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.     

Dynamic demand estimation and prediction for traffic urban networks adopting new data sources

Nowadays, new mobility information can be derived from advanced traffic surveillance systems that collect updated traffic measurements, both in fixed locations and over specific corridors or paths. Such recent technological developments point to challenging and promising opportunities that academics and practitioners have only partially explored so far.The paper looks at some of these opportunities within the Dynamic Demand Estimation problem (DDEP). At first, data heterogeneity, accounting for different sets of data providing a wide spatial coverage, has been investigated for the benefit of off-line demand estimation. In an attempt to mimic the current urban networks monitoring, examples of complex real case applications are being reported where route travel times and route choice probabilities from probe vehicles are exploited together with common link traffic measurements.Subsequently, on-line detection of non-recurrent conditions is being recorded, adopting a sequential approach based on an extension of the Kalman Filter theory called Local Ensemble Transformed Kalman Filter (LETKF).Both the off-line and the on-line investigations adopt a simulation approach capable of capturing the highly nonlinear dependence between the travel demand and the traffic measurements through the use of dynamic traffic assignment models. Consequently, the possibility of using collected traffic information is enhanced, thus overcoming most of the limitations of current DDEP approaches found in the literature.     

State estimation in roll dynamics for commercial vehicles

Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.     

Design of tyre force excitation for tyre–road friction estimation

Knowledge of the current tyre–road friction coefficient is essential for future autonomous vehicles. The environmental conditions, and the tyre–road friction in particular, determine both the braking distance and the maximum cornering velocity and thus set the boundaries for the vehicle. Tyre–road friction is difficult to estimate during normal driving due to low levels of tyre force excitation. This problem can be solved by using active tyre force excitation. A torque is added to one or several wheels in the purpose of estimating the tyre–road friction coefficient. Active tyre force excitation provides the opportunity to design the tyre force excitation freely. This study investigates how the tyre force should be applied to minimise the error of the tyre–road friction estimate. The performance of different excitation strategies was found to be dependent on both tyre model choice and noise level. Furthermore, the advantage with using tyre models with more parameters decreased when noise was added to the force and slip ratio.     

基于响应面和遗传算法的柴油机瞬态过程喷油参数优化

基于台架试验数据,利用响应面法建立了某工程机械用柴油机瞬态过程喷油参数与性能的近似高精度模型,基于此模型采用遗传算法对瞬态过程喷油参数分别进行离线优化研究。结果表明:采用单目标优化确定的燃油消耗率(BSFC)、NO_x比排放量和颗粒质量(PM)比排放量的优化极限分别可达180.23g/(kW·h),8.92g/(kW·h)和0.011 8g/(kW·h),相对原机可降低多达4.5%,34.0%和37.3%。双目标优化的Pareto解集表明,相比于同时优化BSFC和NO_x比排放量,BSFC和PM比排放量更容易同时得到优化。采用权重因子适应度函数的三目标优化结果对应的BSFC,NO_x比排放量及PM比排放量分别为184.70g/(kW·h),12.62g/(kW·h)和0.012 2g/(kW·h),较原机分别降低2.1%,6.6%和35.3%。改进优化模型后,性能优化Pareto解集对应的BSFC和PM比排放量水平都非常接近其优化极限,但NO_x比排放量相对其优化极限仍然较高。     

Power network parameter estimation method based on data mining technology

The parameter values which actually change with the circumstances, weather and load level etc.produce great effect to the result of state estimation. A new parameter estimation method based on data mining technology was proposed. The clustering method was used to classify the historical data in supervisory control and data acquisition (SCADA) database as several types. The data processing technology was impliedto treat the isolated point, missing data and yawp data in samples for classified groups. The measurement data which belong to each classification were introduced to the linear regression equation in order to gain the regression coefficient and actual parameters by the least square method. A practical system demonstrates the high correctness, reliability and strong practicability of the proposed method.     

基于层次分析法的区间估计的车站能力查定误差因子分析研究

本文归纳总结了车站能力查定计算过程中存在的多种影响因素,深入分析了其产生原因和不可避免性。然后利用层次分析法的区间估计方法计算得出各种误差因子对计算结果的影响权重大小,并对比不同误差因子的权重区间,筛选出强误差因子,剔除弱误差因子,为精确计算车站能力值提供依据。     

船用柴油机SCR催化器载体结构参数仿真优化

利用AVL_BOOST软件建立了某船用柴油机SCR催化器仿真模型,通过响应面优化法和BBD试验设计法开展仿真试验,得到以NO_X转化率为目标函数,压降和NH_3滑移量为约束条件的回归方程。通过回归方程对SCR催化器载体结构参数进行优化,优化结果为:横截面积为0.069m~2,长度0.833m,载体孔密度为22/cm~2,涂层厚度0.01mm,载体壁厚0.15mm。     

船舶焊接常见裂纹分析及控制

船体结构焊接过程中,会出现各种不同的焊接缺陷,焊接裂纹则是最危险的一种缺陷。从船厂生产实际出发,分析船舶建造的过程中容易产生焊接裂纹的部位以及裂纹类型,对产生裂纹的原因进行“把脉分析”,进行一系列的会诊;并结合具体的案例及现场试验结果,找到真正的“病因”所在;独辟蹊径,对容易忽略的影响因素进行深入研究。结合具体的生产条件,提出切合实际、经济有效的方法,来提高焊接质量和产品结构的可靠性。     

某万吨级货船轴系液压联轴节更新安装及相关参数分析

文章从某万吨级货船轴系的液压联轴节更新安装出发,介绍液压联轴节的原理,装配及轴上的安装方法,分析影响扭矩传递能力的安装参数的计算及相互关系。为类似船舶液压联轴节的故障分析和更新安装提供参考。