Table 2 Digital technologies and their... | Emerald Publishing

Table 2

Digital technologies and their applications for environmentally sustainable transportation

Digital technology	Application	Key references
Internet of things including - sensor technologies such as GPS, RFID, load sensors (weight), environmental sensors (temperature, humidity) - connectivity technologies such as cellular networks (3G, 4G, 5G, LTE), Wi-Fi, Bluetooth - telematics	- Real-time tracking and tracing of goods and vehicles (GPS, RFID) - Environmental monitoring (e.g. air quality, noise) - Smart bins for waste management (fill level monitoring) - Traffic monitoring and management - Energy consumption tracking - Condition monitoring (e.g. temperature in food distribution) - Incident detection and mitigation - Smart city applications (e.g. waste management, parking) - Vehicle specific data collection and transmission	Sicilia-Montalvo et al. (2013), Hrabec et al. (2019), Sarvari et al. (2020), Akbarpour et al. (2021), Salehi-Amiri et al. (2022), Idrissi et al. (2024)
Artificial intelligence, machine learning, reinforcement learning and deep learning	- Predictive analysis (e.g. waste generation, EV energy consumption, traffic forecasting) - Route optimization and planning (e.g. for vehicles, drones, robots) - Demand forecasting and supply chain management - Anomaly detection in supply chains - Real-time adjustments to dynamic factors (e.g. traffic, weather) - Autonomous vehicles (UVs) - Decision support systems (e.g. waste management, energy management in microgrids) - Improving path selection based on various factors (inventory, cargo, demand)	Basso et al. (2021), Gomes et al. (2021), Giuffrida et al. (2022), Jelen et al. (2022), Ara et al. (2023), Ramírez-Villamil et al. (2023), Fitzpatrick et al. (2024), Wang et al. (2024)
Cloud computing	- Data storage and analysis from IoT devices - Hosting and running AI/ML models - Platform for smart logistics applications - Enables scalability and accessibility of resources	Edirisuriya et al. (2018), Gayialis et al. (2018), Chiarini (2020), Zhou et al. (2024)
Big data and analytics	- Big data provides the fuel for AI and ML algorithms - Big data is crucial for VRP, and analytics are used to process this data and inform optimization algorithms	Edirisuriya et al. (2018), Gayialis et al. (2018), Su and Fan (2020), Giuffrida et al. (2022), Sbai et al. (2024)
Geographic information system (GIS)	- Mapping and visualizing routes, analyzing spatial data, location-based services	Gayialis et al. (2018), Cerrone and Sciomachen (2022)
Unmanned vehicles including autonomous robots, drones	- Used in smart waste management and other automated logistics processes	Gupta et al. (2022), Justo et al. (2023), Dobrilovic et al. (2024)

Digital technology	Application	Key references
Internet of things including- sensor technologies such as GPS, RFID, load sensors (weight), environmental sensors (temperature, humidity) - connectivity technologies such as cellular networks (3G, 4G, 5G, LTE), Wi-Fi, Bluetooth - telematics	- Real-time tracking and tracing of goods and vehicles (GPS, RFID) - Environmental monitoring (e.g. air quality, noise) - Smart bins for waste management (fill level monitoring) - Traffic monitoring and management - Energy consumption tracking - Condition monitoring (e.g. temperature in food distribution) - Incident detection and mitigation - Smart city applications (e.g. waste management, parking) - Vehicle specific data collection and transmission	Sicilia-Montalvo et al. (2013), Hrabec et al. (2019), Sarvari et al. (2020), Akbarpour et al. (2021), Salehi-Amiri et al. (2022), Idrissi et al. (2024)
Artificial intelligence, machine learning, reinforcement learning and deep learning	- Predictive analysis (e.g. waste generation, EV energy consumption, traffic forecasting) - Route optimization and planning (e.g. for vehicles, drones, robots) - Demand forecasting and supply chain management - Anomaly detection in supply chains - Real-time adjustments to dynamic factors (e.g. traffic, weather) - Autonomous vehicles (UVs) - Decision support systems (e.g. waste management, energy management in microgrids) - Improving path selection based on various factors (inventory, cargo, demand)	Basso et al. (2021), Gomes et al. (2021), Giuffrida et al. (2022), Jelen et al. (2022), Ara et al. (2023), Ramírez-Villamil et al. (2023), Fitzpatrick et al. (2024), Wang et al. (2024)
Cloud computing	- Data storage and analysis from IoT devices - Hosting and running AI/ML models - Platform for smart logistics applications - Enables scalability and accessibility of resources	Edirisuriya et al. (2018), Gayialis et al. (2018), Chiarini (2020), Zhou et al. (2024)
Big data and analytics	- Big data provides the fuel for AI and ML algorithms - Big data is crucial for VRP, and analytics are used to process this data and inform optimization algorithms	Edirisuriya et al. (2018), Gayialis et al. (2018), Su and Fan (2020), Giuffrida et al. (2022), Sbai et al. (2024)
Geographic information system (GIS)	- Mapping and visualizing routes, analyzing spatial data, location-based services	Gayialis et al. (2018), Cerrone and Sciomachen (2022)
Unmanned vehicles including autonomous robots, drones	- Used in smart waste management and other automated logistics processes	Gupta et al. (2022), Justo et al. (2023), Dobrilovic et al. (2024)

Source(s): Authors’ own work based on literature review