Solving link-based route guidance problems for large-scale networks is computationally challenging and faces practical issues, such as spatial–temporal data coverage. Thus, regional route guidance has emerged as a promising strategy, which utilizes regional approaches (i.e., network-level macroscopic traffic models) to capture traffic dynamics. Existing regional route guidance models have mainly focused on macroscopic flows and aggregated splitting rates, employing uniformly sampled vehicles as controllable targets. These models, however, overlook the inherent nature of individual drivers’ compliance. It may deteriorate the guidance performance as existing route guidance models cannot effectively generate customized route plans for compliant vehicles. This paper aims to introduce a regional route guidance framework with the utilization of different solution approaches, i.e. model-based and data-driven, considering the compliance pattern. In particular, MPC-based and deep reinforcement learning-based schemes are proposed for the information service provider to send customized route plans to compliant individuals. Within each solution approach, different route guidance strategies are developed for specific purposes and evaluated through numerical experiments under low- and high-congestion scenarios. Besides, the trade-off between total travel time and average trip length is studied, wherein the balance reward in a deep reinforcement learning-based approach enables controllable agents to reduce trip costs while compromising partial system utility. The findings indicate the effectiveness of the proposed strategies in alleviating network congestion. Additionally, multi-agent approaches outperform single-agent ones, highlighting the benefits of cooperative decision-making in traffic management.

Charging while driving, or wireless charging lane (WCL), has been considered a promising solution to promote the widespread adoption of electric vehicles (EVs) for clean transportation systems. However, the WCL location problem is a strategic decision that costs a significant amount of money and greatly impacts network performance due to their interactions with traffic flow patterns. The inappropriate deployment of charging infrastructure may potentially cause more congestion, leading to more pollutants emitted from internal combustion vehicles. This paper proposes a decision-making framework to assist the system planner in finding the preferred optimal WCL location based on incorporating different stakeholders' preferences, i.e. capital cost, congestion and environmental considerations. To capture the traffic evolution of different classes of vehicles (i.e. EVs and internal combustion vehicles), the travellers' optimal time-dependent route choice is modelled using the multi-class dynamic traffic assignment. The proposed framework is transformed into a single-objective problem, i.e. minimizing total system cost, and solved efficiently by a meta-heuristic based on the cross-entropy method. The framework has been intensively tested to demonstrate the capability of tracking congestion propagation and energy consumption. Finally, the trade-offs between social welfare and the capital cost of WCL infrastructure are also investigated.

Wireless charging technologies have now made it possible to charge while driving, which offers the opportunity to stimulate the market penetration of electric vehicles. This paper aims to support the system planner in optimally deploying the wireless charging lanes on the network, considering traffic dynamics and congestion under multiple vehicle classes. The overall objective is to maximise network performance while providing insights into traffic propagation patterns over the network. A multi-class dynamic system optimal model is adopted to compute an approximate representation of the dynamic traffic flow. As a result, the problem is formulated as a mixed-integer linear program by integrating the dynamic routing behaviour into the charging location problem. Finally, the proposed framework has been tested on different sized test-bed networks to examine the solution quality and illustrate the model’s efficacy.

This study aims to seek the optimal deployment of fast-charging stations concerning the traffic flow equilibrium and various realistic considerations to promote Electric Vehicles (EVs) widespread adoption. A bi-level optimization framework has been developed in which the upper level aims to minimize the total system cost (i.e., capital cost, travel cost, and environmental cost). Meanwhile, the lower level captures travellers' routing behaviours with stochastic demands and driving range limitation. A meta-heuristic approach has been proposed, combining the Cross-Entropy Method and the Method of Successive Average to solve the problem. Finally, numerical studies are conducted to demonstrate the proposed framework's performance and provide insights into the impact of uncertain driving range and charging congestion on the planning decision and the system performance. Generally, both on-route congestion and charging congestion tend to be more serious when there are more EVs in the network; however, the system performance can be improved by increasing EVs' driving range limitation and providing appropriate charging infrastructure.

Inappropriate deployment of charging stations not only hinders the mass adoption of Electric Vehicles (EVs) but also increases the total system costs. This paper attempts to address the problem of identifying the optimal locations of fast-charging stations in the urban network of mixed gasoline and electric vehicles with respect to the traffic equilibrium flows and the EVs' penetration. A bi-level optimization framework is proposed in which the upper level aims to locate charging stations by minimizing the total travel time and the installation costs for charging infrastructures. On the other hand, the lower-level captures re-routing behaviours of travellers with their driving ranges. A cross-entropy approach is developed to deliver the solutions with different levels of EVs' penetration. Finally, numerical studies are performed to demonstrate the fast convergence of the proposed framework and provide insights into the impact of EVs' proportion in the network and the optimal location solution on the global system cost.

Although the electrification of transportation can bring long-term sustainability, increasing penetration of Electric Vehicles (EVs) may cause more congestion. Inappropriate deployment of charging stations not only hinders the EVs adoption but also increases the total system costs. This paper attempts to identify the optimal locations for fast-charging stations in the urban network considering heterogeneous vehicles with respect to the traffic congestion at different levels of EVs’ penetration. A bi-level optimization framework is proposed to solve this problem in which the upper level aims to locate charging stations by minimizing the total travel time and the infrastructure costs. On the other hand, the lower level captures re-routing behaviours of travellers with their driving ranges. Finally, numerical study is performed to demonstrate the fast convergence of the proposed framework.

On the international market, Vietnam's rice value is quite low while production costs are too high, especially in energy costs. Moreover, wastes and pollution have become one of the most imperative issues of rice processing industry, leading to waste resources, increase costs, reduce product quality, and adverse impact on the working environment and habitats as well as the reputation of the enterprises. From analyzing the current state of Vietnam rice processing industry and studying Cleaner Production, this research would study the feasibility of cleaner production technology for Vietnam rice processing industry. A roadmap to implement the cleaner production technology would be suggested. A case study on a rice processing factory belonging to Vietnam Southern Food Corporation would be done. It shows that there is a potential demand on cleaner production and it is possible to implement for Vietnam rice processing industry.