Transport network design problem (TNDP) is a well-studied problem for planning and operations of transportation systems. They are widely used to determine links for capacity enhancement, link closures to schedule maintenance, identify new road or transit links and more generally network enhancements under resource constraints. As changes in network capacities result in a redistribution of demand on the network, resulting in changes in the congestion patterns, TNDP is generally modelled as a bi-level problem, which is known to be NP-hard. Meta-heuristic methods, such as Tabu Search Method are relied upon to solve these problems, which have been demonstrated to achieve near optimality in reasonable time. The advent of quantum computing has afforded an opportunity to solve these problems faster. We formulate the TNDP problem as a bi-level problem, with the upper level formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem that is solved using quantum annealing on a D-Wave quantum computer. We compare the results with Tabu Search. We find that quantum annealing provides significant computational benefit. The proposed solution has implications for networks across different contexts including communications, traffic, industrial operations, electricity, water, broader supply chains and epidemiology.

A traffic assignment model is a critical tool for developing future transport systems, road policies, and evaluating future network upgrades. However, the development of the network and demand data is often highly intensive, which limits the number of cases where some form of the models are available on a global basis. These problems include licensing restrictions, bureaucracy, privacy, data availability, data quality, costs, transparency, and transferability. This paper introduces Rapidex, a novel origin–destination (OD) demand estimation and visualisation tool. Firstly, Rapidex enables the user to download and visualise road networks for any city using a capacity-based modification of OpenStreetMap. Secondly, the tool creates traffic analysis zones and centroids, as per the user-specified inputs. Next, it enables the fetching of travel time data from pervasive traffic data providers, such as TomTom and Google. With Rapidex, we tailor the genetic-algorithm (GA)-based metaheuristic approach to derive the OD demand pattern. The tool produces critical outputs such as link volumes, link travel times, OD travel times, average trip length and duration, and congestion level, which can also be used for validation. Finally, Rapidex enables the user to perform scenario evaluation, where changes to the network and/or demand data can be made and the subsequent impacts on performance metrics can be identified. In this article, we demonstrate the applicability of Rapidex on the network of Sydney, which has 15,646 directional links, 8708 nodes, and 178 zones. Further, the model was validated using the Household Travel Survey data of Sydney using the aggregated metrics and a novel project selection method. We observed that 88% of the time, the “estimated” and “observed” OD matrices identified the same project (i.e., the rapid process estimated the more intensive traditional approach in 88% of cases). This tool would help practitioners in rapid decision making for strategic long-term planning. Further, the tool would provide an opportunity for developing countries to better manage traffic congestion, as cities in these countries are prone to severe congestion and rapid urbanisation while often lacking the traditional models entirely.

Extreme events, such as disasters and severe road accidents, have frequently happened in recent years, threatening thousands of people’s lives. Efficient evacuation after extreme events is of utmost importance, which requires transport authorities to plan for and develop solutions to ensure transport infrastructure resiliency to disasters. In this study, we develop a modeling framework for optimizing road network pre-disaster investment strategy under different disaster damage levels. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and reliability of the road network, which are measured by the resilience polygon area, while minimizing the total budget costs, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. Realistic speed data collected from crowd sources is used to explore the dependency between the vulnerability and new steady state with copula functions. The numerical study finds that the dependency of road capacities does affect measurements of road network reliability. The investment strategy is significantly affected by the characteristics of road edges and disaster damage levels. Critical sections that can significantly improve the overall functionality of the network are identified. This paper provides a theoretical basis for authorities to make pre-disaster road network investments based on different disaster damage levels, road attributes and budgets.

In this paper, the multicommodity network flow (MCNF) problem is formulated as a mixed integer programming model which is known as NP-hard, aiming to optimize the vehicle routing and minimize the total travel cost. We explore the potential of quantum computing, specifically quantum annealing, by comparing its performance in terms of solution quality and efficiency against the traditional method. Our findings indicate that quantum annealing holds significant promise for enhancing computation in large-scale transportation logistics problems.

In this paper, the multicommodity network flow (MCNF) problem is formulated as a mixed integer programing model which is known as NP-hard, aiming to optimize the vehicle routing and minimize the total travel cost. We explore the potential of quantum computing, specifically quantum annealing, by comparing its performance in terms of solution quality and efficiency against the traditional method. Our findings indicate that quantum annealing holds significant promise for enhancing computation in large-scale transportation logistics problems.