<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7RW1lcmdlbmN5IG1lZGljYWwgc2VydmljZXMmcXVvdDsiIG9yaWdpbmFsX3ZhbHVlPSImcXVvdDtFbWVyZ2VuY3kgbWVkaWNhbCBzZXJ2aWNlcyZxdW90OyIgLz48L2ZpbHRlcnM+PHJhbmdlcyAvPjxzb3J0cz48c29ydCBmaWVsZD0icHVibGlzaGVkIiBvcmRlcj0iZGVzYyIgLz48L3NvcnRzPjxwZXJzaXN0cz48cGVyc2lzdCBuYW1lPSJyYW5nZXR5cGUiIHZhbHVlPSJwdWJsaXNoZWRkYXRlIiAvPjwvcGVyc2lzdHM+PC9zZWFyY2g+" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A generalized three-stage optimization model for emergency medical services under uncertainties: Integrating rescue station locations, ambulance deployment, and vehicle dispatch</title>
      <link>https://trid.trb.org/View/2614748</link>
      <description><![CDATA[The rapid pace of urbanization and increasing social complexity have made the efficiency of Emergency Medical Services (EMS) crucial for public safety and societal stability. Most existing studies analyze rescue station locations, ambulance deployment, and vehicle dispatch separately. However, these studies mainly focus on deterministic factors, such as travel time and rescue demand, without addressing uncertainties in real-world transportation networks and resource constraints. To overcome these limitations, this paper proposes a three-stage optimization model. The first two stages aim to determine the optimal layout of rescue stations and the number of vehicles to deploy, considering effective travel time instead of traditional Euclidean distance. The concepts of “contribution” and “fairness” are introduced to maximize vehicle service efficiency. The third stage applies a multi-objective scheduling strategy that integrates road network data. This study proposes an exact algorithm based on the greedy (EABG) approach. The results show significant improvements in demand coverage and response efficiency, despite limited resources. Sensitivity analysis confirms that fuzzy logic effectively handles demand uncertainty. These results offer actionable insights for EMS resource allocation and policy design, advancing emergency response strategies in complex urban environments. In addition, the proposed algorithm significantly outperforms GA in terms of solution speed and quality.]]></description>
      <pubDate>Wed, 17 Jun 2026 16:14:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2614748</guid>
    </item>
    <item>
      <title>Treatment Outcomes and Associated Factors among Road Traffic Injury Patients in Emergency Departments of Public Hospitals in Awi Zone Northwest Ethiopia</title>
      <link>https://trid.trb.org/View/2694328</link>
      <description><![CDATA[Globally, road traffic injuries (RTIs) cause numerous tragedies such as serious economic loss to the community and death of young people. In Ethiopia, a large proportion of serious injuries result from RTIs and have become major causes of death in hospitals. However, there is insufficient research conducted on treatment outcomes of road traffic injuries and associated factors in the study area. The primary aim of this study was to determine the magnitude of poor treatment outcomes and identify associated factors among patients in the emergency departments of public hospitals in Awi Zone, Northwest Ethiopia. A facility-based cross-sectional study was employed in Awi Zone public hospitals, northwest Ethiopia. With a sample of medical charts of 461 RTI patients were reviewed and data were collected between January 1, 2022 and June 30, 2024. Data were collected by using data collection checklist. Four nurses and one health officer were employed as data collector and supervisor respectively. Data were entered using Epi Data version 4.7 and cleaned, coded, and analyzed using SPSS version 27. Bivariate analysis was computed and variables with p-value < 0.25 were included in multi-variable logistic regression. The significant of statistical associations were tested using odds ratio and 95% confidence interval (CI) and p-value < 0.05. Finally, the results were presented in texts, tables, and graphs. For this study, 461 study subjects of RTIs victims were enrolled. Among these, 49 (10.6%) patients had poor treatment outcomes. Patients aged 31-50 years [AOR = 0.091, 95% CI: 0.019-0.443], patient age > 50 years [AOR = 0.114, 95% CI: 0.021-0.606], absence of complication [AOR: 0.021; CI: (0.002-0.208)], and patients who received first aid [AOR: 0.340; CI: (0.123-0.938)] were significantly associated with poor treatment outcomes. The study showed a high rate of prognosis but still the poor outcome was not underestimated. Age, absence of complications, and first aid service were statistically significant factors that affect treatment outcomes. Therefore, health care providers should prioritize those RTI victims with complication, not received first aid service, and younger age groups.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694328</guid>
    </item>
    <item>
      <title>Hierarchical Bayesian analysis of prehospital timelines for elderly adults in rural and urban motor-vehicle crashes</title>
      <link>https://trid.trb.org/View/2697027</link>
      <description><![CDATA[Emergency medical services (EMS) play a critical role in improving survival after road crashes by providing timely prehospital care. However, differences in EMS response and transport persist between rural and urban areas. Older adults, who make up a large share of rural populations, face additional risks due to frailty and comorbid conditions, yet studies focusing on their prehospital care remain limited.   This study analyzed linked EMS and crash data from Ohio (2018–2023) to evaluate prehospital timelines for older adults involved in motor vehicle crashes. Rural–urban classification was determined using Rural–Urban Commuting Area (RUCA) codes. A hierarchical Bayesian survival model was used to assess response, on-scene, and transport times jointly, with predictors including distance to emergency centers, weather, crash characteristics, and time of occurrence.   Findings revealed that greater distances to emergency centers, multi-patient crashes, adverse weather, and off-peak or weekend crashes were associated with prolonged EMS timelines. Older adults consistently experienced longer on-scene times, reflecting the added complexity of their assessment and management. Rural areas showed delays, underscoring regional differences in EMS care.   These results highlight the need for targeted strategies to improve EMS performance for elderly crash victims, particularly in rural settings. This study offers valuable insights into mitigating variations in EMS availability, highlighting the critical roles of geographic proximity and community-level conditions in shaping emergency response effectiveness. Strategies to reduce gaps should include optimizing EMS base locations, and tailoring prehospital protocols to the needs of older patients. These interventions can improve response efficiency and reduce preventable delays in critical care for elderly crash victims.]]></description>
      <pubDate>Tue, 19 May 2026 15:12:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2697027</guid>
    </item>
    <item>
      <title>Enhancing Expressway Crash Rescue with Vertical Takeoff and Landing Vehicles: Insights from an Evolutionary Game Study</title>
      <link>https://trid.trb.org/View/2701227</link>
      <description><![CDATA[Expressway traffic crashes often result in higher fatalities and more severe congestion compared with incidents on regular roads, creating significant challenges for timely emergency response. Vertical takeoff and landing (VTOL) vehicles offer a potential solution to bypass surface-level bottlenecks and efficiently deliver emergency personnel and supplies. This study develops a tripartite evolutionary game model to analyze the strategic interactions among crash participants, VTOL operators, and road authorities in the context of expressway rescue. The analysis identifies the most favorable equilibrium as one where point-to-point rescue is adopted, VTOL services are actively provided, and road conditions are effectively managed. This setup encourages coordination among stakeholders and enhances overall rescue efficiency. The evolutionary path is affected by factors such as road regulation costs, subsidy coefficients, and stakeholders’ initial willingness to cooperate. Notably, higher initial cooperation from crash victims and VTOL operators accelerates convergence toward stable outcomes. These findings improve understanding of the feasibility conditions for VTOL deployment in emergency scenarios and guide cost-sharing mechanisms, stakeholder alignment, and policy design to support the practical implementation of VTOL-based rescue strategies.]]></description>
      <pubDate>Mon, 11 May 2026 12:24:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2701227</guid>
    </item>
    <item>
      <title>A two-rescuer-method significantly alters CPR-quality during cardiopulmonary resuscitation in an airliner cabin - a randomized, controlled manikin trial</title>
      <link>https://trid.trb.org/View/2646876</link>
      <description><![CDATA[Between 1/15,000-1/50,000 passengers suffer in-flight medical emergencies (IFME) with cardiac arrest accounting for 0.3 %. Confined space can have a negative impact on quality of chest compressions during cardiopulmonary resuscitation (CPR), thus the authors have conducted a randomized controlled study to find the most effective approach of performing CPR in a one - vs. two-rescuer method in a simulated airliner cabin. The authors randomized 20 healthcare professionals to perform a set of 10 min Basic Life Support (BLS, chest compressions and bag-mask-ventilation) in a one- vs. two-rescuer scenario and in confined space vs. open space in a randomized order using a full-body manikin. The primary outcome was compression depth as sensitive marker for differences in CPR-quality. The study was registered on clinicaltrials.gov (NCT02002481). Mixed ANOVAs with post-hoc false-discovery-rate adjusted pairwise comparisons indicated that one- vs. two-rescuer method showed differences in no-flow-time (confined: 8.05 +/- 0.17 vs. 24.25 +/- 1.05 s/2min and open space: 7.51 +/- 0.02 vs. 21.31 +/- 0.43 s/2min; p < 0.001) and missing releases (confined: 27.09 +/- 5.55 vs. 46.64 +/- 9.66 number/10 minutes and open space: 27.09 +/- 2.44 vs. 43.36 +/- 6.4 number/10minutes; p < 0.001). A confined space significantly elevated no-flow-time in the two-rescuer-method vs. the one-rescuer-method (24.24 +/- 1.06 s/2min vs. 21.26 +/- 0.44 s/2min; p < 0.001), whereas compression frequency and compression depth were different but still within the current recommendations of ERC/AHA in both methods per condition. Limited space in an airliner cabin has significant impact on no-flow-time in a two-rescuer-method. In case of CPR and limited access to the patient, the authors recommend a one-rescuer-method as first approach to ensure early and high-quality CPR for experienced personnel.]]></description>
      <pubDate>Tue, 21 Apr 2026 09:29:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2646876</guid>
    </item>
    <item>
      <title>Integrated Emergency Medical Facility Location and Patient Dispatching Under Uncertainty</title>
      <link>https://trid.trb.org/View/2610644</link>
      <description><![CDATA[In the face of a sudden public health emergency caused by a new infectious disease, it is necessary to establish a multi-level emergency medical facility (including primary and superior facilities) to address the surge in medical needs. In this context, traditional hospitals are responsible for patient screening, primary emergency medical facilities are responsible for treating mild cases, and superior emergency medical facilities are responsible for treating severe cases. Against the backdrop of uncertainties such as patient self-referral and the autonomous progression of the disease, we address an important problem of integrated emergency medical facility location and patient dispatching under uncertainty and propose a multi-stage stochastic programming model to formulate the problem. For a deterministic model under a given set of scenarios, a Decomposition-based Dual-level Heuristic (DDH) algorithm is proposed to efficiently solve the problem, where the upper level employs tabu search to optimize the location scheme, and the lower level utilizes a patient allocation heuristic to provide an optimized patient dispatching solution. Numerical experiments are conducted using Wuhan, China, the epicenter of the COVID-19 outbreak, as an example. The results show that the DDH algorithm achieves high quality solutions close to those obtained by state-of-the-art solver CPLEX but with significantly reduced computational overload. The DDH algorithm is also compared with the progressive hedging algorithm and genetic algorithm, showing its superior performance in terms of solution quality and computational efficiency. Through extensive data analysis, valuable conclusions and managerial insights are obtained, providing useful references for emergency response in similar public health emergencies in the future.]]></description>
      <pubDate>Wed, 25 Mar 2026 17:11:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2610644</guid>
    </item>
    <item>
      <title>Patient transportation in conditions of the Slovak Armed Forces</title>
      <link>https://trid.trb.org/View/2665986</link>
      <description><![CDATA[Provision of high quality and timely medical care is one of the most important force multipliers of armed forces worldwide and undoubtedly for the Slovak Armed Forces as well. Fundamental precondition to deliver accurate medical care is timely transportation of casualties to the medical capabilities of different levels which can provide adequate medical care for diseased, injured or wounded. The Slovak Armed Forces have established system of military patient transportation and as well have dedicated means of transportation to execute medical evacuation tasks. The aim of the paper was based on analysis of historical development of the military patient transportation, comparative analysis of the Slovak, NATO and EU military patient transportation systems and available Slovak military evacuation capabilities, identify possible ways for improvement of the Slovak Armed Forces medical evacuation system.]]></description>
      <pubDate>Mon, 23 Mar 2026 15:15:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665986</guid>
    </item>
    <item>
      <title>Association between prehospital time and injury severity in traffic crash patients</title>
      <link>https://trid.trb.org/View/2673273</link>
      <description><![CDATA[This study aimed to examine the association between on-scene time and trauma severity, with particular attention to differences across age groups and anatomical injury regions among patients injured in traffic crashes. The authors conducted a retrospective cohort study by linking emergency medical services (EMS) prehospital records with hospital-based trauma registry data from a single Level 1 trauma center in metropolitan Taipei between 2016 and 2022. Traffic crash patients transported by EMS were included. Prehospital time was disaggregated into response time, on-scene time, and transport time. Injury severity was assessed using the Injury Severity Score (ISS), with ISS ≥ 9 defined as severe injury. Multivariable logistic regression models were used to evaluate associations between prehospital time components and injury severity. Additional analyses were stratified by age group and anatomical injury region. Among 5,022 patients, 1,858 (37.0%) sustained severe injuries. Longer on-scene time was strongly associated with higher injury severity; each additional minute on scene was associated with a 10.1% increase in the odds of severe injury (adjusted odds ratio [AOR] = 1.101; 95% CI, 1.085–1.117). Older age, poor consciousness, pedestrian involvement, and late-night crashes were also associated with severe injury. Age- and region-stratified analyses demonstrated consistent associations between longer on-scene time and higher severity (AIS ≥ 3) for head, thoracic, abdominal, and extremity injuries, with more pronounced associations among older adults. Longer on-scene time is closely associated with trauma severity and likely reflects greater injury complexity and patient acuity rather than a direct causal effect. Given the observational nature of this study, the findings should be interpreted cautiously and may be influenced by reverse causation and confounding by indication. These results highlight the importance of early severity recognition, appropriate triage, and minimizing avoidable delays while ensuring essential life-saving interventions in prehospital trauma care.]]></description>
      <pubDate>Wed, 18 Mar 2026 08:59:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673273</guid>
    </item>
    <item>
      <title>In-Flight Medical Events on Commercial Airline Flights</title>
      <link>https://trid.trb.org/View/2611420</link>
      <description><![CDATA[In-flight medical events are an inevitable challenge in commercial aviation. Managing these events is complicated by constrained medical resources and delayed access to definitive care.  To characterize the epidemiology of in-flight medical events and identify factors associated with aircraft diversion, hospital transport, and in-flight mortality. This cohort study included 77790 in-flight medical events reported to a global ground-based medical support center from January 1, 2022, through December 31, 2023. All passengers experiencing an in-flight medical event across 84 participating airlines during the study period were included. Data were collected from consultations initiated by flight crew via radio or satellite communication with a dedicated ground-based physician. No demographic or clinical exclusions were applied. Medical conditions occurring during commercial flights that prompted contact with the ground-based support center. Data included clinical presentation, in-flight management, passenger demographics, involvement of volunteer medical professionals, and disposition.  Primary outcome was aircraft diversion, and secondary outcomes were hospital transport and in-flight mortality. Descriptive statistics, univariate analyses, and multivariable analyses were used to identify clinical and operational variables associated with these outcomes. Among 77790 in-flight medical events, the overall incidence was 39 events per 1 million enplanements, with 1 event per 212 flights, or 17 events per billion revenue passenger kilometers. The median (IQR) age of affected passengers (42316 females [54.4%]) was 43 (27-61) years. Aircraft diversion occurred in 1.7% of cases, most frequently due to neurologic (41%) and cardiovascular (27%) conditions. Suspected stroke (adjusted OR [AOR], 20.35; 95% CI, 12.98-31.91) and acute cardiac emergencies (AOR, 8.16; 95% CI, 6.38-10.42) were the factors associated with the highest odds of diversion. The involvement of a physician volunteer was also associated with increased odds of diversion (AOR, 7.86; 95% CI, 4.49-13.78). In this cohort study of 77790 in-flight medical events, these events occur more frequently than previously reported. Serious neurologic conditions, cardiac events, and physician volunteer involvement are each associated with higher odds of diversion. These findings contribute to the understanding of in-flight medical event frequency and outcomes and may inform policy, flight crew training, and diversion protocols.]]></description>
      <pubDate>Wed, 18 Feb 2026 13:22:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611420</guid>
    </item>
    <item>
      <title>Prediction of urban medical emergencies using machine learning models based on spatial and temporal variables</title>
      <link>https://trid.trb.org/View/2655586</link>
      <description><![CDATA[This study aims to develop and evaluate a prediction model for medical emergencies in an urban setting, specifically in the Bucaramanga metropolitan area located in Colombia. Accurate forecasting of emergencies could potentially enhance preparedness and optimize resource allocation of the medical emergency services, improving response times, preventing injuries in the population, and reducing mortality and morbidity rates. Automatic prediction of emergencies is carried out using a random forest model that uses sliding window techniques and integrates temporal variables to forecast emergency events. Feature vectors incorporate spatial locations (via geocoding), meteorological data, road conditions, demographic information, and road traffic statistics. The prediction task corresponds to the estimation of emergency occurrences by day and location. Two different data sets were constructed to train and evaluate the model: Emergency records from the Regulatory Center for Emergencies and Urgencies (RCEU) and traffic accident reports from Bucaramanga’s traffic department for the years 2017 to 2019. The model achieved a mean square error (MSE) of 0.005 and R2 of 0.351 using data from the RCEU. For traffic accident reports from Bucaramanga’s traffic department (2017–2019), the MSE was 0.018 and R2 was 0.150. Aggregated daily spatial predictions yielded R2 of 0.75 and MSE of 2.258 (RCEU) and R2 of 0.898 and MSE of 0.798 (traffic department). The sliding window methodology effectively captures periods with higher emergency occurrences, potentially contributing to potential injury prevention strategies. Although fine-grained prediction accuracy (specific time and location) is limited, the model performs well across an approximately 165 km2 urban area. Though the model’s fine-grained accuracy in predicting specific times and locations of emergencies is limited, the automated prediction model demonstrates a promising ability to identify periods with increased emergency occurrences when aggregating spatial information. Further use of fine-grained data and recent machine learning techniques may improve its precision, particularly at smaller spatial and temporal scales.]]></description>
      <pubDate>Wed, 18 Feb 2026 11:59:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655586</guid>
    </item>
    <item>
      <title>Enabling Mobility of Emergency Medical Service through Connected and Automated Vehicle Preemption</title>
      <link>https://trid.trb.org/View/2669655</link>
      <description><![CDATA[Emergency Medical Service (EMS) vehicles, typically ambulances, have time-critical transportation roles when responding to traffic incidents by bringing first medical responders and equipment from their bases to the incident scenes, and transferring injured persons from the scenes to medical facilities. Addressing the mobility of EMS vehicles supports but public health and safety goals, as well as those related to efficient mobility.     

The traditional way for EMS vehicles to reach their destinations faster is to use audible sirens to alert drivers of their presence. Upon hearing an EMS vehicle’s siren, drivers must yield the right of way to facilitate its passage. Previous research on traffic signal preemption for EMS vehicles has demonstrated its effectiveness in reducing delays at signalized intersections. With the advent of Connected and Automated Vehicle (CAV) technology, vehicles can now communicate directly with each other. EMS vehicles equipped as CAVs could leverage vehicle-to-vehicle (V2V) communication technology to transmit warning messages to the CAVs downstream along their routes, beyond the range of audible sirens. The CAVs that have received these messages can proactively move aside to create a clear lane for the EMS vehicle to pass. This “CAV preemption” concept has the potential to significantly improve EMS mobility, resulting in faster response times, earlier on-scene medical aid, and quicker patient transfer to hospitals. Furthermore, the proposed CAV preemption will accelerate incident clearance and the restoration of highway capacity.  

This research is based on an envisioned CAV preemption system in which an EMS vehicle broadcasts its impending arrival to downstream CAVs, while simultaneously sounding its siren and emitting high-intensity strobe light to request signal preemptions. All CAVs receiving this V2V message will automatically move to the right lane, while only a portion of the non-CAV drivers will comply and respond to the siren. The efficiency of this system depends the following factors: (1) The broadcast range of the warning messages to CAVs, (2) The market penetration rate of CAVs, (3) The move-aside compliance rate of non-CAV drivers, (4) The level of traffic congestion.  

This research will simulate and quantify the efficiency of the proposed CAV preemption system under varying operating conditions. An agent-based simulation model of the El Paso highway network will be used to assess the EMS vehicle’s travel time. Mobility efficiency is defined as the percentage reduction in the average travel time. The travel times of EMS vehicles from their bases (selected fire stations that house ambulances) to multiple incident sites (selected highway locations) will be simulated, extracted, and analyzed. The analyses will assess the impacts of broadcast range, CAV market penetration, non-CAV compliance rate, and traffic volume.   ]]></description>
      <pubDate>Sun, 15 Feb 2026 16:34:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669655</guid>
    </item>
    <item>
      <title>A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort overlap</title>
      <link>https://trid.trb.org/View/2652424</link>
      <description><![CDATA[Reliable estimation of injury severity is essential for informing trauma care, evaluating crash interventions, and guiding EMS resource allocation; however, analyses based on linked administrative datasets are often compromised by incomplete linkage and selection bias. This study employs a doubly robust estimation framework to address potential bias in injury severity estimation when integrating multiple datasets. Using Augmented Inverse Probability Weighting (AIPW), the authors adjust for selection bias introduced by incomplete linkage while improving robustness to misspecification in either the selection or outcome model. Using data from a multi-source linkage of crash, EMS, and trauma records, the authors estimate the Injury Severity Score (ISS) under three approaches: naïve complete-case analysis, inverse probability weighting (IPW), and AIPW. The naïve approach yielded a mean ISS of 13.52, while both IPW (10.86) and AIPW (10.93) provided adjusted estimates accounting for selection. Subgroup analyses revealed substantial differences in effect size and direction between models. For instance, the impact of male gender on ISS was estimated at 3.98 in AIPW versus 2.22 in naïve analysis. Similarly, secondary collisions and frontage-road crashes showed ISS increases exceeding 10 points under AIPW, compared to considerably lower naïve estimates. Several protective factors, including airbag deployment and crash setting, also demonstrated stronger effects when adjusted for bias. The results demonstrate that traditional analyses of linked data may underestimate or misstate key risk and protective associations. The proposed AIPW framework offers a practical, statistically rigorous solution for producing population-level inferences in injury severity research using linked administrative data.]]></description>
      <pubDate>Fri, 06 Feb 2026 08:45:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2652424</guid>
    </item>
    <item>
      <title>Artificial intelligence for enhanced prehospital stroke care : focus on efficient mobile stroke unit allocation and travel time estimation</title>
      <link>https://trid.trb.org/View/2666503</link>
      <description><![CDATA[This thesis aims to use artificial intelligence's power to enhance prehospital stroke care. To accomplish this, we study challenges in prehospital stroke care by focusing on three interrelated research challenges: Mobile stroke unit (MSU) allocation, ambulance travel time estimation, and improving travel time calculations within emergency medical service (EMS) simulation. We develop and analyze different optimization and machine learning (ML) methods to achieve improved analysis and planning of prehospital stroke care. In particular, we propose methods to solve the MSU allocation problem, which aims to identify the optimal locations for a fixed number of MSUs at the existing ambulance station locations within a geographic region. Moreover, we develop a machine learning-based regression method for ambulance travel time estimation. Next, we apply our pre-trained ML-based regression method to improve ambulance travel time estimation within an EMS simulation framework.]]></description>
      <pubDate>Thu, 05 Feb 2026 08:32:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666503</guid>
    </item>
    <item>
      <title>Prehospital Care of Road Traffic Injuries in Chiang Mai, Thailand</title>
      <link>https://trid.trb.org/View/2635334</link>
      <description><![CDATA[Road traffic accidents (RTAs) cause enormous morbidity and mortality in developing countries. The Global Burden of Disease Study projected that RTAs will be the third highest cause of disability adjusted life years (DALYs) by 2020. Ninety percent of the DALYs due to RTAs are in developing countries. Because the majority of trauma deaths in developing nations occur in the prehospital setting, it is imperative that emergency medical systems be established and improved in such countries. Two studies in Central America found that increasing the number of emergency dispatch units and prehospital personnel training increased the utilization of emergency medical devices and lowered the percentage of patients who die en route to the hospital. RTA fatalities are rising faster in Asia than anywhere else in the world. This is the report of a field study conducted between May and June 2003 in Chiang Mai and Bangkok. Information was gathered through oral interviews and written questionnaires and was supplemented by publications of The Narenthorn EMS Center of the Ministry of Health and unpublished documents of the Chiang Mai Health Department Office of Emergency Care. The research objectives were: (1) to learn how emergency rescue services are organized in Chiang Mai, (2) to learn about ongoing public health efforts to improve such services, and (3) to learn about the training, certification, employment and medical device usage of prehospital personnel. There was no specific a priori hypothesis.]]></description>
      <pubDate>Sat, 03 Jan 2026 17:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635334</guid>
    </item>
    <item>
      <title>Pre-hospital Care of Road Traffic Injuries in Chiang Mai, Thailand</title>
      <link>https://trid.trb.org/View/2635339</link>
      <description><![CDATA[In many developing countries, transportation infrastructure development lags behind the tremendous growth in motorization. Road traffic injuries cause enormous morbidity and mortality worldwide, placing heavy burdens on global and national economies. Underdeveloped transportation infrastructures critical to traffic safety include roadway improvement, occupant protection laws, traffic law enforcement, and emergency medical services (EMS). Highlighting one important aspect of lagging infrastructure, this article focuses on emergency medical services. This research study offers a descriptive evaluation of the existing pre-hospital care system in Chiang Mai, Thailand. The research objectives were (a) to describe how emergency rescue services are organized in Chiang Mai, (b) to examine ongoing public health efforts to improve emergency services, and (c) to document the training, certification, employment, and medical use of pre-hospital personnel. Thailand’s national and local pre-hospital services (i.e., services designed to transfer persons with traffic injuries into the country’s hospital infrastructure) are both insufficient and inefficient. The Thai National Government has promised funding to create a national EMS network by 2006. Research recommendations for Thailand EMS include more professional training for emergency workers, standardization of equipment, centralization of communications, and further analysis of competitive services.]]></description>
      <pubDate>Sat, 03 Jan 2026 17:07:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2635339</guid>
    </item>
  </channel>
</rss>