Karthik K. Srinivasan

MODELING INERTIA AND COMPLIANCE MECHANISMS IN ROUTE CHOICE BEHAVIOR UNDER REAL-TIME INFORMATION

This research examines route choice, in the presence of real-time information, as a consequence of two underlying behavioral mechanisms: compliance and inertia. The compliance mechanism reflects the propensity of a user to comply with the information supplied by advanced traveler information systems (ATIS). The inertial mechanism represents the tendency of users to continue on their current paths. These two mechanisms in route choice are neither mutually exclusive nor collectively exhaustive. A framework is proposed to model these mechanisms explicitly. The proposed framework decomposes the route choice into two states by exploiting the user’s path choice structure (resulting from the current choice prior to the decision of interest) and the information supplied by ATIS. In each state, the mechanisms are incorporated by associating their utilities with those that reflect the specific attributes of the alternative paths. The resulting nested choice structure is implemented using the multinomial probit model. This framework is illustrated using route choice data obtained from dynamic interactive simulator experiments. The empirical results strongly support the simultaneous presence of both the compliance and inertia mechanisms in route choice behavior. The results also indicate that information quality, network loading and day-to-day evolution, level-of-service measures, and trip-makers’ prior experience are significant determinants of route choice through the inertial and compliance mechanisms. These findings have important implications in travel behavior forecasting, ATIS design and evaluation, demand management, and network state prediction.

Injury Severity Analysis with Variable and Correlated Thresholds: Ordered Mixed Logit Formulation

An ordered mixed logit (OML) formulation is proposed to model injury severity, given a crash. The proposed formulation extends the ordered probit/logit models by accommodating variable, random, and correlated injury severity thresholds associated with various severity levels. The proposed model is calibrated using a sample from the 1996 National Automotive Sampling System General Estimates System data set. Chi-square tests indicate that the more general OML formulation provides a statistically superior representation of observed injury severity data than corresponding ordered logit models. Model results indicate that injury severity thresholds vary systematically depending on individual, traffic, crash-related, and vehicle characteristics. Further, significant unobserved variability in thresholds is found, and the thresholds are correlated within a given individual. The results suggest drastically increased chances of fatal injury due to certain factors including tripped rollovers and injuries sustained by moped riders. These findings suggest that targeting these drivers, behaviors, and conditions with suitable countermeasures including education, enforcement, or curfews is likely to result in substantial safety benefits. The model and results have important implications for developing effective safety countermeasures and more accurate assessment of their impacts.

Role of Congestion and Information in Trip-Makers' Dynamic Decision Processes: Experimental Investigation

The role of congestion and information on trip-makers’ dynamic decision processes, particularly commuters’ route- and departure-time-switching behavior, is investigated. Using data from interactive simulation experiments, indifference bands for route- and departure-time-switching decisions are calibrated based on a boundedly rational behavioral framework. Trip-maker behavior is influenced not only by the magnitude of congestion but also by its day-to-day evolution. Both information quality and the interaction between information, behavioral inertia, and traffic supply conditions are significant determinants of behavior. Trip-makers’ short-term and longer-term experiences in traffic strongly influence route- and departure-time-switching decisions. These findings have important implications for dynamic traffic modeling, network state prediction, design of Advanced Traveler Information Systems products and services, and assessment of Intelligent Transportation Systems impacts.

Determinants of Changes in Mobility and Travel Patterns in Developing Countries: Case Study of Chennai, India

This study analyzes changes in sociodemographic, activity, land use, and mobility patterns and their effects on travel dimensions in the context of a developing country. More specifically, increase in vehicle ownership (both two-wheelers and cars) and changes in mode choice over time are observed and analyzed with the use of household data from Chennai, India. Three sources of dynamics are analyzed: exogenous variable dynamics, sensitivity changes over time, and the influence of lagged and persistent effects. The key drivers of growth in travel demand include the increase in vehicle ownership, the number of workers, and the increase in female drivers. The influence of social and technological factors on vehicle ownership and mode choice such as peer pressure and mobile phone ownership are also significant. In addition, the effect of land use, accessibility, and activity has been investigated. Results show significant evidence of differences in travel decisions across different user segments (on the basis of driving knowledge and vehicle–worker ratio) and over time. The proposed disaggregate models provide a reasonably good description (goodness of fit is 47% to 64%) of the observed changes in travel patterns. The findings and results assume importance in the context of increasing congestion, declining public transportation share, and the imminent need for enhancing urban transportation system capacity in cities of developing countries.

Tripmaker choice behavior for shopping trips under real-time information: model formulation and results of stated-preference internet-based interactive experiments

Abstract This paper examines behavioral responses of non-commuters under real-time information during shopping trips. Utilizing results from an interactive stated-preference internet-based survey, discrete choice models are developed to investigate factors that influence en-route switching to alternate destinations and alternate routes. The fundamental difficulty in modeling this phenomenon is due to the manner in which information is provided to assist trip-making. The information provided and user choices are interdependent. That is, the choice set presented to a tripmaker at a decision state is predicated on his/her previous decision. Conversely, a tripmakers decision in turn alters his/her information and choice sets. A model structure is formulated to overcome this difficulty. It explicitly captures the conditional nature of the decision process. The developed model provides insight on en-route diversions during the shopping trip together with the factors affecting these decisions, especially with regard to the role of real-time information.

Impact of Mobile Phones on Travel: Empirical Analysis of Activity Chaining, Ridesharing, and Virtual Shopping

Mobile phones are indispensable and ubiquitous tools that afford unprecedented levels of connectivity and accessibility to millions of users. A study investigated the influence of mobile phones on three travel-related dimensions: unplanned activity chaining, unplanned rideshares arranged by using mobile phones, and shopping by phone. These dimensions were investigated by using data from 400 workers in the city of Chennai, India. The results reveal that mobile phones significantly affect not only these travel dimensions but also activity participation. The data also provide evidence that social connectivity, activity characteristics, mobile phone use, and travel patterns are all strongly interlinked. Individual characteristics, such as flexible time and duration of working hours, and personal and household characteristics, such as age, gender, and vehicle availability, were found to be influential. The impact of mobile phones on the dimensions of unplanned stop making, ridesharing, and shopping trip substitution can have important practical implications for mode choice modeling, vehicle occupancy increase measures, and congestion alleviation measures.

Modeling Interaction Between Internet Communication and Travel Activities: Evidence from Bay Area, California, Travel Survey 2000

Increasingly, advanced information technologies affect both activity and travel participation. Although several studies have focused on specific activities such as e-shopping or telecommuting, this study focuses on the relationship between physical and virtual activity participation in maintenance and discretionary activities (specifically using Internet technologies). In particular, three related dimensions are investigated using a series of statistical models: (a) factors affecting information and communications technology (ICT) use and the propensity to perform virtual activities using the Internet, (b) the relationship between physical and virtual activity participation and ICT use, and (c) interactions among ICT use, activity attributes, and observed travel behavior. The models are estimated by using recent empirical activity-diary data from the San Francisco Bay Area (California). The results provide considerable evidence in support of substitution and generation of trips due to ICT use (particularly the Internet). The results highlight the influence of technological familiarity, work-related attributes, time constraints, timing of travel, mobility and connectivity needs, and sociodemographic factors on the interactions between ICT use and travel patterns of users. These results have important implications for travel demand estimation and forecasting given the growing adoption and use of ICT among various segments of the population.

Commute Mode Choice in a Developing Country: Role of Subjective Factors and Variations in Responsiveness Across Captive, Semicaptive, and Choice Segments

This paper investigates mode choice decisions of workers in Chennai City, a metropolis in India. Mode choice in developing countries such as India may differ significantly from their developed counterparts in several respects. These differences pertain to vehicle types (two-wheelers versus four-wheelers), vehicle ownership levels and growth, wide variation in socioeconomic characteristics, perception of subjective factors, and variability in choice set, among other factors. Toward understanding these differences, this paper investigates the role of the following factors on mode choice: (a) differences between mode choice propensity for two-wheelers and four-wheelers; (b) differences in sensitivity to travel time and cost across different user groups based on captivity effects [captive (no vehicles), semicaptive (fewer vehicles than workers), and choice segments]; (c) alternative means of representing the unavailability or infeasibility of some alternatives to some users; and (d) the influence of subjective factors. To achieve these objectives, a series of disaggregate mode choice models are developed to capture the aforementioned effects, on the basis of data from 550 workers in Chennai City. Six alternatives are considered: two-wheeler, car, bus, train, nonmotorized, and other modes. The empirical results underscore significant variations in two-wheeler and car choice propensities. Furthermore, significant differences in sensitivity to travel time, cost, and vehicle availability are observed across different user segments. The cost sensitivity to various modes reduces as the commute distance increases. Results indicate that disregarding the aforementioned effects can lead to poor model fit, biased coefficients, and erroneous forecasts. These findings have important implications for the evaluation of transit ridership improvement strategies and demand forecasting in developing countries.

Experiments with Route and Departure Time Choices of Commuters Under Real-Time Information: Heuristics and Adjustment Processes

An overview of three experiments to study day-to-day departure time and route adjustment behavior of commuters, conducted in the mid 1980’s, is first given, focusing on behavioral mechanisms used by commuters to adjust these decisions in response to experienced congestion and variability in system performance. Experiments conducted in the past five years using a real-time interactive simulator to study users’ responses to real-time information are discussed. Focus is on experiments where quality as well as content of supplied information is varied. The effect of real-time information on the decisions and behavioral processes followed by commuters in online route switching as well as in departure time adjustment from day to day is addressed.

Integrating Household-Level Mode Choice and Modal Expenditure Decisions in a Developing Country: Multiple Discrete-Continuous Extreme Value Model

This paper investigates two mode-related dimensions at the household level, namely, mode choice and modal use intensity (as reflected by modal expenditures). These dimensions are analyzed jointly in the context of Chennai city in India, by using a large disaggregate database consisting of more than 2,000 households. Specifically, the objectives of this study are to analyze mode choice decisions at the household level, to integrate mode choice and mode usage using a suitable model, and to analyze the effect of contextual factors relevant to developing countries on the mode choice propensity and mode use intensity. At the household level, the mode choice problem is a multiple discrete choice problem (multiple alternatives may be selected) in contrast to the singly discrete nature of the individual mode choice problem. Therefore, a multiple discrete–continuous extreme value model is formulated to integrate choice and usage, based on a coherent utility maximization framework. To the authors’ knowledge, this study is the first to model mode choice as a multiple discrete choice problem. The results reveal that several unique and context-specific features in developing countries affect household-level mode choice significantly. Further, the mode use intensity of alternative modes is influenced by prior mode choice decisions, inertia, and users perception of safety and congestion. The results have important planning and policy implications for transit improvement and congestion mitigation strategies.