Malaysia tourism demand forecasting using box-jenkins approach

Tourism industry in Malaysia is crucial and has contributes a huge part in Malaysia’s economic growth. The capability of forecasting field in tourism industry can assist people who work in tourism-related-business to make a correct judgment and plan future strategy by providing the accurate forecast values of the future tourism demand. Therefore, this research paper was focusing on tourism demand forecasting by applying Box-Jenkins approach on tourists arrival data in Malaysia from 1998 until 2017. This research paper also was aiming to produce the accurate forecast values. In order to achieve that, the error of forecast for each model from Box-Jenkins approach was measured and compared by using Akaike Information Criterion (AIC), Mean Absolute Deviation (MAD), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Model that produced the lowest error was chosen to forecast Malaysia tourism demand data. Several candidate models have been proposed during analysis but the final model selected was SARIMA (1,1,1)(1,1,4)12. It is hoped that this research will be useful in forecasting field and tourism industry

Falta de atenção como fator de risco em condutores de moto

Los choques que involucran motociclistas constituyen un problema creciente. No obstante, es poco lo que se conoce sobre sus patrones de desplazamiento, y sus comportamientos de riesgo y protección. En este trabajo se evalúa si la inatención en conductores de motos constituye un factor de riesgo para choques de tránsito y si ciertas variables personales se relacionan con las fallas atencionales durante la conducción. Se trabajó con una muestra de 110 motociclistas de población general de la ciudad de Mar del Plata (Argentina), que respondieron un instrumento de evaluación de la inatención durante la conducción, una medida general sobre error atencional, una escala de experiencias disociativas, una medida de deseabilidad social, un cuestionario de actividades distractoras durante la conducción, y un cuestionario de datos sociodemográficos e historial de incidentes de tránsito. La escala de inatención durante la conducción para motociclistas (Attentional Related Driving Errors Scale-Motorcyclists, ARDES-M) fue desarrollada y validada en este estudio. Se brindan datos sobre su validez dimensional y de constructo y sobre su fiabilidad en términos de consistencia interna. Según los resultados, los errores atencionales durante la conducción están correlacionados con características generales de funcionamiento atencional. Por otra parte, los conductores que incurrieron en más fallas de atención informaron un mayor historial de choques y multas de tránsito. Los resultados de esta investigación pueden proporcionar información relevante para el diseño de medidas preventivas y educativas para motociclistas.Motorcycle crashes are an increasing problem worldwide. However, actual knowledge about motorcyclist risky behaviors, displacement patterns, and protective behaviors is scarce. This paper reports the assessment of inattention as a risk factor for road crashes in motorcyclist and also the relation of personal variables with attentional failures during driving. A sample of 110 motorcyclists from the general population of Mar del Plata answered a response sheet consisting of an instrument for the assessment of inattention during driving (ARDES-M), a scale which evaluates attentional failures in everyday life (ARCES), a scale for the evaluation of dissociative experiences (DES), an instrument that evaluates social desirability, a questionnaire about distracting activities, and a questionnaire about sociodemographic background and driving history. The Attentional Related Driving Errors Scale-Motorcyclists (ARDES-M) was developed and validated in this study. Data regarding dimensional and construct validity as well as internal consistency is given. Results indicate that attentional failures during driving are correlated to general attentional functioning. Besides, drivers who had more attentional failures reported to have been involved in more crashes than those who commited less failures. The present research could give useful information for the design of preventive and educative measures for motorcycle drivers.As colisões que envolvem motociclistas constituem um problema crescente. Não obstante, é pouco o que se conhece sobre os padrões de deslocamento, os comportamentos de risco e os comportamentos de proteção dos motociclistas. Este trabalho avalia se a falta de atenção em condutores de motos constitui um fator de risco para acidentes de trânsito e se certas variáveis pessoais se relacionam com as falhas de atenção durante a condução. Trabalhou-se com uma amostra de 110 motociclistas da população geral da cidade de Mar del Plata (Argentina), que responderam um instrumento de avaliação de desatenção durante a condução (ARDES-M), uma medida geral sobre erro de atenção (ARCES), uma escala de experiências dissociativas (DES), uma medida de desejabilidade social, um índice de atividades distrativas durante a condução e um questionário de dados sociodemográficos e histórico de incidentes de trânsito. A escala de desatenção durante a condução para os motociclistas (Attentional Related Driving Errors Scale - ARDES-M) foi desenvolvida e validada neste estudo. Segundo os resultados, os erros relacionados à falta de atenção durante a condução estão correlacionados com características gerais de funcionamento da atenção. Por outro lado, os condutores que incorrem em mais falhas de atenção informaram um maior histórico de colisões e multas de trânsito. Esta investigação poderia proporcionar informação relevante para o planejamento de medidas preventivas e educativas para motociclistas.Fil: Nucciarone, María Isabel. Universidad Nacional de Mar del Plata; ArgentinaFil: Poó, Fernando Martín. Universidad Nacional de Mar del Plata; ArgentinaFil: Tosi, Jeremías David. Universidad Nacional de Mar del Plata; ArgentinaFil: Montes, Silvana Andrea. Universidad Nacional de Mar del Plata; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Austral de Investigaciones Científicas; Argentin

Risks of Driving While Talking on Mobile Devices: Soccer Parents\u27 Perceptions

The number of motor vehicle accidents that occur as a result of driving while talking on mobile devices increases each year. Distracted driving is dangerous; however, policy researchers have not focused on adults who talk on mobile devices as they drive children to and from daily events. This study focused on the experiences of soccer parents, an important focus because of soccer\u27s year-long duration that requires a large amount of driving in addition to the other daily tasks of parenting. The purpose of this phenomenological study was to investigate the perceptions of parents of child soccer players regarding the motivations for and risks of talking on mobile devices while driving. The theoretical framework for this phenomenological study was the self-determination theory. Data were collected by electronic surveys using a convenience sample of 10 couples and 4 single parents of children who play soccer for a team in a southern state. Data were analyzed using the constant comparative method in which patterns were identified and coded into themes. The key findings were that the parents had different perceptions of the risks and motivations for talking on mobile devices while driving. There were participants who viewed talking on mobile devices as risky while others did not perceive talking on mobile devices while driving as a risk. Recommendations include conducting further research on parents who drive children to and from soccer practices, while talking on mobile devices, in order to gain better understanding of what motivates people to choose to talk on mobile devices while driving. The implications for positive social change include informing policy makers about the importance of increasing awareness and educating the public about the risks of talking on mobile devices while driving

Modeling driver distraction mechanism and its safety impact in automated vehicle environment.

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly pre-defined distraction as a binary outcome and investigated the patterns that contribute to distraction from multiple perspectives. However, the magnitude of AV distraction is not accurately quantified. Moreover, past studies in quantifying distraction have mainly used wearable sensors’ data. In reality, it is not realistic for drivers to wear these sensors whenever they drive. Hence, a research motivation is to develop a surrogate model that can replace the wearable device-based data to predict AV distraction. From the safety perspective, there lacks a comprehensive understanding of how AV distraction impacts safety. Furthermore, a solution is needed for safely offsetting the impact of distracted driving. In this context, this research aims to (1) improve the existing methods in quantifying Human-Vehicle Interaction-induced (HVI-induced) driver distraction under automated driving; (2) develop a surrogate driver distraction prediction model without using wearable sensor data; (3) quantitatively reveal the dynamic nature of safety benefits and collision hazards of HVI-induced visual and cognitive distractions under automated driving by mathematically formulating the interrelationships among contributing factors; and (4) propose a conceptual prototype of an AI-driven, Ultra-advanced Collision Avoidance System (AUCAS-L3) targeting HVI-induced driver distraction under automated driving without eye-tracking and video-recording. Fixation and pupil dilation data from the eye tracking device are used to model driver distraction, focusing on visual and cognitive distraction, respectively. In order to validate the proposed methods for measuring and modeling driver distraction, a data collection was conducted by inviting drivers to try out automated driving under Level 3 automation on a simulator. Each driver went through a jaywalker scenario twice, receiving a takeover request under two types of HVI, namely “visual only” and “visual and audible”. Each driver was required to wear an eye-tracker so that the fixation and pupil dilation data could be collected when driving, along with driving performance data being recorded by the simulator. In addition, drivers’ demographical information was collected by a pre-experiment survey. As a result, the magnitude of visual and cognitive distraction was quantified, exploring the dynamic changes over time. Drivers are more concentrated and maintain a higher level of takeover readiness under the “visual and audible” warning, compared to “visual only” warning. The change of visual distraction was mathematically formulated as a function of time. In addition, the change of visual distraction magnitude over time is explained from the driving psychology perspective. Moreover, the visual distraction was also measured by direction in this research, and hotspots of visual distraction were identified with regard to driving safety. When discussing the cognitive distraction magnitude, the driver’s age was identified as a contributing factor. HVI warning type contributes to the significant difference in cognitive distraction acceleration rate. After drivers reach the maximum visual distraction, cognitive distraction tends to increase continuously. Also, this research contributes to quantitatively revealing how visual and cognitive distraction impacts the collision hazards, respectively. Moreover, this research contributes to the literature by developing deep learning-based models in predicting a driver’s visual and cognitive distraction intensity, focusing on demographics, HVI warning types, and driving performance. As a solution to safety issues caused by driver distraction, the AUCAS-L3 has been proposed. The AUCAS-L3 is validated with high accuracies in predicting (a) whether a driver is distracted and does not perform takeover actions and (b) whether crashes happen or not if taken over. After predicting the presence of driver distraction or a crash, AUCAS-L3 automatically applies the brake pedal for drivers as effective and efficient protection to driver distraction under automated driving. And finally, a conceptual prototype in predicting AV distraction and traffic conflict was proposed, which can predict the collision hazards in advance of 0.82 seconds on average

Doctor of Philosophy

dissertationA safe and secure transportation system is critical to providing protection to those who employ it. Safety is being increasingly demanded within the transportation system and transportation facilities and services will need to adapt to change to provide it. This dissertation provides innovate methodologies to identify current shortcomings and provide theoretic frameworks for enhancing the safety and security of the transportation network. This dissertation is designed to provide multilevel enhanced safety and security within the transportation network by providing methodologies to identify, monitor, and control major hazards associated within the transportation network. The risks specifically addressed are: (1) enhancing nuclear materials sensor networks to better deter and interdict smugglers, (2) use game theory as an interdiction model to design better sensor networks and forensically track smugglers, (3) incorporate safety into regional transportation planning to provide decision-makers a basis for choosing safety design alternatives, and (4) use a simplified car-following model that can incorporate errors to predict situational-dependent safety effects of distracted driving in an ITS infrastructure to deploy live-saving countermeasures

Microscopic Modeling of Driver Behavior Based on Modifying Field Theory for Work Zone Application

Because many freeways in the U.S. and abroad are being reconstructed or rehabilitated, it becomes increasingly important to plan and design freeway work zones with the utmost in safety and efficiency. Central to the effective design of work zones is being able to understand how drivers behave as they approach and enter a work zone area. While simple and complex microscopic models have been used over the years to analyze driver behavior, most models were not designed for application in work zones and thus do not capture the interdependencies between lane-changing and car-following vehicle movements along with the drivers’ cognitive and physical characteristics. With the use of psychology’s field theory, this dissertation develops a framework for creating vector-based, explanatory, deterministic microscopic models, to enhance our understanding of driver behavior in work zones and better aid freeway planners and designers. In field theory, an agent (i.e. the driver) views a field (i.e. the area surrounding the vehicle) filled with stimuli and perceives forces associated with each stimuli once these stimuli are internalized. Based on this theory, the new modeling framework, Modified Field Theory (MFT), is designed to directly incorporate drivers’ perceptions to roadway stimuli along with vehicle movements for drivers of different cognitive and physical abilities. From this framework, specific microscopic models, such as a simple freeway work zone car following model, can be created. It is postulated that models derived from this framework would more accurately reflect the driver decision-making process, naturally modeling the effects of external stimuli such as innovative geometric configurations, lane closures, and technology applications such as variable message boards. A simple freeway work zone car following model was created using the MFT framework. Two MFT car-following agents were created and calibrated. The second agent (Agent 2) followed the first agent (Agent 1) through a one-lane segment of freeway. Car-following data for Agent 2 was plotted on a graph of relative speed vs. distance to the lead vehicle, showing car-following behavior. Car-following behavior for Agent 2 was validated against Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center (TFHRC) Living Laboratory data for simple freeway work zone car-following (Driver 15). The car-following behavior of Agent 2 replicated the “spiraling” trend observed in Driver 15. Unlike other models (such as Wiedemann), this model does not ‘force’ these trends to occur; these trends occur naturally, as a result of the perception-reaction time delay and the nature of the forces involved. Additionally, unusual car following trends reported for Driver 15 were replicated in Modified Field Theory when conditions surrounding each event were synthetically recreated. Results demonstrated that the Modified Field Theory framework can successfully replicate the process by which a driver scans the driving environment and reacts to their surroundings. Microscopic models can successfully be created using this framework. Results demonstrated that models created from this framework naturally recreate behavioral trends observed in empirical data, and that these models are capable of replicating driving behavior in unusual scenarios, such as the car following behavior of a subject vehicle when the lead vehicle has a strong sudden acceleration event. Before this model can be applied to work zones, other calibration and validation efforts are required

Analysis and solutions of congestion of vehicles using DTCA, FUZZY logic, and ITS on highways

Dynamic Traffic Cellular Automata (DTCA) method has been used to develop a mathematical model of vehicular traffic flow based on acceleration, velocity and position. This model is extended to investigate human driver behavior using Fuzzy Logic algorithms including; asymmetric auto driving, symmetric auto driving, and the driver behavior using ITS. Congestions have been created and solutions are offered thus leading to a better understanding traffic flow, aggregate fuel consumption, and emissions caused by clusters of vehicles. In simulation, ITS is used to provide inter-vehicular information leading to avoidance of congestions, fuel control, and emission reduction

Computational framework for modeling infrastructure network performance and vulnerability

Networked infrastructures serve as essential backbones of our society. Examples of such critical infrastructures whose destruction severely impacts the defense or economic security of our society include transportation, telecommunications, power grids, and water supply networks. Among them, road transportation networks have a principal role in people's everyday lives since they facilitate physical connectivity. The performance of a road transportation network is governed by the three principal components: (a) structure, (b) dynamics, and (c) external causes. The structure defines the topology of a network including links and nodes. The dynamics (i.e., traffic flow) defines what processes are happening on the network. The external causes (e.g., disasters and driver distraction) are the phenomena that impact either structure or dynamics. These principal components do tend to influence each other. For example, the collapse of a bridge (i.e., external cause) could render certain nodes and links (i.e., structure) ineffective thereby affecting traffic flow (i.e., dynamics). A distracted driver (i.e., external cause) on a road can also cause accidents that can negatively impact traffic flow. Thus, to model the performance and vulnerability of a network, it is necessary to consider such interactions among these principal components. The main objective of this research is to formalize and develop a computational framework that can: (a) predict the macroscopic performance of a transportation network based on its multiple structural and dynamical attributes (Chapter 2), (b) analyze its vulnerability as a result of man-made/natural disruption that minimizes network connectivity (Chapter 3), and (c) evaluate network vulnerability due to driver distraction (Chapter 4). An integrated framework to address these challenges--which have largely been investigated as separate research topics, such as distracted driving, infrastructure vulnerability assessment and traffic demand modeling--needs to simultaneously consider all three principal components (i.e., structure, dynamics, and external causes) of a network. In this research, the integrated framework is built upon recent developments (theories and methods) in interdisciplinary domains, such as network science, cognitive science and transportation engineering. This is the novelty of the proposed framework compared to existing approaches. Finally, the framework were validated using real-world data, existing studies and traffic simulated results.Ph.D., Civil Engineering -- Drexel University, 201