Using a spatial autoregressive model with spatial autoregressive disturbances to investigate origin-destination trip flows.

Spatial interaction models with spatial origin-destination (OD) filters are powerful tools to characterize trip flows in space, which is a classic and important problem in regional science. To the authors' knowledge, existing studies adopting OD filters mostly specify the spatial dependence as an autoregressive process, which may not be the full picture of spatial effects. To examine the problem, this paper proposes the hypotheses that 1) spatial OD dependences can take place in both the spatial autoregressive term and the spatial error term in a spatial interaction model. 2) Estimating a spatial autoregressive model with spatial autoregressive disturbances (SARAR) model with OD filters would disentangle where the spatial dependence exists and by how much. 3) The marginal effects obtained from SARAR models would be preferred to analysts when SARAR models outperform spatial autoregressive (SAR) models and spatial error models (SEM) from the statistical point of view. To assess these hypotheses, this paper specifies, estimates, and applies SARAR models with OD filters to investigate trip distributions. By comparing against alternative models, this paper investigates the estimation results in SAR, SEM and SARAR models using an empirical data collected from Hangzhou, China. The contribution of this paper is to be the first in developing an SARAR model with OD filters for trip distribution analyses and examining its performance.

Analyzing time-varying trip distributions with a random-effect spatial OD dependence model.

This paper proposes a random-effect spatial OD (origin-destination) dependence model to investigate varying trip distributions over time. By proposing a maximum likelihood estimation with spectral decomposition methods, the effects of spatial dependences and the unobservable zonal heterogeneity at the origin and destination can be estimated simultaneously. A series of numerical experiments and a real-world trip distribution study with cellular signaling data collected in Hangzhou, China, are conducted. This paper enriches the existing literature by developing (1) an innovative specification to allow for random effects in existing spatial OD dependence models; (2) an innovative estimation method to obtain the values of parameters and improve model fittings; and (3) a set of numerical experiments and an empirical trip distribution analysis that jointly captures spatial effects (spatial interaction and spatial OD dependences), and the unobservable zonal heterogeneity. This paper can equip policymakers with an effective tool for analyzing the OD travel flow over time which is a groundwork for making appropriate transportation policies.

Using mixed methods to design service quality evaluation indicator system of railway container multimodal transport.

Multimodal transport can bring the technical and economic advantages in different transportation modes into full play. While ensuring the level of service, it can reduce energy consumption and transport costs. Governments of most countries are actively promoting it. Therefore, it has become a research hot spot. Being a green, fast, and all-day transport mode, railways play an important role in multimodal transport. This article aims to analyze a multimodal transport service quality indicator system involving railways from the perspectives of customers, multimodal service providers, and governments. Qualitative and quantitative research methods were adopted to analyze the secondhand data of academic papers, government policy, and industry reports to clarify the quality characteristics of multimodal transport services. Using grounded theory and to analyze firsthand data from in-depth interviews with multimodal transport practitioners, 25 evaluation indicators of container multimodal transport service quality were chosen to be the evaluation index system. To test and improve the evaluation scale, 270 valid questionnaires were analyzed using SPSS 24.0 and AMOS 21.0 software, including reliability analysis, exploratory factor analysis, and confirmatory factor analysis. The results show that all the indicators meet the standard requirements and have good reliability and validity.

A simulated annealing methodology to multiproduct capacitated facility location with stochastic demand.

A stochastic multiproduct capacitated facility location problem involving a single supplier and multiple customers is investigated. Due to the stochastic demands, a reasonable amount of safety stock must be kept in the facilities to achieve suitable service levels, which results in increased inventory cost. Based on the assumption of normal distributed for all the stochastic demands, a nonlinear mixed-integer programming model is proposed, whose objective is to minimize the total cost, including transportation cost, inventory cost, operation cost, and setup cost. A combined simulated annealing (CSA) algorithm is presented to solve the model, in which the outer layer subalgorithm optimizes the facility location decision and the inner layer subalgorithm optimizes the demand allocation based on the determined facility location decision. The results obtained with this approach shown that the CSA is a robust and practical approach for solving a multiple product problem, which generates the suboptimal facility location decision and inventory policies. Meanwhile, we also found that the transportation cost and the demand deviation have the strongest influence on the optimal decision compared to the others.

Mixed transportation network design under a sustainable development perspective.

A mixed transportation network design problem considering sustainable development was studied in this paper. Based on the discretization of continuous link-grade decision variables, a bilevel programming model was proposed to describe the problem, in which sustainability factors, including vehicle exhaust emissions, land-use scale, link load, and financial budget, are considered. The objective of the model is to minimize the total amount of resources exploited under the premise of meeting all the construction goals. A heuristic algorithm, which combined the simulated annealing and path-based gradient projection algorithm, was developed to solve the model. The numerical example shows that the transportation network optimized with the method above not only significantly alleviates the congestion on the link, but also reduces vehicle exhaust emissions within the network by up to 41.56%.

Combined simulated annealing algorithm for the discrete facility location problem.

The combined simulated annealing (CSA) algorithm was developed for the discrete facility location problem (DFLP) in the paper. The method is a two-layer algorithm, in which the external subalgorithm optimizes the decision of the facility location decision while the internal subalgorithm optimizes the decision of the allocation of customer's demand under the determined location decision. The performance of the CSA is tested by 30 instances with different sizes. The computational results show that CSA works much better than the previous algorithm on DFLP and offers a new reasonable alternative solution method to it.