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Rural two-lane highways make up a large portion of road networks around the world. The special geometric and traffic attributes of these highways pose special challenges to safety and traffic operation. In recent years, microscopic simulation models have gained increased acceptance as a reliable tool for investigating traffic operations and evaluating safety performance. Despite this trend, the development and application of these models to two-lane highway operations has not kept pace with those of freeways and urban networks, and this is due, in large part, to difficulties in modeling the overtaking process. This process has been rendered complex by the large number of inter-related decision factors that need to be considered by the overtaking driver in a bi-directional driving regime. In this research, a new overtaking gap-acceptance model is developed to simulate traffic operation and safety performance on two-lane highways. This model considers a wide spectrum of physical and behavioral variables that could affect overtaking. It does so by introducing a new safety-based gap-acceptance decision variable based on the overtaking driver's perception of time-to-collision (TTC) with an opposing vehicle. The decision to overtake was expressed as a function of the perceived TTC in comparison to an established driver risk threshold (critical TTC). The distribution of critical TTC among drivers are determined through a model calibration and validation procedure based on overtaking observational data obtained from a video-recording of a one-kilometer segment of a two-lane highway. Unlike previous models, the proposed gap-acceptance model makes use of only a few calibration parameters. The proposed overtaking models along with other components of a micro-simulation traffic model are implemented in a software framework that can simulate traffic and safety operation for two-lane highways. The overall simulation results demonstrate that the proposed simulation model can provide reliable measures of traffic and safety for two-lane highway operation. The overtaking model was found to yield both consistent and transferable results. The model is then applied successfully to provide more accurate estimates of traffic measures used in level-of-service analysis for two-lane highways and to compare these results to values reported in the two versions of the Highway Capacity Manual (HCM). In another application, this model is used to investigate the impact of truck mandated speed limiters on safety and traffic operation of two-lane highways and specifically their impact on overtaking. Finally, the potential implications of adaptive cruise control for overtaking and its resultant traffic and safety impacts are studied using the developed simulation model.
This thesis presents the foundations, the initial state, and the progress made in modelling and implementing a real-world and real-time online microscopic traffic simulation system for highway traffic. To successfully model and implement such a simulation system, this thesis recommends the use of a number of formal methods applied at the right places. As part of the recommendation, this thesis proposes a microscopic traffic simulation system. To explore the feasibility and the potential of the recommended methods, it observes and examines the proposed system from multiple views and under various different aspects. As part of the examination, this thesis provides a (semi-)formal specification, a model implementation, an implementation of a productive system, and the benefits that result from validating such a system. The results and any proper application of them have the potential to increase the reliability and the trustworthiness for any future implementation of the proposed simulation system. The presented results additionally motivate to apply the proposed approach to similar simulation systems. The thesis concludes the presentation of the results with some considerations for future implementations.
The increasing power of computer technologies, the evolution of software en- neering and the advent of the intelligent transport systems has prompted traf c simulation to become one of the most used approaches for traf c analysis in s- port of the design and evaluation of traf c systems. The ability of traf c simulation to emulate the time variability of traf c phenomena makes it a unique tool for capturing the complexity of traf c systems. In recent years, traf c simulation – and namely microscopic traf c simulation – has moved from the academic to the professional world. A wide variety of traf- c simulation software is currently available on the market and it is utilized by thousands of users, consultants, researchers and public agencies. Microscopic traf c simulation based on the emulation of traf c ows from the dynamics of individual vehicles is becoming one the most attractive approaches. However, traf c simulation still lacks a uni ed treatment. Dozens of papers on theory and applications are published in scienti c journals every year. A search of simulation-related papers and workshops through the proceedings of the last annual TRB meetings would support this assertion, as would a review of the minutes from speci cally dedicated meetings such as the International Symposiums on Traf c Simulation (Yokohama, 2002; Lausanne, 2006; Brisbane, 2008) or the International Workshops on Traf c Modeling and Simulation (Tucson, 2001; Barcelona, 2003; Sedona, 2005; Graz 2008). Yet, the only comprehensive treatment of the subject to be found so far is in the user’s manuals of various software products.
First Published in 1989. Routledge is an imprint of Taylor & Francis, an informa company.
In recent years, the transport simulation of large road networks has become far more rapid and detailed, and many exciting developments in this field have emerged. Within this volume, the authors describe the simulation of automobile, pedestrian, and rail traffic coupled to new applications, such as the embedding of traffic simulation into driving simulators, to give a more realistic environment of driver behavior surrounding the subject vehicle. New approaches to traffic simulation are described, including the hybrid mesoscopic-microscopic model and floor-field agent-based simulation. Written by an invited panel of experts, this book addresses students, engineers, and scholars, as well as anyone who needs a state-of-the-art overview of transport simulation today.
In safety analysis, two questions typically need to be addressed: 1) how to identify unsafe sites for priority intervention? and 2) how to determine the effectiveness of treatments introduced at these and other sites? Two types of approaches have been considered in the literature to provide answers for these questions: (1) observational models based on historical crash data and (2) observed or simulated higher risk vehicle interactions or traffic conflicts. Observational crash-based models are good at predicting higher severity crashes, but they tend to ignore higher risk vehicle interactions that compromise safety, that have not resulted in crashes (e.g. near misses). Proponents of microscopic simulation argue that ignoring these higher risk interactions can severely understate the safety problem at a given site and lead to a misallocation of scarce treatment funds. Another problem with observational crash prediction models is the need for sufficient crash data reported over an extended period of time to provide reliable estimates of “potential” lack of safety. This requirement can be a challenge for certain types of treatment and different sites or locations. Furthermore, observational approaches are not causal in nature, and as such, they fail to provide a sound “behavioural” rationale for “why” certain treatments affect safety. On the other hand, traffic conflicts occur more frequently than crashes and can provide a stronger experimental basis for estimating safety effects on a short-term basis. This is especially important given the rare random nature of crashes for certain traffic conditions. Additionally, they provide a more rational basis for lack of safety than is normally available from crash occurrence data. Basically, through the application of calibrated behavioural simulation, traffic conflicts can be linked to specific driver actions and responses at a given site, more so than conventional reported crashes. As such, they permit a causal underpinning for possible treatment effects and this is important to decision-makers because it underscores why certain treatments act to enhance safety, rather than simply providing an estimate of the treatment effect itself. Notwithstanding the usefulness of conflict-based measures, observed crashes remain the primary verifiable measure for representing failures in the transportation systems. Unfortunately traffic conflicts have not been formally linked to observed crashes, and hence their values as indicators for treatment effect have not been fully explored. This presents a challenge on how best to use both conflicts and observed crashes to better understand where safety is most problematic, where intervention is needed, and how best to resolve specific safety problems? In this thesis, the position is taken that a complete understanding of safety problems at a given site can only emerge from a more inclusive analysis of both observed crashes and traffic conflicts. This is explored by developing two integrated models: (1) An integrated priority ranking model is presented that combines estimates from observational crash prediction with an analysis of simulated traffic conflicts; (2) An integrated treatment model is presented that uses simulated traffic conflicts that are linked statistically to observed crashes to provide estimates of crash modification factor (CMF). The suitability of these integrated models has been evaluated using data for a sample of signalized intersections from Toronto for the period 1999-2006. In the absence of a benchmark (or true) priority ranking outcome, a number of evaluation criteria were considered, and the integrated ranking model was found to yield better results than both conventional observational crash-based models (including empirical Bayesian, potential for safety improvement methods) and conflict-based models (including conflict frequency and rate for different risk thresholds). For treatment effects, the results suggest that CMFs can be estimated reliably from conflicts derived from microsimulation, where the simulation platform has been sufficiently calibrated. The link between crashes and conflicts provides additional inferences concerning treatment effects, in those cases where treatments were not previously implemented (i.e., no after history). Since there is an absence of crash history, the treatment effect is based exclusively on simulated conflicts. Moreover, the integrated model has the added advantage of providing site-specific CMFs instead of applying a constant CMF across all sites considered for a potential treatment.
This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.
Lane changing manoeuvres have a substantial impact on microscopic and macroscopic traffic flow characteristics due to the interference effect they have on surrounding vehicles. The interference effects of heavy vehicles' lane changing manoeuvres on surrounding traffic are likely to be greater than when passenger cars execute lane changing manoeuvre. While heavy vehicles account for a minority of traffic stream, heavy vehicles have a pronounced effect on traffic flow and produce a disproportionate effect particularly during heavy traffic conditions. Heavy vehicles impose physical and psychological effects on surrounding traffic which are the results of physical and operational characteristics of heavy vehicles. The number of heavy vehicles on urban freeways has increased over the past three decades and this trend is likely to continue over the next decade. Despite the increasing number of heavy vehicles on freeways, previous studies have predominantly focused on the behaviour of passenger car drivers. In the previous lane changing models, heavy vehicles are accommodated in current lane changing models by calibrating the parameters of a general lane changing model for heavy vehicles rather than by incorporating a lane changing model developed specifically for the heavy vehicle drivers. However, heavy vehicle and passenger car drivers may have fundamentally different lane changing behaviour.In this study, the trajectory dataset is based on the video images of two freeway sections. In general, extracting the trajectory dataset from video images makes it impossible to capture some physical (e.g. weight) and operational (e.g. power) characteristics of vehicles. The length of vehicles is one of their physical characteristics that can be extracted from video images. Therefore, vehicle length is used to identify heavy vehicles in this research. The vehicles with the length of equal to or greater than 6 meters are classified as heavy vehicles. This classification is consistent with the definition of the heavy vehicles in the trajectory dataset used for this study.In this research, the lane changing behaviour of a driver has been characterized as a sequence of three stages including motivation to change lanes, selection of the target lane and the execution of the lane change. This research has provided new insight into the role that traffic parameters associated with the surrounding vehicles plays in the lane changing behaviour of heavy vehicle and passenger car drivers. From detailed examination of vehicle trajectory data, differences were identified in the lane changing of heavy vehicle and passenger car drivers in terms of the three stages of lane changing behaviour.To understand the influencing factors on heavy vehicle drivers' lane changing, it is required to analyse the surrounding traffic characteristics at the time that the heavy vehicle drives change lanes as well as when they do not wish to execute lane changing manoeuvre. From detailed examination of the surrounding traffic characteristics, the explanatory variables in heavy vehicle drivers' lane changing decision were identified.A reliable model has been developed in this thesis to estimate the lane changing behaviour of heavy vehicle drivers. Drivers' lane changing behaviour has been characterised as a sequence of two stages: the decision to change lanes and the execution of the lane change. Hence, separate models were considered for those two stages of the heavy vehicle drivers' lane changing behaviour. Fuzzy logic was used to develop a model of the lane changing decision of heavy vehicle drivers. The lane changing decision has been defined as the motivation for selecting either the right adjacent lane (slower lane) or the left adjacent lane (faster lane). Therefore, two separate models were developed for the lane changing decision of heavy vehicle drivers: Lane Changing to Slower Lane (LCSL) and Lane Changing to Faster Lane (LCFL). The explanatory variables in motivating heavy vehicle drivers to move into the slower lane include: the front space gap, the rear space gap, the lag space gap in the right lane and the average speed of the surrounding vehicles in the current lane. The explanatory variables in motivating heavy vehicle drivers to move into the faster lane include: the front relative speed, the lag relative speed in the left lane and the average speeds of the surrounding vehicles in the current lane and the left lane. A triangular membership function was used for all fuzzy sets in the lane changing decision model. The leave-one-out cross-validation method was used to examine the accuracy of the models in estimating the lane changing manoeuvres of heavy vehicle drivers. The obtained results showed that the LCFL model has higher percentage of accurately estimating the heavy vehicle drivers' lane changing decision. This may be due to the fact that heavy vehicle drivers mainly move into the faster lane to gain speed advantages which could be modelled by the microscopic traffic characteristics of surrounding vehicles in the current and the left lanes. However, the heavy vehicle drivers may have other motivations for moving into the slower lane than only the differences in microscopic traffic characteristics in the current and the right lanes.The speed and acceleration/deceleration profiles of heavy vehicles were analysed in detail from the start to the end of lane changing manoeuvres. The results showed that heavy vehicle drivers maintain an almost constant speed during lane changing execution. They do not accelerate or decelerate to adjust their speed according to the speeds of the surrounding vehicles in the target lane. Subsequently, a simple constant speed model could be assumed for the heavy vehicles during the lane changing execution.Finally, the performance of the heavy vehicle drivers' lane changing model was examined macroscopically and microscopically using VISSIM (German abbreviation for 'traffic simulation in cities') microscopic traffic simulation model. The heavy vehicle lane changing model in VISSIM was substituted with a combination of the fuzzy logic heavy vehicle lane changing decision model and a constant speed lane changing execution model. The traffic measurements obtained from the fuzzy logic model were compared to those obtained from a calibrated VISSIM lane changing model and the actual field observations. The results show that using the fuzzy logic heavy vehicle lane changing model provided more accurate estimates of the macroscopic traffic measurements. The number of heavy vehicle lane changing manoeuvres estimated by the fuzzy logic model was found to be more accurate than the estimates from default lane changing model in VISSIM. The microscopic analysis of the lane changing manoeuvres shows that using the fuzzy logic model more accurately replicated the microscopic lane changing behaviour of the heavy vehicle drivers. In particular, the fuzzy logic model accurately replicates the observed speed profile of heavy vehicles and the observed space gap and speed profiles of the surrounding vehicles during lane changing manoeuvres. The superior performance of the fuzzy logic heavy vehicle drivers' lane changing model highlights the importance of developing an exclusive lane changing model for heavy vehicle drivers. Employing a purpose built heavy vehicle lane changing model has been shown to increase the accuracy of the microscopic traffic simulation model.
Microscopic traffic simulations are tools for simulation of traffic in form of individual vehicles. Road types have various traffic characteristics and therefore different models for their traffic simulation and analysis. The Rural Road Traffic Simulator, RuTSim, is a model which was developed by the Swedish National Road and Transport Research Institute, VTI. RuTSim is a microscopic traffic simulator for rural roads. The 2+1 roads are the type of rural roads that allocate 2 lanes to one direction and one lane to the other, with this configuration for the lanes changing sides after a certain distance. In this research a calibration of the current version of RuTSim for 2+1 roads is presented. The project clarifies microscopic traffic simulation models, RuTSim and its specific settings for 2+1 roads, different approaches for calibrating models and finally the calibration process for 2+1 roads in the current version of the RuTSim model.The calibration process provides a better understanding of the specific effects (of the change) of calibration parameters and their role in returning better simulation outputs on traffic of 2+1 roads.