Spatial Perspectives in Business Establishment Behavioral Modeling: A Case-
Study Analysis in Santa Barbara County
Srinath K. Ravulaparthy, Doctoral Candidate, UC Santa Barbara
Advisor: Konstadinos Goulias
Representation of economic agents and their activity in integrated land-use and transportation models is increasingly becoming disaggregate thereby requiring the corresponding spatial environment also to be represented in much finer detail. This representation can greatly benefit by thoroughly operationalizing and emphasizing the critical element of ‘location’ in better understanding spatial behavior of business establishments within this framework. This dissertation research study strives to contribute to this growing body of knowledge in three major directions. First, presented is a methodological framework that captures for each location properties in-terms of its closeness, intermediacy, straightness and accessibility to all other locations in any study area. Second, we present a modeling approach to comprehensively assess the locational impacts on economic efficiency of business establishments, thereby identifying the highest and lowest performers in the region. Finally, we make a case to use dynamic analysis in thoroughly investigating the spatial dimension of business establishment lifecycle events as related to business establishment formation, relocation and dissolution.
Reliable Where-in-Lane Positioning
Yiming Chen, Doctoral Candidate, UC Riverside
Advisor: Jay A. Farrell
Where-in-lane positioning is a highly desired technique for modern surface transpiration vehicles. Global Positioning System (GPS) integrated with Inertial Navigation System (INS) is a widely applied solution to provide high fidelity vehicle navigation and positioning. Conventional Extended Kalman Filter based GPS-INS has inherent limitations. Herein a novel framework referred as Contemplative Real Time (CRT) GPS-INS is proposed. The contemplation part of the CRT framework could do Bayesian estimation and remove faculty measurement by statistical testings. The theoretical analysis and on-vehicle tests show that this CRT approach could improve the state-of-art significantly in accuracy and reliability.
Inventory-based Temporal Modeling for Freight Networks
Miyuan Zhao, Doctoral Candidate, UC Irvine
Advisor: Stephen Ritchie
Freight transportation demand is a highly variable process over time and space. Two challenges in current regional freight forecasting are the lack of consideration of the space-time trade-offs and the lack of behaviorally-based models for temporally assigning annual commodity flows to daily flows. State-of-the-practice models typically use fixed factors for temporal assignment and do not address the tradeoffs between transport costs and inventory costs, which can aid in quantifying the impact of different land uses on monthly truck distributions or the impact of rising fuel costs on shipment frequency and warehousing needs. This dissertation work makes the first step toward explicitly modeling the freight temporal distributions and proposes a novel approach that adopts the concept of Network Economics and Economic Order Quantity (EOQ) inventory in an agent-based freight demand modeling framework.
Unlike other agent-based models that seek to replace the whole freight forecasting process, the proposed model relies on other aggregate models to generate annual distribution channels (commodity OD matrix) and monthly demand distributions by commodity type. This frees the model to focus on trade-offs between transport and inventory without having to bear the burden of limited disaggregate data for other choices.
The modeling framework is composed of two main components: (1) a supplier selection module to indicate the supply chain interactions and determine the order quantity from one firm to another firm while meeting the zone level flow constraints; (2) an EOQ-based inventory operation module to indicate the goods movement daily pattern and determine the daily firm-firm flows by modeling firms’ inventory replenishment decisions. By aggregating the daily firm-firm flows back up to the zone level, we get the average zone-zone daily flows by commodity types as the final output.
The whole framework has been fully examined using the California data. A union of 6 datasets is utilized as inputs to model the daily flows of 503 firm groups in California during the 261 weekdays in year 2007. As one parameter of the normative model, the unit inventory holding cost has been calibrated with the given inventory data. A simple comparison of the model outputs with the fixed factor approach is conducted. Four use cases are presented to demonstrate the effectiveness of such a new model for freight transport analysis.
Inferring and Replicating Activity Selection and Scheduling Behavior of Individuals
Mahdieh Allahviranloo, Doctoral Candidate, UC Irvine
Advisor: Will Recker
Understanding the choices that each individual in the population makes regarding daily plans and activity participation behavior is crucial to forecasting spatial-temporal travel demand in the region. In this dissertation, we develop a comprehensive mathematical/statistical framework to infer and replicate travel behavior of individuals in terms of their socio-demographic profiles. The framework comprises series of distinct modules that employ statistical segmentation, Bayesian econometrics, data mining, and optimization techniques to predict individuals’ activity types, activity frequencies, and the travel linkages that make them possible. The key advantages of the model are: first, providing the likely content of activity agenda as part of the inference procedure; second, integrating transportation network topology within activity scheduling step; and third, capability of integrating modal components. The data used for the analysis is the California Household Travel Survey data, 2000-2001, (Caltrans, 2002). After preprocessing (which includes queries to match, clean, and prepare data), the final cleaned data is consisted of activity patterns of 26,269 individuals.
In the model-building process, we initially cluster individuals in the sample based on their reported (one-day) activity patterns. Later, we argue and demonstrate that clustering activity/travel patterns in terms of such activity characteristics as type, duration, scheduling, and location can be an effective tool to capture preferential distributions of arrival time, departure time, and duration, which are unobservable inputs to activity-based travel models. Representative patterns are found based on two measures of dissimilarities between activity patterns, Sequence Alignment Method and Agenda dissimilarity, resulting in 8 clusters. A decision tree based on socio-demographics of individuals is fitted to infer the cluster to which each individual belongs. 12 Inference on agenda formation in each cluster is based on ensemble of three different modules—“multivariate probit model,” “Markov chains with conditional random fields,” and “Adaptive Boosting”— applied to individuals within each cluster. In each of these modules, the inputs are socio-demographic attributes of individuals, and the outputs are discrete outcomes indicating participation in each activity type. Arrival time and activity duration inference for each activity type in each cluster, is performed using the adaptive boosting algorithm. Having identified the type of activities, and their arrival time and duration, activities are scheduled in the agenda using two approaches: decision rules, and Household Activity Pattern Problem (HAPP: a variation of pickup and delivery problem with time windows, (Recker, 1995) ).
Testing the entire modeling system on an out-of-sample population—15% of the entire sample— shows that the model is able to predict on average 80.3% of daily activities of individuals; correct activities during 867 minutes of 1080 awake minutes in a day was predicted.
An Investigation in Decision Making and Destination Choice Incorporating Place Meaning and Social Network Influences
Kathleen Elizabeth Deutsch, Doctoral Candidate, UC Santa Barbara
Advisor: Konstadinos Goulias
Travel demand models in the field of transportation have become increasingly sophisticated through the past several decades. The use of activity based modeling methods requires the integration of highly detailed information with statistical models but still substantial variation is unobserved. The pursuit of richer and more accurate models requires thinking outside of the proverbial box, and extending our research into various directions. This dissertation examines the process of destination choice, and the potential influence of place meaning and social networks in the process and in our ability to computationally replicate and predict behavior. Aspects of place meaning are examined, including different geographical aggregations, and the contributions of several theories such as sense of place. In addition, the role of individuals as decision makers is examined, in an attempt to determine whether there are different situations in which an individual’s preferences or attitudes have more weight in the decision process. The research presented in this dissertation is motivated by the theoretical assumptions and underpinnings of the discrete choice framework. Misspecification of choice models can lead to incorrect estimations, or biased parameters. It is therefore important to take care in specifying the models as accurately as possible to the actual decision process, and not relying on a stochastic error term to correct for any absent information. Although this work is framed by the discrete choice framework, the implications of the research also apply to broader domains in planning.
Results show that we can and should include sense of place attributes in a quantitative manner in modeling behavior. In addition, attitudes and perceptions of attributes of place can be used to challenge current assessments of accessibility and attraction to parts of a region. Though sense of place is a well-founded and widely discussed theory, there is still a considerable amount of work to do in capturing the emotional aspects of place in a quantitative manner. The work in this dissertation also explores the potential and shortfalls of the quantification of sense of place, and how we might better incorporated the phenomenon in models of decision-making. Lastly, findings of research conducted on the influence of social networks on decision making show that there is a wide range of cooperative decision making strategies, and as such, we must be more careful to model the influence of individuals in decision making more accurately.
Stability and Change in Travel Behavior and the Built Environment
Pamela Dahl, Doctoral Candidate, UC Santa Barbara
Advisor: Konstadinos Goulias
The study of dynamic travel behavior explores the individual and environmental triggers that drive new travel patterns over the life course. In the context of urban planning, these triggers hold vital information regarding the strength of land use policies and demographic shifts on individual responses to public investments in smart growth planning. This study describes a new approach to identifying drivers for change using the life-course behavioral framework to describe dynamics in task allocation over the individuals' lifetime. Using longitudinal travel behavior and land use data from the Puget Sound region, years 1989-2000, and latent growth modeling, the study finds that part of behavioral change is explained by yearly changes and part is explained by long-term planning and adaptation to changes in the household structure, such as having children.
In addition to the study of individual behavior, the dynamics of the built environment are examined in the context of smart growth planning and the jobs-housing balance. Smart growth planning in the Puget Sound attempts to create a jobs-housing balance by intensifying and diversifying residents and businesses in 26 regional growth centers. The success of these planned centers to meet the jobs-housing needs is measured using commuting behavior of the center residents for 2010. Using the National Establishments Time Series dataset, centers with high levels of professional employment (~65%) and low levels of retail employment (~30%) have short commute times and low levels of single-occupancy commutes.
Development and Certification of Low Blend Level Biodiesel Fuels for use in California
Maryam Hajbabaei, Doctoral Candidate, UC Riverside
Renewable transportation fuels become popular in recent decades due to the necessity to reduce dependency on fossil fuels, greenhouse gases, and air pollution. Several regulations on federal and state level promote expanding the use of renewable fuels. Over the past two decades, many studies focused on the impact of renewable fuels on tailpipe emissions. However, more study is needed before expanding the use of these fuels. Biodiesel is one of the popular renewable fuels which has been subject of many research studies. Biodiesel blends can reduce emissions of carbon monoxide (CO), total hydrocarbons (THC), and particulate matter (PM). However, there are uncertainties about the impact of biodiesel on nitrogen oxides (NOx) emissions, especially when it is blended with a clean diesel fuel such as the one being used in California. Therefore, more comprehensive study of biodiesel blends emissions is needed before its commercialization. It is also crucial to look into possibilities of reducing NOx emissions increases with biodiesel for introduction of more renewable fuels to the market. This study will focus on evaluation of emissions from low biodiesel blends. It will also look at the possibility of certification of one or two biodiesel blends for use in State of California.
Modeling, estimation and control of large-scale transportation networks at the age of ubiquitous sensing
Sebastien Blandin, Doctoral Candidate, UC Berkeley
Advisor: Alexandre Bayen
In the recent years, understanding of traffic data and its applicability to operations and planning has widely evolved. With the advent of probe data and crowd-sourced user information, the transportation network is the source of a significant amount of microscopic and macroscopic measurements. The heterogeneous properties of these new data types combined with their considerable volume require the development of novel modeling, estimation, and control algorithms to elevate the state of practice to its full potential.
In this work, the classical theory of macroscopic traffic flow modeling (based on partial differential equations) is used to support joint data assimilation of probe and loop detector data. A robust filtering algorithm is provided for real-time estimation in traffic information systems. Uncertain information from massive probe data volumes is combined in a robust way with classical flow measurements to produce statistical estimates of traffic. Novel optimal control strategies leveraging these improved statistical estimates are developed for reliable routing under uncertain conditions.
The proposed algorithms are implemented in the Mobile Millennium traffic data fusion platform, collecting daily 60 million traffic data points from a variety of sources. This illustrates the operational improvements provided by the novel modeling, estimation, and control methods introduced in this work, on the Northern California road network.
Behavioral change and life course turning points in activity processes
Pamela Dalal, Doctoral Candidate, UC Santa Barbara
Advisor: Konstadinos Goulias
Long-term travel demand forecasts currently do not do enough to address habitual behavior and long term planning by individuals. This is clearly an aspect of travel behavior that needs to be incorporated in long term forecasting models. For example, an individual can be expected to adapt a current state to accommodate future needs. The anticipation of the birth of a child may result in the purchase of a larger home or family sized vehicle before the actual birth. In another circumstance, an individual may look for work with more flexible work hours, or spend more time at home with a significant other. These examples are just assumptions, because little is known about how people prepare and adapt to new circumstances.
A large body of evidence suggests that life cycle changes, such as having children or retirement, significantly impact household time and task allocation, activity spaces, and even residential and work location choices. However, current travel models tend to forecast immediate changes in behavior due to a new life cycle stage, though in reality change may be gradual. When forecasting over long time horizons, as is common with many planning organizations, not knowing the timing of behavioral responses can significantly bias the calculated effect of policies. Thus, it is imperative to the success of transportation planning that policies are based on a comprehensive understanding of behavioral responses in the short-term, the long-term and the adjustments in between the two.
A Support System for Estimation and Monitoring of Real-Time On-Road Emissions
Hang Liu,Doctoral Candidate, UC Irvine
Advisor: Stephen Ritchie
Transportation has been a significant contributor to total greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation's impacts on the environment. In order to effectively develop and evaluate on-road emissions reduction strategies, it is important to have an information support system which can estimate and monitor emissions for real world traffic operations. Emission data provided by such a system can be used to identify emission hotspots and their causes, and to develop and evaluate reduction strategies accordingly. In this research, a web-based support system is proposed to estimate and monitor operational on-road emissions with high accuracy and resolution in real time. The two sets of critical information, vehicle mix and vehicle activity, are directly generated from traffic detection using the inductive vehicle signature technology. The models developed in this study to generate stratified speed by vehicle type, an important measure for accurate emission analyses, will be applied for a proof-of-concept implementation on sections of the I-405 freeway. Case studies will demonstrate how to use the data from the system to make useful decisions and evaluations. With more widespread deployment, the system can be used to perform before-and-after evaluation of certain mitigation strategies, to develop time sensitive optimal traffic control strategies with the purpose to control emissions, and to provide greenhouse gas and air quality information to policymakers, scientists, and the general public.
System-Level Optimization of Maintenance and Replacement Decisions for Heterogeneous Road Networks
Aditya Medury ,Doctoral Candidate, UC Berkeley
Advisor: Samer M. Madanat
The objective of infrastructure management is to determine optimal maintenance, rehabilitation and replacement (MR&R) decisions for a system of facilities over a planning horizon. While most approaches in the literature have studied it as a problem of optimal allocation of limited financial resources, the network configuration of the system has usually not been explicitly accounted for. In fact, MR&R activities on road networks can cause significant delays for users, due to congestion, detours, etc. The proposed bottom-up methodology investigates the significance of the network configuration within a multi-objective decision-making framework, wherein capacity losses due to construction activities are subjected to an agency-defined network capacity threshold. A parametric study is conducted on a stylized network configuration to infer the impact of network-based constraints on the decision-making process. Finally, the effect of different capacity thresholds and budget levels on the system performance is evaluated using a Pareto optimal frontier.
Perceived and Actual Bicycling Safety as Related to Roadway Users' Knowledge, Attitudes, and Behavior
Rebecca Lauren Sanders, Doctoral Candidate, UC Berkeley
Advisor: Elizabeth Deakin
Increasing bicycling for transportation is a national policy goal. However, two decades of probicycling policies and interventions have yielded little success. Research on bicycling has found that the most consistent barrier to becoming a bicyclist is a perceived lack of safety while bicycling near motor vehicle traffic (i.e., not on an off-road path). Yet, no research has thoroughly dissected current and potential bicyclists' perceptions of traffic safety risk. In addition, despite bicyclist and driver behavior being implicated in perceived bicycling risk, little research has examined how driver and bicyclist knowledge, beliefs, and attitudes lead to those behaviors. This research will fill these knowledge gaps using focus groups, a survey, and crash data to 1) explore fears about bicycling safety for current and potential bicyclists, in particular how driver and bicyclist behaviors affect perceptions of risk and whether or not empirical data corroborates perceived risk, and 2) examine how roadway design, knowledge of road rules, personal experience bicycling and driving near bicyclists, and attitudes toward bicycling and driving influence roadway users' behavior. With this understanding, practitioners will be better equipped to design effective interventions that could improve driver-bicyclist interaction, enhance bicycling safety, and help achieve national goals of increased bicycling.
The Rising Car Culture in China: Is sustainable urban development possible? How Planning Organizations Conceive of and Manage Rapid Motorization in China
Alainna Thomas, Doctoral Candidate, UC Berkeley
Advisor: Elizabeth Deakin
This dissertation explores the ways urban planners in China address the competing interests of car owners and public transit users, particularly in second-tier cities, which are now experiencing rapid motorization. Using a multi-case study approach, this research incorporates mixed methods to examine the planning processes undertaken in two Chinese cities— Kunming, capital of the southwestern province Yunnan, and Jinan, capital of the northeast province Shandong. Through identifying, interviewing, and surveying the diverse group of planners, this research looks at how urban planning organizations view, interpret, and coordinate work within the context of rapid motorization. In support of case study findings, a nationwide survey of planning officials and practitioners will be undertaken to understand the prevalence of certain conceptions of motorization as well as sustainable urban planning. PPDiscovering how planning agencies understand the roles of cars and public transit and the actions they take therein provide insight into how more sustainable development can be supported in China. This research will also illuminate what is missing in terms of training/capacity building, technology transfers, and supportive policies. This will benefit US federal agencies and organizations working in China to provide better channels for policy transfers as well as stronger partnerships in the shared goal of reducing carbon emissions.
Seven Students Awarded Dissertation Grants for Fall 2010 Cycle
December 14, 2010—Seven students at four UC campuses have been awarded UCTC Dissertation Grants for the Fall 2010 award cycle. Each grant provides the student with $20,000.
The dissertation grantees are: Kathleen Deutsch (UC Santa Barbara), Hsin-Ping Hsu and Jee Eun (Jamie) Kang (both UC Irvine), Jeffrey Lidicker (UC Berkeley), Eric A. Morris (UCLA), Manish Shirgaokar and Yiguang Xuan (both UC Berkeley).
All proposals were reviewed, evaluated, and ranked independently by a former UCTC Dissertation Award grantee and a senior researcher in the field. Upon completion, links to the dissertations will be posted on the dissertation research page of the UCTC Web site.
A Structural Equations Approach to Time Use and Destination Choice Using a GPS Based Activity Diary Data Collection
Kathleen Deutsch, Doctoral Candidate, Department of Geography, UC Santa Barbara
Advisor: Konstadinos Goulias
Project Description: The ability to obtain accurate and descriptive models of travel behavior has increased over the past few decades with the introduction and improvement of the activity based approach. With current legislation routed in environmental concerns, the requirement for accurate models is heightened. The activity-based model requires a much higher level of detail and understanding of human behavior. Several details of human behavior have been explored, understood and implemented, though several are still fuzzy. In addition to this, data requirements have promoted a renovation in the methodology by which the field collects data. This research uses a GPS enabled activity diary along with a household survey to explore several aspects of decision making relating to time use and destination choice. Sense of place, social networks and variability of activity patterns will all be explored using primary and secondary data from southern Santa Barbara County. Latent classes of people with similar characteristics of sense of place will be developed. Similarly, social network attributes will be examined. Following this, structural equation modeling using these latent variables will be developed to predict behavioral outcomes. Simultaneous equations will be used to model the joint decision making processes of activity type, duration and destination.
Key words: Travel behavior modeling and simulation, integrated land use and transportation models, GPS aided activity diary, prompted recall survey, sense of place, social networks, structural equation modeling, latent variables.
Gender Differences in Non-Work Travel Behavior: Interaction between Land Use and Sociodemographic Characteristics
Hsin-Ping Hsu, Doctoral Candidate, Department of Planning, Policy and Design and Economics, UC Irvine
Advisor: Marlon Boarnet
Project Description: Research on gender differences in travel behavior usually relies on national travel survey data, which contain a rich set of sociodemographic variables but only coarse land use characteristics. On the other hand, research on the link between land use and travel behavior lacks a gender angle, and therefore how do the impacts of land use on travel behavior differ between men and women is not typically considered. This dissertation aims to fill the gap in the literature by exploring simultaneously the interaction between land use and sociodemographic characteristics and its effects on gender differences in non-work travel behavior. Using a regional travel survey data with detailed land use and sociodemographic variables, the initial analysis shows that land use has greater impacts on women's non-work trip frequency than men's, and the impacts vary by women's roles in households. For example, living in a neighborhood near a rail station can reduce the number of non-work trips of married women without children by 31 percent. These results suggest that land use might provide opportunities to mitigate women's travel burdens which come from their gender roles in households, which in turn can contribute to more gender equal transportation policy interventions.
Key words: Gender differences; travel behavior, non-work travel; land use; household responsibility hypothesis.
Jee Eun (Jamie) Kang, Doctoral Candidate, Department of Civil and Environmental Engineering, UC Irvine
Advisor: Will W. Recker
Project Description: Since the California Air Resources Board's legislation of Low Emission Vehicle (LEV) and Zero Emission Vehicle (ZEV) mandates has been adopted in 1990, there have been positive expectations of Alternative Fuel Vehicles (AFVs) adaptation. Recently, concerns about rising gasoline cost, technical feasibility of the "green" AFVs, and the success of the Hybrid Electric Vehicles (HEVs) in the automobile market, achieving sustainable transportation system has never seemed more promising. Many recent assessments on energy and emission of AFVs suggested positive outcomes. Two major advantages of the AFVs are significant reductions in energy use and emission because the conventional Internal Combustion Vehicles (ICVs) run on gasoline which is accountable for massive greenhouse gas emissions and non-renewable energy dependency.
However, the assessments are limited to aggregated analysis, scenario-based trend analysis, or survey of hypothetical conditions. This is due to lack of sufficient data on how consumers' or drivers' behavior towards the AFVs with their technical characteristics such as range and limited access to required infrastructures (i.e. refuel/recharge). As a result, it excludes an important aspect of the possible AFV adaptations that depending on the drivers' response to infrastructure investments, the environmental and energy effects will differ significantly.
To incorporate drivers' behaviors, this dissertation proposes an activity-based travel demand, patterns, and infrastructure analysis. In addition, activity-based disaggregated approach analysis provides temporal and spatial profiles of energy uses and emissions. From this analysis, it is expected to assess bounds of the AFVs with different levels and strategies of infrastructure penetration. Furthermore, new infrastructure location strategies will be developed incorporating each vehicle patterns.
Key words: Activity-based, Environmental sustainability, Alternative fuel vehicles, Household activity pattern problem, Facility location, Location routing Problem.
Pavement Resurfacing Policy for Multi-criteria Minimization of Life-cycle Cost and Greenhouse Gas Emissions
Jeffrey Lidicker, Doctoral Candidate, Department of Civil and Environmental Engineering, UC Berkeley
Advisors: Samer Madanat and Arpad Horvath
Project Description: Traditionally, pavement maintenance optimization has strived to find the balance between User Costs due to pavement roughness and Agency Costs associated with roughness reduction. This study endeavors to expand beyond optimizing maintenance for costs in dollars, to also include GHG emissions reduction. However, optimizing for one criterion may not produce the same results as optimizing for the other. Thus, this research aims to produce a decision tool for transportation departments where it can be easily estimated, for a particular roadway segment with specific traffic usage, how much it will cost to reduce various amounts of GHG emissions by altering pavement maintenance policies. Preliminary results indicate that there is a tradeoff between costs and emissions when developing a pavement maintenance policy. Use of a Pareto curve provides a visual tool to strike the proper balance for decision makers. The curve also provides cost-effectiveness information needed for policy analysis. Future work endeavors to improve on framework accuracy to actual roadway segments, and to further develop a budget-constrained system-level version of the pavement maintenance optimization that will enable estimation of emission savings potential.
Key words: Pavement Maintenance Policy, Multi-criteria optimization, cost minimization, emission minimization.
Eric A. Morris, Doctoral Candidate, Department of Urban Planning, UC Los Angeles
Advisor: Brian Taylor
Project Description: Promoting "access" is a fundamental goal of the planning profession. But, surprisingly, there has been a relative paucity of research on how access affects ultimate life outcomes. This dissertation will explore whether access, which is a function of proximity to opportunities and the transportation that allows people to reach those opportunities, is associated with subjective well-being (SWB). It will employ data from the Gallup/Healthways Well-Being Index survey, a rich source of information on diverse topics including subjects' self-reported SWB on a 10 point scale. For this dissertation, Gallup has temporarily added questions about transportation, including respondents' auto ownership and mode for last trip. I will supplement this with data on respondents' rail and bus access, as well as respondents' neighborhood walkability (calculated based on nearby commercial opportunities). Demographic control variables (such as respondents' genders, ages and incomes) and spatial controls (such as neighborhood incomes, population densities, etc.) will also be included. I hypothesize that, for the most part, better access will be associated with greater SWB, but that this will not necessarily hold true in all models; much will depend on what is controlled for. The results should inform policy in the realms of transportation and land use.
Key words: Subjective well-being, happiness, access, auto ownership, transit access, walkability.
Understanding the links between Regulatory Costs, Aspirations, Social Status, and (Un)sustainable Travel Behaviors
Manish Shirgaokar, Doctoral Candidate, Department of City and Regional Planning, UC Berkeley
Advisor: Elizabeth Deakin
Project Description: Transportation sector policies, working towards an environmentally and economically sustainable future, attempt to charge "appropriate" costs for private travel to encourage transit plus non-motorized mode shifts. However, in emerging economies such as India, social status has a large bearing on household vehicle holdings and travel mode choices, making impacts of pricing policies complex and potentially subject to unanticipated and unintended consequences.
Using a mixed methods approach, my study will explore growing motorization in India in two phases: (Phase 1) develop discrete choice models based on a 66,000 household travel survey dataset for Mumbai looking at (i) household vehicle holdings by number and vehicle type and (ii) mode choice, followed by sensitivity tests to explore responses to higher regulatory costs across working and middle income groups, and (Phase 2) through in-depth interviews, gain more qualitative insights into how social attributes influence vehicle ownership and travel decisions.
My findings will develop a deeper understanding of how social attributes inform travel decisions. This will help inform a set of policy choices that can strategically enhance sustainable transportation options in India. My research will contribute to the field by adding insights regarding the role of social attributes driving household vehicle holdings and travel decisions.
Key words: vehicle ownership level, vehicle type choice, mode choice, aspirations, social status, regulatory costs, knowledge transfer.
Yiguang Xuan, Doctoral Candidate, Department of Civil and Environmental Engineering, UC Berkeley
Advisor: Carlos Daganzo
Project Description: The dissertation explores ways of increasing the flow capacity of congested highway intersections by adding a mid-block signal, termed pre-signal, to re-organize traffic. The emphasis is on re-organizing automobile traffic so that competing (i.e., left-turning and through-moving) streams do not impede each other. The ideas proposed here have much broader application, however. For example, the same ideas can be used to mitigate the capacity-reducing conflicts that arise between distinct modes, such as buses and cars, when these compete for the same right-of-way at an intersection. And because the proposed concept enables intersections to handle the same amount of traffic with less space, it becomes easier to convert car lanes to special lanes that are reserved for greener modes, such as buses or bicycles.
Key words: isolated signalized intersection, oversaturation, capacity, pre-signal.