This paper investigates dynamic railcar planning in railroad classification yards. The plan decid... more This paper investigates dynamic railcar planning in railroad classification yards. The plan decides the assignment of railcars from inbound trains to outbound trains under various size limitations of outbound trains and allows dynamic sequencing of inbound train classification and outbound train assembly. A mixed-integer program is presented for the problem along with a heuristic algorithm based on the harmony search strategy. The numerical results based on both a real-world case and randomly created instances show that dynamic sequencing can reduce total dwell time compared to static sequencing, especially when more trains are classified at a yard. However, the savings on average dwell time for each railcar become smaller when the traffic volume goes up. Once the volume through a yard is close to its capacity, the dwell time per rail car goes up very quickly and the benefit of dynamic sequencing diminishes. It implies that the benefit of dynamic sequencing is more significant for yards with lower traffic volume.
Extreme heat is a leading cause of weather-related human mortality throughout much of the world, ... more Extreme heat is a leading cause of weather-related human mortality throughout much of the world, posing a significantly heavy burden on the development of healthy and sustainable cities. To effectively reduce heat health risk, a better understanding of where and what risk factors should be targeted for intervention is necessary. However, little research has examined how different risk factors for heat-related mortality operate at varying spatial scales. Here, we present a novel application of the multiscale geographically weighted regression (GWR) approach to explore the scale of effect of each underlying risk factor using Hong Kong as a case study. We find that a hybrid of global and local processes via multiscale GWR yields a better fit of heat-related mortality risk than models using GWR and ordinary least squares (OLS) approaches. Predictor variables are categorized by the scale of effect into global variables (i.e., age and education attainment, socioeconomic status), intermedi...
Many scholars have advocated for the use of empirical evidence to assess resilience across scales... more Many scholars have advocated for the use of empirical evidence to assess resilience across scales and over time. Accordingly, we conduct a case study using survey data on individual perceptions of disaster relief that were gathered each month from August to Decem-ber 2015, shortly after the 2015 Nepal earthquake. We construct a scale-specific resilience index (SSRI) based on a set of variables that are validated separately at different spatial scales and over time against the survey data. The regression results show that the variables related to household structure, industrial diversity, community capital, and accessibility and emergency services are validated against the survey data at both the district and sub-district levels, the variables related to ethnic diversity and the capacity of emergency camps are validated only at the district level, and the earthquake experiences variable is validated only at the sub-district level. Consequently, to achieve optimal models, we use six validated variables to construct an SSRI at the district level and seven variables, including those related to the vulnerability of household property and the average elevation, to construct an SSRI at the sub-district level. The SSRI scores are validated via multilevel regression models against the surveyed relief scores after the 2015 Nepal earthquake. The results show that the SSRI scores based on the validated variables correlate favorably and as expected against the survey data at both district and sub-district levels, and outperform the composite resilience index, which considers all of the variables regardless of their individual validation results. The method used to construct the SSRI helps to identify the contributions of multidimensional resilience indicators across spatial scales and over time in real cases, and also provides index scores of scale-specific resilience that are easily understood and applicable to multi-scale decision-making processes.
Building communities' resilience to natural weather hazards requires the appropriate assessment o... more Building communities' resilience to natural weather hazards requires the appropriate assessment of such capabilities. The resilience of a community is affected not only by social, economic, and infrastructural factors but also by natural factors (including both site characteristics and the intensity and frequency of events). To date, studies of natural factors have tended to draw on annual censuses and to use aggregated data, thus allowing only a limited understanding of site-specific hot or cold spots of resilience. To improve this situation , we carried out a comprehensive assessment of resilience to typhoon disasters in Nan-sha district, Guangzhou, China. We measured disaster resilience on 1×1-km grid units with respect to socioeconomic and infrastructural dimensions using a set of variables and also estimated natural factors in a detailed manner with a meteorological modeling tool, the Weather Research and Forecast model. We selected typhoon samples over the past 10 years, simulated the maximum typhoon-borne strong winds and precipitation of each sample , and predicted the wind speed and precipitation volume at the 100-year return-level on the basis of extreme value analysis. As a result, a composite resilience index was devised by combining factors in different domains using factor analysis coupled with the analytic hierarchy process. Resilience mapping using this composite resilience index allows local governments and planners to identify potential hot or cold spots of resilience and the dominant factors in particular locations, thereby assisting them in making more rational site-specific measures to improve local resilience to future typhoon disasters.
Building " disaster-resilient " rather than " disaster-resistant " cities/communities requires th... more Building " disaster-resilient " rather than " disaster-resistant " cities/communities requires the development of response capabilities to natural disasters and subsequent recovery. This study devises a new method to measure resilience via recovery capability to validate indicators from social, economic, infrastructural, and environmental domains. The pollutant discharge data (waste-water and waste-gas discharge/emission data) of local power plants, sewage treatment plants and main factories were used to monitor recovery process of both people's living and local industrial production as the waste water/gas is released irregularly during the short disaster-hit period. A time series analysis of such data was employed to detect the disturbance on these infrastructures from disasters and to assess community recovery capability. A recent record-breaking flash flood in Changzhou, a city in eastern-central China, was selected as a case study. We used ordinal logistic regression to identify leading proxies of flood resilience. A combination of six variables related to socioeconomic factors, infrastructure development and the environment, stood out and explained 61.4% of the variance in measured recovery capability. These findings substantiate the possibility of using recovery measurement based on pollutant discharge to validate resilience metrics, and contribute more solid evidences for policy-makers and urban planners to make corresponding measures for resilience enhancement.
This paper investigates dynamic railcar planning in railroad classification yards. The plan decid... more This paper investigates dynamic railcar planning in railroad classification yards. The plan decides the assignment of railcars from inbound trains to outbound trains under various size limitations of outbound trains and allows dynamic sequencing of inbound train classification and outbound train assembly. A mixed-integer program is presented for the problem along with a heuristic algorithm based on the harmony search strategy. The numerical results based on both a real-world case and randomly created instances show that dynamic sequencing can reduce total dwell time compared to static sequencing, especially when more trains are classified at a yard. However, the savings on average dwell time for each railcar become smaller when the traffic volume goes up. Once the volume through a yard is close to its capacity, the dwell time per rail car goes up very quickly and the benefit of dynamic sequencing diminishes. It implies that the benefit of dynamic sequencing is more significant for yards with lower traffic volume.
Extreme heat is a leading cause of weather-related human mortality throughout much of the world, ... more Extreme heat is a leading cause of weather-related human mortality throughout much of the world, posing a significantly heavy burden on the development of healthy and sustainable cities. To effectively reduce heat health risk, a better understanding of where and what risk factors should be targeted for intervention is necessary. However, little research has examined how different risk factors for heat-related mortality operate at varying spatial scales. Here, we present a novel application of the multiscale geographically weighted regression (GWR) approach to explore the scale of effect of each underlying risk factor using Hong Kong as a case study. We find that a hybrid of global and local processes via multiscale GWR yields a better fit of heat-related mortality risk than models using GWR and ordinary least squares (OLS) approaches. Predictor variables are categorized by the scale of effect into global variables (i.e., age and education attainment, socioeconomic status), intermedi...
Many scholars have advocated for the use of empirical evidence to assess resilience across scales... more Many scholars have advocated for the use of empirical evidence to assess resilience across scales and over time. Accordingly, we conduct a case study using survey data on individual perceptions of disaster relief that were gathered each month from August to Decem-ber 2015, shortly after the 2015 Nepal earthquake. We construct a scale-specific resilience index (SSRI) based on a set of variables that are validated separately at different spatial scales and over time against the survey data. The regression results show that the variables related to household structure, industrial diversity, community capital, and accessibility and emergency services are validated against the survey data at both the district and sub-district levels, the variables related to ethnic diversity and the capacity of emergency camps are validated only at the district level, and the earthquake experiences variable is validated only at the sub-district level. Consequently, to achieve optimal models, we use six validated variables to construct an SSRI at the district level and seven variables, including those related to the vulnerability of household property and the average elevation, to construct an SSRI at the sub-district level. The SSRI scores are validated via multilevel regression models against the surveyed relief scores after the 2015 Nepal earthquake. The results show that the SSRI scores based on the validated variables correlate favorably and as expected against the survey data at both district and sub-district levels, and outperform the composite resilience index, which considers all of the variables regardless of their individual validation results. The method used to construct the SSRI helps to identify the contributions of multidimensional resilience indicators across spatial scales and over time in real cases, and also provides index scores of scale-specific resilience that are easily understood and applicable to multi-scale decision-making processes.
Building communities' resilience to natural weather hazards requires the appropriate assessment o... more Building communities' resilience to natural weather hazards requires the appropriate assessment of such capabilities. The resilience of a community is affected not only by social, economic, and infrastructural factors but also by natural factors (including both site characteristics and the intensity and frequency of events). To date, studies of natural factors have tended to draw on annual censuses and to use aggregated data, thus allowing only a limited understanding of site-specific hot or cold spots of resilience. To improve this situation , we carried out a comprehensive assessment of resilience to typhoon disasters in Nan-sha district, Guangzhou, China. We measured disaster resilience on 1×1-km grid units with respect to socioeconomic and infrastructural dimensions using a set of variables and also estimated natural factors in a detailed manner with a meteorological modeling tool, the Weather Research and Forecast model. We selected typhoon samples over the past 10 years, simulated the maximum typhoon-borne strong winds and precipitation of each sample , and predicted the wind speed and precipitation volume at the 100-year return-level on the basis of extreme value analysis. As a result, a composite resilience index was devised by combining factors in different domains using factor analysis coupled with the analytic hierarchy process. Resilience mapping using this composite resilience index allows local governments and planners to identify potential hot or cold spots of resilience and the dominant factors in particular locations, thereby assisting them in making more rational site-specific measures to improve local resilience to future typhoon disasters.
Building " disaster-resilient " rather than " disaster-resistant " cities/communities requires th... more Building " disaster-resilient " rather than " disaster-resistant " cities/communities requires the development of response capabilities to natural disasters and subsequent recovery. This study devises a new method to measure resilience via recovery capability to validate indicators from social, economic, infrastructural, and environmental domains. The pollutant discharge data (waste-water and waste-gas discharge/emission data) of local power plants, sewage treatment plants and main factories were used to monitor recovery process of both people's living and local industrial production as the waste water/gas is released irregularly during the short disaster-hit period. A time series analysis of such data was employed to detect the disturbance on these infrastructures from disasters and to assess community recovery capability. A recent record-breaking flash flood in Changzhou, a city in eastern-central China, was selected as a case study. We used ordinal logistic regression to identify leading proxies of flood resilience. A combination of six variables related to socioeconomic factors, infrastructure development and the environment, stood out and explained 61.4% of the variance in measured recovery capability. These findings substantiate the possibility of using recovery measurement based on pollutant discharge to validate resilience metrics, and contribute more solid evidences for policy-makers and urban planners to make corresponding measures for resilience enhancement.
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