ATP系统数据记录器应用

主要介绍了德黑兰地铁12号线的ATP系统,它能够控制司机对列车的操作,具有故障-安全特性。该系统是ATC(Automatic Train Control)系统的子系统,用于保障行车安全,避免行车事故的发生。当司机不遵守列车运行指令而盲目驾驶的时候,ATP系统能够向司机报警并自动开启制动,降低列车行驶速度。重点介绍德黑兰地铁12号线ATP系统的安装、调试运行情况。目前德黑兰地铁的车载ATP还没有安装数据记录系统,主要对此进行了探讨并提出了相关解决方案。     

LKJ数据换装施工实行属地管理的利弊

LKJ数据换装施工实行属地管理,在感受其安全及时、节省资源等有利面时,也应客观分析其存在的弊端。结合LKJ数据属地换装实际,针对如何存利去弊,完善LKJ数据换装施工属地管理,谈谈个人的一些建议与看法。     

CTCS-3级列控系统CSD数据传输处理过程及延时性能分析

阐述了列控CSD数据传输在CTCS-3级列控系统中的应用与意义,介绍了CSD传输延时的定义以及铁路应用的指标要求,描述了CSD数据传输处理过程,分析了CSD延时的产生原因与分布情况,针对传输系统保护倒换、R接口不同的串口通信速率,以及不同的空口用户速率、用户数据帧长度给出了实测数据。     

Optimal Genetic View Selection Algorithm for Data Warehouse

To efficiently solve the materialized view selection problem, an optimal genetic algorithm of how to select a set of views to be materialized is proposed so as to achieve both good query performance and low view maintenance cost under a storage space constraint. First, a pre-processing algorithm based on the maximum benefit per unit space is used to generate initial solutions. Then, the initial solutions are improved by the genetic algorithm having the mixture of optimal strategies. Furthermore, the generated infeasible solutions during the evolution process are repaired by loss function. The experimental results show that the proposed algorithm outperforms the heuristic algorithm and canonical genetic algorithm in finding optimal solutions.     

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城市综合交通枢纽评价能否客观有效,将对城市综合交通系统的规划和发展产生重大影响。因此,本文重新整合数据包络分析法（DEA）,在传统CCR模型基础上,补充决策单元（DMU）排序方法,使其不但能够指出各决策单元是否有效及无效的原因和程度,同时得到各决策单元的优先排序。并将该方法应用到城市综合交通枢纽评价领域,不仅得到各枢纽或同一枢纽的不同规划方案的排序值,而且找到DEA无效枢纽或方案所存在的症结和改进的方向。最后,通过实例验证了该方法的可行性。     

基于小波变换的交通流数据集成灵敏度分析

道路上的交通流数据通常是以20-30秒间隔采集并传递到交通管理中心的，为了使这些数据能够广泛地运用于各种交通用途，必须采用合理高效的集成方法对其进行处理。传统的数据集成方法基于数理统计的理论，该方法不能有效地去除数据中的噪音。近年来迅速发展的基于小波变换的交通流数据集成方法克服了这一缺点，通过对交通流数据的频率特性进行详细分析能够使集成更加高效，准确满足不同的交通用途对集成的需求。但该方法尚未作进一步的灵敏度分析，本文通过对数据集成中小波函数和数据样本量这两个重要参数的分析，从理论的角度探讨了这两个参数变化对数据集成的影响。然后以北京市三环上的交通流数据为例，计算得到了数据集成建议采用的小波族和小波阶数，以及合理的最小样本量。该结论对数据集成在将来的实际应用具有指导意义。     

Estimating metro passengers’ path choices by combining self-reported revealed preference and smart card data

With the help of automated fare collection systems in the metro network, more and more smart card (SC) data has been widely accumulated, which includes abundant information (i.e., Big Data). However, its inability to record passengers’ transfer information and factors affecting passengers’ travel behaviors (e.g., socio-demographics) limits further potential applications. In contrast, self-reported Revealed Preference (RP) data can be collected via questionnaire surveys to include those factors; however, its sample size is usually very small in comparison to SC data. The purpose of this study is to propose a new set of approaches of estimating metro passengers’ path choices by combining self-reported RP and SC data. These approaches have the following attractive features. The most important feature is to jointly estimate these two data sets based on a nested model structure with a balance parameter by accommodating different scales of the two data sets. The second feature is that a path choice model is built to incorporate stochastic travel time budget and latent individual risk-averse attitude toward travel time variations, where the former is derived from the latter and the latter is further represented based on a latent variable model with observed individual socio-demographics. The third feature is that an algorithm of combining the two types of data is developed by integrating an Expectation-Maximization algorithm and a nested logit model estimation method. The above-proposed approaches are examined based on data from Guangzhou Metro, China. The results show the superiority of combined data over single data source in terms of both estimation and forecasting performance.     

Understanding travellers’ preferences for different types of trip destination based on mobile internet usage data

New mobility data sources like mobile phone traces have been shown to reveal individuals’ movements in space and time. However, socioeconomic attributes of travellers are missing in those data. Consequently, it is not possible to partition the population and have an in-depth understanding of the socio-demographic factors influencing travel behaviour. Aiming at filling this gap, we use mobile internet usage behaviour, including one’s preferred type of website and application (app) visited through mobile internet as well as the level of usage frequency, as a distinguishing element between different population segments. We compare the travel behaviour of each segment in terms of the preference for types of trip destinations. The point of interest (POI) data are used to cluster grid cells of a city according to the main function of a grid cell, serving as a reference to determine the type of trip destination. The method is tested for the city of Shanghai, China, by using a special mobile phone dataset that includes not only the spatial-temporal traces but also the mobile internet usage behaviour of the same users. We identify statistically significant relationships between a traveller’s favourite category of mobile internet content and more frequent types of trip destinations that he/she visits. For example, compared to others, people whose favourite type of app/website is in the “tourism” category significantly preferred to visit touristy areas. Moreover, users with different levels of internet usage intensity show different preferences for types of destinations as well. We found that people who used mobile internet more intensively were more likely to visit more commercial areas, and people who used it less preferred to have activities in predominantly residential areas.     

User-centric interdependent urban systems: Using time-of-day electricity usage data to predict morning roadway congestion

Urban systems are interdependent as individuals’ daily activities engage using those urban systems at certain time of day and locations. There may exist clear spatial and temporal correlations among usage patterns across all urban systems. This paper explores such a correlation among energy usage and roadway congestion. We propose a general framework to predict congestion starting time and congestion duration in the morning using the time-of-day electricity use data from anonymous households with no personally identifiable information. We show that using time-of-day electricity data from midnight to early morning from 322 households in the City of Austin, can make reliable prediction of congestion starting time of several highway segments, at the time as early as 2 am. This predictor significantly outperforms a time-series predictor that uses only real-time travel time data up to 6 am. We found that 8 out of the 10 typical electricity use patterns have statistically significant affects on morning congestion on highways in Austin. Some patterns have negative effects, represented by an early spike of electricity use followed by a drastic drop that could imply early departure from home. Others have positive effects, represented by a late night spike of electricity use possible implying late night activities that can lead to late morning departure from home.     

A comparative study on data segregation for mesoscopic energy modeling

On-road vehicles have been considered as one of the major contributors to energy consumption and air pollutant emissions. In order to quantify the corresponding environmental impacts, great efforts have been dedicated to the microscopic and macroscopic modeling for vehicle energy consumption and emissions. However, the mesoscopic modeling research that is focused on estimating trip-based energy consumption and is critical to some ITS applications (e.g., environmentally-friendly navigation), is relatively deficient. This study aims to investigate the effects of different data segregation methods on the mesoscopic modeling for vehicle energy consumption. A variety of novel methods, including the so-called conditional operating mode based method, have been proposed and evaluated using field data. Based on real-world data, statistical analyses have demonstrated the superior performance of enhanced models (i.e., conditional operating mode/VSP based models) in estimating vehicle energy consumption on a trip basis, compared to the other four models (velocity binning, time snipping, distance snipping and VSP based models) tested in this study.