Acceptance and intention to use mobile applications in a library context is attracting a great deal of interest in education field. A sparse amount of research was conducted in mobile library applications (MLA) previously, investigating the influential factors of intention to use MLA. Research here aims to provide empirical support on acceptance of MLA, library access through mobile applications, with the model developed by taking a technology acceptance model (TAM) in MLA context by adding perceived mobility value, system accessibility and satisfaction for investigating the influence on behavioural intention to use MLA. A self-administrated cross-sectional survey was conducted to collect data from 321 users of MLA in the COMSATS Institute of Information Technology (CIIT) in Islamabad, while a structural equation model (SEM) using analysis of moment structure (AMOS) software was used for examining quantitative data. Results revealed that satisfaction and perceived ease of use are direct significant predictors of intention to use MLA, whereas system accessibility was influenced by the perceived ease of use. However, the perceived mobility value shows a weak effect on intention to use MLA in terms of perceived usefulness. Results serve as a guide for effective decision-making in development and resource allocation to ensure the success of the library’s vision and mission.

No abstract

No abstract

No abstract

The primary focus of this paper is to govern traffic congestion on urban road networks based upon a cumulative approach comprising of traffic flow modeling, vehicle emission modeling, and air quality modeling. Based upon the traffic conditions, a simulation model is proposed and further tested for performance metrics, which is relative to three main aspects, namely, the waiting time of the vehicles at the junctions/intersections/signals, the type of pollutant emitted by a vehicle, and the traveling time. The experimental analysis and validation are carried out for different case studies in Malaysia, such as Petaling Jaya, Shah Alam, Mont Kiara, and Jalan Tun Razak. Three different scenarios (morning, afternoon, and evening) are analyzed and tested to explore the traffic usage parameter. The results showed that when traffic is modeled and governed based upon traffic flow, vehicle emission, and air quality index (AQI), nearly 75% of traffic congestion is mitigated, hence making the atmosphere pollution free as well as avoiding Urban Heat Island (UHI) effect due to the heat generated from vehicles. The experimental results are tested, validated, and compared with existing solutions for performance analysis. The proposed model is aimed toward overcoming the major drawbacks of existing approaches, such as single-path suggestions, traffic delay during peak hours/emergencies, non-recurring congestion consideration, congestion avoidance instead of recovering from it, improper reporting of road accidents, and notifications about traffic jam ahead to the users and high vehicle usage rate.

No abstract

A framework for the future incorporation of proper right-turn-on-red (RTOR) delay estimation procedures into the Highway Capacity Manual signalized intersection chapters is provided. Models of uniform delay that are based on queuing theory and that incorporate RTORs are developed. Queuing accumulation polygons (QAPs) were used to analyze the effects of RTOR operations. A classification system for placement of any signal phasing scheme into one of four classes based on a sequence of three red regimes and one green regime is showcased. The three red regimes are characterized by the traffic stream that is given priority over the RTOR traffic during the red regime. One red regime gives priority to the intersecting and conflicting traffic. The second red regime gives priority to the intersecting but nonconflicting traffic. The third red regime prioritizes the opposing and conflicting traffic. It is further demonstrated that the four classes of signal phasing schemes result in one of two general QAP patterns. The first pattern consists of two conflicting regimes, followed by two nonconflicting regimes. The second consists of alternating conflicting and nonconflicting regimes. Models used to calculate delay for each basic pattern are presented. The parameters needed to calculate delays based on the models that were developed are discussed. Furthermore, models from related research that may be used to develop any necessary parameter estimates are also presented.

Radio propagation models (RPMs) are generally employed in Vehicular Ad Hoc Networks (VANETs) to predict path loss in multiple operating environments (e.g. modern road infrastructure such as flyovers, underpasses and road tunnels). For example, different RPMs have been developed to predict propagation behaviour in road tunnels. However, most existing RPMs for road tunnels are computationally complex and are based on field measurements in frequency band not suitable for VANET deployment. Furthermore, in tunnel applications, consequences of moving radio obstacles, such as large buses and delivery trucks, are generally not considered in existing RPMs. This paper proposes a computationally inexpensive RPM with minimal set of parameters to predict path loss in an acceptable range for road tunnels. The proposed RPM utilizes geometric properties of the tunnel, such as height and width along with the distance between sender and receiver, to predict the path loss. The proposed RPM also considers the additional attenuation caused by the moving radio obstacles in road tunnels, while requiring a negligible overhead in terms of computational complexity. To demonstrate the utility of our proposed RPM, we conduct a comparative summary and evaluate its performance. Specifically, an extensive data gathering campaign is carried out in order to evaluate the proposed RPM. The field measurements use the 5 GHz frequency band, which is suitable for vehicular communication. The results demonstrate that a close match exists between the predicted values and measured values of path loss. In particular, an average accuracy of 94% is found with R2 = 0.86.