Hooram Halat

Impact of crime statistics on travel mode choice: Case Study of the city of Chicago, Illinois

Whether crime or the perception of it has any direct and significant influence on travelers’ mode choice is a topic for which the evidence remains inconclusive. Studies have revealed various, and in some cases counterintuitive, roles that safety concerns can play in individuals’ travel behavior. In addition, characteristics of the physical environment such as land use and walkability are also influential factors in travelers’ decisions. This study explored these questions through the study of individual travel behavior by using discrete choice models applied to the reported home-based work trips in the Chicago household travel survey. Mode choice was modeled as functions of variables such as sociodemographics, neighborhood crime density (as a safety measure), and walk score (as a measure of walkability). Different crime types were examined, and a crime index was introduced. Results suggest that both walk score and the crime index at the destination can be considered meaningful predictors of individuals’ mode usage. The crime index at origin, however, does not show a significant and meaningful effect.

Online implementation and evaluation of weather-responsive coordinated signal timing operations

This paper presents the development and application of weather-responsive traffic management strategies and tools to support coordinated signal timing operations with traffic estimation and prediction system (TREPS) models. First, a systematic framework for implementing and evaluating traffic signal operations under severe weather conditions was developed, and activities for planning, preparing, and deploying signal operations were identified in real-time traffic management center (TMC) operations. Next, weather-responsive coordinated signal plans were designed and evaluated with the TREPS method and a locally calibrated network. Online implementation and evaluation was conducted in Salt Lake City, Utah—the first documented online application of TREPS to support coordinated signal operation in inclement weather. The analysis results confirm that the deployed TREPS, which is based on DYNASMART-X, is able to help TMC operators test appropriate signal timing plans proactively under different weather forecasts before deployment and is capable of using real-time measurements to improve the quality and accuracy of the systems estimations and future predictions through detectors and roadside sensor coverage.

Effectiveness of Predictive Weather-Related Active Transportation and Demand Management Strategies for Network Management

This paper presents the development, implementation, and evaluation of predictive active transportation and demand management (ATDM) and weather-responsive traffic management (WRTM) strategies to support operations for weather-affected traffic conditions with traffic estimation and prediction system models. First, the problem is defined as a dynamic process of traffic system evolution under the impact of operational conditions and management strategies (interventions). A list of research questions to be addressed is provided. Second, a systematic framework for implementing and evaluating predictive weather-related ATDM strategies is illustrated. The framework consists of an offline model that simulates and evaluates the traffic operations and an online model that predicts traffic conditions and transits information to the offline model to generate or adjust traffic management strategies. Next, the detailed description and the logic design of ATDM and WRTM strategies to be evaluated are proposed. To determine effectiveness, the selection of strategy combination and sensitivity of operational features are assessed with a series of experiments implemented with a locally calibrated network in the Chicago, Illinois, area. The analysis results confirm the models’ ability to replicate observed traffic patterns and to evaluate the system performance across operational conditions. The results confirm the effectiveness of the predictive strategies tested in managing and improving traffic performance under adverse weather conditions. The results also verify that, with the appropriate operational settings and synergistic combination of strategies, weather-related ATDM strategies can generate maximal effectiveness to improve traffic performance.

Schedule Consistency for Daily Activity Chains in Integrated Activity-Based Dynamic Multimodal Network Assignment

The dynamic multimodal network assignment problem at the daily schedule level is addressed by integrating an activity-based model and a dynamic traffic assignment tool through a unified framework. The framework achieves this integration while retaining disaggregated individualized information. The problem is formulated as a fixed-point problem, and equilibrium is achieved by minimizing the gap between the expected travel time, which is used by the activity-based model to generate the travelers’ individual and household activity schedules, and their experienced travel times, simulated by the dynamic traffic assignment tool. The schedule adjustment problem for individuals and households is formulated as a linear optimization problem. Two measures—inconsistent-schedule penalty and number of households with unrealistic schedules—are defined to monitor the status of the equilibrium and convergence gap of the integrated system. To ensure convergence of the applied integration, heuristic strategies for selecting individuals for schedule adjustment and path swap are tested in a subarea network of Chicago, Illinois. Selecting individuals for schedule adjustment based on their inconsistent-schedule penalty reduces both defined measures significantly and leads to the convergence of the planned schedule and the experienced (i.e., simulated) schedule.

Activity Cancellation and Rescheduling by Stressed Households: Improving Convergence in Integrated Activity-Based and Dynamic Traffic Assignment Models

Maintaining consistency between individuals’ activity choices and realistic network conditions is made possible through a unified framework of activity-based and dynamic traffic assignment models. In this study, a model formulation and solution approach is presented for an integrated system of household activity schedule adjustment and multimodal dynamic traffic assignment model. In the proposed framework, individuals are initially assigned an activity pattern that includes their activity start time, end time, sequence, and number of intermediate and final destinations. The planned schedules are loaded to the network and executed to obtain the dynamic traffic conditions. Next, the individuals who experience schedules inconsistent with the planned ones try to adjust their activity durations or trip departure times to accommodate the fluctuations. In some cases, however, the schedule inconsistencies may not be resolved through departure time or duration adjustments; therefore, the traveler may cancel an activity or trip as a response to the fluctuations. Strategies for selecting activities to be canceled are discussed, and the influence of incorporating activity cancellation on numerical convergence is presented.

Integrated Mode Choice and Dynamic Traveler Assignment in Multimodal Transit Networks: Mathematical Formulation, Solution Procedure, and Large-Scale Application

This paper introduces an integrated mode choice–multimodal transit assignment model and solution procedure intended for large-scale urban applications. The cross-nested logit mode choice model assigns travelers to car, transit, or park-and-ride. The dynamic multimodal transit assignment–simulation model determines minimum hyperpaths and assigns and simulates transit and park-and-ride travelers iteratively until the network approaches a state of equilibrium. After a given number of iterations, the updated transit network travel times are fed into the mode choice model and the model reassigns travelers to transit, car, or park-and-ride. The outer feedback loop between the mode choice model and the transit assignment model continues until the mode probabilities for each traveler do not change between iterations. A unique contribution of the method presented in this paper is that it reaches mode choice convergence with the use of disaggregate agents (travelers) instead of aggregate modal flows at the origin–destination level. The integrated model is successfully implemented on the Chicago Transit Agency’s bus and train network in Illinois. Different procedures for reaching convergence are tested; the results suggest that a gap-based formulation is more efficient than the method of successive averages.