The focus of this paper is on the demonstration that some rather well-known network-based formulations in operations research, that have heretofore largely gone unnoticed in activity-based travel research, offer a potentially powerful technique for advancing the general development of the activity-based modeling approach. These formulations can provide an analytical framework that unifies the complex interactions among the resource allocation decisions made by households in conducting their daily affairs outside the home, while preserving the utility-maximizing principles presumed to guide such decisions. A mathematical programming formulation is developed and used to identify the similarities and differences between traditional trip-based modeling methodologies and those pertaining to an activity-based approach. It is demonstrated that the two approaches are directly related.

The 1990 Clean Air Act Amendments (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have defined a set of transportation control measures to counter the increase in the vehicle emissions and energy consumption due to increased travel. The value of these TCM strategies is unknown as there is limited data available to measure the travel effects of individual TCM strategies and the models are inadequate in forecasting changes in travel behavior resulting from these strategies. The work described in this paper begins to provide an operational methodology to overcome these difficulties so that the impacts of the policy mandates of both CAAA and ISTEA can be assessed. Although the framework, as currently developed, falls well short of actually forecasting changes in traveler behavior relative to policy options designed to encourage emissions reduction, the approach can be useful in estimating upper bounds of certain policy alternatives in reducing vehicle emissions. Subject to this important limitation, the potential of transportation policy options to alleviate vehicle emissions is examined in a comprehensive activity-based approach. Conclusions are drawn relative to the potential emissions savings that can be expected from efficient trip chaining behavior, ride-sharing among household members, as well as from technological advances in vehicle emissions control devices represented by replacing all of the vehicles in the fleet by vehicles conforming to present-day emissions technology.

The household activity pattern problem of analyzing/predicting the optimal path of household members through time and space as they complete a prescribed agenda of out-of home activities is posed as a variant of the pickup and delivery problem with time windows. The most general case of the model includes provision for vehicle transfer, selective activity participation, and ridesharing options. A series of examples are solved using generic algorithms. The model is purported to remove existing barriers to the operationalization of activity-based approaches in travel behavior analysis.

This research aims to develop a framework investigating the viability of autonomous vehicles (AVs) in an urban area that enables multiple travel ways that potentially compete or coordinate with them. Based on the Household Activity Pattern Problem (HAPP) developed by Recker (1995), this research attempts to simulate travel-activity patterns with AVs by reformulating the original HAPP model. The revised framework succeeds in assessing the condition under which AVs can be advantageous over conventional vehicles (CVs) for a hypothetical household. It explicitly captures AVs' zero-occupancy trips and searching behavior for parking spots leading to the increase in vehicle travel miles. Finally, this research extends the formulation of HAPP to be capable of simulating multimodal transportation systems. In addition to conventional private vehicles and AVs, various private modes, rideshare (taxi), and public transit are incorporated into the framework. This extension distinguishes the framework from other activity-based models in depicting the coordination or competition between AVs and other modes. Thus, this framework shows that the availabilities of other transportation modes, as well as AVs' costs and households preferences, will affect households' decisions to embrace AVs. The results of this research imply that the proposed framework potentially serves as the demand-side component in an operational system for innovative transportation services such as Mobility as a Service.

Linear and nonlinear multivariate statistical analyses are applied to determine how the types of accidents that occur on heavily used freeways in Southern California are related to both the flow of traffic and weather and ambient lighting conditions. Traffic flow is measured in terms of time series of 30-second observations from inductive loop detectors in the vicinity of the accident prior to the time of its occurrence. Results indicate that the type of collision is strongly related to median traffic speed and to temporal variations in speed in the left and interior lanes. Hit-object collisions and collisions involving multiple vehicles that are associated with lane -change maneuvers are more likely to occur on wet roads, while rear-end collisions are more likely to occur on dry roads during daylight. Controlling for weather and lighting conditions, there is evidence that accident severity is influenced more by volume than by speed.

The focus of this research effort is the study of driver behavior in the presence of real-time traffic condition information. The methodology adapted for this research involves three parts: development of a theoretical model for driver behavior under Advanced Traveler Information Systems (ATIS), interactive simulation experiments, data analysis and behavioral modeling. FASTCARS, an interactive computer-based simulator that has been developed for in-laboratory experimentation to gather data for estimating and calibrating predictive models of driver behavior under conditions of real-time information, is used in the project.

This paper proposes a theoretical methodology and practical data collection approach for modeling enroute driver behavioral choice under Advanced Traveler Information Systems (ATIS). The theoretical framework is based on conflict assessment and resolution theories popularized in psychology and applied to models of individual consumer behavior. It is posed that enroute assessment and adjustment is a reactionary process influenced by increased conflict arousal and motivation to change. When conflict rises to a level at which conflict exceeds a personal threshold of tolerance, drivers are likely to alter enroute behavior to alleviate conflict through either route diversion or goal revision. Assessment and response to conflict arousal directly relate to the driver's abilities to perceive and predict network conditions in conjunction with familiarity of network configurations and accessible alternate routes.

Data collection is accomplished through FASTCARS (Freeway and Arterial Street Traffic Conflict Arousal and Resolution Simulator), an interactive microcomputer-based driving simulator. Limited real-world implementation of ATIS has made it difficult to study or predict individual driver reaction to these technologies. It is contended here that in-laboratory experimentation with interactive route choice simulators can substitute for the lack of real-world applications and provide an alternate approach to data collection and driver behavior analysis. This paper will explain how FASTCARS is useful for collecting data and testing theories of driver behavior.