scholarly journals Road Maintenance Optimization Model Based on Dynamic Programming in Urban Traffic Network

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Jie Ma ◽  
Lin Cheng ◽  
Dawei Li

Urban road maintenance is an important part of urban traffic management. However, in modern cities, road maintenance work needs to occupy some traffic resources; therefore, unreasonable road maintenance schemes often lead traffic networks to unexpected large-scale congestion. In this paper, a dynamic programming model is proposed in order to minimize the delay caused by road maintenance scheme. This model can obtain a globally optimal maintenance scheme which contains the decisions and sequence for every stage of maintenance. Each stage of this model can be boiled down to a discrete network design problem. This model helps make suggestions for the traffic managers with the request of minimizing the delay caused by the maintenance scheme. This paper uses two examples to illustrate this method, one is a small-scale Nguyen-Dupuis network, and the other one is a larger scale Sioux-Falls network.

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Lianbo Deng ◽  
Jing Xu ◽  
Ningxin Zeng ◽  
Xinlei Hu

This paper studies the multistage pricing and seat allocation problems for multiple train services in a high-speed railway (HSR) with multiple origins and destinations (ODs). Taking the maximum total revenue of all trains as the objective function, a joint optimization model of multistage pricing and seat allocation is established. The actual operation constraints, including train seat capacity constraints, price time constraints in each period, and price space constraints among products, are fully considered. We reformulate the optimization model as a bilevel multifollower programming model in which the upper-level model solves the seat allocation problem for all trains serving multiple ODs in the whole booking horizon and the lower optimizes the pricing decisions for each train serving each OD in different decision periods. The upper and lower are a large-scale static seat allocation programming and many small-scale multistage dynamic pricing programming which can be solved independently, respectively. The solving difficulty can be significantly reduced by decomposing. Then, we design an effective solution method based on divide-and-conquer strategy. A real instance of the China’s Wuhan-Guangzhou high-speed railway is employed to validate the advantages of the proposed model and the solution method.


2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Jie Xiao ◽  
Yi Xie ◽  
Haowei Yu ◽  
Hongying Yan

Effective railway freight transportation relies on a well-designed train service network. This paper investigates the train service network design problem at the tactical level for the Chinese railway system. It aims to determine the types of train services to be offered, how many trains of each service are to be dispatched per day (service frequency), and by which train services shipments are to be transported. An integer programming model is proposed to address this problem. The optimization model considers both through train services between nonadjacent yards, and two classes of service between two adjacent yards ( i.e., shuttle train services directly from one yard to its adjacent yard, and local train services that make at least one intermediate stop). The objective of the model is to optimize the transportation of all the shipments with minimal costs. The costs consist of accumulation costs, classification coststrain operation costs, and train travel costs. The NP-hard nature of the problem prevents an exact solution algorithm from finding the optimal solution within a reasonable time, even for small-scale cases. Therefore, an improved genetic algorithm is designed and employed here. To demonstrate the proposed model and the algorithm, a case study on a real-world sub-network in China is carried out. The computational results show that the proposed approach can obtain high-quality solutions with satisfactory speed. Moreover, comparative analysis on a case that assumes all the shuttle train services between any two adjacent yards to be provided without optimization reveals some interesting insights.


2021 ◽  
Vol 10 (11) ◽  
pp. 787
Author(s):  
Chunchun Hu ◽  
Si Chen

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.


2020 ◽  
Vol 21 (4) ◽  
pp. 295-302
Author(s):  
Haris Ballis ◽  
Loukas Dimitriou

AbstractSmart Cities promise to their residents, quick journeys in a clean and sustainable environment. Despite, the benefits accrued by the introduction of traffic management solutions (e.g. improved travel times, maximisation of throughput, etc.), these solutions usually fall short on assessing the environmental impact around the implementation areas. However, environmental performance corresponds to a primary goal of contemporary mobility planning and therefore, solutions guaranteeing environmental sustainability are significant. This study presents an advanced Artificial Intelligence-based (AI) signal control framework, able to incorporate environmental considerations into the core of signal optimisation processes. More specifically, a highly flexible Reinforcement Learning (RL) algorithm has been developed towards the identification of efficient but-more importantly-environmentally friendly signal control strategies. The methodology is deployed on a large-scale micro-simulation environment able to realistically represent urban traffic conditions. Alternative signal control strategies are designed, applied, and evaluated against their achieved traffic efficiency and environmental footprint. Based on the results obtained from the application of the methodology on a core part of the road urban network of Nicosia, Cyprus the best strategy achieved a 4.8% increase of the network throughput, 17.7% decrease of the average queue length and a remarkable 34.2% decrease of delay while considerably reduced the CO emissions by 8.1%. The encouraging results showcase ability of RL-based traffic signal controlling to ensure improved air-quality conditions for the residents of dense urban areas.


2012 ◽  
Vol 27 (3) ◽  
pp. 291-307 ◽  
Author(s):  
Nicolas Barnier ◽  
Cyril Allignol

AbstractAs acknowledged by the SESAR (Single European Sky ATM (Air Traffic Management) Research) program, current Air Traffic Control (ATC) systems must be drastically improved to accommodate the predicted traffic growth in Europe. In this context, the Episode 3 project aims at assessing the performance of new ATM concepts, like 4D-trajectory planning and strategic deconfliction.One of the bottlenecks impeding ATC performances is the hourly capacity constraints defined on each en-route ATC sector to limit the rate of aircraft. Previous works were mainly focused on optimizing the current ground holding slot allocation process devised to satisfy these constraints. We propose to estimate the cost of directly solving all conflicts in the upper airspace with ground holding, provided that aircraft were able to follow their trajectories accurately.We present a Constraint Programming model of this large-scale combinatorial optimization problem and the results obtained with the FaCiLe (Functional Constraint Library). We study the effect of uncertainties on the departure time and estimate the cost of improving the robustness of our solutions with the Complete Air Traffic Simulator (CATS). Encouraging results were obtained without uncertainty but the costs of robust solutions are prohibitive. Our approach may however be improved, for example, with a prior flight level allocation and the dynamic resolution of remaining conflicts with one of CATS’ modules.


Author(s):  
Kriangsak Phalapanyakoon ◽  
Peerapon Siripongwutikorn

This paper investigates the problem of route planning for rechargeable unmanned aerial vehicles (UAV) under the mission time constraint in cases where more than one trip per round is required due to limited battery capacities. The goal is to determine the number of UAVs to be deployed and the flying paths that minimize the total mission cost. Unlike previous works, the electric cost incurred by UAV recharging proportional to actual flying distances is incorporated into our model. The problem is formulated as a mixed-integer programming model to minimize the sum of electric charging cost, the UAV usage cost, and the penalty cost from the violation of the mission time constraint. Extensive numerical experiments are conducted to examine the integrity and performance of the proposed model under various model parameters and deployment scenarios in grid areas and a real terrain area. The optimal solutions can be obtained for small-scale problem instances in a reasonable runtime. For large-scale problems, only feasible solutions can be obtained due to limited computational resources.


2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wenqian Liu ◽  
Xiaoning Zhu ◽  
Li Wang ◽  
Baicheng Yan ◽  
Xuewei Zhang

As the core operational issue in container terminals, yard crane scheduling problem directly affects the overall operation efficiency of port connecting highway or railway transportation and sea transportation. In practice, the scheduling of yard cranes is subject to many uncertain factors, so the scheme may be inapplicable and needs to be adjusted. From the perspective of proactive strategy, considering fluctuations in arrival time of external trucks as well as varied handling volume of yard cranes, a stochastic programming model is established in this paper to obtain a fixed scheme with the minimum expected value of yard crane makespan and total task waiting time over all the scenarios. The scheme does not require rescheduling when facing different situations. Subsequently, two algorithms based on certain rules are proposed to obtain the yard crane operation scheme in the deterministic environment, which are taken as the basic solution in the uncertain conditions, and then a tailored genetic algorithm is adopted to find the optimal solution with good adaptability to the uncertain scenarios. Finally, we use small-scale examples to compare the performance of algorithms in the deterministic and uncertain environment and then analyze the relationship between different yard crane configurations and the number of tasks. Large-scale experiments are performed to study the operation efficiency of the storage yard with different handling volumes assigned to each yard crane.


2021 ◽  
pp. 184-194
Author(s):  
А.П. Шрамко

С ростом экономического потенциала страны, реализацией масштабных инфраструктурных проектов актуализируются проблемы оптимизации многоуровневых и многоэтапных управленческих решений на основе методов математического моделирования. В работе исследуются оптимизационные подходы к моделированию региональных и локальных транспортных систем с использованием принципов динамического программирования при соотнесении объемов грузопотоков провозному потенциалу транспортных системы с определением оптимального маршрута движения потока. Принимая во внимание, что транспорт не создает новых вещественных ценностей, а только их перемещает в пространстве предлагаемые подходы организации, планирования и управления создадут условия для получения существенного эффекта на основе оптимизации прогнозирования и совершенствования организационного механизма управления транспорто – технологическими процессами. Излагаются общие принципы построения модели динамического программирования применительно к региональной и локальной транспортной системам, при распределении ресурсов между различными объектами, их использования по уточнённым периодам времени, для получения максимального эффекта от принятого способа распределения. Обосновано прикладное использование моделей динамического программирования, при организации и исследовании, несмотря на то, что базовые параметры ограничены размерностью переменной состояния системы. Вместе с тем данное обстоятельство не снижает ценности и практической значимости метода, а позволяет провести границу между областями динамического программирования и другими математическими методами, с обоснованием преимуществ и прикладного универсального значения. Предлагается методический инструментарий повышения эффективности региональной и локальных транспортных систем при тенденции к общем увеличении грузооборота. With the growth of the economic potential of the country, the implementation of large-scale infrastructure projects is updated to optimize multi-level and multi-step management solutions based on mathematical modeling methods. The paper explores optimization approaches to modeling regional and local transport systems using the principles of dynamic programming in relation to the volume of freight traffic volumetric potential of transport systems with the definition of the optimal flow route. Taking into account that transport does not create new real values, but only they move them in space. The proposed approaches of the organization, planning and management will create conditions for obtaining a significant effect on the basis of optimizing forecasting and improving the organizational mechanism of transport management - technological processes. General principles for building a dynamic programming model in relation to regional and local transport systems, when distributing resources between different objects, their use on refined periods of time, to obtain a maximum effect from the received distribution method. Applied use of models of dynamic programming, with the organization and study, despite the fact that the basic parameters are limited to the dimension of the system status variable. At the same time, this circumstance does not reduce the value and practical significance of the method A, makes it possible to carry out the border between the areas of dynamic programming and other mathematical methods, with the rationale for the advantages and applied universal value. A methodological toolkit of increasing the efficiency of regional and local transport systems with a tendency to a general increase in cargo turnover is proposed.


2021 ◽  
Vol 11 (21) ◽  
pp. 10143
Author(s):  
Yaling Zhou ◽  
Chengxuan Cao ◽  
Ziyan Feng

In this paper, we investigate the multimodal discrete network design problem that simultaneously optimizes the car, bus, and rail transit network, in which inter-modal transfers are achieved by slow traffic modes including walking and bike-sharing. Specifically, a super network topology is presented to signify the modal interactions. Then, the generalized cost formulas of each type of links in the super network are defined. And based on the above formulas a bi-objective programming model is proposed to minimize the network operation cost and construction cost with traffic flow equilibrium constraints, investment constraints and expansion constraints. Moreover, a hybrid heuristic algorithm that combines the minimum cost flow algorithm and simulated annealing algorithm is presented to solve the proposed model. Finally, the effectiveness of the proposed model and algorithm is evaluated through two numerical tests: a simple test network and an actual multimodal transport network.


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