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由于在模拟密集网络、宏观问题和方案阶段中的优势，连续性模拟方法应用到交通模型的模拟中，正在得到越来越多的关注。此论文意在为二维连续模拟方法的发展和应用提供综合的概括。首先讨论的是理论的发展和对特殊的或多变领域机理的模拟，然后再针对连续模拟方法应用到工厂选址、道路选择、人行道的规划、政策和社会经济分析进行评论。最后，预测其未来研究的前景。     

小鼠10号染色体上致聋突变基因hml的精确定位(英文)

目的　定位小鼠致聋基因 ,识别决定其性状的有关突变 ,为人类耳聋基因研究提供动物模型。方法　利用全基因组扫描来定位名为hml可致小鼠听力丧失突变基因。结果　①hml基因定位在小鼠 10号染色体上 ,距中心粒约4 3cM处。根据已知的鼠 人同源同线性特点 ,提示人的同源基因位于 12 q2 2 -q2 4 ;②获得了 2 5个多态性微卫星标记 ,通过高分辨的小鼠图谱将 3个已知人类基因进行了正确排列 ,并将hml侯选基因限定在一个 5 0 0kb的区域内。     

城市高架道路的中日设计规范比较

在深入推进新型城镇化,中心城市和城市群正成为承载发展要素主要空间形态的背景下,解决城市交通拥堵问题更加具有时代紧迫性.城市高架桥具有便于形成网络化布局和与既有城市道路紧密融合的优点,但有一些有别于公路桥梁的特点.通过对中日两国在城市高架桥梁方面的设计规范进行比较,找出日本在建设城市高架桥梁方面所采用的一些方法.对提出根据设计速度决定桥梁最大纵坡、采用面分布车道荷载、根据作用种类采用不同抗倾覆安全系数、强化铺装层内排水提高耐久性等问题进行探讨.     

Accelerated Service Life Testing of Automotive Vehicles on a Test Course

The accelerated service life testing of automotive vehicles for durability to road roughness induced dynamic loads is often accomplished in the laboratory using road roughness simulation facilities [1-5]. However, such tests can also be accomplished by a carefully designed field operation on a test course [6], where both the speed of the vehicle and the roughness of die test course become variables that control the degree of the test acceleration. Field tests are generally harder to control than laboratory tests, but offer a greater degree of realism since the vehicle is fully operational during the test exactly as it will be in service. This paper formulates the criteria for accelerated service life tests on a test course, evaluates the assumptions that must be enforced to obtain valid results, and explores the sensitivity of the results to the critical test parameters, namely, the vehicle speed and the road roughness severity of the test course relative to the service environment.     

New methodology for fast prediction of wheel wear evolution

In railway applications wear prediction in the wheel–rail interface is a fundamental matter in order to study problems such as wheel lifespan and the evolution of vehicle dynamic characteristic with time. However, one of the principal drawbacks of the existing methodologies for calculating the wear evolution is the computational cost. This paper proposes a new wear prediction methodology with a reduced computational cost. This methodology is based on two main steps: the first one is the substitution of the calculations over the whole network by the calculation of the contact conditions in certain characteristic point from whose result the wheel wear evolution can be inferred. The second one is the substitution of the dynamic calculation (time integration calculations) by the quasi-static calculation (the solution of the quasi-static situation of a vehicle at a certain point which is the same that neglecting the acceleration terms in the dynamic equations). These simplifications allow a significant reduction of computational cost to be obtained while maintaining an acceptable level of accuracy (error order of 5–10%). Several case studies are analysed along the paper with the objective of assessing the proposed methodology. The results obtained in the case studies allow concluding that the proposed methodology is valid for an arbitrary vehicle running through an arbitrary track layout.     

Engine clutch torque estimation for parallel-type hybrid electric vehicles

This paper mainly focuses on the accurate estimation of the torque transferred through the engine clutch installed between the engine and the drive motor in parallel-type hybrid electric vehicles. The estimation of the engine clutch torque primarily relies on the forward-direction observer which uses the nominal engine net torque information. To overcome the limitation of using the nominal engine torque information that it may not be accurate during the transient states or due to the influence of external disturbance such as the road condition and wind, the forward-direction observer is supplemented by the use of reverse-direction observer which uses the driveline model and wheel speed measurements. In addition, the drive motor torque information is used to calibrate the nominal engine torque during the idle charging state, so that the driveline characteristic unique to parallel-type hybrid electric vehicle can be utilized to increase the estimation accuracy. Finally, the estimation performance of the designed observer is tested via simulation and experiments based on a real vehicle.     

Adaptive control of the shifting process in automatic transmissions

This paper focuses on the way of keeping shift quality of automatic transmissions consistent in mass production and with mileage accumulation. We investigate the main factors influencing the consistency of shift quality. Test results show that the torque to pressure (T2P) and pressure to current (P2I) characteristics of shifting elements are easily affected. A simulation model of an 8-speed automatic transmission is established to simulate the dynamic process of clutch-to-clutch shift. Simulation results demonstrate that the change of T2P and P2I characteristics has a significant influence on shift quality. In order to compensate for the influences, we develop two adaptive control strategies, i.e., the adaptive control strategies for torque phase and inertia phase. They make use of the measured speed information and time information to evaluate shift quality. Then the control parameters are tuned to adapt to the change of T2P and P2I characteristics. Vehicle tests verify that the developed adaptive control strategies are effective to keep shift quality consistent in mass production and with mileage accumulation.     

In-depth understanding of lane changing interactions for in-vehicle driving assistance systems

Lane-changing events are often related with safety concern and traffic operational efficiency due to complex interactions with neighboring vehicles. In particular, lane changes in stop-and-go traffic conditions are of keen interest because these events lead to higher risk of crash occurrence caused by more frequent and abrupt vehicle acceleration and deceleration. From these perspectives, in-depth understanding of lane changes would be of keen interest in developing in-vehicle driving assistance systems. The purpose of this study is to analyze vehicle interactions using vehicle trajectories and to identify factors affecting lane changes with stop-and-go traffic conditions. This study used vehicle trajectory data obtained from a segment of the US-101 freeway in Southern California, as a part of the Next Generation Simulation (NGSIM) project. Vehicle trajectories were divided into two groups; with stop-and-go and without stop-and-go traffic conditions. Binary logistic regression (BLR), a well-known technique for dealing with the binary choice condition, was adopted to establish lane-changing decision models. Regarding lane changes without stop-and-go traffic conditions, it was identified based on the odd ratio investigation that he subject vehicle driver is more likely to pay attention to the movement of vehicles ahead, regardless of vehicle positions such as current and target lanes. On the other hand, the subject vehicle driver in stop-and-go traffic conditions is more likely to be affected by vehicles traveling on the target lane when deciding lane changes. The two BLR models are adequate for lane-changing decisions in normal and stop-and-go traffic conditions with about 80 % accuracy. A possible reason for this finding is that the subject vehicle driver has a tendency to pay greater attention to avoiding sideswipe or rear-end collision with vehicles on the target lane. These findings are expected to be used for better understanding of driver’s lane changing behavior associated with congested stop-and-go traffic conditions, and give valuable insights in developing algorithms to process sensor data in designing safer lateral maneuvering assistance systems, which include, for example, blind spot detection systems (BSDS) and lane keeping assistance systems (LKAS).     

Lubricating performance of carbon nanotubes in internal combustion engines – engine test results for CNT enriched oil

The main purpose of this research is to reduce friction losses by adding carbon nanotubes to engine oil. Extremely favorable tribological properties of carbon nanotubes have been extensively studied on the microscopic scale and using tribometers, have not yet been verified in the engine. Enriching oil with nanotubes can lead to significant, exceeding 7 %, reduction in the motoring torque of the engine at low crankshaft rotational speed. The phenomena associated with the dispersion of carbon nanotubes in the engine are stated and discussed. It has been shown that the oil shear during normal operation of the engine can effectively improve the dispersion of nanotubes. At the same time the oil filtration system removes agglomerates of nanotubes very quickly.     

Design of emergency braking algorithm for pedestrian protection based on multi-sensor fusion

Globally, safety has become an increasingly important issue in the automotive industry. In an attempt to reduce traffic fatalities, UNECE launched a new EU Road Safety Program which aims to decrease the number of road deaths by half by 2020. AEB (Autonomous Emergency Braking) is a very effective active safety system intended to reduce fatalities. This study involves the design of a multi-sensor data fusion strategy and decision-making algorithm for AEB pedestrian. Possible collision avoidance scenarios according to the EuroNCAP protocol are analyzed and a robust pedestrian tracking strategy is proposed. The performance of the AEB system is enhanced by using a braking model to predict the collision avoidance time and by designing the system activation zone according to the relative speed and possible distance required to stop for pedestrians. The AEB activation threshold requires careful consideration. The test results confirm the advantages of the proposed algorithm, the performance of which is described in this paper.