We have reviewed disaster management research papers published in major operations management, management science, operations research, supply chain management and transportation/logistics journals. In reviewing these studies, our objective is to assess and present the macro level "architectural blue print" of disaster management research with the hope that it will attract new researchers and motivate established researchers to contribute to this important field. The secondary objective is to bring this disaster research to the attention of disaster administrators so that disasters are managed more efficiently and more effectively. We have mapped the disaster management research on the following five attributes of a disaster: (1) Disaster Management Function (decision-making process, prevention and mitigation, evacuation, humanitarian logistics, casualty management, and recovery and restoration), (2) Time of Disaster (before, during and after), (3) Type of Disaster (accidents, earthquakes, floods, hurricanes, landslides, terrorism and wildfires etc.), (4) Data Type (Field and Archival data, Real data and Hypothetical data), and (5) Data Analysis Technique (bidding models, decision analysis, expert systems, fuzzy system analysis, game theory, heuristics, mathematical programming, network flow models, queueing theory, simulation and statistical analysis). We have done cross tabulations of data among these five parameters to gain greater insights into disaster research. Recommendations for future research are provided.

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Disaster Management from a POM Perspective: Mapping a New Domain

This paper presents a study aimed at comparing the outcome of two geostatistical-based approaches, namely kernel density estimation (KDE) and kriging, for identifying crash hotspots in a road network. Aiming at locating high-risk locations for potential intervention, hotspot identification is an integral component of any comprehensive road safety management programs. A case study was conducted with historical crash data collected between 2003 and 2007 in the Hennepin County of Minnesota, U.S. The two methods were evaluated on the basis of a prediction accuracy index (PAI) and a comparison in hotspot ranking. It was found that, based on the PAI measure, the kriging method outperformed the KDE method in its ability to detect hotspots, for all four tested groups of crash data with different times of day. Furthermore, the lists of hotspots identified by the two methods were found to be moderately different, indicating the importance of selecting the right geostatistical method for hotspot identification. Notwithstanding the fact that the comparison study presented herein is limited to one case study, the findings have shown the promising perspective of the kriging technique for road safety analysis.

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Identification of crash hotspots using kernel density estimation and kriging methods: a comparison

https://hal.laas.fr/hal-02383753/document

Optimization models for large scale network evacuation planning and management: A literature review

Greenhouse gas emissions in the construction industry: An analysis and evaluation of a concrete supply chain

Role of Analytics for Operational Risk Management in the Era of Big Data

An assessment of the CO2 emissions reduction in high speed rail lines: Two case studies from Turkey

Real time data collection in traffic engineering is crucial for better traffic corridor control and management In the literature, many data collection methods have been used such as; magnetic loops, road tube counters, piezo sensors, radars, Bluetooth etc to estimate the link occupancy, average speed or density of a corridor More recently, Floating Car Data (FCD) has become another important traffic data source and has an increasing usage due to its lower cost and higher coverage despite its reliability problems FCD obtained from GPS equipped vehicles moving in the traffic can provide speed or travel speed data for many segments for even 1-min intervals in real-time Though not totally diverse providing more than one of the traffic flow parameters, measuring the effectiveness of this extensive data source in detecting some critical urban traffic states is the ultimate goal of this study As a case study, 1-min interval FCD for an urban arterial in Ankara has been collected during the morning peak hour for 2 months Average speed values were transformed into a qualitative 4-scale state parameter based on the Level of Service (LOS) definitions for urban roads Pattern searches over consecutive segment states using different search length (ie 2 segments, 3 segments, etc) showed that FCD is capable to detect recurrent congestion or bottleneck locations, and even have an idea about the length of queue formed before the bottlenecks

Detection of Urban Traffic Patterns from Floating Car Data (FCD)

Comprehensibility of traffic signs among urban drivers in Turkey

Evaluation of emission cost of inefficiency in road freight transportation in Turkey

Evacuations are massive operations that create heavy travel demand on road networks some of which are experiencing major congestions even with regular traffic demand. Congestion in traffic networks during evacuations, can be eased either by supply or demand management actions. This study focuses on modeling demand management strategies of optimal departure time, optimal destination choice and optimal zone evacuation scheduling (also known as staggered evacuation) under a given fixed evacuation time assumption. The analytical models are developed for a system optimal dynamic traffic assignment problem, so that their characteristics can be studied to produce insights to be used for large-scale solution algorithms. While the first two strategies were represented in a linear programming (LP) model, evacuation zone scheduling problem inevitable included integers and resulted in a mixed integer LP (MILP) one. The dual of the LP produced an optimal assignment principle, and the nature of the MILP formulations revealed clues about more efficient heuristics. The discussed properties of the models are also supported via numerical results from a hypothetical network example.

Hediye Tuydes-Yaman

Papers

An assessment of the CO2 emissions reduction in high speed rail lines: Two case studies from Turkey

Detection of Urban Traffic Patterns from Floating Car Data (FCD)

Comprehensibility of traffic signs among urban drivers in Turkey

Evaluation of emission cost of inefficiency in road freight transportation in Turkey

Modeling demand management strategies for evacuations