The bustling urban environment of Kathmandu Valley is characterized by unprecedented traffic congestion. Due to its bowel-shaped geography, gusty winds rarely remove vehicular emissions from the urban atmosphere, making Kathmandu one of Asia's most polluted cities, 100 th city in global pollution index. Over 500,000 vehicles travel daily on over 1600 km of roads covering over 675 sq•km urban area. Thousands of low occupancy vehicles are added each year to the urban public transit system (UPTS). Kathmandu faces worse and unreliable traffic from the current UPTS mostly with low occupancy vehicles. Around 4.5 million urban denizens, both permanent and transient residents, suffer from unreliable UPTS. Traffic rules and daily transportation schedules are rarely followed, resulting in frequent traffic jams and accidents. Once experienced, visitors try avoiding the UPTS. Tourism, annually contributing almost 8 percent to Nepal's total annual GDP, also suffers from poor UPTS. Planners, policy makers, and politicians (P-actors) are seeking ways to improve sustainable UPTS to ameliorate stresses to family life and working hours for the urban majority. Aiming to help P-actors, we propose a transit-tracker model that uses real time information (RTI) in mobile phones and web-embedded devices to inform travelers, drivers, government authorities, and sub-admins. We argue that unreliability in the UPTS motivates urban elites to add more low occupancy vehicles, which in turn reduces already shrunken urban spaces and contributes more per capita air pollution than multi-occupancy vehicles. Since mobile and smart phones are capable of processing RTI to generate meaningful information and inform various How to cite this paper: Bhattarai, K., Yousef, M., Greife, A. and Lama, S. (2019) Decision-Aiding Transit-Tracker Methodology for Bus Scheduling Using Real Time Information to Ameliorate Traffic Congestion in the Kathmandu Valley of Nepal.

For several years, the mechanism of recording attendance has evolved from traditional manual systems, such as recording in daybooks, to electronic systems, where modern systems have included the integration of fingerprint devices and data management systems, including ERP systems. Earlier, the manual attendance method used, which is not only consuming the time, but it also gives erroneous results. The attendance controlling system provides many benefits to organizations. This diminishes the need of a pen-and-paper-based manual attendance system. Following this thought, this paper has proposed a smartphone auto attendance application based on the global positioning system (GPS). We are going to use GPS, wireless fidelity (Wi-Fi) and network data to determine the location of the mobile device with the desired accuracy. The solution is implemented and tested on an Android and iOS device, which results in "no need of additional biometric scanner devices and other readers". The application can be configured with the organization locations that were identified in the system, which can be determined by GPS. Each user's location can be logged by GPS using a smartphone. This location is declared as a key of time and attendance tracking in this paper.

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