Networks and Spatial Economics最新文献

This paper presents two novel algorithmic frameworks to address the logit-based stochastic user equilibrium traffic assignment problem (SUE-TAP). Following the different variant of the gradient projection (termed as GP2) algorithm, we propose an improved GP2 algorithm (IGP) for the SUE-TAP. This study initially presents a smart approach for determining the allocation of more or less effort to specific origin–destination (OD) pairs. Subsequently, the TAP can be decomposed by different OD pairs, whereas the proposed IGP algorithm is designed based on the serial scheme (i.e., the Gauss–Seidel method). Therefore, a new parallel algorithm P-IGP is proposed, which integrates the block coordinate descent (BCD) method and the IGP algorithm. In specific, the independent OD pairs can be separated into several blocks, and the OD-based restricted subproblems within each block can be solved in parallel. Then, we outline the entire process of implementing the P-IGP algorithm to address the SUE-TAP. Several numerical experiments are conducted to verify the proposed algorithms. The results reveal that the proposed IGP algorithm demonstrates significantly speeder convergence in comparison to the traditional GP2 algorithm, achieving a remarkable acceleration of approximately 12%. Furthermore, the performance of the P-IGP algorithm surpasses that of the proposed IGP algorithm, and it can further achieve a notable 4–5-fold enhancement in convergence efficiency.

As the world is reopening from the unprecedented global pandemic, investigating how the intensity of transmission and responsive policies affect passenger air traffic demand is valuable for the aviation industry recovery and post-pandemic economic development. This paper investigates the effects of confirmed COVID-19 cases and state government policies at 28 hub airports in the United States from March 2020 to September 2021 by proposing an origin-destination (OD) spatial temporal econometric model. The investigation finds that (1) confirmed COVID-19 cases and state government policies had the highest effects on air traffic in the same month as these events occurred and the effects were diminishing in the following months; (2) The policy of internal movement restrictions in a given state generated a higher impact for trips arriving at this state, while confirmed COVID-19 cases and the testing policy generated a higher impact for trips departing from this state; (3) Reopening offices, lifting movement restrictions, maintaining flexibility in accessing COVID-19 tests, and using facial covering onboard are effective policies for aviation industry recovery. This paper aims to be a timely study on air travel demand when the domestic traffic has almost achieved the pre-pandemic level, offering insights into recovery of the aviation industry and preparation for future uncertainties. In addition, the proposed OD spatial temporal model which captures OD spatial dependences and temporal correlations simultaneously can equip spatial economists with an innovative and powerful tool.

Abstract

This paper is concerned with spatial single-index autoregressive model (SSIM), where the spatial lag effect enters the model linearly and the relationship between variables is a nonparametric function of a linear combination of multivariate regressors. It addresses challenges related to the curse of dimensionality and interactions among non-independent variables in spatial data. The local Walsh-average regression has proven to be a robust and efficient method for handling single-index models. We extend this approach to the spatial domain, propose a regularized local Walsh-average (RLWA) estimation strategy where the nonparametric component is established by a local Walsh-average approach and the estimation of the parametric part by Walsh-average method. Under specific assumptions, we establish the asymptotic properties of both parametric and nonparametric partial estimators. Additionally, we propose a robust shrinkage method termed regularized local Walsh-average (RLWA) that can construct robust parametric variable selection and robust nonparametric component estimation simultaneously. Theoretical analysis reveals RLWA works beautifully, including consistency in variable selection and oracle property in estimation. We propose a parameter selection process based on a robust BIC-type approach with an oracle property. The effectiveness of the proposed estimation procedure is evaluated through three Monte Carlo simulations and real data applications, demonstrating its performance in finite samples.

In recent times, the significance of variable selection has amplified because of the advent of high-dimensional data. The regularization method is a popular technique for variable selection and parameter estimation. However, spatial data is more intricate than ordinary data because of spatial correlation and non-stationarity. This article proposes a robust regularization regression estimator based on Huber loss and a generalized Lasso penalty to surmount these obstacles. Moreover, linear equality and inequality constraints are contemplated to boost the efficiency and accuracy of model estimation. To evaluate the suggested model’s performance, we formulate its Karush-Kuhn-Tucker (KKT) conditions, which are indicators used to assess the model’s characteristics and constraints, and establish a set of indicators, comprising the formula for the degrees of freedom. We employ these indicators to construct the AIC and BIC information criteria, which assist in choosing the optimal tuning parameters in numerical simulations. Using the classic Boston Housing dataset, we compare the suggested model’s performance with that of the model under squared loss in scenarios with and without anomalies. The outcomes demonstrate that the suggested model accomplishes robust variable selection. This investigation provides a novel approach for spatial data analysis with extensive applications in various fields, including economics, ecology, and medicine, and can facilitate the enhancement of the efficiency and accuracy of model estimation.

The popularization of electric vehicles (EVs) is limited by their driving range and long charging times. To address this, in-motion wireless power transfer systems (WPTSs) are currently attracting attention as a new power supply system. In-motion WPTSs have coils embedded under the road to transfer power from the WPTSs to EVs while driving. However, the main drawback of WPTSs is their large investment, especially in supporting the long-distance trips of EVs on expressways. Therefore, this study proposes a new mixed-integer programming model (MIP) to determine the optimal location of WPTSs for maximized total feasible flow demand. By focusing on long-distance trips on expressways, we propose the first flow-capturing model for WPTS locations that can (i) solve for the distance of WPTS installed as continuous variables, and (ii) solve problems based on real-scale data using a general MIP solver. Our method is extended to a discussion of WPTS installations on expressways in Japan. We observe that WPTS has strong potential as an EV power supply system in terms of coverage and economic rationality. In particular, WPTS has economic rationality not only in busy networks but also in sparsely populated networks that connect urban and rural areas. Thus, this study clarifies the important insights of WPTSs in improving their effectivity to narrow down the demand and ensure the flexibility in the locations of WPTS.

This study seeks to plan and evaluate the cost of the logistics in manufacturing tetra duplex board using prime grade and recycled materials. The real-world data for this study is obtained from one of the largest paper and board industries in Asia. The bi-objective problem is formulated by developing a mixed integer linear programming (MILP) model considering the constraints related to raw material supplies, processing, and storage. The metaheuristic optimization techniques are applied based on the concept of epsilon dominance to balance the conflicting objectives to counter the complex problem in the real world of transportation for the ease of the decision-makers to make the best-informed decisions in the selection of raw material. The investigation results indicate that the cost of prime-grade material in the tetra duplex board supply chain is 71 percent higher than recycled fiber. Furthermore, this study can be extended by evaluating the environmental aspects of prime and recycled-grade transportation. Moreover, the logistics of the prime grade can further be narrowed down by investigating it in various modes of transportation such as highways, waterways, rail, and air.

Maritime transport plays a key role in the global and local economy, accounting for 80% of global trade by volume. This makes smooth operation of the maritime transport essential. However, the sector faces the constant risk of various crises and their potential consequences that may significantly impact and disrupt the movement of goods on local, regional, and global levels. In recent years, two notable crises, namely the COVID-19 pandemic and the war in Ukraine, have been observed. This paper aims to analyse how international crises, such as armed conflicts and pandemics, influence maritime traffic and assess their impact on both global and local economies. A comparison is drawn between the periods before and during the COVID-19 pandemic and before and during the war in Ukraine to exemplify the effects of crises. The findings are then extrapolated to apply to potential future crises. Vessel movements are studied using data collected from Automatic Identification Systems (AIS). In our quantitative approach, we analyse big data using dedicated tools and visualisation techniques to gain insights into specific phenomena. The paper identifies economically significant regions for maritime traffic and examines the impact of crises on their performance. Its unique value lies in its flow-based analysis of changes in maritime traffic. The main conclusion is that China's importance for worldwide maritime traffic is increasing. This makes the global economy heavily reliant on China to a substantially greater extent than it is, for example, on Russia. Consequently, any crisis in the China region could exert a dramatic impact on the global economy. The paper also discusses observations of changes in maritime traffic following the outbreak of the war in Ukraine.

Since its outbreak in December 2019, COVID-19 has spread rapidly across the world. To slow down the spread of the pandemic, various countries have implemented a series of policies and measures. The transportation system is not only an important carrier for COVID-19, but also a vital means for the prevention and control of the spread of the pandemic. Therefore, most anti-pandemic measures are based on travel restrictions, thereby slowing down the spread of the pandemic. As a result, because of the impact of the pandemic and corresponding control measures, the transportation system has undergone tremendous changes. By analyzing the evolution of the transportation system in response to the influence of COVID-19, it is possible to better understand socioeconomic changes and the changes in residents' daily life. Based on rich license plate recognition data, the characteristics of urban motorized travel under the influence of COVID-19 has been analyzed. According to the processes associated with the control of the pandemic and the resumption of work and production, the analysis period is divided into four stages. The changes in indicators of macroscopic traffic status are analyzed for each stage. The three types of typical motor vehicle groups (i.e., non-localized operating vehicles, taxis, and localized operating vehicles) are characterized by the traffic flow they contribute, the number of vehicles in transit, the average travel intensity, the average daily travel time of a vehicle, the average daily travel distance of a vehicle, and the spatiotemporal distributions of origins and destinations of trips. These data clarify the spatiotemporal evolution characteristics of peoples’ travel behavior at different stages of the pandemic. The results of data analysis show that COVID-19 has deeply changed the motorized travel behavior of urban residents. In the initial stage of resumption of work and production, the willingness to engage in motorized travel had decreased significantly compared with that in the first stage. This willingness gradually resumed until the third and fourth stages, but still did not fully reach the level before the onset of the pandemic. Specifically, the traffic status during morning and evening peaks has basically recovered, and has even increased beyond the level before the pandemic; however, a certain gap was still found between off-peak hours. There were also significant differences in the extent to which different types of vehicles were affected by the pandemic. Among these, taxis were impacted the most by the pandemic. In the fourth stage (at the end of April), the average daily travel time of a vehicle and the average daily travel distance of a vehicle still decreased by 29.25% and 22.63% compared with the first stage, respectively. The operating time of many taxis was shortened from 22:00 PM to 19:00 PM. The spatiotemporal characteristics of vehicles show that the reduction of flexible travel demand (e.g., shopping, cater

This paper uses insights from new economic geography (NEG) to examine eight commodities that span the COVID-19 medical value chain from 2000 to 2020 based on the multilayer network approach. Such an approach is distinguished from single layer approaches by taking into consideration interlayer connections. Centrality measures are based on the HITs algorithm to identify dominant exporters (hubs) and importers (authorities). We further examine the determinants of the multilayer trade networks with a gravity model. Three results are reported. First, the core-periphery structure under NEG has weakened over time with the internationalization of production in certain Covid medical commodities (CMCs). Second, home market effect remains relatively strong over time reflecting the role of NEG’s agglomeration economies in locations with large home market demand. Finally, countries that restricted certain CMCs (especially PPEs and masks) even as they simultaneously facilitated the imports of other CMCs saw a decrease in exports.