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Improving Traffic Flows and Safety at Highway-Rail Grade Crossings via Multi-Criteria Optimization
Abstract and Figures
Many counties around the world experience urbanization, where large metropolitan areas are characterized with a high population density and a variety of different business activities. The movement of people from rural areas to new urban centers needs faster, safer, and cheaper commute alternatives. The railway network is experiencing a rapid growth and expansion to meet the demand for passenger travel and provide effective commute services. Furthermore, rail transportation has been playing an important role for freight movements as well, especially in North America (i.e., United States and Canada). Containers delivered by oceangoing vessels to marine container terminals are effectively distributed by rail to major cities of the United States.
A rapid development and expansion of rail services not only creates economic opportunities but also imposes some major challenges. Some of these challenges are associated with accidents between trains and highway vehicles at highway-rail grade crossings (HRGCs). Each HRGC represents a geographical location where rail tracks intersect a highway at the same elevation. Due to increasing demand for passenger and freight rail transportation, there are chances of increased severity of accidents at HRGCs. Various types of countermeasures, such as advanced warning devices, quad gates, advanced signal systems, and grade separation, can be used at HRGCs in order to reduce the number of accidents. Deployment of warning devices or other types of countermeasures is a challenge for the relevant stakeholders because of the uniqueness of each crossing in terms of geometry, location, and type of traffic. Also, the selection of appropriate countermeasures within a specified budget is a quite challenging task, since each countermeasure induces traffic delays at HRGCs. These delays negatively impact the continuity of passenger and freight flows and have long-term financial implications.
The main objective of this dissertation is to develop a multi-objective optimization model that can be used to identify the HRGCs and upgrade them with necessary countermeasures. The multi-objective optimization model relies on two conflicting objectives when performing resource allocation: (1) reduce the overall hazard severity at HRGCs; and (2) maximize the overall freight and passenger flows by reducing the overall traffic delays. A set of multi-objective solution algorithms, including an exact optimization algorithm and a group of heuristic algorithms, are developed to solve the proposed multi-objective optimization model. Performance of the developed optimization model and proposed solution algorithms is evaluated for the HRGCs located in the State of Florida (United States), where rail transportation is heavily used for both passenger and freight movements.
A set of sensitivity analyses for resource allocation decisions among the HRGCs is conducted as well. In particular, the sensitivity of resource allocation decisions to the following elements is performed: (a) number of crossings; (b) number of countermeasures; and (c) total available budget. The results from the conducted analyses reveal some important patterns. More specifically, the overall hazard severity values generated for various instances reduce with the increase of the total available budget but generally increase with the number of crossings. Moreover, availability of countermeasures creates more flexibility in resource allocation, and more effective countermeasures can be selected to increase the safety level. A significant reduction in the values of the overall hazard severity is observed after introducing inexpensive (i.e., low-cost) countermeasures. Since the cost of these countermeasures is much lower compared to the traditional countermeasures, more crossings can be upgraded. The implementation of upgrading at additional crossings, however, causes an increase in the values of the overall traffic delay.
The outcomes of this work (including the developed multi-objective optimization model for resource allocation among HRGCs, proposed solution algorithms, and managerial insights revealed during the numerical experiments) are expected to assist relevant stakeholders with safety improvement projects at HRGCs in the United States and other countries as well. Moreover, this research could also benefit other scholars who are intensively working in the area of HRGC safety.
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A significant number of accidents occur each year at level crossings globally. Substantial efforts are being made by different railway authorities and other stakeholders to prevent accidents by installing various countermeasures at level crossings (e.g., mountable curbs, two-quad gates, four-quad gates). However, due to budgetary constraints, it is not possible to deploy countermeasures at all level crossings. Besides, countermeasures have certain effectiveness factors and the associated installation cost. Usually, the cost of the countermeasure increases with its effectiveness. Therefore, it is essential to select the most effective countermeasures at the riskiest level crossings. The implementation of countermeasures at level crossings may lead to certain negative externalities as well. In particular, the deployment of countermeasures can result in a decreased traffic flow, causing traffic delays and adversely affecting the continuity of freight and passenger train movements. This study proposes a multi-objective mathematical model for resource allocation among level crossings, which aims not only to minimize the total hazard severity due to potential accidents but the associated traffic delays as well. Exact and heuristic solution approaches are designed to solve the developed multi-objective model. A set of computational experiments are conducted for the level crossings located in the State of Florida (United States). The results demonstrate superiority of the exact optimization method, as it obtained optimal Pareto Fronts within acceptable computational time. Moreover, a number of sensitivity analyses are conducted to showcase certain managerial insights that would be of interest to railway authorities and other stakeholders involved in level crossing safety improvements.
The level crossings in the United States experience a significant number of accidents every year. The accidents can be reduced with the application of various countermeasures (e.g., traffic signal preemption, flashing lights, barrier cubs, gates). However, the application of countermeasures for all the level crossings in the United States is not feasible due to monetary limitations. Moreover, each countermeasure has a unique level of effectiveness and installation cost (e.g., the most effective countermeasures are typically more expensive than the least effective ones). Hence, selection of potent and cost-effective countermeasures at the riskiest level crossings is imperative to improve safety. While improving safety at level crossings with the application of countermeasures, there is a significant risk of waning highway vehicle flows, increasing delays, and negatively affecting the continuity of passenger and freight flows. In such a scenario, multi-objective resource allocation models could be instrumental, since such models can analyze the tradeoffs between conflicting objectives (e.g., minimizing the number of accidents vs. minimizing the total delay). Hence, this chapter presents a framework for multi-objective resource allocation to minimize the number of accidents and to minimize the total delay at level crossings. Furthermore, various methods for quantifying the number of accidents as well as delays due to the application of countermeasures at level crossings are reviewed. Solution methods for multi-objective resource allocation models, including exact and approximate optimization approaches, are also discussed. Finally, future research avenues for multi-objective resource allocation among level crossings are outlined.
There is so much interest in online purchasing within supply chain networks nowadays. After expanding the internet access and services, customers’ behavior has changed. Today, a customer’s shopping manner usually begins with the internet search. With this approach, we face some new trends in this field, such as online-to-offline (O2O) commerce that aims to balance online and offline sales. Regarding the supply chain management, the O2O commerce can help the managers to conduct both online and offline businesses. The tire industry is one of the applications of the O2O approach, which also directly affects the supply chain network design (SCND). Therefore, this work for the first time proposes a dual-channel, multi-product, multi-period, multi-echelon closed-loop SCND under uncertainty for the tire industry. To tackle the uncertain parameters of the problem (e.g., prices and demand), a fuzzy approach, so-called the Jimenez’s method, is applied. Another main innovation of this work is two new hybrid meta-heuristic algorithms with new procedures. Two recent nature-inspired algorithms (i.e., red deer algorithm (RDA) and whale optimization algorithm (WOA)) are hybridized with the genetic algorithm (GA) and simulated annealing (SA) to strengthen the diversification and intensification phases, respectively. The numerical experiments demonstrate that the hybrid version of WOA and SA returns high-quality solutions and requires an acceptable amount of computational time. The conducted sensitivity analyses underline the importance of tire remanufacturing. Furthermore, setting the appropriate prices in different channels for the available tire types is critical for sustainable tire supply chain management.
Nowadays, an efficient and robust plan for maintenance activities can reduce the total cost significantly in the equipment-driven industry. Maintenance activities are directly associated with the impact on the plant output, production quality, production cost, safety, and the environmental performance. To address this challenge more broadly, this paper presents an optimization model for the problem of flexible flowshop scheduling in a series-parallel waste-to-energy (WTE) system. To this end, a preventive maintenance (PM) policy is proposed to find an optimal sequence for processing tasks and minimize the delays. To deal with the uncertainty of the flexible flowshop scheduling of waste-to-energy in practice, the work processing time is modeled to be uncertain in this study. Therefore, a robust optimization model is applied to address the proposed problem. Due to the computational complexity of this model, a novel scenario-based genetic algorithm is proposed to solve it. The applicability of this research is shown by a real-life case study for a WTE system in Iran. The proposed algorithm is compared against an exact optimization method and a canonical genetic algorithm. The findings confirm a competitive performance of the proposed method in terms of time savings that will ultimately save the cost of the proposed PM policy.
Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.
Highway-rail grade crossing (HRGC) accidents pose a serious risk of safety to highway users, including pedestrians trying to cross HRGCs. A significant increase in the number of HRGC accidents globally calls for greater research efforts, which are not limited to the analysis of accidents at HRGCs but also understanding user perception, driver behavior, potential conflicting areas at crossings, effectiveness of countermeasures and user perception towards them. HRGC safety is one of the priority areas in the State of Florida, since the state HRGCs experienced a total of 429 injuries and 146 fatalities between 2010 and 2019 with a significant increase in HRGC accidents over the last years. The present study aims to conduct a comprehensive analysis of the HRGCs that experienced accidents in Florida over the last years. The databases maintained by the Federal Rail Administration (FRA) are used to gather the relevant information for a total of 578 crossings that experienced at least one accident from 2010 to 2019. In contrast with many of the previous efforts, this study investigates a wide range of various factors, including physical and operational characteristics of crossings, vehicle and train characteristics, spatial characteristics, temporal and environmental characteristics, driver actions and related characteristics, and other relevant information. The outcomes of this research will help better understanding the major causes behind accidents at the HRGCs in the State of Florida in a holistic way by considering a variety of relevant factors, which will assist the appropriate stakeholders with implementation of safety improvement projects across the state.
The maritime transportation flows and container demand have been increasing over time, although the COVID-19 pandemic may slow down this trend for some time. One of the common strategies adopted by shipping lines to efficiently serve the existing customers is the deployment of large ships. The current practice in the liner shipping industry is to deploy a combination of ships of different types with different carrying capacities (i.e., heterogeneous fleet), especially at the routes with a significant demand. However, heterogeneous fleets of ships have been investigated by a very few studies addressing the tactical liner shipping decisions (i.e., determination of service frequency, ship fleet deployment, optimization of ship sailing speed, and design of ship schedules). Moreover, limited research efforts have been carried out to simultaneously capture all the major tactical liner shipping decisions using a single solution methodology. Therefore, this study proposes an integrated optimization model that addresses all the major tactical liner shipping decisions and allows the deployment of a heterogeneous ship fleet at each route, considering emissions generated throughout liner shipping operations. The model's objective maximizes the total turnaround profit generated from liner shipping operations. A decomposition-based heuristic algorithm is presented in this study to solve the model proposed and efficiently tackle large-size problem instances. Numerical experiments, carried out for a number of real-world liner shipping routes, demonstrate the effectiveness of the proposed methodology. A set of managerial insights, obtained from the proposed methodology, are also provided.
An efficient product distribution is critical for proper supply chain operations. Many supply chains handle perishable products that decay over time. Due to mismanagement of supply chain operations, a significant portion of perishable products is wasted, resulting in substantial monetary losses. Cross-docking terminals (CDTs) have been widely used in cold supply chains for the product distribution but have not received adequate attention in the scientific literature. To improve the efficiency of perishable product distribution, this study introduces for the first time a novel mixed-integer mathematical formulation for the truck scheduling optimization at a cold-chain CDT. The model explicitly captures the decay of perishable products throughout the service of arriving trucks and accounts for the presence of temperature-controlled storage areas that are specifically designated for perishable products. The objective minimizes the total cost incurred during the truck service. Considering the complexity of the proposed model, a customized Evolutionary Algorithm is developed to solve it. The computational performance of the developed algorithm is assessed throughout the numerical experiments based on a detailed comparative analysis against the other metaheuristics. The developed Evolutionary Algorithm is found to be the most promising metaheuristic, considering both solution quality and CPU time perspectives. Furthermore, the proposed algorithm demonstrates an acceptable stability of the solution quality at termination. A set of additional sensitivity analyses are performed in order to draw some significant managerial implications, which would be of potential interest to the supply chain stakeholders that are involved in the distribution of perishable products in cold supply chains.
Energy supply of megacities is considered as an active research topic in the new aspects of urban management, especially in developing countries like Iran. With an introduction to the sustainable development goals, the smart city concept presents a novel idea for providing energy in a city with the use of Artificial Intelligence (AI), renewable energy, such as Photovoltaic (PV) technologies, and Transformational Participation (TP) based on motivational programs for citizens. This study aims to evaluate the electrical energy consumption in Mashhad, Iran, based on machine learning tools and present the dynamic strategies for promoting citizens’ willingness for renewable energy generation based on the experts’ knowledge. The main novelty of this research is simultaneous application of Artificial Neural Network (ANN) and statistical analysis for creating a Decision Support System (DSS). Then, the solar energy potential is appraised by the PV system simulation tool during one year in our case study in Mashhad, Iran. Furthermore, a Classical Delphi (CD) method is applied for motivational strategies and further TP implementation. In particular, the motivational strategies are suggested by 45 experts and then are prioritized in sequential expert meetings. The outcomes of this research indicate that the ANN model can successfully forecast the electrical energy consumption in summer and winter periods with a 99% accuracy. Then, based on the solar energy computations in the PV system, the peak of electrical energy consumption can be controlled in the hottest and coldest months. Last but not least, the superposition of experts’ and citizens’ opinions reveal A4 (sharing benefits of optimized costs with the citizens by solar energy generation), B2 (reducing the electrical energy cost for solar energy generation, especially in peak times) and C1 (creating the energy coin in the city with credits instead of spending money in urban activities fits to solar energy generation) as the main motivational strategies for solar energy generation in short, middle and long-term planning horizons.
Automation is expected to effectively address the growing demand for passenger and freight
transportation, safety issues, human errors, and increasing congestion. The growth of autonomous vehicles
using the state-of-the-art connected vehicle technologies has paved the way for the development of
passenger and freight autonomous trains (ATs), also known as driverless trains. ATs are fully automated
trains that are centrally controlled using advanced communication and internet technologies, such as highspeed internet (5G) technology, Internet of Things, dedicated short range communications, digital video
detection cameras, and artificial intelligence-based methods. The current study focuses on a detailed up-todate review of the existing trends, technologies, advancements, and challenges in the deployment of ATs
with a full automation level in rail transportation. The basic AT features along with the key technologies
that are instrumental for the AT deployment and operations are discussed in detail. Furthermore, a
comprehensive evaluation of the state-of-the-art research efforts is performed as well with a specific
emphasis on the issues associated with the AT deployment, user perception and outlook for ATs, innovative
concepts and models that could be used for the AT design, and the AT operations at highway-rail grade
crossings. Based on the conducted review, this study determines the main advantages and challenges from
the AT deployment. The identified challenges have to be collaboratively addressed by the relevant
stakeholders, including railroad companies, researchers, and government representatives, to facilitate the
AT development and deployment considering the perspectives of future users and without affecting the
safety level.