## 6 Smart Transportation
### The Improvements of Traffic Efficiency  
Previous literature has discussed methods for enhancing transportation efficiency, primarily through the efficient processing of data from smart transportation IoT, citizen engagement, and the use of IoT-enabled transportation infrastructure. In smart transportation IoT systems, scholars generally use presence sensors (e.g., GPS [77], Bluetooth sensor [78], and infrared laser light sensor [79]) to detect passing people or vehicles and speed sensors (e.g., inductive loop detector [77], optical/Laser sensors [80]) to detect their speed. Cellular data transmission (e.g., GSM [77], 3G [79]) is the most commonly used technology for data transmission. Scholars have different insights on efficiently processing data from smart transportation IoT systems. [PERSON] and [PERSON] [77] developed an IoT traffic system. This system can predict traffic congestion by comparing normalized speed and identify traffic accidents through the accident detection model. [PERSON] et al. [81] suggested to use parallel processing systems and big graph processing technology to process big data from IoT transportation systems. [PERSON] et al. [82] used IoT technology, cloud computing, fog computing, microservices, and DevOps infrastructure to develop the PROMENADE platform for real-time monitoring and analysis of transportation data generated by IoT devices in large smart cities. This platform combines cloud computing, fog computing, and IoT technology. It can provide information such as shortest paths and the betweenness centrality of each node. [PERSON] et al. [78] proposed a framework for real-time smart commuting behavior analysis based on artificial intelligence and IoT. Traffic data are obtained in real time via Bluetooth sensors located on Victoria's main roads. When a vehicle with a Bluetooth device passes through its scanning area, the MAC address of the Bluetooth device is recorded by the scanner along with the timestamp of that event and transmitted to a central database. The data generated by the sensor include the analysis of traffic flow and traffic trajectory under stable and fluctuating commuting behavior scenarios. To analyze pedestrian mobility, [PERSON] et al. [PERSON] et al. (2018) used infrared laser light sensors to scan and count passing pedestrians. They conducted static analysis using Microsoft Excel(r) and more advanced interactive visualizations and analyses with PowerBI(r). Citizen engagement plays a crucial role in smart transportation IoT systems, as citizens are significant participants in transportation systems. Therefore, the data from the smart transportation IoT system can be analyzed in conjunction with citizen participation data. Multiple modal data can complement and reinforce each other [PERSON] et al. (2018). For example, [PERSON] et al. [PERSON] et al. (2018) combine the quantitative transportation data and the qualitative transportation data from citizen engagement to obtain more accurate data. Social media can be a useful way to access citizen engagement data. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) and [PERSON] et al. [PERSON] et al. (2018) both combined sensor-generated data and social media data for traffic event detection. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) suggested using a Multimodal Neural Network. They used a Recurrent Neural Network to process sensor-generated data and a Convolutional Neural Network to process social media data. [PERSON] et al. [PERSON] et al. (2018) suggested using a multimodal generative adversarial network. They used LSTM-RNN for the feature extraction of sensor-generated data in this network. IoT-enabled transportation infrastructures include IoT road monitoring systems and IoT lampposts. [PERSON] et al. [PERSON] et al. (2018) introduced a real-time IoT-based system to monitor the occupancy of loading and unloading days on roadsides in Hong Kong.