Alireza Talebpour

Speed harmonization: evaluation of effectiveness under congested conditions

The objective of this study was to explore the impacts of early shock wave detection on breakdown formation and driving hazards (safety) and the possible corresponding improvements from the use of speed harmonization as a control strategy. Several efforts have been made to evaluate speed harmonization systems by means of various traffic microsimulation models. However, further related behavioral-based studies are needed, especially in light of the development of connected-vehicle technology. The adopted approach relies on a cognitive risk-based microscopic simulation model capable of endogenously accounting for incidents to study the effects of speed harmonization strategies on traffic flow characteristics and safety. An algorithm based on wavelet transform—to detect shock wave formation—was combined with a reactive speed limit selection algorithm to implement speed harmonization within the microscopic simulation model. Three sets of scenarios were simulated. The results showed significant improvement in traffic flow characteristics through the implementation of the speed harmonization control strategy under congested conditions. Analysis of a fundamental diagram revealed the existence of an optimal location to implement the speed limit changes upstream of the point of shock wave detection. The analysis also revealed the role of speed limit compliance for the success of a speed harmonization system.

Multiregime sequential risk-taking model of car-following behavior: Specification, calibration, and sensitivity analysis

Car-following models constitute the main component of operational microscopic simulation models and are intended to capture intervehicle interactions on highway sections. Most existing car-following models are deterministic and do not capture the effects of surrounding traffic conditions on the decision-making process of the driver. An extension to a previously introduced sequential risk-taking model is offered to capture the effects of surrounding conditions on driving behavior. The model extension recognizes two behavioral regimes that depend on the complexity of the decision situation associated with the prevailing congestion. With each regime is associated a value function capturing driver preferences for gains associated with a particular acceleration. A probabilistic regime selection mechanism relates the drivers choices to prevailing traffic conditions. The model is calibrated against actual trajectory data. Initial results show that the model provides realistic behavioral patterns previously identified in the literature.

Modeling Driver Behavior in a Connected Environment: Integrated Microscopic Simulation of Traffic and Mobile Wireless Telecommunication Systems

Connected and autonomous vehicles will influence the future of the surface transportation system by enhancing safety, improving mobility, reducing energy consumption, and controlling emissions. The real-time information provided by connected vehicles technology allows drivers to become more aware of the surrounding traffic conditions and to maneuver safely and more efficiently. Furthermore, when coupled with onboard sensing technologies, the connected vehicles technology can improve the efficiency and the reliability of a driverless transportation network. This paper presents a comprehensive simulation framework to model driver behavior in a connected driving environment with connected vehicles. The framework, which consists of a microscopic traffic simulator integrated with a discrete-event communications network simulator, Network Simulator 3, forms a basis for exploration of the properties of the resulting traffic systems and assessment of the system-level impacts of these technologies. Furthermore, the connectivity of a vehicle-to-vehicle and vehicle-to-infrastructure communications network was investigated with the FHWA Next Generation Simulation: US-101 Highway data set (to represent vehicular movements in a highway environment). It was found that signal interference can result in information loss and partial connectivity. Finally, through the implementation of a speed harmonization algorithm, the paper discusses the importance of consideration of telecommunications along with vehicular movements to investigate the effects of connected vehicle applications on mobility and emissions.

Near-Crash Identification in a Connected Vehicle Environment:

The main objective of this study was to identify near crashes in vehicle trajectory data with interdriver heterogeneity and situation dependency considered. Several efforts have been made to evaluate the effects of near-crash events on safety with the use of naturalistic driving data, driving simulators, and test tracks. However, these efforts have faced some challenges because the observations reflected only the equipped vehicles. The development of connected vehicle technology provided the essential data to study high-risk maneuvers in the entire traffic stream. In this study, two near-crash detection algorithms were proposed. One algorithm had its basis in fixed thresholds, while the other considered interdriver heterogeneity and estimates driver-specific thresholds. The models were tested against two NGSIM trajectory data sets. Initial results showed that consideration of driver preferences resulted in more realistic identification of near crashes than otherwise.

Safety First: Microsimulation Approach to Assessing Congestion Effects on Risk Experienced by Drivers

Prevailing traffic conditions affect highway safety and the processes by which drivers perceive a stimulus, evaluate it, and execute a corresponding driving maneuver. Several efforts have been made to use microscopic traffic simulation for evaluating highway safety. However, these efforts faced serious challenges because previous acceleration and lane-changing models had been built in an accident-free environment with different layers of safety constraints. A new approach relies on a cognitive risk-based microscopic model to study the relationship between prevailing traffic conditions and the risk experienced by drivers in a traffic stream. The model can consider accidents endogenously through lane-changing logic and provide an indicator of relative roadway safety as experienced by drivers. Six scenarios are simulated. The results show the importance of lane changing to understanding accident and near-accident occurrence in simulation models. A risk value comparison reveals that work zone bottlenecks have a greater impact on drivers’ risk-taking tendencies than bottlenecks caused by uphill grades.

Effect of Information Availability on Stability of Traffic Flow: Percolation Theory Approach

Abstract: Connectivity and automation are expected to enhance safety and efficiency in transportation systems. Connectivity will provide information to drivers/autonomous vehicles to enhance decision-making reliability at the operational and tactical levels. Consequently, drivers are more likely to execute safe and efficient maneuvers and autonomous vehicles will have a more accurate perception of the traffic condition and an “error-free” execution of the driving maneuvers. At the operational level, ensuring string stability is a key consideration since unstable traffic flow results in shockwave propagation and possibly crashes. While several studies have examined the effects of information availability on string stability in a connected environment, most of the approaches are focused on automated driving (e.g., Cooperative Adaptive Cruise Control systems) and do not consider a mixed environment with regular, connected, and autonomous vehicles. To ensure connectivity in such a mixed environment, the correlation between communication range and connected vehicles density should be considered. To capture the effects of this correlation, this study uses the Continuum Percolation theory to determine the effects of the vehicular density and communication range on the connectivity level in telecommunications network and consequently, on the string stability of traffic flow. Based on the Continuum Percolation theory, there is a critical density of connected vehicles above which the entire system is connected. This critical density also depends on the communication range. This study presents an analytical derivation of this critical density. Moreover, this study evaluates the string stability under different communication ranges and market penetration rates of connected and autonomous vehicles. Results revealed that as communication range increases, the system becomes more stable and at high communication ranges, the system performs similar to the system with full connectivity. Moreover, results indicated the existence of an optimal communication range to maximize stability and ensure information delivery.

Investigating the Effects of Reserved Lanes for Autonomous Vehicles on Congestion and Travel Time Reliability

Autonomous vehicles are expected to influence daily travel significantly. Despite autonomous vehicles’ potential to enhance safety and to reduce congestion, energy consumption, and emissions, many studies suggest that the system-level effects will be minimal at low market penetration rates. Introducing reserved lanes for autonomous vehicles is one potential approach to address this limitation because these lanes increase autonomous vehicles’ density. However, preventing regular vehicles from using specific lanes can significantly increase congestion in other lanes. Accordingly, this study explored the potential effects of reserving one lane for autonomous vehicles on traffic flow dynamics and travel time reliability. A two-lane hypothetical segment with an on-ramp and a four-lane highway segment in Chicago, Illinois, was simulated under different market penetration rates of autonomous vehicles. Three strategies were evaluated: (a) mandatory use of the reserved lane by autonomous vehicles, (b) optional use of the reserved lane by autonomous vehicles, and (c) limiting autonomous vehicles to operate autonomously in the reserved lane. Policies based on combinations of these strategies were simulated. It was found that optional use of the reserved lane without any limitation on the type of operation could improve congestion and could reduce the scatter in a fundamental diagram. Throughput analysis showed the potential benefit of reserving a lane for autonomous vehicles at market penetration rates of more than 50% for the two-lane highway and 30% for the four-lane highway. Travel time reliability analysis revealed that the optional use of the reserved lane was also significantly beneficial.

Travel Time Reliability Versus Safety: A Stochastic Hazard-Based Modeling Approach

This paper presents a modeling approach to linking stochastic acceleration and lane-changing behavior to travel time reliability on congested freeways. Individual driving behavior is represented by a prospect-theory-based model that takes into account uncertainty and risk evaluation in terms of gains and losses while following a lead vehicle. Given a set of stimuli (i.e., headways, relative speeds, etc.), the stochastic acceleration model generates acceleration probability distribution functions rather than deterministic acceleration values. Such distribution functions may be associated with travel time reliability through the construction of travel time distributions. In addition, lane-changing decision is represented by a stochastic hazard-based duration model that accounts for the surrounding traffic conditions (i.e., traffic density, distance to ramp, etc.). Numerical results from Monte Carlo simulations demonstrate that the proposed microscopic stochastic modeling approach produces realistic macroscopic traffic flow patterns and can be used to generate travel time distributions. With proper experimental setup and sensitivity analysis, travel time distributions may be estimated and linked to safety-based parameters.

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.

Traffic Flow of Connected and Automated Vehicles: Challenges and Opportunities

Significant progress has been observed in recent years in the development of connected and automated vehicles (CAVs). Such progress has been publicized through the latest products/applications being released or announced by the industry. However, there is a limited knowledge on the impact of CAV technologies on surface transportation network performance. In particular, the technological specifications associated with CAVs and the response of drivers to such technologies are not well integrated into traffic flow models. These models are needed to assess and evaluate the safety and mobility impact on our roadway conditions. Accordingly, a more elaborate discussion is needed between three entities: (1) the industry partners leading the efforts in developing CAVs; (2) the academic traffic flow modeling community researching the impact of CAVs on traffic flow performance; and (3) the public/government agencies devising the standards and the rules to regulate the deployment of CAVs on our roadway network. This chapter summarizes the presentations of speakers from these three entities during the Automated Vehicles Symposium 2016 (AVS16) held in San Francisco, California on July 19–21, 2016. These speakers participated in the break-out session titled “Traffic Flow of Connected and Automated Vehicles”. The corresponding discussion and recommendation are presented in terms of the lessons learned and the future research direction to be adopted. This session was organized by the AHB45(3) Subcommittee on Traffic Flow Modeling for Connected and Automated Vehicles.