Development of a path planning system using mean shift algorithm for driver assistance

The longitudinal and lateral vehicle control techniques have been widely used in several active driver assistance systems. The adaptive cruise control, lane keeping assistant control, vehicle platooning and stop-and-go control are typical examples of the most important applications. In this study, a novel path planning method is proposed considering the driving environment such as road shape, ego vehicle and surrounding vehicles’ movement. The relative distance and velocity between the ego vehicle and surrounding vehicles are identified with respect to the predicted lane shape in front of the ego vehicle. Based on the identified information, the road shape and surrounding vehicles are mapped into the intensity image and the desired vector for the ego vehicle’s movement is determined by the maximum intensity density tracing method. The desired vehicle path is followed by the acceleration/deceleration control and the steering assist control, respectively. In order to evaluate the performance of the proposed system, simulations are conducted and compared with ACC systems.     

Derivation and validation of nonlinear governing equations for an analysis of vehicle handling

Nonlinear governing equations used to analyze the handling of a ground vehicle are derived from the Lagrange equations of motion. The derived equations are coded using VBA (Visual Basic for Applications) embedded in Microsoft’s Excel Software and simulated in the time domain using the 4th-order Runge-Kutta method. A total of six degrees of freedom are used in the equations; three of these are the directional translation, lateral translation, and yaw of a platform (unsprung) on the base of an inertial ground coordinate, and the other three are the roll, pitch, and yaw of a body (sprung) by a platform-fixed coordinate. Four driving torques and four wheel angles of all tires are used as input control parameters. A simplified Calspan tire model is adopted for the generalized forces of the equations. This is a combined model that can be used to obtain tractional (or braking) and side forces using the inputs of the directional and side-slip ratios and the vertical force. The VBA code realized in this study is validated by comparisons with trimmed equilibrium results and the test data cited in published papers. The major characteristics of this study are: (1) the coordinate systems of the equations are mixed with the inertial frame and the platform-fixed frame, and, as a result, almost all types of driving conditions with long mileages can be simulated; (2) vertical movement is eliminated due the focus on the handling analysis; (3) the body-yaw degree of freedom is separated from the platform-yaw degree of freedom; and (4) the programming is performed by VBA, which is rarely used in the vehicle dynamics field.     

新加坡南洋理工大学地下技术及岩土工程项目研究进展

以新加坡南洋理工大学“地下技术及岩土工程项目”为依托,总结了南洋理工大学近期在岩土工程技术与地下防护技术领域所开展的研究工作.     

Panama Canal expansion: alleviating global climate change

This paper quantifies likely changes in greenhouse gas emissions which contribute to anthropogenic climate change, resulting from the expansion of the Panama Canal and the consequent increase in ship-borne commercial transportation from east Asia to the US east coast. Ocean transportation offers higher fuel economy and lower pollutant emissions compared with land transport. Additionally, truck and train transportation of cargo along the US land bridge threatens to overwhelm existing highway and rail systems and limit economic growth. The alternate transportation route for easterly transit of the Panama Canal will reduce overland traffic congestion and enhance economic development by maintaining freight flow efficiency.     

Estimating the agglomeration benefits of transport investments: some tests for stability

The case for including agglomeration benefits within transport appraisal rests on an assumed causality between access to economic mass and productivity. Such causality is justified by the theory of agglomeration, but is difficult to establish empirically because estimates may be subject to sources of bias from endogeneity and confounding. The paper shows that conventional panel methods used to address these problems are unreliable due to the highly persistent nature of accessibility measures. Adopting an alternative approach, by applying semiparametric techniques to restricted sub-samples of the data, we find considerable nonlinearity in the relationship between accessibility and productivity with no positive effect to be discerned over broad ranges of the data. A key conclusion is that we are unable to distinguish the role of accessibility from other potential explanations for productivity increases. For transport appraisal, this implies that the use of conventional point elasticity estimates could be highly misleading.     

Microeconomic determinants of dry bulk shipping freight rates and contract times

While the literature has established macroeconomic determinants of shipping freight (charter) rates, there has been no systematic investigation of the microeconomic determinants of shipping freight rates. Therefore, the purpose of this paper is to investigate microeconomic determinants of freight rates in the dry bulk shipping market, using a large sample of individual dry bulk charter contracts from January 2003 to July 2009. Differences in freight rates across major dry bulk shipping routes, the geographical distribution of shipping activities around the world, and the duration of the laycan period of shipping contracts are also investigated. Estimated results suggest that the laycan period and dry bulk freight rates are interrelated and determined simultaneously. Furthermore, vessel deadweight, age and voyage routes are important determinants of dry bulk shipping freight rates, while determinants of the laycan period of chartered vessels include vessel age, freight rate level, and freight rate volatility.     

Modeling interdependencies between vehicle transaction,residential relocation and job change

This paper introduces a vehicle transaction timing model which is conditional on household residential and job relocation timings. Further, the household residential location and members’ job relocation timing decisions are jointly estimated. Some researchers have modeled the household vehicle ownership decision jointly with other household decisions like vehicle type choice or VMT; however, these models were basically static and changes in household taste over time has been ignored in nearly all of these models. The proposed model is a dynamic joint model in which the effects of land-use, economy and disaggregate travel activity attributes on the major household decisions; residential location and members’ job relocation timing decisions for wife and husband of the household, are estimated. Each of these models is estimated using both the Weibull and log-logistic baseline hazard functions to assess the usefulness of a non-monotonic rather than monotonic baseline hazard function. The last three waves of the Puget Sound Panel Survey data and land-use, transportation, and built environment variables from the Seattle Metropolitan Area are used in this study as these waves include useful explanatory variables like household tenure that were not included in the previous waves.     

Modeling the choice continuum: an integrated model of residential location,auto ownership,bicycle ownership,and commute tour mode choice decisions

The integrated modeling of land use and transportation choices involves analyzing a continuum of choices that characterize people’s lifestyles across temporal scales. This includes long-term choices such as residential and work location choices that affect land-use, medium-term choices such as vehicle ownership, and short-term choices such as travel mode choice that affect travel demand. Prior research in this area has been limited by the complexities associated with the development of integrated model systems that combine the long-, medium- and short-term choices into a unified analytical framework. This paper presents an integrated simultaneous multi-dimensional choice model of residential location, auto ownership, bicycle ownership, and commute tour mode choices using a mixed multidimensional choice modeling methodology. Model estimation results using the San Francisco Bay Area highlight a series of interdependencies among the multi-dimensional choice processes. The interdependencies include: (1) self-selection effects due to observed and unobserved factors, where households locate based on lifestyle and mobility preferences, (2) endogeneity effects, where any one choice dimension is not exogenous to another, but is endogenous to the system as a whole, (3) correlated error structures, where common unobserved factors significantly and simultaneously impact multiple choice dimensions, and (4) unobserved heterogeneity, where decision-makers show significant variation in sensitivity to explanatory variables due to unobserved factors. From a policy standpoint, to be able to forecast the “true” causal influence of activity-travel environment changes on residential location, auto/bicycle ownership, and commute mode choices, it is necessary to capture the above-identified interdependencies by jointly modeling the multiple choice dimensions in an integrated framework.     

Identifying variations and co-variations in discrete choice models

This study introduces an extended version of a standard multilevel cross-classified logit model which takes co-variations into account, i.e., variations jointly caused by two or more unobserved factors. Whilst focusing on mode choice behavior, this study deals with four different types of variation: spatial variations, inter-individual variations, intra-individual variations and co-variations between inter-individual and spatial variations. Such co-variations represent individual-specific spatial effects, reflecting different responses to the same space among individuals, which may for example be due to differences in their spatial perceptions. In our empirical analysis, we use data from Mobidrive (a continuous six-week travel survey) to clarify the existence of co-variation effects by comparing two models with and without co-variation terms. The results of this analysis indicate that co-variations certainly exist, especially for utility differences in bicycle and public transport use in comparison with car use. We then sequentially introduce four further sets of explanatory variables, examine the sources of behavioral variations and determine what types of influential factors are dominant in mode choice behavior.     

An empirical analysis of children’s after school out-of-home activity-location engagement patterns and time allocation

Children are an often overlooked and understudied population group, whose travel needs are responsible for a significant number of trips made by a household. In addition, children’s travel and activity participation during the post-school period have direct implication for adults’ activity-travel patterns. A better understanding of children’s after school activity-travel patterns and the linkages between parents and children’s activity-travel needs is necessary for accurate prediction and forecasting of activity-based travel demand modeling systems. In this paper, data from the 2002 Child Development Supplement of the Panel Study of Income Dynamics is used to undertake a comprehensive assessment of the post-school out-of-home activity-location engagement patterns of children aged 5–17 years. Specifically, this research effort utilizes a multinomial logit model to analyze children’s post-school location patterns, and employs a multiple discrete–continuous extreme value model to study the propensity of children to participate in, and allocate time to, multiple activity episode purpose-location types during the after-school period. The results show that a wide variety of demographic, attitudinal, environmental, and others’ activity-travel pattern characteristics impact children’s after school activity engagement patterns.