Binglei Xie

Kernel linear regression for face recognition

AbstractLinear regression uses the least square algorithm to solve the solution of linear regression equation. Linear regression classification (LRC) shows good classification performance on face image data. However, when the axes of linear regression of class-specific samples have intersections, LRC could not well classify the samples that distribute around intersections. Moreover, the LRC could not perform well at the situation of severe lighting variations. This paper proposes a new classification method, kernel linear regression classification (KLRC), based on LRC and the kernel trick. KLRC is a nonlinear extension of LRC and can offset the drawback of LRC. KLRC implicitly maps the data into a high-dimensional kernel space by using the nonlinear mapping determined by a kernel function. Through this mapping, KLRC is able to make the data more linearly separable and can perform well for face recognition with varying lighting. For comparison, we conduct on three standard databases under some evaluation protocols. The proposed methodology not only outperforms LRC but also takes the better performance than typical kernel methods such as kernel linear discriminant analysis and kernel principal component analysis.

Modeling the Joint Choice Decisions on Urban Shopping Destination and Travel-to-Shop Mode: A Comparative Study of Different Structures

The joint choice of shopping destination and travel-to-shop mode in downtown area is described by making use of the cross-nested logit (CNL) model structure that allows for potential interalternative correlation along the both choice dimensions. Meanwhile, the traditional multinomial logit (MNL) model and nested logit (NL) model are also formulated, respectively. This study uses the data collected in the downtown areas of Maryland-Washington, D.C. region, for shopping trips, considering household, individual, land use, and travel related characteristics. The results of the model reveal the significant influencing factors on joint choice travel behavior between shopping destination and travel mode. A comparison of the different models shows that the proposed CNL model structure offers significant improvements in capturing unobserved correlations between alternatives over MNL model and NL model. Moreover, a Monte Carlo simulation for a group of scenarios assuming that there is an increase in parking fees in downtown area is undertaken to examine the impact of a change in car travel cost on the joint choice of shopping destination and travel mode switching. The results are expected to give a better understanding on the shopping travel behavior.

Cross-Nested Joint Model of Travel Mode and Departure Time Choice for Urban Commuting Trips: Case Study in Maryland Washington, DC Region

The aim of this paper is to contribute to describing the simultaneous choice of travel mode and departure time by making use of a cross-nested logit structure that allows for the joint representation of interalternative correlation along the both choice dimensions. Traditional multinomial logit model and nested logit model are formulated respectively. The analysis uses the revealed preference data collected from Maryland-Washington, DC, regional household travel survey during 2007–2008 for commuting trips, considering more work-related characteristics than previous studies. A comparison of the different model results shows that the presented cross-nested logit structure offers significant improvements over multinomial logit and nested logit models. The empirical results of the analysis reveal significant influences on commuter joint choice behavior of travel mode and departure time. Moreover, a Monte Carlo simulation for two groups of scenarios arising from transportation policies, congestion pricing, and improvements to transit service during peak period is undertaken respectively to examine the impact of a change in car travel cost and transit travel time on the travel mode and departure time switching. The simulation results show that US

Evaluation Index System on Relationship between Urban Mass Rail Transit and Its Surrounding Land Use Based on TOD Mode

5 increase in car travel cost during peak period has a similar effect on reducing drive alone in peak hours as 30% saving in transit travel time but only half of the latter policy in the transit ridership increase.

Interactive Relations between Urban Arterial Transit Route and Land-Use under TOD Mode

To establish the evaluation index system on relationship between urban mass rail transit and its surrounding land use based on transit oriented development (TOD) mode, the suitable methods and principles of evaluation index system were analyzed. An evaluation procedure frame was proposed based on data envelopment analysis (DEA) model. Then the evaluation indexes from urban mass rail transit system and land use system selected based on interactive relations between urban mass rail transit and land use under TOD mode.

Travel Mode Choice Model with Multiple Attributes Based on Interval Value Information

To study interactive relations between urban arterial transit route and land-use under TOD mode, the features of urban arterial transit route and its surrounding land-use were analyzed. An interactive relation model between urban arterial transit route and land-use was established with nonlinear differential equations. Dynamic evolvement and trends of urban arterial transit route and land-use were analyzed, and stability and conditions on equilibrium states were shown by differential equation stability theory. A simulation was done to show that the development of arterial transit route in urban had a trend to surpass traffic capacity of equilibrium state.

Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity

In travel behavior survey, especially in stated preference (SP) survey, travelers were often unable to provide exact perceptions of the attribute of travel mode, as a result, interval values were used to denote the alternatives. After the least-square method based on complementary judgment matrix was used to obtain the weight of every mode under every attribute, the weight of each attribute was computed based on bias maximization model, and the composite weight of each mode choice was obtained. Considering travelers’ preference sufficiently, the model simulated the process of decision making in traveler mode choice perfectly.