Archak Mittal

Network Flow Relations and Travel Time Reliability in a Connected Environment

Connected vehicle technology provides the opportunity to create a connected network of vehicles and infrastructure. In such a network, individual vehicles can communicate with each other and with the infrastructure, including a traffic management center. The effects of connectivity on reducing congestion and improving throughput and reliability have been extensively investigated at the segment (facility) level. To complement the segment-level studies and to assess the large-scale effects of connectivity, this paper presents a networkwide evaluation of the effect of connectivity on travel time reliability. This study uses a microscopic simulation framework to establish the speed–density relationships at different market penetration rates (MPRs) of connected vehicles. Calibrated speed–density relationships are then used as inputs to the mesoscopic simulation tools to simulate the networkwide effects of connectivity. The Chicago, Illinois, and Salt Lake City, Utah, networks are simulated. Numerical results from the simulations confirm that the linear relationship between distance-weighted travel time rate and standard deviation holds for both networks and is not affected by either the demand level or the MPR of connected vehicles. In addition, with an increase in the MPR of connected vehicles, the network attains a lower maximum density and gets an increased flow rate for the same density level. Highly connected environment has the potential to help a congested network to recover from a breakdown and avoid gridlock. It is shown that a connected environment can improve a system’s performance by providing increased traffic flow rate and better travel time reliability at all demand levels.

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.

Predictive Dynamic Speed Limit in a Connected Environment for a Weather Affected Traffic Network: A Case Study of Chicago

Dynamic speed limits (DSLs) are used to improve safety and mobility on freeways in unfavorable traffic conditions due to recurring congestion, roadworks, incidents, or adverse weather. The evaluation of in-field deployment reveals that the effectiveness of DSLs can be hampered by low compliance rates or lack of inherent capacity. With the emergence of vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communication, it is believed that the operation of DSLs will be able to take advantage of vehicle connectivity. In this paper, the effectiveness of the predictive DSL operation in a connected environment is investigated on the weather affected traffic network of Chicago city under different operational conditions. For the sensitivity test, different market penetration rates of connected vehicles are tested in microsimulation. Microscopic models are used to simulate information exchange by V2V or V2I communication. However, such an application over a large network with mixed traffic can be computationally expensive. A mesoscopic or macroscopic tool is needed that can scale and be computationally economical at the network level. This study integrates the microscopic aspect of V2V communication and the macroscopic for dynamic traffic assignment at a network level. The evaluation of effectiveness at network level is conducted by the Traffic Estimation and Prediction System (TREPS), which is a mesoscopic simulator. The results show, depending on the strategy applied, meaningful increases in both throughput and prevailing speed.

Exploring the Role of Social Media Platforms in Informing Trip Planning: Case of Yelp.com

Understanding how travelers make mobility decisions has always been central to transportation studies. The growing availability of information and communication technologies in everyday life and their role in conveying more recent, relevant, and customized information have substantially changed the context within which trip decisions are made. Whether travelers are actively seeking pretrip information or merely surfing the web, they have access through social media to user-generated information that may affect their mobility decisions. This study forms an exploratory step in understanding how one such social media platform, Yelp.com, designed to allow users to review and rate their experiences at any visited business, can serve as an information source for activity and trip planning in the pretrip process. In particular, the study explored (a) the relative depth, (b) the sentiment associated with, and (c) the type of information in transport-related reviews on Yelp.com. This research has implications for the study of travel behavior in a highly connected environment and can inform efforts to design information and communication technology tools aimed at affecting behavior.