Multiobjective optimisation of bogie suspension to boost speed on curves

To improve safety and maximum admissible speed on different operational scenarios, multiobjective optimisation of bogie suspension components of a one-car railway vehicle model is considered. The vehicle model has 50 degrees of freedom and is developed in multibody dynamics software SIMPACK. Track shift force, running stability, and risk of derailment are selected as safety objective functions. The improved maximum admissible speeds of the vehicle on curves are determined based on the track plane accelerations up to 1.5?m/s². To attenuate the number of design parameters for optimisation and improve the computational efficiency, a global sensitivity analysis is accomplished using the multiplicative dimensional reduction method (M-DRM). A multistep optimisation routine based on genetic algorithm (GA) and MATLAB/SIMPACK co-simulation is executed at three levels. The bogie conventional secondary and primary suspension components are chosen as the design parameters in the first two steps, respectively. In the last step semi-active suspension is in focus. The input electrical current to magnetorheological yaw dampers is optimised to guarantee an appropriate safety level. Semi-active controllers are also applied and the respective effects on bogie dynamics are explored. The safety Pareto optimised results are compared with those associated with in-service values. The global sensitivity analysis and multistep approach significantly reduced the number of design parameters and improved the computational efficiency of the optimisation. Furthermore, using the optimised values of design parameters give the possibility to run the vehicle up to 13% faster on curves while a satisfactory safety level is guaranteed. The results obtained can be used in Pareto optimisation and active bogie suspension design problems.     

Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle’s dynamics and improve stability and curving performance. A half-car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H_∞ control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball-screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second-order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one-car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.     

Direct generation of level of service maps from images using convolutional and long short-term memory networks

Congestion in transport stations could result in stampede development and deadly crush situations. Closed circuit television (CCTV) cameras enable station managers to monitor the crowd and reduce overcrowding risks. However, identifying congestion conditions is a very laborious task for a human operator who has to monitor multiple locations at the same time. This paper presents a new approach to automated image-based identification of congestion as measured by level of service (LOS), which is the most widely accepted standard for measuring congestion. Existing methods for measuring LOS based on crowd density estimation from images have the disadvantages that, crowd density cannot be estimated accurately. In addition, the calculation of flow parameters involves a complex process, and consequently these parameters are not indicative of congestion in real-time. This paper proposes a novel method based on machine learning to directly classify LOS without calculating flow parameters. In the proposed method, visual features extracted by a deep convolutional neural network are classified using a support vector machine classifier and the classification results are further refined by using a long short-term memory network. A second contribution of this research is to develop a web-based LOS map visualization platform to monitor pedestrian distribution and variation of distribution in real-time. Experimental evaluation at Flinders Street Station in Melbourne shows that this method can achieve an accuracy of 81.9% and efficiency of 0.40?seconds per frame in LOS classification using CCTV images.     

The role of socio-economic,built environment and psychological factors in parental mode choice for their children in an Iranian setting

This research investigated the role of parental psychological and socio-economic factors as well as built environment for the choice of their children’s (primary school pupils, aged 7–9 years) travel mode to school in Rasht, Iran. A total of 1078 questionnaires were distributed (return rate of 80 percent) among pupils in nine primary schools in January 2014. A mixed logit (ML) model was employed due to its ability to test heterogeneity among parents and also to determine its possible sources. Results of random coefficient ML modelling showed that several psychological, socio-economic and built environment characteristics were significant factors in parental mode choice. Only walking time perception to school had a significant random normal distribution coefficient and no other psychological and socio-economic variable had a random effect. Further investigation by random coefficient analysis showed that the possible source of household preference heterogeneity could be to own two or more cars. Regarding psychological variables, strong parental worry about their children walking alone to school had a negative impact on allowing them to walk to school. Parents who evaluated poor contextual and design preconditions for walking tended to choose school service more than private car and walking. Parents with stronger environmental personal norms were more willing to allow their children to walk. The findings suggest that infrastructural measures, such as sidewalk facilities, neighborhood security and safety, encourage parents to allow children to walk to school. Information campaigns targeting environmental norms may increase walking among pupils in an Iranian setting.     

Pedestrian crowd tactical‐level decision making during emergency evacuations

This paper investigates pedestrian crowd tactical‐level decision making during emergency evacuations. Of particular interest is crowd exit‐choice behaviour. Two sources of stated choice data are collected and combined. One data set is derived from an experiment linked to a real‐life exit choice experience of participants (in a non‐evacuation setting). We examine aspects that have often been taken for granted in the literature in connection with egress behaviour of crowds during emergencies. We quantify evacuees' trade‐off between “distance”, “density”, “exit visibility” and “directional density” as well as the interactive effect between exit visibility and tendency to follow others. A comprehensive random‐utility analysis is conducted ranging from traditionally practiced models to the state‐of‐the‐practice methods such as random‐coefficient nested logit. Our findings suggest that (i) unless evacuees face certain levels of uncertainty in the escape environment; flows of crowd are unlikely to be followed. Otherwise, most evacuees perceive other individuals as potential sources of congestion and extra delay (generalisation to situations where crowd is completely unfamiliar with the egress geometry, however, may require careful scrutiny). (ii) Evacuees mostly prefer visible exits over the exits whose congestion level is unknown to them (i.e. the tendency to minimise ambiguity). (iii) The presence of attribute uncertainty (e.g. exit visibility) significantly changes the impact of observing decisions of others on each individual choice maker. We also found out that (iv) spatial distribution of exits has a significant influence on evacuees' decisions (presenting itself in the form of violating the IIA assumption). (v) The marginal weights that different individuals place upon attributes of exits are significantly heterogeneous. (vi) There is meaningful correlation between certain utility weights of individual evacuees. These behavioural findings can provide significant behavioural insight essential for safe evacuation planning and accurate forecast of evacuees' behaviour. Copyright © 2016 John Wiley & Sons, Ltd.     

Robustness analysis of bogie suspension components Pareto optimised values

Bogie suspension system of high speed trains can significantly affect vehicle performance. Multiobjective optimisation problems are often formulated and solved to find the Pareto optimised values of the suspension components and improve cost efficiency in railway operations from different perspectives. Uncertainties in the design parameters of suspension system can negatively influence the dynamics behaviour of railway vehicles. In this regard, robustness analysis of a bogie dynamics response with respect to uncertainties in the suspension design parameters is considered. A one-car railway vehicle model with 50 degrees of freedom and wear/comfort Pareto optimised values of bogie suspension components is chosen for the analysis. Longitudinal and lateral primary stiffnesses, longitudinal and vertical secondary stiffnesses, as well as yaw damping are considered as five design parameters. The effects of parameter uncertainties on wear, ride comfort, track shift force, stability, and risk of derailment are studied by varying the design parameters around their respective Pareto optimised values according to a lognormal distribution with different coefficient of variations (COVs). The robustness analysis is carried out based on the maximum entropy concept. The multiplicative dimensional reduction method is utilised to simplify the calculation of fractional moments and improve the computational efficiency. The results showed that the dynamics response of the vehicle with wear/comfort Pareto optimised values of bogie suspension is robust against uncertainties in the design parameters and the probability of failure is small for parameter uncertainties with COV up to 0.1.     

Application of particle swarm optimization and robust net present value for BOT-type contracts

Major infrastructure construction projects contracted to private companies by governments are important for maximizing profitability. This paper extends an existing build–operate–transfer (BOT) concession model (BOTCcM) for identifying the reasonable concession period which would be profitable both to the government and to the private sector. There are some major limitations with BOTCcM – for example, the total investment cost is pre-given and the impact of uncertainty of parameters affecting the concession period were not considered. In this research, the total investment cost is assumed as variable which should be optimally determined and the uncertainty of net cash flows is considered. Further, the proposed model is implemented to calculate the robust concession period and required capital for the construction period, using the obtained values and particle swarm optimization method.     

Merging ICT and informal transport in Jakarta's ojek system

This paper discusses how information and communication technologies (ICT) may influence travel behavior of an informal transport system, and whether and in which ways merging digital technologies represents disruption for a specific sociotechnical ensemble. For decades, ojek has been a form of informal passenger transport, based on motorcycles in Jakarta, and recently smartphone apps have become available in that sociotechnical arrangement. We randomly surveyed passengers and drivers in the central district, for both those who do and do not use these apps. Despite many similarities between both groups, the drivers who use smartphones for their trips reported higher daily incomes, longer average trip distances and a larger coverage area. Passengers using conventional ojek transport reported lower levels of both safety perception and satisfaction when compared to app-based ojek users. For both categories of user, ojek seems to compete with, complement, and be an alternative to bus rapid transit and the suburban rail system (KRL) in the area covered by our sample. The areas covered by regular buses and vans roughly match the origins and destinations of ojek trips.     

Active school travel: homogeneity or heterogeneity? That is the question

ABSTRACT

To explain and predict active school travel (AST), most studies have not investigated to what extent considering taste heterogeneity is an important influence on AST share. The main aim of the present study was to evaluate whether considering unobserved taste heterogeneity through mixed logit models – including random coefficient and random coefficient analysis (RCA) – materially improves/influences the AST prediction compared to a simpler model – the multinomial logit (MNL) model. The database comprises 735 valid observations. The results show that, with a 10% increase in perceived walking time to school, the MNL model predicts that the AST share would decrease by 7.8% (from 18.9% to 17.4%) while the RCA model predicts that it would decrease by 8.5% (from 18.9% to 17.3%). Thus, the expected share of AST is overestimated by MNL by one-tenth of a percentage point. Although there might be random taste variations around perceived distance to school, it seems the other important policy-sensitive variables, such as safety perception, homogeneously impacts on the AST share across households with different socioeconomic and built environment characteristics. Our empirical assessment suggests that considering taste heterogeneity does not necessarily improve the accuracy of analysis for the aggregate share of the AST concerning policy-sensitive variables.     

“I’ll never stop driving my child to school”: on multimodal and monomodal car users

Transportation - This study investigates the behavior of multimodal and monomodal car users in school trips using 1 week of travel information of 735 Iranian students between 7 and...