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Normal Deceleration Behavior of Passenger Vehicles at Stop Sign-Controlled Intersections Evaluated with In-Vehicle Global Positioning System Data
Abstract
Deceleration characteristics of passenger cars are often used in traffic simulation, vehicle fuel consumption and emissions models, and intersection and deceleration-lane design. Most previous studies collected spot speed data with detectors or radar guns. Because of the limitations of the data collection methods, these studies could not determine when and where drivers began to deceate. Therefore, the studies may not provide an accurate estimation of deceleration time and distance. Furthermore, most previous studies are based on outdated and limited data, and their conclusions may not be applicable to the current vehicle fleet and drivers. The normal deceleration behavior of current passenger vehicles is evaluated at stop sign-controlled intersections on urban streets on the basis of in-vehicle Global Positioning System data. This study determined that drivers with higher approach speeds decelerated over a longer time and distance. Higher initial deceleration rates were also associated with higher approach speeds. However, the collected data in this study did not indicate a clear relationship between the average and maximum deceleration rates and approach speeds. With second-by-second deceleration profile data, the authors found that most drivers reached their maximum deceleration rates about 5 s or less than 5 s before stopping, and the maximum deceleration rate (3.4 m/s 2) recommended by AASHTO was applicable to most of the study drivers. This review verified several previous deceleration models with the current observations and found that the polynomial model developed by Akcelik and Biggs provides the best fit for the data set in this study. Finally, this study developed a new deceleration model based on the approach speeds and deceleration time.
... Chassis dynamometer method uses driving cycle approach. However, many researchers (Ross, 1994;Black, 1991;Joumard et al., 1995;Andrews et al., 2004;Li et al., 2005) have reported that driving cycles do not represent actual driving behavior of vehicles on roads. Particularly acceleration, creeping and other off-cycle events are not properly represented in driving cycles. ...
... Vehicle acceleration and deceleration were calculated using Equation (1), (Wang et al., 2004) and (2), (Wang et al., 2005). ...
... This was done to examine average behaviour of emission with speed, acceleration and deceleration. A similar procedure was adopted by Wang et al. (2005) for evaluating acceleration of passenger cars at stop-controlled intersections. The maximum acceleration was 2.035 m/s 2 and the idealized maximum acceleration was 1.63 m/s 2 . ...
Several studies, in the past, have used chassis dynamometer and remote-sensing method to describe effects of speeds on pollutant emissions. These studies reasonably lacked data on important modal events such as acceleration, deceleration, speed, and their effects on emissions. Present study includes on-road experiments carried out to examine the impacts of car speed, acceleration, and deceleration on their tailpipe emissions. The study was carried out on cars, with and without catalytic converter on a two-lane roadway in engine operating modes of acceleration and deceleration. The power to weight ratio of the cars was 0.03hp/lb. The relationships of pollutant emissions with the speeds were examined at two acceleration levels (a = 1.0 m/s² and a = 1.6 m/s²). A prominent relationship of tailpipe emission with the averaged speed was seen at both accelerations. Further, the pollutant emissions were different at different speed ranges of 0-3 m/s (0-10.8 km/h), 3-6 m/s (10.8-21.6 km/h) and above 6 m/s (21.6 km/h). A second-order statistical emission - speed model has been presented and discussed. The effect of deceleration on tailpipe emission was not clear in the study.
... Though, deceleration rate is an important input for duration of amber light at signalized intersection, limited work (Akcelik and Biggs, 1987;Bennet and Dunn, 1995;Wang et al., 2005)) is reported in the past on deceleration modelling of vehicles in comparison to acceleration modelling. Bennet and Dunn (1995) reported second order polynomial deceleration model for vehicles in New Zealand. ...
... where, d t2 is deceleration at time t 2 and v 1 and v 2 are the speeds at time t 1 and t 2 respectively. Starting of deceleration process was defined from the time onwards where deceleration values calculated from Equation 2 are greater or equal to 0.1 m/s 2 for five consecutive seconds (Wang et al., 2005). At the end of deceleration process vehicle speed become zero. ...
... Vehicles having lower weight to horse power ratio take lesser time to complete deceleration maneuver. However, unlike Bennet and Dunn (1995) and Wang et al. (2005) no relationship between approach speed and deceleration time is observed in this study. The reason is that the rate of deceleration depends on many other factors like available deceleration space at intersection, vehicles' capacity to decelerate etc. rather than approach speed. ...
Acceleration/deceleration (A/D) behaviour of vehicles is important for various applications like length of yellow light at inter- section, determination of sight distances at intersection, determination of length of A/D lanes, ramp design, traffic simulation modelling, vehicular emission modelling, instantaneous fuel consumption rate modelling, etc. Literature reports A/D studies for cars in lane disciplined homogeneous traffic. However, Indian traffic stream is weak lane disciplined and heterogenous, containing various vehicle types like truck, motorized three and two wheeler and diesel and petrol driven cars. Also, the reported studies are based on out of date data, collected using traditional and less accurate methods. Hence, this work aims to study the A/D behaviour of various vehicle types using modern instruments like Global Positioning System (GPS) in controlled manner including maximum A/D envelop.
... The acceleration rate was then calculated from the measured travel times between each station and the distance between those stations. A challenge of this method is that the researchers could not determine where and when drivers stopped their acceleration process or started their deceleration process (12). Other studies used the test vehicle's speedometer to collect speed data (10). ...
... Several recent studies (4,9,11,12) adopted GPS technology to collect speed data. Rakha et al. installed a GPS device on test vehicles to measure the maximum acceleration of passenger vehicles and trucks on a test road (9). ...
... The study concluded that GPS data contained a high resolution of speed data and were appropriate for determining acceleration rates. The other studies (4,11,12) developed acceleration or deceleration models using the GPS data under ideal conditions, namely, including only the first vehicle in the queue. ...
Acceleration and deceleration characteristics are basic driving behaviors that influence signal control and road geometry. Most previous studies focused on acceleration and deceleration rates under ideal conditions, i.e., on characteristics of lead vehicles, which might not adequately reflect the full spectrum of traffic operations. This paper presents a methodology for determining acceleration and deceleration rates and zone lengths for vehicles approaching and leaving intersections under likely uncongested traffic conditions, regardless of queue position. These characteristics are derived from speed profiles gained from vehicles equipped with Global Positioning System (GPS) instrumentation. With the speed profiles obtained from GPS-equipped vehicles, a series of data-processing algorithms is developed to measure the acceleration and deceleration behavior of vehicles approaching and departing an intersection. Acceleration and deceleration rates and zone lengths are estimated for several road categories. These zone lengths are compared with recommended values from AASHTO's A Policy on Geometric Design of Highways and Streets (Green Book), in which it is seen that the zone lengths under light traffic conditions are longer than the Green Book's values. In addition, determined acceleration and deceleration rates and zone lengths are found to depend on the underlying assumptions and calculation methods of the analysis.
... The selected slow and fast rates correspond to two different braking scenariosmild and moderately hard. The 4-ft/s 2 (1.22-m/ s 2 ) rate has been characterized as "mild" braking (Lee, 1976) and approximates the 4.53-ft/s 2 (1.38-m/s 2 ) mean deceleration rate observed when passenger vehicles approached a stop sign at speeds similar to the lead car in our study (Wang, Dixon, Li, & Ogle, 2005). The 10-ft/s 2 rate (3.05 m/s 2 ) has been characterized as "moderately hard" braking (Lee, 1976) and corresponds to the upper end of comfortable braking rates used by drivers in non-emergency situations (e.g., stopping at a stop sign). ...
... The Institute of Transportation Engineers (1999) recommends deceleration rates of less than 3 m/s 2 (10 ft/s 2 ) as reasonably comfortable for occupants of passenger vehicles, and this value is used to determine stopping distances for traffic signals. This value also approximates the maximum deceleration rates observed for most drivers approaching stop signs (Wang et al., 2005): The maximum deceleration rates for 87.6% of the deceleration trips observed were lower than the Institute of Transportation Engineer's recommended 3 m/s 2 (10 ft/s 2 ). ...
Objective:
Two experiments were conducted to determine whether detection of the onset of a lead car's deceleration and judgments of its time to contact (TTC) were affected by the presence of vehicles in lanes adjacent to the lead car.
Background:
In a previous study, TTC judgments of an approaching object by a stationary observer were influenced by an adjacent task-irrelevant approaching object. The implication is that vehicles in lanes adjacent to a lead car could influence a driver's ability to detect the lead car's deceleration and to make judgments of its TTC.
Method:
Displays simulated car-following scenes in which two vehicles in adjacent lanes were either present or absent. Participants were instructed to respond as soon as the lead car decelerated (Experiment 1) or when they thought their car would hit the decelerating lead car (Experiment 2).
Results:
The presence of adjacent vehicles did not affect response time to detect deceleration of a lead car but did affect the signal detection theory measure of sensitivity d' and the number of missed deceleration events. Judgments of the lead car's TTC were shorter when adjacent vehicles were present and decelerated early than when adjacent vehicles were absent.
Conclusion:
The presence of vehicles in nearby lanes can affect a driver's ability to detect a lead car's deceleration and to make subsequent judgments of its TTC.
Application:
Results suggest that nearby traffic can affect a driver's ability to accurately judge a lead car's motion in situations that pose risk for rear-end collisions.
... Most of the studies conducted at signalized intersections have focused on vehicle's deceleration behavior and its importance in dilemma zone analysis [25][26][27][28][29]. A deceleration rate of 5 m/s 2 was assumed to know the time required by a Car to stop completely [30]. ...
Turning movements at uncontrolled intersections depend on both longitudinal and lateral vehicular behavior which becomes complex due to lack of understanding of priority rules and weak lane discipline. This study analyzes the acceleration characteristics of right-turning vehicles from minor roads at uncontrolled intersections. Data were collected using a Differential GPS mounted on various vehicle types (2w, 3w, and Car). The analysis categorized maneuvers into approaching, turning, and exiting sections. It was observed that 2 W exhibit higher deceleration rates during approach (−0.18 to −0.20 m/s²) and greater longitudinal acceleration while merging (0.12–0.18 m/s²), attributed to their lower weight-to-power ratio. Notably, 2 W also experienced the highest lateral acceleration during turns (up to 0.13 m/s²), reflecting their dynamic instability. Regression models (0.66–0.83) validated the strong dependence of acceleration on speed, with vehicle type significantly influencing these relationships. Compared to previous studies the findings reveal that two-wheelers (2 W) exhibit 20–30% higher lateral acceleration and more aggressive deceleration during approach, while three-wheelers (3 W) show lower longitudinal acceleration than reported in literature, reflecting their constrained power-to-weight ratios. Longer ISD enables smoother deceleration, while tighter turns increase lateral forces by 30–35% for 2 W. The findings highlight the need for intersection designs that accommodate heterogeneous vehicle behaviors, offering actionable insights for improving safety and traffic flow in uncontrolled intersections.
... Wortman & Fox [14] collected and analysed speed and deceleration rate data on vehicles decelerating at signalised intersections and found that the deceleration rate is related to the approach speed, with higher approach speeds resulting in greater deceleration rates. Wang et al. [16] confirmed that vehicles with higher approach speeds have longer deceleration times and distances and higher initial deceleration rates based on the onboard GPS data. Da Lio et al. [17] developed decelerationstop behaviour models for intersections with different types based on parameters such as time, speed, deceleration rate and distance. ...
To reveal the speed control behaviour and manoeuvring characteristics of direct vehicles that stop-go through signalised intersections, a large-scale field driving test was carried out in Chongqing to collect vehicle data under natural driving conditions. The characteristics of speed, longitudinal acceleration rate and their two-dimensional correlation were analysed for deceleration and acceleration behaviour at signalised intersections. Further, a sensitivity analysis of the simulation model on measured data was done with the micro-traffic simulation experiment of a signalised intersection. The following were observed: (1) Drivers’ speed-selection behaviours become more concentrated with closer distance from the stop point. The transects ±25 m from the stop point are abrupt change points in the discrete nature of driver speed-selective behaviours. (2) Drivers’ desire to decelerate during the stop-go through signalised intersections is more robust, with the magnitude of pedal manoeuvres for deceleration behaviours being more intense than that for acceleration behaviours. (3) There is a nonlinear correlation between longitudinal acceleration rate and speed. The longitudinal acceleration rate increased with increase in speed and then decreased with the inflection point at 15 km/h. (4) The micro-traffic simulation’s acceleration rate model is sensitive to measured acceleration rate parameters. This study guides the parameter setting of speed, deceleration rate and acceleration rate models for microscopic traffic simulation and for parameter calibration of the car-following model.
... in simulations. 10 This study only aimed at studying the acceleration and deceleration capabilities of 11 motorized three wheeler. Similarly, efforts should be made to study the acceleration and 12 deceleration characteristics for other vehicles type plying on the road traffic. ...
Acceleration and deceleration characteristics of vehicle is an important aspect in traffic engineering, which has a significant impact on various design elements. Most of the acceleration and deceleration models present in the literature have been developed for passenger car and truck. But vehicles tend to differ in their performance to accelerate and decelerate due to their varying characteristics. This study is aimed at studying the acceleration and deceleration capabilities of a motorized three wheeler, which one of the most commonly found vehicle type in developing countries like India. Motorized three wheelers (commonly known as auto rickshaws or tuk-tuks) are primarily used as an Intermediate public transport in many Asian countries. This study captured has the entire acceleration and deceleration procedure using a GPS based performance box inside the vehicles and by capturing their speed and position data at every 1 second interval. Acceleration rates were found to be higher at lower speeds and lower at higher speeds in accordance with the literature. Maximum acceleration rate and deceleration rate for motorized three wheeler was found to be in range of 1.88 m/s 2 to 2.08 m/s 2 , and 2.81 m/s 2 to 3.21 m/s 2 over various speed ranges, respectively. This study has proposed an exponential model to describe the acceleration behaviour of a motorized three-wheeler and a polynomial model to describe its deceleration behaviour observed. The application of the developed models can help in better traffic operation and management and will yield realistic vehicle behavior in simulations when used in micro-simulation software.
... However, these rules may differ for different drivers and under different driving situations [29,30]. The studies have found that the acceleration varies from vehicle to vehicle depending on prevailing traffic conditions [31][32][33][34][35]. The authors observed lower rates of acceleration for heavy vehicles like Bus, Truck, and light commercial vehicles as compared to Cars, Three-wheelers, and Two-wheelers and estimated maximum and average acceleration values at different speeds of vehicle types on highways and suggested exponential models for acceleration and deceleration. ...
Urban roads in India comprise of a variety of motorized vehicles that share common space with non-motorized vehicles. The traffic conditions become very complex and chaotic due to the presence of side friction on the urban roads. The roadside frictions such as on-street parking, bus stops, road encroachments, etc., are the primary reasons for roadway congestion. They influence the operating performance of vehicles such as speed and acceleration/deceleration characteristics. The present study is carried out to analyse the acceleration and deceleration characteristics of various vehicle types at curbside bus stops. The data for the study was collected using a V-box instrument mounted on different subject vehicle types, at 4 bus stop different locations on four-lane and six-lane divided urban roads in Hyderabad city, India. Speed-distance profiles for different vehicle types are plotted for each bus stop location during the stoppage of a bus and it is divided into three sections for detailed analysis. Dynamic characteristics are analysed using statistical methods at a 95% confidence interval. The linear and non-linear regression analysis has been performed for the development of acceleration and deceleration models for each subject vehicle type traversing the bus stop section. Developed models are validated using the MAPE value which is observed to be less than 5% at all the sections. The paper also attempts to study vehicle acceleration and deceleration behavior at varying approach speeds on different sections such as upstream, bus stop, and downstream. The findings of the present study provide insights for improvement in the geometric design of roadway bus stop and for understanding vehicular dynamic characteristics when traffic stream is interrupted due to a bus stop without bus bay. Vehicle-wise acceleration and deceleration characteristics measured at the bus stop locations may be used as an important input for microscopic simulation studies for realistic replication of field conditions.
... These trajectories are completely excluded from further consideration. This is done as values that fall out of the aforementioned range can be considered unrealistic in the context of urban driving [28,35]. In a real-world context, the data would be collected by means of sensors. ...
Many state-of-the-art approaches in autonomous driving make use of highly precise maps. Among other annotations, these maps must contain information regarding traffic regulations.
In this thesis an offline approach aiming at the inference of traffic regulations at German intersections on a sub-lane level is suggested. The representation and likelihood-based inference of regulations is realized using Hidden Markov Models. These are parametrized and evaluated on an artificially created set of trajectories crossing several intersections. In a real-world context, the trajectories could be opportunistically collected from a sensor-equipped fleet of vehicles over an extended period of time. In a series of experiments, a suitable trajectory representation is determined and the approach is tested and improved. Classification performance is evaluated in a cross-validation manner. Mean test F1 scores associated to the best classification results range between 0.809 and 0.832. High performance is achieved in the context of the traffic regulations priority, stop and traffic-light. However, regarding the yield and yield-to-right regulation, challenges remain. As initial results are promising, the approach is worth being developed and improved further.
... The minimum and maximum values of the brake reaction time are set according to Mai (2017). The mean comfort deceleration of 1.24 m/s 2 is set according to Wang et al. (2005) and the maximum comfort deceleration is limited to 3 m/s 2 . The maximum deceleration of the driver, which is used in critical scenarios, is modelled according to Chen et al. (2016), with the maximum deceleration limited to above 4 m/s 2 . ...
With the rise of Advanced Driver Assistant Systems (ADAS) and the introduction of Highly Automated Driving (HAD), understanding and predicting road traffic accidents becomes increasingly important. Especially for the assessment of HAD/ADAS systems and of road safety, the precise prediction of the system’s impact on the occurrence of road traffic accidents is essential.
Traffic simulations, as one option of virtual assessment, enable the assessment of safety systems in virtual test fields. By modelling the human driver, it is possible to simulate and predict future accident constellations and accident severities. In addition, the influence of vehicle factors, such as the presence of safety systems or their characteristics, on the occurrence of accidents can be investigated.
This article elucidate virtual assessment by executing the whole procedure for an automatic emergency braking (AEB) system capable to detect cyclists at urban intersections. The authors implement a new driver behaviour model (DReaM) to predict road traffic accidents at urban intersections. Using the AEB as an example, the influence of different sensor opening angles on crash frequency is investigated by varying the sensors in three steps: 100°, 180°, and 210°. A total of 240,000 situations are simulated of which 6,674 are crashes. Within the two investigation scenarios, the crash frequency is reduced by up to 88.43%, or 93.92% by introducing the AEB system.
The article shows that virtual assessment enables the prediction of new safety systems regarding road safety at early design stages. In addition, different system characteristics can be compared efficiently, e.g., the sensor opening angle.
... The human driver deceleration/acceleration behavior model [123] is shown in Eq. (2.7). The model was evaluated in [124] and confirmed to match experimental data very well. ...
Connected Automated Vehicles (CAV) technologies are developing rapidly, and one of its more popular application is to provide mobility-on-demand (MOD) services. However, with CAVs on the road, the fuel consumption of surface transportation may increase significantly. Travel demands could increase due to more accessible travel provided by the flexible service compared with the current public transit system. Trips from current underserved population and mode shift from walking and public transit could also increase travel demands significantly. In this research, we explore opportunities for the fuel-saving of CAVs in an urban environment from different scales, including speed trajectory optimization at intersections, data-drive fuel consumption model and eco-routing algorithm development, and eco-MOD fleet assignment. First, we proposed a speed trajectory optimization algorithm at signalized intersections. Although the optimal solution can be found through dynamic programming, the curse of dimensionality limits its computation speed and robustness. Thus, we propose the sequential approximation approach to solve a sequence of mixed integer optimization problems with quadratic objective and linear constraints. The speed and acceleration constraints at intersections due to route choice are addressed using a barrier method. In this work, we limit the problem to a single intersection due to the route choice application and only consider free flow scenarios, but the algorithm can be extended to multiple intersections and congested scenarios where a leading vehicle is included as a constraint if an intersection driver model is available. Next, we developed a fuel consumption model for route optimization. The mesoscopic fuel consumption model is developed through a data-driven approach considering the tradeoff between model complexity and accuracy. To develop the model, a large quantity of naturalistic driving data is used. Since the selected dataset doesn’t contain fuel consumption data, a microscopic fuel consumption simulator, Autonomie, is used to augment the information. Gaussian Mixture Regression is selected to build the model due to its ability to address nonlinearity. Instead of selected component number by cross-validation, we use the Bayesian formulation which models the indicator of components as a random variable which has Dirichlet distribution as prior. The model is used to estimate fuel consumption cost for routing algorithm. In this part, we assume the traffic network is static. Finally, the fuel consumption model and the eco-routing algorithm are integrated with the MOD fleet assignment. The MOD control framework models customers’ travel time requirements are as constraints, thus provides flexibility for cost function design. At the current phase, we assume the traffic network is static and use offline calculated travel time and fuel consumption to assign the fleet. To rebalance the idling vehicles, we developed a traffic network partition algorithm which minimizing the expected travel time within each cluster. A Model Predictive Control (MPC) based algorithm is developed to match idling fleet distribution with the demand distribution. A traffic simulator using Simulation of Urban MObility (SUMO) and calibrated using data from the Safety Pilot Model Deployment (SPMD) database is used to evaluate the MOD system performance. This dissertation shows that if the objective function of fleet assignment is not designed properly, even if ride-sharing is allowed, the fleet fuel consumption could increase compared with the baseline where personal vehicles are used for travel.
... Spanish and Italian design standards also adopt an approach that factors the coefficient of friction into the SSD estimates (Ministero delle Infrastrutture e dei Trasporti, 2001;Ministerio de Fomento, 2016) In general, there seems to be a significant variation in what is assumed to be a comfortable maximum deceleration rate in the literature, with mean values ranging from as low as 1.96 m/s 2 to as high as 4.2 m/ s 2 . Since multiple studies have shown that drivers only maintain the maximum deceleration rate for a short duration of the braking distance (Wang et al., 2005;Li and Abbas, 2009;Deligianni et al., 2017), it was decided that using a maximum mean deceleration rate of 4.2 m/s 2 , recommended by Fambro et al. (1998), was unrealistic. Moreover, the fact that more recent research has reported lower deceleration rates may indicate that these modifications are due to changes in population demographics or other environmental factors. ...
Stopping Sight Distance (SSD) is the distance defined in most highway design guides as the distance required by drivers to safely come to a complete stop in case of an emergency. Accordingly, design guides define theoretical values for SSD and recommend that these requirements are satisfied at all points along a highway corridor. SSD is estimated as a function of speed, driver reaction time, and deceleration rate, which are all factors that vary by both driver and driving conditions. Despite the anticipated uncertainty in those variables, they are all modelled deterministically. Unfortunately, this is an inaccurate assumption and provides no information about the extent to which roads designed to meet SSD requirements are able to satisfy road user demand for SSD. Design guides also fail to provide information about the impact a segment that fails to meet driver needs has on safety. To overcome those limitations, this paper assesses the ability of existing roads to satisfy stochastically modelled road user demand for SSD. The Available Sight Distance (ASD) was first quantified for a group of top crash-prone segments, and a Monte Carlo Simulation was used to model demand for SSD. The proportion of the test highways that failed to meet driver demands for SSD was then quantified by comparing the ASD to the required SSD at different levels of driver demand. Furthermore, the paper also compares the safety performance between regions that meet SSD and those that fail to do so. Among other findings, the paper shows that, on average, 6.8 % of the length of the test segments are noncompliant to the SSD demands of 70 % of the driving population. On the other hand, the average percent noncompliance for 30 % of the driving population (the 30 % with limited abilities) was 12.1 %. It was also found that, on average, crash rates in the noncompliant regions were two to three times higher than those in the compliant regions at the 70 % level (i.e., in regions that fail to meet the SSD demands of 70 % of the driving population).
... Also, we analyzed the acceleration and deceleration behavior of vehicles for the sub-scenario 40-25-15 and found that all the values are within the range of comfortable deceleration (see Fig. 8) [52]. ...
Safety issues at school zones have been an important topic in the traffic safety field. This study assesses the safety effects of different roadway countermeasure at school zones. Although several studies have evaluated the effectiveness of various traffic control devices (e.g., sign, flashing beacon), there is lack of studies proposing innovative operation and engineering countermeasures , which might have significant improvement of safety at school zones. In this study, the most crash-prone school zone is identified based on crash rates in Orange and Seminole Counties in Florida. Afterward, a microsimulation network is built to evaluate different safety countermeasures. Three different countermeasures i.e., two-step speed reduction, decreasing the number of driveways, and replacing the two-way left-turn lane (TWLTL) with raised median are implemented in the microsimulation. Multiple surrogate safety measures are utilized as indicators for safety evaluation. The results show that both two-step speed reduction and decreasing driveway access significantly reduce crash risks compared with the base condition. Moreover, the combination of these two countermeasures outperforms their individual effectiveness. On the other hand, for TWLTL to the raised median, the crash risk is higher than the base condition. The results of this study could help transportation planners and decision makers to understand the effect of these countermeasures prior to implementing them in the real field.
... Regarding the characteristics of driving behavior, the movement is defined by the speed and acceleration [5]. Throughout the speed-time and acceleration-time profile, this approach can gain deeper understanding of trip activity and driving behavior [2] [6] [7]. Because it is very essential to determine the real value during the operation, this study will analyze the movement of buses based on different sampling rates to evaluate their effect on the instantaneous speed and estimated acceleration. ...
Presently, Global Positioning System (GPS) receiver is a very popular tool to track the movement of an object and obtain its associated information in real-time. It has been commonly used in various fields including transportation. In this research, the highly sampled rate GPS of 5 Hertz is utilized to track real-time movement of public buses in Bangkok. By extracting and analyzing the GPS data record, it can provide more meaningful information on the operations of public bus services. The aim of this study is to evaluate the movement of public bus by comparing among different recording intervals; i.e., 0.4, 0.6, 0.8, 1, 2, 3, 4 and 5-s in order to see the effects of sampling rate on the trajectories. The results show that the high sampling rate of less than 1s tends to provide a discrete fluctuation rather than continuous trajectory of the real movement. A sampling rate of 1-2s seems to be appropriate with respect to the reasonable instantaneous speed and acceleration value.
... Majority of the studies were conducted the study on acceleration behavior on signalized intersections (Akcelik and Beseley, 2001;Akcelik and Biggs, 1987;Bham and Benekohal, 2002;RaiChowdhury and Rao, 1989;Wang et al., 2004). Limited work is done in the past on deceleration modelling in comparison to acceleration modelling (Akcelik and Biggs, 1987;Wang et al., 2005). Limited studies have been conducted on A/D behavior in mixed and weak lane discipline traffic like India (Bokare and Maurya, 2016;Dey and Biswas, 2011;Maurya and Bokare, 2012;RaiChowdhury and Rao, 1989). ...
On heterogeneous and non-lane discipline traffic, the drivers have to control the vehicle’s position in longitudinal direction of motion along with the lateral direction, i.e., the width of the roadway. The relation between dynamic parameters (speed and lateral/longitudinal acceleration) can very well represent the driving behavior of vehicles. In the present paper, driving behavior of vehicles are studied by analyzing longitudinal and lateral acceleration/deceleration (A/D) with operating speed of vehicles on different straight roadway sections. Data are collected from five major cities of India using GPS based instrument (Video-VBOX) mounted on five different type of vehicles. The probability distribution of longitudinal A/D and the lateral acceleration are analysed, and their relationship with operating speed of vehicles are studied on roads with a different number of lanes for different vehicle types. A two-term exponential and linear relationship with operating speed are observed for lateral and longitudinal A/D respectively.
... Wang et al. recommended a maximum deceleration level of 3.4 m/s 2 (11.2 ft/s 2 ) for passenger cars approaching stop-controlled intersection. They reported that the 3.4 m/s 2 (11.2 ft/s 2 ) was corresponding to the 92.5th percentile deceleration level at stop-controlled intersections [15]. Another research done at rural stop sign-controlled intersections in southern Michigan to study the factors explaining the variation in the observed deceleration and acceleration rates. ...
The ability to model the driver’s perception reaction time (PRT) and deceleration level is important for signal-timing design. The current state of practice considers PRT and the deceleration level deterministic and uses of constant values for them. The state of practice ignores the differences in PRT and deceleration level between individual drivers approaching the same intersection at the onset of yellow. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model brake PRT and the deceleration level at the onset of a yellow indication for different roadway surface conditions. The paper uses many of the recent stat of art machine learning to train models that can be used to predict the brake PRT and deceleration level of approaching driver.
... Wang et al. recommended a maximum deceleration level of 3.4 m/s 2 (11.2 ft/s 2 ) for passenger cars approaching stop-controlled intersection. They reported that the 3.4 m/s 2 (11.2 ft/s 2 ) was corresponding to the 92.5th percentile deceleration level at stop-controlled intersections [15]. Another research done at rural stop sign-controlled intersections in southern Michigan to study the factors explaining the variation in the observed deceleration and acceleration rates. ...
The ability to model the driver’s perception-reaction time (PRT) and deceleration level is important for signal timing design. The current state of practice considers PRT and the deceleration level deterministic and uses of constant values for them. The state of practice ignores the differences in PRT and deceleration level be-tween individual drivers approaching the same intersection at the onset of yellow.
The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model brake PRT and the deceleration level at the onset of a yellow indication for different roadway surface conditions. The paper uses many of the recent stat of the art machine learning to train models that can be used to predict the brake PRT and deceleration level of approaching driver.
... Accelerations greater than 0.1 g were assumed to be an intentional acceleration by the driver. The deceleration threshold was based on a study by Wang et al. (2005 ) that found the average maximum braking deceleration of vehicles approaching stop sign–controlled intersections to be −2.65 m/s 2 (−0.27 g). The acceleration threshold was taken from another study by Wang et al. (2004) that found the normal average acceleration rate of vehicles from a stopped position to be 1.25 m/s 2 (0.13 g) when traveling straight and 1.16 m/s 2 (0.12 g) when turning. ...
Objective:
Intersection crashes account for over 4,500 fatalities in the United States each year. Intersection Advanced Driver Assistance Systems (I-ADAS) are emerging vehicle-based active safety systems that have the potential to help drivers safely navigate across intersections and prevent intersection crashes and injuries. The performance of an I-ADAS is expected to be highly dependent upon driver evasive maneuvering prior to an intersection crash. Little has been published, however, on the detailed evasive kinematics followed by drivers prior to real-world intersection crashes. The objective of this study was to characterize the frequency, timing, and kinematics of driver evasive maneuvers prior to intersection crashes.
Methods:
Event data recorders (EDRs) downloaded from vehicles involved in intersection crashes were investigated as part of NASS-CDS years 2001 to 2013. A total of 135 EDRs with precrash vehicle speed and braking application were downloaded to investigate evasive braking. A smaller subset of 59 EDRs that collected vehicle yaw rate was additionally analyzed to investigate evasive steering. Each vehicle was assigned to one of 3 precrash movement classifiers (traveling through the intersection, completely stopped, or rolling stop) based on the vehicle's calculated acceleration and observed velocity profile. To ensure that any significant steering input observed was an attempted evasive maneuver, the analysis excluded vehicles at intersections that were turning, driving on a curved road, or performing a lane change. Braking application at the last EDR-recorded time point was assumed to indicate evasive braking. A vehicle yaw rate greater than 4° per second was assumed to indicate an evasive steering maneuver.
Results:
Drivers executed crash avoidance maneuvers in four-fifths of intersection crashes. A more detailed analysis of evasive braking frequency by precrash maneuver revealed that drivers performing complete or rolling stops (61.3%) braked less often than drivers traveling through the intersection without yielding (79.0%). After accounting for uncertainty in the timing of braking and steering data, the median evasive braking time was found to be between 0.5 to 1.5 s prior to impact, and the median initial evasive steering time was found to occur between 0.5 and 0.9 s prior to impact. The median average evasive braking deceleration for all cases was found to be 0.58 g. The median of the maximum evasive vehicle yaw rates was found to be 8.2° per second. Evasive steering direction was found to be most frequently in the direction of travel of the approaching vehicle.
Conclusions:
The majority of drivers involved in intersection crashes were alert enough to perform an evasive action. Most drivers used a combination of steering and braking to avoid a crash. The average driver attempted to steer and brake at approximately the same time prior to the crash.
... In this paper, if both of the leading (n À 2)th and following (nth) vehicle of the inserted vehicle (n À 1)th have negative accelerations, then it is assumed that the inserted vehicle will have a negative acceleration as well and the deceleration value is determined by Table 2. The table shows the relationship between the current vehicle speed (v nÀ1 ) and the average deceleration rate based on empirical data (Wang et al., 2005). If the criteria is not satisfied, then the acceleration rate of the inserted vehicle is set to be zero. ...
The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.
... A study conducted by Brown et al. (2001) found that drivers could avoid a collision in all test conditions if they could react in 1.2 s or less from the onset of a warning. Deceleration (a c ) is the assigned comfortable deceleration of the test vehicle, with the Institute of Transportation Engineers Traffic Engineering Handbook (Kraft 2009) suggesting that deceleration rates up to 3.0 m/s 2 are comfortable for passenger cars (Wang et al. 2005). Safety margin (x off ) is the distance of the closest point of approach between the 2 vehicles. ...
The objective of this research was to examine the effects on driving performance of a prototype intersection collision warning system (ICWS) that is based on the dedicated short-range communication (DSRC) technology concept.
An experimental study with 32 participants using a driving simulator was conducted to examine the effects of ICWS on the driving performance of different drivers sorted by gender (male, female) and experience (experienced, novice) compared across 3 warning conditions: no warning, audio warning, and visual warning. Each participant completed 3 test, drives with each drive comprising 3 events. Each event contained a combination of scenario type (cross-traffic from left/right scenario, right turn against scenario) and warning condition.
The results showed that an ICWS, in general, could reduce the number of intersection crashes by approximately 40 to 50 percent and shorten drivers' reaction times to brake in response to emergent events occurring at intersections. Moreover, drivers' reaction times to brake in response to the audio warning system were significantly shorter than in response to the visual warning system in the cross-traffic from right scenario. In addition, speeds were reduced in all 3 scenarios and deceleration rates increased in the cross-traffic from left and right turn against scenario under the influence of ICWS.
In general, the results provide overall support for ICWSs based on the DSRC technology concept. The broader implications of the research are discussed.
... The preliminary analysis showed two completely different behaviours: acceleration (Fig. 2a) and deceleration. Consistently with Wang et al. (2005), several deceleration patterns are depicted, such as constant deceleration, two-phase constant deceleration, linear decreasing deceleration, and polynomial deceleration. Because of the significant variability between the different deceleration profiles, it seemed appropriate to assume only two deceleration models. ...
Aim of the study was to investigate, by means of a driving simulator experiment, drivers' behaviour in terms of speed, deceleration, and lateral position on major approaches of rural intersections in relation to different perceptual cues. In the experiment, ten different design conditions with and without speed-reducing treatments along the approach to the intersection were tested. Twenty-three drivers drove a test route two times and data from the second drive were used for comparison. The order of the ten design conditions was counterbalanced for all the drivers to minimize the presentation order effect. Three different data analysis techniques were used: (a) cluster analysis of speed and lateral position data, (b) statistical tests of speed and lateral position data, and (c) categorical analysis of deceleration behaviour patterns. The most effective treatments were the dragon teeth markings (based on the principle of optical road narrowing), the colored intersection area (based on the principle of intersection highlighting), and the raised median island (based on the principle of physical road narrowing). These measures, in comparison to the base intersection, produced: (1) a significant speed reduction starting from 250 m before the intersection in the range between 13 and 23 km/h, (2) a significant change in the deceleration behaviour with a reduction in the proportion of drivers which did not decelerate, and (3) a shift away from the intersection of the deceleration beginning. Given the significant effects on drivers' behaviour, the dragon teeth markings, the colored intersection area, and the raised median island are strongly recommended for real world implementation.
Low-speed urban streets are designed to provide both access and mobility and to accommodate multiple road users, such as bicyclists and pedestrians. However, speeds on these facilities often exceed the intended operating speeds, as well as their design speeds. Several studies have indicated that the design speed concept, as implemented in the roadway design process in the United States, does not guarantee a consistent alignment promoting uniform operating speeds less than design speeds. A promising design approach to overcome these apparent shortfalls of the design speed approach is a performance-based design procedure with the incorporation of operating speeds. However, this approach requires a clear understanding of the relationships between operating speeds and various road environments. Although numerous previous studies have developed operating-speed models, most of these studies have concentrated on high-speed, rural two-lane highways. In contrast, highway designers and planners have little information about the influence of low-speed urban street environments on drivers’ speeds. This study investigated the relationship between drivers’ speed choices and their associated low-speed urban roadway environments by analyzing second-by-second in-vehicle global positioning system (GPS) data from more than 200 randomly selected vehicles in the Atlanta, Georgia, area. The authors developed operating-speed models for low-speed urban street segments on the bases of roadway alignment, cross-section characteristics, roadside features, and adjacent land uses. The results of this research effort can help highway designers and planners better understand expected operating speeds when they design and evaluate low-speed urban roadways.
South Korea has the tenth highest greenhouse gas (GHG) emissions worldwide, of which 16% originates from the road sector. Existing estimation methods of road GHG emissions have various limitations, such as low accuracy or the ability to only estimate GHG emissions within a limited area. Therefore, this study aimed to develop a methodology to estimate GHG emissions while considering various geometric designs of roads, including both vertical and horizontal alignment. The developed method is more objective and reliable than existing methodologies that consider only vertical alignment. First, Lamm's theory on travel speed profiles was applied to predict GHG emissions. Then, this study attempted to overcome the limited spatial estimation capacity of existing methods by considering upstream and downstream geometric design parameters simultaneously. Second, this study used the GHG operation mode extracted from the MOtor Vehicle Emission Simulator (MOVES), a modeling system that estimates emissions for mobile sources at the national, county, and project levels for criteria air pollutants, GHGs, and air toxicity. The operation mode includes vehicle type, fuel, and other factors, and is designed to estimate GHG emissions at 1-s intervals. Based on the results of the analysis, the effectiveness of the new method was compared to existing methods using an economic analysis (e.g., cost–benefits from the reduced emissions). This study presents a method for performing sensitive estimations of GHG emissions according to the geometric design of roads, which can be used to collect more accurate data on GHG emissions.
This paper investigates modelling vehicle following using Fuzzy-Neural Network (FNN). Architecture, training sets, and learning rate are manipulated to create 24 combinations of FNN. Generating two sets of weights per combination yields 48 simulations. Acceleration and deceleration profiles from seven electrical tricycles are observed while navigating through the University of the Philippines. A force equation has been applied to simulate vehicular dynamics. Each combination is then subjected to test run simulations to examine vehicular reactions to distance maintenance, velocity matching, and change in applied force to the vehicle. Results show that two 2 hidden layer FN and two NN allow a vehicle to successfully follow a lead vehicle.
The objective of this paper is to model driver intersection approach and traversal trajectories in response to traffic signals and driver behavior based on stopping behavior. This study analyzed 12,688 observations from signalized intersections from the CICAS-V project (1) database. The selected data were subjected to Multivariate Adaptive Regression Splining to develop a model of a typical driver’s velocity on their approach to the intersection based on the vehicle’s proximity to the stop bar (range [m]) and other categorical factors such as vehicle type, time of day, road surface condition, and weather. The resulting models highlight how driver approach speeds vary as a function of range and other factors depending on the signal phase and intended course of action. The models predict the vehicle speed as a function of distance to the stop bar of the intersection (range). The results suggest that the behaviors of red and yellow light runners are difficult to distinguish from each other, but it is possible. These models will be used to calculate vehicle approach speeds in real-world intersection crashes.
To develop a driver assistance system with the goal to increase driving efficiency, we aimed at understanding unassisted driving behaviour. With this knowledge, we will then be able to estimate the potential of the assistance system to support drivers in avoiding unnecessary deceleration and acceleration when approaching traffic lights and to estimate the amount of influence the driver assistance system could have on normal driving. Efficient driving was defined as driving behaviour that leads to reduced fuel consumption and emissions. In a driving simulator experiment with twelve participants and a within-subjects design, drivers approached intersections while the traffic light was either solid green or solid red, or changed from red to green or from green to red during the approach. In addition, we varied whether there was a lead vehicle present and manipulated visibility through the presence or absence of fog. Driving speed, acceleration and pedal usage were analysed and interpreted due to their relation with fuel consumptions and emissions, which is well known from the literature. Participants avoided strong accelerations and decelerations when approaching a solid green traffic light compared to a changing red to green traffic light. Speed was reduced earlier, when the traffic light was solid red compared to when the traffic light changed from green to red. Higher visibility in the non-fog conditions compared to the fog condition was only an advantage in terms of more efficient driving behaviour when the traffic light phase did not change during the approach. The potential for improvements in driving efficiency was higher when drivers were in free driving compared to when following a lead vehicle. We propose that approaching traffic light intersections takes place in three phases: an orientation, a preparation and a realisation phase. A driver assistance system is expected to improve drivers’ anticipation of the driving scene and could recommend efficient driving behaviour in all three phases.
The operating speed profile is a useful tool for diagnosing potential safety issues on existing highways and for alignment design. Unfortunately, existing operating speed profile prediction models do not take into account the presence of intersections along roads because a procedure to estimate a reliable operating speed profile on the road section along which driver speed behavior is affected by an intersection is not currently available. To overcome this limitation, driver speed behavior approaching and departing from at-grade intersections and modern roundabouts was studied and a procedure to predict the operating speed profile along a road section characterized by the presence of an intersection was developed. This simple procedure uses the same construction rules as the existing operating speed profile models in order to be easily integrated into them. The procedure developed predicts a reliable profile in the proximity of at-grade intersections and roundabouts along two-lane rural roads, thereby improving significantly the possibility of applying this useful tool to safety evaluations. DOI: 10.1061/(ASCE)TE.1943-5436.0000471. (C) 2012 American Society of Civil Engineers.
This paper discusses driver deceleration levels in a controlled field environment at the onset of a yellow indication on high-speed signalized intersection approaches using an in-vehicle differential Global Positioning System. The impacts of driver gender, driver age, roadway grade, mean approach speed, platooning scenarios (leading, following, or alone), and time to intersection (TTI) on driver deceleration levels were analyzed. This information is critical for the efficient and safe design of traffic signal clearance timings. The IntelliDrive initiative can gather information about the driver, subject vehicle, and surrounding traffic conditions to execute safe and customizable traffic signal indication change warnings. The results indicate that driver deceleration levels are significantly higher than the 3-m/s(2) deceleration level used in the state-of-the-practice traffic signal design guidelines. The mean deceleration level is 3.6 to 4.1 m/s(2). The results can be used to enhance the design of yellow timings and may be integrated with the new IntelliDrive initiative to provide customizable driver warnings. The results demonstrate that driver deceleration levels are higher at shorter TTIs at the onset of yellow. Drivers are willing to exert deceleration levels in excess of 7 m/s(2) at short TTIs (less than 2.5 s). Furthermore, older drivers (60 years of age or older) employ greater deceleration levels compared with younger (under 40 years old) and middle-aged (between 40 and 59 years old) drivers. A driver following another vehicle that proceeds legally through an intersection without stopping exerts higher deceleration levels than drivers driving alone on a roadway or leading another vehicle, and drivers leading a platoon of vehicles are not affected by vehicles behind them.
The primary objective of this study was to provide guidelines and tools for quantifying the environmental impacts in terms of fuel consumption and emissions of various highway geometric design conditions related to crest vertical curves. Second-by-second speed profiles were generated with speed prediction and polynomial models, and fuel consumption and emission rates based on vehicle-specific power and speed were extracted with the recently developed motor vehicle emission simulator (MOVES) modeling software. The generated speed profiles were matched with the extracted rates and aggregated during a trip on the curves. A benefit cost analysis was also carried out with existing data from Washington State. The results demonstrated that the design vehicle respectively consumed and produced 10% less fuel and carbon dioxide on the crest vertical curve designed with a rate 1.5 times greater than the minimum rate of the vertical curvature documented in the AASHTO Green Book. For other emissions carbon monoxide, oxides of nitrogen, hydrocarbons, and particulate matter of 2.5 mu m or less there were also reductions by up to 31% on the curve. The results also showed that the environmental economic benefits from flattening the curve design exceeded additional construction costs for a 30-year design period. Vertical curve design providing a flattened curvature can be environmentally and economically beneficial throughout the life of the highway. This study shows the efficacy of environmentally friendly design for sustainable transportation.
Majority of intersections in the United States are unsignalized. The total number of crashes at unsignalized intersections is higher than the number of crashes at signalized intersections. While stop signs have been a common safety treatment for unsignalized intersections, they also introduce additional travel delays, fuel consumption and emissions. In some cases, stop signs induce increased safety hazards. Until recently, unsignalized intersections and stop signs have mostly been seen as design problems using traditional traffic engineering approaches. This paper studies the characteristics of stop signs, their unintended impacts, and potential improvements by ITS technologies.
We investigate two causes for the instability of tra‐c ∞ow: The time lag caused by flnite accelerations of the vehicles, and the delay caused by the flnite reaction times of the drivers. Furthermore, we simulate to which degree drivers may compensate for these delays by looking several vehicles ahead and anticipate future tra‐c situations. Since vehicular tra‐c ∞ow is a multi-particle system with many degrees of freedom, two concepts of linear stability have to be considered: Local stability of a car following a leader that drives at constant velocity, and string (chain) stability of a \platoon" of several vehicles following each other. Typically, string stability is a much more restrictive criterion than local stability. We simulate both types of stability with the human driver model (HDM)[M. Treiber et al., Physica A, Vol. 360 (1), 71-88 (2006)], which includes all the features above. We found several remarkable results: (i) with a suitable anticipation, we obtained string stability for reaction times exceeding the "safe time headway", which, to date, has not yet been obtained for any other car-following model; (ii) parameter changes that destabilize the model variant with zero reaction time may stabilize the model with flnite reaction times and vice versa, (iii) distributed reaction times (every driver has a difierent reaction time) can stabilize the system compared to drivers with identical reaction times that are equal to the mean. Keywords: Car-following model, microscopic tra‐c simulation, human reaction
Low-speed urban streets are designed to provide both access and mobility and to accommodate multiple road users, such as bicyclists and pedestrians. However, speeds on these facilities often exceed the intended operating speeds, as well as their design speeds. Several studies have indicated that the design speed concept, as implemented in the roadway design process in the United States, does not guarantee a consistent alignment promoting uniform operating speeds less than design speeds. A promising design approach to overcome these apparent shortfalls of the design speed approach is a performance-based design procedure with the incorporation of operating speeds. However, this approach requires a clear understanding of the relationships between operating speeds and various road environments. Although numerous previous studies have developed operating-speed models, most of these studies have concentrated on high-speed, rural two-lane highways. In contrast, highway designers and planners have little information about the influence of low-speed urban street environments on drivers' speeds. This study investigated the relationship between drivers' speed choices and their associated low-speed urban roadway environments by analyzing second-by-second in-vehicle global positioning system (GPS) data from more than 200 randomly selected vehicles in the Atlanta, Georgia, area. The authors developed operating-speed models for low-speed urban street segments on the bases of roadway alignment, cross-section characteristics, roadside features, and adjacent land uses. The results of this research effort can help highway designers and planners better understand expected operating speeds when they design and evaluate low-speed urban roadways.
This study analyzes field data gathered from 60 test subjects to characterize driver deceleration rates at the onset of a yellow-phase transition on high-speed signalized intersection approaches using an in-vehicle Global Positioning System. The driver rate of deceleration is analyzed for five yellow-phase trigger times to stop line (1.6, 2.7, 3.3, 4.4, and 5.6 s) as drivers approach the intersection at a speed of 72 km/h (45 mph). Results of the study, based on a sample of 821 deceleration events, indicate that driver deceleration rates range between 1.51 and 7.47 m/s(2) (5-24.5 ft/s(2)) with a mean value of 3.27 m/s(2) (10.7 ft/s(2)). Mean deceleration rates varying from 2.2 m/s(2) (7.2 ft/s(2)) for the longest time to stop line (5.6 s) to 5.9 m/s(2) (19.4 ft/s(2)) for the shortest time to stop line (1.6 s) demonstrate that drivers use more time to decelerate if they are sufficiently away from the intersection. Statistical analyses were used to investigate the effects of the time to stop line, gender, age group, and grade on the average deceleration rates. Results demonstrate that male drivers appear to show slightly higher rates of deceleration when compared with female drivers. This difference increases as the trigger time to stop line decreases. Younger drivers (younger than 40 years old) and older drivers (60 years of age or older) exhibit greater deceleration rates when compared with drivers in the 40- to 59-year age group.
We intend to define a continuous car-following model exclusively based on the time gap. A model of the interaction between a vehicle and its predecessor is produced by adjusting the time gap to a targeted safety time that is a function of speed. The model is defined by a differential system, to which a consistent numerical scheme is associated. The parameters of the model are statistically estimated by maximum likelihood. In order to reproduce a heterogeneous traffic flow, vehicles are differentiated by type, and to recreate asymmetric longitudinal behavior, acceleration phases are distinguished from deceleration phases. Introducing a reaction time, inducing a delay in the perception and processing of information about vehicles in interaction, can alter the stability of the flux through appearance of kinematic waves. By simulation, the types and domains of parameters which are asymptotically unstable are identified. The results reveal that, in the model, the statistically estimated parameters form is strongly factor of instability.
When modeling the acceleration and decel- eration of drivers, there are three characteristic time con- stants that influence the dynamics and stability of traffic flow: The reaction time of the drivers, the velocity adap- tation time needed to accelerate to a new desired velocity, and the numerical update time. By means of numerical simulations with a time-continuous car-following model, we investigate how these times interplay with each other and effectively influence the longitudinal instability mech- anisms for a platoon of vehicles. The long-wavelength string instability is mainly driven by the velocity adapta- tion time while short-wavelength local instabilities arise for sufficiently high reaction and update times. Further- more, we investigate the relation between large update time steps and finite reaction times as they both introduce delays in the reaction to the traffic situation. Remarkably, the numerical update time is dynamically equivalent to about half the reaction time, which clarifies the meaning of the time step in models formulated as iterated maps such as the Newell and the Gipps model. Furthermore, with respect to stability, there is an optimal adaptation time as a function of the reaction time.
These models yield the S-shaped speed-time trace indicated by data from driving in real-life traffic conditions, satisfy the realistic conditions of zero jerk (except the two-term sinusoidal model) and zero acceleration at the start and end of the acceleration, and allow for the position and the value of the maximum acceleration to vary for a given average acceleration rate. A comparative evaluation of these three models and the previously known constant and linear-decreasing acceleration models is reported. The evaluation criteria are distance traveled and fuel consumed during acceleration. The performances of the five models are compared under three sets of conditions: acceleration time and distance known, time known but distance unknown, and both time and distance unknown. The comparisons are made separately for central business district (CBD), other urban and nonurban traffic conditions.
The results of a study that monitored driver deceleration behavior on a freeway in New Zealand are presented. A series of axle detectors were placed over a 500-m interval and the speeds were recorded using a data logger. The speeds of the same vehicle at different stations along the road were established for more than 1,200 vehicles. The speed profiles showed that vehicles decelerated over the same distance irrespective of the initial speed. As a result, the deceleration rate was proportional to the initial speed. A relationship was developed to predict the speed at any time as a function of the approach speed.
Vehicles stopping at signalized intersections were examined for the purpose of evaluating the validity of the common assumption of constant and uniform deceleration rates. The data set consisted of the first vehicles to stop upon the onset of the yellow signal interval with measurements of the initial approach speed, deceleration time, and deceleration distance.
The deceleration rate may be computed using only two of the three measured values; thus the rate for each vehicle can be determined by three different equations. With nonuniform deceleration profiles, the equations will produce different values; and the degree of nonuniformity can be determined by comparing the differences in the computed deceleration rates.
The analysis of the field observations indicated that 69 percent of the vehicles demonstrated deceleration profiles associated with nonuniform deceleration rates. Furthermore, the deceleration profile and the degree of nonuniformity were found to be a function of the initial approach speed.