Construction and Application of Knowledge Graph for Quality and Safety Supervision of Transportation Engineering

Knowledge graph technology play a more and more important role in various fields of industry and academia.This paper firstly introduces the general framework of the knowledge graph construction,which includes three stages:information extraction,knowledge fusion and knowledge processing.In order to improve the efficiency of quality and safety supervision of transportation engineering construction,this paper constructs a knowledge graph by acquiring multi-sources heterogeneous data from supervision of transportation engineering quality and safety.It employs a bottom-up construction strategy and some natural language processing methods to solve the problems of the knowledge extraction for transportation engineering construction.We use the entity relation extraction method to extract the entity triples from the multi-sources heterogeneous data,and then employ knowledge inference to complete the edges in the constructed knowledge graph,finally perform quality evaluation to add the valid triples to the knowledge graph for updating.Subgraph matching technology is also exploited to retrieve the constructed knowledge graph for efficiently acquiring the useful knowledge about the quality and safety of transportation engineering projects.The results show that the constructed knowledge graph provides a practical and valuable tool for the quality and safety supervision of transportation engineering construction.

Transportation engineering for enhanced production of plant natural products in microbial cell factories

Plant natural products(PNPs)exhibit a wide range of biological activities and have essential applications in various fields such as medicine,agriculture,and flavors.Given their natural limitations,the production of high-value PNPs using microbial cell factories has become an effective alternative in recent years.However,host metabolic burden caused by its massive accumulation has become one of the main challenges for efficient PNP production.Therefore,it is necessary to strengthen the transmembrane transport process of PNPs.This review introduces the discovery and mining of PNP transporters to directly mediate PNP transmembrane transportation both intracellularly and extracellularly.In addition to transporter engineering,this review also summarizes several auxiliary strategies(such as small molecules,environmental changes,and vesicles assisted transport)for strengthening PNP transportation.Finally,this review is concluded with the applications and future perspectives of transportation engineering in the construction and optimization of PNP microbial cell factories.

Introduction to the Institute of Transportation Engineering, Tsinghua University

The Institute of Transportation Engineering （ITE） at Tsinghua University was founded in 1995. The Institute grew out of the urban transportation studies group in the Department of Civil Engineering established in 1986. The Institute seeks to promote the country＇s development through its research program and by training graduates with initiative and creativity. The Institute has made remarkable advance in its fundamental research program with several national key research projects with numerous publications that extend the university＇s reputation.

Special issue: Management of road and railway traffic and transportation engineering

In recent years,with the development of road and railway transportation industries,a variety of complicated decisionmaking problems have emerged in real-world applications.It is urgent to analyze these problems from the perspective of theoretical and methodological innovations,and provide methods in management,decision-making and application so as to achieve efficient operations of traffic and transportation systems.These problems have

Engineering two-dimensional pores in freestanding TiO_2/graphene gel film for high performance lithium ion battery

As the key component of electrochemical energy storage devices, an electrode with superior ions transport pores is the important premise for high electrochemical performance. In this paper, we developed a unique solution process to prepare freestanding TiO_2/graphene hydrogel electrode with tunable density and porous structures. By incorporating room temperature ionic liquids（RTILs）, even upon drying, the non-volatile RTILs that remained in the gel film would preserve the efficient ion transport channels and prevent the electrode from closely stacking, to develop dense yet porous structures. As a result, the dense TiO_2/graphene gel film as an electrode for lithium ion battery displayed a good gravimetric electrochemical performance and more importantly a high volumetric performance.

A panel data model to predict airline passenger volume

Airline passenger volume is an important reference for the implementation of aviation capacity and route adjustment plans.This paper explores the determinants of airline passenger volume and proposes a comprehensive panel data model for predicting volume.First,potential factors influencing airline passenger volume are analyzed from Geo-economic and service-related aspects.Second,the principal component analysis(PCA)is applied to identify key factors that impact the airline passenger volume of city pairs.Then the panel data model is estimated using 120 sets of data,which are a collection of observations for multiple subjects at multiple instances.Finally,the airline data from Chongqing to Shanghai,from 2003 to 2012,was used as a test case to verify the validity of the prediction model.Results show that railway and highway transportation assumed a certain proportion of passenger volumes,and total retail sales of consumer goods in the departure and arrival cities are significantly associated with airline passenger volume.According to the validity test results,the prediction accuracies of the model for 10 sets of data are all greater than 90%.The model performs better than a multivariate regression model,thus assisting airport operators decide which routes to adjust and which new routes to introduce.

关于我国高校英文科技期刊国际影响力提升的思考

本文从办刊体制、编辑人员专业水平、期刊评价体制、学术界唯SCI论四个方面对高校英文科技期刊的发展弊端进行了调研,总结出Journal of Traffic and Transportation Engineering (English Edition)(JTTE,交通运输工程学报(英文版))创刊以来所采取的提升期刊国际影响力的措施和方法:在“中国科技期刊卓越行动计划”的支持下,JTTE充分发挥自身优势,依靠长安大学在道路工程、交通运输领域强大的学术影响力,团结作者、学者、编委三方力量,将期刊介绍给全世界,从而提高了期刊的国际显示度;在新媒体背景下,期刊利用跨平台推荐、社交媒体推广等方式宣传期刊内容,加快了期刊内容传播。

An Importance Segregation Based Multi-Objective Optimization Model for Traffic Safety Improvement Countermeasure Selection

This paper presents a multi-objective optimization model to decide effective and beneficial portfolio for implementing traffic safety improvements under budgetary constraints.Different from the conventional multi-objective approaches,the proposed model segregates decision criteria into different importance levels and designs a successive optimization approach to obtain the final solution(s).Such modeling features offer the advantages to:1)prevent the arbitrariness for transportation agencies to determine weights for decision criteria;2)remedy the deficiency of over-weighting less important criteria in the traditional multi-objective optimization approach;and 3)minimize the number of potential solutions for final decision by transportation agencies with enhanced screening of the sub-optimal solutions.Case study results reveal that the proposed model is efficient not only for deciding the most suitable traffic safety countermeasure for a specific site,but also for determining the plans for implementing multiple countermeasures among multiple sites given the budget constraint.Comparative study results have also indicated that the proposed model outperforms the traditional criteria in objectively selecting traffic safety improvements in a multi-criteria decision-making process.The clarity of model inputs and the interpretation of results with respect to different selection criteria offer its best potential to be used as an effective decision-support tool for transportation authorities to assess and refine their safety improvement investments.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

Emergency response facility location in transportation networks: A literature review

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption,transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility,location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, datadriven optimization approach, and machine learning-based algorithms.

Sustainable transit vehicle tracking service,using intelligent transportation system services and emerging communication technologies:A review

The most salient problems of transit vehicle service in Latin American intermediate cities include:the high number of passengers involved in traffic accidents;traffic congestion caused by transit vehicles,and pollution generated by these vehicles,which increases in high congestion scenarios.To improve upon this situation,a research was conducted on the transit vehicle tracking service,which is a basic service for implementing mobility solutions for the aforementioned problems,the most relevant characteristics of this service for the context of Latin American intermediate cities were identified,and an implementation was proposed.This paper presents the four stages of the study:(a)a review of the state-of-the-art of services or systems related to vehicle tracking,including wireless communications technologies,implemented sustainability approaches,usage of special algorithms for efficiency improvement,and the intelligent transportation system(ITS)architecture used as a basis;(b)the process of identifying relevant characteristics of the service for a given context;(c)proposal of an ITS architecture for this service in an intermediate city,its requirements and the suggested technologies;and(d)development of experiments for validating usage of the key suggested technologies.The review allowed to identify the main service characteristics,with regard to vehicle positioning technologies,the recommended wireless communication technology(long range,LoRa),energy consumption considerations,and use of artificial intelligence(AI)to calculate waiting time of users at bus stops.Finally,an ITS architecture for the city of Popayan(Colombian city)considering the aforementioned characteristics is proposed,and the experiments related to the use of these technologies are described in detail.

Public transport inclusion and active aging: A systematic review on elderly mobility

Global aging has brought attention to addressing the elderly mobility dilemma,and a commitment to address inclusive transport in aging.Transport inclusion for older adults aims at establishing an age-friendly transport system.As a direct policy goal and promoting active aging as an indirect social goal,to help older populations gain access to transport and social opportunities.Based on the analysis of scientific knowledge mapping over the past 30 years,this study visualizes the hotspots and trends in elderly mobility research.Through a qualitative analysis of 138 papers,this paper reviews the theoretical foundations for transport inclusion and three themes of elderly mobility research,proposes a framework for public transport(PT)inclusion research as well as future research directions.The study suggests that transport inclusion can help enhance elderly health,social participation,and subjective well-being.It can improve their travel opportunities for active aging in four ways:opportunity accessibility,physical accessibility,age-friendly informationalization,and cost affordability.Future research should pay attention to examining the mechanism of perceived transport inclusion on active aging,expanding the multilevel research space of the capability approach(CA),and exploring the subjectiveobjective integrated research model for PT inclusion.This research contributes to the overall knowledge of the theoretical and empirical researches on PT inclusion for the elderly and promotes the integrated application of interdisciplinary knowledge in the field.

Integrated multi-item packaging and vehicle routing with split delivery problem for fresh agri-product emergency supply at large-scale epidemic disease context

Fresh agri-product emergency supply is crucial to secure the basic livelihood of residents at large-scale epidemic disease context. Considering the massive demand and limited transportation resources, this study integrates multi-item packaging and vehicle routing with split delivery to improve the emergency supply capacity. Firstly, three specific objectives of fresh agri-product emergency supply at large-scale epidemic disease context are formulated, i.e., average response time, infectious risk possibility and transportation resource utilization. Then, a multi-item packaging strategy is proposed to consolidate different categories of fresh agri-products according to the food cold chain temperatures.An optimization model integrating multi-item packaging and vehicle routing with split delivery is developed to jointly decide the optimal packaging scheduling, vehicle assignment and delivery routing. Next, an improved genetic algorithm based on solution features (IGA-SF) is designed to solve the integrated model with multiple decision variables. Finally,a case on fresh agri-product emergency supply of Huangpi District, Wuhan in the context of the Corona Virus Disease 2019(COVID-19) is carried out to illustrate the efficiency and feasibility of the proposed model. The numerical results of medium-to-largescale cases demonstrate that the proposed IGA-SF could save 23.91% CPU time and 37.80% iteration number on average than genetic algorithm. This study could satisfy different emergency scenario requirements flexibly, and provide scientific decision support for provincial and national governments on fresh agri-product emergency supply.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution(VREMD)is one of the core issues of emergency management,which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events.To summarize the latest research progress,we collected 511VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software.Subsequently,we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords,research gaps,and future works.The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers.However,these organizations with the most publications performed poorly regarding the number of literature citations.China and the US have contributed the vast majority of the literature,and there are close collaborations between researchers from both countries.The optimization model of VREMD can be divided into single-,multi-,and joint-objective models.The shortest travel time is the most common optimization objective in the single-objective optimization model.Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously.In recent literature,scholars have focused on the impact of uncertainty and special events(e.g.,COVID-19)on VREMD.Moreover,some scholars focus on joint optimization models to optimize vehicle routes and central locations(or material allocation)simultaneously.Solution algorithms can be divided into two primary categories,i.e.,mathematical planning methods and intelligent evolutionary algorithms.The branch and bound algorithm is the most dominant mathematical planning algorithm,while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms.It is shown that the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ)can effectively solve the multiobjective model of VREMD.To further improve the algorithm’s performance,researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-Ⅱand certain other algorithms.Scholars have also proposed a series of optimization algorithms for specific scenarios.With the development of new technologies and computation methods,it will be exciting to construct optimization models that consider uncertainty,heterogeneity,and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.

Analyzing driver's response to yellow indication subjected to dilemma incursion under mixed traffic condition

Under mixed traffic conditions prevailing on Indian roads,drivers show complex response when faced with yellow signal because lane assignment gets dynamic in nature.The present study analyzes the effect of surrounding vehicles on response of the drivers while facing dilemma at intersections.Although dilemma zone definitions hold true in case of homogeneous traffic mix,a statistical analysis is performed to check the consistency across the definitions under mixed traffic condition.Present study shows a significant difference in percentage of red light running in comparison to homogeneous traffic as reported by various studies.For carrying out the research,study locations are chosen in such a way to reflect diversity in road geometry,traffic composition and signal characteristics.The results deduced in this study indicate a strong correlation between the driver's decision making choice and the effect of presence of surrounding vehicle at the onset of yellow signal.The effect of critical time analysis has been found out to be one of the parameters other than critical distance in categorizing driver's aggressiveness while facing the yellow signal.In the process of identifying the statistical significance of dilemma zone definitions,it has been found that under heterogeneous traffic condition,drivers behave differently as compared to homogenous traffic when it comes to dilemma zone.It is observed that the percentage of vehicles crossing the intersection when faced with dilemma by violating the red light is 11.6%according to dilemma zone definition I whereas the definition II has yielded about 10.8%violation covering different vehicle types.The above violation figures derived based on the above definition is somewhat higher as compared to homogeneous traffic condition which is observed to be of the order of 5%-6%.

Machine learning applied to road safety modeling:A systematic literature review

Road safety modeling is a valuable strategy for promoting safe mobility,enabling the development of crash prediction models(CPM)and the investigation of factors contributing to crash occurrence.This modeling has traditionally used statistical techniques despite acknowledging the limitations of this kind of approach(specific assumptions and prior definition of the link functions),which provides an opportunity to explore alternatives such as the use of machine learning(ML)techniques.This study reviews papers that used ML techniques for the development of CPM.A systematic literature review protocol was conducted,that resulted in the analysis of papers and their systematization.Three types of models were identified:crash frequency,crash classification by severity,and crash frequency and severity.The first is a regression problem,the second,a classificatory one and the third can be approached either as a combination of the preceding two or as a regression model for the expected number of crashes by severity levels.The main groups of techniques used for these purposes are nearest neighbor classification,decision trees,evolutionary algorithms,support-vector machine,and artificial neural networks.The last one is used in many kinds of approaches given the ability to deal with both regression and classification problems,and also multivariate response models.This paper also presents the main performance metrics used to evaluate the models and compares the results,showing the clear superiority of the ML-based models over the statistical ones.In addition,it identifies the main explanatory variables used in the models,which shows the predominance of road-environmental aspects as the most important factors contributing to crash occurrence.The review fulfilled its objective,identifying the various approaches and the main research characteristics,limitations,and opportunities,and also highlighting the potential of the usage of ML in crash analyses.