Healthier routes planning: A new method and online implementation for minimizing air pollution exposure risk

Air pollution exposure during daily traveling is growing as an increasingly serious factor affecting public health with rapid incensement of travel distance in urban sprawl. Finding a healthier route with least exposure risk might be an alternative way to mitigate adverse health outcomes under the truth that worldwide air pollution in urban area cannot be eliminated within a short period of time. Integrating techniques of fine scale mapping of air pollutant concentration, risk weight estimation of road segment exposure to air pollutants, and dynamic Dijkstra algorithm capable of updating route, this study for the first time proposes a healthier route planning (HRP) method to minimize personal travel exposure risk to air pollution. Effectiveness of HRP in mitigating exposure risk was systematically tested based on hundred pairs of origins and destinations located in Beijing-Tianjin-Hebei (BTH) of China with necessarily dense air quality observations. Results show that the spatiotemporal variations of air pollutant concentrations were significant and these differences indeed occurred with time at hourly scale. Meanwhile, the grid-based estimation of exposure risk is time dependent with risk ranging from 5 to 109, which echoes the necessity of healthier route planning. Compared to routes with the shortest distance and least travel time, healthier route has the least exposure risk. And this risk mitigation effect is more significant in areas with wide exposure risk variations than those in areas without obvious risk difference over space (e.g., 21.38% vs. 0.86%). Results suggest that HRP method is promising to minimize personal exposure risk during daily travel based on the accurate exposure risk estimation of road segment at high spatiotemporal resolution. This role could be more important in areas with longer travel distance and greater heterogeneous distribution of air pollution in great metropolis.     

An inexact log-normal distribution-based stochastic chance-constrained model for agricultural water quality management

In this study, an inexact log-normal-based stochastic chance-constrained programming model was developed for solving the non-point source pollution issues caused by agricultural activities. Compared to the general stochastic chance-constrained programming model, the main advantage of the proposed model is that it allows random variables to be expressed as a log-normal distribution, rather than a general normal distribution. Possible deviations in solutions caused by irrational parameter assumptions were avoided. The agricultural system management in the Erhai Lake watershed was used as a case study, where critical system factors, including rainfall and runoff amounts, show characteristics of a log-normal distribution. Several interval solutions were obtained under different constraint-satisfaction levels, which were useful in evaluating the trade-off between system economy and reliability. The applied results show that the proposed model could help decision makers to design optimal production patterns under complex uncertainties. The successful application of this model is expected to provide a good example for agricultural management in many other watersheds.     

Identifying spatial interaction patterns of vehicle movements on urban road networks by topic modelling

The development of mobile positioning technologies makes massive individual trajectory data easily accessible, which facilitates the revisit of spatial interaction issue in recent years. Researchers have proposed many methods to investigate the spatial interactions derived from human movements, such as the gravity model and radiation model. However, these studies have mainly focused on the interactions among areal units at an aggregated level, neglecting that in most cases, human movements are carried by vehicles and constrained by the underlying road network, which causes the interactions among roads. To fill this gap, we propose a novel approach to identify spatial interaction patterns of vehicle movements on urban road network. As the topic model originating from the domain of natural language processing has powerful advantages in extracting semantic relations of words from corpus, we utilize it to extract interaction relations of urban roads from massive vehicle trajectories. First, "strokes" (i.e., natural streets) are chosen as geographical units to represent the vehicle moving paths. Then, an analogy between geographical elements (i.e., stroke, moving path) and textual elements (i.e., word, document) is established, and a topic model is applied to the moving paths to identify the spatial interaction patterns on road network. From a mass of trajectory data collected by GNSS-equipped taxis in Beijing, the "topic patterns", which can be viewed as clusters of spatially interacted strokes, are identified, visualized and verified. It is argued that our proposed approach is effective in identifying spatial interaction patterns, which provides a new perspective for spatial interaction modelling and enriches the current spatial interaction studies.     

Rainfall-runoff modeling,parameter estimation and sensitivity analysis in a semiarid catchment

We present a study on the Hydro-Informatic Modelling System (HIMS) rainfall-runoff model for a semiarid region. The model includes nine parameters in need of calibration. A master-slave swarms, shuffling evolution algorithm based on self-adaptive dynamic particle swarm optimization (MSSE-SDPSO) is proposed to derive model parameters. In comparison with SCE-UA, PSO, MSSE-PSO and MSSE-SPSO algorithms, MSSE-SDPSO has faster convergence and more stable performance. The model is used to simulate discharge in the Luanhe River basin, a semiarid region. Compared with the SimHyd and SMAR models, HIMS model has the highest Nash-Sutcliffe efficiencies (NSE) and smallest relative errors (RE) of volumetric fitness for the periods of calibration and verification. In addition, the studies indicate that the HIMS model with all-gauge data improves runoff prediction compared with single-gauge data. A distributed HIMS model performs better than a lumped one. Finally, the Morris method is used to analyze model parameters sensitivity for the objective functions NSE and RE.     

Detecting anomalous spatial interaction patterns by maximizing urban population carrying capacity

Rapid urbanization in China has prompted plenty of urban facilities to be constructed with the expectation of harmonizing with the rapid growth of urban population. However, regarding the spatial interactions produced by cross-area human mobility, the diversity and variability of residents' trip requirements inevitably cause the deviations of the real interaction patterns from the optimal status determined by the current allocation of urban facilities. To maximize the utility of urban facility allocation, we designed a bipartite network-based approach to explore anomalous spatial interaction patterns within cities. First, considering the potential area attractiveness, a weighted origin-destination bipartite network was constructed to structure the spatial interactions between traffic analysis zones. Then, a branch and bound (BnB) based augmenting path algorithm was proposed to optimize the distribution of spatial interactions, which can maximize the urban population carrying capabilities. Finally, anomalous interaction patterns causing both overload and underload were detected through comparisons between the actual and optimal spatial interaction distribution. The experimental results show that the two types of anomalous interaction patterns have significantly different spatial distribution characteristics. Through further analyzing the relationships between the two types of anomalous interaction patterns and urban evolution process, this study can also provide targeted decision supports for the accommodating of urban facility allocations to the distributions of resident trips in space.     

Process mechanization models for improved Eucalyptus plantation management in Southern China based on the analysis of currently applied semi-mechanized harvesting operations

Within the last decades ecological and environmental dimensions were in the focus of Chinese forestry and forest research, but forest operations were much less studied. However, a sustainable, efficient, and continuous forest biomass supply is crucial for a socio-economic development in rural areas, and will contribute to national forest management aims. Today, high supply costs and insufficient biomass supply are bottlenecks for bioenergy and wood processing industry in the P.R. China.The aim of this study was (1) to characterize, and obtain process data of the current harvesting operations in forest plantations in Southern China, (2) to develop a harvesting productivity model based on the current performance, and (3) to develop applicable models for improved mechanized operations. The study was conducted in Eucalyptus (Eucalyptus grandis x urophylla) and Mytilaria (Mytilaria laosensis) plantations in steep terrain (8.5–27.5°; respectively 15–52%) in Guangxi province, with a tree diameter at 1.3 m height ranging from 9.8 to 15.4 cm. Via conducting time and motion studies, 237.8 h were recorded and 121.5 m³ were harvested between October 2008 and May 2010.The clear cut operations are characterized by basic manual work, except motor-manual work for tree felling and cross-cutting. The overall time consumption for harvesting and extraction was 115.07 min m⁻³, respectively the productivity was 0.58 m³ h⁻¹. Manual hauling of the logs was identified as the most time consuming activity. Therefore, priority should be given to extraction activities when trying to improve productivity and work safety through mechanization.     

Complexity analysis of manufacturing service ecosystem: a mapping-based computational experiment approach

The trend of servitisation is increasingly affecting manufacturing enterprises. Traditional manufacturing enterprises cannot handle the related challenges of service innovation by themselves. Recently, manufacturing service ecosystem (MSE) has been proposed to support service innovation by facilitating collaboration. The construction and development of MSE need to handle a series of complexities, such as individual complexity, interaction complexity and ecological complexity. However, it is still very difficult to clearly identify the possible effect of various influence factors on MSE evolution, which is necessary analyse the complex dynamic interactive relationship among participants, so as to maintain the sustainable and healthy development of MSE. To change such a situation, this paper proposes a mapping-based computational experiment approach to analyse the evolution of MSE. This approach has three main parts, i.e. model construction of real world, model mapping of computational system and experiment evaluation of various factors of MSE evolution. By adopting the proposed approach, several case studies are conducted to investigate the possible effect of cooperation preference on the MSE evolution in various market environments. The results demonstrate that the proposed approach is effective.     

Effects of urban planning in guiding urban growth: Evidence from Shenzhen,China

Improved understanding the effects of urban planning in guiding urban growth is conducive to the establishment of orderly spatial control systems. Few findings emphasized and systematic interpreted on the effects directly caused by urban planning. Thus, the effects of urban planning and its detailed urban functional planning, as well as different planning combinations were studied in this paper, using Shenzhen as an example. The results show that: 1) During the study period of 2000–2010, urban planning has had a conspicuous controlling effect on urban growth of the Special Economic Zone (SEZ). However, disordered growth has occurred outside the SEZ. 2) Urban planning plays a not particularly significant role in guiding urban renewal and development. The influence of residential planning on residential renewal is not significant. Commercial renewal is positively correlated with traffic accessibility and the distance from subway stations. Industrial renewal only has a significant positive correlation with industrial planning. 3) The planning combinations have played a significant positive guiding role in land development. The renewal probability of residential land equipped with rail planning is increased 19.22 times compared by itself.     

Assessing the flexibility of public squares the case of Grande Poste square in Algiers

Changes in cities affect public squares. They host temporal, virtual and conflicting uses at different times. For these reasons, they must be multifunctional and adaptable to rapid and unpredictable changes to meet the users´ needs which change over time. These changes have repercussions on the planning and the development of these spaces. In this context, planners introduce flexibility of use in urban planning as a feature of adapting spaces to changes. Current references cite characters and models of flexible places, as well as flexible uses of these places. However, how can planners improve the potential of the flexibility of use in existing public squares? This article proposes an approach to assess the flexibility of use in public squares. It requires the combination of two relevant qualitative methods used to evaluate the flexibility of use in buildings. We used them because there is still no evaluation method for public squares. The application of this method in the central square of the Grand Poste in Algiers aims to show the degree of flexibility of this place and identify aspects that help planners and architects to propose planning recommendations for more flexibility in using this square.     

An exploration of the interaction between urban human activities and daily traffic conditions: A case study of Toronto,Canada

Understanding how citizens interact with transportation system is a key to solving a variety of urban issues in general and traffic congestion in particular. Recently, scholars have put efforts on the pertinent work ranging from developing traffic predictors to understanding human mobility and activity patterns. Multiple types of data have been used, of which crowdsourced data (e.g. social media data) plays an essential role. Due to the limitation of traffic information extraction from social media data raised in the existing work, this paper aims to develop an approach which allows us to explore the potential influence of human activities on daily traffic congestions through linking human activities derived from geotagged tweets to the daily traffic conditions. The result of a case study of Toronto, Canada exhibits that entertainment related activities are more likely to appear during evening peak hours, while it seems that morning rush hours are less sensitive to human activities. In addition, it is learned that the activities involved in international events tend to have a long-term impact on urban traffic. This work provides a new tool for urban planners and policy makers to deal with complex urban issues effectively using low-cost social media data and sheds light on the research on analyzing urban traffic and urban dynamics based on crowdsourced data.