A heuristic model of bounded route choice in urban areas

There is substantial evidence to indicate that route choice in urban areas is complex cognitive process, conducted under uncertainty and formed on partial perspectives. Yet, conventional route choice models continue make simplistic assumptions around the nature of human cognitive ability, memory and preference. In this paper, a novel framework for route choice in urban areas is introduced, aiming to more accurately reflect the uncertain, bounded nature of route choice decision making. Two main advances are introduced. The first involves the definition of a hierarchical model of space representing the relationship between urban features and human cognition, combining findings from both the extensive previous literature on spatial cognition and a large route choice dataset. The second advance involves the development of heuristic rules for route choice decisions, building upon the hierarchical model of urban space. The heuristics describe the process by which quick, ‘good enough’ decisions are made when individuals are faced with uncertainty. This element of the model is once more constructed and parameterised according to findings from prior research and the trends identified within a large routing dataset. The paper outlines the implementation of the framework within a real-world context, validating the results against observed behaviours. Conclusions are offered as to the extension and improvement of this approach, outlining its potential as an alternative to other route choice modelling frameworks.     

Optimal biofuel supply chain design under consumption mandates with renewable identification numbers

The Renewable Identification Number (RIN) system is a tracking mechanism that enforces the U.S. Renewable Fuel Standard by monitoring obligated parties’ compliance with the biofuel consumption mandates. This paper incorporates the RIN system into the design of a biofuel supply chain that addresses independent decisions of non-cooperative farmers, biofuel manufacturers, and blenders. Game-theoretic models are developed to examine the impacts of the RIN system on individual stakeholders’ decisions (e.g., on farmland use, bio-refinery investment, biofuel production) and the competition between food and biofuel industries, in both a perfectly competitive scenario and a monopoly scenario. For the perfectly competitive scenario, Nash equilibrium can be obtained by solving a convex optimization problem. For the monopoly scenario, a bi-level Stackelberg leader–follower model is developed, from which we found that a rigid mandate on blenders may suppress the total biofuel production. To avoid such unintended consequences, a relaxed unit-RIN based penalty scheme is proposed and shown to improve the overall biofuel supply chain performance. Managerial insights are drawn from a numerical case study for the state of Illinois.     

A spatial Difference-in-Differences estimator to evaluate the effect of change in public mass transit systems on house prices

Evaluating the impact of public mass transit systems on real-estate values is an important application of the hedonic price model (HPM). Recently, a mathematical transformation of this approach has been proposed to account for the potential omission of latent spatial variables that may overestimate the impact of accessibility to mass transit systems on values. The development of a Difference-in-Differences (DID) estimator, based on the repeat-sales approach, is a move in the right direction. However, such an estimator neglects the possibility that specification of the price equation may follow a spatial autoregressive process with respect to the dependent variable. The objective of this paper is to propose a spatial Difference-in-Differences (SDID) estimator accounting for possible spatial spillover effects. Particular emphasis is placed on the development of a suitable weights matrix accounting for spatial links between observations. Finally, an empirical application of the SDID estimator based on the development of a new commuter rail transit system for the suburban agglomeration of Montréal (Canada) is presented and compared to the usual DID estimator.     

A hierarchical rule-based land use extraction system using geographic and remotely sensed data: A case study for residential uses

Lack of detailed land use (LU) information and of efficient data gathering methods have made modeling of urban systems difficult. This study aims to develop a hierarchical rule-based LU extraction system using very high resolution (VHR) remotely sensed imagery and geographic vector data. Land cover information extracted from remote sensing and several types of geographic data from the study area, City of Fredericton, Canada, are fused into a comprehensive database, in order to develop a sophisticated LU Extraction Expert System (LUEES). This paper illustrates how the proposed LUEES though a case study for residential uses in the study area. Morphological (individual-based) analysis at the building-level is carried out through a step-wise binary logistic regression model, which differentiates residential and non-residential buildings and results in an overall accuracy of 93.1%. The results derived from morphological analysis are then subject to a post-correction process using a spatial arrangement analysis, in order to further mitigate the misclassification issues arising from the morphological analysis. In this regard, Gabriel Graph connectivity examines the spatial structure and arrangements of urban features concerning different LU types. It is found that the spatial arrangement analysis further enhances the residential LU classification accuracy, which gives rise to an overall accuracy of 97.4%. It is believed that, equipped with such a powerful LU data collection tool and resulting detailed/accurate LU data, urban planners/modelers should be able to more reliably and precisely represent/predict economic interactions, activity locations, space and housing developments, business expansion, and trip patterns.     

Economic analyses of sea-level rise adaptation strategies in transportation considering spatial autocorrelation

This paper reports the impacts of economic analysis results on sea-level rise adaptation decision making with different economic analysis methods. The methodology was applied to Hillsborough County, Florida. A general conclusion is that partial shoreline protection should be implemented to reduce the potential impacts of sea-level rise on important land use, then transportation infrastructure is preferred to be protected or accommodated, and finally managed relocation should be adopted. More specifically, the results show that the best adaptation strategy is shoreline protection plus transportation infrastructure accommodation; the length of shoreline protection plays an important role in the economic analysis results, and shoreline protection and accommodation adaptation strategies for all areas are not recommended because of either high costs or low benefits; the value of travel time saving and spatial autocorrelation play important roles in the economic analysis results of accommodation strategy, which highlights the importance of including indirect economic factors and spatial autocorrelation impacts when making sea-level rise adaptation decisions.     

依托铁路货场的临港物流园区规划研究——以莆田东站物流园区规划为例

临港物流园区凭借其优越的区位条件及强大的综合交通能力在我国发展迅速。为指导依托铁路货场的临港物流园区规划,以莆田东站物流园区规划为案例,基于MSFLB物流园区规划方法,着重分析影响规划的控制要素,创新规划理念,强化战略定位和发展目标,最终提出打造联运中心、中转枢纽及联动核心的规划构想,并据此对莆田东站物流园区空间布局进行详细设计,为指导此类物流园区规划提供了参考与借鉴。     

ArcGIS空间分析功能在道路交通环境影响后评价中的应用

以ArcGIS空间分析模块为工具，在吸收道路建设环境影响后评价各种常用方法优点的基础上，提出了一种简便实用的评价方法，并给出了评价实例的详细操作步骤。     

Incumbent responses to low-cost airline entry and exit: A spatial autoregressive panel data analysis

While the US airline industry has been substantially transformed in recent years by the growth of low-cost airlines, the cost-saving benefits of lower airfares are difficult to gauge empirically. There are two important ways in which this paper contributes to the existing literature on the impact of the low-cost carriers (LCCs). First, the availability of route-level panel data allows us to examine the role of the LCCs in the long-run adjustment of airfares as well as the responses of the incumbent carriers to LCC entry and exit in a dynamic setting. Second, we capitalize on recent developments in spatial econometrics and explicitly model the spatial dependence among adjacent airline routes, an issue often ignored by previous studies. Although most of the pro-competitive effects of LCC entry take place after entry, we find evidence that the incumbent carriers also cut airfares in anticipation of entry by the LCCs. Moreover fares remain lower even after Southwest Airlines exits. Our empirical analysis confirms the spatial dependence among airfares in adjacent routes, provides estimates of the consumer benefits from lower airfares in routes affected by LCCs, and shows that there are substantial indirect benefits, i.e. lower fares in spatially-linked, nearby routes.     

Climate–ocean variability and Pacific hake: A geostatistical modeling approach

Climate forcing of the California Current has been known to impact the distribution and abundance of a number of local fish populations, but the mechanisms involved remain poorly understood. Climate metrics such as the Pacific Decadal Oscillation (PDO) and the El Niño Southern Oscillation (ENSO) are usually used to represent climate processes and direct links are made between climate forcing and production variability. This involves aggregation of impacts across large spatial scales and range of species. However, fluctuations in productivity are often the result of changes in physical habitat. In order to fully understand the relationship between climate and productivity, habitat changes should be addressed. In this study we use a geostatistical approach to quantify adult Pacific hake habitat during different climate regimes. Several authors have suggested that the distribution and intensity of the sub-surface poleward flow (the undercurrent) plays a key role in defining adult hake habitat along the west coast of North America. Here we build a model designed to predict hake habitat distribution in space based on sub-surface poleward flow distribution and bottom depth. Our results show that hake habitat expands in 1998 El Niño year compared to 1995. Given the important predatory role that hake plays in the CC, the amount and distribution of adult hake habitat has large implications for the Pacific Northwest food web and could thus serve as an ecosystem indicator representing important physical–biological interactions. Spatially based ecosystem indicators such as the one we develop here address two important yet neglected areas in the ‘Ecosystem Indicators debate’: the importance of developing metrics explicitly representing spatial and environmental processes shaping ecosystem structure. Without these, our power to fully describe ecosystems will be limited.