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Rapid urbanization and natural hazards are posing threats to local ecological processes and ecosystem services worldwide. Using land use, socioeconomic, and natural hazards data, we conducted an assessment of the ecological vulnerability of prefectures in Sichuan Province for the years 2005, 2010, and 2015 to capture variations in its capacity to modulate in response to disturbances and to explore potential factors driving these variations. We selected five landscape metrics and two topological indicators for the proposed ecological vulnerability index (EVI), and constructed the EVI using a principal component analysis-based entropy method. A series of correlation analyses were subsequently performed to identify the factors driving variations in ecological vulnerability. The results show that: (1) for each of the study years, prefectures with high ecological vulnerability were located mainly in southern and eastern Sichuan, whereas prefectures in central and western Sichuan were of relatively low ecological vulnerability; (2) Sichuan’s ecological vulnerability increased significantly (p = 0.011) during 2005–2010; (3) anthropogenic activities were the main factors driving variations in ecological vulnerability. These findings provide a scientific basis for implementing ecological protection and restoration in Sichuan as well as guidelines for achieving integrated disaster risk reduction.

The adverse effect of climate change continues to expand, and the risks of flooding are increasing. Despite advances in network science and risk analysis, we lack a systematic mathematical framework for road network percolation under the disturbance of flooding. The difficulty is rooted in the unique three-dimensional nature of a flood, where altitude plays a critical role as the third dimension, and the current network-based framework is unsuitable for it. Here we develop a failure model to study the effect of floods on road networks; the result covers 90.6% of road closures and 94.1% of flooded streets resulting from Hurricane Harvey. We study the effects of floods on road networks in China and the United States, showing a discontinuous phase transition, indicating that a small local disturbance may lead to a large-scale systematic malfunction of the entire road network at a critical point. Our integrated approach opens avenues for understanding the resilience of critical infrastructure networks against floods., The spread of flood-induced failures in critical infrastructure systems is understudied. Here the authors apply the CaMa-Flood global river flood simulation model to estimate the flood-induced failures and their spread in China and the US and find that the number of flood-induced total failures is in-between that of random and localized damage given the same intensity.

Social media data (SMD) is a new data source in disaster research, which can be used in hazard identification, disaster analysis, risk assessment and emergency rescue. This data-driven disaster research needs to find an appropriate method considering the aspect of data sensitivity. So far, the research in this area is focused on the types of hazard, but rarely considers the relationship between the technical methods and applicable tasks. By emphasizing data and method dependencies, we have attempted to summarize the characteristics of SMD in disaster research, viz., “sociality, rapidity, subjectivity, and un-authenticity”, and explore the processing methods in the applications of disaster management. Our work provides ideas and reference to the researchers working in this area from the perspectives of data and research goals.

In 2019, the typhoon Lekima hit China, bringing strong winds and heavy rainfall to the nine provinces and municipalities on the northeastern coast of China. According to the Ministry of Emergency Management of the People’s Republic of China, Lekima caused 66 direct fatalities, 14 million affected people and is responsible for a direct economic loss in excess of 50 billion yuan. The current observation technologies include remote sensing and meteorological observation. But they have a long time cycle of data collection and a low interaction with disaster victims. Social media big data is a new data source for natural disaster research, which can provide technical reference for natural hazard analysis, risk assessment and emergency rescue information management.We propose an assessment framework of social media data-based typhoon-induced flood assessment, which includes five parts: (1) Data acquisition. Obtain Sina Weibo text and some tag attributes based on keywords, time and location. (2) Spatiotemporal quantitative analysis. Collect the public concerns and trends from the perspective of words, time and space of different scales to judge the impact range of typhoon-induced flood. (3) Text classification and multi-source heterogeneous data fusion analysis. Build a hazard intensity and disaster text classification model by CNN (Convolutional Neural Networks), then integrate multi-source data including meteorological monitoring, population economy and disaster report for secondary evaluation and correction. (4) Text clustering and sub event mining. Extract subevents by BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) text clustering algorithms for automatic recognition of emergencies. (5) Emotional analysis and crisis management. Use time-space sequence model and four-quadrant analysis method to track the public negative emotions and find the potential crisis for emergency management.This framework is validated with the case study of typhoon Lekima. The results show that social media big data makes up for the gap of data efficiency and spatial coverage. Our framework can assess the influence coverage, hazard intensity, disaster information and emergency needs, and it can reverse the disaster propagation process based on the spatiotemporal sequence. The assessment results after the secondary correction of multi-source data can be used in the actual system.The proposed framework can be applied on a wide spatial scope and even full coverage; it is spatially efficient and can obtain feedback from affected areas and people almost immediately at the same time as a disaster occurs. Hence, it has a promising potential in large-scale and real-time disaster assessment.

International journal of disaster risk science 9(3), 292-305 (2018). doi:10.1007/s13753-018-0185-6, Published by Springer, Heidelberg

The Sendai Framework for Disaster Risk Reduction 2015–2030 shifts the focus from managing disasters to reducing risks. Such a shift requires a better understanding of risk in all its dimensions of environment, hazards, exposure, and vulnerability; a disaster risk governance that ensures disaster risk is factored into planning and development at all levels across all sectors, as well as into disaster pre-paredness, rehabilitation, recovery, and reconstruction; and cost–benefit analyses to support the prioritization of investments in disaster risk reduction (DRR) for long-term resilience.

Understanding the environmental impacts of road networks and the success of policy initiatives is crucial to a country’s socioeconomic development. In this study, we propose a comprehensive approach to quantitatively assessing whether a given response is effective in mitigating the impacts of environmental shocks on roads. Our approach includes factor analysis, direct and indirect loss quantification, and cost-benefit analysis. Using nationwide data on road malfunctions and weather service performance in China, we found that the macro-level indirect economic losses from road malfunctions were more than the direct losses in multiples ranging from 11 to 21, and that information provided by the weather service could reduce losses, with benefits exceeding costs by a ratio of 51. The results of our study provide a quantitative tool as well as evidence of the effectiveness of sustainability investment, which should provide guidance for future disaster mitigation, infrastructure system resilience, and sustainability-building policy-making.

The negative impact of climate change continues to escalate flood risk. Floods directly and indirectly damage highway systems and disturb the socioeconomic order. In this study, we propose an integrated approach to quantitatively assess how floods impact the functioning of a highway system. The approach has three parts: (1) a multi-agent simulation model to represent traffic, heterogeneous user demand, and route choice in a highway network; (2) a flood simulator using future runoff scenarios generated from five global climate models, three representative concentration pathways (RCPs), and the CaMa-Flood model; and (3) an impact analyzer, which superimposes the simulated floods on the highway traffic simulation system, and quantifies the flood impact on a highway system based on car following model. This approach is illustrated with a case study of the Chinese highway network. The results show that (i) for different global climate models, the associated flood damage to a highway system is not linearly correlated with the forcing levels of RCPs, or with future years; (ii) floods in different years have variable impacts on regional connectivity; and (iii) extreme flood impacts can cause huge damages in highway networks; that is, in 2030, the estimated 84.5% of routes between provinces cannot be completed when the highway system is disturbed by a future major flood. These results have critical implications for transport sector policies and can be used to guide highway design and infrastructure protection. The approach can be extended to analyze other networks with spatial vulnerability, and it is an effective quantitative tool for reducing systemic disaster risk.

Disaster information acquisition and assessment in China primarily depends on disaster-related governmental departments at all levels. As new challenges faced by disaster emergency management arise, a gap still exists in the timeliness and completeness of rapid disaster assessment. The development and popularity of social media has opened up new channels. Social media data, containing massive amounts of disaster information, has the advantages of timeliness, efficiency, and multiple spatiotemporal scales and can supplement existing methods. Based on the dominant social media platform in China, Sina Weibo, this paper proposes a method of extracting intensity information from Weibo texts, including social perceptions such as shaking, emergency reaction, mood and visible damage, which can support the rapid assessment of seismic intensity. By taking the Changning earthquake in Sichuan Province, China, as a case study, we verify the feasibility of hazard information extraction from Weibo. The results show that the intensity information derived from Weibo in the short-term can respond to a sudden earthquake promptly, then effectively identify the main earthquake affected area, locate the possible orientation close to the meizoseismal area and depict the situation in 10 min. We also propose a grid-based correction method, which synthesizes the hotness-intensity matrix and a seismic attenuation-based model. Compared with taking the seismic attenuation model directly, the proposed correction method improves the accuracy of correct recognition rate of seismic intensity assessment to 82% on average and reduces the false recognition rate significantly. The proposed framework of rapid assessment and correction reveals that the combination of social perception and hazard intensity plays a valuable and promising role as a supplement of traditional disaster assessment approach.

Estimating flood impacts on daily activities is vital to disaster risk reduction and urban resilience. Human mobility on complex road networks has caused citizens, especially automobile commuters, to suffer from remarkable impacts on rainy days. However, quantitative studies of these harms have been limited. In this paper, we develop a new approach to estimating flood-affected populations, including their quantity, structure, and spatial distribution, by integrating commuter mobility patterns and road inundation records using multi-source data. Mobility patterns were built by the Gravity model with point-of-interest (POI) data and then downscaled to individual routes by Monte Carlo simulation. Inundation locations and impassable roads were retrieved by combining flood hazard maps with social media data. This approach was applied to Wuhan, a large city in Central China, to estimate the affected automobile commuters under a typical flood event (July 6, 2016) and three flood scenarios (10-year, 20-year, and 50-year). Our approach produces an estimate of the affected population that is more accurate and detailed than existing approaches. It also provides high-resolution impact information for urban studies and can be applied easily to other cities and disasters that include road closures. It will help governments to estimate affected populations rapidly and assess indirect economic losses caused by labor disruptions and work delays.

Social media, as a new data source, is a promising field in disaster research. Despite doubts about its validity, a growing number of research institutes and commercial companies are exploring the potential of social media in disaster risk management. To understand the development and trends in this domain, a bibliometric analysis was performed using 1573 related published articles in Web of Science between 1991 and 2019. We found that (1) the number of annual publications and new research institutes in this field grew rapidly but seems to have become saturated in recent years. (2) The main research force is independent universities with limited cooperation, and a knowledge network has not yet been formed in this arena. (3) Research hotspots evolve in the path of “conceptualization - refinement - application”. Due to the three features of social media data, namely, timeliness, subjectivity, and disequilibrium, obstacles still exist in applicable disaster types and population representativeness. We anticipate new scientific advances emerging to overcome some technical difficulties. Knowledge sharing, advanced computer science, and multiorganizational cooperation will benefit this arena. These findings indicate potential directions for the development of and innovation in social media-based disaster research.

Efficient evacuation routing and allocation of evacuees to safe shelters can effectively reduce casualties and property losses caused by storm surge disasters. A biobjective model is proposed to tackle shelter allocation and emergency routing problems while simultaneously considering network reliability. The proposed model maximizes the total reliability of routes connecting residential communities and shelters and those connecting shelters. A nondominated sorting genetic algorithm is developed to solve this biobjective model with a specially designed chromosome structure, initialization process, and genetic operators. The mathematical model is validated with a case study using storm surge scenario simulation data and real-world community and infrastructure data from Pingyang County, China. The case study illustrates the effectiveness of the proposed model and its potential application in real-world disaster risk reduction.

Globalization and technological revolutions are making the world more interconnected. International trade is one of the major approaches linking the world. Since the 2011 Tohoku earthquake and tsunami in Japan shocked the global supply chain, more attention has been paid to the global impact of large-scale disasters. China is the second largest trader in the world and faces the most frequent natural disasters. Therefore, this study proposes a gravity model for China's bilateral trade tailored to national circumstances, and estimates the impact of natural disasters in China and trading partner countries on Chinese imports and exports. We analyzed Chinese and trading partner statistical data from 1980 to 2012. Study results show that: (1) China's natural disasters have a positive impact on imports, but have no significant impact on exports, (2) trading partner countries' natural disasters reduce Chinese imports and exports, (3) both development level and land area of the partners are important in determining the intensity of natural disaster impacts on China's bilateral trade. The above findings suggest that the impact of natural disasters on trade is asymmetric and significantly affected by other factors, which demand further study.

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical-cyclone-induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the network's functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real-world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction.

Air pollutant emissions from agricultural burning are observed every year after harvest in China. While agriculture is not the main contributor to air pollution in China, agricultural activities can cause severe pollution events. Recognizing the key mechanisms involved in this process offers an opportunity to minimize pollution events caused by agricultural burning. In this paper, we review the meteorological conditions present during a selected air pollution episode and discuss those conditions using standard meteorological observations. The spatio-temporal variations of PM2.5 concentrations following agricultural burning in Beijing were measured from October 4 to October 7, 2013. This time period coincides with a Chinese public holiday and was selected because the influence of other anthropogenic emissions on air quality was strongly reduced during those days. As a result, we were able to identify the key sources and progress of a severe air pollution event. On October 4, average PM_(2.5) concentration in Beijing continuously increased from 49.7 μg m^(-3) at 1:00 to 302.5 μg m^(-3) at 23:00. Heavily polluted air (＞ 300 μg m^(-3)) initially appeared in southeastern Beijing on the afternoon of October 4. On October 5 and in the early morning of October 6, heavily polluted air masses moved into central Beijing, the inner suburbs, and the suburbs. From 0:00 on October 6 to 15:00 on October 7, the average PM_(2.5) concentration in Beijing decreased from 291.6 μg m^(-3) to 19.2 μg m^(-3). Active fire information derived from the MODIS sensors and back trajectory analysis show that field burning of agricultural residues after a harvest triggered and massively contributed to this severe air pollution event. The results improve our understanding of PM_(2.5) air pollution development processes, and they provide scientific support for the Chinese government to accelerate emission reductions from the field burning of agricultural residues.

China is amongst the countries most severely affected by coastal and marine disasters. In this study, the annual variation and geographic distribution of the direct economic losses and fatalities caused by rapid-onset coastal and marine disasters in China have been analysed. This was based on a collection of historical documents and official records. The five main hazards include storm surges, rough seas, sea ice, red tides and green tides. The results show that: (1) Storm surges caused the most economic losses (92% of the total); (2) At national scale, direct economic losses induced by coastal and marine disasters fluctuated with no clear trend; the number of fatalities per year declined, and in relative terms both economic losses and fatalities decreased dramatically throughout time; (3) Substantial heterogeneity exists across the 11 provincial-level administrative regions in terms of the spatial pattern and temporal trends of coastal and marine hazards, exposure, vulnerability and observed impacts. Guangzhou, Fujian, Zhejiang and Hainan provinces experienced the highest direct economic losses and fatalities due to repeated typhoon-induced storm surges. The decline in adverse impacts caused by hazards is due to substantial progress in coastal and marine disaster prevention and migration in China, largely thanks to institutional measures, plus adaptation and mitigation actions at both national and regional levels. Coastal China still faces growing risks due to socio-economic development, climate change, as well as subsidence and new emerging marine disasters (e.g. green tides). Further management needs to promote integrated solutions across socio-economic development, disaster risk reduction and environmental conservation in coastal regions. This should happen at national and international levels as disasters can affect neighboring countries, and their marine environments and socio-ecological systems. Lessons may be learnt from countries experiencing similar problems over the long-term.

In recent decades, losses resulted from natural disasters have dramatically increased and combined with a significant rise in the intensity and frequency of natural hazards, and in exposure. China is prone to multiple hazards and has experienced huge changes in its social fabric during the past two decades, especially in its population density, development patterns, and other social characteristics. These changes may have exposed the population and society to higher risks than ever before. It is, therefore, important to identify and assess regional social vulnerability to natural hazards to strengthen decision making in disaster risk reduction planning and risk governance. To quantify regional social vulnerability to natural hazards and map its temporal–spatial distribution in China, we carefully selected variables from an extensive compilation of provincial socioeconomic and demographic data. Because of the data issue of high dimension and low sample size, we used a two-level approach to calculate social vulnerability index scores. Dynamic geographic patterns of social vulnerability were captured, and spatial autocorrelations were then calculated. The results indicated a quasi-dualistic structure of social vulnerability in China, with a higher distribution in eastern China compared with that in western China, and a declining trend in spatial clustering in recent years.

When a region is hit by a severe disaster, humanitarian supplies must be provided to victims/evacuees efficiently throughout the entire disaster and post-disaster periods. The emergency packages include but not limited to food, water, sanitation supplies, medicine, medical equipment, etc. Delivery of the humanitarian aids from suppliers to shelters must be done within certain time limits. Though helicopters are used for transporting a fraction of the daily requirements, the capacity of small aircrafts limits the throughput of this mode. Ground transportation modes are still playing a major role in the humanitarian logistics. Because of the severe weather during and after disaster, e.g. storm after hurricane or after-shock after an earthquake, it is common to have failures on the road and infrastructure, such as flooding, surface cave-in and sedimentation, which may delay the traffic or even make part of the network unusable. The expected reliability of a route is one of the main variables when planning a trip. The route choice coheres with the choice of the departure time to reach a destination in time with an acceptable probability. To increase the possibility of providing supply to evacuees under uncertainty without disruptions is a challenging problem. An efficient and reliable routing and scheduling model needs to be developed for both disaster and post–disaster conditions. In this study, we address a sub-problem of the general humanitarian supply-chain problem, which is the humanitarian response planning for a fleet of vehicles with reliability considerations. Routing and scheduling of humanitarian supply transportation is formulated as a mathematical model. To apply this routing and scheduling method in real operations with on-line information, efficient algorithms are necessary. A genetic algorithm based heuristic is proposed to solve the problem in reasonable computational time. The performance of the mathematical optimization model and heuristic algorithm are evaluated using test networks. The results show that the proposed approach can provide prompt delivery while reducing the risk of undesirable delay caused by uncertainty. The algorithm can provide high quality solution within short computational time to fulfill the on-line operation requirement.

After the Wenchuan earthquake (magnitude 7.9, May 12, 2008), intensive debates on how China should establish a natural disaster insurance system were initiated among researchers, policymakers, and insurance professionals. Our focus was the social aspects of disaster insurance, explored in China through a nationwide survey. Our questionnaires investigated people's risk awareness, insurance acceptance, their opinions on governmental measures for disaster management, and their willingness to pay for disaster house insurance. We analyzed the results at both regional and individual scales. We found that the integrated hazard index and respondents' experience of insurance (considered objective factors), and their opinions on the importance of insurance and government responsibility (considered subjective factors) showed strong correlation with the regional overall acceptance of disaster insurance. An individual's decision to participate highly depended on his/her experience of both insurance and disaster and his/her opinion on the importance of insurance as a coping mechanism. Respondents from poverty-stricken or less-developed counties were not necessarily more reluctant to accept natural disaster insurance, though they exhibited relatively lower ability to afford insurance. In general, respondents had correct perceptions of natural disasters in their areas; however, people from regions with a greater multihazard threat showed less willingness to accept disaster insurance because they tended to expect the government to undertake to cover losses and considered insurance to be less important. People's willingness to pay for an assumed disaster house insurance was also investigated and analyzed. We consequently discuss the policy implications for developing a disaster insurance system in China.

Highway bridges played an important role in lifeline systems. Their seismic damage status made a direct influence on the implement plan of the earthquake relief work and the life and property security of the people. Therefore, accurate assessment and prediction for the seismic damage of highway bridges were of vital significance, based on which reasonable earthquake fortification could be implemented. This paper summarized and classified the existing seismic damage assessment methods for highway bridges, including certain index evaluation methods based on statistical results, vulnerability index evaluation methods based on probability theory and vulnerability index evaluation methods based on aggregation model. Then the capacity-spectrum method which could be used for calculating the fragility curve and the seismic assessment model of highway bridges used in HAZUS were specially explained. At last, the proposal for the development of seismic assessment systems related with highway bridges in China was put forward. Considering HAZUS’s good performance in seismic assessment, a similar assessment system should be built in China as soon as possible. The first step should be classifying highway bridges in China based on their current situation and obtaining their seismic assessment model parameters roughly, which tend to be safe. The second step should be carrying out sufficient experiments and numerical analysis and revising the model parameters obtained in the first step.

To cope with increasing customer demand and market changes, airline companies need to organize and manage their route networks in a more cost-efficient way. In addition, the robustness of flight operations against unpredictable accidents such as terrorist attacks and natural disasters has become more important to airlines. In this study, the concepts and techniques from complex network theory are used to model airline route networks, and then an effective and efficient genetic algorithm is developed to optimize airline route networks in terms of network properties that may have crucial roles to play in improving the cost-effectiveness and reliability of airspace systems. The simulation results illustrate that the work reported in this study has a good potential to improve the topology of airline route networks in terms of given network properties such as operating costs and network robustness.

In an emergency medical service (EMS) system, the depot location and fleet assignment greatly affect the average response time, which is the main criterion for measuring system performance. Whereas the EMS depot location problem is a strategic problem, the fleet assignment problem is a tactical one. As such, the EMS depot location and fleet assignment problems are usually solved separately under some simplified assumptions. However, there is a potential for savings in both the average response times and the capital and operating costs if these problems can be solved simultaneously. A simulation model for EMS vehicle dispatching was developed. This model is calibrated with real-world data, and it is incorporated in a genetic algorithm to help solve the EMS depot location and fleet assignment problems simultaneously. Emergency types, their response priorities, and whether or not they require dispatching of multiple units are taken into consideration in the model. The average response time and the capital and operating costs are used as criteria for evaluation.

In countries with a high population density, evacuating large amounts of people in a short time post-disaster is a critical challenge for emergency response agencies. This paper analyzes the reliability of a highway evacuation network post earthquake disaster. The capacity of a road segment after disasters is modeled as a discrete probability function. A modified stochastic flow algorithm based on k-shortest path algorithm is proposed to calculate the possibility of transporting a given the number of people in a pre-defined time window on a potentially damaged highway network. Northeast Sichuan is used as case study site and real world data are collected to develop multiple scenarios in order to test the proposed methodology. The results show that the reliability of an evacuation network is highly sensitive to the tightness of an evacuation time window and the damage probability of a critical road segment. The proposed methodology illustrates prominent potential of application in regional emergency planning and risk assessment.

The efficiency and effectiveness of the disaster relief transportation has direct impact on the health of those in needs. Earthquake and secondary hazards can also cause huge delays in relief distribution and threaten the safety of emergency response teams. In this study, a hierarchical time-dependent dispatching approach is proposed based upon fuzzy clustering of stricken areas and landslide risk analysis. A time dependent fuzzy multi-objective program is built to solve the regional relief assignment with three objective functions: efficiency, fairness, and cost. Based on the regional assignment plan in each time period, a local delivery problem is formulated as a multi-depot vehicle routing problem. LINGO is used to solve the mathematical model. The solution approach and proposed models are tested in the Yushu earthquake case study. The results show that the hierarchical model can coordinate the relief distribution in a timely, but reasonable way under the preference of decision-maker.

Dynamic response to emergencies requires real time information from transportation agencies, public safety agencies and hospitals as well as the many essential operational components. In emergency response operations, good vehicle dispatching strategies can result in more efficient service by reducing vehicles’ travel times and system preparation time and the coordination between these components directly influences the effectiveness of activities involved in emergency response. In this chapter, an integrated emergency response fleet deployment system is proposed which embeds an optimization approach to assist the dispatch center operators in assigning emergency vehicles to emergency calls, while having the capability to look ahead for future demands. The mathematical model deals with the real time vehicle dispatching problem while accounting for the service requirements and coverage concerns for future demand by relocating and diverting the on-route vehicles and remaining vehicles among stations. A rolling-horizon approach is adopted in the model to reduce the relocation sites in order to save computation time. A simulation program is developed to validate the model and to compare various dispatching strategies

The advantages of mesh network provide huge potential for its application in transportation network security and first response service. The application of mesh network in transportation security will not only improve emergency response, but also help the coordination and integration across agencies, regions, and public safety and transportation disciplines. The goal of this paper is to explore the potential applications of mesh network in emergency response service and to analyze the performance of various emergency dispatching policies using simulations. The case study shows that with help of mesh network and integrated management, promising savings in average response time and reduction in the percentage of long response times can be achieved.

While cellular probe traffic data shows its great potential in applications of Intelligent Transportation System (ITS), especially, the Traveler Information System (TIS), the data quality becomes a more and more important issue for real world application. The purpose of the paper is to discuss the issues in probe data evaluation and their potential solution approaches. The selections of data source, sampling strategies and data analysis techniques can be complicated problems. A case study is presented. It is observed in the study that cellular probe traffic data provide ideal spatial and temporal coverage and the precision of the cellular probe traffic travel times on freeways qualifies immediate ITS application. It indicates that it is necessary to further improve the algorithm on arterials and to coordinate signal setting within the algorithm. With future improvement of cellular probe technology, ITS applications will be further benefited.

Cities are centers of socioeconomic activities, and transport networks carry cargoes and passengers from one city to another. However, transport networks are influenced by meteorological hazards, such as rainstorms, hurricanes, and fog. Adverse weather impacts can easily spread over a network. Existing models evaluating such impacts usually neglect the transdisciplinary nature of approaches for dealing with this problem. In this article, a mesoscopic mathematical model is proposed to quantitatively assess the adverse impact of rainstorms on a regional transport network in northern China by measuring the reduction in traffic volume. The model considers four factors: direct and secondary impacts of rainstorms, interdependency between network components, and recovery abilities of cities. We selected the Beijing-Tianjin-Hebei region as the case study area to verify our model. Socioeconomic, precipitation, and traffic volume data in this area were used for model calibration and validation. The case study highlights the potential of the proposed model for rapid disaster loss assessment and risk reduction planning.