Methods for pre-processing smartcard data to improve data quality

In recent years smartcards have been implemented in many transit systems around the world as a means by which passengers pay for travel. In addition to allowing speedier boardings there are many secondary benefits of smartcard systems including better understanding of travel patterns and behaviour of travellers. Such research is dependent on the smartcard correctly recording the boarding stop, and where available the alighting stop. It is also dependent on the smartcard system correctly aggregating individual rides into trips.This paper identifies causes for why smartcard systems may not correctly record such information. The first contribution of the paper is to propose a set of rules to aggregate individual rides into a single trip. This is critical in the research of activity based modelling as well as for correctly charging the passenger. The second contribution of the paper is to provide an approach to identify erroneous tap-out data, either caused by system problems or by the user. An approach to detecting this phenomenon is provided. The output from this analysis is then used to identify faulty vehicles or data supply using the “comparison against peers approach”. This third contribution of the paper identifies where transit agencies and operators should target resources to improve performance of their Automatic Vehicle Location systems. This method could also be used to identify users who appear to be tapping out too early.The approaches are tested using smartcard data from the Singapore public transport network from one week in April 2011. The results suggest that approximately 7.7% of all smartcard rides recorded the passenger as alighting one stop before the bus stop that they most probably alighted at. A further 0.7% of smartcard rides recorded the passenger as alighting more than one stop before the bus stop that they most probably alighted at. There was no evidence that smartcards overestimated the distance travelled by the passenger.     

Trip generation modeling using data collected in single and repeated cross‐sectional surveys

The majority of US metropolitan regions still use the four‐step urban transportation modeling system to develop their travel forecasts. Trip generation, the first step of this system, has as objective of predicting the expected total travel demand in a region. The commonly used methods in planning practice for predicting this expected total travel demand typically use only the most recent cross‐sectional data available from a study region for model development, which ties the resulting travel‐forecast model to the economic environment prevailing at the time of data collection. Applying such models to generate forecasts of travel in economic environments significantly different from those embodied in the estimated model parameters could result in greater errors than would otherwise be the case. To address the aforementioned problem, this paper proposes the development of trip generation models estimated on multiple independent cross‐sectional datasets collected in the same urban region but at different times representing different economic environments. Data used in the research were collected in cross‐sectional household travel behavior surveys undertaken in the Greater Toronto Area, Canada in 1986, 1996, 2001, and 2006. The results lead to the conclusion that well‐specified models, estimated on pooled multiple cross‐sectional datasets, yield travel predictions in the base and horizon years, respectively, that have smaller error compared with corresponding travel predictions generated with single cross‐sectional models. Copyright © 2012 John Wiley & Sons, Ltd.     

高铁运行计划自动调整系统的研究与实现

目前列车运行线的调整主要依靠人工完成。但随着铁路建设的不断发展.列车的密度不断增加,运行线手动调整的结果往往不能满足预期需求,特别是在极端恶劣天气或是设备故障的情况下。本文根据大量工作经验.总结并分析了运行线自动调整的应用场景,研究实现了一套运行线自动调整系统。     

Implicit choice set generation in discrete choice models: Application to household auto ownership decisions

Latent choice set models that account for probabilistic consideration of choice alternatives during decision making have long existed. The Manski model that assumes a two-stage representation of decision making has served as the standard workhorse model for discrete choice modeling with latent choice sets. However, estimation of the Manski model is not always feasible because evaluation of the likelihood function in the Manski model requires enumeration of all possible choice sets leading to explosion for moderate and large choice sets. In this study, we propose a new group of implicit choice set generation models that can approximate the Manski model while retaining linear complexity with respect to the choice set size. We examined the performance of the models proposed in this study using synthetic data. The simulation results indicate that the approximations proposed in this study perform considerably well in terms of replicating the Manski model parameters. We subsequently used these implicit choice set models to understand latent choice set considerations in household auto ownership decisions of resident population in the Southern California region. The empirical results confirm our hypothesis that certain segments of households may only consider a subset of auto ownership levels while making decisions regarding the number of cars to own. The results not only underscore the importance of using latent choice models for modeling household auto ownership decisions but also demonstrate the applicability of the approximations proposed in this study to estimate these latent choice set models.     

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有效的乘务调度能够为公交企业带来巨大的成本节约,但是,公交乘务调度问题因受制于一系列劳动法规的约束变得十分复杂.我国公交普遍存在"中式用餐"约束,进一步加大了问题的复杂性,使西方主流调度系统在国内实施面临困难.本文基于"生成与选择"方法解决乘务调度问题,关键在于"生成"阶段处理"中式用餐"难题;利用"中式用餐"约束和乘务问题特点,设计一种基于启发式规则的换班机会筛选方法;在所选换班机会集合的基础上构造能满足"中式用餐"约束的潜在乘务班次集合.对实际公交乘务调度问题中的12组实例进行测试,表明本文方法不仅能处理"中式用餐"约束,而且能极大减少所求问题的规模,因此适用于解决大规模的带有"中式用餐"约束的乘务调度问题.     

基于时间步长自适应技术的海上浮基风电平台绕流数值模拟分析

圆柱绕流问题对于研究海上浮基风电平台在波浪和海流作用下的动力特性以及开发深海风能具有重要的理论和工程应用价值,很多不可压缩流体力学数值模拟方法都基于圆柱绕流的实验或计算结果进行验证。基于自适应时间步长理论及小雷诺数（Re=100）情况下,采用有限体积法,借助FLUENT软件中的用户自定义（UDF）功能,通过二次开发在FLUENT中实现圆柱绕流的数值模拟,并对计算结果与先前的研究结果作了比较,依此来验证时间步长自适应技术和精细边界层网格设计的合理性。计算结果表明了该方法能有效获得准确的流体动力学参数并提高数值模拟计算精度,为该领域的深入研究提供依据。     

基于PID的船舶航向控制

文章根据实船参数设计出PID自动舵,并在此基础上对控制器进行了模糊控制优化,在simulink环境下进行了仿真检验,最后对仿真结果进行了比较。结果表明,PID船舶航向控制器具有更强控制性和稳定性。     

Comparison of pedestrian trip generation models

Using Poisson regression and negative binomial regression, this paper presents an empirical comparison of four different regression models for the estimation of pedestrian demand at the regional level and finds the most appropriate model with reference to the National Household Travel Survey (NHTS) 2001 data for the Baltimore (USA) region. The results show that Poisson regression seems to be more appropriate for pedestrian trip generation modeling in terms of χ² ratio test, Pseudo R², and Akaike's information criterion (AIC). However, R² based on deviance residuals and estimated log‐likelihood value at convergence confirmed the empirical studies that negative binomial regression is more appropriate for the over‐dispersed dependent variable than Poisson regression. Copyright © 2011 John Wiley & Sons, Ltd.     

Forecasting Travel Demand with Alternatively Structured Models of Trip Frequency

Abstract

This paper develops alternatively structured trip frequency/generation models, and investigates their forecast performance. The first model presented is the simple linear model with a discussion of its theoretical shortcomings. Models that address, in a progressive fashion, the underlying shortcomings of the linear model are then presented. These models are namely the truncated normal model, the Poisson model, the negative binomial model, and an ordered logit model. The modeling unit employed in the study is the individual. The models are assessed by how closely they are able to replicate trips produced by each individual in the dataset, and by each traffic zone. This assessment of performance in prediction is conducted on an estimation dataset collected in the Toronto Region in 1986, and on an independent dataset collected in the same geographic region, 10 years later, in 1996. The results show that, notwithstanding the simplicity of the simple linear model and its lack of an explicit underlying travel behavioral theory, it predicts travel in the base and forecast years with less error compared to any of the more complex models.