Statistical modelling for ship propulsion efficiency

This paper presents a state-of-the-art systems approach to statistical modelling of fuel efficiency in ship propulsion, and also a novel and publicly available data set of high quality sensory data. Two statistical model approaches are investigated and compared: artificial neural networks and Gaussian processes (GP). The data presented is a publicly available full-scale data set, with a whole range of features sampled over a period of 2 months. We further discuss interpretations of the operational data in relation to the underlying physical system.     

Development of control logic for hydraulic active roll control system

Developed in this research is a control logic for the ARC (Active Roll Control) system that uses rotary-type hydraulic stabilizer actuators at the front and rear axles. The hydraulic components of the system were modeled in detail using AMESim, and a driving logic for the hydraulic circuit was constructed based upon the model. The performance of the driving logic was evaluated on a test bench, and it demonstrated good pressure tracking capability. The control logic was then designed with the target of reducing the roll motion of the vehicle during cornering. The control logic consists of two parts: a feedforward controller that generates anti-roll moments in response to the centrifugal force, and a feedback controller that generates anti-roll moments in order to make the roll angle to follow its target value. The developed ARC logic was evaluated on a test vehicle under various driving conditions including a slowly accelerated circular motion and a sinusoidal steering. Through the test, the ARC system demonstrated successful reduction of the roll motion under all conditions, and any discomfort due to the control delay was not observed even at a fast steering maneuver.     

Testing electric vehicle demand in ‘hybrid households’ using a reflexive survey

The debate over electric vehicles (EVs) pivots largely on issues of market demand: will consumers purchase a vehicle that provides substantially less driving range, yet can be refueled at home, than an otherwise comparable gasoline vehicle? Also, what role do other unique attributes of EVs play in the purchase decision? Most previous studies find that limited driving range is a serious market barrier; many of those same studies ignore or under-value other novel attributes. To probe these future consumer decision processes deeply and robustly, we first devised and conducted detailed, interactive and experiment-oriented interviews. Then, incorporating what we learned, we designed an innovative mail survey and administered it to 454 multi-car households in California. The four-stage mail survey included a video of EV use and recharging and other informational material, completion of a 3-day trip diary and map of activity locations, and vehicle choice experiments. In addition to propulsion systems, respondents made choices of body styles, driving ranges, and other features. We formalized and tested what we call the hybrid household hypothesis: households who choose EVs will be purposefully diversifying their vehicle holdings to achieve the unique advantages of different propulsion systems. The hypothesis is supported, given the assumptions in our experimental design. In fact, a significantly larger number of EVs are chosen than the minimum number that would support our hypothesis. We find that purchases of battery-powered EVs by hybrid households would account for between 7 and 18% of annual light duty vehicle sales in California. EVs sold to fleets and other households would be in addition to those identified by this study.     

High downforce race car vertical dynamics: aerodynamic index

Race car performance is strongly affected by aerodynamics. Due to downforce generated by the vehicle floor (i.e. diffuser), vehicle ride heights are key parameters to improve performance, and the coupling of aerodynamics and suspension is one of the key points of race car setting. This work focuses on the suspension and aerodynamic coupling from the vertical dynamics point of view. Besides road holding performance, for race cars, aerodynamic performance and stability are major factors. Downforce decreases laptime (the main performance target) but pitch instability is a non-desired effect that can happen in high downforce race cars. A new vertical dynamic performance index is proposed through the use of simulation to improve aerodynamic performance and understand the pitch instability phenomenon. This new index uses all relevant vehicle nonlinearities related to vertical dynamics and can handle a specific track profile and vehicle speed range, allowing the analysis be conducted according to a circuit specification. A previously validated Formula 3 car model was used as an example.     

Social distancing in public transport: mobilising new technologies for demand management under the Covid-19 crisis

Dense urban areas are especially hardly hit by the Covid-19 crisis due to the limited availability of public transport, one of the most efficient means of mass mobility. In light of the Covid-19 pandemic, public transport operators are experiencing steep declines in demand and fare revenues due to the perceived risk of infection within vehicles and other facilities. The purpose of this paper is to explore the possibilities of implementing social distancing in public transport in line with epidemiological advice. Social distancing requires effective demand management to keep vehicle occupancy rates under a predefined threshold, both spatially and temporally. We review the literature of five demand management methods enabled by new information and ticketing technologies: (i) inflow control with queueing, (ii) time and space dependent pricing, (iii) capacity reservation with advance booking, (iv) slot auctioning, and (v) tradeable travel permit schemes. Thus the paper collects the relevant literature into a single point of reference, and provides interpretation from the viewpoint of practical applicability during and after the pandemic.

     

Crowdsourced data for bicycling research and practice

ABSTRACT

Cities are promoting bicycling for transportation as an antidote to increased traffic congestion, obesity and related health issues, and air pollution. However, both research and practice have been stalled by lack of data on bicycling volumes, safety, infrastructure, and public attitudes. New technologies such as GPS-enabled smartphones, crowdsourcing tools, and social media are changing the potential sources for bicycling data. However, many of the developments are coming from data science and it can be difficult evaluate the strengths and limitations of crowdsourced data. In this narrative review we provide an overview and critique of crowdsourced data that are being used to fill gaps and advance bicycling behaviour and safety knowledge. We assess crowdsourced data used to map ridership (fitness, bike share, and GPS/accelerometer data), assess safety (web-map tools), map infrastructure (OpenStreetMap), and track attitudes (social media). For each category of data, we discuss the challenges and opportunities they offer for researchers and practitioners. Fitness app data can be used to model spatial variation in bicycling ridership volumes, and GPS/accelerometer data offer new potential to characterise route choice and origin-destination of bicycling trips; however, working with these data requires a high level of training in data science. New sources of safety and near miss data can be used to address underreporting and increase predictive capacity but require grassroots promotion and are often best used when combined with official reports. Crowdsourced bicycling infrastructure data can be timely and facilitate comparisons across multiple cities; however, such data must be assessed for consistency in route type labels. Using social media, it is possible to track reactions to bicycle policy and infrastructure changes, yet linking attitudes expressed on social media platforms with broader populations is a challenge. New data present opportunities for improving our understanding of bicycling and supporting decision making towards transportation options that are healthy and safe for all. However, there are challenges, such as who has data access and how data crowdsourced tools are funded, protection of individual privacy, representativeness of data and impact of biased data on equity in decision making, and stakeholder capacity to use data given the requirement for advanced data science skills. If cities are to benefit from these new data, methodological developments and tools and training for end-users will need to track with the momentum of crowdsourced data.     

Incorporating features of autonomous vehicles in activity-based travel demand model for Columbus,OH

Transportation - Autonomous vehicles (AVs) could change travel patterns of the population significantly and with the rapid improvements in AV technology, transportation planners should address AV...     

Computational model for analyzing the kinematics and compliance characteristics of a commercial vehicle’s front suspension system

This paper develops a computational model that can analyze the kinematics and compliance characteristics of the front suspension of a commercial vehicle. This computational model is called the flexible multi-body dynamic model because it is developed by interfacing the finite element model of the multi-leaf spring with the dynamic model of the front suspension. In this paper, the bump mode and roll mode tests are performed with a suspension parameter measuring device (SPMD). An excitation load for creating the bump mode and roll mode motion is applied on the left and right tires slowly in in-phase and out-of-phase modes. In the test, wheel rate, toe angle change, caster angle change, and camber angle change, which together represent the wheel alignment, are measured along with the longitudinal and lateral wheel center loci which together represent the wheel center trajectory change. The reliability of the developed computational model is verified by comparing the simulation results with the SPMD test results. The developed flexible multi-body computational model will provide useful information on kinematics and compliance characteristics in the earliest stages of the commercial vehicle design process.     

Current sensorless drive method for electric power steering

This paper proposes a current sensorless drive method by using the mathematical modeling of a surface-mounted permanent magnet synchronous motor (SPMSM) and a dead-time compensation method to reduce the calculated current error caused by differences between real output voltages and voltage commands. The proposed method is implemented by using only a processor algorithm internally, i.e., without any voltage-sensing circuit hardware, such as filtering circuits and current sensor feedback circuits. To show the effectiveness of the proposed method, MATLAB simulations are carried out with an Electric Power Steering (EPS) System containing an SPMSM, a motor drive system and a mechanical gear system. The result shows the validity of the proposed method.     

New appraisal values of travel time saving and reliability in Great Britain

Transportation - This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium...