Effect of roadway geometrics and environmental factors on rural freeway accident frequencies.

Models of urban traveler route choice are reviewed in the context of Intelligent Transportation Systems, particularly Advanced Traveler Information Systems. Existing models suffer from assumptions of perfect information about travel conditions and infinite information processing capabilities of drivers. We present evidence that a majority of travelers fail to minimize travel time or distance. We also show that travelers with more network knowledge appear to vary their commute route to respond to changing travel conditions. Coefficient estimates of a model of network knowledge, based on the geographical idea of spatial ability, are presented. To better understand habitual route choice behavior, we examine many possible route generation algorithms. A simulation approach is preferred because it allows for heterogeneity in driver perceptions and it has a quick computational time. Alternative route choice model specifications such as Multinomial Logit, CLogit, Path Size Logit, Cross-Nested Logit and Logit Kernel Probit are evaluated. The exponential specification of the Path Size term, using a large parameter value, offers a considerable improvement in fit over MNL, C-Logit and CNL. A hybrid Path Size Logit and Logit Kernel Probit model offers the best overall fit; however, the stability of these estimates requires further examination. The hybrid Path Size Logit and CNL model provides the next best empirical fit. Random coefficient specifications of MNL, PSL and LK Probit models were also examined. Significant random coefficient parameter estimates were only obtained for the MNL model. This result suggests that random coefficients capture variation in route choice models that would be more effectively explained by a Path Size or LK Probit specification. Model fit can be further improved by adding an Implicit Availability/Perception term that includes estimated network knowledge. However, this term provides limited explanatory power, as can be seen by its standard errors and by forecasts that are relatively insensitive to changes in traveler knowledge. These results suggest that continued development of better attitudinal surveys to assess network knowledge and wayfinding strategies would allow estimation of route choice models with better explanatory power. Thesis Supervisor: Moshe Emanuel Ben-Akiva Title: Edmund K. Turner Professor of Civil and Environmental Engineering

Network knowledge and route choice

This paper presents a statistical analysis of commuters' route choice including the effect of traffic information. The paper utilizes data collected from two stated preference survey techniques. Two route choice models were estimated. The first model used five hypothetical binary choice sets collected in a computer-aided telephone interview. The objective of the model was to determine how travel time variation affects route choice, and the potential interplay among travel time variation, traffic information acquisition and route choice. The second model used data collected in a mail survey from three binary route choice stated preference scenarios customized according to each respondent's actual commute route and travel time. The objective of the model was to investigate the potential effect of advanced traveler information systems on route choice. The correlation among error components in repeated measurement data was addressed in this paper with individual-specific random error components in a binary logit model with normal mixing distribution. The results underscored the significance of traffic information and the potential effect of advanced traveler information systems (ATIS) on route choice.

Using stated preference data for studying the effect of advanced traffic information on drivers' route choice

Temporal instability and the analysis of highway accident data

Since the beginning of civilization, the viability and economic success of communities have been, to a major extent, determined by the efficiency of the transportation infrastructure. To make informed transportation infrastructure planning decisions, planners and engineers have to be able to forecast the response of transportation demand to changes in the attributes of the transportation system and changes in the attributes of the people using the transportation system. Travel demand models are used for this purpose; specifically, travel demand models are used to predict travel characteristics and usage of transport services under alternative socio-economic scenarios, and for alternative transport service and land-use configurations.

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Activity-Based Modeling of Travel Demand

This paper studies the effects of traffic information on Seattle-area commuters. Models of commuters' route-change frequency, the duration of traffic delay needed to induce a route change, and the influence of pre-trip traffic information on departure-time choice, mode choice, and route choice are estimated. Two modeling techniques are used in these estimations: an ordered logit probability approach and a Weibull duration model with a heterogeneity correction term. The findings of these model estimations provide important insights into how traveler information systems should be designed, implemented, and marketed.

Statistical analysis of commuters' route, mode, and departure time flexibility

This paper explores the temporal stability of activity type-choice models and models of travelers' home-stay duration. To empirically evaluate this stability, a nested logit model of activity-type choice and a proportional hazards model of home-stay duration are estimated using data from two-day travel diaries collected in the fall of 1989 and again, from the same individuals, in the fall of 1990. The results show that the models are not temporally stable over the one year time period separating the two travel-diary samples. A number of possible reasons for this instability are discussed.

Temporal stability of travelers' activity choice and home-stay duration: Some empirical evidence

A sample of travelers' preferences toward in-vehicle traffic information systems was undertaken and appropriate statistical models were estimated. Specifically, ordered logit and regression analyses were conducted to quantify travelers' ratings of the importance of in-vehicle system attributes, and the distance ahead that they prefer to be notified of various types of information provided by in-vehicle systems. Model estimation results show that travelers' socio-economics, habitual travel patterns, commute congestion levels and attitudes toward in-vehicle technologies are significant determinants of travelers' importance ratings and the preferred distance ahead of in-vehicle system information. These model results provide important information for both marketing and design of in-vehicle information systems.

Travelers' preferences for in-vehicle information systems: an exploratory analysis

The availability of panel data and the continued development of activity-based travel demand models has cast a very promising light on the prospect of significantly improving our understanding of travel demand and our ability to forecast it. A lingering concern, however, relates to the econometric structure of activity-based models given the availability of panel data. This chapter presents a number of econometric alternatives that can be used to model individuals’ activity duration (e.g. time spent shopping, participating in recreational activities, etc.). Using panel data from the Puget Sound Region in Washington State, we estimate a number of econometric models and focus on potential specification errors and predictive capabilities. The primary econometric focus is on estimating survival models of activity duration using the fully-parametric Weibull proportional hazards with Gamma heterogeneity. The estimation concerns deal with the effects of state dependence and heterogeneity and the determination of true state dependence.

Panel Data and Activity Duration Models: Econometric Alternatives and Applications

Converting general-purpose lanes to high-occupancy vehicle (HOV) lanes is a policy that has been meticulously avoided since the public outcry opposing the lane conversion projects of the 1970s. Now that HOV lanes are firmly established in many metropolitan areas one has to wonder if public sentiments toward such lane conversions have changed. Public opinion of an HOV lane conversion recently completed in the Seattle metropolitan area is assessed. A series of multinomial logit and ordered probit models are estimated to isolate factors that determine commuters' attitudes toward various HOV policies (including lane conversion). The results show that although lane conversions are still strongly opposed by a substantial portion of the population, the intense public resistance encountered in the 1970s appears to be waning. Most of the survey respondents were either neutral or in favor of lane conversion projects.

Soon-Gwan Kim

Papers

Statistical analysis of commuters' route, mode, and departure time flexibility

Temporal stability of travelers' activity choice and home-stay duration: Some empirical evidence

Travelers' preferences for in-vehicle information systems: an exploratory analysis

Panel Data and Activity Duration Models: Econometric Alternatives and Applications

Statistical assessment of public opinion toward conversion of general-purpose lanes to high-occupancy vehicle lanes