Delineation of an Urban Community Life Circle Based on a Machine-Learning Estimation of Spatiotemporal Behavioral Demand

Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.

DEMAND ESTIMATION AND ASSORTMENT PLANNING IN WIRELESS COMMUNICATIONS

This paper provides an efficient and useful approach for demand estimation and assortment planning of cell phone cards in wireless communication industry. We use maximum likelihood estimation to estimate the primary demand and substitution probability of each cell phone card based on historical sales data. This estimation model is nonlinear, so we transform it to a mixed integer linear programming model by logarithmic transformations and piecewise＇linear approximation. On the basis of the estimation results, we can make assortment planning. Considering the resource of cell phone cards is limited, we jointly optimize the assortment and quantity planning of cell phone cards. In numerical study, we apply our approach to a large mobile service provider in China and frod our approach can increase the revenue of this mobile service provider by 23.69%. Sensitivity analysis shows the mobile service provider should provide more assortments to increase revenue when the types of cell phone cards that can be assigned to each store are limited.

An improved modal pushover analysis procedure for estimating seismic demands of structures

The pushover analysis （POA） procedure is difficult to apply to high-rise buildings, as it cannot account for the contributions of higher modes. To overcome this limitation, a modal pushover analysis （MPA） procedure was proposed by Chopra et al. （2001）. However, invariable lateral force distributions are still adopted in the MPA. In this paper, an improved MPA procedure is presented to estimate the seismic demands of structures, considering the redistribution of inertia forces after the structure yields. This improved procedure is verified with numerical examples of 5-, 9- and 22-story buildings. It is concluded that the improved MPA procedure is more accurate than either the POA procedure or MPA procedure. In addition, the proposed procedure avoids a large computational effort by adopting a two-phase lateral force distribution..

Code Sharing and Merger： Continental, Delta and Northwest

When Delta joined the code share between Continental and Northwest in 2003, these three legacy carriers formed the only three-way U.S. domestic code sharing partnership. In 2008, Delta and Northwest announced their intention to merge. The merger was granted by the Department of Justice six months later. This paper analyzes the effects of the three-way code share and the effects of the merger between these two previous code sharing partners. I find both competitive effects and anti-competitive effects of code sharing, i.e., mean market price decrease in non-hub markets and price increase in hub-to-hub markets. Meanwhile, passenger volume increases significantly. Moreover, rivals＇ responses are to move price in the same direction as code sharing partners. I find an increase in Herfindahl-Hirschman Index （HHI） and a reduction in traffic after the merger. Specifically, HHI increases for over 1,000 points in markets where merging airlines were either duopolistic or with a third carrier. Pre-merger code sharing markets experience an increase in HHI due to the disappearance of the code sharing contract. Low cost carrier entry mitigates merger effects.