智能交通系统中的交通信息采集技术研究进展

实时、准确的交通信息采集是智能交通的基础。根据被采集车辆是否与采集系统进行交互,交通信息采集技术分为两大类:独立式采集技术和协作式采集技术。分析了各种采集技术的特点、适用场合和发展现状,并提出了未来的发展趋势:车辆内部信息的直接获取、交换式信息采集、多种采集技术的融合。     

基于混沌高效遗传算法优化SVM的交通量预测

针对交通量预测本身所存在的小样本、非线性和复杂性等特点,利用支持向量机建立了基于RBF核函数的SVM交通量预测模型,采用基于混沌映射和加速遗传算法的混沌高效遗传算法对SVM模型参数C,ε和δ2进行优选,结合某市1978～2008年交通量实测资料进行了仿真验证,与GA-SVM模型和BP神经网络模型的仿真预测结果对比表明:该模型取得了较好的预测效果,可有效应用于城市交通量的预测.     

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.     

Application of the support vector machine and heuristic k-shortest path algorithm to determine the most eco-friendly path with a travel time constraint

This study aims to determine an eco-friendly path that results in minimum CO₂ emissions while satisfying a specified budget for travel time. First, an aggregated CO₂ emission model for light-duty cars is developed in a link-based level using a support vector machine. Second, a heuristic k-shortest path algorithm is proposed to solve the constrained shortest path problem. Finally, the CO₂ emission model and the proposed eco-routing model are validated in a real-world network. Specifically, the benefit of the trade-off between CO₂ emission reduction and the travel time budget is discussed by carrying out sensitivity analysis on a network-wide scale. A greater spare time budget may enable the eco-routing to search for the most eco-friendly path with higher probability. Compared to the original routes selected by travelers, the eco-friendly routes can save an average of 11% of CO₂ emissions for the trip OD pairs with a straight distance between 6 km and 9 km when the travel time budget is set to 10% above the least travel time. The CO₂ emission can also be reduced to some degree for other OD pairs by using eco-routing. Furthermore, the impact of market penetration of eco-routing users is quantified on the potential benefit for the environment and travel-time saving.     

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.     

Comparing smart scheme effects for congested highways

A high level objective for many international governments and local operators is that highways should be managed in a way that is sustainable in terms of a Low Carbon Energy future. Recent initiatives such as the Strategic Transport Technology Plan and the policy and legal framework promoted by the European Commissions’ Intelligent Transport System (ITS) Directive and ITS Action Plan may assist with this objective. However, many levels of complexity are inherent within the (ITS) schemes that are now part of highway management, due to the linkage of various technological components to complex systems and services. Maintaining efficient, sustainable co-operative performance is therefore a major task, with inconsistencies between product suppliers, network managers and operators. As a result, it is of considerable interest to the highway operators and high level policy makers to be able to assess the performance of individual ITS schemes and furthermore, to be able to compare performance between ITS schemes. In this paper, an illustration is provided of a methodology that can be used to assess the performance of ITS schemes according to a set of sustainability criteria. A case study is introduced which compares the performance of anticipated Active Traffic Management (ATM) schemes for what the road network operator (Highways England) perceive to be the four most congested highways in England (in terms of annual average daily traffic flows). Appropriate action can then be taken to improve the energy and sustainable management of Information Communication Technology (ICT) and transport systems for the benefit of a smarter, sustainable and efficient future.     

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.     

A context aware system for driving style evaluation by an ensemble learning on smartphone sensors data

There are many systems to evaluate driving style based on smartphone sensors without enough awareness from the context. To cover this gap, we propose a new system namely CADSE system to consider the effects of traffic levels and car types on driving evaluation. CADSE system includes three subsystems to calibrate smartphone, to classify the maneuvers, and to evaluate driving styles. For each maneuver, the smartphone sensors data are gathered in three successive time intervals referred as pre-maneuver, in-maneuver, and post-maneuver times. Then, we extract some important mathematical and experimental features from these data. Afterwards, we propose an ensemble learning method on these features to classify the maneuvers. This ensemble method includes decision tree, support vector machine, multi-layer perceptron, and k-nearest neighbors. Finally, we develop a rule-based fuzzy inference system to integrate the outputs of these algorithms and to recognize dangerous and safe maneuvers. CADSE saves this result in driver’s profile to consider more for dangerous driving recognition. The experimental results show that accuracy, precision, recall, and F-measure of CADSE system are greater than 94%, 92%, 92%, and 93%, respectively that prove the system efficiency.     

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.     

关于深度学习的综述与讨论

  目的  针对传统有限元数值计算方法耗时长、占用计算资源多等缺点,提出基于机器学习算法对船舶结构强度进行评估的回归预测模型。  方法  以已有的有限元数值计算数据为样本,以外部载荷和结构尺寸为特征,以应力和变形为目标,引入4种传统机器学习算法和4种集成机器学习算法,对船舶上吊杆处的局部结构响应进行预测分析。  结果  与传统有限元数值模拟方法相比,机器学习算法在保证结果准确性的同时大幅度提升了计算效率,其中Light Gradient Boosting Machine（LightGBM）算法与其他算法相比,其准确度和计算效率的表现更好。  结论  为进一步研究更复杂的船体结构设计提供了一条可行且高效的技术途径。