基于遗传算法的BP神经网络短时交通流预测

对BP神经网络模型、方法及遗传算法的基本原理进行了分析,将遗传算法与标准BP神经网络算法相结合构建了基于遗传算法的神经网络算法;建立了基于BP神经网络的短时交通流预测模型,对BP神经网络算法及基于遗传算法的BP神经网络算法进行设计,并将其应用于短时交通流预测模型的仿真计算,仿真结果表明基于遗传算法的BP神经网络算法具有更快的计算速度及更好的适应能力,该方法可以较好地应用于短时交通流预测。     

动态车辆路径问题的遗传算法研究

文章针对动态车辆路径的特点及模型对其算法进行了研究，并设计了改进的遗传算法对最优路径进行求解，结果显示采用改进的遗传算法提高了全局寻优能力与收敛速度，取得了较好的效果。     

遗传算法在精益物流配送线路优化中的应用

精益物流配送活动中出现的一类散户配送问题是典型的组合优化问题,利用传统的优化方法计算复杂、难以求得全局最优解,遗传算法在求解这类问题中表现出了强劲的优势.利用遗传算法对这类特殊问题进行了研究分析,针对具体的配送问题进行了仿真试验,在实验的基础上探索了交叉算子、变异算子的优化设计,实验结果证明了遗传算法在解决这类问题上的可行性和有效性.     

基于混合遗传算法的供水管道漏损安全预测

为解决供水行业中供水管道漏损安全性问题,建立了供水管道漏损安全预测模型,并引入遗传算法对其优化进行漏损预测,同时,针对传统的遗传算法优化该模型的过程中出现的缺陷,提出了混合遗传算法对其求解.模拟实例的预测结果表明：该模型预测方法准确可靠,具有较高的实用性。     

基于遗传算法的地面常规公交与城市轨道交通接驳研究

为了研究地面常规公交与城市轨道接驳问题,构建了基于乘客交通出行时间最短优化模型,采用遗传算法进行求解,并通过具体案例进行了模型验证。结果表明建立的优化模型及遗传算法适用于接驳问题。     

综合客运枢纽集散控制策略分析

差异化控制策略设计可以有效提高综合客运枢纽集散服务网络的运作效率,体现"以人为本"的综合交通服务理念,决定城市综合交通网络效能的发挥。以集散服务网络为研究对象,依据枢纽特征聚合态所处阶段是否为常态,利用Stackelberg博弈建立双层规划控制策略模型,并用遗传算法对模型进行求解。通过研究枢纽特征聚合态的差异化,构建模型和算法设计,为高性能集散服务网络的设计与实现提供了一种思路。     

基于IC卡数据的深圳地铁客流特征分析

随着我国经济的快速发展和城市化进程加快,近年来以地铁为代表的城市轨道交通实现跨越式发展。客流数据是城市轨道交通规划设计、建设和运营调度的基本依据。地铁IC卡数据具有数据量大、真实准确、时间连续、易获取的特点,广泛应用于地铁站点、线路和网络客流特征分析、长短期客流预测以及地铁用户出行模式识别等方面。本文主要对深圳地铁IC卡刷卡数据进行分析,重点提取地铁客流时空分布特征以及用户出行特征,研究客流变化规律,对于地铁运营调度管理以及站点客流疏导有重要意义。     

层状地层横观各向同性粘弹性优化反分析与优化方法比较

针对基坑开挖过程中所表现出来的时间效应以及地层横向与竖向变形的差异,采用Voigt模型进行横观各向同性粘弹性模拟,并结合具体的施工过程,进行横观各向同性粘弹性位移优化反分析。结合具体的工程实例,分析了阻尼最小二乘法、遗传算法以及混合遗传算法对横观各向同性粘弹性位移反分析的适应性,结果发现,阻尼最小二乘法对流变参数不敏感,而遗传算法则不同,可以对参数进行全面优化,而混合遗传算法集合了两者的优点,克服了阻尼最小二乘法的不足。     

基于局部切空间排列和支持向量机的S700k转辙机齿轮组故障诊断模型

针对S700k转辙机齿轮组故障信号和故障征兆的非线性、多样性和复杂性等诊断问题,提出一种基于局部切空间排列和支持向量机的故障诊断模型。该模型首先通过局部切空间排列算法实现对振动信号的降维与消噪,然后利用EMD方法分解振动信号,计算各分量的能量熵作为故障特征向量,在保证故障特征整体几何结构信息不发生改变的前提下,降低了诊断数据的维数及噪音。最后利用多故障单值支持向量机对故障特征向量进行分类识别,增强了故障模式识别的分类性能。     

我国城市群交通模式选择对策

本文对交通模式选择的重要性、交通模式种类及适用范围、我国城市群发展特征、空间结构和交通需求进行了分析研究,在以人为本、集约利用资源、有效满足运输需求的理念指导下,提出了未来各类城市群交通模式选择的意见建议。     

Generating lane-based intersection maps from crowdsourcing big trace data

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.     

Comparison of pattern recognition techniques for the classification of impact acoustic emissions

Current day condition monitoring applications involving wood are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. These inspections are mainly done intuitively by skilled personnel. In this paper, a pattern recognition approach has been considered to automate such intuitive human skills for the development of robust and reliable methods within the area. The study presents a comparison of several pattern recognition techniques combined with various stationary feature extraction techniques for classification of impact acoustic emissions. Further issues concerning feature fusion are discussed as well. It is hoped that this kind of broad analysis could be used to handle a wide spectrum of tasks within the area, and would provide a perfect ground for future research directions. A brief introduction to the techniques is provided for the benefit of the readers unfamiliar with the techniques.Pattern classifiers such as support vector machines, etc. are combined with stationary feature extraction techniques such as linear predictive cepstral coefficients, etc. Results from support vector machines in combination with linear predictive cepstral coefficients delivered good classification rates. However, Gaussian mixture models delivered higher classification rates when feature fusion is proposed.     

Estimation of rail passenger flow and system utilization with ticket transaction and gate data

Capturing the dynamics in passenger flow and system utilization over time and space is extremely important for railway operators. Previous studies usually estimated passenger flow using automatic fare collection data, and their applications are limited to a single stopping pattern and/or a single type of ticket. However, the conventional railway in Taiwan provides four types of ticket and five types of train service with a number of stopping patterns. This study develops a comprehensive framework and corresponding algorithms to map passenger flow and evaluate system utilization. A multinomial logit model is constructed and incorporated in the algorithms to estimate passenger train selection behavior. Results from the empirical studies demonstrate that the developed framework and algorithms can successfully match passengers with train services. With this tool, operators can efficiently examine passenger flow and service utilization, thereby quickly adjusting their service strategies accordingly to improve system performance.     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

The missing data problem remains as a difficulty in a diverse variety of transportation applications, e.g. traffic flow prediction and traffic pattern recognition. To solve this problem, numerous algorithms had been proposed in the last decade to impute the missed data. However, few existing studies had fully used the traffic flow information of neighboring detecting points to improve imputing performance. In this paper, probabilistic principle component analysis (PPCA) based imputing method, which had been proven to be one of the most effective imputing methods without using temporal or spatial dependence, is extended to utilize the information of multiple points. We systematically examine the potential benefits of multi-point data fusion and study the possible influence of measurement time lags. Tests indicate that the hidden temporal–spatial dependence is nonlinear and could be better retrieved by kernel probabilistic principle component analysis (KPPCA) based method rather than PPCA method. Comparison proves that imputing errors can be notably reduced, if temporal–spatial dependence has been appropriately considered.     

Pedestrian detection for traffic safety based on Accumulate Binary Haar features and improved deep belief network algorithm

ABSTRACT

In order to improve traffic safety and protect pedestrians, an improved and efficient pedestrian detection method for auto driver assistance systems is proposed. Firstly, an improved Accumulate Binary Haar (ABH) feature extraction algorithm is proposed. In this novel feature, Haar features keep only the ordinal relationship named by binary Haar features. Then, the feature brings in the idea of a Local Binary Pattern (LBP), assembling several neighboring binary Haar features to improve discriminating power and reduce the effect of illumination. Next, a pedestrian classification method based on an improved deep belief network (DBN) classification algorithm is proposed. An improved method of input is constructed using a Restricted Bolzmann Machine (RBM) with T distribution function visible layer nodes, which can convert information on pedestrian features to a Bernoulli distribution, and the Bernoulli distribution can then be used for recognition. In addition, a middle layer of the RBM structure is created, which achieves data transfer between the hidden layer structure and keeps the key information. Finally, the cost-sensitive Support Vector Machine (SVM) classifier is used for the output of the classifier, which could address the class-imbalance problem. Extensive experiments show that the improved DBN pedestrian detection method is better than other shallow classic algorithms, and the proposed method is effective and sufficiently feasible for pedestrian detection in complex urban environments.     

A robust method for estimating transit passenger trajectories using automated data

Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.     

Speed limits,speed selection and network equilibrium

This paper investigates the local and global impact of speed limits by considering road users’ non-obedient behavior in speed selection. Given a link-specific speed limit scheme, road users will take into account the subjective travel time cost, the perceived crash risk and the perceived ticket risk as determinant factors for their actual speed choice on each link. Homogeneous travelers’ perceived crash risk is positively related to their driving speed. When travelers are heterogeneous, the perceived crash risk is class-specific: different user classes interact with each other and choose their own optimal speed, resulting in a Nash equilibrium speed pattern. With the speed choices on particular roads, travelers make route choices, resulting in user equilibrium in a general network. An algorithm is proposed to solve the user equilibrium problem with heterogeneous users under link-specific speed limits. The models and algorithms are illustrated with numerical examples.     

Bus running time prediction using a statistical pattern recognition technique

Abstract

Given that real-time bus arrival information is viewed positively by passengers of public transit, it is useful to enhance the methodological basis for improving predictions. Specifically, data captured and communicated by intelligent systems are to be supplemented by reliable predictive travel time. This paper reports a model for real-time prediction of urban bus running time that is based on statistical pattern recognition technique, namely locally weighted scatter smoothing. Given a pattern that characterizes the conditions for which bus running time is being predicted, the trained model automatically searches through the historical patterns which are the most similar to the current pattern and on that basis, the prediction is made. For training and testing of the methodology, data retrieved from the automatic vehicle location and automatic passenger counter systems of OC Transpo (Ottawa, Canada) were used. A comparison with other methodologies shows enhanced predictive capability.     

基于支持向量机的隧道衬砌空洞机器识别方法

探地雷达是检测隧道衬砌空洞最为有效的方法之一,但检测数据的解析始终是限制其广泛应用的关键。基于支持向量机的基本理论,文章建立了一套隧道衬砌空洞探地雷达图像的机器识别方法,该方法包括图像预处理、特征提取和支持向量机识别三个步骤。首先,探地雷达图像需经过零时修正、滤波、偏移、增益等预处理以提高信噪比;其次,对图像的时域信号进行分段,在分段信号上提取方差、标准绝对偏差和四阶矩三个统计量作为图像特征;最后,利用已知数据对支持向量机模型进行训练,并用数值模拟和模型试验数据对训练好的支持向量机模型进行测试。结果表明,该方法不仅能够准确识别隧道衬砌和围岩内的空洞,还可以对空洞埋深及横向分布范围做出较准确的判断。