Yujie Hu

Detecting and Analyzing Mobility Hotspots using Surface Networks

Capabilities for collecting and storing data on mobile objects have increased dramatically over the past few decades. A persistent difficulty is summarizing large collections of mobile objects. This article develops methods for extracting and analyzing hotspots or locations with relatively high levels of mobility activity. We use kernel density estimation (KDE) to convert a large collection of mobile objects into a smooth, continuous surface. We then develop a topological algorithm to extract critical geometric features of the surface; these include critical points (peaks, pits and passes) and critical lines (ridgelines and course-lines). We connect the peaks and corresponding ridgelines to produce a surface network that summarizes the topological structure of the surface. We apply graph theoretic indices to analytically characterize the surface and its changes over time. To illustrate our approach, we apply the techniques to taxi cab data collected in Shanghai, China. We find increases in the complexity of the hotspot spatial distribution during normal activity hours in the late morning, afternoon and evening and a spike in the connectivity of the hotspot spatial distribution in the morning as taxis concentrate on servicing travel to work. These results match with scientific and anecdotal knowledge about human activity patterns in the study area.

Decomposing excess commuting: a Monte Carlo simulation approach

Excess or wasteful commuting is measured as the proportion of actual commute that is over minimum (optimal) commute when assuming that people could freely swap their homes and jobs in a city. Studies usually rely on survey data to define actual commute, and measure the optimal commute at an aggregate zonal level by Linear Programming (LP). Travel time from a survey could include reporting errors and respondents might not be representative of the areas they reside; and the derived optimal commute at an aggregate areal level is also subject to the zonal effect. Both may bias the estimate of excess commuting. Based on the 2006–2010 Census for Transportation Planning Package (CTPP) data in Baton Rouge, Louisiana, this research uses a Monte Carlo approach to simulate individual resident workers and individual jobs within census tracts, estimate commute distance and time from journey-to-work trips, and define the optimal commute based on simulated individual locations. Findings indicate that both reporting errors and the use of aggregate zonal data contribute to miscalculation of excess commuting.

Local Indicator of Colocation Quotient with a Statistical Significance Test: Examining Spatial Association of Crime and Facilities

Most existing point-based colocation methods are global measures (e.g., join count statistic, cross K function, and global colocation quotient). Most recently, a local indicator such as the local colocation quotient has been proposed to capture the variability of colocation across areas. Our research advances this line of work by developing a simulation-based statistical test for the local indicator of colocation quotient (LCLQ). The study applies the indicator to examine the association of land use facilities with crime patterns. Moreover, we use the street network distance in addition to the traditional Euclidean distance in defining neighbors because human activities (including facilities and crimes) usually occur along a street network. The method is applied to analyze the colocation of three types of crimes and three categories of facilities in a city in Jiangsu Province, China. The findings demonstrate the value of the proposed method in colocation analysis of crime and facilities and, in general, colocation analysis of point data.

Temporal Trends of Intraurban Commuting in Baton Rouge, 1990–2010

Based on the 1990–2010 Census Transportation Planning Package data of Baton Rouge, Louisiana, this research analyzes the temporal trends of commuting patterns in both time and distance. In comparison to previous work, commuting length is calibrated more accurately by Monte Carlo–based simulation of individual journey-to-work trips to mitigate the zonal effect. First, average commute distance kept climbing between 1990 and 2010, whereas average commute time increased between 1990 and 2000 but then slightly dropped toward 2010. Second, urban land use remained a good predictor of commuting pattern over time (e.g., explaining up to 90 percent of mean commute distance and about 30 percent of mean commute time). Finally, the percentage of excess commuting increased significantly between 1990 and 2000 and stabilized afterward.

A novel algorithm to identifying vehicle travel path in elevated road area based on GPS trajectory data

In recent years, the increasing development of traffic information collection technology based on floating car data has been recognized, which gives rise to the establishment of real-time traffic information dissemination system in many cities. However, the recent massive construction of urban elevated roads hinders the processing of corresponding GPS data and further extraction of traffic information (e.g., identifying the real travel path), as a result of the frequent transfer of vehicles between ground and elevated road travel. Consequently, an algorithm for identifying the travel road type (i.e., elevated or ground road) of vehicles is designed based on the vehicle traveling features, geometric and topological characteristics of the elevated road network, and a trajectory model proposed in the present study. To be specific, the proposed algorithm can detect the places where a vehicle enters, leaves or crosses under elevated roads. An experiment of 10 sample taxis in Shanghai, China was conducted, and the comparison of our results and results that are obtained from visual interpretation validates the proposed algorithm.

Commuting Variability by Wage Groups in Baton Rouge, 1990–2010

ABSTRACT Residential segregation has recently shifted to become more class- and income-based in the United States, and neighborhoods have undergone significant changes in commuting patterns over time. To better understand the commuting pattern across neighborhoods of different income levels, this research analyzes commuting variability (in both distance and time) across wage groups as well as stability over time using the Census Transportation Planning Package (CTPP) data for 1990 through 2010 in Baton Rouge, Louisiana. In comparison to previous work, commuting distance is estimated more accurately by Monte Carlo simulation of individual trips to mitigate aggregation error and scale effect. The results based on neighborhoods mean wage rate indicate that commuting behavior varies across areas of different wage rates and such variability is captured by a convex relationship. Affluent neighborhoods tended to commute more, but the highest wage neighborhoods displayed a reduced level of commuting. This trend remains relatively stable over time despite a general overall transportation improvement. An exploratory analysis based on the distribution of wage groups is conducted to gain more detailed insights and uncovers the lasting poor mobility (e.g., fewer location and transport options) of the lowest wage workers between 1990 and 2010.

Disparities in Spatial Accessibility of Pharmacies in Baton Rouge, Louisiana

Abstract This study examines spatial accessibility of pharmacies in Baton Rouge, Louisiana. Two popular geographic information systems ()—based methods are compared: the proximity method uses the distance (travel time) from the nearest pharmacy, and the two‐step floating catchment area (2) method considers the match ratio between providers and population as well as the complex spatial interaction between them. The study indicates that disproportionally higher percentages of African‐Americans are in areas with shorter travel time to the nearest pharmacies than whites, but suffer from poorer accessibility measured by the 2 method—that is, fewer pharmacies per 10,000 residents. Seniors, particularly those of seventy‐five years or older, tend to be disproportionally concentrated in areas that not only are closer to pharmacies, but also have more pharmacies per 10,000 residents. The two methods used in the study capture different elements in spatial accessibility: one being physically close to a facility and another adding the crowdedness in service. Both properties can be valuable for residents. The two may not always coincide with each other in spatial variability, as it is the case for racial disparity in our study area. However, when they do, as in the case for seniors, it may imply a true (dis)advantage for a demographic group in terms of both properties of spatial accessibility.

Automated Delineation of Hospital Service Areas and Hospital Referral Regions by Modularity Optimization

OBJECTIVE To develop an automated, data-driven, and scale-flexible method to delineate hospital service areas (HSAs) and hospital referral regions (HRRs) that are up-to-date, representative of all patients, and have the optimal localization of hospital visits. DATA SOURCES The 2011 state inpatient database in Florida from the Healthcare Cost and Utilization Project. STUDY DESIGN A network optimization method was used to redefine HSAs and HRRs by maximizing patient-to-hospital flows within each HSA/HRR while minimizing flows between them. We first constructed as many HSAs/HRRs as existing Dartmouth units in Florida, and then compared the two by various metrics. Next, we sought to derive the optimal numbers and configurations of HSAs/HRRs that best reflect the modularity of hospitalization patterns in Florida. PRINCIPAL FINDINGS The HSAs/HRRs by our method are favored over the Dartmouth units in balance of region size and market structure, shape, and most important, local hospitalization. CONCLUSIONS The new method is automated, scale-flexible, and effective in capturing the natural structure of the health care system. It has great potential for applications in delineating other health care service areas or in larger geographic regions.

An Entropy-based Approach for Evaluating Travel Time Predictability Based on Vehicle Trajectory Data

With the great development of intelligent transportation systems (ITS), travel time prediction has attracted the interest of many researchers, and a large number of prediction methods have been developed. However, as an unavoidable topic, the predictability of travel time series is the basic premise for travel time prediction, which has received less attention than the methodology. Based on the analysis of the complexity of the travel time series, this paper defines travel time predictability to express the probability of correct travel time prediction, and proposes an entropy-based method to measure the upper bound of travel time predictability. Multiscale entropy is employed to quantify the complexity of the travel time series, and the relationships between entropy and the upper bound of travel time predictability are presented. Empirical studies are made with vehicle trajectory data in an express road section to shape the features of travel time predictability. The effectiveness of time scales, tolerance, and series length to entropy and travel time predictability are analyzed, and some valuable suggestions about the accuracy of travel time predictability are discussed. Finally, comparisons between travel time predictability and actual prediction results from two prediction models, ARIMA and BPNN, are made. Experimental results demonstrate the validity and reliability of the proposed travel time predictability.

Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Abstract Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.