高速公路立交区驾驶员视点分布特征研究

为研究在不同匝道类型情况下驾驶员的视点分布规律,用Smart EyePro5.7型非侵入式眼动仪记录了在不同情况下驾驶员的眼球运动数据,并进行分析,主要采用的指标为瞳孔直径、XY视角、注视次数百分数、注视时间百分比、平均注视时间。结果表明:对于左转弯匝道,环圈式匝道给驾驶员的舒适感要好于半定向式匝道;环圈式匝道视点分布偏向于匝道内侧区域;在半定向式匝道的左转段和右转段内,驾驶员的心理负荷和视点分布较相似,视点都关注前方和左侧区域,并不受半定向式匝道内转向变化的影响。     

5kW汽油发电机组排气消声器改进设计

汽、柴油发电机组作为一种辅助的发电装置应用日渐广泛，但其产生的噪声特别是排气噪声，对环境产生极大污染，为人们生活带来不便。因此，本文对一款5kW汽油发电机组正常：工作时所产生的排气噪声作了详细测量及分析，找出了原装消声器的不足之处，并进行了改进设计，后对其性能进行了测试，取得良好效果。     

轿车车内空气污染源的分析及其空气品质的评价方法

对轿车内的污染源作了详细的分析,并由此选择了11种污染物作为车内空气品质客观评价的评价因子。对客观评价的方法做出了规范,并给出了数据的处理办法。提出了用客观评价和主观评价相结合的办法来评价轿车内的空气品质。     

基于声固耦合非线性系统的汽车车内噪声计算研究

采用具有线性和非线性连接子结构的自由界面模态综合法，建立整车系统声固耦合非线性动力学模型。用该模型对发动机激励产生的车内噪声进行数值仿真，并通过试验对仿真结果进行验证。     

ADSL接入网与上海地铁通信网建设

介绍了复双绞线并采用ＡＤＳＬ技术的接入网基本原理与接口方式，并根据上海地铁通信系统的要求，提出了采和双绞线作为物理介质的接入网人步行了铁通信系统接入网的过渡方案，并对其实现提出了具体的方案。     

Large-scale application of MILATRAS: case study of the Toronto transit network

This paper documents the efforts to operationalize the conceptual framework of MIcrosimulation Learning-based Approach to TRansit Assignment (MILATRAS) and its component models of departure time and path choices. It presents a large-scale real-world application, namely the multi-modal transit network of Toronto which is operated by the Toronto Transit Commission (TTC). This large-scale network is represented by over 500 branches with more than 10,000 stops. About 332,000 passenger-agents are modelled to represent the demand for the TTC in the AM peak period. A learning-based departure time and path choice model was adopted using the concept of mental models for the modelling of the transit assignment problem. The choice model parameters were calibrated such that the entropy of the simulated route loads was optimized with reference to the observed route loads, and validated with individual choices. A Parallel Genetic Algorithm engine was used for the parameter calibration process. The modelled route loads, based on the calibrated parameters, greatly approximate the distribution underlying the observed loads. 75% of the exact sequence of transfer point choices were correctly predicted by the off-stop/on-stop choice mechanism. The model predictability of the exact sequence of route transfers was about 60%. In this application, transit passengers were assumed to plan their transit trip based on their experience with the transportation network; with no prior (or perfect) knowledge of service performance.     

Analysis of the environmental benefits of a motorcycle idling stop policy at urban intersections

In an attempt to reduce CO₂ emissions from motorized transport, the Taiwanese government introduced an idling stop policy for vehicles in early 2007. This paper seeks to quantify the environmental benefits of the policy based on a stated preference analysis. Motorcyclists were surveyed at urban intersections in Taiwan, to identify the amount of time they would be willing to turn off their engines while waiting at traffic lights (the WTO). A contingent valuation framework based on stated preference questions was designed to determine the WTO. Results obtained from the Spike model showed that the average motorcyclist’s WTO is 82 s. In another analysis, in which other variables were taken into consideration, such as the possibility that the policy will be enacted as legislation, the expected WTO increased to 101 s. In both cases, an idling stop policy would have positive environmental effects, reducing gasoline usage by 1021 L per hour and reducing CO₂ emissions by 0.56 metric tons per hour at the intersection studied during peak periods.     

Estimating the value of risk reductions for car drivers when pedestrians are involved: a case study in Spain

We estimated the benefits associated with reducing fatal and severe injuries from traffic accidents using a stated choice experiment where choice situations were generated through a statistically efficient design. Specifically, the risk variables were defined as the expected annual number of vehicle car-users that suffered their death or were severely injured in a traffic accident. In addition, and differing from previous research, the number of pedestrians that died or were severely injured in traffic accidents per year was also included as a risk attribute in the choice experiment, to attempt at measuring drivers’ willingness to pay to reduce the risk of hitting pedestrians in a crash. The empirical setting was a choice of route for a particular trip that a sample of car drivers periodically undertakes in Tenerife, Spain. Models were estimated accounting for random taste heterogeneity and pseudo-panel data correlation. The median of the distribution of simulated parameters was used to obtain a representative measure for the monetary valuation of risk reductions. We found that the ratio between the values of reducing the risk of suffering a serious injury and that of reducing a fatality was approximately 18 %. Further, and quite novel, we also found that the value of reducing a pedestrian fatality was 39 % of the value of reducing a car occupant fatality.     

Are we ready to embrace connected and self-driving vehicles? A case study of Texans

While connected, highly automated, and autonomous vehicles (CAVs) will eventually hit the roads, their success and market penetration rates depend largely on public opinions regarding benefits, concerns, and adoption of these technologies. Additionally, the introduction of these technologies is accompanied by uncertainties in their effects on the carsharing market and land use patterns, and raises the need for tolling policies to appease the travel demand induced due to the increased convenience. To these ends, this study surveyed 1088 respondents across Texas to understand their opinions about smart vehicle technologies and related decisions. The key summary statistics indicate that Texans are willing to pay (WTP) $2910, $4607, $7589, and $127 for Level 2, Level 3, and Level 4 automation and connectivity, respectively, on average. Moreover, affordability and equipment failure are Texans’ top two concerns regarding AVs. This study also estimates interval regression and ordered probit models to understand the multivariate correlation between explanatory variables, such as demographics, built-environment attributes, travel patterns, and crash histories, and response variables, including willingness to pay for CAV technologies, adoption rates of shared AVs at different pricing points, home location shift decisions, adoption timing of automation technologies, and opinions about various tolling policies. The practically significant relationships indicate that more experienced licensed drivers and older people associate lower WTP values with all new vehicle technologies. Such parameter estimates help not only in forecasting long-term adoption of CAV technologies, but also help transportation planners in understanding the characteristics of regions with high or low future-year CAV adoption levels, and subsequently, develop smart strategies in respective regions.     

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.