Disturbance Observer-Based Sideslip Angle Control for Improving Cornering Characteristics of In-Wheel Motor Electric Vehicles

In this paper, a robust sideslip angle controller based on the direct yaw moment control (DYC) is proposed for in-wheel motor electric vehicles. Many studies have demonstrated that the DYC is one of the effective methods to improve vehicle maneuverability and stability. Previous approaches to achieve the DYC used differential braking and active steering system. Not only that, the conventional control systems were commonly dependent on the feedback of the yaw rate. In contrast to the traditional control schemes, however, this paper proposes a novel approach based on sideslip angle feedback without controlling the yaw rate. This is mainly because if the vehicle sideslip angle is controlled properly, the intended sideslip angle helps the vehicle to pass through the corner even at high speed. On the other hand, the vehicle may become unstable because of the too large sideslip caused by unexpected yaw disturbances and model uncertainties of time-varying parameters. From this aspect, disturbance observer (DOB) is employed to assure robust performance of the controller. The proposed controller was realized in CarSim model described actual electric vehicle and verified through computer simulations.     

Effects of Residual Ash on Dpf Capture and Regeneration

To study the effects of residual ash on the capture and regeneration of a diesel particulate filter (DPF), repeated capture and complete regeneration experiments were conducted. An engine exhaust particulate sizer was used to measure the particle size distribution of diesel in the front and back of DPF. Discrepancies in the size distribution of the particulate matter in repeated trapping tests were analyzed. To achieve complete DPF regeneration, a DPF regeneration system using nonthermal plasma technology was established. The regeneration carbon removal mass and peak temperatures of DPF internal measuring points were monitored to evaluate the effect of regeneration. The mechanism explaining the influence of residual ash on DPF capture and regeneration was thoroughly investigated. Results indicate that the DPF trapping efficiencies of the nuclear-mode particles and ultrafine particles have significant improvements with the increase quantity of residual ash, from 90 % and 96.01 % to 94.17 % and 97.27 %, respectively. The exhaust backpressure of the DPF rises from 9.41 kPa to 11.24 kPa. Heat transfer in the DPF is improved with ash, and the peak temperatures of the measuring points accordingly increase. By comparing the regeneration trials, the elapsed time for complete regeneration and time difference for reaching the peak temperature between adjacent reaction interfaces are extended with increased quantity of ash. The carbon removal mass rises by 34.00 %.     

Tire wear estimation based on nonlinear lateral dynamic of multi-axle steering vehicle

This paper presents a novel nonlinear dynamic model of a multi-axle steering vehicle to estimate the lateral wear amount of tires. Firstly, a 3DOF nonlinear vehicle dynamic model is developed, including dynamic models of the hydropneumatic suspension, tire, steering system and toe angle. The tire lateral wear model is then built and integrated into the developed vehicle model. Based on the comparison of experimental and simulation results, the nonlinear model is proved to be better than a linear model for the tire wear calculation. In addition, the effects of different initial toe angles on tire wear are analyzed. As simulation results shown, the impact of the dynamic toe angle on the tire wear is significant. The tire wear amount will be much larger than that caused by normal wear if the initial toe angle increases to 1° - 1.5°. The results also suggest that the proposed nonlinear model is of great importance in the design and optimazation of vehicle parameters in order to reduce the tire wear.     

Characteristics of premium transit services that affect mode choice

This research seeks to improve the understanding of the full range of determinants for mode choice behavior and to offer practical solutions to practitioners on representing and distinguishing these characteristics in travel demand forecasting models. The principal findings were that the representation of awareness of transit services is significantly different than the underlying assumption of mode choice and forecasting models that there is perfect awareness and consideration of all modes. Furthermore, inclusion of non-traditional transit attributes and attitudes can improve mode choice models and reduce bias constants. Additional methods and analyses are necessary to bring these results into practice. The work is being conducted in two phases. This paper documents the results of Phase I, which included data collection for one case study city (Salt Lake City), research and analysis of non-traditional transit attributes in mode choice models, awareness of transit services, and recommendations for bringing these analyses into practice. Phase II will include data collection for two additional case study cities (Chicago and Charlotte) with minor modifications based on limitations identified in Phase I, additional analyses where Phase I results indicated a need, and a demonstration of the research in practice for at least one case study city.     

The cost saving potential of carsharing in a US context

Carsharing is a vehicle sharing service for those with occasional need of private transportation. Transportation planners are beginning to see great potential for carsharing in helping to create a more diversified and sustainable transport system. While it has grown quickly in the US in recent years, it is still far from the level where it can deliver significant aggregate benefits. A key element to the potential growth of carsharing is its ability to provide cost savings to those who adopt it in favor of vehicle ownership. This research seeks to quantify these potential cost savings. The costs of carsharing and vehicle ownership are compared based on actual vehicle usage patterns from a large survey of San Francisco Bay Area residents. The results of this analysis show that a significant minority of Bay Area households own a vehicle with a usage pattern that carsharing could accommodate at a lower cost. Further research is required to indentify how these cost savings translate to the adoption of carsharing.     

An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Surveys of behavior could benefit from information about people’s relative ranking of choice alternatives. Rank ordered data are often collected in stated preference surveys where respondents are asked to rank hypothetical alternatives (rather than choose a single alternative) to better understand their relative preferences. Despite the widespread interest in collecting data on and modeling people’s preferences for choice alternatives, rank-ordered data are rarely collected in travel surveys and very little progress has been made in the ability to rigorously model such data and obtain reliable parameter estimates. This paper presents a rank ordered probit modeling approach that overcomes limitations associated with prior approaches in analyzing rank ordered data. The efficacy of the rank ordered probit modeling methodology is demonstrated through an application of the model to understand preferences for alternative configurations of autonomous vehicles (AV) using the 2015 Puget Sound Regional Travel Study survey data set. The methodology offers behaviorally intuitive model results with a variety of socio-economic and demographic characteristics, including age, gender, household income, education, employment and household structure, significantly influencing preference for alternative configurations of AV adoption, ownership, and shared usage. The ability to estimate rank ordered probit models offers a pathway for better utilizing rank ordered data to understand preferences and recognize that choices may not be absolute in many instances.     

Exploring the impact of walk–bike infrastructure,safety perception,and built-environment on active transportation mode choice: a random parameter model using New York City commuter data

This study estimates a random parameter (mixed) logit model for active transportation (walk and bicycle) choices for work trips in the New York City (using 2010–2011 Regional Household Travel Survey Data). We explored the effects of traffic safety, walk–bike network facilities, and land use attributes on walk and bicycle mode choice decision in the New York City for home-to-work commute. Applying the flexible econometric structure of random parameter models, we capture the heterogeneity in the decision making process and simulate scenarios considering improvement in walk–bike infrastructure such as sidewalk width and length of bike lane. Our results indicate that increasing sidewalk width, total length of bike lane, and proportion of protected bike lane will increase the likelihood of more people taking active transportation mode This suggests that the local authorities and planning agencies to invest more on building and maintaining the infrastructure for pedestrians. Further, improvement in traffic safety by reducing traffic crashes involving pedestrians and bicyclists, will increase the likelihood of taking active transportation modes. Our results also show positive correlation between number of non-motorized trips by the other family members and the likelihood to choose active transportation mode. The model would be an essential tool to estimate the impact of improving traffic safety and walk–bike infrastructure which will assist in investment decision making.     

Effects of built environment and weather on bike sharing demand: a station level analysis of commercial bike sharing in Toronto

Bike Share Toronto is Canada’s second largest public bike share system. It provides a unique case study as it is one of the few bike share programs located in a relatively cold North American setting, yet operates throughout the entire year. Using year-round historical trip data, this study analyzes the factors affecting Toronto’s bike share ridership. A comprehensive spatial analysis provides meaningful insights on the influences of socio-demographic attributes, land use and built environment, as well as different weather measures on bike share ridership. Empirical models also reveal significant effects of road network configuration (intersection density and spatial dispersion of stations) on bike sharing demands. The effect of bike infrastructure (bike lane, paths etc.) is also found to be crucial in increasing bike sharing demand. Temporal changes in bike share trip making behavior were also investigated using a multilevel framework. The study reveals a significant correlation between temperature, land use and bike share trip activity. The findings of the paper can be translated to guidelines with the aim of increasing bike share activity in urban centers.     

Impacts of urban built environment on empty taxi trips using limited geolocation data

This study identifies the determinants of the empty taxi trip duration (ETTD) by combining three high-resolution databases—geolocation data in New York City, geodatabase of urban planning data, and transportation facilities data. Considering the nature of duration data, hazard-based duration model is proposed to explore the relationships between causal factors and ETTD, coupling with three variations of baseline hazard distribution, i.e., Weibull distribution with heterogeneity, Weibull distribution, and log-logistic. Furthermore, the likelihood ratio test is presented to implement comparisons of three baseline hazard distributions, as well as spatial and temporal transferability of causal factors. The results show significant complementary effects by subway system and competitive effects by city bus and bicycling system, as well as significant impacts of trip length, airport trip, average annual income, and employment rate. Urban built environment, for instance, density of road, public facilities, and recreational sites and ratio of green space, has various impacts on ETTD. The elasticity estimations confirm significant spatial and temporal heterogeneity in impacts on ETTD. In addition, the analysis on elasticity also reveals the considerable impacts of severe traffic congestion on ETTD within Manhattan. The modeling can assist stakeholders in understanding empty taxi movements and measuring taxi system efficiency in urban areas.     

Comparison on travel scheduling between driving and walking trips by habitual car users

Research on walking behavior has become increasingly more important in the field of transportation in the past decades. However, the study of the factors influencing the scheduling decisions related to walking trips and the exploration of the differences between travel modes has not been conducted yet. This paper presents a comparison of the scheduling and rescheduling decisions associated with car driving trips and walking trips by habitual car users using a data set collected in Valencia (Spain) in 2010. Bivariate probit models with sample selection are used to accommodate the influence of pre-planning on the decision to execute a travel as pre-planned or not. The explicative variables considered are: socio-economic characteristics of respondents, travel characteristics, and facets of the activity executed at origin and at destination including the scheduling decisions associated with them. The results demonstrate that a significant correlation exists between the choices of pre-planning and rescheduling for both types of trips. Whether for car driving or walking trips, the scheduling decisions associated with the activity at origin and at destination are the most important explicative factors of the trip scheduling and rescheduling decisions. However, the rescheduling of trips is mainly influenced by modifications in the activity at destination. Some interesting differences arise regarding the rescheduling decision processes between travel modes: if pre-planned, walking trips are less likely to be modified than car driving trips, showing a more rigid rescheduling behavior.