Samer H. Hamdar

Modeling Driver Behavior as Sequential Risk-Taking Task

Acceleration models are at the core of operational driving behaviors and include car-following models that capture interactions between a lead and following vehicles. The main assumption in these models is that the behavior of the following vehicle (e.g., change in acceleration) is related directly to a stimulus observed or perceived by the driver, defined relative to the lead vehicle (e.g., difference in speeds or headways). An important aspect missing from previous formulations pertains to the stochastic character of the cognitive processes used by drivers, such as perception, judgment, and execution while driving. A car-following model that reflects the psychological and cognitive aspects of the phenomenon and captures risk-taking behavior under uncertainty is explored and evaluated. In this model, Tversky and Kahnemans prospect theory provides a theoretical and operational basis for weighing a drivers different alternatives. The model is implemented and tested to assess its properties and those of the resulting traffic stream behavior.

From Existing Accident-Free Car-Following Models to Colliding Vehicles: Exploration and Assessment

The study explores the specifications of microscopic traffic models that could capture congestion dynamics and model accident-prone behaviors on a highway section in greater realism than existing models currently used in practice (commercial software). A comparative assessment of several major acceleration models is conducted, especially for congestion formation and incident modeling. On the basis of this assessment, alternative specifications for car-following and lane-changing models are developed and implemented in a microscopic simulation framework. The models are calibrated and compared for resulting vehicle trajectories and macroscopic flow-density relationships. Experiments are conducted with the models under different degrees of relaxation of the safety constraints typically applied in conjunction with simulation codes used in practice. The ability of the proposed specifications to capture traffic behavior in extreme situations is examined. The results suggest that these specifications offer an improved basis for microscopic traffic simulation for situations that do not require an accident-free environment. As such, the same basic behavior model structure could accommodate both extreme situations (evacuation scenarios, oversaturated networks) as well as normal daily traffic conditions.

Aggressiveness propensity index for driving behavior at signalized intersections

The development of a quantitative intersection aggressiveness propensity index (API) is described in this paper. The index is intended to capture the overall propensity for aggressive driving to be experienced at a given signalized intersection. The index is a latent quantity that can be estimated from observed environmental, situational and driving behavior variables using structural equations modeling techniques. An empirical study of 10 major signalized intersections in the greater Washington DC metropolitan area was conducted to illustrate the approach. The API is shown to provide (a) an approach for capturing and quantifying aggressive driving behavior given certain measurements taken at a particular intersection, (b) understanding of the factors and intersection characteristics that may affect aggressiveness, and (c) an index for the cross comparison of different traffic areas with different features. This index has the potential to support safety policy analysis and decision-making.

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.

Life in the Fast Lane: Duration-Based Investigation of Driver Behavior Differences Across Freeway Lanes

The objective of this paper is to explore driver behavior in separate lane groups from a duration perspective by using hazard-based models. Driving is a continuous story divided into different experienced episodes. Each episode is terminated probabilistically on the basis of the surrounding traffic conditions as well as the drivers’ characteristics. This termination reflects mainly lane-changing behaviors. By means of the next generation simulation (NGSIM) trajectory data, an analysis is performed of the time durations until lane-changing maneuvers are executed. The distributions of such durations are found to be lane specific in cases in which the leftmost lanes show higher duration mean than the rightmost lanes. This indicates that drivers wait longer before deciding to change lanes in the leftmost lanes. In the middle lanes a bipeak distribution constitutes a transition between satisfaction in traveling in the fast lanes (left) versus impatience while traveling in the slower lanes (right).

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 propensity index for signalized and unsignalized intersections: Exploration and assessment

The objective of this study is to develop a safety propensity index (SPI) for both signalized and unsignalized intersections. Through the use of a structural equation modelling (SEM) approach safety is quantified in terms of multiple endogenous variables and related to various dimensions of exogenous variables. The singular valued SPI allows for identification of relationships between variables and lends itself well to a comparative analysis between models. The data provided by the Highway Safety Information System (HSIS) for the California transportation network was utilized for analysis. In total 22,422 collisions at unsignalized intersections and 20,215 collisions at signalized intersections (occurring between 2006 and 2010) were considered in the final models. The main benefits of the approach and the subsequent development of an SPI are (1) the identification of pertinent variables that effect safety at both intersection types, (2) the identification of similarities and differences at both types of intersections through model comparison, and (3) the quantification of safety in the form of an index such that a ranking system can be developed. If further developed, the adopted methodology may assist in safety related decision making and policy analysis.

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.