Vehicle Speed Data Research Articles

Assessing the accuracy and reliability of freeway traffic monitoring stations using GPS probe vehicle speed data

Freeway performance measures are extracted from Traffic Monitoring Stations (TMS) which gather traffic volume and speed data. Reported freeway speeds are usually aggregated over periods of 20 or 30-second or 1, 5, or 15-minute by traffic agencies. This paper examines the accuracy and reliability of reported TMS detector speeds. The study compares speeds collected by GPS-equipped probe vehicles with aggregated detector speeds. The paper evaluates how various aggregation intervals impact accuracy and reliability of TMS detector speeds and determines the optimal aggregation interval. Results indicate that shorter aggregation intervals are appropriate for high variability traffic conditions during peak periods.

KIDDS – kids involved in driving down speed

Kids involved in driving down speed (KIDDS) represents an innovative and creative partnership approach to addressing road safety issues around local primary schools. In 2007, representatives from local road safety...

Diesel-engined vehicle nitric oxide and soot emissions during the European light-duty driving cycle using a transient mapping approach

An engine mapping-based methodology is developed to gain a first approximation of a vehicle’s performance and emissions during a light-duty cycle. The procedure is based on a steady-state experimental investigation of the engine with an appropriate vehicle drivetrain model applied so that the cycle vehicle speed data can be transformed into engine speed and torque. Correction analysis is then applied based on transient experimentation to account for the transient discrepancies during real driving. The developed algorithm is applied for the case of a diesel-engined vehicle running on the European light-duty cycle. A comparative analysis is performed for each section of the cycle revealing its individual transient characteristics.

Bayesian mixture modeling approach to account for heterogeneity in speed data

Speed is one of the most important parameters describing the condition of the traffic flow. Many analytical models related to traffic flow either produce speed as a performance measure, or use speed to determine other measures such as travel time, delay, and the level of service. Mathematical models or distributions used to describe speed characteristics are very useful, especially when they are utilized in the context of simulation and theoretical derivations. Traditionally, normal, log–normal and composite distributions have been the usual mathematical distributions to characterize speed data. These traditional distributions, however, often fail to produce an adequate goodness-of-fit when the empirical distribution of speed data exhibits bimodality (or multimodality), skewness, or excess kurtosis (peakness). This often occurs when the speed data are generated from several different sub-populations, for example, mixed traffic flow conditions or mixed vehicle compositions. The traditional modeling approach also lacks the ability to explain the underlying factors that lead to different speed distribution curves. The objective of this paper is to explore the applicability of the finite mixture of normal (Gaussian) distributions to capture the heterogeneity in vehicle speed data, and thereby explaining the aforementioned special characteristics. For the parameter estimation, Bayesian estimation method via Markov Chain Monte Carlo (MCMC) sampling is adopted. The field data collected on IH-35 in Texas is used to evaluate the proposed models. The results of this study show that the finite mixture of normal distributions can very effectively describe the heterogeneous speed data, and provide richer information usually not available from the traditional models. The finite mixture modeling produces an excellent fit to the multimodal speed distribution curve. Moreover, the causes of different speed distributions can be identified through investigating the components.

Traffic Monitoring of Motorcycles during Special Events Using Video Detection

Because of a recent federal initiative, states are required to submit motorcycle vehicle miles traveled data to the FHWA. Data are needed to obtain better counts of motorcycles to evaluate their impact on crashes and traffic flow, but there is concern about the quality of data. Many states have identified problems with automatic traffic recorders accounting for motorcycle traffic. Existing sensors exhibit difficulties counting motorcycles that travel side by side or close behind each other, they have difficulty distinguishing larger motorcycles from passenger vehicles, and magnetic counters in particular do not sense motorcycles that do not pass over or travel close enough to sensors. Alternatively, some states conduct manual classification counts, which are labor intensive and lead to sparse data. Because classification counts are frequently conducted during the week, they do not capture weekend numbers. This paper evaluates a video-based traffic monitoring system that was developed at Clemson University and can classify vehicles, including motorcycles. The processor uses vehicle tracking, rather than virtual detection, to collect vehicle count, speed, and classification data. An algorithm calculates a motorcycle's length, width, and height through a series of frames. The system is evaluated by using traffic data for more than 2,000 motorcycles, collected at two locations in Myrtle Beach, South Carolina, during a motorcycle rally. The difference between actual and system motorcycle counts ranged from 0.6% to just over 6%, depending on direction and location. The difference for all vehicles ranged from 0.25% to 3.6%. While the system successfully classifies motorcycles traveling in close pairs and in small groups, it experienced difficulty in cases of severe occlusion.

GPS-based speed collection method for road traffic noise mapping

One of the most difficult and expensive tasks in making noise pollution maps is the collection and processing of the data needed to create acoustic models. In the case of road traffic noise maps, obtaining speed data for light and heavy vehicles a problem that has usually been avoided by using a road’s speed limit or by making assumptions based on experience from similar road types. Here global positioning systems-based techniques are applied for acquiring vehicle speed data and adapted to fulfill the requirements of noise prediction models.

Development of New Performance Measure for Winter Maintenance by Using Vehicle Speed Data

The study investigated vehicle speed changes during winter weather events. A total of 954 winter maintenance logs in 24 counties over three winter seasons were analyzed. Several variables of interest were developed, such as storm duration, maintenance operation hour, crew delayed, maximum speed reduction, and the storm start and end times. Also, t1, which represents the start of vehicle speed reduction, and t3, the time at which vehicle speeds recover to normal winter driving speeds, were measured from automatic traffic recorder data. The study confirms that vehicle speed seems to be a good measure of representing driving conditions during winter weather events and winter maintenance performance alike. Speed recovery duration was found to be a dependent variable, defined as a possible evaluation of winter maintenance operations using vehicle speed data.

The dynamic response of the Berwick-upon-Tweed bypass bridge

The Al trunk road bridge over the River Tweed upstream from Berwick-upon-Tweed was monitored as part of the routine condition monitoring strategy. This paper presents details of the principal inspection report, traffic count, vehicle speed, and axle weight data derived from in situ induction loops and weigh-in-motion sensors, followed by data arising from the non-destructive testing of the bridge. The NDT used the random dynamic traffic loading to excite the bridge's natural frequency response, which was detected by accelerometers mounted under the deck. Modal analysis was then used to draw conclusions about the amplitudes, frequencies and modes of vibration of the bridge, especially with respect to the effect of the bearings and the expansion joints which were potentially problematic and as such, would be subject to rigorous inspection. Data presented here established a baseline case for future condition monitoring. The main theme of the research was to use modal analysis to determine the bridge's dynamic response.

Development of a practical driving cycle construction methodology: A case study in Hong Kong

A practical methodology for constructing a representative driving cycle reflecting the real-world driving conditions is developed for vehicle emissions testing and estimation. The methodology tackles three major tasks, i.e., data collection, route selection and cycle construction. Both car chasing and on-board measurement techniques were employed to collect vehicle speed data. Route selection was based on the records of average annual daily traffic of the road network between major residential areas and commercial/industrial areas. A variety of parameters were employed as the target statistics characterising the driving pattern in the construction of driving cycles. The performance value and speed-acceleration probability distribution were utilised to determine the best synthesised driving cycle. The method is easy to follow and the driving cycles are comparative to other renounced cycles.

Traffic signal phasing at intersections to improve safety for alcohol-affected pedestrians

Alcohol-affected pedestrians are among the highest-risk groups involved in pedestrian casualty crashes. This paper investigates the opportunities to use a modified form of traffic signal operation during high-risk periods and at high-risk locations to reduce alcohol-affected pedestrian crashes and the severity of injuries that might otherwise occur. The ‘Dwell-on-Red’ treatment involves displaying a red traffic signal to all vehicle directions during periods when no vehicular traffic is detected, so that drivers approach high-risk intersections at a lower speed than if a green signal were displayed. Vehicle speed data were collected before and after treatment activation at both a control and treatment site. Speed data were collected both 30 m prior to and at the intersection stop line. The treatment was associated with a reduction in mean vehicle speeds of 3.9 kph (9%) and 11.0 kph (28%) at 30 m and stop line collection points, respectively, and substantial reductions in the proportion of vehicles travelling at threatening speeds with regard to the severity of pedestrian injury. Other important road safety concerns may also benefit from this form of traffic signal modification, and it is recommended that other areas of application be explored, including the other severe trauma categories typically concentrated around signalised intersections.

Automatic Speed Calibration Methodology for Traffic Monitoring Sites

Traffic monitoring sites are installed in highways to collect speed, volume, and classification data to support various planning and intelligent transportation system applications. There is minimal research that addresses speed accuracy and methods to evaluate it without manually collecting vehicle speed data. Accurate speeds are important because the measured speed is used to calculate axle spacing distances and determine the vehicle classification. If the speed measurements are not accurate, the axle spacing distances may not conform to expected values making it difficult to classify the vehicle. This paper identifies values for the drive tandem axle spacing on Federal Highway Administration (FHwA) Scheme F Class 9 vehicles based on truck-manufacturer sales data. A relationship between the drive tandem axle spacing and speed calibration accuracy is verified by collecting and analyzing data from two weigh-in-motion (WIM) sites in Indiana. Based on this relationship, a traffic monitoring lane can be autom...

VEHICLE-DRIVER BEHAVIOURAL-RESPONSES DEFINED BY RADIO-NAVIGATION SYSTEM GPS

From the very beginning of its usage the navigation systemGPS has been applied in studying the cases of the car followingbehaviour of two-vehicle platoons. The navigation receiver isused for vehicle position and speed data collections. The measurementaccuracy is to be improved by the real-time dGPScorrection-data or by additional data post-processing. For additionalmeasurement of the vehicle-to-vehicle separation, bothvehicles should be equipped with a GPS receiver. The vehicle-to-vehicle separation is calculated later on, during the datapost-processing.This paper describes the choice of the experimental setup,on the basis of the radio-navigation system GPS, which may beused for recording the data on location, speed and time. Thepresented measurement results have been attained in two steps:first, the data were collected, through the process of tracking oftwo-vehicle platoons in real life traffic-flow, and then processed.The selected mathematical model is based on the PIDcontroller in the closed-loop control system. It enables evaluationof the measurement results and assessment of the behaviouralelements of each driver.

Method for Identifying Vehicle Movements for Analysis of Field Operational Test Data

The independent evaluation of a roadway-departure crash-warning system will involve identifying and classifying conflicts and near-collisions from a vast quantity of field data. The classification of these events includes identifying vehicle movements such as negotiating a curve, making a turn, changing lanes, and merging. A unified algorithm that relies solely on vehicle kinematic data is developed for identifying such movements. The method works reliably for various types of road surfaces, road types, speeds, and conditions. The algorithm relies on metrics such as heading angle, lateral position, radius of curvature, and peak yaw rate—calculated from yaw rate and vehicle speed data—to classify vehicle-movement events from a large file of vehicle data. Tuning parameters for the algorithm, set both analytically and empirically, are provided. The results of the tests show that the algorithm makes correct identifications of vehicle movement about 80% of the time. The highest success is for turns, followed by curves and lane changes. Although these numbers improve on previous methods, there is additional room for improvement that gives impetus for future work.

Collecting Vehicle-Speed Data by Using Time-Lapse Video Recording Equipment

An innovative application of time-lapse video recording is used to assist in an evaluation of a highway safety improvement. The improvement is an icy-curve warning system near Fredonyer Summit in northern California that activates real-time motorist warnings via extinguishable message signs, based on weather readings collected from road weather information systems. A measure of effectiveness is whether motorist speed is reduced as a result of real-time warnings to drivers. Why indirect speed measurement with video was preferred over radar for this case is discussed, as is how specific methodological issues related to the custom-built equipment, including camera location and orientation, distance benchmarking, and data collection and reduction. Theoretical and empirical accuracy measurements show that the video surveillance trailers yield results comparable to radar and, hence, would be applicable for studies in which speed change is measured. Because this particular technology had not been used previously, several lessons are documented that may help determine where and how similar equipment may be optimally used in future studies.

Speed Behavior Analysis at Curved Ramp Sections of Minor Interchanges

On and off ramps act as interfaces between freeways and the surface street network (nonfreeway highways) and in effect should provide a smooth and safe transition from one facility to another. The speed behavior of passenger cars at minor-interchange on and off ramps with unusual curved arrangements is analyzed in depth for the first time. Vehicle speed data were collected at 20 on and off ramps in Greece that have a variety of geometric features. Regression analyses were applied to investigate the variables affecting operating speed— V85. The best models resulted in coefficients of determination of nearly 0.75, indicating that operating speed is influenced by curvature change rate and superelevation rate. Comparison of the “Greek speed profile” with those of Australia, Austria, Germany, South Africa, and the United States proved that for on and off ramps it very closely approximates the values of Australia, Austria, and Germany. Austria is the only country using V85 to determine ramp geometrics. The differences between the Greek V85 and the speed values recommended by the German guidelines are considered small, ranging from 6 to 9 km/h for on ramps and −1.5 up to −4 km/h for off ramps. The analysis produced regression models that will provide designers with a tool to estimate the operating speed during the design process.

A Framework for the Evaluation of System Safety Benefits of Intelligent Cruise Control Systems

ABSTRACT This paper describes a framework for assessing the system safety impacts of an adaptive or intelligent Cruise Control (ICC) system. This framework incorporates a time-to-collision model that is developed from vehicle headway, speed, and acceleration data that are typically gathered as part of field operational tests. Specifically, the framework combines car-following models with vehicle deceleration and acceleration constraints to compute instantaneous rear-end time-to-collision estimates. These instantaneous time-to-collision estimates are then translated into an accident risk estimate that is accumulated over the entire trip. The proposed approach can be applied directly to field data that are typically gathered as part of field operational tests. In addition, the proposed approach can be easily incorporated within microscopic traffic simulation models