Decision-Aiding Transit-Tracker Methodology for Bus Scheduling Using Real Time Information to Ameliorate Traffic Congestion in the Kathmandu Valley of Nepal

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, 100th 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 stakeholders in communicable languages, we argue that replacing low occupancy vehicles with multi-occupancy buses within a Bus Rapid Transit (BRT) system, on main roads with fixed schedules and strict traffic rules, would not only improve UPTS, but also reduce pollution in the Kathmandu Valley.

Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics

We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins;natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.

Approximate controllability results of semilinear integrodifferential equations with infinite delays

We study the approximate controllability of control systems governed by a class of semilinear integrod- ifferential equations with infinite delays. Sufficient conditions for approximate controllability of semilinear control systems are established provided the approximate controllability of the corresponding linear control systems. The results are obtained by using fixed-point theorems and semigroup theory. As an illustration of the application of the approximate controllability results, a simple example is provided.

A Privacy-Preserving Scheme for Location-Based Services in the Internet of Vehicles

Ubiquitous information exchange is achieved among connected vehicles through the increasingly smart environment.The concept of conventional vehicular ad hoc network is gradually transformed into the Internet of vehicles(IoV).Meanwhile,more and more locationbased services(LBSs)are created to provide convenience for drivers.However,the frequently updated location information sent to the LBS server also puts user location privacy at risk.Thus,preserve user location privacy while allowing vehicles to have high-quality LBSs is a critical issue.Many solutions have been proposed in the literature to preserve location privacy.However,most of them cannot provide real-time LBS with accurate location updates.In this paper,we propose a novel location privacy-preserving scheme,which allows vehicles to send accurate real-time location information to the LBS server while preventing being tracked by attackers.In the proposed scheme,a vehicle utilizes the location information of selected shadow vehicles,whose route diverge from the requester,to generate multiple virtual trajectories to the LBS server so as to mislead attackers.Simulation results show that our proposed scheme achieves a high privacy-preserving level and outperforms other state-of-the-art schemes in terms of location entropy and tracking success ratio.

Real-time and generic queue time estimation based on mobile crowdsensing

People often have to queue for a busy service in many places around a city, and knowing the queue time can be helpful for making better activity plans to avoid long queues. Traditional solutions to the queue time monitoring are based on pre-deployed infrastructures, such as cameras and infrared sensors, which are costly and fail to deliver the queue time information to scattered citizens. This paper presents CrowdQTE, a mobile crowdsensing system, which utilizes the sensor-enhanced mobile devices and crowd hu- man intelligence to monitor and provide real-time queue time information for various queuing scenarios. When people are waiting in a line, we utilize the accelerometer sensor data and ambient contexts to automatically detect the queueing behav- ior and calculate the queue time. When people are not waiting in a line, it estimates the queue time based on the information reported manually by participants. We evaluate the perfor- mance of the system with a two-week and 12-person deploy- ment using commercially-available smartphones. The results demonstrate that CrowdQTE is effective in estimating queu- ing status.