Effect of intake valve swirl on fuel-gas mixing and subsequent combustion in a CAI engine

A fully three-dimensional model was used to investigate the optimal value for intake valve lift in a CAI engine. Uniform mixing in the engine is a key parameter that affects the auto-ignition reliability and thermal efficiency. The method of intake of the air supply often determines the uniformity (or quality) of the fuel-air mixture. In this paper, four strategies were applied for controlling the swirl intensity of intake air. The variation of the intake valve lift induces different swirling and tumbling intensities. Both experimental data and 1D WAVE software (Ricardo, Co.) were coupled with the 3D model to provide pressure and temperature boundary conditions. The initial condition of the EGR mass fraction was also provided by the 1D model. The benchmark scenario (Case 1) was considered as a valve lift with 2 mm for all intake valves. We found that an intake valve lift of 6 mm with the other intake valve closed (i.e., Case 5) yielded the largest swirling (helical motion in the axial direction) and tumbling, which in turn rendered optimal fuel-gas mixing. We also found that fuel distribution affected the auto-ignition sites (or spot). The better the mixing, the greater the gas temperature and combustion efficiency achieved, as seen in Case 5. The NOx level, however, was increased due to the gas temperature. The optimal operating condition is selected from the viewpoints of environmental protection and combustion efficiency.     

Explaining the “immigrant effect” on auto use: the influences of neighborhoods and preferences

Since immigrants will account for most urban growth in the United States for the foreseeable future, better understanding their travel patterns is a critical task for transportation and land use planners. Immigrants initially travel in personal vehicles far less than the US-born, even when controlling for demographics, but their reliance on autos increases the longer they live in the US. Cultural or habitual differences, followed by assimilation to auto use, could partly explain this pattern; and it may also be partly due to changes in locations and characteristics of home and work neighborhoods. Previous studies have rarely investigated non-work travel, and have not tested workplace land use measures, compared the relative influences of enclave and home neighborhood measures, or looked at the role of culturally-bound residential preferences or motivations for migration. This study relies on a unique and rich dataset consisting of a survey of US residents born in South Asia, Latin America, and the US, joined to spatial information in a GIS. I find that the home built environment is the most consistently influential factor in explaining the lower auto use of both recent and settled Latin American immigrants. Indian immigrants use autos less than would be expected given their home and work neighborhoods. There is little evidence that either ethnic enclaves, or cultural differences, play a role in lower auto use by immigrants. These results suggest there may be a role for neighborhood built environment policies in delaying immigrant assimilation to auto use in the US.     

A meta-model of vehicle ownership choice parameters

This paper builds a meta-model of vehicle ownership choice parameters to predict how their values might vary across extended periods as a function of macroeconomic variables. Multinomial logit models of vehicle ownership are estimated from repeated cross-sectional data between 1971 and 1996 for large urban centers in Ontario. Three specifications are tested: a varying constants (VC) model where the alternative specific constants are allowed to vary each year; a varying scales (VS) model where the scale parameter varies instead; and a varying scales and constants model. The estimated parameters are then regressed on macroeconomic variables (e.g., employment rate, gas prices, etc.). The regressions yield good fit and statistically significant results, suggesting that changes in the macroeconomic environment influence household decision making over time, and that macroeconomic information could potentially help predict how model parameters evolve. This implies that the common assumption of holding parameters constant across forecast horizons could potentially be relaxed. Furthermore, using a separate validation dataset, the predictive power of the VC and VS models outperform conventional approaches providing further evidence that pooling data from multiple periods could also produce more robust models.     

Accounting for travel time variability in the optimal pricing of cars and buses

A number of studies have shown that in addition to travel time and cost as the common influences on mode, route and departure time choices, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean–variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars, buses and walking as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes.     

Introducing shared life experience metric in urban planning

Transportation - Historically cities are formed to provide interaction and communication opportunities for communities. As cities become smarter, new forms of interactions are formed and the...     

Study on State Parameter Control of Intake Air in Utility Tunnels Based on BB-MOPSO北大核心CSCD

In order to explore the control method of the intake air state in utility tunnels when the outside fresh air humidity is too high, taking the ventilation works of a utility tunnel section in Pingtan comprehensive experimental area as the research background, this paper proposes the control method of intake air state parameters for ventilation and dehumidification in utility tunnels under normal and accident conditions, by means of the effective ventilation and dehumidification duration prediction model, the evolutionary multi-objective algorithm (EMOA), and in combination with the ventilation control requirements. The results show that the intake air state parameter controller can be adjusted freely according to the different requirements of decision makers for ventilation fan working duration and dehumidifier working temperature and moisture content, and the optimized appropriate control objectives can be achieved, which is conducive to the real-time decision-making of the intake air state parameter control for ventilation and dehumidification in utility tunnels. © 2022, Editorial Office of Modern Tunnelling Technology. All right reserved.     

Multi‐criteria analysis for evaluating the impacts of intelligent speed adaptation

Road safety is a policy priority due to the high casualties and costs associated with road accidents. Since speed is a major cause of road accidents, in‐vehicle speed limiters or Intelligent Speed Adaptation (ISA), seems a promising solution. ISA implementation, however, is hindered by large uncertainties, for example about the impacts of ISA, the way users might respond to ISA, and the relationship between speed and accidents. Traditional Multi‐Criteria Analysis (MCA) has limitations in handling these uncertainties. We present an MCA approach based on exploratory modeling, which uses computational experiments to explore the multiple outcomes of ISA policies (safety, emissions, throughput, and cost) across a range of future demand scenarios, functional relationships for performance criteria, and user responses to ISA. As an illustration, by testing the impacts of different ISA penetration levels on two driver groups, we show that when compliance with ISA is expected to be low, a policy aimed only at novice drivers outperforms other ISA policies on safety improvement.     

When strike comes to town… anticipated and actual behavioural reactions to a one-day,pre-announced,complete rail strike in the Netherlands

We conducted secondary analysis on data collected among rail users, days before and after a national rail strike in the Netherlands. Our aim was to compare anticipated and actual behavioural reactions to the rail strike, investigate associations with traveller and trip characteristics, and perceived behavioural control and satisfaction with the chosen alternative. Forty-four percent of the people who had anticipated to travel by train on the day of the strike abandoned their trip, 24% switched to car as driver, 14% switched to another mode (as passenger), 18% stayed with the train and rescheduled the planned activity to another day. Almost half of people who had anticipated travelling by car expected to change behaviour as well. Multinomial logistic regression showed low preference for car among rail users. Considerable marginal effects were found for several variables, e.g.: young people and females were less likely to switch to car; short and middle distance trips were less likely abandoned or switched to another day; commute and business trips were more likely done by car, and business trips less likely cancelled. Despite high levels of perceived behavioural control and satisfaction with the chosen alternative, permanent modal shift as result of this strike is not expected.     

GPS-based speed collection method for road traffic noise mapping

One of the most difficult and expensive tasks in making noise pollution maps is the collection and processing of the data needed to create acoustic models. In the case of road traffic noise maps, obtaining speed data for light and heavy vehicles a problem that has usually been avoided by using a road’s speed limit or by making assumptions based on experience from similar road types. Here global positioning systems-based techniques are applied for acquiring vehicle speed data and adapted to fulfill the requirements of noise prediction models.     

Causal linkages between highways and sector-level employment

While transport infrastructure investments have usually been viewed to have long-term impacts on employment, what is perhaps not immediately clear is the direction of causality. This paper has sought to disentangle the causal relationship between highway infrastructure and employment, using panel data for the 48 contiguous US states from 1984 to 1997. Of particular emphasis in this analysis is the sectoral differences in the causal and spatial effects of highway capacity expansions for employment growth in alternative sectors of the economy. The results indicate that lane-mile additions of own-state major highways could increase state employment growth in the services sector while reducing growth in manufacturing. However, the causal relationship is also found to work the other way around. That is, both the rapid growth in services employment and the slowdown in manufacturing jobs temporally lead to increases in roadway capacity of non-interstate major roads. Our analysis also shows that highway infrastructure could produce both positive and negative employment spillovers across states. We find that improvements in non-interstate major roads outside the state border are beneficial to the manufacturing sector which generally serves regional and national markets. For the services sector, however, employment gains from interstate highways in the same state may come at the expense of other states as there is clear evidence of negative employment spillovers from interstate lane-mile additions.