published journal article

The location selection problem for the household activity pattern problem

Publication Date

August 31, 2013

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

Travel Behavior, Land Use, & the Built Environment

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Abstract

In this paper, an integrated destination choice model based on routing and scheduling considerations of daily activities is proposed. Extending the Household Activity Pattern Problem (HAPP), the Location Selection Problem (LSP–HAPP) demonstrates how location choice is made as a simultaneous decision from interactions both with activities having predetermined locations and those with many candidate locations. A dynamic programming algorithm, developed for PDPTW, is adapted to handle a potentially sizable number of candidate locations. It is shown to be efficient for HAPP and LSP–HAPP applications. The algorithm is extended to keep arrival times as functions for mathematical programming formulations of activity-based travel models that often have time variables in the objective.

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On Activity-based Network Design Problems

Phd Dissertation

Improving On-Road Emissions Estimates with Traffic Detection Technologies

Publication Date

September 14, 2013

Associated Project

A Support System for Estimation and Monitoring of Real-Time On-Road Emmissions–Dissertation Grant for PhD Candidate Hang Liu

Author(s)

Hang Liu

Areas of Expertise

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

View Phd Dissertation

Abstract

Transportation has been a significant contributor to greenhouse gas and criteria air pollutant emissions. Emission mitigation strategies are essential in reducing transportation’s impacts on our environment. In order to effectively develop and evaluate on-road emissions reduction strategies, accurate quantification of emissions is the critical first step. The accuracy and resolution of the traffic measures needed by the emission models will directly affect the emission estimation results. This dissertation investigates the ability of traffic detection technologies to provide the traffic measures needed for accurate on-road emissions estimation. A review of traffic detection technologies is provided with insight into their capability and suitability for estimating emissions. The Inductive Vehicle Signature (IVS) system is identified as currently the most promising technology to couple with EPA’s latest MOVES emission model for estimating emissions. Models and algorithms based on the IVS detection system are developed to generate the two most important traffic measures for emission estimation: vehicle mix and average speed. The performances of the models are verified using real-world data. Assuming the IVS system and the models developed are deployed to generate vehicle mix and average speeds, the accuracy and reliability of the emissions estimation results based on these traffic measures are evaluated by simulating the operations of the models in the field using NGSIM data. Very promising results are obtained, which clearly demonstrates the capability of the IVS system for on-road emissions estimation. A Real-Time Emissions Estimation and Monitoring System based on the IVS technology is implemented on the I-405 freeway to estimate operational emissions on the road in real-time. Although average speed has been the most common input into emission models, the MOVES model is capable of using second-by-second vehicle speed trajectories to estimate emissions more accurately. Vehicle speed trajectories are becoming increasingly available thanks to the proliferation of GPS-enabled personal navigation devices and smartphones. Crowd sourced GPS data can also be used by emission models like MOVES to estimate emissions. This dissertation studies the use of a limited number of GPS speed trajectories to estimate emissions for all traffic on the road. Two fundamental questions are answered by this work: 1) how can GPS data be used for emissions estimation, and 2) how does the penetration rate of the GPS probes affect the emission results. With the methods proposed in this study, it is found that emissions can be estimated with high accuracy and reliability with even a very small penetration rate of GPS probes, when combined with the vehicle mix data generated from the IVS system. Discussions on the applications of the proposed systems and methods to various emissions analysis scenarios are also provided in this dissertation.

working paper

Integration of Weigh-in-Motion and Inductive Signature Technology for Advanced Truck Monitoring

Publication Date

July 31, 2013

Author(s)

Sarah Hernandez, Andre (Yeow Chern) Tok, Stephen Ritchie

Areas of Expertise

Freight, Logistics, & Supply Chain Infrastructure Delivery, Operations, & Resilience Intelligent Transportation Systems, Emerging Technologies, & Big Data

View working paper

Abstract

Trucks have a significant impact on infrastructure, traffic congestion, energy consumption, pollution and quality of life. To better understand truck characteristics, comprehensive high resolution truck data is needed. Higher quality truck data can enable more accurate estimates of GHGs and emissions, allow for better management of infrastructure, provide insight to truck travel behavior, and enhance freight forecasting. Currently, truck traffic data is collected through limited means and with limited detail. Agencies can obtain or estimate truck travel statistics from surveys, inductive loop detectors (ILD) and weigh-in-motion (WIM) stations, or from manual counts, each of which have various limitations. Of these sources, WIM and ILD seem to be the most promising tools for capturing detailed truck information. Axle spacing and weight from existing WIM devices and unique inductive signatures indicative of body type from ILDs equipped with high sampling rate detector cards are complementary data sources that can be integrated to provide a synergistic resource that otherwise does not exist in practice, a resource that is able to overcome the drawbacks of the traditional truck data collection methods by providing data that is detailed, link specific, temporally continuous, up-to-date, and representative of the full truck population. This integrated data resource lends itself very readily toward detailed truck body classification which is presented as a case study. This body classification model is able to predict 35 different trailer body types for FHWA class 9 semi-tractors, achieving an 80 percent correct classification rate. In addition to the body classification model, the large data set resulting from the case study is itself a valuable and novel resource for truck studies.

research report

Spatially Focused Travel Survey Data Collection and Analysis: Closing Data Gaps for Climate Change Policy

Publication Date

August 26, 2013

Author(s)

Doug Houston, Marlon Boarnet, Gavin Ferguson, David Nyenhuis

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View research report

Abstract

This research explored the effect of small area land use policies on land use–travel behavior relationships. The authors pioneered methods to obtain travel data with sufficient spatial focus to shed light on how land use influences vehicle miles of travel. Travel diary surveys were obtained from four small neighborhoods in southern California. Results suggest differences in walking, transit, and passenger vehicle travel behavior associated with residing in areas with different built environment, land use, and transit access characteristics. Households in areas with higher employment accessibility tended to have more walking travel and lower vehicle miles of travel (VMT). Households within 1.5 miles of a rail transit station tended to have more transit ridership. Households within 0.5–1.0 miles of a rail transit station tended to have more walking travel, while households with higher levels of transit service were associated with lower household VMT. The methods developed advanced efforts toward low-cost, rapid travel data collection that can be used in before-and-after transportation program evaluations in the future.

working paper

Stochastic Dynamic Itinerary Interception Refueling Location Problem with Queue Delay for Electric Taxi Charging Stations

Publication Date

June 30, 2013

Author(s)

Joseph Chow, R. (Jay) Jayakrishnan, Ji Young Park

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Zero-Emission Vehicles & Low-Carbon Fuels

View working paper

Abstract

A new facility location model and a solution algorithm are proposed that feature 1) itinerary-interception instead of flow-interception; 2) stochastic demand as dynamic service requests; and 3) queueing delay. These features are essential to analyze battery-powered electric shared-ride taxis operating in a connected, centralized dispatch manner. The model and solution method are based on a bi-level, simulation-optimization framework that combines an upper level multiple-server allocation model with queueing delay and a lower level dispatch simulation based on earlier work by Jung and Jayakrishnan. The solution algorithm is tested on a fleet of 600 shared-taxis in Seoul, Korea, spanning 603 km2, a budget of 100 charging stations, and up to 22 candidate charging locations, against a benchmark “naïve” genetic algorithm that does not consider cyclic interactions between the taxi charging demand and the charger allocations with queue delay. Results show not only that the proposed model is capable of locating charging stations with stochastic dynamic itinerary-interception and queue delay, butt that the bi-level solution method improves upon the benchmark algorithm in terms of realized queue delay, total time of operation of taxi service, and service request rejections. Furthermore, we show how much additional benefit in level of service is possible in the upper-bound scenario when the number of charging stations approaches infinity.

working paper

An Alternative Method to Estimate Balancing Factors for the Disaggregation of OD Matrices

Publication Date

July 31, 2013

Author(s)

Neda Masoud, Fatemeh Ranaiefar, Michael McNally, Daniel Rodriguez-Roman, Stephen Ritchie

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View working paper

Abstract

The solution algorithms for the family of flow distribution problems, which include (1) the trip distribution problem of travel forecasting, (2) the OD estimation from link counts problem, and (3) the trip matrix disaggregation problem, are usually based on the Maximum Entropy (ME) principle. ME-based optimization problems are hard to solve directly by optimization techniques due to the complexity of the objective function. Thus, in practice, iterative procedures are used to find approximate solutions. These procedures, however, cannot be easily applied if additional constraints are needed to be included in the problem. In this paper a new approach for balancing trip matrices with application in trip matrix disaggregation is introduced. The concept of generating the most similar distribution (MSD) instead of the Most Probable Distribution of Maximum Entropy principle is the basis of this approach. The goal of MSD is to minimize the deviation from the initial trip distribution, while satisfying additional constraints. This concept can be formulated in different ways. Two MSD-based objective functions have been introduced in this paper to replace the ME-based objective function. One is the Sum of Squared Deviations MSD (SSD-MSD), and the other is Minimax-MSD. While SSD-MSD puts more emphasis on maintaining the base year trip shares as a whole, Minimax-MSD puts more emphasis on maintaining the share of each individual element in the trip table. The main advantage of replacing the entropy-based objective functions with any of these functions is that the resulting problems can include additional constraints and still be readily solved by standard optimization engines. In addition, these objective functions could produce more meaningful results than entropy-based functions in regional transportation planning studies, as shown in the case study and some of the examples in the paper. Several examples and a case study of the California Statewide Freight Forecasting Model (CSFFM) are presented to demonstrate the merits of using MSD-based formulations.

Phd Dissertation

Online Advertising: A Large Scale Computing Perspective

Publication Date

June 29, 2013

Author(s)

Tianbing Xu

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data

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Abstract

Online advertising is emerging as a primary industry for major computer science companies such as Facebook, Google, Microsoft and Yahoo!. From a computing perspective, we face challenges to match the best ads to suitable users within fast enough response times on the massive industrial Petabyte scale of data. In this thesis, centered on computational problems related to online ad problems, the main contributions are: to provide a new statistical model for ad response prediction; a new parallel computing algorithm for model inference; a new optimization method in an online stochastic environment; and exploration and exploitation for ad selection in time-varying dynamic systems. First, we introduce a Bayesian Regression Model for click through rate prediction, and develop and parallelize learning and inference algorithms in the distributed Hadoop Map-Reduce framework. Then, we propose a Multi-Core Gibbs Sampling. Our exact parallel inference achieves near linear speedup in the complex statistical models on 1.8 million news articles from over 20 years of the New York Times. Moreover, we develop a voted Dual Average method for online classification, derive the training and generalization error bounds, and achieve state-of-the-art performance in parsing reranking. Finally, we discuss the exploration and exploitation in a more realistic scenario under a time-varying environment. We introduce different discount factors (e.g.exponential decay) according to the underlying dynamics; and consequently our algorithm is able to trade-off exploration and exploitation adaptively.

Phd Dissertation

The development and evaluation of a highly-resolved California electricity market model to characterize the temporal and spatial grid, environmental, and economic impacts of electric vehicles

Publication Date

June 29, 2013

Author(s)

Ghazal Razeghi

Areas of Expertise

Transportation Economics, Funding, & Finance Zero-Emission Vehicles & Low-Carbon Fuels

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Abstract

Drastic changes need to occur in the electricity generation and transportation sectors in order to address environmental concerns that have attracted attention in recent years. These concerns, combined with increasing energy prices, have led to elevated interest in alternative, and low to non-carbon technologies in both sectors from both researchers and policymakers. In the state of California, integration of renewable resources, and switching to more environmental-friendly transportation options, have been mandated by stringent environmental regulations such as AB 32, AB 118, and RPS goals. A spatially and temporally resolved resource dispatch model is developed that simulates the operations of an electricity market while taking into account all the physical constraints associated with various components of an electricity grid such as transmission system constraints. Multiple modules are also developed to provide inputs to the model and also determine the interaction between electricity generation and transportation sectors. This dispatch model and its modules are used to assess a selected set of future transportation and electricity generation scenarios. These scenarios include various dispatch strategies, integration of renewables, and deploying plug-in electric vehicles. The results show that with appropriate planning, the generation, transmission, and distribution sectors will be able to accommodate a high penetration of plug-in vehicles, and they will result in an overall reduction in criteria pollutant and greenhouse gas emissions. With vehicle smart charging, the need for planning in the generation sector is minimized and the installed generators will be able to handle the extra load caused by the vehicles. Different dispatch strategies are developed and results indicate that the best approach to reduce emissions while keeping the system’s costs at acceptable levels is a combination of economic and environmental dispatch strategies. This strategy can also be used to dispatch renewable resources as a part of the market instead of using the current must-take strategy. The methodology and the tools developed provide a means to examine various aspects of future scenarios and their impacts on different sectors, and can be used for decision making and planning purposes.

Phd Dissertation

Choices and constraints : gender differences in travel behavior

Publication Date

June 29, 2013

Author(s)

Hsin Hsu

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

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Abstract

The purpose of my dissertation is to explore how gender interacts with other factors such as personal attitudes, earning power, household structure, and the built environment to influence travel behavior, with a focus on whether these factors strengthen or relieve the constraints women face when making travel choices. In this context, my dissertation is organized around three separate case studies in California that rely on various discrete choice econometric models. Results from my first case study indicate that chauffeuring trips in two-adult households with children are intensely gendered, and women bear most of the chauffeuring burden. It is partly because women’s income earning potential is generally lower than that of their male partners. However, living in neighborhoods with access to bus stop and with less single-family housing can reduce this gender chauffeuring gap. It suggests that compact urban development and better bus service may yield social benefits that help alleviate women’s household burdens. In my second case study, I find that mothers are more likely to extend their greater concerns about traffic safety to their children, which in turn reduces the chance that their children will walk or bike to school. However, mothers bear most of the burden to chauffeur their children to school not because they worry more, but because chauffeuring children is still seen more as a mother’s responsibility. It suggests that interventions targeting an increase in children’s active commuting to school should focus on the concerns of mothers, especially as they relate to traffic characteristics. My findings in the third case study reveal that both environmental and safety concerns are associated with sustainable travel behavior, but the influence of safety concerns is more prominent and women have greater safety concerns. Moreover, proximity to transit service can increase sustainable travel behavior, but having higher safety concerns can totally offset this effect. For women with higher safety concerns, the reduction is even greater. It suggests that to encourage sustainable travel behavior, reducing personal safety concerns about transit use may be more effective than increasing public environmental awareness, especially for attracting potential female riders.

Phd Dissertation

Essays on econometric methodology and application

Publication Date

June 14, 2013

Author(s)

Alicia Lloro

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

This dissertation is composed of three chapters on estimation of vehicle choice and utilization models, simulated likelihood estimation, and Bayesian non-parametric additive methods for neighborhood effect models. The first chapter exploits differences in fuel efficiency between hybrid vehicles and their gasoline counterparts to investigate two behavioral questions relating to fuel economy standards: how car buyers value fuel economy (the energy paradox) and whether improved fuel efficiency increases travel (the rebound effect). Emphasis is placed on handling methodological and data issues that are typically ignored in prior studies, such as partially observed choice, endogeneity, and measurement error. Estimates of the rebound effect and consumer valuation of fuel economy remain imprecise despite the use of the most detailed household level data available and sound methodology to handle limitations with these data. The inability to precisely estimate these important policy questions suggests it is a worthwhile endeavor to obtain reliable, detailed data on household vehicles. The following chapter (joint with Ivan Jeliazkov) presents techniques, based on Markov chain Monte Carlo (MCMC) theory, for construction of the likelihood function in a broad class of hierarchical models where direct evaluation of the likelihood function is not possible. We review existing estimators, introduce new MCMC estimators, and examine their performance in applications to the Poisson-log normal and mixed logit models. The MCMC techniques outperform existing methods in both settings, with the existing methods performing especially poorly in the Poisson-log normal case. The final chapter applies Bayesian semiparametric additive methods to a neighborhood effects model. The baseline model assumes all covariates enter linearly, whereas the approach in this paper allows for flexible functional forms. An efficient Markov chain Monte Carlo (MCMC) algorithm that exploits the properties of banded matrices is proposed for estimation. The efficiency gains offered by the banded matrix algorithm are critical, as they permit the estimation of applications with large sample sizes. The model and estimation methodology are used to examine foreclosure contagion in California. The results reveal the impact of neighborhood effects on foreclosure rates as nonlinear, where the relationship resembles a tipping point phenomenon.