Verification and validation of models

Simulation Models;econometrics

The Calibration And Verification Of Simulation Models For Toll Plazas

A great deal of research has been conducted on Central Florida toll roads to better understand the characteristics of the tolling operation. In this thesis, the development and calibration of a toll plaza simulation models will be analyzed using two simulation programs varying mostly in their modeling theory. The two models utilized are, SHAKER, a deterministic queuing model for vehicles utilizing toll collection facilities, and VISSIM, a globally popular stochastic simulation software. The benefits of simulation models leads to the purpose of this thesis, which is to examine the effectiveness of two toll modeling programs that are similar in purpose but vary in approach and methodology. Both SHAKER and VISSIM toll plaza models have the potential to work as a tool that can estimate the maximum throughput and capacity of toll plazas. Major operational benefits resulting from developing these models are to simulate and evaluate how traffic conditions will change when demand increases, when and if queues increase when a lane is closed due to maintenance or construction, the impact of constructing additional lanes, or determining whether or not the best lane type configuration is currently implemented. To effectively calibrate any model available site data must be used to compare simulation results to for model validity. In an effort to correctly calibrate the SHAKER toll plaza tool and VISSIM model, an extensive field collection procedure was conducted at four Florida Turnpike operated toll facilities located in Central Florida. Each site differed from the others in terms of number of lanes, lane configuration, toll base fee, highway location, traffic demand, and vehicle percentage. The sites chosen for data collection were: the Lake Jesup Mainline Plaza along the Seminole Expressway (SR-417), the Beachline West Expressway Toll Plaza along the SR-528, the Daniel Webster Western Beltway Plaza along SR-429, and the Leesburg Toll Plaza along the Florida Turnpike Mainline SR-91. Upon completion of calibration of the two simulation models it is determined that each of the two software are successful in modeling toll plaza capacity and queuing. As expected, each simulation model does possess benefits over the other in terms of set up time, analysis reporting time, and practicality of results. The SHAKER model setup takes mere seconds in order to create a network and input vehicle, another few seconds to calibrate driving parameters, and roughly 10 additional seconds to report analysis. Conversely, setting up the VISSIM model, even for the most experienced user, can take several hours and the report analysis time can take several more hours as it is dependant on the number of required simulation runs and complexity of the network. VISSIM is most beneficial by the fact that its modeling allows for driver variability while SHAKER assumes equilibrium amongst lane choice and queuing. This creates a more realistic condition to observed traffic patterns. Even though differences are prevalent, it is important that in each simulation model the capacity is accurately simulated and each can be used to benefit operational situations related to toll plaza traffic conditions

The MATSim Network Flow Model for Traffic Simulation Adapted to Large-Scale Emergency Egress and an Application to the Evacuation of the Indonesian City of Padang in Case of a Tsunami Warning

The evacuation of whole cities or even regions is an important problem, as demonstrated by recent events such as evacuation of Houston in the case of Hurricane Rita or the evacuation of coastal cities in the case of Tsunamis. This paper describes a complex evacuation simulation framework for the city of Pandang, with approximately 1,000,000 inhabitants. Padang faces a high risk of being inundated by a tsunami wave. The evacuation simulation is based on the MATSim framework for large-scale transport simulations. Different optimization parameters like evacuation distance, evacuation time, or the variation of the advance warning time are investigated. The results are given as overall evacuation times, evacuation curves, an detailed GIS analysis of the evacuation directions. All these results are discussed with regard to their usability for evacuation recommendations.BMBF, 03G0666E, Verbundprojekt FW: Last-mile Evacuation; Vorhaben: Evakuierungsanalyse und Verkehrsoptimierung, Evakuierungsplan einer Stadt - Sonderprogramm GEOTECHNOLOGIENBMBF, 03NAPAI4, Transport und Verkehr: Verbundprojekt ADVEST: Adaptive Verkehrssteuerung; Teilprojekt Verkehrsplanung und Verkehrssteuerung in Megacitie

Integrating driving and traffic simulators for the study of railway level crossing safety interventions: a methodology

Safety at Railway Level Crossings (RLXs) is an important issue within the Australian transport system. Crashes at RLXs involving road vehicles in Australia are estimated to cost $10 million each year. Such crashes are mainly due to human factors; unintentional errors contribute to 46% of all fatal collisions and are far more common than deliberate violations. This suggests that innovative intervention targeting drivers are particularly promising to improve RLX safety. In recent years there has been a rapid development of a variety of affordable technologies which can be used to increase driver’s risk awareness around crossings. To date, no research has evaluated the potential effects of such technologies at RLXs in terms of safety, traffic and acceptance of the technology. Integrating driving and traffic simulations is a safe and affordable approach for evaluating these effects. This methodology will be implemented in a driving simulator, where we recreated realistic driving scenario with typical road environments and realistic traffic. This paper presents a methodology for evaluating comprehensively potential benefits and negative effects of such interventions: this methodology evaluates driver awareness at RLXs , driver distraction and workload when using the technology . Subjective assessment on perceived usefulness and ease of use of the technology is obtained from standard questionnaires. Driving simulation will provide a model of driving behaviour at RLXs which will be used to estimate the effects of such new technology on a road network featuring RLX for different market penetrations using a traffic simulation. This methodology can assist in evaluating future safety interventions at RLXs

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model