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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

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

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport and Planning

Headway is a safety measure commonly used to investigate driving behaviour and driver performance. Its purpose is to reflect the following distance or time between a leading and following vehicle in traffic. It is therefore associated with drivers’ response time, such as in braking or swerving, during safety critical events. In the literature, distance and time headway are defined in different ways, despite standard definitions in the traffic engineering literature, which prompted this systematic review of headway definitions across a range of study designs, in order to recommend approaches to improve the accuracy and reproducibility of headway definitions used in road safety contexts. PRISMA guidelines were followed to search four databases (EMBASE, COMPENDEX, SCOPUS and MEDLINE) for studies that reported on headways or discussed methodological approaches. The search and filtering of abstracts identified 110 articles for a qualitative synthesis. Four broad approaches to measuring headways were detected: studies using simulation, roadside external features, on-road features, and on-vehicle features. Studies were coded as to whether they included written explanation, mathematical statements, or pictorial depictions of headway. Only 49.6% of studies contextualised headway sufficiently for reproducibility. Reproducibility is crucial for accurate interpretation of research findings and comparisons across studies. It is recommended that headway definitions should a) exclude vehicle or parts of vehicle lengths, b) include reference points (e.g., bumper/axle/rear), c) have a consistent terminology, and d) include the accuracy of headway measuring devices to report the precision of a study’s findings.

This study aims to model traffic flow under weak lane based heterogonous (mixed) traffic conditions. Unlike homogeneous traffic, when a follower (subject) vehicle in mixed traffic moves closer to its leader vehicle, it tends to adjust its longitudinal movement or change its lane and acts discretely. Due to this phenomenon, traffic flow modeling under such conditions is always challenging. A new driver behavioral logic is conceptualized for the vehicles' movement within a combination of surrounding vehicles. In which the following behavior was dissected with the lateral shift distance between vehicles. Two car-following models for homogeneous traffic conditions, the IDM and Gipps models were adapted with relevant lateral behavior parameters to different vehicle classes under mixed-traffic conditions. The new driving behavior logic was incorporated externally in place of default logic. The results showed that the performance of the adapted models was better accurate than the classical models., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport and Planning

Drivers know that smartphones distract. Trying to limit distraction, drivers can use handsfree devices, where they onlybriefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating back todriving adds an underappreciated, potentially long period to the total distraction time. This project measured the effectsof handsfree smartphones on driving behaviors by engaging ninety-seven 21- to 78-year-old individuals whoselfidentified as active drivers and smartphone users in a simulated driving scenario that included smartphonedistractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eyetracking. This research found that simulated driving performance drops to dangerous levels after smartphone distractionfor all ages and for both voice and texting. The participants swerved for 15.1 seconds after a voice distraction and for alonger 20.6 seconds after a text distraction. Participants from the 71+ age group missed seeing about 50% of peripheralcues within 4 seconds of the distraction. Coherence with the lead car during following task dropped from 0.54 to 0.045during distraction, and seven participants rear-ended the lead car.

Advancements in technology are bringing Automated vehicles (AVs) closer to reality and it is not so far in future when these vehicles will have a significant penetration rate in our existing transport network. However, in the early phases of automation, penetration rate of these vehicles will be very low. This would lead to a mixed traffic situation where AVs will coexist and interact with other human driven vehicles (HDVs). This one to one interaction might lead to change in driving behavior of HDVs when they interact with AVs. Thus it is crucial to understand the behavioral adaptation of HDVs due to their interaction with AVs. A field test was conducted to gain insights about such behavioral adaptation focusing on gap acceptance, car following, and overtaking behavior. Furthermore, learning effet over multiple interactions with AVs, effect of positive and negative information, stress during interaction and trust in AVs were also studies. This research concludes following findings. First, significantly lower critical gaps were accepted during interaction with AVs. Second, significantly lower headways were found at the end of overtaking during interaction with AVs. Third, higher trust was reported in AV scenarios. Fourth, positive information leaded to even closer interactions with AVs in comparison to HDVs. These findings indicate the potential exploitation of AV technology by HDV-drivers., SAMEN, Civil Engineering | Transport and Planning

Drivers know that smartphones distract. Trying to limit distraction, drivers can use handsfree devices, where they onlybriefly glance at the smartphone. However, the cognitive cost of switching tasks from driving to communicating back todriving adds an underappreciated, potentially long period to the total distraction time. This project measured the effectsof handsfree smartphones on driving behaviors by engaging ninety-seven 21- to 78-year-old individuals whoselfidentified as active drivers and smartphone users in a simulated driving scenario that included smartphonedistractions. Peripheral-cue and car-following tasks were used to assess driving behavior, along with synchronized eyetracking. This research found that simulated driving performance drops to dangerous levels after smartphone distractionfor all ages and for both voice and texting. The participants swerved for 15.1 seconds after a voice distraction and for alonger 20.6 seconds after a text distraction. Participants from the 71+ age group missed seeing about 50% of peripheralcues within 4 seconds of the distraction. Coherence with the lead car during following task dropped from 0.54 to 0.045during distraction, and seven participants rear-ended the lead car.

Advancements in technology are bringing Automated vehicles (AVs) closer to reality and it is not so far in future when these vehicles will have a significant penetration rate in our existing transport network. However, in the early phases of automation, penetration rate of these vehicles will be very low. This would lead to a mixed traffic situation where AVs will coexist and interact with other human driven vehicles (HDVs). This one to one interaction might lead to change in driving behavior of HDVs when they interact with AVs. Thus it is crucial to understand the behavioral adaptation of HDVs due to their interaction with AVs. A field test was conducted to gain insights about such behavioral adaptation focusing on gap acceptance, car following, and overtaking behavior. Furthermore, learning effet over multiple interactions with AVs, effect of positive and negative information, stress during interaction and trust in AVs were also studies. This research concludes following findings. First, significantly lower critical gaps were accepted during interaction with AVs. Second, significantly lower headways were found at the end of overtaking during interaction with AVs. Third, higher trust was reported in AV scenarios. Fourth, positive information leaded to even closer interactions with AVs in comparison to HDVs. These findings indicate the potential exploitation of AV technology by HDV-drivers., SAMEN, Civil Engineering | Transport and Planning

Numerical simulations have shown that the network fundamental diagram (NFD) of a signalized network is significantly affected by the green ratio. An analytical approximation of the NFD has been derived from the link transmission model. However, the consistency between these approaches has not been established, and the impacts of other factors are still unrevealed. This research evalutes the impacts of start-up and clearance behaviors in a signalized network from a network fundamental diagram approach. Microscopic simulations based on Newell’s car-following model are used for testing the bounded acceleration (start-up) and aggressiveness (clearance) effects on the shape of the NFD in a signalized ring road. This new approach is shown to be consistent with theoretical results from the link transmission model, when the acceleration is unbounded and vehicles have the most aggressive clearance behaviors. This consistency validates both approaches; but the link transmission model cannot be easily extended to incorporate more realistic start-up or clearance behaviors. With the new approach, this project demonstrates that both bounded acceleration and different aggressiveness lead to distinct network capacities and fundamental diagrams. In particular, they lead to start-up and clearance lost times of several seconds; and these lost times are additive. Therefore, the important role that these behaviors play in the NFD shape is studied to reach a better understanding of how the NFD responds to changes. This will help with designing better start-up and clearance behaviors for connected and autonomous vehicles.

This paper incorporates the driver compliance behaviour into a connected vehicle driving strategy (CVDS) that can be integrated with traditional car-following (CF) models to better describe the connected vehicle CF behaviour. Driver compliance, a key human factor for the success of connected vehicles technology, is modelled using a celebrated theory of decision making under risk – the Prospect theory (PT). The reformulated value and weighting functions of PT are consistent with the driver compliance behaviour and also preserve the integral elements of PT. Furthermore, the connected vehicle trajectory data collected from a carefully designed advanced driving simulator experiment are utilised to calibrate CVDS integrated with Intelligent Driver Model (IDM), i.e., CVDS-IDM. The calibration results reveal that drivers in the connected environment drive safely and efficiently. Moreover, the CVDS-IDM can successfully model and predict the CF dynamics of connected vehicles and is more behaviourally and numerically sound than a traditional CF model.

Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing SAE level 2 and 3 automation systems into the market. The main advantage of Automated Vehicles (AV) will be the significant decrease in road accidents and casualties. However, a significant shift from conventional to automated vehicles must occur before it can have a positive impact on society. If the behaviour of the vehicle is not perceived as natural, the user will most likely not activate the ADAS features again. During this study a naturalistic dataset is used to investigate the driver behaviour, in the hope of bringing the current ACC logic to a more human-like behaviour that will feel more natural to the driver. The research question summarizes the final objective of this study: How can Naturalistic Driving Study (NDS) datasets be used in target performance setting for ACC systems? This study will answer the research question by studying human behaviour in the scene of following an accelerating vehicle. The main body of this thesis is divided in three chapters, one for each step of the research. First the information about the used datasets are provided together with the methodologies used to extract the relevant time-series data. Secondly driver behaviour models are created in order to mathematically characterize human behaviour. The strength of the created models is their ability to represent the full range of driver behaviour in terms of driving style. The aggressiveness parameter of the model can be easily adjusted to represent different percentiles of driver behaviour. This allows for a quick and effective tuning process: by changing a single parameter the driving style of the model can be fully modified. Finally, the driver behaviour models are implemented into a simulation environment. The models are simulated against an existing ACC logic in order to assess the difference, Mechanical Engineering | Vehicle Engineering

Numerical simulations have shown that the network fundamental diagram (NFD) of a signalized network is significantly affected by the green ratio. An analytical approximation of the NFD has been derived from the link transmission model. However, the consistency between these approaches has not been established, and the impacts of other factors are still unrevealed. This research evalutes the impacts of start-up and clearance behaviors in a signalized network from a network fundamental diagram approach. Microscopic simulations based on Newell’s car-following model are used for testing the bounded acceleration (start-up) and aggressiveness (clearance) effects on the shape of the NFD in a signalized ring road. This new approach is shown to be consistent with theoretical results from the link transmission model, when the acceleration is unbounded and vehicles have the most aggressive clearance behaviors. This consistency validates both approaches; but the link transmission model cannot be easily extended to incorporate more realistic start-up or clearance behaviors. With the new approach, this project demonstrates that both bounded acceleration and different aggressiveness lead to distinct network capacities and fundamental diagrams. In particular, they lead to start-up and clearance lost times of several seconds; and these lost times are additive. Therefore, the important role that these behaviors play in the NFD shape is studied to reach a better understanding of how the NFD responds to changes. This will help with designing better start-up and clearance behaviors for connected and autonomous vehicles.

Advanced Driving Assistance Systems (ADAS) technologies like Adaptive Cruise Control (ACC) are becoming the normality for many users, and many major car manufacturers are introducing SAE level 2 and 3 automation systems into the market. The main advantage of Automated Vehicles (AV) will be the significant decrease in road accidents and casualties. However, a significant shift from conventional to automated vehicles must occur before it can have a positive impact on society. If the behaviour of the vehicle is not perceived as natural, the user will most likely not activate the ADAS features again. During this study a naturalistic dataset is used to investigate the driver behaviour, in the hope of bringing the current ACC logic to a more human-like behaviour that will feel more natural to the driver. The research question summarizes the final objective of this study: How can Naturalistic Driving Study (NDS) datasets be used in target performance setting for ACC systems? This study will answer the research question by studying human behaviour in the scene of following an accelerating vehicle. The main body of this thesis is divided in three chapters, one for each step of the research. First the information about the used datasets are provided together with the methodologies used to extract the relevant time-series data. Secondly driver behaviour models are created in order to mathematically characterize human behaviour. The strength of the created models is their ability to represent the full range of driver behaviour in terms of driving style. The aggressiveness parameter of the model can be easily adjusted to represent different percentiles of driver behaviour. This allows for a quick and effective tuning process: by changing a single parameter the driving style of the model can be fully modified. Finally, the driver behaviour models are implemented into a simulation environment. The models are simulated against an existing ACC logic in order to assess the difference, Mechanical Engineering | Vehicle Engineering

Driving brings great convenience and efficiency to people’s daily travel. On the other hand, possible road crashes while driving pose a great challenge to the safety of road users. More insights into human driving behaviour are needed to improve driving safety. As the key part of driving behaviour, vehicle following, characterised by the interaction between a vehicle and its leader in a single-lane roadway, has been extensively studied for more than sixty years. However, perception and decision making have seldom been mathematically incorporated in dominant methods of vehicle following modelling. The investigation of these two components is expected to expand the knowledge boundary regarding human driving behaviour. Specifically, perception of traffic dynamics and risk perception are introduced as the representation of perception while decision theory under risk and risk attitudes are incorporated for modelling decision making. After the microscopic modelling of perception and decision making in single-lane vehicle following, the flow-density relation is also derived. Through macroscopic sensitivity analysis, a more risk-averse attitude, the perception of a more severe crash, a greater standard deviation of perceived time headway, a longer time interval for calculating the future speed and a longer vehicle length are found to lower the capacity of traffic facilities and cause more congestion in macroscopic traffic. The model calibration and validation are performed against the vehicle-trajectory data that were collected at a freeway section of I-80 Emeryville. It is found that drivers when following a car show a more accurate and stable time-headway perception than following a truck. Truck drivers tend to have a more stable time-headway perception and less risk aversion than car drivers. When following a different type of vehicle, drivers are shown to perceive a more severe crash. Subsequently, the comparison between the predicted space headway and the observed valu

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance. The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string. Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance, Transport and Planning

Literature has shown potentials of Connected/Cooperative Automated Vehicles (CAVs) in improving highway operations, especially on roadway capacity and flow stability. However, benefits were also shown to be negligible at low market penetration rates. This work develops a novel adaptive driving strategy for CAVs to stabilise heterogeneous vehicle strings by controlling one CAV under vehicle-to-infrastructure (V2I) communications. Assumed is a roadside system with V2I communications, which receives control parameters of the CAV in the string and estimates parameters imperfectly of non-connected automated vehicles. It determines the adaptive control parameters (e.g. desired time gap and feedback gains) of the CAV if a downstream disturbance is identified and sends them to the CAV. The CAV changes its behaviour based on the adaptive parameters commanded by the roadside system to suppress the disturbance. The proposed adaptive driving strategy is based on string stability analysis of heterogeneous vehicle strings. To this end, linearised vehicle dynamics model and control law are used in the controller parametrisation and Laplace transform of the speed and gap error dynamics in time domain to frequency domain enables the determination of sufficient string stability criteria of heterogeneous strings. The analytical string stability conditions give new insights into automated vehicular string stability properties in relation to the system properties of time delays and controller design parameters of feedback gains and desired time gap. It further allows the quantification of a stability margin, which is subsequently used to adapt the feedback control gains and desired time gap of the CAV to suppress the amplification of gap and speed errors through the string. Analytical results are verified via systematic simulation of both homogeneous and heterogeneous strings. Simulation demonstrates the predictive power of the analytical string stability conditions. The performance, Transport and Planning

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behaviour under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety; this realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modelling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few. Thus, this special issue will focus on latest developments in modelling, calibrating, and validating two primary vehicular interactions observed in traffic flow: CF and LC.

Car following (CF) and lane changing (LC) are two primary driving tasks observed in traffic flow, and are thus vital components of traffic flow theories, traffic operation and control. Over the past decades a large number of CF models have been developed in an attempt to describe CF behavior under a wide range of traffic conditions. Although CF has been widely studied for many years, LC did not receive much attention until recently. Over the last decade, researchers have slowly but surely realized the critical role that LC plays in traffic operations and traffic safety. This realization has motivated significant attempts to model LC decision-making and its impact on traffic. Despite notable progresses in modeling CF and LC, our knowledge on these two important issues remains incomplete because of issues related to data, model calibration and validation, human factors, just to name a few.

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

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.

[EN] Engineering and psychophysiological car following models emerge in the late 1950s (Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and explanatory mechanisms, but both assume a fundamental tenet: following each other, drivers invariably attempt to couple, keeping safety distance. More recent models focus on the spontaneous emergence of traffic jams that results from the properties of a system of interacting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., (2008) have successfully recreated the conditions that allow the observation of the typical soliton wave going backwards through several car clusters. When certain speed, density and inter-vehicular distance join, so do traffic jams. Some of us have built upon these and other factors (e.g., wave movement in nature) exploring the mathematical properties of a system with three incognita that also needs three variables to be solved (Melchor & Sánchez, 2014). Two canonical car-following techniques emerge as a consequence: Driving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers actually understand and follow either way, or do they stick to a basic normative driving behavior? This paper summarizes the results after three experimental studies done with a driving simulator. Several performance measures from individual drivers (accelerations, decelerations, average speed, distance to leader, and so on) were taken. As an overall indicator, results consistently announce in the three studies that DI trips consume less fuel (about 20%) than DD ones.

Driving a car is common but complex everyday task. Nowadays the mobile phone, infotainment and navigation systems are used while driving. These distract the driver from their driving task. What is the implication on driving safety? How is the car following behaviour influenced by distraction? Can this be described by a car following model? The multiple resource model tells us that there is limited resources to perform a task. Consequently when a driver performs a secondary task it is expected that its performance decrease. The car following model should be able to capture this behaviour. In this study data from a simulator experiment performed by IFSTTAR is used. Participants drove with and without a visual secondary (VST) task. The obtained driving behaviour is then modelled using the Helly and IDM model. With a traffic flow model, the influence of distracted driving on traffic flow is studied. Analysis of the simulator data showed that against expectations the subjects with VST drive closer to the lead vehicle with less speed difference. Their car following performance increased. This behavior can be explained by assuming an underload situation, where performance is suboptimal, when driving without VST. The Helly model can be used to describe the different conditions by using different parameters. The subjects are more vigilant when performing a secondary task. This better car following performance leads to an increased road capacity for the visual ST condition., BMD, Biomechanical Engineering, Mechanical, Maritime and Materials Engineering

Driving a car is common but complex everyday task. Nowadays the mobile phone, infotainment and navigation systems are used while driving. These distract the driver from their driving task. What is the implication on driving safety? How is the car following behaviour influenced by distraction? Can this be described by a car following model? The multiple resource model tells us that there is limited resources to perform a task. Consequently when a driver performs a secondary task it is expected that its performance decrease. The car following model should be able to capture this behaviour. In this study data from a simulator experiment performed by IFSTTAR is used. Participants drove with and without a visual secondary (VST) task. The obtained driving behaviour is then modelled using the Helly and IDM model. With a traffic flow model, the influence of distracted driving on traffic flow is studied. Analysis of the simulator data showed that against expectations the subjects with VST drive closer to the lead vehicle with less speed difference. Their car following performance increased. This behavior can be explained by assuming an underload situation, where performance is suboptimal, when driving without VST. The Helly model can be used to describe the different conditions by using different parameters. The subjects are more vigilant when performing a secondary task. This better car following performance leads to an increased road capacity for the visual ST condition., BMD, Biomechanical Engineering, Mechanical, Maritime and Materials Engineering

This paper comprehensively reviews recent developments in modeling lane-changing behavior. The major lane changing models in the literature are categorized into two groups: models that aim to capture the lane changing decision-making process, and models that aim to quantify the impact of lane changing behavior on surrounding vehicles. The methodologies and important features (including their limitations) of representative models in each category are outlined and discussed. Future research needs are determined.

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

This research investigated the effectiveness of using an eco-driving strategy at urban signalised intersections from both the individual driver and the traffic flow perspective. The project included a field driving experiment and a series of traffic simulation investigations. The study found that the prevailing eco-driving strategy has negative impacts on traffic mobility and environmental performance when the traffic is highly congested. An improved eco-driving strategy has been developed to mitigate these negative impacts.

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existi

Tato práce se zabývá vytvořením kombinovaného simulačního jádra pro spojitou simulaci včetně on-line animace založeného na metodě snímání aktivit a využitím kvadraturních vzorců. Teoretická část je věnována popisu základních pojmů oblasti simulace a numerické integrace, konkrétně kvadraturních vzorců. Praktická část se zabývá implementací samotného jádra a simulačního modelu realizující přímočarý pohyb vozidel pohybující se v modelové infrastruktuře bez přejíždění., This diploma thesis deals with the creation of the combined simulation engine for continuous simulation based on method of scanning activities and the use of quadrature formulas. The theoretical part of thesis is devoted to describing the basic concepts of simulation and numerical integration methods, namely quadrature formulas. The practical part deals with the implementation of simulation engine and simulation model realizing linear motion of vehicle moving on the model infrastructure without crossing., Katedra softwarových technologií, Cílem diplomové práce byl návrh, implementace a ověření synchronizačního mechanismu simulačního výpočtu spojité simulace s využitím numerické integrace. Dle vedoucího práce byly splněny veškeré cíle. Výsledná aplikace je plně funkční. Aplikaci lze použít v praxi při simulování reálných situací. Též typograficky je práce na velmi dobré úrovni. Obhajoba proběhla úspěšně s demonstrativními ukázkami. Zkoušená úspěšně zodpověděla všechny dotazy a připomínky. Vydařená diplomová práce, hodnocena klasifikací výborně. Rovněž odpovědi na otázky odpovídají celkovému hodnocení výborně. S přihlédnutím ke skutečnosti, že studentka prospěla s vyznamenáním navrhuje komise pro státní závěrečné zkoušky udělit diplomové práci s názvem "Výpočetní jádro pro spojitou simulaci uplatňující metody numerické integrace" cenu rektora 1. stupně.

This exploratory study investigates the potential of developing an Eco-Driving application that utilizes an eco-cruise control (ECC) system within state-of-the-art car-following models. The research focuses on integrating predictive cruise control and optimal vehicle acceleration and deceleration controllers within car-following models to minimize vehicle fuel consumption levels. This system makes use of topographic information, spacing to lead vehicle, and a desired (or target) vehicle speed and distance headway as input variables.

This exploratory study investigates the potential of developing an Eco-Driving application that utilizes an eco-cruise control (ECC) system within state-of-the-art car-following models. The research focuses on integrating predictive cruise control and optimal vehicle acceleration and deceleration controllers within car-following models to minimize vehicle fuel consumption levels. This system makes use of topographic information, spacing to lead vehicle, and a desired (or target) vehicle speed and distance headway as input variables.

This exploratory study investigates the potential of developing an Eco-Driving application that utilizes an eco-cruise control (ECC) system within state-of-the-art car-following models. The research focuses on integrating predictive cruise control and optimal vehicle acceleration and deceleration controllers within car-following models to minimize vehicle fuel consumption levels. This system makes use of topographic information, spacing to lead vehicle, and a desired (or target) vehicle speed and distance headway as input variables.

Traffic oscillations are typical features of congested traffic flow that are characterized by recurring decelerations followed by accelerations. However, people have limited knowledge on this complex topic. In this research, 1) the impact of traffic oscillations on freeway crash occurrences has been measured using the matched case-control design. The results consistently reveal that oscillations have a more significant impact on freeway safety than the average traffic states. 2) Wavelet Transform has been adopted to locate oscillations' origins and measure their characteristics along their propagation paths using vehicle trajectory data. 3) Lane changing maneuver's impact on the immediate follower is measured and modeled. The knowledge and the new models generated from this study could provide better understanding on fundamentals of congested traffic; enable improvements to existing traffic control strategies and freeway crash countermeasures; and instigate people to develop new operational strategies with the objective of reducing the negative effects of oscillatory driving.

This research effort aims to compare car-following models when the models are calibrated to individual drivers with the naturalistic data. The models used are the GHR, Gipps, Intelligent Driver, Velocity Difference, Wiedemann, and the Fritzsche model. This research effort also analyzes the Wiedemann car-following model using car-following periods that occur at different speeds. The Wiedemann car-following model uses thresholds to define the different regimes in car following. Some of these thresholds use a speed parameter, but others rely solely upon the difference in speed between the subject vehicle and the lead vehicle. This research effort also reconstructs the Wiedemann car-following model for truck driver behavior using the Naturalistic Truck Driving Study's (NTDS) conducted by Virginia Tech Transportation Institute. This Naturalistic data was collected by equipping 9 trucks with various sensors and a data acquisition system. This research effort also combines the Wiedemann car-following model with the GHR car-following model for trucks using The Naturalistic Truck Driving Study's (NTDS) data.

The paper develops procedures for calibrating the steady-state component of various car following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways.

This report documents the second part of the FHWA research study involving analysis of the microscopic impacts of adverse weather on traffic flow, but is a third phase of the research effort on the impacts of weather on traffic flow. The first phase of FHWA research involved macroscopic analysis, which focused on the impacts of adverse weather on aggregate traffic flow. The second phase of research analyzed the impacts of adverse weather on microscopic traffic behavior. This report documents the results of three research efforts (1) The impacts of icy roadway conditions on driver behavior at a microscopic level, using field measured car-following data,; (2) An investigation of the influence of weather precipitation and roadway surface condition on left-turn gap-acceptance behavior using traffic and weather data collected during the winter of 2009-2010 at a signalized intersection in Blacksburg, Virginia; and (3)The development and demonstration of methodologies for the use of weather-related adjustment factors in microsimulation models, including general approaches to construct simulation models accounting for the impact of precipitation. For the third effort, the general approach was applied to the calibration of the VISSIM and INTEGRATION simulation software.

The paper develops procedures for calibrating the steady-state component of various car following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways.

This report documents the second part of the FHWA research study involving analysis of the microscopic impacts of adverse weather on traffic flow, but is a third phase of the research effort on the impacts of weather on traffic flow. The first phase of FHWA research involved macroscopic analysis, which focused on the impacts of adverse weather on aggregate traffic flow. The second phase of research analyzed the impacts of adverse weather on microscopic traffic behavior. This report documents the results of three research efforts (1) The impacts of icy roadway conditions on driver behavior at a microscopic level, using field measured car-following data,; (2) An investigation of the influence of weather precipitation and roadway surface condition on left-turn gap-acceptance behavior using traffic and weather data collected during the winter of 2009-2010 at a signalized intersection in Blacksburg, Virginia; and (3)The development and demonstration of methodologies for the use of weather-related adjustment factors in microsimulation models, including general approaches to construct simulation models accounting for the impact of precipitation. For the third effort, the general approach was applied to the calibration of the VISSIM and INTEGRATION simulation software.

This report documents the second part of the FHWA research study involving analysis of the microscopic impacts of adverse weather on traffic flow, but is a third phase of the research effort on the impacts of weather on traffic flow. The first phase of FHWA research involved macroscopic analysis, which focused on the impacts of adverse weather on aggregate traffic flow. The second phase of research analyzed the impacts of adverse weather on microscopic traffic behavior. This report documents the results of three research efforts (1) The impacts of icy roadway conditions on driver behavior at a microscopic level, using field measured car-following data,; (2) An investigation of the influence of weather precipitation and roadway surface condition on left-turn gap-acceptance behavior using traffic and weather data collected during the winter of 2009-2010 at a signalized intersection in Blacksburg, Virginia; and (3)The development and demonstration of methodologies for the use of weather-related adjustment factors in microsimulation models, including general approaches to construct simulation models accounting for the impact of precipitation. For the third effort, the general approach was applied to the calibration of the VISSIM and INTEGRATION simulation software.

The paper develops procedures for calibrating the steady-state component of various car following models using macroscopic loop detector data. The calibration procedures are developed for a number of commercially available microscopic traffic simulation software, including: CORSIM, AIMSUN2, VISSIM, Paramics, and INTEGRATION. The procedures are then applied to a sample dataset for illustration purposes. The paper then compares the various steady-state car-following formulations and concludes that the Gipps and Van Aerde steady-state car following models provide the highest level of flexibility in capturing different driver and roadway characteristics. However, the Van Aerde model, unlike the Gipps model, is a single-regime model and thus is easier to calibrate given that it does not require the segmentation of data into two regimes. The paper finally proposes that the car-following parameters within traffic simulation software be link-specific as opposed to the current practice of coding network-wide parameters. The use of link-specific parameters will offer the opportunity to capture unique roadway characteristics and reflect roadway capacity differences across different roadways.