铁路智能运输系统——现状、挑战与发展

在对铁路发展过程进行简要回顾的基础上,指出通过在信息化基础上的智能化,使铁路 运输系统向铁路智能运输系统转化是保持和提高铁路运输业在21世纪竞争力的核心战略.之 后给出了铁路智能运输系统(RITS)的定义、特点及一般构成,并在对RITS相关研究发展简要 回顾的基础上,提出了中国RITS的发展框架,系统结构体系以及构成我国RITS的核心技术. 以及中国ITS系统的发展模式.     

交通运输与现代物流

现代物流是国民经济发展的产物,同时又是促进国民经济进一步发展的一个重要环节, 传统的交通运输业如何进行适度调整,以开拓物流领域这个大市场,逐渐成为人们关注的焦点 本文从阐述交通运输与物流的不同特点和关系出发,探讨了交通运输开拓物流市场所应进行的 调整,在本文的最后说明了交通运输进人物流领域所应该注意的问题。     

中国智能铁路系统的若干设想

智能铁路系统IRS属智能交通系统ITS的轨道交通研究领城,许多中国专家和学者都在研究智能交通系统并取得了许多研究成果.总结了智能交通系统ITS的发展状况和智能铁路系统的国内外研究情况,提出了中国智能铁路系统CIRS的基本组成和总体结构,在此基础上,指出了中国智能铁路系统CIRS的若干关键技术,对发展中国铁路智能系统CIRS有借鉴意 义.     

基于模糊聚类的铁路智能运输系统结构分析方法研究

铁路智能运输系统(RITS)规模大、结构复杂、要素繁多,系统设计难度大.为降低系统设计复杂程度,提出了一种基于模糊聚类的RITS逻辑结构分解方法,通过建立模糊关联强度矩阵描述过程间的关联程度,在此基础上采用模糊聚类将RITS逻辑结构分解为若干相对独立的功能单元,使系统分解过程更为精细和严谨,为系统物理实现及相关研究提供了基础.     

Definition of a merging assistant strategy using intelligent vehicles

In the area of active traffic management, new technologies provide opportunities to improve the use of current infrastructure. Vehicles equipped with in-car communication systems are capable of exchanging messages with the infrastructure and other vehicles. This new capability offers many opportunities for traffic management. This paper presents a novel merging assistant strategy that exploits the communication capabilities of intelligent vehicles. The proposed control requires the cooperation of equipped vehicles on the main carriageway in order to create merging gaps for on-ramp vehicles released by a traffic light. The aim is to reduce disruptions to the traffic flow created by the merging vehicles. This paper focuses on the analytical formulation of the control algorithm, and the traffic flow theories used to define the strategy. The dynamics of the gap formation derived from theoretical considerations are validated using a microscopic simulation. The validation indicates that the control strategy mostly developed from macroscopic theory well approximates microscopic traffic behaviour. The results present encouraging capabilities of the system. The size and frequency of the gaps created on the main carriageway, and the space and time required for their creation are compatible with a real deployment of the system. Finally, we summarise the results of a previous study showing that the proposed merging strategy reduces the occurrence of congestion and the number of late-merging vehicles. This innovative control strategy shows the potential of using intelligent vehicles for facilitating the merging manoeuvre through use of emerging communications technologies.     

Real time location prediction with taxi-GPS data streams

The prediction of the destination location at the time of pickup is an important problem with potential for substantial impact on the efficiency of a GPS-enabled taxi service. While this problem has been explored earlier in the batch data set-up, we propose in this paper new solutions in the streaming data set-up. We examine four incremental learning methods using a damped window model namely, Multivariate multiple regression, Spherical-spherical regression, Randomized spherical K-NN regression and an Ensemble of these methods for their effectiveness in solving the destination prediction problem. The performance of these methods on several large datasets are evaluated using suitably chosen metrics and they were also compared with some other existing methods. We found that the Multivariate multiple regression method has the best performance in terms of prediction accuracy but the Spherical-spherical regression method is the best performer when we take into account the accuracy time trade-off criterion. The next pickup location problem, where we are interested in predicting the next pickup location for a taxi given the dropoff location coordinates of the previous trip as input is also considered and the aforementioned methods are examined for their suitability using real world datasets. As in the case of destination prediction problem, here also we find that the Multivariate multiple regression method gives better performance than the rest when we consider prediction accuracy but the Spherical-spherical regression method is the best performer when the accuracy-time trade-off criterion is taken into account.     

Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling

Traffic congestion is rapidly increasing in urban areas, particularly in mega cities. To date, there exist a few sensor network based systems to address this problem. However, these techniques are not suitable enough in terms of monitoring an entire transportation system and delivering emergency services when needed. These techniques require real-time data and intelligent ways to quickly determine traffic activity from useful information. In addition, these existing systems and websites on city transportation and travel rely on rating scores for different factors (e.g., safety, low crime rate, cleanliness, etc.). These rating scores are not efficient enough to deliver precise information, whereas reviews or tweets are significant, because they help travelers and transportation administrators to know about each aspect of the city. However, it is difficult for travelers to read, and for transportation systems to process, all reviews and tweets to obtain expressive sentiments regarding the needs of the city. The optimum solution for this kind of problem is analyzing the information available on social network platforms and performing sentiment analysis. On the other hand, crisp ontology-based frameworks cannot extract blurred information from tweets and reviews; therefore, they produce inadequate results. In this regard, this paper proposes fuzzy ontology-based sentiment analysis and semantic web rule language (SWRL) rule-based decision-making to monitor transportation activities (accidents, vehicles, street conditions, traffic volume, etc.) and to make a city-feature polarity map for travelers. This system retrieves reviews and tweets related to city features and transportation activities. The feature opinions are extracted from these retrieved data, and then fuzzy ontology is used to determine the transportation and city-feature polarity. A fuzzy ontology and an intelligent system prototype are developed by using Protégé web ontology language (OWL) and Java, respectively. The experimental results show satisfactory improvement in tweet and review analysis and opinion mining.     

基于韧性理论的盾构隧道智能建造北大核心CSCD

以盾构隧道为主的城市轨道交通在"十三五"期间已取得了跨越式的进步,盾构掘进设备在智能化方面取得了飞速发展,但隧道结构设计、结构制造和现场管片拼装的智能化方面,仍需要大的创新与突破。面对建设韧性智慧城市的战略目标,盾构隧道还存在系列问题亟需解决,如对于新材料的物性认识浅、理论少;传感的布置缺乏针对性,监测感知差;隧道管片拼装大量依靠人工,误差大。解决这些问题的关键在于构建一个基于韧性理论的智能化盾构隧道建造系统,通过利用材料和结构的韧性特点,结合计算机等信息技术,采用韧性设计、智能感知、智能制造、智能拼装等一系列措施,使得隧道结构中的材料可智能感知、结构可精准监测、数据可实时孪生、信息可高效管理、制造可自动操控、过程可全域感知、模型可动态调节、管片可智能拼装,最终实现韧性城市的盾构隧道智能化建设。     

An empirical investigation on consumers’ intentions towards autonomous driving

Major steps towards implementation of autonomous and connected transport are being taken nowadays. The trend of automation technology being used in vehicles by the most important vehicle manufacturing industries is expected to move closer to high or fully Autonomous Vehicles (AVs) through technological advancements in sectors of robotics and artificial intelligence. Vehicles with autonomous driving capabilities are planning to be available on market, in full scale, in the next years. In the longer term substantial benefits are mainly expected for accessibility to transport, safety, traffic flow, emissions, fuel use and comfort. All these potential societal benefits will not be achieved unless AVs are accepted and used by a critical mass of people. Addressing these challenges, this paper: (a) proposes a technology acceptance modelling process by extending the original Technology Acceptance Model (TAM) to explain and predict consumers’ intensions towards AVs, (b) based on the proposed TAM-extended framework, a 30-question survey was conducted in order to investigate the factors influencing consumers’ intensions to use and accept AVs. Results show that the constructs of perceived usefulness, perceived ease to use, perceived trust and social influence, are all useful predictors of behavioral intentions to have or use AVs, with perceived usefulness having the strongest impact. The insights derived from this study could significantly contribute to ongoing research related to technology acceptance of AVs and are expected to allow automobile industries to improve their design and technology.     

An inference engine for smartphones to preprocess data and detect stationary and transportation modes

A smartphone can be utilized as a cost-effective device for the purposes of intelligent transportation system. To detect the movement and the stationary statuses in the motorized and non-motorized modes, this study develops a new inference engine, including two sets of rules. The first sets of rules are defined by the related thresholds on the features of smartphone sensors while the second sets are extracted from the human knowledge to improve the results of the first rules. The experimental results reveal that by utilizing Inertial Measurement Unit (IMU) sensors in the proposed inference engine, it is possible to save 40% energy in comparison with the previous research. Moreover, this engine increases the accuracy of the motorized mode detection to 95.2% and determines the stationary states in motorized mode with 97.1% accuracy.