Anthropogenic is defined as one of the influencing factors of the climatic phenomenon, called Urban Heat Island (UHI), in which urban areas have higher air temperatures than their rural surroundings. Analyzing the impact of anthropogenic factors, such as vehicular traffic, has implications for the potential benefits of health monitoring systems; however, the spatiotemporal impact of anthropogenic factors, as well as vehicle mobility, has not been thoroughly investigated. This study incorporates vehicle mobility data by leveraging two different sensors; fixed station sensor instruments designed to integrate with the Internet of Things (IoT), and the camera of an area traffic control system (ATCS) that uses CCTV visualization with object detection. Using object detection and time-based traffic volume analysis, we obtain the level of the queue rate (λ) to present the level of the traffic flow based on the average velocity of the vehicle flow. Based on the results, it showed that the average temperature in urban areas is higher than in suburban areas, and the severe traffic jams caused a significant increase in temperature, that is until 7 Celsius when the weather is sunny. In addition, the theoretical UHI (UHI_T) model developed in this study can be used to estimate the UHI which is influenced by the queue rate.

Development in technology bring digital world to be border-less. It's proven through a developed technology, when trade and transaction can be done not only using real money but also virtual one. Shopping process using virtual money has even more supported by existed Near Field Communication (NFC) device. This particular device works using radio frequency. In the year of 2011, Google was integrating this device into a Android-based cell phone, which made transactions using virtual money gradually developed. E-Ticket is one of the utmost services in online trading. E-ticket is basically a paper-less electronic document, which commonly used for passenger tickets of a transportation system. One of the aims in using NFC in daily life can be implemented within public transportation application. This particular research offers a application prototype for train ticket purchasing and exterminating that use a Near Field Communication (NFC) integrated cell phone. This train ticketing application integrated with NFC device that had been planted in an Android 2.3.3 Operation System based cell phone. Designed system made for passenger interface and train conductor interface. NFC technology applied within two created applications. Cell phone that is used within this research is Google Nexus S, which has been integrated with NFC device; therefore created application can provide a circumstance where applications for passenger and conductor are able to work as a single entity of train ticketing application.

The route recommendation system helps the driver find the best route between origin and destination. A recommendation system often suggests its decision without considering some criteria. This paper proposes a multicriteria decision-making method, namely Fuzzy—Analytic Hierarchy Process—Express (F-AHP-Express) for recommending a personal travel route from several alternative routes. It is calculated based on the driving preferences of a driver and road conditions for each road segment. We compare the F-AHP-Express to others; Fuzzy—Analytic Hierarchy Process (F-AHP) and Fuzzy—Analytic Hierarchy Process—Technique for Others Preference by Similarity to Ideal Solution (F-AHP-TOPSIS), for its recommendation results, time processing, agility, and complexity. Our experiments show that F-AHP-Express could deliver similar recommendation results compared to other methods, and it is additionally the fastest method. F-AHP-Express is 45% and 23% faster than F-AHP and F-AHP-TOPSIS, respectively. F-AHP-Express not only has the fastest time processing among the others but also has the least judgments in agility testing. It needs 37.5% and 16.67% fewer judgments from F-AHP and F-AHP-TOPSIS, respectively. Moreover, AHP-Express has a complexity of O(n), meanwhile, the others have O(n²) for their complexity. Thus, the results show that F-AHP-Express is the best method for recommending a personal route.

Communication between the near field communication (NFC) devices occurs in a very close distance of less than 10 cm. In the NFC-based payment system, close proximity between devices will increase the security of transactions. The disadvantage is the interaction between devices requires more physical activity of device owners because the device must be brought near to other devices some times. Besides requiring more physical activity, NFC-based interaction also takes a longer time because the device needs to be moved from one position to another. This paper proposed Secure and efficient protocol that will reduce the physical activity of the device owners and reducing transaction time. The data sending between merchant and payer will be executed without waiting for each other and one transaction will require two data transmissions are performed by the merchant and payer. Transactions are secured by the use of encryption on each data which sent by the merchant and payer. In addition, the protocol also guarantees the security of offline micro transactions and online macros transactions.

The traffic composition in developing countries comprises of variety of vehicles which include cars, buses, trucks, and motorcycles. Motorcycles dominate the road with 77.5% compared to other types. Meanwhile, route recommendation such as navigation and Advanced Driver Assistance Systems (ADAS) is limited to particular vehicles only. In this research, we propose a framework for a contextual route recommendation system that is compatible with traffic conditions and vehicle type, along with other relevant attributes (traffic prediction, weather, temperature, humidity, heterogeneity, current speed, and road length). The framework consists of two phases. First, it predicts the traffic conditions by using Knowledge-Growing Bayes Classifier on which the dataset is obtained from crawling the public CCTV feeds and TomTom digital map application for each observed road. The performances of the traffic prediction are around 60.78–73.69%, 63.64–77.39%, and 60.78–73.69%, for accuracy, precision, and recall respectively. Second, to accommodate the route recommendation, we simulate and utilize a new measure, called road capacity value, along with the Dijkstra algorithm. By adopting the compatibility, the simulation results could show alternative paths with the lowest RCV (road capacity value).

Road extraction plays a crucial role in various sectors, including transportation systems, disaster relief distribution, and urban planning, necessitating a more efficient method than the current labor-intensive approach. The conventional process, reliant on operator efforts, proves costly, time-consuming, and energy intensive. To address this, the employment of deep learning models such as DeepLab V3+ are done, leveraging encoders like ResNet 50, ResNet 101, and MobileNet V2, known for their effectiveness in deep learning applications. Although Deep Learning has demonstrated faster and more automated road extraction, the bottleneck persists in the manual creation of training data through road annotations by operators. Our research focuses on accelerating road extraction by incorporating enhanced annotations from Object-Based Image Analysis (OBIA) along with organic annotations into the training data to expedite dataset creation while ensuring extraction model accuracy. Specifically, we investigate the optimal ratio of synthetic to organic annotations that yields the highest road extraction accuracy. Moreover, we enhance OBIA-derived road annotations and regulate their integration into the training data. Our findings reveal an optimal composition of 25% for OBIA annotations and 50% for improved OBIA annotations, as exceeding these numbers results in diminished model performance. Significantly, the further improvement of OBIA annotations substantially boosts model performance metrics exemplified in the use of 100% composition in training data. On average, each model produces Pixel Accuracy of 0.942, IoUr of 0.012, mean IoU of 0.477, and Dice Score of 0.495 for every use of 100% OBIA annotations in the training data. Improvement in model performance evaluation metrics occurs when using 100% improved OBIA annotations in the training data where on average each model produces Pixel Accuracy values of 0.954, IoUr of 0.433, mean IoU of 0.692, and Dice Score of 0.771. The experimental results demonstrate the advantages of our proposed method, indicating a reduction in the time required to prepare Deep Learning datasets, reducing the number of organic annotations required to as little as 50% while maintaining model performance by leveraging OBIA road annotations as training data.