With the advent of the Internet of Things (IoT), it has become possible to have a variety of data sets generated through numerous types of sensors deployed across large urban areas, thus empowering the notion of smart cities. In smart cities, various types of sensors may fall into different administrative domains and may be accessible through exposed Application Program Interfaces (APIs). In such setups, for traffic prediction in Intelligent Transport Systems (ITS), one of the major prerequisites is the integration of heterogeneous data sources within a preprocessing data pipeline resulting into hybrid feature space. In this paper, we first present a comprehensive algorithm to integrate heterogeneous data obtained from sensors, services, and exogenous data sources into a hybrid spatial–temporal feature space. Following a rigorous exploratory data analysis, we apply a variety of deep learning algorithms specialized for time series geospatial data and perform a comparative analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and their hybrid combinations. The hybrid LSTM–GRU model outperforms the rest with Root Mean Squared Error (RMSE) of 4.5 and Mean Absolute Percentage Error (MAPE) of 6.67%.

Blockchain, smart contracts, and the Internet of Things (IoT) are essential technologies for the business process re-engineering of supply chains in the era of Industry 4.0. The agricultural food supply chain is one of the research areas where these disruptive technologies can play a crucial role in automating business processes, providing real-time goods monitoring, and securing transactions. With the help of blockchain, smart contracts, and the IoT, a product’s health and environment can be monitored throughout the supply chain. In this study, we have critically examined the relevance of these technologies through various activities of the agriculture supply chain using the approach of Business Process Modeling (BPM). The blockchain and smart contracts-based findings of the BPM were then incorporated along various layers of the Reference Architecture for Modeling Industry 4.0 (RAMI 4.0). This enabled us to introduce the IoT, blockchain, and smart contract based smart agriculture framework, Agri-4-All. Agri-4-All can be used to automate the intra-organizational and inter-organizational processes of the agricultural supply chain. We developed, deployed, and tested the intra-organizational and inter-organizational smart contracts written in the Solidity language for a typical scenario related to the agriculture supply chain. Our hybrid smart algorithms implemented using Ganache and Truffle suite reduce the gas cost in our proposed intra-organizational smart contracts by 13.89 times as compared to the traditional smart contract-based model.

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method’s efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.

With the rapid expansion of sensor technologies and wireless network infrastructure, research and development of traffic associated applications, such as real-time traffic maps, on-demand travel route reference and traffic forecasting are gaining much more attention than ever before. In this paper, we elaborate on our traffic prediction application, which is based on traffic data collected through Google Map API. Our application is a desktop-based application that predicts traffic congestion state using Estimated Time of Arrival (ETA). In addition to ETA, the prediction system takes into account various features such as weather, time period, special conditions, holidays, etc. The label of the classifier is identified as one of the five traffic states i.e. smooth, slightly congested, congested, highly congested or blockage. The results demonstrate that the random forest classification algorithm has the highest prediction accuracy of 92 percent followed by XGBoost and KNN respectively.

Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.

In smart cities of the future, data will be generated, integrated, processed and utilized from heterogeneous sources and at varying levels of complexity. For urban traffic planning in smart cities, one of the biggest challenges is traffic congestion prediction and its avoidance. Traffic congestion is a complex phenomenon and it is a manifestation of various contributing factors. In addition to vehicular mobility, properties of road network, weather, holidays and peak hours play a significant role in traffic congestion especially on arterial roads within a city. In this paper, we proposed a hybrid GRU-LSTM based deep learning model and applied it on city-wide novel traffic data integrated from heterogeneous sources. We have devised our indigenous data pipeline that is composed of a set of algorithms dealing with map matching, sparsity handling, outlier removal, zero speed adjustments, Open Street Map (OSM) and segment mapping etc. Extensive experimentations have been carried out to demonstrate the improved performance of the proposed method. The comparative analysis reveals that our methodology yields 95 % accuracy that outperforms other deep neural network models.