A hybrid scheme for real‐time prediction of bus trajectories

The uncertainty associated with public transport services can be partially counteracted by developing real‐time models to predict downstream service conditions. In this study, a hybrid approach for predicting bus trajectories by integrating multiple predictors is proposed. The prediction model combines schedule, instantaneous and historical data. The contribution of each predictor as well as values of respective parameters is estimated by minimizing the prediction error using a linear regression heuristic. The hybrid method was applied to five bus routes in Stockholm, Sweden, and Brisbane, Australia. The results indicate that the hybrid method consistently outperforms the timetable and delay conservation prediction method for different route layouts, passenger demands and operation practices. Model validation confirms model transferability and real‐time applicability. Generating more accurate predictions can help service users adjust their travel plans and service providers to deploy proactive management and control strategies to mitigate the negative effects of service disturbances. Copyright © 2017 John Wiley & Sons, Ltd.     

Measuring uncertainty in discrete choice travel demand forecasting models

In transportation projects, uncertainty related to the difference between forecast and actual demand is of major interest for the decision-maker, as it can have a substantial influence on the viability of a project. This paper identifies and quantifies discrete choice model uncertainty, which is present in the model parameters and attributes, and determines its impact on risk taking for decision-making applied to a case study of the High-Speed Rail project in Portugal. The methodology includes bootstrapping for the parameter variation, a postulated triangular distribution for the mode-specific input and a probabilistic graphical model for the socio-economic input variation. In comparison to point estimates, the findings for mode shift results in a wider swing in the system, which constitutes valuable information for decision-makers. The methodology, findings and conclusions presented in this study can be generalized to projects involving similar models.     

Efficient solution algorithm for finding spatially dependent reliable shortest path in road networks

Travel times are generally stochastic and spatially correlated in congested road networks. However, very few existing route guidance systems (RGS) can provide reliable guidance services to aid travellers planning their trips with taking account explicitly travel time reliability constraint. This study aims to develop such a RGS with particular consideration of travellers' concern on travel time reliability in congested road networks with uncertainty. In this study, the spatially dependent reliable shortest path problem (SD‐RSPP) is formulated as a multi‐criteria shortest path‐finding problem in road networks with correlated link travel times. Three effective dominance conditions are established for links with different levels of travel time correlations. An efficient algorithm is proposed to solve SD‐RSPP by adaptively using three established dominance conditions. The complexities of road networks in reality are also explicitly considered. To demonstrate the applicability of proposed algorithm, a comprehensive case study is carried out in Hong Kong. The results of case study show that the proposed solution algorithm is robust to take account of travellers' multiple routing criteria. Computational results demonstrate that the proposed solution algorithm can determine the reliable shortest path on real‐time basis for large‐scale road networks. Copyright © 2016 John Wiley & Sons, Ltd.     

Wanting to travel,more or less: Exploring the determinants of the deficit and surfeit of personal travel

This study investigates the determinants of peoples desire to increase or decrease the amount of travel they do. We use data from 1,357 working commuters, residents of three different neighborhoods in the San Francisco Bay Area, California. The dependent variables are indicators of Relative Desired Mobility for ten categories of travel (short- and long-distance overall and by several mode- and purpose-specific categories). These variables are measured on a five-point ordinal scale ranging from much less to much more, through which the respondents indicated the amount of travel they want to do (in the category in question) compared to what they are doing now. Censored ordered probit models were developed for these variables, with explanatory variables including general travel attitudes, specific liking for travel in each of the same separate categories, objective and subjective measures of the amount currently traveled in each category, and personality, lifestyle, and socio-demographic characteristics. The results support the hypotheses that the liking for travel has a strong positive impact, and subjective qualitative assessments of mobility have a strong negative impact, on the desire to increase ones travel. Finally, a number of general types of effects on Relative Desired Mobility were identified, among them complementarity and substitution effects. The results of this study can provide policy makers and researchers with new and valuable insight into key principles that affect individual travel demand.     

Specification of a tour-based neighborhood shopping model

This paper presents a state-of-the practice neighborhood shopping travel demand model. The model structure is designed to incorporate decisions across five dimensions of shopping travel, including decisions of: (1) household tour frequency; (2) participating party; (3) shopping tour type; (4) mode, and (5) destination choices using a tour-based nested-logit model. As a neighborhood model, we have also captured the interrelated effects of three main factors associated with shopping travel decisions both within and outside of the neighborhood, including the residential location within the neighborhood, the neighborhood regional setting and the household structure. The model was validated using the travel data collected in three neighborhoods located in the Puget Sound region, WA. Results show that household socio-demographics have significant effects on the decisions for household tour frequency, mode and destination choices, while the characteristics of the traveling party have considerable impacts on the decisions for tour type. The level of service and the zone attractions influence decisions about mode and destination choices. The day of week variable (weekday versus weekend) is statistically significant in all models, indicating that weekday shopping travel decisions differ from weekend, across all five dimensions of interest. The paper concludes with a discussion about how the model can be used to examine policy-related neighborhood issues (e.g. accessibility).     

城市轨道交通乘客上下车行为与停站时间研究

乘客的上下车时间是城市轨道交通列车停站时间的重要组成部分,而列车停站时间关系到轨道交通系统的运行效率。以上海轨道交通1号线彭浦新村站、2号线南京东路站为背景,结合实地数据采集,分析不同客流特征下的乘客上下车行为;通过多元线性回归模型指出了乘客上下车混行对上下车时间的影响。通过定量指标的计算,对列车停站时间的组成进行分析,指出了运营管理中应关注无效停站时间在列车停站时间中过长的问题,提出在保证乘客有足够的时间完成上下车的前提下,尽量缩短列车的无效停站时间,从而提高城市轨道交通运行效率的建议。     

锈蚀钢筋与混凝土粘结性能的试验研究

鉴于现在广泛应用的拉拔式试验结果不能反映实际受弯构件中钢筋与混凝土的粘结性能,设计了梁式试件来研究钢筋与混凝土之间粘结性能.采用半干法的钢筋通电快速锈蚀法对钢筋进行锈蚀.在此基础上,对锈蚀钢筋的混凝土梁受力各阶段特征点的粘结应力及滑移值进行试验测定,拟合出粘结滑移曲线及锈蚀对粘结性能影响曲线.基于试验数据,提出了锈蚀率及锈胀裂缝宽度对钢筋与混凝土粘结强度影响系数的建议表达式.     

广珠城际铁路票制选择的思考

从广珠城际铁路的票制现状引发对广珠城际铁路储值票问题的思考。分析非接触式IC卡的应用情况、主要类型及发展趋势。探讨广珠城际铁路票制选择的方向。     

地铁列车高峰时段越站不停车问题探讨

由于各种原因,某些地铁车站存在着客流过少、站间距过短等问题.分析了地铁列车越站不停车的主要优缺点,把乘客旅行时间的总体节省作为主要考虑因素,提出了越站不停车的一般性原则;给出了站间距和车上车下乘客比例两个关键因素的表达式.以北京地铁南礼士路站为案例,实地调研客流等相关数据.计算分析后得出了南礼士路站高峰时段越站不停车有...     

长株潭城际铁路利用规划轨道交通方案研究

研究目的:长株潭城市群("3+5"城市群)城际铁路网是湖南省"两型社会"建设的先导工程,采用客运专线标准,规划长株潭轨道交通先于城际铁路网规划研究,采用城市轨道交通模式和标准。为充分整合和利用轨道交通资源,从不同客流影响、技术标准、运输组织、通过能力、设备制式及工程投资等方面对长株潭城市群城际铁路网引入长株潭地区利用规划轨道交通方案进行研究。研究结论:城际铁路利用长株潭轨道交通一方面能力不能满足运输需求;另一方面相关设备技术还不成熟,工程投资增加较大。根据长株潭城市群城际铁路网布局,近期宜采用新建长株潭城际铁路方案,城区内可适当兼顾城市轨道交通功能,规划轨道交通可根据长株潭一体化进程适时新建。