Ram M. Pendyala

IMPACT OF TELECOMMUTING ON SPATIAL AND TEMPORAL PATTERNS OF HOUSEHOLD TRAVEL

A spatial and temporal analysis of travel diary data collected during the State of California Telecommuting Pilot Project is performed to determine the impacts of telecommuting on household travel behavior. The analysis is based on geocoded trip data where missing trips and trip attributes have been augmented to the extent possible. The results confirm the earlier finding that the Pilot Project telecommuters substantially reduced travel; on telecommuting days, the telecommuters made virtually no commute trips, reduced peak-period trips by 60%, total distance traveled by 75%, and freeway miles by 90%. The spatial analysis of the trip records has shown that the telecommuters chose non-work destinations that are closer to home; they exhibited contracted action spaces after the introduction of telecommuting. Importantly, this contraction took place on both telecommuting days and commuting days. The telecommuters distributed their trips, over the day and avoided peak-period travel on telecommuting days. Non-work trips, however, show similar patterns of temporal distribution on telecommuting days and commuting days. Non-work trips continued to be made during the lunch period and late afternoon and evening hours.

A conceptual analysis of the impact of travel demand management on private car use

A conceptual framework is presented that may be utilized when analyzing changes in household travel arising from the range of potential measures available to policy makers. The proposed framework draws on goal setting theory in order to understand how travel is influenced by the impact various travel demand management (TDM) measures have on time, cost, and convenience of travel options. Travel is understood from a perspective assuming that it is controlled by negative feedback functioning to minimize deviations from goals nested at different levels. The conceptual framework, with its basis in goal setting and control theories, is then applied to understanding strategic and operational choice related to travel as well as habitual travel. Finally, the proposed conceptual framework is used to highlight and focus attention on key research issues that ought to be addressed if our understanding of the impact of TDM measures on household travel, and private car use in particular, is to improve.

Modeling crashes involving pedestrians and motorized traffic

This paper presents an empirical inquiry into the predictive modeling of crashes involving pedestrians and motorized traffic on roadways. Empirical models based on the negative binomial distribution and mixing distributions, such as the zero-inflated Poisson distribution, are presented and discussed in terms of their applicability to pedestrian crash phenomena. Key modeling issues relating to the presence of excess zeros as well as unobserved heterogeneity in pedestrian crash distributions are addressed. The empirical results show that zero-inflated count distributions, such as the zero-inflated Poisson, are promising methodologies for providing explanatory insights into the causality behind pedestrian-traffic crashes.

On the formulation of time-space prisms to model constraints on personal activity-travel engagement

The notion of time-space prisms has often been used in the context of describing activity-travel patterns of individuals. This paper presents a methodology for estimating the temporal vertices of time-space prisms using the stochastic frontier modeling technique. Observed trip starting and ending times are used as dependent variables and socio-economic characteristics and commute characteristics serve as independent variables. The models are found to offer plausible results indicating that temporal vertices of time-space prisms, though unobservable, can be estimated based on temporal characteristics of observed activity-travel patterns. Comparisons of stochastic frontier models of prism vertices and the distributions of prism vertices are presented using two activity data sets collected in the United States – San Francisco and Miami. Differences and similarities in temporal vertex locations are highlighted in the paper.

Florida Activity Mobility Simulator: Overview and Preliminary Validation Results

The development of modeling systems for activity-based travel demand ushers in a new era in transportation demand forecasting and planning. A comprehensive multimodal activity-based system for forecasting travel demand was developed for implementation in Florida and resulted in the Florida Activity Mobility Simulator (FAMOS). Two main modules compose the FAMOS microsimulation model system for modeling activity-travel patterns of individuals: the Household Attributes Generation System and the Prism-Constrained Activity-Travel Simulator. FAMOS was developed and estimated with household activity and travel data collected in southeast Florida in 2000. Results of the model development effort are promising and demonstrate the applicability of activity-based model systems in travel demand forecasting. An overview of the model system, a description of its features and capabilities, and preliminary validation results are provided.

An Activity-Based Microsimulation Analysis of Transportation Control Measures

This paper describes the development and application of an activity-based microsimulation model system capable of simulating changes in individual travel patterns in response to a transportation control measure. A unique activity-based time use survey was conducted to obtain information on peoples activity and travel patterns and their likely behavioral adjustment in response to various transportation control measures. This paper describes the survey and the use of the ensuing data set in estimating various components of the simulator, called AMOS. The first application in the Washington DC area demonstrated the capabilities of AMOS as a transportation policy analysis tool. Sample results from the Washington DC demonstration are presented.

DISCRETE CHOICE MODELS OF TRAVELER PARTICIPATION IN DIFFERENTIAL TIME OF DAY PRICING PROGRAMS

Tolls that vary based on time of day or congestion are gaining attention around the world as a potential travel demand management strategy that can shift peak period travel to off peak periods thereby contributing to peak period congestion relief. However, despite the widespread interest in the concept, there is very little empirical data available on the impacts of variable tolls on traveler choices and disaggregate models that can be used to predict traveler response to variable pricing are few. This paper reports on results from two bridges with differential time of day tolls in the Lee County area of Florida in the United States. Using travel survey data collected at these two bridges, discrete choice models of traveler response to the variable toll rates are estimated. The models indicate that travelers who are retired, have a low income, have flextime at their place of employment, or have a flexible travel schedule are more likely to alter their time of travel with greater frequency due to the variable toll.

GENERATION OF SYNTHETIC DAILY ACTIVITY-TRAVEL PATTERNS

Microsimulation approaches to travel demand forecasting are gaining increased attention because of their ability to replicate the multitude of factors underlying individual travel behavior. The implementation of microsimulation approaches usually entails the generation of synthetic households and their associated activity-travel patterns to achieve forecasts with desired levels of accuracy. A sequential approach to generating synthetic daily individual activity-travel patterns was developed. The sequential approach decomposes the entire daily activity-travel pattern into various components, namely, activity type, activity duration, activity location, work location, and mode choice and transition. The sequential modeling approach offers practicality, provides a sound behavioral basis, and accurately represents an individual’s activity-travel patterns. In the proposed system each component may be estimated as a multinomial logit model. Models are specified to reflect potential associations between individual activity-travel choices and such factors as time of day, socioeconomic characteristics, and history dependence. As an example results for activity type choice models estimated and validated with the 1990 Southern California Association of Governments travel diary data set are provided. The validation results indicate that the predicted pattern of activity choices conforms with observed choices by time of day. Thus, realistic daily activity-travel patterns, which are requisites for microsimulation approaches, can be generated for synthetic households in a practical manner.

Development of Time-of-Day-Based Transit Accessibility Analysis Tool

The measurement of transit accessibility and availability is important both for forecasting transit ridership and for planning and evaluating transit service. When measuring transit accessibility, both spatial and temporal dimensions of demand and supply are important considerations. Between these two dimensions, the temporal dimension has lagged behind the spatial dimension with respect to its explicit recognition and incorporation in transit accessibility measurement. A transit accessibility measurement tool has been developed that explicitly considers two facets of the time dimension. Considered first is the supply side of the time dimension by incorporating the span and headway of service and the willingness to wait to determine the actual time duration when service is truly available to potential riders. Next considered is the demand side of the time dimension by incorporating the time-of-day distribution of travel demand to determine the relative value of the transit service provided in each time period of the day. The tool measures transit availability with regard to the daily trips per capita in each zone that is exposed to transit service. The flexible spreadsheet-based tool can be easily and quickly applied at the local level in support of service planning and service delivery analysis. The application of the tool is demonstrated with a simple numerical illustration.

Application of an Activity-Based Travel-Demand Model Incorporating a Rule-Based Algorithm

In this paper an activity-based travel-demand model called AMOS is described. The model system is capable of simulating changes in individual activity and travel behavior that may be brought about by a change in the transportation system. These simulations may then be used to predict the impacts of various transportation policies on regionwide travel characteristics. A rule-based activity-scheduling algorithm is at the heart of AMOS. The algorithm simulates changes in activity and travel patterns while recognizing the presence of constraints under which travelers make decisions. Operationally, the algorithm reads the baseline activity and travel pattern of an individual and then determines the most probable adjustments that the individual may make in response to a transportation policy. In this paper, the scheduling algorithm is described in detail and sample results from a case study in the Washington, DC metropolitan area are provided.