Estimation of capacity utilization in world shipping

The surplus of tonnage which has characterized the shipping industry for over a decade has severely affected profitability and investment opportunities in the industry. This has prompted various schemes to reduce the surplus. A proper assessment of the size of the surplus is an essential requirement for sound policy measures. This paper, which focuses on the tanker sector, examines selected methods used to compute surplus tanker tonnage and proposes other methods. It observes that methods currently used give estimates of surplus tonnage which are basically identical to those obtained by using alternative methods. It concludes that while from 1984 the surplus tanker tonnage has been declining, from 1986 the decline has been rather small and the volume of the surplus is likely to stabilize in the next few years at more or less current levels. However, the paper points out conceptual and practical problems of determining the appropriate base periods or base operating conditions upon which surplus tonnage may be computed.     

The Bathside Bay story

In this article the author considers whether an opportunity to develop harbour facilities at a strategically located site in Harwich Harbour on the east coast of England may have been missed in the 1960s and 1970s. The complications of divided ownership and powers to develop the site are explained; and successive attempts to obtain Government approval for harbour development under section 9 of the Harbours Act 1964 (since repealed) are described. The effective operation of that control in this case, influenced at one stage by political considerations, is questioned.     

A capacity related reliability for transportation networks

A capacity related reliability for transportation networks with random link capacity is introduced. It is defined as the probability that the road network can accommodate a certain level of traffic demand, and is built on the concept of network reserve capacity. Network reserve capacity is defined as the largest multiplier applied to an existing origin-destination demand matrix that can be allocated to a transportation network in a user-optimal way without violating the link capacities. Due to large variability associated with link capacities, a probabilistic approach is adopted to model the different physical and operational factors that often degrade the capacity of roadways. A Monte Carlo simulation procedure is developed to estimate the capacity related reliability measure. Numerical results are provided to demonstrate the feasibility of the approach.     

Multimodal traveling and its impact on urban transit network design

Multimodal trip making, that is trips using a combination of several modes between origin and destination, is expected to be beneficial to society and might offer advantages to the traveler as well. This article looks at some of the implications of multi‐modality in trip making for the design of urban transit systems since these play an important role in multi‐modal transportation systems. In this respect, the article looks at the strategic design characteristics of urban transit networks, that is line density, stop density and service frequencies for the case of multimodal access to urban transit networks and for hierarchical network structures in urban transit systems. The analyses show that multimodal access does not require alternative network structures. For hierarchical network structures it is concluded that these are primarily determined by the hierarchy in demand densities and thus by hierarchy in urban structures.     

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.     

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.