A Comparative Analysis of Different Dilemma Zone Countermeasures at Signalized Intersections based on Cellular Automaton Model

In the United States, intersections are among the most frequent locations for crashes. One of the major problems at signalized intersection is the dilemma zone, which is caused by false driver behavior during the yellow interval. This research evaluated driver behavior during the yellow interval at signalized intersections and compared different dilemma zone countermeasures. The study was conducted through four stages. First, the driver behavior during the yellow interval were collected and analyzed. Eight variables, which are related to risky situations, are considered. The impact factors of drivers\u27 stop/go decisions and the presence of the red-light running (RLR) violations were also analyzed. Second, based on the field data, a logistic model, which is a function of speed, distance to the stop line and the lead/follow position of the vehicle, was developed to predict drivers\u27 stop/go decisions. Meanwhile, Cellular Automata (CA) models for the movement at the signalized intersection were developed. In this study, four different simulation scenarios were established, including the typical intersection signal, signal with flashing green phases, the intersection with pavement marking upstream of the approach, and the intersection with a new countermeasure: adding an auxiliary flashing indication next to the pavement marking. When vehicles are approaching the intersection with a speed lower than the speed limit of the intersection approach, the auxiliary flashing yellow indication will begin flashing before the yellow phase. If the vehicle that has not passed the pavement marking before the onset of the auxiliary flashing yellow indication and can see the flashing indication, the driver should choose to stop during the yellow interval. Otherwise, the driver should choose to go at the yellow duration. The CA model was employed to simulate the traffic flow, and the logistic model was applied as the stop/go decision rule. Dilemma situations that lead to rear-end crash risks and potential RLR risks were used to evaluate the different scenarios. According to the simulation results, the mean and standard deviation of the speed of the traffic flow play a significant role in rear-end crash risk situations, where a lower speed and standard deviation could lead to less rear-end risk situations at the same intersection. High difference in speed are more prone to cause rear-end crashes. With Respect to the RLR violations, the RLR risk analysis showed that the mean speed of the leading vehicle has important influence on the RLR risk in the typical intersection simulation scenarios as well as intersections with the flashing green phases\u27 simulation scenario. Moreover, the findings indicated that the flashing green could not effectively reduce the risk probabilities. The pavement marking countermeasure had positive effects on reducing the risk probabilities if a platoon\u27s mean speed was not under the speed used for designing the pavement marking. Otherwise, the risk probabilities for the intersection would not be reduced because of the increase in the RLR rate. The simulation results showed that the scenario with the pavement marking and an auxiliary indication countermeasure, which adds a flashing indication next to the pavement marking, had less risky situations than the other scenarios with the same speed distribution. These findings suggested the effectiveness of the pavement marking and an auxiliary indication countermeasure to reduce both rear-end collisions and RLR violations than other countermeasures

Vehicle and Traffic Safety

The book is devoted to contemporary issues regarding the safety of motor vehicles and road traffic. It presents the achievements of scientists, specialists, and industry representatives in the following selected areas of road transport safety and automotive engineering: active and passive vehicle safety, vehicle dynamics and stability, testing of vehicles (and their assemblies), including electric cars as well as autonomous vehicles. Selected issues from the area of accident analysis and reconstruction are discussed. The impact on road safety of aspects such as traffic control systems, road infrastructure, and human factors is also considered

Situational Awareness Enhancement for Connected and Automated Vehicle Systems

Recent developments in the area of Connected and Automated Vehicles (CAVs) have boosted the interest in Intelligent Transportation Systems (ITSs). While ITS is intended to resolve and mitigate serious traffic issues such as passenger and pedestrian fatalities, accidents, and traffic congestion; these goals are only achievable by vehicles that are fully aware of their situation and surroundings in real-time. Therefore, connected and automated vehicle systems heavily rely on communication technologies to create a real-time map of their surrounding environment and extend their range of situational awareness. In this dissertation, we propose novel approaches to enhance situational awareness, its applications, and effective sharing of information among vehicles.;The communication technology for CAVs is known as vehicle-to-everything (V2x) communication, in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) have been targeted for the first round of deployment based on dedicated short-range communication (DSRC) devices for vehicles and road-side transportation infrastructures. Wireless communication among these entities creates self-organizing networks, known as Vehicular Ad-hoc Networks (VANETs). Due to the mobile, rapidly changing, and intrinsically error-prone nature of VANETs, traditional network architectures are generally unsatisfactory to address VANETs fundamental performance requirements. Therefore, we first investigate imperfections of the vehicular communication channel and propose a new modeling scheme for large-scale and small-scale components of the communication channel in dense vehicular networks. Subsequently, we introduce an innovative method for a joint modeling of the situational awareness and networking components of CAVs in a single framework. Based on these two models, we propose a novel network-aware broadcast protocol for fast broadcasting of information over multiple hops to extend the range of situational awareness. Afterward, motivated by the most common and injury-prone pedestrian crash scenarios, we extend our work by proposing an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection for vulnerable road users. Finally, as humans are the most spontaneous and influential entity for transportation systems, we design a learning-based driver behavior model and integrate it into our situational awareness component. Consequently, higher accuracy of situational awareness and overall system performance are achieved by exchange of more useful information

Guidance Compliance Behavior on VMS Based on SOAR Cognitive Architecture

SOAR is a cognitive architecture named from state, operator and result, which is adopted to portray the drivers' guidance compliance behavior on variable message sign (VMS) in this paper. VMS represents traffic conditions to drivers by three colors: red, yellow, and green. Based on the multiagent platform, SOAR is introduced to design the agent with the detailed description of the working memory, long-term memory, decision cycle, and learning mechanism. With the fixed decision cycle, agent transforms state through four kinds of operators, including choosing route directly, changing the driving goal, changing the temper of driver, and changing the road condition of prediction. The agent learns from the process of state transformation by chunking and reinforcement learning. Finally, computerized simulation program is used to study the guidance compliance behavior. Experiments are simulated many times under given simulation network and conditions. The result, including the comparison between guidance and no guidance, the state transition times, and average chunking times are analyzed to further study the laws of guidance compliance and learning mechanism

Reliability and Efficiency of Vehicular Network Applications

The DSRC/WAVE initiative is forecast to enable a plethora of applications, classified in two broad types of safety and non-safety applications. In the former type, the reliability performance is of tremendous prominence while, in the latter case, the efficiency of information dissemination is the key driving factor. For safety applications, we adopt a systematic approach to analytically investigate the reliability of the communication system in a symbiotic relationship with the host system comprising a vehicular traffic system and radio propagation environment. To this aim, the¬ interference factor is identified as the central element of the symbiotic relationship. Our approach to the investigation of interference and its impacts on the communication reliability departs from previous studies by the degree of realism incorporated in the host system model. In one dimension, realistic traffic models are developed to describe the vehicular traffic behaviour. In a second dimension, a realistic radio propagation model is employed to capture the unique signal propagation aspects of the host system. We address the case of non-safety applications by proposing a generic framework as a capstone architecture for the development of new applications and the efficiency evaluation of existing ones. This framework, while being independent from networking technology, enables accurate characterization of the various information dissemination tasks that a node performs in cooperation with others. As the central element of the framework, we propose a game theoretic model to describe the interaction of meeting nodes aiming to exchange information of mutual or social interests. An adaptive mechanism is designed to enable a mobile node to measure the social significance of various information topics, which is then used by the node to prioritize the forwarding of information objects

Modelling vehicle-pedestrian interactions at unsignalised locations employing game-theoretic models

There are some aspects of driver-pedestrian interactions at unsignalised locations that remain poorly understood. Understanding these aspects is vital for promoting road traffic safety in general which involves the interaction of human road users. Recent developments in vehicle automation have called for investigating human-robot interactions before the deployment of highly automated vehicles (HAVs) on roads so that they can communicate effectively with pedestrians making them trustworthy and reliable road users. To understand such interactions, one can simulate interactive scenarios studying various factors affecting road user decision-making processes through lab and naturalistic studies. To quantify such scenarios, mathematical models of human behaviour can be useful. One of these mathematical models that is capable of capturing interactions is game theory (GT). GT can provide valuable insights and strategies to help resolve road user interactions by analysing the behaviour of different participants in traffic situations and suggesting optimal decisions for each party. Thus, the current doctoral thesis aimed to investigate vehicle-pedestrian interactions at unsignalised crossings using GT models, applied to both lab-based and naturalistic data. One of the main aims of the current thesis was to understand how two or more human road users can communicate in a safe and controlled manner demonstrating behaviours of a game-theoretic nature. Thus, an experimental paradigm was created in the form of a distributed simulator study (DSS), by connecting a motion-based driving simulator to a CAVE-based pedestrian simulator to achieve this goal. It was found that the DSS could generate scenarios where participants interact actively showing similar communication patterns to those observed in real traffic. Another prominent finding was the stronger role of vehicle kinematics than personality traits for determining interaction outcomes at unmarked crossings, i.e. whether the pedestrian or driver passed first. To quantify the observations made from the DSS, five computational models namely four GT and one logit model were developed, tested and compared using this dataset. The GT models were obtained from both conventional and behavioural GT literature (CGT and BGT, respectively). This was done to bridge a gap in the previous research, specifically the lack of a comparison between these two modelling approaches in the context of vehicle-pedestrian interactions. Overall, the findings showed that: 1) DSS is a reliable source for the testing and development of GT models; 2) there is a high behaviour variability among road users highlighting the value of studying individualised data in such studies; 3) the BGT models showed promising results in predicting interaction outcomes and simulating the whole interaction process, when compared to the conventional models. These findings suggest that future studies should proceed to adopt, test, and develop BGT approaches for future HAV-human road user interaction studies. To validate the findings of the first two studies, a naturalistic study was conducted in the city of Leeds using state-of-the-art sensors. The sensors gathered road user data including their trajectory and speed over time. The findings from observations revealed similar communication patterns between drivers and pedestrians as in the DSS, suggesting a high degree of relative validity of the experimental paradigm. The results for the computational models were similar but the differences among the models were less noticeable compared to when the models tested against the controlled dataset. Overall, this thesis illustrates that the experimental paradigm and BGT models developed as part of the PhD programme have potential applications for HAV decision-making and motion planning algorithms, as well as traffic safety in general

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp