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Traffic flow detection plays a significant part in freeway traffic surveillance systems. Currently, effective autonomous traffic analysis is a challenging task due to the complexity of traffic delays, despite the significant investment spent by authorities in monitoring and analysing traffic congestion. This study builds an intelligent analytic method based on machine‐learning algorithms to investigate and predict road traffic flows in four locations in the United Kingdom (London, Yorkshire and the Humber, North East, and North West) with a range of relevant factors. While aiming to conduct the study, the dataset ‘estimated annual average daily flows (AADFs) Data—major and minor roads’ from the UK government was used. Machine‐learning algorithms are used for this research and classification applied consists of Logistic Regression, Decision Trees, Random Forests, K‐Nearest Neighbors, and Gradient Boosting. Each of these algorithms achieves an accuracy of over 93% and the F1 score of over 95%, with Random Forest outperforming the other algorithms. This analytical approach helps to focus attention on critical areas to reduce traffic flows on major and minor roads in the area. In summary, the findings on traffic analysis have been discussed in detail to demonstrate the practical insights of this study.

Air cargo plays an important role in supporting global supply chains; this becomes more vital when facing uncertainties in a crisis such as the COVID-19 pandemic. This motivates our study on air cargo forwarding plans, considering demand uncertainties and economic conditions. Cargos are placed into air containers based on weights and volumes, and then flown from regional collection points into a hub, for consolidation before transporting to onward destinations. Decisions are made in advance by cargo forwarders as to the containers to book, both in regions and in the hub, since airlines offer discounts on containers booked in advance; however, cargo quantities are uncertain when advance bookings are made. We develop a two-stage stochastic programming model, where the first stage determines both the quantities and types of air containers to book; the second stage deals with ordering any extra containers, at higher cost, or returning unused containers, as well as making loading and consolidation plans. The objective is to minimise the total expected costs. We then extend it into a multistage case and design a genetic algorithm as the solution method. Experimental results demonstrate that the proposed approaches provide a cost-efficient plan and responsive to demand as it arises.

Container terminals serve as an interface between marine and land transportation. Since the introduction of containerisation in 1960s, the number of containers handled worldwide has dramatically grown every year. With the increasing containerisation, nowadays container terminals are working at maximum capacity. Therefore, the efficiency of stacking and transportation of large number of containers to and from the quayside is critical to any container terminal. We have investigated the integration of container-handling equipment (such as quay cranes, yard cranes, automated guided vehicles and straddle carriers) scheduling and container storage allocation problems in two types of container-handling system: one is automated container terminal, which represents the current container terminal development and the other is straddle-carrier system, which has been used by most European container terminals. For each type of container terminal, we have studied three integrated problems respectively considering container unloading process (during which containers are unloaded from a ship and delivered to the storage yard), container loading process (during which containers are picked up from the yard and delivered to the quayside to be loaded onto a ship) and dual-cycle process (unloading and loading of containers simultaneously). Our aims are to determine the optimal schedules of container-handling equipment and assign optimal yard locations for containers. The objective is to minimise the berth time of the ship, which is the most important factor to evaluate the efficiency of container terminals. We have developed six models for the above problems. Optimal solutions can be obtained in small sizes of the problems under investigation; however, large-sized problems are hard to solve optimally in a reasonable time. Therefore, genetic algorithms are designed for each model to solve the problem in large sizes. The computational results show the effectiveness of th