Joshua Auld

Dynamics of Activity Time-of-Day Choice

This study explored travelers’ decision behavior in selecting activity start times. The study examined the problem in the context of the Agent-based Dynamic Activity Planning and Travel Simulation (ADAPTS) activity-based travel demand model for the Chicago, Illinois, metropolitan area. A unique feature of the ADAPTS framework is its consideration of planning horizons for various activity attributes. Naturally, the various attributes of an activity—such as start time, duration, location, party involvement, and mode of travel—can be planned in different time horizons. An attribute that is planned affects the choice of other activity attributes. Therefore, developing a true behavioral time-of-day choice model would not be possible unless the planning order of activity attributes and the dynamics of travelers’ decision-making processes are taken into account. Similarly, it can be argued that there should be fundamental differences in the time-of-day decision process when other attributes of the activity are not yet planned but are to be decided at a later time. The presented time-of-day model aims to capture the dynamics of this decision process by considering the planning time horizons of other attributes of the activity, as well as the outcomes of the decisions. The study adopted the discrete choice approach to model activity timing decisions and a hybrid utility maximization and developed a regret minimization model to account for the heterogeneity of decision rules across choice variables. Analysis of the estimation results and parameter elasticities indicates that higher expected travel time, variations in travel time, and schedule occupancy rates for different time choices can significantly increase the regret value of the corresponding choice and therefore affect the time-of-day choice.

Analysis of the Effects of Connected–Automated Vehicle Technologies on Travel Demand

Connected–automated vehicle (CAV) technologies are likely to have significant effects not only on how vehicles operate in the transportation system, but also on how individuals behave and use their vehicles. While many CAV technologies—such as connected adaptive cruise control and ecosignals—have the potential to increase network throughput and efficiency, many of these same technologies have a secondary effect of reducing driver burden, which can drive changes in travel behavior. Such changes in travel behavior—in effect, lowering the cost of driving—have the potential to increase greatly the utilization of the transportation system with concurrent negative externalities, such as congestion, energy use, and emissions, working against the positive effects on the transportation system resulting from increased capacity. To date, few studies have analyzed the potential effects on CAV technologies from a systems perspective; studies often focus on gains and losses to an individual vehicle, at a single intersection, or along a corridor. However, travel demand and traffic flow constitute a complex, adaptive, nonlinear system. Therefore, in this study, an advanced transportation systems simulation model—POLARIS—was used. POLARIS includes cosimulation of travel behavior and traffic flow to study the potential effects of several CAV technologies at the regional level. Various technology penetration levels and changes in travel time sensitivity have been analyzed to determine a potential range of effects on vehicle miles traveled from various CAV technologies.

A Flexible Framework for Developing Integrated Models of Transportation Systems Using an Agent-based Approach

Abstract Travel demand, traffic flow and land-use models are typically modeled in a decoupled way, i.e. each of the components is modeled separately assuming that parameters related to the other components are fixed. Moreover, the models are often developed by different groups for different contexts, requirements, etc. In this paper we present a prototype of a software framework which allows the user to develop an integrated simulation of a transportation system and also to link additional models to the new simulation in a standardized way. We use an agent-based approach as the basis of such a model. Integrated transportation system models allow model users to overcome the limitations of traditional aggregated, independent transportation models, particularly with respect to sensitivity to behavioral aspects of the travelers. Another requirement, which the software is to satisfy, is the interoperability of models developed in the new framework with legacy models. By interoperability we mean, that any component of the of the model can be interchanged by a legacy software and be used for the integrated simulation. This would allow disparate research groups working on modeling different aspects of a transportation model to plugnplay their models into the framework and test those as a part of an integrated model of an entire system, providing a benefit to researchers, modelers and institutional users of such models.

Activity start time and duration: incorporating regret theory into joint discrete–continuous models

ABSTRACT Activity start time and duration decisions are two key elements of activity-travel behaviour. As an effort towards a more realistic representation of individuals’ decision behaviour, this paper presents a new copula-based joint model of activity start time and duration. We incorporate the regret theory into the joint modelling framework, which assumes that individuals tend to avoid the situation where non-chosen alternatives outperform the chosen one in one or more attributes. The proposed joint model comprises a hybrid utility-regret model as the discrete component to estimate activity start time and a hazard duration model as the continuous component to estimate activity duration. The comparative analysis of estimation results reveals that the proposed structure is statistically superior to the utility-based joint model and independent models. We also found that applying the regret-based decision rule for variables of travel time and travel time variability significantly improves the model.

Impact of Privately-Owned Level 4 CAV Technologies on Travel Demand and Energy

Abstract This study analyzes the mobility and energy impact of CAV technologies. Under different VOT, WTP and market penetration scenarios, changes in mobility in terms of VMT and average travel time, as well as changes in energy consumption are estimated. The state-of-the-art integrated activity-based modeling (ABM) and traffic simulation software POLARIS is used to model the mobility impact. Moreover, POLARIS is coupled with AUTONOMIE to analyze the resulting fuel consumption.

Demographic Characterization of Anonymous Trace Travel Data

Research was undertaken to determine whether demographic characteristics of individual travelers could be derived from travel pattern information when no information about the individual was available. This question is relevant in the context of anonymously collected travel information, such as cell phone traces, when used for travel demand modeling. Determining the demographics of a traveler from such data could partially obviate the need for large-scale collection of travel survey data, depending on the purpose for which the data were to be used. This research complements methodologies used to identify activity stops, purposes, and mode types from raw trace data and presumes that such methods exist and are available. The paper documents the development of procedures for taking raw activity streams estimated from GPS trace data and converting these into activity travel pattern characteristics that are then combined with basic land use information and used to estimate various models of demographic characteristics. The work status, education level, age, and license possession of individuals and the presence of children in their households were all estimated successfully with substantial increases in performance versus null model expectations for both training and test data sets. The gender, household size, and number of vehicles proved more difficult to estimate, and performance was lower on the test data set; these aspects indicate overfitting in these models. Overall, the demographic models appear to have potential for characterizing anonymous data streams, which could extend the usability and applicability of such data sources to the travel demand context.

Enhancing traveler context through transferable activity patterns

Developing a model of the needs of a mobile traveler is critical to good personalization. Transportation planners have been modeling these needs for years, but these models have not been used to date due to two outstanding questions: 1) are they applicable to individual travelers 2) are they useful beyond the studied region. This study demonstrates these studies can directly enhance the model of mobile users, and be done in a practical way through the transference of activity patterns across cities. This work then demonstrates how these studies can be combined with patterns of an individual mobile user successfully.

Developing a Spatial Transferability Platform to Analyze National-Level Impacts of Connected Automated Vehicles

A recent application of the spatial transferability approach is to assess the potential impacts of the emerging connected automated mobility technology on people’s travel behavior at the national level. While there are a few transportation simulation frameworks which can account for potential impacts of this technology in a simulated geographical context, there is yet to be any literature documenting disaggregated estimates of large-scale impacts of connected automated vehicles (CAVs) on travel behavior at the national level. Therefore, in order to provide a platform to assess national-level impacts of CAVs, this study develops a methodological framework based on transferability techniques, which uses data and models from a smaller geographical area—the POLARIS simulation results for the CAVs scenario in the Chicago metropolitan area—to generate disaggregate travel data at the national level. Comparison of the distributions of the transferred variables at the regional and the national contexts indicates that the platform is capable of transferring travel behavior indices to the national level with high level of accuracy.

Time-Dependent Intermodal A* Algorithm: Methodology and Implementation on a Large-Scale Network:

This paper proposes a time-dependent intermodal A* (TDIMA*) algorithm. The algorithm works on a multimodal network with transit, walking, and vehicular network links, and finds paths for the three major modes (transit, walking, driving) and any feasible combination thereof (e.g., park-and-ride). Turn penalties on the vehicular network and progressive transfer penalties on the transit network are considered for improved realism. Moreover, upper bounds to prevent excessive waiting and walking are introduced, as well as an upper bound on driving for the park-and-ride (PNR) mode. The algorithm is validated on the large-scale Chicago Regional network using real-world trips against the Google Directions API and the Regional Transit Authority router.

Assessing the energy impact of traffic management and vehicle hybridisation

We provide a review of methodologies previously used to evaluate impacts of transportation systems and changes in transportation infrastructure on energy consumption. We present a new framework that allows estimating the energy impacts of managed traffic lanes in the context of vehicle automation. The presented framework relies on two major components, an integrated transportation system simulator and a powertrain simulator. For the transportation system simulator we propose using integrated transportation system simulator POLARIS. For the powertrain simulator we use AUTONOMIE, a tool funded by the US Department of Energy. Both tools are developed at Argonne National Laboratory. We demonstrate our approach by modelling a transportation corridor along a major highway. Two scenarios are considered, unmanaged, when both trucks and cars use all the lanes of the highway and managed, under which one of the highway lanes is a dedicated lane for truck traffic and trucks are forming platoons using adaptive cruise control technology. We provide the numerical results of the experiment at the end of the paper. We also present the impact of vehicle hybridisation combined with automation on the energy consumption.