## 4 Applications of WSN in Urban Areas
### Transportation Applications
It is important to consider user preferences and characteristics in the design and planning of transportation systems to improve efficiency, accessibility, and sustainability. [PERSON] et al. (2014) [23] introduced the land-use transportation model for urban policy assessment in smart community building, employing light, temperature, and acoustic sensor nodes. Using a centralized network type and mesh network topology, the model addressed urban policy formulation and assessment challenges, particularly concerning energy consumption. The model provided a comprehensive framework for evaluating the impact of city policies on energy consumption in a reliable manner by incorporating actor location-related decisions and travel behavior. Through simulations and analysis, the study offered insights into the effectiveness of different policy interventions in promoting smart and sustainable urban development. Then, the results highlighted the potential for integrating sensor technologies and transportation modeling to support evidence-based decision-making in urban planning and policy development, ultimately creating more efficient and resilient smart communities. The importance of leveraging technological advancements to address complex urban challenges and achieve sustainable development goals. An integrated approach was designed by [PERSON] et al. (2021) [24] who aimed to enhance the functionality of cities by incorporating public infrastructure systems. Using Radio Frequency Identification (RFID) and Hall Effect sensors, coupled with a centralized network type and mesh network topology, this approach sought to optimize urban planning procedures, particularly in public transportation, using the Public Transport Accessibility Level (PTAL) methodology. By integrating sensors and network technologies, the study addressed challenges related to transportation efficiency and accessibility, ultimately aiming to improve the overall functionality of cities. The conceptual model by [PERSON] et al. (2021) [25], on the other hand, focused on connecting transportation and health. Employing multimedia sensors and a centralized network type with a grid-based network topology, the model aimed to leverage expert knowledge and information to assess the health outcomes associated with transportation systems. By integrating sensor data and expert insights, this model provided a comprehensive framework for understanding the health implications of transportation decisions. These two methodologies highlight the potential of integrating sensor technologies with urban planning and health assessment to address complex challenges and improve the overall well-being of urban residents. An integrated urban transportation system (IUTS) developed by [PERSON] et al. (2012) [26] aimed to address challenges in metropolitan transportation by integrating various modes of transportation through the use of magneto resistive sensors. Using a decentralized network type with a star network topology, the system facilitated data collection and decision-making processes involving city planners, traffic engineers, and municipal authorities. By combining public and private transportation options, the IUTS sought to improve mobility standards in metropolitan areas and positively influence resident travel behavior. Implementing the IUTS led to the design of advanced transportation solutions and changes in resident travel habits, ultimately enhancing the efficiency and effectiveness of urban transportation systems. The planning process relied on Multi-Criteria Decision Making/Analysis (MCDM/A) methodology for ensuring a comprehensive and systematic approach to addressing transportation challenges. Thus, the study highlighted the potential of integrated transportation systems to transform urban mobility and improve the overall quality of life for residents in metropolitan areas. Table 5 compares various urban transport methodologies aimed at improving transportation systems and understanding user behavior.