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.

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.

New application domains have faded the barriers between humans and robots, introducing a new set of complexities to robotic systems. The major impediment is the uncertainties associated with human decision making, which makes it challenging to predict human behavior. A realistic model of human behavior is thus vital to capture humans’ interactive behavior with their surroundings and provide robots with reliable estimates on what is most likely to happen. Focusing on operations of connected and automated vehicles (CAVs) in areas with a high presence of human actors (i.e., pedestrians), this study creates an interactive decision-making framework to predict pedestrians’ trajectories when walking in a shared environment with vehicles and other pedestrians. It develops a game theoretical structure to approximate the movement and directional components of pedestrian motion using the theory of Nash equilibria in non-cooperative games. It also introduces a novel payoff structure to address the inherent uncertainties in human behavior. Ground truth pedestrian trajectories are then used to calibrate the game parameters and evaluate the model’s performance in approximating the motion decisions of human agents in interaction with interfering vehicles and pedestrians. The main contribution of the study is to develop an interactive human–vehicle decision-making framework toward realizing human–vehicle coexistence by capturing the effect of pedestrian–vehicle and pedestrian–pedestrian interactions on choice of walking strategies. The derived knowledge could be used in CAV navigation algorithms to provide the vehicle with more accurate predictions of pedestrian behavior, and in turn, improve CAV motion planning in human-populated areas.

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.

This study investigates the prediction and mitigation of the phenomenon of traffic flow breakdown when affected by varying weather conditions. First, the probability of breakdown occurrence is examined using a survival analysis approach to obtain distributions of pre-breakdown flow rates under different weather conditions. Second, pre-breakdown flow rate distributions were applied in breakdown prediction for the implementation of breakdown mitigation strategies. In the first part, a set of data from the network of Kansas City was used to demonstrate the applicability of the Kaplan–Meier Product Limit method to estimating the breakdown probability under various weather conditions. Then, using simulated data on the network of Chicago, the K-M approach was used again to obtain survival likelihood distributions, which in turn yield breakdown probability, for 13 different weather cases as combinations of weather categories for different levels of visibility, rain, and snow precipitation. In the second part, continuing with the simulated data, dynamic speed limits (DSL) were applied to demonstrate the effectiveness of the prediction method presented. A sensitivity analysis of the threshold probability and upstream distance at which DSL should be implemented was performed for clear and inclement weather conditions. In clear weather the performance of the strategy is better at a lower probability threshold and farther upstream location, whereas in inclement weather the performance is better at a lower probability threshold and closer upstream location. The paper demonstrates the effect of changing weather conditions on the likelihood of breakdown occurrence and the implementation of breakdown mitigation strategies.