working paper

Simulating Travel Reliability

Abstract

We present a simulation model designed to determine the impact on congestion of policies for dealing with travel time uncertainty. The model combines a supply side model of congestion delay with a discrete choice econometric demand model that predicts scheduling choices for morning commute trips. The supply model describes congestion technology and exogenously specifies the probability, severity, and duration of non-recurrent events. From these, given traffic volumes, a distribution of travel times is generated, from which a mean, a standard deviation, and a probability of arriving late are calculated. The demand model uses these outputs from the supply model as independent variables and choices are forecast using sample enumeration and a synthetic sample of work start times and free flow travel times. The process is iterated until a stable congestion pattern is achieved. We report on the components of expected cost and the average travel delay for selected simulations.

working paper

Airport choice and airline choice in the market for air travel between the San Francisco Bay area and greater Los Angeles in 1995

Publication Date

September 1, 2005

Author(s)

Abstract

This paper empirically investigates the impact of airport and airline supply characteristics on the air travel choices of passengers departing from one of three San Francisco Bay area airports and arriving at one of four airports in greater Los Angeles. It does so by estimating a conditional logit model for the market of air travel between both metropolitan areas in 1995, and using the estimated model to simulate three counterfactual scenarios. First, reducing access times to San Francisco International airport by 10% for all travelers increases the market share of that airport by 4.5%-point. United Airlines benefits from the reduced access times, as its market share increases by 2.9%-point. Second, reducing average delays at San Francisco International airport by 10% has similar aggregate effects to the first scenario, but indicates that leisure travelers value access time reductions more than reduced delays. Third, entry of Southwest airlines in San Francisco International airport increases the market share of Southwest airlines by 5%-point to 15-%point, depending on assumptions concerning its continuation of services at Oakland International Airport, and assuming that rival carriers do not respond in terms of prices or service levels.

working paper

The Location Selection Problem for the Household Activity Pattern Problem

Publication Date

September 5, 2012

Working Paper

UCI-ITS-WP-12-1

Areas of Expertise

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.

Suggested Citation
Jee Eun Kang and Will W. Recker (2012) The Location Selection Problem for the Household Activity Pattern Problem. Working Paper UCI-ITS-WP-12-1. Institute of Transportation Studies, Irvine. Available at: https://escholarship.org/uc/item/5865d8xf.

working paper

Johnny Walks to School - Does Jane? Sex Differences in Children's Active Travel to School

Abstract

Communities are traditionally built with one transportation mode and user in mind—the adult automobile driver. Recently, however, there has been an international focus on the trip to school as an opportunity to enhance children’s independent active travel. Several factors must be considered when designing programs to promote walking and bicycling. This paper examined the influence of child sex on caregivers’ decisions about travel mode choice to school.

Caregivers of children in grades three to five from ten California Safe Routes to School communities were surveyed on their child’s normal travel mode to school and factors that determined travel decisions. Results indicate that the odds of walking and bicycling to school are 40 percent lower in girls than boys; however, this relationship is significantly moderated by the caregiver’s own walking behavior. The findings suggest that programs that focus on increasing children’s active travel to school should consider multiple influences on health behavior, including the neighborhood physical activity of parents.

Phd Dissertation

Modeling individual route choice with automated real -time vehicle trip histories

Abstract

Collecting rich individual trip data at an individual level has long been viewed as a hard task and has become a bottleneck in modeling and calibrating travel behavior models since traditional survey methods are both costly and time-consuming. New technologies make such data a possibility and thus there is a need for frameworks that model individual behavior in real-time using such data. Such modeling will find use in a variety of real-time network optimization and prediction schemes. This dissertation describes the details of plausible behavioral modeling of this kind, and develops new data structures that are needed both for handling the network combinatorics in the analysis and in the data storage. The work is presented in the context of a new technology we propose called the Persistent Traffic Cookie (PTC) system which uses the short range wireless connection between vehicles and road side controllers to store authenticated, time-stamped node sequences on an onboard database. The dissertation makes the premise that traditional travel behavior models, including those based on disaggregate decision paradigms were developed primarily for application in aggregate level prediction and are thus not very applicable for an individual’s route choice prediction in real-time. A scheme that does not require variation of explanatory variables across the choice sets or variation in the individual’s decisions for calibration may be essential. Thus the dissertation developed models based on observed frequencies of decisions. The research also stresses the importance of path and sub-path notions in route choice decisions and provides appropriate data structures that enable modeling with such notions. Two methods that directly query the collected sequence data using efficient data structures based on the suffix tree and the suffix array schemes and node/edge transition probability model, are proposed to predict individual travels from trip diary database. A day-to-day PTC simulation framework with behavior components is proposed to generate consistent PTC data and implemented in Paramics microscopic traffic simulator. Day-to-day PTC simulations are carried out for two Paramics networks, including the Irvine Triangle network, which is a well-calibrated real world network. Various scenarios are created to test the sensitivities of the proposed prediction methods. The simulation results shows that it seems the prediction methods are robust with regard to the underlying behavior models, traffic conditions and tracking periods.

Suggested Citation
Yu Zhang (2006) Modeling individual route choice with automated real -time vehicle trip histories. Ph.D.. University of California, Irvine. Available at: https://uci.primo.exlibrisgroup.com/permalink/01CDL_IRV_INST/1gpb62p/alma991035092986404701 (Accessed: October 14, 2023).

Phd Dissertation

Modeling Activity Pattern Generation and Execution

Abstract

Activity-based approaches are perhaps the most promising alternative to the current travel forecasting methodology. This dissertation first presents a pattern generation model that can serve as a link between activity and trip-based methodologies. The model uses a clustering approach to identify groups of similar activity-travel behavior and relates them to household socioeconomic attributes. Minimally, the pattern generation model is then expanded to serve as the core component of a proposed activity-based microsimulation model that constructs complete origin-destination tables using a wholly activity-based approach. The techniques developed provide due diligence to the complex nature of activity-travel behavior in terms of spatial and temporal constraints, household interactions, and the derived nature of such behavior. A successful application of the expanded model is outlined using data from the 1994 Portland activity-travel survey.

Suggested Citation
Anup Kulkarni (2001) Modeling Activity Pattern Generation and Execution. PhD Dissertation. UC Irvine. Available at: https://escholarship.org/uc/item/99v1m5x0.