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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.10.019 Byline: Kazuki Matsumoto (a), Hiroyoshi Miwa (b), Toshihide Ibaraki (b) Keywords: Scheduling; Corrugated paper; Complexity; Contiguous set; Integer programming Abstract: Corrugated paper is produced by gluing three types of papers of the same breadth. Given a set of orders, we first assign each order to one of the standard breadths, and then sequence those assigned to each standard breadth so that they are continuously manufactured from the three rolls of the specified standard breadth equipped in the machine called corrugator. Here we are asked to achieve multi-goals of minimizing total length of roll papers, total loss of papers caused by the differences between standard breadths and real breadths of the orders, and the number of machine stops needed during production. We use integer programming to assign orders to standard breadths, and then develop a special purpose algorithm to sequence the orders assigned to each standard breadth. This is a first attempt to handle scheduling problems of the corrugator machine. Author Affiliation: (a) Department of Applied Mathematics and Physics, Graduate School of Informatics, Kyoto University, Sakyo-ku, Kyoto 606-8501, Japan (b) Department of Informatics, School of Science and Technology, Kwansei Gakuin University, 2-1 Gakuen Sanda, Hyogo 669-1337, Japan Article History: Received 24 March 2007; Accepted 5 October 2007

Vehicle detection and tracking from unmanned aerial vehicles (UAVs) aerial images are among the main tasks of intelligent traffic systems. Especially in tasks with long distances, extensive backgrounds, and small objects, it increases the difficulty of localization and regression, which can easily lead to missed detections and false positives. This paper proposes a detection-based small-scale vehicle tracking framework that integrates an improved YOLOX network and the DeepSORT algorithm to address these issues. Based on the original YOLOX network, a shallow feature extraction network, 160×160 pixels, is added to enhance the ability to extract small-scale object features. A convolutional block attention module (CBAM) is inserted in front of the neck network to select crucial information for vehicle detection tasks while suppressing noncritical ones. EIoU_Loss is introduced as the bounding box regression loss function in training to speed up their convergence and improve the localization accuracy of the small objects. Furthermore, an image segmentation method is proposed to effectively reduce missed and false detection events. It divides the original high-definition image into multiple subimages, first detected and then reassembled. Finally, the improved YOLOX network is used as the detector of the DeepSORT to perform small-scale vehicle detection and tracking tasks in various traffic scenarios. Experiments show that the proposed method can significantly improve the detection accuracy of the network and effectively solve the problems of missed detection and false positives in small-scale vehicle tracking tasks in high-resolution aerial images captured by high-altitude UAVs. Significantly, the algorithm proposed in this paper has sufficient robustness for small-scale tracking tasks of aerial videos captured at different altitudes., Author(s): Heshan Zhang [1]; Xin Tan [1]; Mengwei Fan [1]; Cunshu Pan [1]; Zhanji Zheng [1]; Shuang Luo [1]; Jin Xu (corresponding author) [1] 1. Introduction With the rapid development [...]

This paper studies a class of gradient-descent heuristic algorithms for the bi-level demand adjustment problem (DAP), which seeks to adjust origin-destination (OD) matrices based on observed link flows in congested transportation networks. We first present a general gradient-descent solution framework for the bi-level DAP and then examine and further develop its two building blocks, namely, the gradient approximation and stepsize calculation. This paper presents two gradient approximation and four stepsize calculation methods, of which two stepsize methods are newly developed. Similarities and differences between these algorithms, as well as the relevant implementation issues are discussed in great detail. The numerical results show that algorithms employing the new stepsize calculation strategies consistently outperform existing algorithms in terms of both computational precision and efficiency., Author(s): Lan Cheng [1]; Jun Xie [1]; Jun Huang (corresponding author) [1]; Liyang Feng [1]; Qianni Wang [2]; Hongtai Yang [1] 1. Introduction OD matrices (ODM) are essential inputs for [...]

Real-time status acquisition of parking spaces is highly valuable for an intelligent urban parking system. Crowdsourcing-based parking availability sensing via connected and automated vehicles (CAVs) provides a feasible method with the advantages of high coverage and low cost. However, data trust issues arise from incorrect detection and incomplete information. This paper proposes a trustworthiness assessment method for crowdsourced CAV data considering different impact factors, such as the distance between the CAV and the target parking space, line abrasion, scene complexity, and image sharpness. The crowdsourced CAV data are collected through extensive field experiments and PreScan simulations. The classical line detection algorithm of VPS-Net and the target detection algorithm of YOLO-v3 are applied to detect on-street parking availability. A failure probability model based on the XGBoost algorithm is then developed to establish the relationship between data trustworthiness and different impact factors. The results show that the proposed model has an average accuracy of 78.29% and can effectively assess the degrees of external influences on the trustworthiness of the crowdsourced data. This paper provides a new tool to identify the data quality and improve the sensing accuracy for a crowdsourcing-based parking availability information system., Author(s): Shiyu Wang [1]; Cong Zhao (corresponding author) [1]; Zekai Wang [2]; Yupeng Shi [1]; Shengchuan Jiang [1,3]; Yuchuan Du [1] 1. Introduction Due to the rapid increase in car [...]

This paper presents a methodology to study non-twist invariant circles and their bifurcations for area preserving maps, which is supported on the theoretical framework developed in Gonzalez-Enriquez et al. (Mem. Amer. Math. Soc. 227:vi+115, 2014). We recall that non-twist invariant circles are characterized not only by being invariant, but also by having some specified normal behavior. The normal behavior may endow them with extra stability properties (e.g., against external noise), and hence, they appear as design goals in some applications, e.g., in plasma physics, astrodynamics and oceanography. The methodology leads to efficient algorithms to compute and continue, with respect to parameters, non-twist invariant circles. The algorithms are quadratically convergent and, when implemented using FFT, have low storage requirement and low operations count per step. Furthermore, the algorithms are backed up by rigorous a posteriori theorems, proved and discussed in detail in Gonzalez-Enriquez et al. (Mem. Amer. Math. Soc. 227:vi+115, 2014), which give sufficient conditions guaranteeing the existence of a true non-twist invariant circle, provided an approximate invariant circle is known. Hence, one can compute confidently even very close to breakdown. With some extra effort, the calculations could be turned into computer-assisted proofs, see Figueras et al. (Found. Comput. Math. 17:1123-1193, 2017) for examples of the latter. The algorithms are also guaranteed to converge up to the breakdown of the invariant circles, and then, they are suitable to compute regions of parameters where the non-twist invariant circles exist. The calculations involved in the computation of the boundary of these regions are very robust, and they do not require symmetries and can run without continuous manual adjustments, largely improving methods based on the computation of very long period periodic orbits to approximate invariant circles. This paper contains a detailed description of our algorithms, the corresponding implementation and some numerical results, obtained by running the computer programs. In particular, we include calculations for two-dimensional parameter regions where non-twist invariant circles (with a prescribed frequency) exist. Indeed, we present systematic results in systems that do not contain symmetry lines, which seem to be unaccessible for previous methods. These numerical explorations lead to some open questions, also included here., Author(s): Alejandra González [sup.1], Àlex Haro [sup.2], Rafael de la Llave [sup.3] Author Affiliations: (1) Foundations of Learning, Zurich, , Schwäntenmos 4, 8126, Zumikon, Switzerland (2) grid.5841.8, 0000 0004 1937 [...]

The rapid increase in e-commerce and the emergence of combined passenger/freight systems in urban areas have raised the question of how best to integrate public transport services into door-to-door deliveries. This paper develops a variant of the pickup and delivery problem, called the two-echelon pickup and delivery problem using public transport (2E-PDP-PT). In the 2E-PDP-PT, the transportation network is split into two echelons. Different vehicles are utilized across the first and second echelons to ensure distribution efficiency. Parcels are delivered by public transport with free capacity or via trucks between satellites in the first echelon, and logistics vehicles are operated in the second echelon. The satellites are located at the echelon borders to transfer commodities between echelons. The 2E-PDP-PT aims to minimize total delivery costs and improve public transport capacity utilization. We formulate a new mathematical model based on a space-time network and adopt an adaptive large neighborhood search (ALNS) algorithm for the 2E-PDP-PT. The effectiveness of the ALNS algorithm is validated using newly generated small-scale instances. Furthermore, we investigate large-scale instances based on the Beijing Yizhuang transportation network. The computations show that an average total delivery cost savings of 4.5% is feasible. In addition, we analyze the impact of demand distributions and compare the ALNS algorithm and the LNS algorithm. Finally, we conclude that dynamically integrating public transport into freight transport services can benefit both logistics companies and public transport operators., Author(s): Shuai Wang [1]; Xiaoning Zhu [1]; Pan Shang (corresponding author) [1]; Wenqian Liu [1]; Xiao Lin [2]; Lóránt Tavasszy [2] 1. Introduction The substantial growth of e-commerce contributes to [...]

This review paper presents the current state-of-the-art pertains to water pipe failure prediction and risk assessment, published in the last ten years (2009-2019). This paper has been motivated by the lack of comprehensive review articles that integrates water network failure and risk modeling. Some of the current practices reviewed the pipe condition and its failure. Others focused on the statistical prediction models, whereas the rest outlined failure prediction models of large diameter mains only. The mainstream of the current practice, highlighted in this paper characterizes the structural deterioration and failure rates using various statistical techniques, whereas the remainder of research covers a proliferation of machine learning and soft computing applications to forecast and model the pipeline risk of failure. The review offers descriptions of the models together with their proposed methodologies, algorithms and equations, contributions and drawbacks, comparisons and critiques, and types of data used to develop the models using the bibliographic review method. Finally, future work and research challenges are recommended to assist the civil engineering research community in setting a clear agenda for the upcoming research. Key words: pipe failure, risk analysis, prediction models, water main deterioration, water main rehabilitation, infrastructure, state-of-the-art review. Le present article de synthese presente l'etat actuel des realisations en matiere de prevision des defaillances de conduites d'eau et revaluation des risques, tel que publie au cours des dix dernieres annees (2009-2019). C'est l'absence d'etudes globales integrant la defaillance d'un reseau d'eau et la modelisation des risques qui a motive cette etude. Certaines des pratiques actuelles ont examine l'etat de la conduite et sa defaillance. D'autres ont porte sur les modeles de prediction statistique, tandis que les autres ont decrit les modeles de prediction de defaillances de conduites principales de grand diametre seulement. Le courant dominant de la pratique actuelle, mis en evidence dans le present document, caracterise la deterioration structurelle et les taux de defaillance a l'aide de diverses techniques statistiques, alors que le reste de la recherche porte sur une multitude d'applications d'apprentissage automatique et de calcul electronique pour prevoir et modeliser le risque de defaillance de pipeline. La revue propose des descriptions de modeles ainsi que leurs methodologies, algorithmes et equations proposes, leurs contributions et inconvenients, des comparaisons et critiques, et les types de donnees utilisees pour elaborer les modeles a l'aide de la methode de la revue bibliographique. Enfin, on suggere les prochains travaux et les defis associes a la recherche pour aider le milieu de la recherche en genie civil a etablir un programme clair pour la recherche a venir. [Traduit par la Redaction] Mots-cles: defaillance de conduites, analyse des risques, modeles de prediction, deterioration de conduite principale, rehabilitation de conduite principale, infrastructure, revue sur l'etat actuel des connaissances., Introduction Water supply networks have always been an integral part of public infrastructure and a vital constituent of the national assets. Unfortunately, communities all over the world experience undesired incidents [...]

Nowadays, vehicle line pressure detection is an important function of an intelligent transportation system. At present, the line pressure detection algorithms mainly include algorithms based on traditional features and models and algorithms based on deep learning. However, these algorithms also have shortcomings such as low detection accuracy or relying on specific scenarios. In this regard, this paper proposes a fast and accurate vehicle line detection algorithm based on deep learning for vehicle images. The algorithm builds a GooleNet-based FCN semantic segmentation network and adds a BN layer, 1×1 convolution, and FPN structure to improve the segmentation effect of the GooleNet-FCN network and reduce network parameters. The MobileNet-SSD (no pretrained model) network structure is used for vehicle detection. According to the relationship between the receptive field and the anchor, and then combined with specific data, the prediction branch of the network and the Default Box on the branch are modified and the FPN structure is added for feature fusion to form the final improved MobileNet-SSD network. The experimental results show that the algorithm takes an average time of 67.8ms per frame, the detection rate of line pressing for a vehicle is 96.6%, and the deep learning models are 25.5M and 19.2M, respectively. The experimental results verify the effectiveness and practicality of the detection algorithm proposed in this paper., Author(s): Xuexi Zhang [1]; Ying Li [1]; Ruidian Zhan (corresponding author) [1]; Jiayang Chen [1]; Junxian Li [1] 1. Introduction With the development of science and technology, automobiles have become [...]

Machine learning is a discipline that covers probability theory, statistics, approximate theoretical knowledge, and complex algorithm knowledge. It is committed to real-time simulation of human learning methods, which can effectively improve learning efficiency. The main function of this calculation method is to use a relatively open platform to integrate the Internet, computers, memory, and other terminal applications through integrated technical means. For providing short-distance services and applications, we should start from the edge of the program to create a faster network. Service response covers the basic needs of real-time processing industry, intelligent applications, and security and privacy protection. This paper aims to study the recognition of urban traffic conditions and refine the recognition through the improvement of edge computing algorithms. This paper proposes a method to calculate traffic flow parameters, preprocess the traffic flow data, delete irrelevant features, use flow theory to delete wrong data, and change the data in time according to actual needs, carrying out refined discrimination and analysis of urban traffic status. The experimental results in this paper show that the use of edge computing to fine-tune the state of urban traffic can divert traffic, greatly reduce traffic congestion, and increase traffic safety by 13%. Among them, the efficiency of road network time and space resources has also increased by 23%., Author(s): Lijuan Liu (corresponding author) [1] 1. Introduction At present, the economy is developing rapidly and the demand for road traffic is also increasing. However, the increase in traffic accidents [...]

Machine learning algorithms can now identify patterns and correlations in (big) datasets and predict outcomes based on the identified patterns and correlations. They can then generate decisions in accordance with the outcomes predicted, and decision-making processes can thereby be automated. Algorithms can inherit questionable values from datasets and acquire biases in the course of (machine) learning. While researchers and developers have taken the problem of algorithmic bias seriously, the development of fair algorithms is primarily conceptualized as a technical task. In this paper, I discuss the limitations and risks of this view. Since decisions on 'fairness measure' and the related techniques for fair algorithms essentially involve choices between competing values, 'fairness' in algorithmic fairness should be conceptualized first and foremost as a political question and be resolved politically. In short, this paper aims to foreground the political dimension of algorithmic fairness and supplement the current discussion with a deliberative approach to algorithmic fairness based on the accountability for reasonableness framework (AFR)., Author(s): Pak-Hang Wong [sup.1] Author Affiliations: (1) grid.9026.d, 0000 0001 2287 2617, Department of Informatics, Universität Hamburg, , Vogt-Kölln-Straße 30, 22527, Hamburg, Germany Introduction Friedman and Nissenbaum (1996) have shown [...]

If dedicate a lane to connected autonomous vehicle (CAV) on a multilane road, the traffic congestion and safety risks remain a major problem but in a different style. Random and disorderly mandatory lane-changing behaviour before approaching the next ramp or intersection would have a disturbing effect on the following vehicles of the traffic flow. This paper mainly establishes the optimal mandatory lane-changing location matching model for each target vehicle in the dedicated CAV lane environment. The aim is to minimizing the total travel time, which could take the disturbing effect into account. This model nests the cell transmission model (CTM) to describe vehicle running. The constraints include the relation between target CAV lane-changing cell and the corresponding behaviour start time, the updating of the flow, and occupancy for varied cells. We use the Ant Colony Optimization (ACO) algorithm to solve the problem. Through the case study of a basic two-lane road scenario in Ningbo, we acquire the convergence results based on the ACO algorithm. Our optimal lane-changing location matching scheme can save 5.9% total travel time when compared to the near-end location lane-changing scheme. We test our model by increasing the total number of upstream input vehicles with 4%, 11%, 15%, and the mandatory lane-changing vehicles with 60%, 200%, respectively. The testing results prove that out optimization method could deal with varied road traffic flow situations. Specifically, when the traffics and mandatory lane-changing vehicles increase, our method could perform better., Author(s): Gao Gao [1,2]; Zhengfeng Huang (corresponding author) [1,2]; Wei Ji [1,2]; Pengjun Zheng [1,2] 1. Introduction To realize the coordinated optimization of vehicles, roads, and networks to alleviate related [...]

Search engines are important contemporary sources of information and contribute to shaping our beliefs about the world. Each time they are consulted, various algorithms filter and order content to show us relevant results for the inputted search query. Because these search engines are frequently and widely consulted, it is necessary to have a clear understanding of the distinctively epistemic role that these algorithms play in the background of our online experiences. To aid in such understanding, this paper argues that search engine algorithms are providers of 'bent testimony'-that, within certain contexts of interactions, users act as if these algorithms provide us with testimony-and acquire or alter beliefs on that basis. Specifically, we treat search engine algorithms as if they were asserting as true the content ordered at the top of a search results page-which has interesting parallels with how we might treat an ordinary testifier. As such, existing discussions in the philosophy of testimony can help us better understand and, in turn, improve our interactions with search engines. By explicating the mechanisms by which we come to accept this 'bent testimony,' our paper discusses methods to help us control our epistemic reliance on search engine algorithms and clarifies the normative expectations one ought to place on the search engines that deploy these algorithms., Author(s): Devesh Narayanan [sup.1] [sup.2], David De Cremer [sup.2] Author Affiliations: (1) grid.4280.e, 0000 0001 2180 6431, Department of Philosophy, National University of Singapore, , 3 Arts Link Block AS3, [...]

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers showing that mean square error is not always the best measure when looking for a solution to the problem. The fitness developed in this paper contains features and equations from the mathematical background of proportional integrative derivative controllers to calculate the best performance of the system. Such results are applied to quantitatively evaluate the performance of twenty-one optimization algorithms. Furthermore, improved versions of the fitness function are considered, in order to investigate which aspects are enhanced by applying the optimization algorithms. Results show that the right fitness function is a key point to get a good performance, regardless of the chosen algorithm. The aim of this paper is to present a novel objective function to carry out optimizations of the gains of a PID controller, using several computational intelligence techniques to perform the optimizations. The result of these optimizations will demonstrate the improved efficiency of the selected control schema., Author(s): Luis Fernando de Mingo López [1]; Francisco Serradilla García [1,2]; José Eugenio Naranjo Hernández (corresponding author) [1,2]; Nuria Gómez Blas [1] 1. Introduction Many optimization problems are nondeterministic polynomial-time [...]

An airport ferry vehicle is a ground service vehicle used to transfer passengers between the far apron and the terminal. The travel time of ferry tasks in the airport ferry network is an important decision-making basis for ferry vehicle scheduling. This paper presents a graph-based method to mine the travel time between nodes in the airport ferry network. Firstly, combined with map and trajectory information, the method takes the terminal boarding gates, parking lots, and remote stands as road network nodes to build a complete airport ferry road network. Then, this paper uses big data processing technology to identify the travel time between regional connection nodes by data fusion through the temporal and spatial relationship between flight schedule and ferry vehicle GPS travel trajectory. Finally, the Floyd shortest path algorithm in graph theory is used to obtain the shortest path and travel time of all OD points. The experimental results show that all the ferry times calculated by the method proposed in this paper can better reflect the actual driving situation. This method saves the manpower, material resources, and time cost of on-site investigation and lays a foundation for the scheduling of ferry vehicles., Author(s): Cong Ding [1]; Jun Bi (corresponding author) [2]; Dongfan Xie [1,2]; Xiaomei Zhao [1,2]; Yi Liu [1] 1. Introduction When the aircraft is parked at a far apron, passengers [...]

In this paper we initiate the study of tropical Voronoi diagrams. We start out with investigating bisectors of finitely many points with respect to arbitrary polyhedral norms. For this more general scenario we show that bisectors of three points are homeomorphic to a non-empty open subset of Euclidean space, provided that certain degenerate cases are excluded. Specializing our results to tropical bisectors then yields structural results and algorithms for tropical Voronoi diagrams., Author(s): Francisco Criado [sup.1], Michael Joswig [sup.1] [sup.2], Francisco Santos [sup.3] Author Affiliations: (1) grid.6734.6, 0000 0001 2292 8254, TU Berlin, Chair of Discrete Mathematics/Geometry, , Berlin, Germany (2) grid.419532.8, [...]

Shunting yards are one of the main areas impacting the reliability of rail freight networks, and delayed departures from shunting yards can further also affect the punctuality of mixed-traffic networks. Methods for automatic detection of departures, which are likely to be delayed, can therefore contribute towards increasing the reliability and punctuality of both freight and passenger services. In this paper, we compare the performance of tree-based methods (decision trees and random forests), which have been highly successful in a wide range of generic applications, in classifying the status of (delayed, early, and on-time) departing trains from shunting yards, focusing on the delayed departures as the minority class. We use a total number of 6,243 train connections (representing over 21,000 individual wagon connections) for a one-month period from the Hallsberg yard in Sweden, which is the largest shunting yard in Scandinavia. Considering our dataset, our results show a slight difference between the application of decision trees and random forests in detecting delayed departures as the minority class. To remedy this, enhanced sampling for minority classes is applied by the synthetic minority oversampling technique (SMOTE) to improve detecting and assigning delayed departures. Applying SMOTE improved the sensitivity, precision, and F-measure of delayed departures by 20% for decision trees and by 30% for random forests. Overall, random forests show a relative better performance in detecting all three departure classes before and after applying SMOTE. Although the preliminary results presented in this paper are encouraging, future studies are needed to investigate the computational performance of tree-based algorithms using larger datasets and considering additional predictors., Author(s): Niloofar Minbashi (corresponding author) [1]; Markus Bohlin [1]; Carl-William Palmqvist [2]; Behzad Kordnejad [1] 1. Introduction The “single wagonload” railway traffic has the potential to increase the modal share [...]

This paper studies the saddle point problem of polynomials. We give an algorithm for computing saddle points. It is based on solving Lasserre's hierarchy of semidefinite relaxations. Under some genericity assumptions on defining polynomials, we show that: (i) if there exists a saddle point, our algorithm can get one by solving a finite hierarchy of Lasserre-type semidefinite relaxations; (ii) if there is no saddle point, our algorithm can detect its nonexistence., Author(s): Jiawang Nie [sup.1], Zi Yang [sup.1], Guangming Zhou [sup.2] Author Affiliations: (1) grid.266100.3, 0000 0001 2107 4242, Department of Mathematics, University of California San Diego, , 9500 Gilman Drive, [...]

China Railway Express is developing rapidly, but the point-to-point direct organization mode has brought many problems to it. Therefore, this paper proposes to construct a hub-and-spoke network and adopt the “collecting and transportation” organization mode. In this paper, based on the single distribution p-hub median problem, a dual-objective planning model was constructed by considering the characteristics of CR Express in terms of cost and time. In addition, considering the port as a key node of international rail transport network, it plays a vital role for CR Express. Therefore, a hub-port allocation model was constructed to determine the hub-port allocation relationship. Furthermore, a Lagrangian relaxation heuristic algorithm was designed to solve the model built for CR Express transportation network. Finally, based on the constructed model, the actual operation data of CR Express were used in the designed example to verify the effectiveness and applicability of the models and methods., Author(s): Yinying Tang [1,2,3]; Si Chen (corresponding author) [1,2,3]; Guangyu Lu [1,2,3]; Qisheng Zhang [1,2,3] 1. Introduction The CR Express is organized by China National Railway Group Corporation Ltd and [...]

In order to improve the prediction accuracy of train passenger load factor of high-speed railway and meet the demand of different levels of passenger load factor prediction and analysis, the influence factor of the train passenger load factor is analyzed in depth. Taking into account the weather factor, train attribute, and passenger flow time sequence, this paper proposed a forecasting method of train passenger load factor of high-speed railway based on LightGBM algorithm of machine learning. Considering the difference of the influence factor of the passenger load factor of a single train and group trains, a single train passenger load factor prediction model based on the weather factor and passenger flow time sequence and a group of trains' passenger load factor prediction model based on the weather factor, the train attribute, and passenger flow time sequence factor were constructed, respectively. Taking the train passenger load factor data of high-speed railway in a certain area as an example, the feasibility and effectiveness of the proposed method were verified and compared. It is verified that LightGBM algorithm of machine learning proposed in this paper has higher prediction accuracy than the traditional models, and its scientific and accurate prediction can provide an important reference for the calculation of passenger ticket revenue, operation benefit analysis, etc., Author(s): Bing Wang [1]; Peixiu Wu [1]; Quanchao Chen [1,2,3]; Shaoquan Ni (corresponding author) [1,2,3] 1. Introduction High-speed railway has become the main transportation mode for passengers' mode of transportation [...]

The present era is marked by rapid improvement and advances in technology. One of the most essential areas that demand improvement is the traffic signal, as it constitutes the core of the traffic system. This demand becomes stringent with the development of Smart Cities. Unfortunately, road traffic is currently controlled by very old traffic signals (tri-color signals) regardless of the relentless effort devoted to developing and improving the traffic flow. These traditional traffic signals have many problems including inefficient time management in road intersections; they are not immune to some environmental conditions, like rain; and they have no means of giving priority to emergency vehicles. New technologies like Vehicular Ad-hoc Networks (VANET) and Internet of Vehicles (IoV) enable vehicles to communicate with those nearby and with a dedicated infrastructure wirelessly. In this paper, we propose a new traffic management system based on the existing VANET and IoV that is suitable for future traffic systems and Smart Cities. In this paper, we present the architecture of our proposed Intelligent Traffic Management System (ITMS) and Smart Traffic Signal (STS) controller. We present local traffic management of an intersection based on the demands of future Smart Cities for fairness, reducing commute time, providing reasonable traffic flow, reducing traffic congestion, and giving priority to emergency vehicles. Simulation results showed that the proposed system outperforms the traditional management system and could be a candidate for the traffic management system in future Smart Cities. Our proposed adaptive algorithm not only significantly reduces the average waiting time (delay) but also increases the number of serviced vehicles. Besides, we present the implemented hardware prototype for STS., Author(s): Samir A. Elsagheer Mohamed (corresponding author) [1,2,3]; Khaled A. AlShalfan [4] 1. Introduction In many countries, including several developed countries, regardless of the extensive efforts devoted to develop and [...]

Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment., Author(s): Ji Guo [1,2]; Yujia Lou [1]; Wanyi Wang [1]; Xianhua Wu (corresponding author) [1,2] 1. Introduction As the world's most consumed light petroleum product, gasoline is one of the [...]

As one of the key algorithms in supporting AV (autonomous vehicle) to complete the LC (lane changing) maneuver, the LTP (LC trajectory planning) algorithm generates safe and efficient LC trajectory for the AV. This paper proposes a novel dynamic LTP algorithm based on the quintic polynomial curve. This algorithm is capable of adjusting LC trajectory according to the state changes of the surrounding driving environment. The formulation of our proposed algorithm mainly consists the underlying form of trajectory equation, the optimization objective function, the corresponding constrains, and the SQP (sequential quadratic programming) algorithm. For each planning step, the time-based quintic polynomial function is introduced to model the trajectory equation. The problem of solving the parameters of the corresponding equation is then transformed into an optimization problem, which takes driver's safety, comfort, and efficiency into account. After that, the SQP algorithm is employed to solve this optimization problem. Finally, both numerical simulation and field-data validation are used to verify the effectiveness of our proposed algorithm. We anticipate that the research could provide certain valuable insights for developing more reliable LC algorithms for AVs., Author(s): Yang Li [1]; Linbo Li (corresponding author) [1]; Daiheng Ni [2] 1. Introduction Numerous research studies indicate that the advent of AVs (autonomous vehicles) could significantly enhance traffic safety, [...]

Various solutions, such as parking reservation systems, have been proposed to alleviate the difficulty in finding parking slots. In such systems, parking requests are submitted in advance by drivers, and the systems will reserve appropriate parking spots for drivers if their requests are accepted. However, the parking slots may be allocated unreasonably, which may lead to a waste of space and time resources. In addition, there is a game relationship between operator's profit (OP) and users' benefits (UB), which may affect the sustainable development of the system, if balanced improperly. Given the drivers' arrival and departure time and their parking preference, the paper proposes a periodic reservation and allocation mode (PRAM) and establishes a dual-objective binary integer linear model to solve the reservation and allocation problem. The model aims to maximize the comprehensive benefits of the operator and users and to take full advantage of parking resources. We proposed a TOPSIS-SA algorithm (Technique for Order Preference by Similarity to an Ideal Solution and Simulated Annealing algorithm) to solve our model. Numerical experiments show that our model performs better than the baseline models on all performance metrics such as total operating profit, users' average walking distance, acceptance rate, and utilization of parking slots., Author(s): Xianmin Song [1]; Tianshu Zhan [1]; Haitao Li (corresponding author) [1]; Bo Liu [1]; Yunxiang Zhang [1]; Xin Liu [2] 1. Introduction With the acceleration of urbanization, car ownership [...]

Bike sharing systems are becoming more and more common around the world. One of the main difficulties is to ensure the availability of bicycles in order to satisfy users. To achieve this objective, managers of these systems set up rebalancing vehicles that displace bicycles to stations that are likely to be in a situation of bike shortage. In order to determine which stations must be supplied on a priority basis and the number of bicycles to be supplied (named in this paper as rebalancing plan), the aim is generally to reduce the lost demand for each station, i.e., the gap between the demand for bicycles and the number of bicycles at a station. On the one hand, this paper proposes an algorithm that evaluates the lost demand in a more realistic way, by describing the behaviour of users faced with a bike-shortage station. It takes into account the possibility that a proportion of users who cannot find bicycles will move to a neighbouring station that is not empty. This proportion depends on the distance between stations and corresponds to the number of users willing to walk a given distance to a neighbouring station. On the other hand, this algorithm provides the value of the objective function to be minimized to a static rebalancing plan algorithm based on a Random Search metaheuristic. The quantities of bicycles to be picked up and dropped off at each station are calculated in a static rebalancing context. The calculation of lost demand based on this algorithm, which simulates user behaviour, was compared with that one obtained by the classical method on a real numerical example obtained from the open data of Parisian Vélibʼ (more than 1200 stations). In addition, the efficiency of the rebalancing algorithm coupled with the user behaviour simulation algorithm was evaluated on this numerical example and allowed to obtain very good results compared to the rebalancing performed by the system operator., Author(s): Roberta Costa Affonso (corresponding author) [1]; Florent Couffin [1]; Patrice Leclaire [1] 1. Introduction Most medium and large cities have installed a bike-sharing system (BSS) since the first appearance [...]

In recent years, the prohibition of trucks which could cause environmental pollution on urban roads has become widespread in China. However, some truck restriction policies might lead to a reduction in logistics transportation efficiency. With the help of big data, technology companies have developed many truck applications, such as HCB, truck home, and truck help, to provide the drivers available traffic information. In this context, this paper put forward a truck path optimization model considering environmental impact (TPOM-EI), which is solved by a heuristic algorithm--ant colony optimization (ACO) algorithm. Most previous studies focused on unilateral benefits rather than overall benefits; this paper aims to propose a path optimization model based on real-time minimization of social and transportation costs. Finally, data of Xiqing Economic and Technological Development Zone in Tianjin city (XQ-EDZ) have been used to demonstrate the applicability of the proposed algorithm. The results show that logistics truck path has a huge impact on social costs, and real-time activities in various areas will also change the path of a truck. This research will also help logistics truck drivers to choose the best route in real time., Author(s): Qianqian Han [1]; Yao Sun [2]; Qingdong Wu (corresponding author) [3]; Zijian Bai [2] 1. Introduction With the rapid development of economy and the explosive growth of urban population [...]

In this paper, a single-vehicle static partial repositioning problem (SPRP) is investigated, which distinguishes the user dissatisfaction generated by different stations. The overall objective of the SPRP is to minimize the weighted sum of the total operational time and the total absolute deviation from the target number of bikes at all stations. An iterated local search is developed to solve this problem. A novel loading and unloading quantity adjustment operator is proposed to further improve the quality of the solution. Experiments are conducted on a set of instances from 30 to 300 stations to demonstrate the effectiveness of the proposed customized solution algorithm as well as the adjustment operator. Using a small example, this paper also reveals that the unit penalty cost has an effect on the repositioning strategies., Author(s): Qiong Tang [1,2,3]; Zhuo Fu (corresponding author) [1,3]; Dezhi Zhang [1,3]; Meng Qiu [4]; Minyi Li [5] 1. Introduction As urban traffic congestion and environmental pollution problems become more [...]

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises., 1. Introduction 1.1. Background. In the past two decades, with the rapid development of economy and e-commerce in China, the annual parcel deliveries have been growing continuously, bringing opportunities and [...]

The automated fare collection (AFC) system has gained increasing popularity among transit systems worldwide. The AFC system is usually an entry-only system that only records the serial number of the smart card and the transaction time of each use. Neither the AFC data nor the bus global positioning system (GPS) could reveal the passenger's alighting information, namely, alighting time and station. Hence, the station-to-station origin-destination (OD) trip information cannot be obtained directly from the available data sources. To address this problem, this paper proposes a methodology that estimates the OD matrix by using smart card and GPS data. In this paper, the characteristics of the basic data sources are first analyzed, based on which the bus arrival time is generated using the density-based clustering algorithm and a time correction strategy, based on which the passenger's boarding station is identified. The alighting stations are inferred based on the characteristics of bus trip chaining, which could identify over 80% of the alighting stations on average. Finally, the proposed methodology is verified by a comprehensive field survey in Suzhou, China, with 100% sample rate., 1. Introduction The bus origin-destination (OD) matrix estimation is one of the most fundamental steps for urban transit planning, operation, and management strategies, such as passenger demand forecasting [1, 2], [...]

In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied to real-world instances with various problem sizes whose data is collected from H Bus Company located in southern Taiwan. To validate the effectiveness and evaluate the efficiency of the proposed solution framework, this paper compares the solution obtained from the BPC algorithm with that of a benchmark optimization package. The results show that the proposed BPC algorithm can solve problems with large real-world instances within a reasonable computational time. Moreover, in the numerical experiments, this paper finds that the scheduling and rostering results of the bus drivers are more sensitive to the rostering constraints. Also, the proposed integrated framework can yield a better solution than the solution from a conventional two-phase approach, which demonstrates the advantage of the integration in this paper. The proposed method provided can be employed to deal with the challenges in driver planning for bus companies., 1. Introduction As the public bus transport system produces less pollution than private transport, improving the quality of bus services and encouraging their use have become significant issues with the [...]

The invention and implementation of smart connected cars will change the way how the transportation networks in cities around the world operate. This technological shift will not happen instantaneously--for many years, both human-driven and smart connected vehicles will coexist. In this paper, using a multiagent simulation framework, we model a complex urban transportation system that involves heterogeneous participants. Vehicles are assigned into two groups: the first one consists of smart cars and the second one involves regular ones. Vehicles in the former group are capable of rerouting in response to changes in the observed traffic while regular ones rely on historical information only. The goal of the paper is to analyze the effect of changing smart cars penetration on system characteristics, in particular, the total travelling time. The smart car routing algorithm proposed in this paper reduced travelling time up to 30%. Analysis has shown that the behaviour of the system and optimal configuration of underlying algorithms change dynamically with smart vehicles penetration level., 1. Introduction Due to an increasing number of traffic participants, especially in urban areas, the current transportation infrastructure becomes insufficient. Big cities suffer great economic losses and resident dissatisfaction from [...]

Electricity consumption of metro stations increases sharply with expansion of a metro network and this has been a growing cause for concern. Based on relevant historical data from existing metro stations, this paper proposes a support vector regression (SVR) model to estimate daily electricity consumption of a newly constructed metro station. The model considers some major factors influencing the electricity consumption of metro station in terms of both the interior design scheme of a station (e.g., layout of the station and allocation of facilities) and external factors (e.g., passenger volume, air temperature and relative humidity). A genetic algorithm with five-fold cross-validation is used to optimize the hyper-parameters of the SVR model in order to improve its accuracy in estimating the electricity consumption of a metro station (ECMS). With the optimized hyper-parameters, results from case studies on the Beijing Subway showed that the estimating accuracy of the proposed SVR model could reach up to 95% and the correlation coefficient was 0.89. It was demonstrated that the proposed model could outperform the traditional methods which use back-propagation neural network or multivariate linear regression. The method presented in this paper can be an adequate tool for estimating the ECMS and should further assist in the delivery of new, energy-efficient metro stations., 1. Introduction A metro system plays an important role among urban mass transit systems and has a number of advantages over other public transportation modes in metropolitan areas, such as [...]

In order to make safety risk assessment more accurately and more reasonably for high-speed railway station in China, this paper analyzes risk factors of fault tree and transfers the fault tree of risk accident into fuzzy petri net and then builds the FPN-FTA model by combining the dynamic weighting fuzzy petri net (FPN) and fault tree analysis (FTA) based on the latter. This paper simulates the FTA-FPN model with Stateflow of Matlab software. Then, it builds up a bi-objective risk control model, making the minimum safety risk level and minimum necessary cost as the objectives, and it designs discrete particle swarm optimization algorithm to solve the risk control model. Finally, this paper selects stampede accident of Shijiazhuang high- speed railway station as an example in case study for assessing stampede risk level and gets the risk control schemes for this station. The results verify the feasibility and validity of the model and algorithm., 1. Introduction The pressure on railway transport safety management has increased constantly with the rapid development of railway. Therefore, safety risk control is the key to secure railway transport. Safety [...]

In order to improve the transshipment efficiency of transit containers in the port or the port-type railway network container freight station (PRNCS) with the condition that each transit container matches a railway flat-car, this paper studied the optimization of operation path of the rail mounted gantry crane (RMG) in the loading and unloading track for containers transshipped directly from highway to railway. Based on the basic model of TSP, the paper constructed the optimization model for the operation path of RMG, and designed the Ant Colony Algorithm (ACA) to solve it, and then obtained the operation scheme of RMG having the highest efficiency. Finally, the validity and correctness of the model and algorithm were verified by a case., 1. Introduction As the key node of transportation network for international containers, PRNCS is not only the hub for distribution of containers, but also the junction of various transportation modes [...]

In this paper, a vision-based crash detection framework was proposed to quickly detect various crash types in mixed traffic flow environment, considering low-visibility conditions. First, Retinex image enhancement algorithm was introduced to improve the quality of images, collected under low-visibility conditions (e.g., heavy rainy days, foggy days and dark night with poor lights). Then, a Yolo v3 model was trained to detect multiple objects from images, including fallen pedestrians/cyclists, vehicle rollover, moving/ stopped vehicles, moving/stopped cyclists/pedestrians, and so on. Then, a set of features were developed from the Yolo outputs, based on which a decision model was trained for crash detection. An experiment was conducted to validate the model framework. The results showed that the proposed framework achieved a high detection rate of 92.5%, with relatively low false alarm rate of 7.5%. There are some useful findings: (1) the proposed model outperformed empirical rule-based detection models; (2) image enhancement method can largely improve crash detection performance under low-visibility conditions; (3) the accuracy of object detection (e.g., bounding box prediction) can impact crash detection performance, especially for minor motor-vehicle crashes. Overall, the proposed framework can be considered as a promising tool for quick crash detection in mixed traffic flow environment under various visibility conditions. Some limitations are also discussed in the paper., 1. Introduction Emergency response to roadway crashes is very important for traffic management. On the one hand, people injured in a crash need to be sent to the nearest hospital [...]

All persons in self-driving vehicle would like to receive each service. To do it, the system has to know the person's state from emotion or stress, and to know the person's state, it has to catch by analyzing the person's bio-information. In this paper, we propose a system for inferring emotion using EEG, pulse, blood pressure (systolic and diastolic blood pressure) of user, and recommending color and music according to emotional state of user for a user service in self-driving vehicle. The proposed system is designed to classify the four emotional information (stability, relaxation, tension, and excitement) by using EEG data to infer and classify emotional state according to user's stress. SVM algorithm was used to classify bio information according to stress index using brain wave data of the fuzzy control system, pulse, and blood pressure data. When 80% of data were learned according to the ratio of training data by using the SVM algorithm to classify the EEG, blood pressure, and pulse rate databased on the biometric emotion information, the highest performance of 86.1% was shown. The bio-information classification system based on the stress index proposed in this paper will help to study the interaction between human and computer (HCI) in the 4th Industrial Revolution by classifying emotional color and emotional sound according to the emotion of the user it is expected., 1. Introduction In recent years, a new future technology called emotional artificial intelligence (AI) has emerged owing to the advancement of the fourth industrial revolution era. Particularly, AI-based emotional computing [...]

The operational aircraft maintenance routing problem (OAMRP) plays a critical part in producing considerable cost reductions for airlines, since its solution directly influences the number of operating leased aircraft. To reduce the quantity of required aircraft, adopting cruise speed control in OAMRP is a good strategy. In this paper, we investigate the OAMRP with cruise speed control. The objective is to minimize the required quantity of aircraft by finding the optimal aircraft routes through cruise time optimization. The focus is on solving two issues simultaneously: (i) optimization of cruise times and (ii) determination of aircraft routes. Since the combination of two intricate sets of decisions poses significant methodological challenges, the difficulty lies in how to efficiently solve it. Accordingly, the goal of this study is twofold: (i) to design a preprocessing step to reduce the network size and (ii) to develop an improved ant colony optimization (IACO) algorithm with a new state transition mechanism to provide the guidance for cruise times optimization and a new pheromone updating mechanism to enhance the search efficiency and precision. Using data from the Bureau of Transportation Statistics (BTS), we demonstrate the computational efficiency of the preprocessing step and the IACO algorithm., Author(s): Qing Zhang [1]; Felix T. S. Chan (corresponding author) [2]; Xiaowen Fu [1] 1. Introduction 1.1. Background and Motivation The airline scheduling plan plays an indispensable role in both [...]

The reference and error channels of active noise control (ANC) systems may be saturated in real-world applications if the noise level exceeds the dynamic range of the electronic devices. This nonlinear saturation degrades the performance of ANC systems that use linear adaptive filters with the filtered-X least-mean-square (FXLMS) algorithm. This paper derives a bilinear FXLMS algorithm for nonlinear adaptive filters to solve the problems of signal saturation and other nonlinear distortions that occur in ANC systems used for practical applications. The performance of this bilinear adaptive filter is evaluated in terms of convergence speed, residual noise in steady state, and the computational complexity for different filter lengths. Computer simulations verify that the nonlinear adaptive filter with the associated bilinear FXLMS algorithm is more effective in reducing saturation effects in ANC systems than a linear filter and a nonlinear Volterra filter with the FXLMS algorithm. Index Terms--Active noise control (ANC), nonlinear adaptive filters, adaptive bilinear filter, adaptive Volterra filter, signal saturation.

In this paper, by modifying the well-known Viterbi algorithm, an adaptive Viterbi algorithm that is based on strongly connected trellis decoding is proposed. Using this algorithm, the design and a field-programmable gale array implementation of a low-power adaptive Viterbi decoder with a constraint length of 9 and a code rate of 1/2 is presented. In this design, a novel systolic array-based architecture with time multiplexing and arithmetic pipelining for implementing the proposed algorithm is used. It is shown that the proposed algorithm can reduce by up to 70% the average number of ACS computations over that by using the nonadaptive Viterbi algorithm, without degradation in the error performance. This results in lowering the switching activities of the logic cells, with a consequent reduction in the dynamic power. Further, it is shown that the total power consumption in the implementation of the proposed algorithm can be reduced by up to 43% compared to that in the implementation of the nonadaptive Viterbi algorithm, with a negligible increase in the hardware. Index Terms--Adaptive Viterbi decoder, field-programmable gate array (FPGA) implementation, low-power design, systolic array architecture.

We are given a uniformly elliptic coefficient field that we regard as a realization of a stationary and finite-range ensemble of coefficient fields. Given a right-hand side supported in a ball of size [Formula omitted] and of vanishing average, we are interested in an algorithm to compute the solution near the origin, just using the knowledge of the given realization of the coefficient field in some large box of size [Formula omitted]. More precisely, we are interested in the most seamless artificial boundary condition on the boundary of the computational domain of size L. Motivated by the recently introduced multipole expansion in random media, we propose an algorithm. We rigorously establish an error estimate on the level of the gradient in terms of [Formula omitted], using recent results in quantitative stochastic homogenization. More precisely, our error estimate has an a priori and an a posteriori aspect: with a priori overwhelming probability, the prefactor can be bounded by a constant that is computable without much further effort, on the basis of the given realization in the box of size L. We also rigorously establish that the order of the error estimate in both L and [Formula omitted] is optimal, where in this paper we focus on the case of [Formula omitted]. This amounts to a lower bound on the variance of the quantity of interest when conditioned on the coefficients inside the computational domain, and relies on the deterministic insight that a sensitivity analysis with respect to a defect commutes with stochastic homogenization. Finally, we carry out numerical experiments that show that this optimal convergence rate already sets in at only moderately large L, and that more naive boundary conditions perform worse both in terms of rate and prefactor., Author(s): Jianfeng Lu [sup.1], Felix Otto [sup.2] Author Affiliations: (1) grid.26009.3d, 0000 0004 1936 7961, Mathematics Department, Duke University, , Box 90320, 27708, Durham, NC, USA (2) grid.419532.8, Max Planck [...]

This paper introduces a new notion of a Fenchel conjugate, which generalizes the classical Fenchel conjugation to functions defined on Riemannian manifolds. We investigate its properties, e.g., the Fenchel-Young inequality and the characterization of the convex subdifferential using the analogue of the Fenchel-Moreau Theorem. These properties of the Fenchel conjugate are employed to derive a Riemannian primal-dual optimization algorithm and to prove its convergence for the case of Hadamard manifolds under appropriate assumptions. Numerical results illustrate the performance of the algorithm, which competes with the recently derived Douglas-Rachford algorithm on manifolds of nonpositive curvature. Furthermore, we show numerically that our novel algorithm may even converge on manifolds of positive curvature., Author(s): Ronny Bergmann [sup.1], Roland Herzog [sup.1], Maurício Silva Louzeiro [sup.1], Daniel Tenbrinck [sup.2], José Vidal-Núñez [sup.3] Author Affiliations: (1) grid.6810.f, 0000 0001 2294 5505, Faculty of Mathematics, Technische Universität [...]