Rajesh Paleti

An empirical investigation into the time-use and activity patterns of dual-earner couples with and without young children

This paper examines the time-use patterns of adults in dual-earner households with and without children as a function of several individual and household socio-demographics and employment characteristics. A disaggregate activity purpose classification including both in-home and out-of-home activity pursuits is used because of the travel demand relevance of out-of-home pursuits, as well as to examine both mobility-related and general time-use related social exclusion and time poverty issues. The study uses the Nested Multiple Discrete Continuous Extreme Value (MDCNEV) model to analyze data from the 2010 American Time Use Survey (ATUS). A major finding of the study is that the presence of a child in dual-earner households not only leads to a reduction in in-home activity participation, but also a substantially larger decrease in out-of-home activity participation, suggesting a higher level of mobility-related social exclusion relative to overall time-use social exclusion.

T-communities and Sense of Community in a University Town: Evidence from a Student Sample using a Spatial Ordered-response Model

An emerging interest in transport research concerns the factors that can help to create strong, sustainable and ‘livable’ communities; however, relatively limited empirical work has been conducted to date. In this paper the perception of sense of community among neighbourhood residents is investigated. Drawing from research on tertiary street-communities (t-communities), the paper explores the effect of the urban landscape, particularly street networks, and neighbourhood and individual characteristics on sense of community. A sample of students at McMaster University in Hamilton, Canada, is used for the analysis. In addition to providing an opportunity to study sense of community, a student sample is interesting in its own right, as students are often a component of essential but at times uneasy relations between universities and towns. Analysis is based on the application of an ordered probit model with a spatial lag. The results provide evidence that t-community membership can influence sense of community.

Analysis of injury severity of large truck crashes in work zones

Work zones are critical parts of the transportation infrastructure renewal process consisting of rehabilitation of roadways, maintenance, and utility work. Given the specific nature of a work zone (complex arrangements of traffic control devices and signs, narrow lanes, duration) a number of crashes occur with varying severities involving different vehicle sizes. In this paper we attempt to investigate the causal factors contributing to injury severity of large truck crashes in work zones. Considering the discrete nature of injury severity categories, a number of comparable econometric models were developed including multinomial logit (MNL), nested logit (NL), ordered logit (ORL), and generalized ordered logit (GORL) models. The MNL and NL models belong to the class of unordered discrete choice models and do not recognize the intrinsic ordinal nature of the injury severity data. The ORL and GORL models, on the other hand, belong to the ordered response framework that was specifically developed for handling ordinal dependent variables. Past literature did not find conclusive evidence in support of either framework. This study compared these alternate modeling frameworks for analyzing injury severity of crashes involving large trucks in work zones. The model estimation was undertaken by compiling a database of crashes that (1) involved large trucks and (2) occurred in work zones in the past 10 years in Minnesota. Empirical findings indicate that the GORL model provided superior data fit as compared to all the other models. Also, elasticity analysis was undertaken to quantify the magnitude of impact of different factors on work zone safety and the results of this analysis suggest the factors that increase the risk propensity of sustaining severe crashes in a work zone include crashes in the daytime, no control of access, higher speed limits, and crashes occurring on rural principal arterials.

Prediction of secondary crash frequency on highway networks

Secondary crash (SC) occurrences are major contributors to traffic delay and reduced safety, particularly in urban areas. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, determining the primary contributing factors for their occurrence is crucial. The non-recurring nature of SCs makes it imperative to predict their occurrences for effective incident management. In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs. Given the count nature of secondary crash frequency data, the authors used count modeling methods including the standard Poisson and Negative Binomial (NB) models and their generalized variants to analyze secondary crash occurrences. Specifically, Generalized Ordered Response Probit (GORP) framework that subsumes standard count models as special cases and provides additional flexibility thus improving predictive accuracy were used in this study. The models developed account for possible effects of geometric design features, traffic composition and exposure, land use and other segment related attributes on frequency of SCs on freeways. The models were estimated using data from Shelby County, TN and results show that annual average daily traffic (AADT), traffic composition, land use, number of lanes, right side shoulder width, posted speed limits and ramp indicator are among key variables that effect SC occurrences. Also, the elasticity effects of these different factors were also computed to quantify their magnitude of impact.

Analysis of passenger-car crash injury severity in different work zone configurations

Work zone safety remains a priority to the Federal Highway Administration, State Highway Departments, highway engineers, and the traveling public. Work zones create a hospitable environment for crashes; an issue that gained tremendous share of attention in recent years. Therefore, every effort should be sought out to reduce the injury severity of crashes in work zones. In this paper we attempt to investigate factors contributing to the injury severity of passenger-car crashes in different work zone configurations. Considering the discrete ordinal nature of injury severity categories, a Mixed Generalized Ordered Response Probit (MGORP) modeling framework was developed. The model estimation was undertaken by compiling a database consisting of 10 years of crashes that involved at least one passenger car, and occurred in a work zone. Revealing the underlying factors contributing to injury severity levels for different work zone configurations will allow for distinguishing mitigation methods for higher severity outcomes that best suit each of the depicted work zone layouts. This can be accomplished through the implementation of specific safety measures based on the specific configuration of a work zone as a potential crash location. Elasticity analysis suggests that partial control of access, roadways classified as rural, crashes during evening times, crashes during weekends, and curved roadways are key factors that increase the likelihood of severe outcomes. Also, the effects of several covariates were found to vary across the different work zone configurations.

Investigation of Alternative Methods for Modeling Joint Activity Participation

This study represents a research effort to capture explicitly the intrahousehold interactions involved in the decision to participate in a joint activity. Joint activity participation is a lesser-explored step in activity-based travel demand modeling, since enlisting all possible subsets of household members in a large household results in many alternatives. For example, the number of possible subsets of members out of 10 persons is 210 = 1,024. After the exclusion of one empty subset and 10 subsets with a single member, 1,013 distinct subsets should be considered with two or more members for joint activity participation. Even more important, a joint choice model formulation is behaviorally unappealing and would require the formulation of a complicated utility function for each possible subset. Additionally, different subsets would have a highly different degree of similarity that would require a sophisticated error structure. This paper analyzes three methods to model joint activity participation that are relatively easy to estimate and implement for households of any size. In all three methods, the travel party is constructed on the basis of the individual and pairwise propensities of the household members to be engaged in a joint activity. These propensities are statistically estimated on survey data in the form of relatively simple binary choice models. The travel party emerges in the process of microsimulation as a result of the reconciliation of the decisions of different household members. This approach is an example of the use of the agent-based modeling paradigm to frame an intrahousehold decision-making mechanism in addition to econometric models.

Telecommuting and Its Impact on Activity–Time Use Patterns of Dual-Earner Households

Telecommuting choices of workers in multiworker households are likely to be interdependent. These telecommuting choices may also affect the activity–time use choices of all people in the household. From the standpoint of travel behavior and travel demand forecasting, it is important to test these hypotheses and quantify the relationship between telecommuting choices and activity–time use patterns. To do this, the present study developed a generalized extreme value–based joint count model for analyzing the monthly frequency of choosing to telecommute of workers in dual-earner households. A panel multiple discrete continuous extreme value model was also developed to study activity–time use decisions while accounting for household-level interaction effects. The study findings confirm the presence of strong intrahousehold interaction effects in both the telecommuting and activity–time use choices of workers. Telecommuting choices were found to have a significant influence on daily activity–time use decisions for both mandatory and nonmandatory activities.

Truck Parking Utilization Analysis Using GPS Data

AbstractUnavailability of sufficient parking spaces during various periods at rest areas results in illegal and unsafe parking at on/off-ramps and other unauthorized areas, which may lead to traffi...

Modeling the impact of latent driving patterns on traffic safety using mobile sensor data

Smartphones are now equipped with sensors capable of recording vehicle performance data at a very fine temporal resolution in a cost-effective way. In this paper, mobile sensor data from smartphones was used to identify and quantify unsafe driving patterns and their relationship with traffic crash incidences. Statistical models that account for measurement error associated with microscopic traffic measures computed using mobile sensor data were developed. The models with microscopic traffic measures were shown to be statistically better than traditional models that only control for roadway geometry and traffic exposure variables. Also, generalized count models that account for measurement error, spatial dependency effects, and random parameter heterogeneity were found to perform better than standard count models.