希望花儿开 朵朵放光彩

莘莘的学子,当你迈着矫健的步伐前行,有多少人在为你默默地祝福,有多少人在为你保驾护航,在你迈向康庄大道的途中,还有无数默默为你奉献的,素昧平生的可人儿,那些奉献大爱的人们……不见不散的约定一座大山,一架小桥,一片爱心,一次凝聚,永恒的瞬间被幸福地定格     

虽千万桥吾往矣——纪念首座“茅以升公益桥”竣工

当爱心小桥在重庆市彭水自治县竣工的那一刻,孩子们笑了,村民们笑了,志愿者们笑了,为小桥付出心血的人们都笑了。那一刻是满足,那一刻是凝聚,那一刻是传承,那一刻是心灵的共鸣。"道之所在,虽千万人吾往矣。"道之所在,虽千万桥吾往矣。修桥过程是枯燥的、艰难的,但想到人心所向的小桥能够造福一方百姓,关注小桥工程的爱心人士们都会勇往直前,     

我国寒带地区客运车型的选择

一、安全性的原则 寒带地区由于车内外温差大,车内空气水分含量高,所以非常容易在车辆的前风挡,司机窗上出现结霜的情况,从而会影响司机的视野清晰度;由于风雪较多,还会出现后视镜积雪的情况,都会影响到司机的视野,进而影响到安全性.同时寒冷的气候会导致制动管路结冰,影响行车的安全性.     

网络话题

热心的网友总能提出各种各样的问题,当然,我们不能苛求网友提的问题能"一针见血",或者,总能提出一些新问题。不过,有时候,网友确实是"与时俱进"了,他们根据正在发生的事情,提出了自己的问题,但还是有人觉得这些问题是老问题,那么,症结就不在网友,而在环境了。大型汽车集团对客车总是有"野心",有"想法",但也好像不是很重视,于是,他们在客车行业中,姿态甚高,但成绩却并不理想,而且颇有反反复复的态势。今年客车行业整体态势不错,有几个汽车集团,也在今年高调进军,成绩如何,看网友如何议论。     

借助"比较优势"再攀运输高峰——河北省道路运输管理局局长于志伟专访

2003年以来,河北省道路运输管理部门结合行业实际,提出了"抓重点,促发展,抓科技,上档次,抓重点,求突破"的发展思路,明确了"以人为本",依法行政,安全稳定,科学发展"的发展理念.     

做中国公交文化捍卫者——访中国城市公共交通博物馆筹建人侯翔

一个秋日的午后,打开紧闭的房门,映入眼帘的是满地的纸张、公交车照片和堆砌的书籍、文件,房间不大,却盛满各种各样的收藏,有大的、小的,厚的、薄的,彩色的、黑白的:还有显露的、尘封的…….     

关于道路货物运输三个关键问题的探讨

改革开放以来,道路货物运输行业以市场化改革为导向,从无到有,从小到大,从弱到强,初步形成了多种经济成分并存、服务种类齐全的运输体系,为社会经济可持续发展奠定了坚实的基础。但是,随着货物运输行业市场化程度的不断深入,道路运输的管理模式、发展模式均受到了一定的挑战,尤其是随着燃油税费改革的实施,这个问题更加凸显。如何在新的形势,分析道路货物运输行业的关键问题,转变管理模式,厘清管理思路,进而促进道路货物运输的发展,已成为道路运输管理机构亟待正视的问题。     

杨传堂履新

在8月1日召开的交通运输部领导干部大会上,中组部王尔乘副部长对杨传堂有一段介绍。他说,杨传堂同志政治上坚定,经过多岗位特别是复杂艰苦环境的锻炼,领导经验丰富,熟悉党务和经济工作。思路清晰,组织领导和驾驭全局的能力比较强。敢于负责,处事果断,推动工作力度大。事业心强,工作热情高,重视调查研究。作风民主,善于听取各方面意见,注意调动大家的积极性。为人正派,团结同志,要求自己严格。中央认为,杨传堂同志是交通运输部主要领导的合适人选。     

历史大道的惊鸿一瞥——探寻北京西山古道

冉冉时光,悠悠岁月。中国,历史悠久的文明古国,在蹉跎与峥嵘的洗礼下,造就了极为丰富的文化遗产,而这些遗产是沉淀、是智慧、是财富,是各族人民精神的传承。当下,保护、管理、利用好祖国的文化遗产成为这代人维系中华民族血脉,弘扬优秀传统文化的历史使命。在近期全国第三次全国文物普查百大新发现评选活动中,经过普查机构的遴选、推荐,以投票方式选出     

桥之所至 心之所安

<正>当城市的孩子们坐着轿车去上学的时候,在边远贫困地区,有一群和他们同样如花年龄的孩子,或踩着水中的石头,或溜着跨江的钢索,或伏在老师的背上,正跋涉在艰难的上学路上……这些孩子随时都可能上演生死考验,有些孩子甚至付出了生命的代价——2011年11月4日,在海南屯昌县乌坡镇一名15岁花季少女小敏,在她每天上学必经的断桥处被河水冲走,永远告别了她的学校。     

Probabilistic model for destination inference and travel pattern mining from smart card data

Transportation - Inferring trip destination in smart card data with only tap-in control is an important application. Most existing methods estimate trip destinations based on the continuity of trip...     

Isolating high-priority metro and feeder bus transfers using smart card data

Fixed-rail metro (or ‘subway’) infrastructure is generally unable to provide access to all parts of the city grid. Consequently, feeder bus lines are an integral component of urban mass transit systems. While passengers prefer a seamless transfer between these two distinct transportation services, each service’s operations are subject to a different set of factors that contribute to metro-bus transfer delay. Previous attempts to understand transfer delay were limited by the availability of tools to measure the time and cost associated with passengers’ transfer experience. This paper uses data from smart card systems, an emerging technology that automatically collects passenger trip data, to understand transfer delay. The primary objective of this study is to use smart card data to derive a reproducible methodology that isolates high priority transfer points between the metro system and its feeder-bus systems. The paper outlines a methodology to identify transfer transactions in the smart card dataset, estimate bus headways without the aid of geographic location information, estimate three components of the total transfer time (walking time, waiting time, and delay time), and isolate high-priority transfer pairs. The paper uses smart card data from Nanjing, China as a case study. The results isolate eight high priority metro-bus transfer pairs in the Nanjing metro system and finally, offers several targeted measures to improve transfer efficiency.     

Estimation of a route choice model for urban public transport using smart card data

This paper describes a logit model of route choice for urban public transport and explains how the archived data from a smart card-based fare payment system can be used for the choice set generation and model estimation. It demonstrates the feasibility and simplicity of applying a trip-chaining method to infer passenger journeys from smart card transactions data. Not only origins and destinations of passenger journeys can be inferred but also the interchanges between the segments of a linked journey can be recognised. The attributes of the corresponding routes, such as in-vehicle travel time, transfer walking time and to get from alighting stop to trip destination, the need to change, and the time headway of the first transportation line, can be determined by the combination of smart card data with other data sources, such as a street map and timetable. The smart card data represent a large volume of revealed preference data that allows travellers' behaviour to be modelled with higher accuracy than by using traditional survey data. A multinomial route choice model is proposed and estimated by the maximum likelihood method, using urban public transport in ?ilina, the Slovak Republic, as a case study     

Bus arrival time calculation model based on smart card data

Bus arrival time is usually estimated using the boarding time of the first passenger at each station. However, boarding time data are not recorded in certain double-ticket smart card systems. As many passengers usually swipe the card much before their alighting, the first or the average alighting time cannot represent the actual bus arrival time, either. This lack of data creates difficulties in correcting bus arrival times. This paper focused on developing a model to calculate bus arrival time that combined the alighting swiping time from smart card data with the actual bus arrival time by the manual survey data. The model was built on the basis of the frequency distribution and the regression analysis. The swiping time distribution, the occupancy and the seating capacity were considered as the key factors in creating a method to calculate bus arrival times. With 1011 groups of smart card data and 360 corresponding records from a manual survey of bus arrival times, the research data were divided into two parts stochastically, a training set and a test set. The training set was used for the parameter determination, and the test set was used to verify the model’s precision. Furthermore, the regularity of the time differences between the bus arrival times and the card swiping times was analyzed using the “trend line” of the last swiping time distribution. Results from the test set achieved mean and standard error rate deviations of 0.6% and 3.8%, respectively. The proposed model established in this study can improve bus arrival time calculations and potentially support state prediction and service level evaluations for bus operations.     

Estimation method for railway passengers’ train choice behavior with smart card transaction data

Smart card systems have become the predominant method of collecting public transport fares in Japan. Transaction data obtained through smart cards have resulted in a large amount of archived information on how passengers use public transportation. The data have the potential to be used for modeling passenger behavior and demand for public transportation. This study focused on train choices made by railway passengers. If each passenger’s train choice can be identified over a long period of time, this information would be useful for improving the customer relationship management of the railway company and for improving train timetables. The aim of this study was to develop a methodology for estimating which train is boarded by each smart card holder. This paper presents a methodology and an algorithm for estimation using long-term transaction data. To validate the computation time and accuracy of the estimation, an empirical analysis is carried out using actual transaction data provided by a railway company in Japan. The results show that the proposed method is capable of estimating passenger usage patterns from smart card transaction data collected over a long time period.     

Geodemographic analysis and the identification of potential business partnerships enabled by transit smart cards

Smart card automated fare payment systems are being adopted by transit agencies around the world. The data-storage characteristics of smart cards present novel opportunities to enhance transit services. On the one hand, there are fare policies, where smart card holders are given specific rebates on the use of the service based on usage patterns or levels. On the other, there are non-fare policies, for instance if holders receive advantages, such as rebates and offers, from commercial partners. The purpose of this paper is to present a geodemographic framework to identify potential commercial partnerships that could exploit the characteristics of smart cards. The framework is demonstrated using data from Montreal, Canada. Household survey data, specifically trip ends, and business data points are jointly used to determine the exposure of various types of establishments to users of the Montreal Metro network. Spatial analysis of business establishments in the neighborhood of metro stations helps to identify potential commercial partners. The results illustrate the potential of geodemographic analysis to generate intelligence of commercial interest.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

Hierarchical evaluation scheme on technology sourcing for advanced public transport systems

Advanced public transport system (APTS) technologies have received much attention from industry researchers in recent years for their evident importance to economic growth. The development of critical APTS technology, such as the contact-less smart card (CSC), in newly industrialized areas receives its impetus from the experience of developed countries. The evaluation of technology sourcing with a higher growth potential in CSC technology has become a critical issue for Taiwanese firms. However, past research rarely emphasized it. This paper utilizes the grey statistical method with survey techniques and the analytic hierarchy process to develop an integrated evaluation model for solving the technology-sourcing problem. An empirical case of the CSC technology sourcing in Taiwan was chosen to demonstrate the application of the proposed model on this issue. The research results suggest that the application of the model provides a sensible path for company policy makers to effectively cope with the technology-sourcing evaluation problem.     

Inferring patterns in the multi-week activity sequences of public transport users

The public transport networks of dense cities such as London serve passengers with widely different travel patterns. In line with the diverse lives of urban dwellers, activities and journeys are combined within days and across days in diverse sequences. From personalized customer information, to improved travel demand models, understanding this type of heterogeneity among transit users is relevant to a number of applications core to public transport agencies’ function. In this study, passenger heterogeneity is investigated based on a longitudinal representation of each user’s multi-week activity sequence derived from smart card data. We propose a methodology leveraging this representation to identify clusters of users with similar activity sequence structure. The methodology is applied to a large sample (n = 33,026) from London’s public transport network, in which each passenger is represented by a continuous 4-week activity sequence. The application reveals 11 clusters, each characterized by a distinct sequence structure. Socio-demographic information available for a small sample of users (n = 1973) is combined to smart card transactions to analyze associations between the identified patterns and demographic attributes including passenger age, occupation, household composition and income, and vehicle ownership. The analysis reveals that significant connections exist between the demographic attributes of users and activity patterns identified exclusively from fare transactions.     

Impact evaluation of a mass transit fare change on demand and revenue utilizing smart card data

Transit fares are an effective tool for demand management. Transit agencies can raise revenue or relieve overcrowding via fare increases, but they are always confronted with the possibility of heavy ridership losses. Therefore, the outcome of fare changes should be evaluated before implementation. In this work, a methodology was formulated based on elasticity and exhaustive transit card data, and a network approach was proposed to assess the influence of distance-based fare increases on ridership and revenue. The approach was applied to a fare change plan for Beijing Metro. The price elasticities of demand for Beijing Metro at various fare levels and trip distances were tabulated from a stated preference survey. Trip data recorded by an automatic fare collection system was used alongside the topology of the Beijing Metro system to calculate the shortest path lengths between all station pairs, the origin–destination matrix, and trip lengths. Finally, three fare increase alternatives (high, medium, and low) were evaluated in terms of their impact on ridership and revenue. The results demonstrated that smart card data have great potential with regard to fare change evaluation. According to smart card data for a large transit network, the statistical frequency of trip lengths is more highly concentrated than that of the shortest path length. Moreover, the majority of the total trips have a length of around 15 km, and these are the most sensitive to fare increases. Specific attention should be paid to this characteristic when developing fare change plans to manage demand or raise revenue.