Phd Dissertation

Active travel, built environment and transit access : a micro-analysis of pedestrian travel behavior

Publication Date

August 13, 2014

Author(s)

Gaby Abdel-Salam

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

The introduction of Senate Bill (SB 375) in 2008 stimulated more research linking travel behavior to the built environment. Smart growth tools mandated by this bill aim to reduce vehicle miles traveled (VMT), greenhouse gas (GhG) emissions and promote alternative modes to motorized travel. These tools encompass an array of land use improvements that are expected to influence active travel. Potential changes in the built environment may impact the frequency, amount and even the selection of routes for walking. Data used in this dissertation was obtained from Phase I of the Expo Study, a three-phase travel survey of residents living near the Expo Light Rail Line in Los Angeles, CA. Respondents carried GPS devices and accelerometers to track locations and activity levels; and completed seven-day trip logs. Phase I of the survey was administered in Fall 2011, prior to the introduction of the Expo Line in April 2012. This dissertation is comprised of three research topics. The first topic uses a “place-oriented” approach to examine where active travel occurs in neighborhoods adjacent to the Expo Light Rail Line. This chapter is based on the Behavioral Model of Environments, which emphasizes the influence of the physical environment on individuals’ travel behavior and route choices. Results indicate that the routes selected by pedestrians have higher densities of commercial and retail centers and better access to more transit stations. The second research topic uses an ecological modeling approach. Multilevel analysis of the effects of the built environment on active transport was performed in three geographic levels of aggregation near respondents’ homes. Examination of land uses at the half-mile extent yield the least number of significant results. In contrast, land uses examined at the segment-level and quarter-mile distance from homes emphasize the importance of street connectivity and green space on increasing transport-related physical activity (TPA). This suggests the importance of analyzing the data at finer geographic levels. The third research topic proposes a practical methodology of pedestrian route analysis in which observed GPS-tracked routes were examined and compared to GIS-simulated shortest paths. The two route types were compared over deviations in trip-level travel indices, respondents’ socio-demographic traits, time of day variations and differences in objectively measured built environment features along both sets of routes. Results suggest that observed routes diverged more from shortest paths with increasing distance and were more circuitous beyond the 2.4-mile threshold. Most walks were completed after the AM Off Peak time. With the exception of the Evening time, observed routes were found to be much longer in all time periods especially in the AM Peak time. Moreover, higher densities of commercial centers, local businesses and green spaces were observed more for GPS-tracked routes than for shortest paths. These routes also had more street intersections and transit stops. Overall, results imply that pedestrians selected routes that were longer than the respective shortest paths and that may have been due to greater access to amenities and activity centers

Phd Dissertation

Inferring and replicating activity selection and scheduling behavior of individuals

Publication Date

August 6, 2014

Author(s)

Mahdieh Allahviranloo

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

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. 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.

published journal article

Strategic Hydrogen Refueling Station Locations with Scheduling and Routing Considerations of Individual Vehicles

Publication Date

July 7, 2014

Associated Project

An Activity-Based Assessment of the Bounds of Sustainable Alternative Transportation Futures

Author(s)

Jee Eun (Jamie) Kang, Will Recker

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Travel Behavior, Land Use, & the Built Environment Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

A hydrogen refueling station siting model that considers scheduling and routing decisions of individual vehicles is presented. By coupling a location strategy of the set covering problem (SCP) and a routing and scheduling strategy of the household activity pattern problem, this problem falls into the category of location routing problems. It introduces a tour-based approach to refueling station siting, with tour-construction capability within the model. There are multiple decision makers in this problem: the public sector as the service provider and the collection of individual households that make their own routing decisions to perform a given set of out-of-home activities together with a visit to a refueling location. A solution method that does not require the full information of the coverage matrix is developed to reduce the computational burden. Compared to the point-based SCP the results indicate that the minimum infrastructure requirement may be overestimated when vehicle (refueling demand)-infrastructure (refueling supply) interactions with daily out-of-home activities are excluded.

Related Publications

published journal article | August 31, 2013

The location selection problem for the household activity pattern problem

published journal article | June 6, 2013

On Activity-based Network Design Problems

Phd Dissertation

Assessment of Constant Volume Sampler Based Test Procedure and Charging Scenarios Based Energy Impact of Plug-in Hybrid Electric Vehicles

Publication Date

June 29, 2014

Author(s)

Li Zhang

Areas of Expertise

Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

The advent of plug-in hybrid electric vehicles (PHEVs) introduces a new vehicle paradigm that consumes both gasoline and electricity. The new concept presents new questions. In particular, (1) what modifications need to be made for test procedures in terms of the emissions and fuel economy measurements for individual vehicles, and (2) what methodology needs to be established to evaluate the energy and emission impacts of a PHEV fleet? For the test procedure, the emission testing has been done by using the continuous sampling method for continuous diluents, the smooth approach orifice (SAO) measurement for ambient air flow, and fuel flow meter (FFM) measurement for fuel consumption in addition to the industry standard constant volume sampler (CVS) system, which faces challenges for PHEVs. Results show that the current CVS dilution factor (DF) exhibits an error resulting in higher emission mass calculation; an alternative procedure can be proposed for the charge depleting cycle to eliminate the overdilution; the CVS system has an error resulting from exhaust left in the tailpipe and CVS sampling line. For the evaluation of the energy impact of PHEVs, the South Coast Air Basin of California (SoCAB) was selected as an example by considering different charging scenarios consisting of different charging powers, locations and time. Results show that petroleum reduction is significant; the all-electric ability is crucial to cold start emission reduction; the benefit of higher power charging is small; delayed and average charging are better than immediate charging for home; and non-home charging increases peak grid load.

MS Thesis

Advising and optimizing the deployment of sustainability-oriented technologies in the integrated electricity, light-duty transportation, and water supply system

Publication Date

June 29, 2014

Author(s)

Brian Tarroja

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

The convergence of increasing populations, decreasing primary resource availability, and uncertain climates have drawn attention to the challenge of shifting the operations of key resource sectors towards a sustainable paradigm. This is prevalent in California, which has set sustainability-oriented policies such as the Renewable Portfolio Standards and Zero-Emission Vehicle mandates. To meet these goals, many options have been identified to potentially carry out these shifts. The electricity sector is focusing on accommodating renewable power generation, the transportation sector on alternative fuel drivetrains and infrastructure, and the water supply sector on conservation, reuse, and unconventional supplies. Historical performance evaluations of these options, however, have not adequately taken into account the impacts on and constraints of co-dependent infrastructures that must accommodate them and their interactions with other simultaneously deployed options. These aspects are critical for optimally choosing options to meet sustainability goals, since the combined system of all resource sectors must satisfy them. Certain operations should not be made sustainable at the expense of rendering others as unsustainable, and certain resource sectors should not meet their individual goals in a way that hinders the ability of the entire system to do so. Therefore, this work develops and utilizes an integrated platform of the electricity, transportation, and water supply sectors to characterize the performance of emerging technology and management options while taking into account their impacts on co-dependent infrastructures and identify synergistic or detrimental interactions between the deployment of different options. This is carried out by first evaluating the performance of each option in the context of individual resource sectors to determine infrastructure impacts, then again in the context of paired resource sectors (electricity-transportation, electricity-water), and finally in the context of the combined tri-sector system. This allows a more robust basis for composing preferred option portfolios to meet sustainability goals and gives a direction for coordinating the paradigm shifts of different resource sectors. Overall, it is determined that taking into account infrastructure constraints and potential operational interactions can significantly change the evaluation of the preferred role that different technologies should fulfill in contributing towards satisfying sustainability goals in the holistic context.

MS Thesis

Off-Street Parking Cost Forecasting Models for Southern California

Publication Date

June 29, 2014

Author(s)

Biling Liu

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

Parking cost is an important and sensitive factor in understanding travel behavior and is typically utilized in the mode choice model of regional demand forecasting models. There are various socio-economics variables that can affect the value of parking cost by employment type, time periods, and trip purposes. In this study, a set of parking cost forecasting models are developed using survey data and local socio-economic data with the objective of identifying parking cost patterns and forecasting future parking costs. This study first summarizes methods applied in previous parking cost forecasting models. Two categories of models were estimated. The first category does not consider parking space supply as a factor in forecasting TAZ parking; the second category considers both parking space supply and parking demand as explanatory variables. For each category, using current off-street parking cost survey data, linear regression models are built for hourly, daily and monthly pricing for SCAG Tier 2 Transportation Analysis Zones (TAZ) using R and Matlab. Daily parking rates are set as the base rates to generate the hourly and monthly parking cost models. The consideration of parking demand is a major contribution of this study, with demand generated based on home-based-work trip attractions for commuters by income groups in all models. This study found that daily parking rates can be explained by total employment, the proportion of office to total jobs, and the proportion of multiple to total households. Hourly parking cost can be explained based on daily parking rates and travel behavior associated with education, hospital, finance, entertainment and other employment types. The monthly parking cost model is built base on both daily and hourly parking rates as independent variables. Future work includes, integration of on-street parking costs with the current models for off-street parking.

MS Thesis

A Performance Assessment of the Elimination of Left-Turns at Selected Intersections

Publication Date

June 29, 2014

Author(s)

Xinyun Cao

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience

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Abstract

For most signalized intersections, left-turn movements are considered as a primary contributor to intersection delay. The concept of eliminating left-turn movements is now feasible with the rise of GPS-based routing which will allow the active routing of vehicles in networks with reduced left turns. This research seeks to estimate the impact of left turn reductions on overall travel time and left turn delay at intersections. The research objective is to evaluate the effect of left turn movement elimination in sample networks. The type of intersections considered is restricted to a grid network but is defined by the roadway hierarchy. A sample network was selected based on a real world network and reflecting observed volumes, travel times, and delays. The analysis approach is to eliminate left turn movements in three types of intersections by applying turn prohibitions and adjusting cycle lengths and turn penalties on other movements. Network performance is then assessed based on delay reduction, total travel time, and fuel consumption. It was concluded that selective reduction of left turn movements can improve network performance.

Phd Dissertation

Estimating Emissions by Modeling Freeway Vehicle Speed Profiles Using Point Detector Data

Publication Date

June 14, 2014

Author(s)

Jin Choi

Areas of Expertise

Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

A method for accurate emissions estimation that will contribute to promoting public health has been increasingly important. The purpose of this study is to develop a novel method that is designed to make accurate real-time emissions estimation from individual vehicles on freeways possible. The benefit of this method is that it can overcome the weakness of macroscopic emissions estimation methods, which underestimated emissions. The most distinguishing feature of the Speed Profile Estimation (SPE) method is that it uses a speed profile (SP) that is generated by the sum of a basic SP (BSP), which is calculated by the basic travel information of an individual vehicle obtained from vehicle reidentification (REID), and a residual SP (RSP), which is estimated by categorized traffic information. In order to estimate RSP this research employs Autoregressive (AR) model and Fourier series (FS). And to find the parameters of RSP, the total absolute difference between actual SP emissions and estimated SP emissions was optimized by genetic algorithm. For this, parameters are calculated for all possible combinations of three categorizations and clusters by K-mean clustering. Individual vehicle trajectories from two freeways, US101 and I-80, were provided by the Next Generation Simulation (NGSIM) dataset. US101 was examined for calibration, and I-80 for validation. And then, transferability tests were conducted for various section distances to verify model transferability. Finally, REID is simulated with low vehicle signatures match rates to test its applicability to real situations. Unlike previous methods, the SPE is notable for its real-time, transferable, reliable, and cost efficient emissions estimation. The calibration and validation account only 4.0 % and 4.1 % MAPEs, respectively. Moreover, transferability tests showed that MAPEs are lower than 4.4 % in both longer and shorter section distances. Furthermore, REID simulation increases only 0.2 % MAPE even in low vehicle signatures match rates, which is lower than 5 % MAPE in emissions estimation. Any signal-like formulation other than AR or FS can perform better emissions estimation when it replaces the RSP. Also, in this research the SPE method was calibrated only for LOS F, when it is arguably of greatest value, but further research should be coordinated to extend the models in other possible traffic conditions such as LOS ÃE.

Phd Dissertation

Assessing costs and benefits of the kaohsiung rail system

Publication Date

June 14, 2014

Author(s)

Chung Yeh

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

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Abstract

This dissertation assesses costs and benefits of two recent public rail transit systems in Kaohsiung, Taiwan’s second largest city: Kaohsiung’s mass rapid transit (MRT) system, which was completed and inaugurated in 2008 and Kaohsiung light rail transit (LRT) loop line, which is now under construction. I first focus on the benefits of the opening of Kaohsiung’s MRT system as reflected in the price of apartments with elevators. I combine two stage least squares with geographically weighted regression to analyze transactions of apartments with elevators in 2007 and 2009. This approach allows accounting for the joint determination of time-on-market information (TOM) and price while allowing hedonic parameters to vary spatially. Results show that the opening of the MRT had a statistically significant and positive impact on the value of apartments with elevators. However, accounting for TOM has a negligible impact on my results. Second, I apply the theory of real options to capture uncertainty in operating revenues and costs in the context of build-operate-transfer (BOT) and operate-transfer (OT) contracts for Kaohsiung’s LRT loop line project. Unlike the traditional net present value (NPV) approach, real options analysis includes option values embedded in a project. Here, I rely on the binomial pricing approach to explore the value of the options to abandon and to expand the project. My findings show that the options to abandon or expand the LRT system are not sufficient to make a BOT contract attractive to a private firm, even under the best case scenario; however, accounting for the value of these options makes an OT contract at least 10% more attractive. These results show that accounting for uncertainty in large urban transportation projects can be important although the value of flexibility may not be sufficient to offset large construction costs.

Phd Dissertation

Simulation Study of Day-Night Variations in Emissions Impacts and Network Augmentation Schemes: An Application to PierPASS Policy for Port Trucks in California

Publication Date

June 1, 2014

Author(s)

Ankoor Bhagat

Areas of Expertise

Freight, Logistics, & Supply Chain Safety, Public Health, & Mobility Justice

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Abstract

Freight operations are critical to our prosperity, but they also generate substantial external costs in the form of additional congestion, air pollution, and health impacts. Unfortunately these external costs are not well understood. In this dissertation, I focus on the drayage trucks that serve the San Pedro Bay Ports (or SPBP, i.e. the Ports of Los Angeles and Long Beach in Southern California), which is the largest port complex in the country. This research focuses on the PierPASS program, which shifts drayage trucks traffic from mid-day and peak hours to the evening and night hours. External costs from drayage trucks remain a major concern for communities adjacent to the ports because they bear a disproportionate fraction of the health impacts (respiratory and cardiovascular illnesses, cancers, and premature deaths) associated with the pollution generated by ports operations. In this context, the purpose of my dissertation is analyze the impacts of shifting freight traffic to off-peak periods with an emphasis on congestion, air pollution (NOx, and PM) and related health impacts. This impact analysis was conducted using a framework that integrates microscopic traffic simulation with emission estimation, air dispersion, and a health impact assessment. The research also developed a new approach for origin-destination demand estimation on large microscopic simulation network that is made by augmenting an existing simulation network. Thus the research makes both policy analysis and methodological contributions, and is expected to help enable policy makers to craft cleaner logistics policies. I found that PierPASS had little impact on traffic congestion and on overall emissions of various pollutants. However, PierPASS had a significant impact on the distribution of these emissions between day and night. During night-time, total port truck emissions increased by 71% for NOx and 72% for PM, while day-time emissions decreased by 9% for both NOx and PM. My dispersion analysis shows that PierPASS increased air pollutant concentrations during both day time and night time because of boundary layer effects. Finally, my health impact analyses using EPA’s BenMAP model show that the annual social costs due to PierPASS are $438 million.