and late delivery of prefabs, the project contractor requires the right quantity and exact type of prefabs are carried to construction sites according to its assembly schedule. This is known as just-in-time purchasing (JITP). However, JITP may increase the pressure on the prefabricated factory to hold excessive inventory or to compress production time. For these reasons, the prefabricated factory may be reluctant to switch to JITP. To initiate the operation of JITP and establish a win-win outcome, side-payment contracts including a delivery-time dependent subsidy and two constant transfer terms are designed as coordinate schemes. Employing Stackelberg, we explore participants' optimal decisions. The results show that the JITP yields higher profit for PCSC, and the proposed contracts are capable of achieving a win-win coordination. In particular, the constant transfer cost term is relatively equitable to participants, while the constant cost-sharing transfer term outperforms the constant transfer cost term under a high double handling cost. Moreover, the prefabricated factory earns more profit when the double handling cost for early or late delivery is high. Some managerial implications are also obtained and help to strengthen cooperation among participants and promote the sustainable development of the PCSC. • The effect of just-in-time purchasing on double handling costs is investigated by mathematical model. • Information sharing between participants under just-in-time purchasing is considered and quantified. • The different contract combinations are employed to provide participants with various coordination schemes. • Just-in-time purchasing yields higher profits for the prefabricated construction supply chain. • Side-payment contracts can resolve profit conflicts between participants arising from just-in-time purchasing. [ABSTRACT FROM AUTHOR]

Identifying the determinants of metro ridership is essential for metro planning and passenger flow management. However, few studies to date have empirically examined how accessibility affects metro ridership and even fewer have emphasized the non-linear impacts from a spatiotemporal perspective. This study demarcates station areas via the network-distance method and precisely quantifies the accessibility of metro stations both internally and externally. This is combined with a gradient boosting regression trees (GBRT) model and a Shapley additive explanations (SHAP) model to understand the non-linear impacts of accessibility on metro ridership from a spatiotemporal perspective. The results show that accessibility indicators collectively contribute more than 60% of the predictive power for metro ridership at different times and the external accessibility has a greater impact on metro ridership than internal accessibility. Some indicators, such as the shortest path and population density show threshold effects on metro ridership. More importantly, the results demonstrate significant spatial heterogeneity in the effects of accessibility indicators on metro ridership and geographic trends generally from urban to suburban areas. The findings are expected to help planning departments and transit agencies improve the coordinated development of metro systems. [ABSTRACT FROM AUTHOR]