conference paper
Phd Dissertation
Given our rapidly changing society, the complexity of residential energy often hinders the efficacy of energy conservation policies designed to address our current social and environmental problems. Therefore, understanding this complexity appears to be essential to successfully building and efficiently implementing energy policies. The present dissertation attempts to advance our understanding of the dynamics and complexity of residential energy consumption by investigating various determinants and contextual factors through the three interrelated pieces of applied research. Using American Housing Survey (AHS) data, the first study investigates the dynamics of residential energy consumption at the micro level. It is found that the electricity consumption of households who have moved into new homes is generally lower than average, and their consumption is found to increase as the period of residence increases. The second study examines the relationship between the choice of energy-efficient systems and inter-agent dynamics. By employing a logistic regression model with two national datasets, the Residential Energy Consumption Survey (RECS) and the American Community Survey Public Use Microdata Sample (ACS PUMS), the empirical analysis reveals statistically significant differences in the installation of solar energy systems among households with different degrees of two major inter-agent issues—split incentives and split decision-making problems. The last study focuses on the complexity of residential energy consumption relevant to the surrounding environments, and it pays special attention to seasonality. Based on city-wide data from Chicago and using a special econometric model, the empirical analysis reveals the seasonal dynamics between urban forms and residential energy consumption. Through these three empirical studies, this dissertation explores the dynamics of residential energy consumption in various dimensions and reveals the complicated mechanisms that determine residents’ choices with respect to energy consumption. The evidence from this study is especially important because it reinforces the conclusion that there is no panacea when addressing energy issues. This study suggests that policy-makers and planners should instead thoroughly understand a wide range of contextual factors and their influences in order to develop more effective, context-specific energy policies that best fit each distinct geographical and socio-economic situation.
published journal article
Crash type is an informative indicator to infer driving behaviors and conditions that cause a crash. For example, rear-end and side-swipe crashes are typically caused by improper vehicle interaction such as sudden lane-changing or speed control while hit-object crashes are likely the result of a single driver’s mistake. This study investigated the impact of vehicles travelling as a group (platoon) and its configuration (i.e., types of vehicles consisting of the platoon) on crash type and severity since the vehicles could affect each other when travelling in close proximity. This study applied Generalized Structure Equation Modeling (GSEM) to capture the complex relationships among the various crash factors such as traffic condition, driver characteristics, environmental conditions, and vehicle interaction to the crash attributes including type and severity. This study collected over 3 million individual vehicle data from 39 traffic count sites in California to estimate the vehicle interactions and driving behaviors. The microscopic traffic data are matched to 1417 crash reports. Results showed that vehicles traveling in platoons are associated with more rear-end and side-swipe crashes. Speed difference in the platoon had a positive effect on hit-object crashes if the platoon comprises vehicles of homogeneous type – either trucks or non-trucks. In addition, human factors such as age and gender were identified as significant influential factors in all type of crashes, however truck involvement particularly played an important role amongst side-swipe crashes. Crash severity was negatively affected by total flow, and rear-end crashes were more likely to be severe compared with hit-object crashes. Based on findings, this study suggests practical operational strategies to reduce traffic instability associated with platooned vehicle patterns. Understanding the high-risk factors for different crash types and severities would provide valuable insights for decision-makers and transportation engineers to develop targeted intervention strategies in consideration of road users and traffic conditions such as fleet mix and speed.
research report
This project showcased how advanced infrastructure data supports connected automated driving systems in perceiving their surroundings cooperatively. The UCLA Mobility Lab established a smart intersection on the UCLA main campus, collecting infrastructure LiDAR data and combining it with sensor and global navigation satellite system data for research on cooperative perception. It also examined the system’s resilience to data spoofing attacks via the V2X channel from a compromised onboard unit (OBU), evaluating different attack scenarios to understand emerging security risks in V2X-based cooperative perception technologies.
presentation |
Jun 2023
published journal article
Since spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.
published journal article
working paper
B2B e-Commerce facilitates the reduction of supply chain intermediaries and reduces transaction costs. This revolution has spawned a number of online marketplaces for freight transportation service procurement. The paper looks into the operational models of existing electronic freight marketplaces and the strategic behavior of shipper and carriers conducting their business in these market places. A literature survey of market clearing mechanisms models for online freight transportation market places is provided. Models for shipper-carrier strategic interaction are presented for freight transportation procurement. Some of the key research questions for developing methodologies to aid both the shippers and carriers are discussed.
Phd Dissertation
More frequent vehicle movements are required for moving containers in a local area due to low unit volume that a single vehicle can handle compared with vessels and rails involved in the container supply chain. For this reason, truck operations for moving containers significantly affect not only transportation cost itself but also product price. They have inherent operational inefficiencies associated with empty container movements and container processes at facilities such as warehouses, distribution centers and intermodal terminals. One critical issue facing the trucking industry is the pressing need for truck routing plans that reduce such inefficiencies. Hence, this dissertation proposes to apply the concept of sharing resources, which is an emerging economic model, to container truck operations in order to resolve this issue. Two shareable resources – vehicles and containers – are considered.This study extends the literature on routing and scheduling problems that arise from container movements, and examines the possible benefits of sharing resources across customers. A series of truck container routing and scheduling problems were developed by assuming different levels of resource sharing among; (1) customers of one trucking operator, (2) customers across collaborations of multiple operators, and (3) customers over multi-day operations. To enable a trucking company to operate its fleet under a shared resource environment, two operational strategies – street turning and decoupling operations – together with temporal precedence constraints – in addition to the time constraints that are typically included in the vehicle routing problem with time windows (VRPTW) – were adopted to address the proposed problems. Two meta-heuristic algorithms based on a variable neighborhood search (VNS) scheme were developed to solve the proposed problems, including temporal precedence constraints – which are computationally more expensive – for real-world applications. To address flexible time windows resulting from temporal precedence constraints, a novel feasibility check algorithm was developed. Results from a series of numerical experiments confirm that the proposed approach leverages the advantages of resource sharing, and the meta-heuristic algorithms are efficient solution approaches for each problem with the targeted resource sharing. Consequently, this dissertation offers a platform for the development of a decision-support tool for drayage companies by applying three different levels of resource sharing into their operations.
conference paper
conference paper
The Los Angeles County Metropolitan Transportation Authority created in October 2021 the largest free transit pass pilot program to-date. Known as GoPass, it serves K-14 students in Los Angeles County, the most populated county in the United States. GoPass has three main goals: regain ridership lost during COVID-19, provide reliable transportation to students, and explore seamless payment options. We examined the characteristics of the students who enrolled in GoPass and compared them to census data to assess opportunities for additional growth. To understand GoPass usage, we estimated a generalized spatial regression model that explains boardings aggregated by census tract because detailed usage data is unavailable. Our explanatory variables include socioeconomic variables, crime, the social vulnerability index, and built environment characteristics. Our results confirm the presence of strong spatial effects. Average direct impacts show that census tracts with more African Americans and Asians, more transit stops, mixed land use, and more participating schools accessible within 30 minutes by transit have more GoPass boardings. Conversely, GoPass boardings decrease with more property crime, more multi-family & non-residential units, and a higher population density. A better understanding of the characteristics of GoPass users and of GoPass usage is important for LA Metro to improve this program, and more generally for transit agencies interested in creating similar programs.
