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The increasing availability of real-time data collected from dynamic systems brings opportunities for simulation models to be calibrated online for improving the accuracy of simulation-based studies. Systematical methods are needed for assimilating real-time measurement data into simulation models. This paper presents a particle filter-based data assimilation method to support online model calibration in discrete event simulation. A joint state-parameter estimation problem is defined, and a particle filter-based data assimilation algorithm is presented. The developed method is applied to a discrete event simulation of a one-way traffic control system. Experiments results demonstrate the effectiveness of the developed method for calibrating simulation models' parameters in real time and for improving data assimilation results. [ABSTRACT FROM AUTHOR]

Microscopic traffic simulation is a powerful tool for studying complex traffic systems when analytical or empirical approaches cannot define response patterns adequately and accurately. The application of simulation techniques for evaluating the characteristics of controlled intersections is effective due to their quick and reliable outcomes. This paper presents a methodology for model development and a unique calibration process for mixed traffic streams using the widely used simulation software Vissim. An isolated signalised intersection was selected for the model development. Most of the calibration methodologies described in previous literature were based on informal practices based on a homogeneous traffic stream. This study frames a novel calibration process suitable for mixed traffic streams. The validity of the proposed methodology was evaluated using field data from another signalised intersection by means of a feasibility comparison of field data and simulation outputs. The calibrated parameters produced suitable simulation outputs, with the measure of effectiveness matching the result obtained from actual field conditions. In contrast, simulations with default or best-guess parameters resulted in significant discrepancies between the observed and simulated outputs. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the viability of microsimulation in estimating critical gaps at unsignalised intersections. Critical gaps are generally estimated by extracting gap acceptance data from traffic videos. Extracting such data is a strenuous task, especially under mixed traffic conditions. In this study, gap acceptance data were generated from simulation models developed in the Vissim software package. Critical gaps of three unsignalised intersections were estimated using field data and simulated data of the intersections. Critical gaps were estimated by the logit method and the clearing time method. The critical gaps estimated using simulated data were reasonably close to those obtained using field data for both methods. The results also showed that the logit method was more robust than the clearing time method in estimating critical gaps under mixed traffic conditions. Moreover, the time needed for generating simulated data was only 3.6% of that required for obtaining field data. The method proposed in this study will help traffic engineers and practitioners in critical gap estimation by saving a considerable amount of time and effort. [ABSTRACT FROM AUTHOR]

In microscopic traffic simulation, the fidelity of the road network model has a significant impact on the difference between the simulation and the actual urban traffic state. Accurately matching data on the simulated road network and the surroundings has become a central concern in traffic simulation research. This study provides a multi-source data-based framework for automatic road network generation (ARNG) to address the issue of manual procedures in the creation of the simulated road network and surroundings. First, the proposed method of fusion and matching of diverse road network data is used to acquire the basic road network information, and the combining of the features of different road network data can enhance the authenticity of the basic road network. Second, a multi-modal simulation road network is developed based on multi-modal traffic operation data to serve as the simulation operation's foundational environment. To address the requirements of the dynamic evolution of the simulated road network, an editor for the dynamic road network is built based on spatial closest neighbor matching. The case study illustrates the process of building the simulated road network and environment in the old city zone of Suzhou. Real-world examples demonstrate that the data-based ARNG approach provided in this study is highly automatic and scalable. [ABSTRACT FROM AUTHOR]

Virtualized traffic via various simulation models and real‐world traffic data are promising approaches to reconstruct detailed traffic flows. A variety of applications can benefit from the virtual traffic, including, but not limited to, video games, virtual reality, traffic engineering and autonomous driving. In this survey, we provide a comprehensive review on the state‐of‐the‐art techniques for traffic simulation and animation. We start with a discussion on three classes of traffic simulation models applied at different levels of detail. Then, we introduce various data‐driven animation techniques, including existing data collection methods, and the validation and evaluation of simulated traffic flows. Next, we discuss how traffic simulations can benefit the training and testing of autonomous vehicles. Finally, we discuss the current states of traffic simulation and animation and suggest future research directions. [ABSTRACT FROM AUTHOR]

Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators' cost and passenger's service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions. Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first [ABSTRACT FROM AUTHOR]

Traffic micro-simulation is the newly developed approach for loading calculation of long span bridges. The approach is quite precise, but computationally expensive to consider the full extent of traffic loading scenarios during a bridge lifetime. To address this shortfall, an efficient multi-scale traffic modelling approach is proposed. The proposed approach uses micro- and macro-simulation with different load model varieties (LMVs), or fidelities (levels of detail) of traffic loading in different bridge regions, to achieve optimal computation efficiency while maintaining the precision of loading calculation. Metrics of influence line (IL) characteristics, such as degree of nonlinearity, are proposed to evaluate the appropriateness of the choice of LMV, and standards of the metrics are also investigated to quantify the implementation of LMVs on bridge IL regions in the multi-scale modelling. Finally, two typical ILs are used along with random traffic modelling to study the feasibility of the proposed approach. It is shown that the multi-scale modelling approach proposed here achieves high computational efficiency and accuracy, which is significant for the massive traffic load simulation for lifetime bridge load effect analysis. [ABSTRACT FROM AUTHOR]

Pavement design, initially based on empirical methods, has been improved and, currently, there is a tendency to replace these methods by mechanistic-empirical ones. This new methodology uses detailed information on traffic, climate and pavement structure to obtain accurate performance predictions, providing a better design. In this study, it was performed a sensitivity analysis of flexible pavements to investigate the effect that the variation of annual average daily traffic (AADT) and traffic growth rate has on structural pavement responses. Therefore, simulations were done using two computer programs, MEPDG and AASHTOWare Pavement ME Design, whose results indicated that the pavement has sensitivity to both variations that were studied (AADT and traffic growth rate), demonstrating higher deterioration values when the traffic growth rate was nonlinear. It was also concluded that the difference between sensitivity values obtained by both programs was insignificant. [ABSTRACT FROM AUTHOR]

Major international automakers have considered the deployment of the 5.9 GHz dedicated short-range communications (DSRC) on their vehicle fleets for wireless connectivity. DSRC-enabled vehicle-to-vehicle (V2V) communication through broadcast of basic safety messages enables safety applications for crash warning and avoidance. However, in dense traffic conditions as the V2V deployment scales up, the resultant channel load increases and leads to channel congestion and may adversely affect the performance of the safety applications. The Society of Automotive Engineers J2945/1 standard that builds atop Institute of Electrical and Electronics Engineers (IEEE) 802.11p and IEEE 1609 standards provides the minimum performance requirements for V2V safety communications. Specifically, it provides a congestion control protocol for transmission rate and power adaptations to achieve robust performance in dense vehicular networks. The primary contribution of this paper is that using a congestion generation testbed that emulates channel congestion including a large number of remote vehicles, we can validate and test any V2V equipped vehicle for compliance with the J2945/1 standard. Our paper also demonstrates that under heavy congestion, even with 600 ms of inter-transmit time, a moving vehicle can be tracked to a lane-level accuracy. [ABSTRACT FROM AUTHOR]

This paper attempts to apply both global and local sensitivity analyses (SA) to fully test the integrated activity-based model (ABM) within several experiments based on a Chinese medium-sized city, Baoding. MATSim (Multi-agent Transport Simulation) that is a typical integrated ABM is used as an example for the SA. The global SA, which is based on the elementary effect method, is firstly applied to identify the influential parameters. Then the once-at-a-time (OAT)-based Local SA is employed to further reveal the relationship between the influential parameters and the outputs of interest, such as traffic flow. The SA results show the extent to which and how three influential MATSim parameters (population scaling factor, the number of iterations and time step size) influence the outputs of interest. In addition, the SA results of MATSim suggest that the parameters of time mutation rate and performing utility can heavily influence the outputs of interest and properly setting them can optimize the daily plans of agents. Finally, this paper concludes with suggestions on how to wisely use the SA findings for both MATSim and other ABM users. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the equilibrium properties of the morning commute problem at the network level with heterogeneous trip lengths. Congestion is modeled with a Macroscopic Fundamental Diagram relating the space-mean speed of a network to the vehicular accumulation. It is shown for a large class of scheduling preferences that if users have continuously distributed characteristics, the network accumulation at equilibrium is a continuous function of time. With α − β − γ preferences and under certain conditions, a partial First-In, First-Out (FIFO) pattern emerges at equilibrium among early and late users. This FIFO pattern is strict only within families of users having heterogeneous trip lengths and identical preferences, or vice versa. Simulation results confirmed that an attracting steady-state exists for a wide range of demands and that the predicted patterns are indeed observed. [ABSTRACT FROM AUTHOR]

Considering congestion effects in realistic network environments, we give a new method to adjust dynamically adjust the weight of the congested edge. We calculate the load on an edge based on the revised betweenness method and propose a novel model with three states of edges to investigate the dynamics of cascading failures in the ring network, the BA scale-free network, and the real traffic networks in London. By two robust metrics, we surprisingly observe the abnormal dynamics of cascading propagation, especially compared with that in the unadjustable weight, the curves of cascading dynamics in the adjustable weight are more irregular, which means that enhancing the capacity of each edge is not always better to avoid the cascading propagation. In addition, our simulation results show that the dynamical change of the edge's weight makes the heterogeneous BA networks more vulnerable. [ABSTRACT FROM AUTHOR]

In this paper, we revisit the principle of bounded rationality applied to dynamic traffic assignment to evaluate its influences on network performance. We investigate the influence of different types of bounded rational user behavior on (i) route flows at equilibrium and (ii) network performance in terms of its internal, inflow, and outflow capacities. We consider the implementation of a bounded rational framework based on Monte Carlo simulation. A Lighthill-Whitham-Richards (LWR) mesoscopic traffic simulator is considered to calculate time-dependent route costs that account for congestion, spillback, and shock-wave effects. Network equilibrium is calculated using the Method of Successive Averages. As a benchmark, the results are compared against both Deterministic and Stochastic User Equilibrium. To model different types of bounded rational user behavior we consider two definitions of user search order (indifferent and strict preferences) and two settings of the indifference band. We also test the framework on a toy Braess network to gain insight into changes in the route flows at equilibrium for both search orders and increasing values of aspiration levels. [ABSTRACT FROM AUTHOR]

A promising framework for understanding flow-density relationship in traffic flow theory is the Fundamental Diagram, originally developed for uninterrupted traffic flow facilities. The concept has been extended to the Arterial Fundamental Diagram (AFD), which has shown that the same relationship holds on arterial streets. However, constructing an AFD is subject to considerable variability in the measured quantities, due to the highly cyclical nature of signalized intersections. In most cases, these diagrams are based on the data from upstream detectors, located away from traffic signals. Recent scientific literature has shown a value of using stop-line detection data to develop AFDs, opening a plethora of opportunities to further investigate traffic dynamics utilizing the data from adaptive traffic control systems (ATCSs). This can, however, be problematic for two major reasons. First, the data may come from detectors unfit to provide good-quality inputs to develop an AFD. Second, such ATCSs may use their own surrogate measures of density and traffic flow, primarily developed for the purpose of controlling traffic, which may be inappropriate for developing fundamental relationships. This study aims to address these issues by investigating appropriateness of using Degree of Saturation (DS), a density-like measure from Sydney Coordinated Adaptive Traffic System (SCATS), to develop an AFD. Empirical SCATS data shows an interesting pattern of the AFD, which cannot be explained by the data itself. Hence, we derive a new analytical model of DS based on the high-resolution signal and detection data, which reveals parameters that drive its behavior. Additionally, we develop the Cyclical Vehicle Arrival and Discharge Model to simulate SCATS-like operations and derive causal relationships between traffic flow variables and density-like performance measures in a controlled environment. The findings show that DS does not have to be a poor estimator of traffic conditions, but when it is combined with SCATS-measured traffic flows it gives a false representation of near-capacity and over-saturated conditions. [ABSTRACT FROM AUTHOR]

We propose a novel approach to pedestrian flow characterization. The definitions of density, flow and velocity existing in the literature are extended through a data-driven spatio-temporal discretization framework. The framework is based on three-dimensional Voronoi diagrams. Synthetic data is used to empirically investigate the performance of the approach and to illustrate its advantages. Our approach outperforms the considered approaches from the literature in terms of the robustness with respect to the simulation noise and with respect to the sampling frequency. Additionally, the proposed approach is by design (i) independent from an arbitrarily chosen discretization; (ii) appropriate for the multidirectional composition of pedestrian traffic; (iii) able to reflect the heterogeneity of the pedestrian population; and (iv) applicable to pedestrian trajectories described either analytically or as a sample of points. [ABSTRACT FROM AUTHOR]

The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated short-range communications (DSRC). As opposed to fixed sampling schemes, which transmit at regular intervals, the sampling policy we propose is adaptive to the road network and the importance of the links that the vehicle traverses. Since cellular communications are costly, in the basic centralized scheme, the vehicle is not aware of the road conditions on the network. We extend the scheme to handle non-cellular communications via roadside units and vehicle-to-vehicle (V2V) communication. Under a general traffic model, we prove that our scheme always outperforms the baseline scheme in terms of transmission cost while satisfying accuracy and real-time requirements. Our analytical results are further supported via simulations based on actual road networks for both the centralized and V2V settings. [ABSTRACT FROM AUTHOR]

This article presents a procedure to estimate the macroscopic traffic state in a pre-defined space-time mesh using relative flow data collected by stationary and moving observers. The procedure consist of two consecutive and independent processes: (1) estimate point observations of the cumulative vehicle number in space-time, i.e., N ( x, t ), based on relative flow data from the observers and (2) estimate flow and density in a pre-define space-time mesh based on the point observations of N . In this paper, the principles behind the first process are explained and a methodology (the Point-Observations N (PON) estimation methodology) is introduced for the second process. This methodology does not incorporate information in the form of a traffic flow model or historical data. To evaluate this performance and improve our understanding of the methodology, a microscopic simulation study is conducted. The estimation performance is effected by the homogeneity and stationarity of traffic in estimation area and in the sample area. In case of large changes in traffic conditions, e.g., from free-flow to congestion or stop-and-go waves, a better sampling resolution will improve localizing these changes in space and time and hence improve the estimation performance. In the simulation study, the proposed methodology is also compared with estimates based on loop-detector data. This indicates that the combination of the proposed methodology and data yields an alternative for existing combinations of methodology and data. Especially, in terms of density estimation the introduced methodology shows promising results. [ABSTRACT FROM AUTHOR]

Modern cities are currently facing rapid urban growth and struggle to maintain a sustainable development. In this context, “eco-neighbourhoods” became the perfect place for testing new innovative ideas that would reduce congestion and optimize traffic flow. The main motivation of this work is a true and stated need of the Department of Transport in Nancy, France, to improve the traffic flow in a central eco-neighbourhood currently under reconfiguration, reduce travel times and test various traffic control scenarios for a better interconnectivity between urban intersections. Therefore, this paper addresses a multi-objective simulation-based signal control problem through the case study of “Nancy Grand Cœur” (NGC) eco-neighbourhood with the purpose of finding the optimal traffic control plan to reduce congestion during peak hours. Firstly, we build the 3D mesoscopic simulation model of the most circulated intersection (C129) based on specifications from the traffic management centre. The simulation outputs from various scenario testing will be then used as inputs for the optimisation and comparative analysis modules. Secondly, we propose a multi-objective optimization method by using evolutionary algorithms and find the optimal traffic control plan to be used in C129 during morning and evening rush hours. Lastly, we take a more global view and extend the 3D simulation model to three other interconnected intersections, in order to analyse the impact of local optimisation on the surrounding traffic conditions in the eco-neighbourhood. The current proposed simulation-optimisation framework aims at supporting the traffic engineering decision-making process and the smart city dynamic by favouring a sustainable mobility. [ABSTRACT FROM AUTHOR]

This paper presents a traffic simulation framework to reproduce urban freight movements, particularly concerning double-parked delivery operations. Since freight movements affect traffic and vice versa, we propose a hybrid framework that simulates traffic phenomena macroscopically and, at the same time, allows tracking delivery vehicles along their routes throughout the entire simulation. The traffic simulation framework is based on the Lighthill–Whitham–Richards macroscopic model as well as the theory of bottlenecks. The traffic component of the model can be coupled with a generic parking model. Since a novel faster version of the Lax-Hopf Formula is used in the traffic simulation, the proposed framework can perform efficient simulations. Because of this hybrid nature, the framework is suitable for simulations of large scenarios and for evaluations of City Logistic measures to tackle the last-mile problem. We show this in the second part of the study with two different measures: shifting delivery operations to off-peak hours, and prohibiting deliveries on critical streets. While the benefits deriving from the first strategy are evident, the effects of the second one are less clear because of the complexity of network interactions. [ABSTRACT FROM AUTHOR]

Deployment of a simulation-based dynamic traffic assignment (DTA) model requires calibration of a large number of demand and supply input parameters. In this paper, we present a data-intensive framework for deployment, calibration, and validation of a simulation-based DTA model of Melbourne, Australia as a large-scale congested network. The model consists of 55,719 links and 24,502 nodes and simulates almost 2.1 million commuters in a 4-h morning peak period. We propose a machine learning based technique to classify and calibrate the traffic flow fundamental diagrams against empirical data obtained from a large number of freeway loop detectors across the network. An optimization framework for estimating the time-dependent origin-destination (TDOD) demand is also presented. To enhance the quality of the TDOD demand estimation, we apply a departure time profiling technique to account for the spatial differences between OD pairs. The paper also demonstrates the impact of adaptive driving on the quality of the DTA calibration. A comparison between the simulated and observed link volumes over 1,250 locations across the entire network shows that the calibration procedure generates an approximately 30% improvement in the root mean square error (RMSE). The impact of pedestrians and cyclists on the vehicular traffic is also implicitly considered in the central business district (CBD) area to improve underestimation of the simulated travel times. Validation results suggest that the calibrated DTA model successfully replicates traffic patterns in the network and support the future applications of the model for various transportation operations and planning purposes. [ABSTRACT FROM AUTHOR]

This paper reports a review of the extensions and application of the Cell Transmission Model (CTM). Those extensions are models able to simulate complex urban traffic dynamics with all the advantages of macroscopic and microscopic urban traffic model. Over the past few years researchers have been trying to increase the level of detail by extending CTM and introducing new formulations to improve the application of the model in urban traffic. The authors classified the papers while taking into consideration all those factors characterizing the urban traffic, arterial and intersection traffic flow in particular. One of the primary goals of transport research is to develop a general framework of urban traffic networks that might be applied from a realistic point of view. Recent studies about traffic simulations have shown that, among various macroscopic simulation models, the CTM has the potential to achieve this objective. We have also reported our model the CTM_UT that improves the CTM for Urban Traffic. We believe that it is possible to apply this model to ITS application, hence increase the accuracy of the macroscopic model while maintaining the computational advantages and provide an accurate prediction of travel time approach. [ABSTRACT FROM AUTHOR]

We study the long time behavior of arches and membrane-like structures in the strong damping case. Our main goal is to incorporate moving points load, and to establish the existence of a non-empty, compact, uniform attractor for the associated semi-flows. The existence of such an attractor is established for almost periodic forcing terms. The method employs Kuratowski's measure of non-compactness. The presentation is mostly self-contained. Examples simulating a vehicular traffic across a long bridge are considered. [ABSTRACT FROM AUTHOR]

Based on our previously developed ship collision avoidance steering system, this paper develops a more extensive collision avoidance decision-making system for non-uniformly moving ships. A real-time simulator based on the Six-Dimensional (6D) Manoeuvring Modelling Group (MMG) model is used to simulate the ship's motion. To validate the manoeuvring mathematical model, sea trial measurements of a container ship (C-3) have been selected. This study incorporates Nomoto's second-order model into a numerical model to calculate the turning characteristics of the ship. The manoeuvring indices of Nomoto's model are the knowledge base of the simplified ship simulation model. To verify the ship collision avoidance system with respect to different traffic factors, simple and complex collision avoidance cases have been designed in fast-time simulations with multi-ship encounter conditions. The simplified simulation model developed here can quickly determine the helm angle when the ship makes a collision avoidance manoeuvre, which is helpful for the safety of ship navigation in heavy traffic areas. [ABSTRACT FROM AUTHOR]

This paper investigates roundabout capacity analysis using mathematical modelling and microscopic simulation. The capacity in approach section in roundabout is calculated by estimating the number of vehicles that can enter a roundabout for a given approach given a certain circulating volume. Since roundabouts are working with only yield conditions, capacity is dependent on gap acceptance model. Priority rules are used to simulate the gap acceptance model and define the right-of-way for conflicting movements. In the case of roundabouts, priority rules can be utilized to establish right-of-way at each of the conflict points where the approach traffic merges with the circulating traffic of the roundabout. By altering the minimum acceptable gap and related parameters, it is possible to calibrate a simulation model to be that of a real-life roundabout or that of a theoretical roundabout that meets the operating characteristics defined in current capacity models. The proposed roundabout capacity analysis methodology is expected to assist modelling operational conditions for roundabouts. Results are presented that provide evidence to validate the proposed approach. [ABSTRACT FROM AUTHOR]

Abstract: This article describes a method for compressing position and identification data for files containing comprehensive trajectory records of all vehicles traversing the same roadway segment over an arbitrary time period, in such a way that no loss of information content occurs. Such complete trajectory records are important for the study of traffic flow theory and could become increasingly relevant as test data against which to study the behavior of autonomous vehicles in a mixed traffic environment. Compression steps include differential encoding, motion prediction, and parsimonious binary storage, plus steps that are unique to the context of vehicle trajectories, including vehicle ID numbers and lane occupancy. The effectiveness of the compression is due, in part, to the strong correlations between positions (and speeds) of vehicles traveling near each other, as well as recognition that certain trajectory artifacts change with spatial and temporal frequencies much lower than the sampling rates. The algorithm is demonstrated on two sets of publicly available complete trajectory records, and compression performance statistics are given. Compression ratios in the range 10:1 to more than 20:1 are achieved for the sample files. [ABSTRACT FROM AUTHOR]

Variable Speed Limit (VSL) control contributes to potential crash risk reduction by suggesting a suitable dynamic speed limit to achieve more stable and uniform traffic flow. In recent studies, researchers adopted macroscopic traffic flow models and perform prediction-based optimal VSL control. The response of drivers to the advised VSL is one of the most critical parameters in VSL-controlled speed dynamics modeling, which significantly affects the accuracy of traffic state prediction as well as the control reliability and performance. Nevertheless, the variations of driver responses were not explicitly modeled. Thus, in this research, the authors proposed a dynamic driver response model to formulate how the drivers respond to the advised VSL during various traffic conditions. The model was established and calibrated using field data to quantitatively analyze the dynamics of drivers’ desired speed regarding the advised VSL and current traffic state variables. A proactive VSL control algorithm incorporating the established driver response model was designed and implemented in field-data-based simulation study. The design proactive control algorithm modifies VSL in real-time according to the traffic state prediction results, aiming to reduce potential crash risks over the experiment site. By taking into account the real-time driver response variations, the VSL-controlled traffic state dynamics was more accurately predicted. The experimental results illustrated that the proposed control algorithm effectively reduces the crash probabilities in the traffic network. [ABSTRACT FROM AUTHOR]

Objective To investigate the extent of a driver’s mental model with irrevocable visual occlusion and analysing the distance to crash. Background Drivers have a mental model of the immediate surroundings which allows them to predict their own as well as others’ travel paths. To navigate safely through traffic, this mental model has to be updated frequently to remain valid. In between information sampling events, the mental model will become outdated over time, as the traffic system is dynamic. Method A simulator study with 22 participants was conducted to investigate the information decay in the mental model. This was implemented by extending visual occlusion until the driver collided with another vehicle or ran off the road, thus providing an estimate of how long it takes until the mental model becomes obsolete. Results An analysis of variance with the factors curve direction, curve radius and traffic showed that curve radius did not influence the distance to crash. Without traffic, drivers veered off the road sooner in right curves. Adding traffic eliminated this difference. Traffic ahead led to a shortened distance to crash. Compared to a tangential travel path from the current lateral position at the time of the occlusion, drivers crashed on average 2.6 times later than they would have, had they not had any mental model of the situation. Conclusions The drivers’ mental representation of the future situation seems to include information on how to act, to alleviate deviations in yaw angle, including and considering the presence of other road users. [ABSTRACT FROM AUTHOR]

Mixed traffic flow is a main feature of urban traffic in developing countries. Mixed bicycle flow includes human-powered and electric-powered bicycles and plays an important role in this mixed traffic flow. In mixed bicycle flow, cyclist behavior is flexible and variable. Cyclists move arbitrarily in the road and may influence the vehicle flow at intersections. Therefore, we first propose an improved bicycle model that can reproduce the main features of cyclist behavior, such as overtaking and self-protection. Then a simulation model based on the improved bicycle model is proposed to research the characteristics of mixed traffic flow in intersections. The conflict avoidance rules are then applied in the simulation model to eliminate traffic accidents. The simulation results indicate that the improved bicycle model is practical and that the density–velocity diagram of bicycle flow is similar to real traffic data. The existence of bicycle flow decreases vehicle velocity and worsens traffic flow in mixed traffic flow. However, the influence of bicycle flow might disappear at a particular vehicle density. [ABSTRACT FROM AUTHOR]

The traditional traffic microscopic simulation models consider driver and vehicle as a single unit to represent the movements of drivers in a traffic stream. Due to this very fact, the traditional car-following models have the driver behavior related parameters, but ignore the vehicle related aspects. This approach is appropriate for homogeneous traffic conditions where car is the major vehicle type. However, in heterogeneous traffic conditions where multiple vehicle types are present, it becomes important to incorporate the vehicle related parameters exclusively to account for the varying dynamic and static characteristics. Thus, this paper presents a driver-vehicle integrated model hinged on the principles involved in physics-based spring-mass-damper mechanical system. While the spring constant represents the driver's aggressiveness, the damping constant and the mass component take care of the stability and size/weight related aspects, respectively. The proposed model when tested, behaved pragmatically in representing the vehicle-type dependent longitudinal movements of vehicles. [ABSTRACT FROM AUTHOR]

Collision avoidance behavior has become an open challenging problem since it is one of critical factors that influence the pedestrian flow dynamics. In this paper, a cellular automaton (CA) model is developed to depict the pedestrian movements when collision avoidance behaviors exist during evacuation. Then, we utilize the proposed model to simulate the influences of the collision avoidance on the pedestrian movements during the evacuation in a classroom with two exits. The numerical results indicate that more collision avoidance behaviors have negative influences on the evacuation efficiency, and that more competition behaviors generate more collisions while have no prominent positive impacts on the evacuation efficiency. Moreover, the evacuation time increases with the decreasing number of aisles in the classroom and the number of collisions increases with the increasing number of parts in the classroom divided by aisles. The above results are helpful to develop effective evacuation strategies and design the internal layouts of buildings. [ABSTRACT FROM AUTHOR]

Mixed traffic flux composed of crossing pedestrians and vehicles extensively exists in cities. To study the characteristics of the interference traffic flux, we develop a pedestrian-vehicle cellular automata model to present the interaction behaviors on a simple cross road. By realizing the fundamental parameters (i.e. injecting rates α 1 , α 2 , the extracting rate β and the pedestrian arrival rate α P ), simulations are carried out. The vehicular traffic flux is calculated in terms of rates. The effect of the crosswalk can be regarded as a dynamic impurity. The system phase diagrams in the ( α 1 , α P ) plane are built. It is found that the phase diagrams consist essentially of four phases namely Free Flow, Congested, Maximal Current and Gridlock. The value of the Maximal current phase depends on the extracting rate β , while the Gridlock phase is achieved only when the pedestrians generating rate is higher than a critical value. Furthermore, the effect of vehicles changing lane ( P ch 1 , P ch 2 ) and the location of the crosswalk X P on the dynamic characteristics of vehicles flow are investigated. It is found that traffic situation in the system is slightly enhanced if the location of the crosswalks X P is far from the intersection. However, when P ch 1 , P ch 2 increase, the traffic becomes congested and the Gridlock phase enlarges. [ABSTRACT FROM AUTHOR]

Predicting the risk of traffic demands and delays exceeding critical limits at road junctions, airports, hospitals, etc., requires knowing how both mean and variance of queue size vary over time. Microscopic simulation can explore variability but is computationally demanding and gives only sample results. A computationally efficient approximation to the mean is used in many modelling tools, but only empirical extensions for variance in particular situations have been available. The paper derives theoretical formulae for time-dependent and equilibrium variance, believed to be novel and to apply generally to queues covered by the Pollaczek–Khinchin mean formula, and offering possible structural insights. These are applied in an extended approximation giving mutually consistent mean and variance estimates with improved accuracy. Tests on oversaturated peak demand cases are compared with Markov probabilistic simulation, demonstrating accuracy (R2 > 0.99) for typical random, priority-like (M/M/1) and traffic-signal-like (M/D/1) queues. Implications for risk analysis, planning and policy are considered. [ABSTRACT FROM AUTHOR]

This study presents a thorough microscopic simulation investigation of a recently developed model-based approach for per-lane density estimation, as well as on-ramp and off-ramp flow estimation, for highways in the presence of connected vehicles. The estimation methodology is mainly based on the assumption that a certain percentage of vehicles is equipped with Vehicle Automation and Communication Systems (VACS), which provide the necessary measurements used by the estimator, namely vehicle speed and position measurements. In addition, a minimum number of conventional flow detectors is needed. In the investigation, a calibrated and validated, with real data, microscopic multi-lane model is employed, which concerns a stretch of motorway A20 from Rotterdam to Gouda in the Netherlands. It is demonstrated that the proposed methodology provides satisfactory estimation performance even for low penetration rates of connected vehicles, while it is also shown that the method is little sensitive to the parameters (two in total) of the model utilized by the estimator. [ABSTRACT FROM AUTHOR]

This paper addresses an interesting and practical bus headway adherence issue for a given public bus route with a number of bus stops. It first defines the random headway adherence ratio (HAR) at a particular bus stop of a specific bus route as the ratio of difference between actual bus headway and scheduled headway with respect to the scheduled headway. This study proceeds to customize a four-step procedure to estimate a probability distribution that can describe the random HAR at each bus stop of the bus route by using the automatic vehicle location (AVL) data. Our real case studies with 44,025 HAR data show that the 19 existing probability distributions including Lognormal, Gamma, Beta and Wakeby are unable to well fit these HAR data. This study thus proposes a tailored Wakeby-type distribution with five parameters. After deriving two fundamental propositions for the tailored Wakeby-type distribution, a tangible L -moment based method to estimate those parameters involved the tailored Wakeby distribution is presented. The tailored Wakeby-type distributions can meet our expectation via our real case studies. Finally, applications of the tailored Wakeby-type distribution derived for the random HAR are conducted. [ABSTRACT FROM AUTHOR]

For the purpose of presenting running processes, assessing the efficiencies of operation plans of modern trams and corresponding adjustments of road traffic signal timing, and reflecting the influence of interactions between tram signals and road traffic signals to the regional transport network, this paper formulates a microscopic model for the modern tram system integrated with a road traffic simulation which contains six modules: the passenger module, the tram vehicle module, the tram equipment module, the road traffic module, the interaction module, and the performance module. Then, validation of the model is presented by comparing the travel time of field observations and simulation results of the proposed model, and statistical testing of the average passenger waiting time and number of waiting passengers. Overall, two applications are developed to illustrate the model capabilities and application value. The first example compares average queuing lengths of urban road traffic at an intersection under different departing intervals of trams; the second one designs the plans of traffic signal coordination control and tram running processes to realize a two-directional green wave for trams. [ABSTRACT FROM AUTHOR]

The use of modeling and simulation aids in deriving many decisions related to transportation planning and traffic operations. Representing the real systems via simulation allows exploring system behavior in an articulated way, which is often impossible in the real world. In this paper, a simulation-based framework is presented to evaluate the impact of congestion charging on daily activity plans of the individuals. Personal decision to accept the congestion charges is evaluated by comparing the value of time with congestion charge. Value of time varies throughout the day depending upon the time pressure at any moment exerted by preceding and succeeding activities. Time pressure during an activity increases if available time for that activity is insufficient to attain the perceived utility. Daily activities in the schedules are modeled using bell-shaped marginal utility that results in sigmoid utility. A model is presented which derives the activity-specific parameters of the marginal utility function for the specific individual. To examine value of time of each person, the congestion charging is applied where personal willingness-to-pay is determined by comparing the ratios of cost to utility for original and adapted schedules. A large-scaled microsimulation of the modeled framework is used to simulate the whole population, which is created by FEATHERS, an operational activity-based model for Flanders, Belgium. The results of the simulation show that the number of individuals who avoid the congestion charges by adapting their schedules is almost three times the number of those who agree to pay it. The proposed framework can be useful to evaluate the tradeoff between value of time and costs where flexibility in selection of time defines the variability in cost. [ABSTRACT FROM AUTHOR]

The aim of this study was to simulate pedestrian movements in the queuing process in subway stations using microscopic simulation models. The queuing process was divided into two stages - walking and selecting a queue, and entering a queue and waiting in that queue. To represent the boundary of these two stages, a novel queuing line concept was developed. This queuing line dynamically changes with factors such as pedestrian preference, queue shape and queue length. Furthermore, two corresponding microscopic simulation models were developed to handle those two stages on the basis of the social force model and the queuing rule, respectively. Typically, the queuing rule is determined by the desired force, the impact of the nearest pedestrian ahead in the same direction and a self-stopping mechanism. Two examples - a ticket office and a security check in a subway station - were used to verify the validity of the proposed models. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important problem faced by Intelligent Transportation Systems (ITS), requiring models that allow predicting the impact of different solutions on urban traffic flow. Such an approach typically requires the use of simulations, which should be as realistic as possible. However, achieving high degrees of realism can be complex when the actual traffic patterns, defined through an Origin/Destination (O-D) matrix for the vehicles in a city, remain unknown. Thus, the main contribution of this paper is a heuristic for improving traffic congestion modeling. In particular, we propose a procedure that, starting from real induction loop measurements made available by traffic authorities, iteratively refines the output of DFROUTER, which is a module provided by the SUMO (Simulation of Urban MObility) tool. This way, it is able to generate an O-D matrix for traffic that resembles the real traffic distribution and that can be directly imported by SUMO. We apply our technique to the city of Valencia, and we then compare the obtained results against other existing traffic mobility data for the cities of Cologne (Germany) and Bologna (Italy), thereby validating our approach. We also use our technique to determine what degree of congestion is expectable if certain conditions cause additional traffic to circulate in the city, adopting both a uniform pattern and a hotspot-based pattern for traffic injection to demonstrate how to regulate the overall number of vehicles in the city. This study allows evaluating the impact of vehicle flow changes on the overall traffic congestion levels. [ABSTRACT FROM AUTHOR]

The pedestrian flow with evading and surpassing behavior in a walking passageway is simulated based on a modified social force model in order to explore the influence of this behavior on evacuation efficiency, bottleneck passing capacity, and the macroscopic phenomenon. A pair of conjugated self-driven forces is introduced to enable a pedestrian to avoid a direct collision and keep a normal velocity magnitude while confronting an obstacle. The pedestrian avoiding time is used to define the triggering conditions of evading and surpassing behavior, and has been estimated through practical experiments. Simulation results show that in a passageway without spatial obstacles, the evading and surpassing behavior will increase the evacuation time under the condition that the pedestrian number is larger than a critical value. Moreover, when a spatial obstacle exists, both the rise of pedestrian numbers and the decline of bottleneck width would increase the evacuation time. Meanwhile, it is observed that compared with a bar-shaped obstacle, a circle-shaped obstacle corresponds to a larger bottleneck passing capacity and less evacuation time when the size of the spatial obstacle is above a critical value. In addition, a phenomenon of a triangle “evading region” caused by the evading and surpassing behavior also can be observed before the spatial obstacle through simulation and experiments. Furthermore, it can be concluded that a circle-shaped obstacle corresponds to a stronger guiding function and a larger area of “evading region” compared with a bar-shaped one, and induces a relatively higher bottleneck passing capacity in a walking passageway. [ABSTRACT FROM AUTHOR]

The goal of this study is to model drivers’ cognition-based en route planning behaviors in a large-scale road network via the Extended Belief-Desire-Intention (E-BDI) framework. E-BDI is a probabilistic behavior modeling framework based on agents’ own preferences of multiple attributes (e.g., travel time and its variance) and daily driving experiences. However, it is challenging to use the E-BDI framework for the demonstration of drivers’ en route planning behavior in a large-scale road network due to its high computational demand. To handle the computation issue, a hierarchical en route planning approach is proposed in this study. The proposed E-BDI-based en route planning approach consists of three major procedures: (1) network partitioning, (2) network aggregation, and (3) E-BDI-based en route planning. The Java-based E-BDI module integrated with DynusT ® traffic simulation software is developed to demonstrate the proposed en route planning approach in Phoenix, Arizona road network involving 11,546 nodes and 24,866 links. The demonstration results reveal that the proposed approach is computationally efficient and effective in representing various en route planning behaviors of drivers in a large-scale road network. [ABSTRACT FROM AUTHOR]

Road traffic simulation is a useful tool for studying road traffic and evaluating solutions to traffic problems. Large-scale agent-based road traffic simulation is computationally intensive, which triggers the need for conducting parallel simulation. This paper deals with the synchronization problem in parallel agent-based road traffic simulation to reduce the overall simulation execution time. We aim to reduce synchronization operations by introducing some redundant computation to the simulation. There is a trade-off between the benefit of reduced synchronization operations and the overhead of redundant computation. The challenge is to minimize the total overhead of redundant computation and synchronization. First, to determine the amount of redundant computation, we proposed a way to define extended layers of partitions in the road network. The sizes of extended layers are determined by the behavior of agents and the topology of road networks. Second, due to the dynamic nature of road traffic, a heuristic was proposed to adjust the amount of redundant computation according to traffic conditions during simulation run-time to minimize the overall simulation execution time. The efficiency of the proposed method was investigated in a parallel agent-based road traffic simulator using real-world network and trip data. Results have shown that the method can reduce synchronization overhead and improve the overall performance of the parallel simulation significantly. [ABSTRACT FROM PUBLISHER]

This paper presents a model to simulate non-signalized pedestrian crosswalks. Principal scope is to develop a tool to be used by decision-makers to evaluate the necessity of introducing a new crosswalk and/or switching to a traffic light and estimate the potential benefits of such a measure in terms of Level of Service. The model is based on empirical evidence gained during an observation of a non-signalized crosswalk. Pedestrian motion is simulated using a Cellular Automata model which is capable to simulate pedestrian dynamics at low density conditions, as observed in the considered scenario. Vehicles use a continuous car following model inspired on Gipps equations in which driver's reaction time is considered. Pedestrian's decision-making process on crossing attempt and model parameters are directly obtained from the analysis of pedestrian-vehicle interactions observed in reality. The model developed employs small time steps, thus allowing the consideration of different pedestrian speeds (intrinsically considering pedestrians with reduced mobility, such as elderlies) and smoothly reproducing car-pedestrian interactions. This aspect required the definition of distinct behavioral rules for vehicles and pedestrians that, in their dynamic interaction, implement an ad-hoc coordination model. In order to validate the model, delays (or waiting times) measured for both pedestrians and drivers were compared with simulated values. Results show a good agreement between empirically obtained time delay and values computed in the simulation. [ABSTRACT FROM AUTHOR]

Building safe and effective roundabouts requires optimizing traffic (operational) efficiency (TE) and traffic safety (TS) while taking into account geometric factors, traffic characteristics and local constraints. Most existing simulation-based optimization models do not simultaneously optimize all these factors. To capture the relationship among geometry, efficiency and safety, we put forward a model formulation in this paper. We present a new multi-criteria and simultaneous multi-objective optimization (MOO) model approach to optimize geometry, TE and TS of urban unsignalized single-lane roundabouts. To the best of our knowledge, this is the first model that uses the multi-criteria decision-making method known as analytic hierarchy process to evaluate and rank traffic parameters and geometric elements of urban single-lane roundabouts. The model was built based on comprehensive review of the research literature and existing roundabout simulation software, a field survey of 61 civil and traffic expert engineers in Croatia, and field studies of roundabouts in the Croatian capital city of Zagreb. We started from the basis of Kimber’s capacity model, HCM2010 serviceability model, and Maycock and Hall's accident prediction model, which we extended by adding sensitivity analysis and powerful MOO procedures of the bounded objective function method and interactive optimization. Preliminary validation of the model was achieved by identifying the optimal and most robust of three geometric alternatives (V.1-V.3) for an unsignalized single-lane roundabout in Zagreb, Croatia. The geometric parameters in variant V.1 had significantly higher values than in the existing design V.0, while approaches 1 and 3 in variant V.2 were enlarged as much as possible within allowed spatial limits and Croatian guidelines, reflecting their higher traffic demand. Sensitivity analysis indicated that variant V.2 showed the overall highest TE and TS across the entire range of traffic flow demand and pedestrian crossing flow demand at approaches. At the same time, the number of predicted traffic accidents was similar for all three variants, although it was lowest overall for V.2. The similarity in predicted accident frequency for the three variants suggests that V.2 provides the greatest safety within the predefined constraints and parameter ranges explored in our study. These preliminary results suggest that the proposed model can optimize geometry, TE and TS of urban single-lane roundabouts. [ABSTRACT FROM AUTHOR]

Cellular Automaton (CA), an efficient dynamic modeling method that is widely used in traffic engineering, is newly introduced for traffic load modeling. This modeling method significantly addresses the modest traffic loads for long-span bridges. It does, however, require improvement to calculate precise load effects. This paper proposed an improved cellular automaton with axis information, defined as the Multi-axle Single-cell Cellular Automaton (MSCA), for the precise micro-simulation of random traffic loads on bridges. Four main ingredients of lattice, cells’ states, neighborhoods and transition rules are redefined in MSCA to generate microscopic vehicle sequences with detailed vehicle axle positions, user-defined cell sizes and time steps. The simulation methodology of MSCA is then proposed. Finally, MSCA is carefully calibrated and validated using site-specific WIM data. The results indicate: (1) the relative errors (REs) for the traffic parameters, such as volumes, speeds, weights, and headways, from MSCA are basically no more than ±10% of those of WIM data; (2) the load effects of three typical influence lines (ILs) with varied lengths of 50, 200 and 1000 m are also confidently comparable, both of which validate the rationality and precision of MSCA. Furthermore, the accurate vehicle parameters and gaps generated from MSCA can be applied not only for precise traffic loading on infrastructures but also for the accurate estimation of vehicle dynamics and safety. Hence, wide application of MSCA can potentially be expected. [ABSTRACT FROM AUTHOR]

Transportation agencies often resort to the use of traffic simulation models to evaluate the impacts of changes in network design or network operations. They often have multiple traffic simulation tools that cover the network area where changes are to be made. These multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration), as well as in their modeling scale (e.g., city-scale versus regional-scale). The choice of which simulation model to rely on, let alone of how to combine their use, is intricate. A larger-scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This paper proposes an optimization framework that enables multiple simulation models to be jointly and efficiently used to address continuous urban transportation optimization problems. We propose a simulation-based optimization algorithm that embeds information from both a high-accuracy low-efficiency simulator and a low-accuracy high-efficiency simulator. At every iteration, the algorithm decides which simulator to evaluate. This decision is based on an analytical approximation of the accuracy loss as a result of running the lower-accuracy model. We formulate an analytical expression that is based on a differentiable and computationally efficient to evaluate traffic assignment model. We evaluate the performance of the algorithm with a traffic signal control problem on both a small network and a city network. We show that the proposed algorithm identifies signal plans with excellent performance, and can do so at a significantly lower computational cost than when systematically running the high-accuracy simulator. The proposed methodology contributes to enable large-scale high-resolution traffic simulation models to be used efficiently for simulation-based optimization. More broadly, it enables the use of multiple simulation models that may differ, for instance, in their scale, their resolution, or their computational costs, to be used jointly for optimization. [ABSTRACT FROM AUTHOR]

The increasing requirement of transmission network sizes would result in huge energy consumption with communication traffic. Green communication technologies are expected to help in reducing energy consumption impact to environment. Therefore, it is important to design energy-efficient strategy that can decrease energy consumption. This paper proposes to use the acquaintance and improved targeted immunization strategies from complex systems to resolve energy consumption issues and uses traffic as measure standard to obtain a stable threshold. The simulation results show that the improved control strategy is better and more effective to save as much energy as possible. [ABSTRACT FROM AUTHOR]

This paper reviews the simplified procedure proposed by Ghosn and Sivakumar to model the maximum expected traffic load effect on highway bridges and illustrates the methodology using a set of Weigh-In-Motion (WIM) data collected on one site in the U.S.A. The paper compares different approaches for implementing the procedure and explores the effects of limitations in the site-specific data on the projected maximum live load effect for different bridge service lives. A sensitivity analysis is carried out to study changes in the final results due to variations in the parameters that define the characteristics of the WIM data and those used in the calculation of the maximum load effect. The procedure is also implemented on a set of WIM data collected in Slovenia to study the maximum load effect on existing Slovenian highway bridges and how the projected results compare to the values obtained using advanced simulation algorithms and those specified in the Eurocode of actions. [ABSTRACT FROM PUBLISHER]

Vehicle headway distribution models are widely used in traffic engineering fields, since they reflect the fundamental uncertainty in drivers' car-following maneuvers and meanwhile provide a concise way to describe the stochastic feature of traffic flows. This paper presents a systematic review of vehicle headway distribution studies in the last few decades. Since it is impossible to enumerate the merits and drawbacks of all of existing distribution models, we emphasize four advances of headway distribution modeling in this paper. First, we highlight the chronicle of key assumptions on the existing distribution models and explain why this evolution occurs. Second, we show that departure headways measured for interrupted flows on urban streets and headways measured for uninterrupted flows on freeways have common features and can be simulated by a unified microscopic car-following model. The interesting finding helps gather two kinds of headway distribution models under one umbrella. Third, we review different approaches that aim to link microscopic car-following models and mesoscopic vehicle headway distribution models. Fourth, we show that both the point scattering on the density-flow plot and the shape of traffic flow breakdown curve implicitly depend on the vehicular headway distribution. These findings reveal pervasive connections between macroscopic traffic flow models and mesoscopic headway distribution. All these new insights bring new vigor into vehicle headway studies and open research frontiers in this field. [ABSTRACT FROM AUTHOR]

This paper proposes test scenarios for evaluation of yaw stability for medium commercial vehicles. Maneuvering, speed, longitudinal tire force, tire-road friction coefficient, road slope, and load condition are considerable factors that have effect on the medium commercial vehicle yaw stability. After conducting an analysis on these six factors, effective test scenarios were developed. A sine with dwell test is well known as a test scenario for evaluation of performance of electronic stability control (ESC) on passenger vehicles and heavy commercial vehicles. The SWD test was modified considering medium commercial vehicle dynamics, and the ramp steer maneuver with maximum acceleration test was proposed. Simulation validation has been conducted using field test data. From simulation study, it was shown that the ESC system for medium commercial vehicle is effectively evaluated by the proposed test scenarios. [ABSTRACT FROM AUTHOR]

Driver behavior analysis has recently been the focus of research to enhance traffic simulation modeling. Previous research has developed algorithms to model different driver types in both urban networks and freeways, and defined driver behavioral parameters based on pre-categorized driver groups. However, driver behaviors within the more confined, low speed, high pedestrian flow environment (such as CBD of a major city, urban downtown, campus) may be quite different and research concentrating on such locations is limited. The objective of this study is to assess and evaluate the relation between driver type and driver behavior within a relatively restrictive environment with high levels of pedestrian–vehicle interactions and develop suitable schemes to categorize driver types. To meet this objective, the University of Florida campus environment was used as the starting point of analyzing such driver types and behaviors, and a diverse pool of drivers was selected based on age, gender, driving experiences, etc. to participate in surveys and an instrumented vehicle study. The instrumented vehicle was used to collect vehicle trajectory and driver behavior data from the study subjects on the campus at the University of Florida. Two performance measures (namely, driver-desired speed and yield behavior) were applied to categorize driver types. These two categorizations provided consistent results and indicated that either one can be effectively applied in future studies for driver type classifications. Additional comparisons of driver background and personal characteristics obtained from questionnaires further confirmed the driver classification results. [ABSTRACT FROM AUTHOR]