Microscopic simulation software use car-following models to capture the interaction of a vehicle and the preceding vehicle traveling in the same lane. In the literature, much research has been carried out in the field of car-following and traffic stream modeling. Microscopic car-following models have been characterized by using the relationship between a vehicle's desired speed and the distance headway (h) between the lead and follower vehicles. On the other hand, macroscopic traffic stream models describe the motion of a traffic stream by approximating for the flow of a continuous compressible fluid. This research work develops and compares three different formulations of car-following models — speed formulation, molecular acceleration, and fluid acceleration formulation. First, four state-of-the-art car-following models namely, Van Aerde, Greenshields, Greenberg and Pipes models, are selected for developing the three aforementioned formulations. Then a comprehensive car-following behavior encompassing steady-state conditions and two constraints — acceleration and collision avoidance — is presented. Specifically, the variable power vehicle dynamics model proposed by Rakha and Lucic (2002) is utilized for the acceleration constraint. Subsequently, the thesis describes the issues associated with car-following formulations. Recognizing that many different traffic flow conditions exist, three distinct scenarios are selected for comparison purposes. The results demonstrate that the speed formulation ensures that vehicles typically revert to steady-state conditions when vehicles experience a perturbation from steady-state conditions. On the other hand, both acceleration formulations are unable to converge to steady-state conditions when the system experiences a perturbation from a steady-state. The thesis also attempts to address the question of capacity drop associated with vehicles accelerating from congested conditions. Specifically, the capacity drop proposition is analyzed for the case of a backward recovery (typical of a signalized intersection) and stationary shockwave (typical of a capacity drop on a freeway). In the case of the backward recovery shockwave, the acceleration constraint results in a temporally and spatially confined capacity drop as vehicles accelerate to their desired steady-state speed. This temporally and spatially confined capacity drop results in what is typically termed the start loss of a signalized phase. Subsequently, vehicles attain steady-state conditions, in the case of the speed and molecular acceleration formulations, at the traffic signal stop bar after the initial five vehicle departures. The analysis also demonstrates that after attaining steady-state conditions the capacity may drop for the initial vehicle departures as a result of traffic stream dispersion. This traffic dispersion capacity drop increases as vehicles travel further downstream. Alternatively, in the case of a stationary bottleneck the aggressiveness of vehicle accelerations plays a major role in defining the capacity drop downstream of a bottleneck. The study demonstrates that any temporal headways that may be lost while vehicles accelerate to steady-state conditions may not be recuperated and thus result in capacity drops downstream of a bottleneck. A typical example of this scenario is the traffic stream flow rate downstream of a stop sign, which is significantly less than the roadway capacity. The reduction in capacity is caused by losses in temporal headways between successive vehicles which are not recuperated. The study also demonstrates that the ability to model such a capacity drop does not require the use of a dual-regime traffic stream model as is proposed in the Highway Capacity Manual (HCM). Instead, the use of a single-regime model captures the observed capacity with the introduction of an acceleration constraint to the car-following system of equations. / Master of Science

Forward Collision Avoidance Systems (FCAS) are a type of active safety system which have great potential for rear-end collision avoidance. These systems use either radar, lidar, or cameras to track objects in front of the vehicle. In the event of an imminent collision, the system will warn the driver, and, in some cases, can autonomously brake to avoid a crash. However, driver acceptance of the systems is paramount to the effectiveness of a FCAS system. Ideally, FCAS should only deliver an alert or intervene at the last possible moment to avoid nuisance alarms, and potentially have drivers disable the system. A better understanding of normal driving behavior can help designers predict when drivers would normally take avoidance action in different situations, and customize the timing of FCAS interventions accordingly. The overall research object of this dissertation was to characterize normal driver behavior in car following events based on naturalistic driving data. The dissertation analyzed normal driver behavior in car-following during both braking and lane change maneuvers. This study was based on the analysis of data collected in the Virginia Tech Transportation Institute 100-Car Naturalistic Driving Study which involved over 100 drivers operating instrumented vehicles in over 43,000 trips and 1.1 million miles of driving. Time to Collision in both braking and lane change were quantified as a function of vehicle speed and driver characteristics. In general, drivers were found to brake and change lanes more cautiously with increasing vehicle speed. Driver age and gender were found to have significant influence on both time to collision and maximum deceleration during braking. Drivers age 31-50 had a mean braking deceleration approximately 0.03 g greater than that of novice drivers (age 18-20), and female drivers had a marginal increase in mean braking deceleration as compared to male drivers. Lane change maneuvers were less frequent than braking maneuvers. Driver-specific models of TTC at braking and lane change were found to be well characterized by the Generalized Extreme Value distribution. Lastly, driver's intent to change lanes can be predicted using a bivariate normal distribution, characterizing the vehicle's distance to lane boundary and the lateral velocity of the vehicle. This dissertation presents the first large scale study of its kind, based on naturalistic driving data to report driver behavior during various car-following events. The overall goal of this dissertation is to provide a better understanding of driver behavior in normal driving conditions, which can benefit automakers who seek to improve FCAS effectiveness, as well as regulatory agencies seeking to improve FCAS vehicle tests. / Ph. D.

The driver-specific data from a naturalistic driving study provides car-following events in real-world driving situations, while additionally providing a wealth of information about the participating drivers. Reducing a naturalistic database into finite car-following events requires significant data reduction, validation, and calibration, often using manual procedures. The data collection performed herein included: the identification of commuting routes used by multiple drivers, the extraction of data along those routes, the identification of potential car-following events from the dataset, the visual validation of each car-following event, and the extraction of pertinent information from the database during each event identified. This thesis applies the developed process to generate car-following events from the 100-Car Study database, and applies the dataset to analyze four car-following models. The Gipps model was found to perform best for drivers with greater amounts of data in congested driving conditions, while the Rakha-Pasumarthy-Adjerid (RPA) model was best for drivers in uncongested conditions. The Gipps model was found to generate the lowest error value in aggregate, with the RPA model error 21 percent greater, and the Gaxis-Herman-Rothery model (GHR) and the Intelligent Driver Model (IDM) errors 143 percent and 86 percent greater, respectively. Additionally, the RPA model provides the flexibility for a driver to change vehicles without the need to recalibrate parameter values for that driver, and can also capture changes in roadway surface type and condition. With the error values close between the RPA and Gipps models, the additional advantages of the RPA model make it the recommended choice for simulation. / Master of Science

This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details. This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods. Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters. / MS / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.

Driver psychology is one of the most difficult phenomena to model in the realm of traffic flow theory because mathematics often cannot capture the human factors involved with driving a car. Over the past several decades, many models have attempted to model driver aggressiveness with varied results. The recently proposed Longitudinal Control Model (LCM) makes such an attempt, and this paper offers evidence of the LCM's usefulness in modeling road dynamics by analyzing deceleration rates that are commonly associated with various levels of aggression displayed by drivers. The paper is roughly divided into three sections, one outlining the LCM's ability to quantify forces between passive and aggressive drivers on a microscopic level, one describing the LCM's ability to measure aggressiveness of platoons of drivers, and the last explaining the meaning of the model’s derivative. The paper references some attempts to capture driver aggressiveness made by classic car-following models, and endeavors to offer some new ideas in study of driver characteristics and traffic flow theory.

Platooning is a method where a chain of vehiclesdrive with small inter-vehicular distances. The many benefitsof autonomous platooning includes improved fuel economy,less congestion and safer transportation. To create a safe andfunctional platoon the operational software needs to be able tohandle various road surfaces without the risk of a crash. Thisreport is aiming to improve the safety of a platoon by includingcommunication of data between vehicles in the chain. Specificallythe focus has been on transferring information about the tireslip, to model a cooperative adaptive cruise control (C-ACC)and combine the two. A system was designed using the dynamicsfor a quarter-car model and then connected to a controller and aplatoon of four vehicles. Simulations of when the leading vehiclebraked hard on two different road surfaces with and withoutthe slip awareness was conducted. The tire slip awareness in thecontroller consisted of proportional control on the error and alow-pass filter. The simulations showed that the inclusion of thetire slip in the controller improved the platooning performance,in the sense that the inter-vehicle distance could be contained.It was also shown the controller could be tuned so that the slipratios were limited. / Konvojkörning är en metod där en kedjaav fordon åker med små interna distanser. De många fördelarnamed förarlösa konvojer inkluderar förbättrad bränsleförbukning, mindre trafik och säkrare transportering. För atten säker och funktionell konvoj ska kunna skapas krävs detatt mjukvaran kan handskas med varierande vägunderlag utanrisk att krocka. Den här rapporten siktar på att förbättrasäkerheten i konvojkörning genom att överföra data till andrafordon i konvojkedjan. Speciellt har fokuset legat på överförainformation om däcksliring, att modellera en kooperative adaptivfarthållare (C-ACC) och sedan kombinera de två. Ett systemdesignades genom att använda dynamiken av en fjärdedelsbil och sen ansluta modellen till en konvoj med fyra fordon.Simulationer av när det ledande fordonet tvärbromsade på olikavägunderlag med och utan däcksliringsinfromation genomfördes.Däckslirnings i regulatorn bestod av proportionerlig kontroll påfelet och ett lågpassfilter. Simulationerna visade att inkluderingenav däcksliringsinformation i regulatorn förbättrar konvojensprestanda, på så sätt att de interna distanserna kan hanteras.Det kunde också påvisas att kontrollern kunde kalibreras så attslirningen begränsades. / Kandidatexjobb i elektroteknik 2022, KTH, Stockholm

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In this thesis, multiple on-road vehicle tracking problem is explored, with greater consideration of road constraints and interactions between vehicles. A comprehensive method for tracking multiple on-road vehicles is proposed by making use of domain knowledge of on-road vehicle motion. Starting with raw measurements provided by sensors, bias correction methods for sensors commonly used in vehicle tracking are briefly introduced and a fast but effective bias correction method for airborne video sensor is proposed. In the proposed method, by assuming errors in sensor parameter measurements are close to zero, the bias is separately addressed in converted measurements of target position by a linear term of errors in sensor parameter measurements. Based on this model, the bias is efficiently estimated by addressing it while tracking or using measurements of targets that are observed by multiple airborne video sensors simultaneously. The proposed method is compared with other airborne video bias correction methods through simulations. The numerical results demonstrate the effectiveness of the proposed method for correcting bias as well as its high computational efficiency. Then, a novel tracking algorithm that utilizes domain knowledge of on-road vehicle motion, i.e., road-map information and interactions among vehicles, by integrating a car-following model into a road coordinate system, is proposed for tracking multiple vehicles on single-lane roads. This algorithm is extended for tracking multiple vehicles on multi-lane roads: The road coordinate system is extended to two-dimension to express lanes on roads and a lane-changing model is integrated for modeling lane-changing behavior of vehicles. Since the longitudinal and lateral motions are mutually dependent, the longitudinal and lateral states of vehicles are estimated sequentially in a recursive manner. Two estimation strategies are proposed: a) The unscented Kalman filter combined with the multiple hypothesis tracking framework to estimate longitudinal and lateral states of vehicles, respectively. b) A unified particle filter framework with a specifically designed computationally-efficient joint sampling method to estimate longitudinal and lateral states of vehicles jointly. Both of two estimation methods can handle unknown parameters in motion models. A posterior Cramer-Rao lower bound is derived for quantifying achievable estimation accuracy in both single-lane and multi-lane cases, respectively. Numerical results show that the proposed algorithms achieve better track accuracy and consistency than conventional multi-vehicle tracking algorithms, which assumes that vehicles move independently of one another. / Thesis / Doctor of Philosophy (PhD)

Ce travail présente des modèles d'écoulement en temps continu de flux de trafic routier. En premier lieu, il s'agit de modèles microscopiques de poursuite. Un modèle par systèmes d'équations différentielles couplées est proposé, basé sur le temps inter-véhiculaire. Ce modèle intègre un temps de réaction et des possibilités d'anticipation pour chaque véhicule. Les paramètres sont estimés par maximum de vraisemblance dans un modèle statistique à deux niveaux. Des simulations permettent de caractériser le comportement d'une file de véhicules. Dans une approche stochastique, un modèle d'évolution de la distance inter-véhiculaire est étudié à l'aide du processus Markovien de saut zero-range. L'introduction d'un temps de réaction tend à produire des ondes cinématiques. D'autre part, un modèle d'écoulement de trafic par le processus Markovien de saut des misanthropes est proposé. Il s'agit d'une modélisation au niveau mésoscopique, adaptée à la simulation de flux de trafic sur un réseau / This work presents different continuous-time traffic flow models. Microscopic models are considered first. A model by coupled differential equation system is proposed, based on the time gap. It incorporates a reaction time parameter and some anticipation possibilities, for each vehicle. The parameters are estimated by maximum likelihood over a two-level statistical model. Simulations allow to characterise the behaviour of a vehicles line. In a stochastic approach, a model of the distance gap evolution is studied with a zero-range process. The introduction of a reaction time parameter produces kinematics waves. On the other hand, traffic flow model by a misanthropes process is proposed. It is a mesoscopic approach, adapted to the simulation of traffic flow on a network

Η επιρροή του παράγοντα χρονοαπόσταση σε ένα σύστημα ακολουθίας οχημάτων μπορεί να προσδιορίσει την επικινδυνότητα του πλήγματος που υφίσταται το σύστημα. Με βάση μια παρ’ολίγον οπισθο-μετωπική σύγκρουση σε αυτοκινητόδρομο 3 λωρίδων, εξετάζεται ο ρόλος της χρονοαπόστασης μεταξύ των οχημάτων σε συνδυασμό με τους χρόνους αντίδρασης των οδηγών στην αντίληψη του επικείμενου κινδύνου. Το μοντέλο ακολουθίας οχημάτων κατά Brill, που συσχετίζει την χρονοαπόσταση, τον χρόνο αντίδρασης του οδηγού και την επιβράδυνση με τη συχνότητα των ατυχημάτων, χρησιμοποιείται ως κύριο εργαλείο για την εκτίμηση της ευαισθησίας της πιθανότητας ενός ατυχήματος. Μέσω της μικροσκοπικής ανάλυσης του βίντεο καταγραφής του ατυχήματος και της επεξεργασίας των δεδομένων και με πηγή έμπνευσης τα προγενέστερα επίμαχα ερωτήματα που θέτει και απαντά ο G. Davis και οι συνεργάτες του, προκύπτουν οι απαραίτητες πληροφορίες για την αριθμητική περιγραφή του ατυχήματος. Με τη χρήση έπειτα του λογισμικού προγράμματος OpenBUGS, το οποίο βασίζεται στη μέθοδο Monte Carlo Markov Chain, γίνεται προσομοίωση του προτύπου ατυχήματος και υπολογίζονται οι τιμές των παραμέτρων που επηρεάζουν τη μορφή του πλήγματος. Από τα αποτελέσματα προκύπτει ο βαθμός που ο συνδυαστικός παράγοντας χρονοαπόσταση και χρόνος αντίδρασης επηρεάζει το πλήγμα και αξιολογείται. Τέλος, με συγκεκριμένες επεμβάσεις επιχειρείται η βελτίωση ολόκληρου του συστήματος ακολουθίας οχημάτων. / The influence of time headway on a car-following system can determine the severity of a shockwave. Based on a near-miss rear-end collision on a 3-lane highway, this study examines the importance of time headway in combination with the driver’s reaction time upon perception of the upcoming hazard. The car-following model developed by Ed. Brill, relating driver’s reaction time, temporal headway and deceleration response to accident frequency, is used as a main tool for assessing the sensitivity of collision probability. Through a microscopic analysis of the video record and data processing and inspired by earlier critical questions that G. Davis and his associates have posed and answered, all the necessary information for the arithmetical description of the accident is extracted. Using the OpenBUGS software, and based on the Monte Carlo Markov Chain method, simulation of the collision prototype is achieved along with the calculation of other main parameters that affect the shockwave form. Simulation results, revealing the influence that the combined factor headway-reaction time has on a shockwave are derived and evaluated. Through certain modifications, the improvement of the whole car-following system is attempted.