Time Mean Speed Research Articles

Perceived cognitive fatigue has only marginal effects on static balance control in healthy young adults.

We examined the influence of perceived cognitive fatigue on static balance control in healthy young adults to gain greater clarity about this issue than provided in previous research. Based on the prevailing assumption in pertinent literature, we hypothesized that the influence of cognitive fatigue on balance control depends on the attentional effort required by the balance tasks being performed. To test this hypothesis, 44 young adults (24 women and 20 men) were alternately assigned to either the experimental group that was cognitively fatigued (using the 16-min TloadDback-task with individualized settings) or the control group (who watched a documentary). Before and after the intervention, the participants performed six balance tasks that differed in (attentional) control requirements, while recording the center of pressure (COP). From these time series, sway variability, mean speed, and sample entropy were calculated and analyzed statistically. Additionally, perceived cognitive fatigue was assessed using VAS scales. Statistical analyses confirmed that the balance tasks differed in control characteristics and that cognitive fatigue was elevated in the experimental group, but not in the control group. Nevertheless, no significant main effects of cognitive fatigue were found on any of the COP measures of interest, except for some non-robust interaction effects related primarily tosample entropy. These results suggest that, in young adults, postural control in static balance tasks is largely automatic and unaffected by task-induced state fatigue.

Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data

Precise and detailed speed information is indispensable for ensuring safe and efficient transportation. This is particularly true within unstable flow (UF) segments, which are especially prone to accidents due to the significant speed variations between vehicles and across lanes, and in the context of evolving transportation systems, where autonomous and non-autonomous vehicles are increasingly mixing. To address the limitations of existing methods in providing such data, this study aims to improve the detail, accuracy, and granularity of road information for micro-segments by leveraging individual vehicle big data. The proposed approach utilizes the geohash algorithm for spatial segmentation and introduces a novel criterion for identifying UF segments based on the relationship between space mean speed (SMS) and time mean speed (TMS). The presented strategy was validated through a comprehensive analysis of DTG (Digital Tachograph) data from freight vehicles on Expressway No. 50 in the Gyeonggi region in the Republic of Korea. As a result, a total of 301 segments were identified, including 178 eastbound and 123 westbound segments. UF segments corresponded to partitions falling beyond the reference standard deviation range. Compared with VDS (Vehicle Detection System) and conzone speeds, the proposed method provided more precise and continuous speed information, surpassing those obtained from conventional link-based approaches.

Critical Analysis of Road Side Friction on an Urban Arterial Road

This study reviews the impact of roadside friction on urban arterial roads in the published literature and aims to identify the various side frictional parameters that impact urban arterial roads. The side frictional parameters are non-motorized vehicles, pedestrian crossing or moving along the road, street vendors, on-street parking, animal movement, and land-use activity. The impact of all these parameters on traffic performance leads to a reduction in capacity, speed (instantaneous speed, journey speed, travel speed, time mean speed, and space mean speed), level of service, delay, travel time, and travel cost. This study considered online published studies from 1995 to 2022. The available effective width and the percentage reduction in road capacity are related. Road capacity is reduced by 3.37% when the effective width is reduced by 2.95%, while road capacity is reduced by 26.08% when the effective width is reduced by 21.81%.

Traffic impact on last mile parcel delivery with cargo bikes

In the course of global warming, low-emission last mile delivery concepts are being discussed as alternatives to conventional concepts. Complementing the logistical perspective, this study evaluates the impact of surrounding traffic on parcel delivery with cargo bikes. We propose a scenario-based methodological approach including an agent-based, multimodal simulation analysis. The approach includes the identification of infrastructural potential for cargo bike route shortcuts as well as effects of provider-joint collaboration. Key findings from a use case in the city of Hamburg, Germany, show similar traffic impact on delivery with cargo bikes and vans according to simulation KPIs such as time loss and mean speed. However, significantly lower CO2 emissions for cargo bike scenarios are observed as well as considerable synergy effects in collaboration scenarios.

Optimization of Dynamic Speed-Homogeneous Space Using Location-Based Vehicle Data

Road extent compared with the number of automobiles registered in South Korea is the lowest among OECD (Organization for Economic Cooperation and Development) countries. Currently, traffic information is provided based on nodes and links using the centerlines of the roads. However, detailed speed information is unavailable. This study presents an optimization method for space to represent a dynamic speed-homogeneous space. Vehicle directions were separated by route using azimuth data based on global navigation satellite system (GNSS) location information, and the time mean speed and space mean speed were used to determine the homogeneous space of vehicle speed through the gradual separation of space. The processing speed of large-capacity data was increased by using Geohash, which applied a quaternary basis segmentation method that split data into two directions of longitude and latitude. The numbers of homogeneous speed spaces along the two directions of the Gyeongbu Expressway were determined to be 127 and 170, respectively, by the 12-step segmentation method. Spaces not included in the range of homogeneous speed spaces amounted to 5.19% and 7.51%, respectively, along the northbound and southbound roadways of the Gyeongbu Expressway. These non-convergence spaces were analyzed by the influence of vehicles entering or exiting slowly from the merging and diverging areas of the freeway junctions and interchanges. In other words, these were analyzed by the difference of speed by lane and not the driving direction.

Analysis of the Effect of Pavement Conditions on Vehicle Speed

The Jakarta Outer Ring Road (JORR) Toll Road is a logistics route that acts as an essential transportation artery to facilitate logistics distribution. This toll road connects strategic locations to support logistics distribution. It can lead to the repetition of traffic loads, causing a decrease in the pavement quality marked by road damage. Road damage that occurs can reduce the level of safety and comfort of road users. To minimize accidents due to road damage, road users will drive their vehicles at lower speeds in locations/segments that have damage. In the study, the level of road damage was analyzed using the Pavement Condition Index (PCI) method. Vehicle speed using the time mean speed method and then statistically analyzed using linear regression using SPSS v.26 software to determine how much influence road pavement conditions have on vehicle speed. Based on the results of the analysis on the Jakarta Outer Ring Road (JORR) KM 35+000 – 45+000 toll road, the pavement conditions of the toll road are in the good category with various vehicle speeds for each group with a range of 44.80 km/hour to 79,98 km/hour and the effect of pavement conditions on vehicle speed is 51.6% for class 1 vehicle, 58,1% for class 2 vehicle, 42,2% for class 3 vehicle, 42,9% for class 4 vehicle, 44,8% for class 5 vehicle.

Novel Vehicle Detection in Real Time Road Traffic Density Using Haar Cascade Comparing with KNN Algorithm based on Accuracy and Time Mean Speed

Aim: The main objective of the paper is to detect objects in iconic real time traffic density videos from CCTVs and Cameras using Haar Cascade algorithm and to compare algorithms with K-Nearest Neighbour algorithm (KNN). In this case we tried improving the rate of accuracy in predicting the traffic density. Materials and methods: Haar Cascade algorithm is applied on 5 realistic videos and which consists of more than 250 frames. For the same we evaluated the Accuracy and Precision values. Harr-like function displays the vehicle’s visual structure, and the AdaBoost machine learning algorithm was used to create a classifier by combining individual classifiers. The significance value achieved for finding the accuracy and precision was 0.445 and 0.754 respectively. Results and Discussions: Detection of vehicles in high speed videos is performed by using Haar Cascade which has mean accuracy with 85.22% and mean precision with 90.63% and 60% of mean accuracy and 58.53% mean precision in KNN classifiers. Conclusion: The performance of the Haar Cascade appears to be better than KNN in terms of both Accuracy and Precision.

Modelling and analysis of an urban road traffic noise pollution: Case study of an interrupted flow traffic noise at a signalized intersection

This research aimed at modeling interrupted flow traffic noise at a signalized intersection. In this study, a new approach has been used with SPSS and Arc GIS with urban road traffic noise. The measured parameters of noise include noise equivalent level, volume of light vehicles, heavy vehicles and time mean speed. The results showed that the Spearman correlation coefficient between the interface and the level of the noise at morning, evening and total time were 0.241, 0.35 and 0.45. The spearman correlation coefficient between the interface of the buildings and the intersection of all types of vehicles passing through the morning, evening and total time equal to 0.534, 0.328 and 0.409. The results showed that noise level can be predicted through the speed variables of the vehicles, the volume of vehicles passing heavy and the use of land use of buildings. According to the results and the noise maps for the modeling noise of the estimated coefficients from the equation, it can be found that in building highway in urban, closer look on internationally acting Green Building Labels and noise protection is seen as an important part of the social sustainability aspects of a building.

Using Artificial Potential Field Theory for a Cooperative Control Model in a Connected and Automated Vehicles Environment

Artificial potential field (APF) theory has been extensively applied in traffic path planning as an efficient method to avoid collision. However, studies in collision avoidance based on APF theory only considered the movement of single vehicle. In this paper, a vehicle cooperative control model for avoiding collision in the connected and autonomous vehicles (CAVs) environment is presented, using APF theory. The proposed model not merely guarantees the travel safety of vehicles in avoiding collision, but also promotes driving comfort and improves traffic efficiency. To verify the cooperative control model, simulations of four scenarios are designed and compared with the human driving environment. Five indicators are selected to evaluate the results, that is, time–space diagram, time mean speed (TMS), the rate of large deceleration time (large deceleration is that deceleration larger than –2 m/s2), the inverse time-to-collision ([Formula: see text]), and lane-changing times. According to the simulation results, the cooperative control model could alleviate the capacity drop and increase the TMS to improve traffic efficiency, reduce the rate of the large deceleration time to promote driving comfort, and decrease [Formula: see text] to promote safety in small and large input flow rates. The results reveal the proposed model is significantly superior to the human driving environment whether in free or congested situations, except for the lane-change times, which are slightly larger.

Evaluation of Effectiveness of Speed Reduction Markings on Driving Speed in Highway Tunnel Entrance and Exit Areas

Tunnels are critical areas for highway safety because the severity of crashes in tunnels tends to be more serious. Controlling vehicle speed is regarded as a feasible measure to reduce the accident rate in the tunnel entrance and exit areas. This paper aims to evaluate the effectiveness of three types of speed reduction markings (SRMs) in tunnel entrance and exit zones by conducting a driving simulation experiment. For this study, 25 drivers completed the driving tasks in the day and night scenarios. The vehicle speed and acceleration data were collected for analysing and the relative speed contrast, time mean speed and acceleration were adopted as indices to evaluate the effectiveness of SRMs. The repeated ANOVA test results revealed that SRMs have a significant effect in reducing vehicle speed, especially in the exit zone. Colour Anti-skid Markings (CASMs) produced a more obvious deceleration in the entrance zone. In the entrance zone, a similar downward trend was performed in the situation of NSRMs and SRMs, but a lower speed occurred in case of SRMs. Besides, CASMs work better and cause an obvious gap of 10 km/h in daytime and 5 km/h at night compared to the speed without SRMs. In the exit zone, the present study supports the conclusion that the drivers are prone to accelerate. Our results showed that the drivers accelerated in case of NSRMs, while they slowed down in case of SRMs. Thus, SRMs are necessarily implemented in the highway tunnel entrance and exit zones. Our study also indicates that though CASMs result in lower speed at night, the Transverse Speed Reduction Markings(TSRMs) have a better performance than CASMs in daytime. The investigation provides essential information for developing a new marking design criterion and intelligent driver support systems in the highway tunnel zones.

Projected Changes in European and North Atlantic Seasonal Wind Climate Derived from CMIP5 Simulations

Abstract Future changes in geostrophic winds over Europe and the North Atlantic region were studied utilizing output data from 21 CMIP5 global climate models (GCMs). Changes in temporal means, extremes, and the joint distribution of speed and direction were considered. In concordance with previous research, the time mean and extreme scalar wind speeds do not change pronouncedly in response to the projected climate change; some degree of weakening occurs in the majority of the domain. Nevertheless, substantial changes in high wind speeds are identified when studying the geostrophic winds from different directions separately. In particular, in northern Europe in autumn and in parts of northwestern Europe in winter, the frequency of strong westerly winds is projected to increase by up to 50%. Concurrently, easterly winds become less common. In addition, we evaluated the potential of the GCMs to simulate changes in the near-surface true wind speeds. In ocean areas, changes in the true and geostrophic winds are mainly consistent and the emerging differences can be explained (e.g., by the retreat of Arctic sea ice). Conversely, in several GCMs the continental wind speed response proved to be predominantly determined by fairly arbitrary changes in the surface properties rather than by changes in the atmospheric circulation. Accordingly, true wind projections derived directly from the model output should be treated with caution since they do not necessarily reflect the actual atmospheric response to global warming.

Evaluation of the Traffic Performance Measure for Exclusive Motorcycle Lane at Merging Section

Exclusive Motorcycle Lane (EML) is defined as a roadway meant exclusively for motorcycles or can be stated as motorcyclists who are compelled by law to use it and other vehicles are prohibited by law from using it. It is physically separated from the mainstreams carriageway and is grade separated from the main carriageway at intersections, interchanges or points of conflicts. Merging is one of the parts of way that motorcycle deceleration the speed from main stream lane to enter the Exclusive Motorcycle Lane (EML) with safe. The urban expressways in Malaysia have serious problems regarding the safety, efficiency and comfort ability at merging sections of an EML because of the insufficient geometric due to limited land and the right of way design configuration condition. The increase of traffic volume and demand lead towards the degradation of motorcyclist performance while on their wheels. This research is performed in order to achieve the objectives, i.e. to obtain and analyze local empirical data, such as flow rate, headway and speed on exclusive motorcycle lane at merging section that reflects on the local traffic condition on Federal Highway Route 2, and to obtainregression modeling that relates to Flow Rate with regards to the Headway 1-3, Time Mean Speed and Length of Merging Ramp section, Lm on the merging section of EML and merging ramp section. Tworegression models have successfully been performed with R-Sq 94.8 % for EML section and 31.1 % for merging ramp section. Its also highlighted the attributes of EML.

Analysis of Speed Differences in Data from Remote Traffic Microwave Sensors and Floating Car Data Systems on Expressways

The remote traffic microwave sensor (RTMS) and the floating car data (FCD) system are two important sources of traffic data; both provide key speed information. However, these two data processes gather data differently. The RTMS detects spot speeds at specific cross sections. The FCD system collects travel speed along a segment of a road link. Although the difference between time mean speed (TMS) and space mean speed (SMS) has been discussed for decades, the speed differences between RTMS and FCD have been underestimated in many engineering applications. This study examined the speed differences between the RTMS and FCD data on expressways in Beijing. First, the differences between the two data collections over 5 days were analyzed. The correlation between the difference and the value of the speeds was investigated. The relationships between TMS and SMS in existing studies were then compared with the relationship derived from the field data. It was found that the existing relationships between TMS and SMS were not valid for representing the relationship between the RTMS and FCD speeds. The flow–speed relationship from each data group was then investigated by using the Van Aerde traffic flow model; it was found that free-flow speed and speed at capacity determined on the basis of the RTMS data were significantly overestimated. It was inaccurate to apply the RTMS speed to the analysis of fundamental traffic flow diagrams. Finally, the repeatability and stability of the relationship between these two data groups were validated.

Estimating Macroscopic Volume Delay Functions with the Traffic Density Derived from Measured Speeds and Flows

This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit fromR2of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations.

Comparison of the Methodologies for Calculating Expressway Space Mean Speed Using Vehicular Trajectory Information from a Radar Detector

This study was initiated to evaluate the performance of methodologies to estimate the space mean speed(SMS) using the time mean speed(TMS) which was collected from the vehicle detection system(VDS) in expressways. To this end, the methodologies presented in prior studies were firstly summarized. It is very hard to achieve exact SMSs and TMSs due to mechanical and communication errors in the field. Thus, a microscopic traffic simulation model was utilized to evaluated the performance. As a result, the harmonic mean and volume-distance weighted harmonic mean were close to the SMS in the case in which the TMSs of individual vehicles were used. However, when the 30-second-interval aggregated TMS were used, the volume-distance weighted harmonic mean was outstanding. In this study, a radar detector was installed in the Joongbu expressway to collect the SMS. The trajectory of individual vehicles collected from the detector were used to calculate the SMS, which was compared with the estimates using other methodologies selected in this study. As a result, the volume-distance weighted mean was turned out to be close to the SMS. However, as the congestion becomes severe. the deviation between the two speed becomes bigger.

Application of Data Mining Techniques for Traffic Density Estimation and Prediction

Advanced Traveller Information Systems (ATIS) is one of the functional areas of Intelligent Transportation Systems (ITS) and it aims at providing real time traffic information to the travellers for making better travel decisions. This information would be most effective if provided to travellers during or before the start of their trip. Therefore, accurate prediction models are required in ATIS for conveying reliable information about the future state of traffic. Different methods used for the prediction of traffic parameters include historic averaging, regression analysis, Kalman filtering, time series analysis, machine learning, etc. The objective of this research is to explore the use of automated sensor data and data driven techniques for traffic state prediction under Indian traffic conditions. Travel time and traffic density (as an indicator of congestion) are used commonly to inform users about the state of a traffic system. However, these two parameters are spatial in nature and direct measurement from field is difficult. Therefore, estimation of these parameters from location based data is a challenge in many of the ITS implementations. The present study addresses the problem of estimation of traffic density with the help of location based sensors which are capable of measuring parameters such as volume and Time Mean Speed (TMS). Machine learning techniques namely, k-Nearest Neighbour (k-NN) and Artificial Neural Network (ANN) are selected as the estimation and prediction tool in this study, based on acceptable performance of the same in earlier studies.

AN ALTERNATIVE METHOD OF ESTIMATING SPACE-MEAN SPEED

Average travelling speed usually applied in the traffic analysis as required in the fundamental traffic model. As one type of speed under the space-mean speed, most of the researchers have derived the equation to estimate the space-mean speed. There are a few factors that influence the speed changes along the segment. The fluctuate speed along the segment give an idea to average the spot mean speed from at least two points in order to estimate the space-mean speed. This paper explores an alternative method on how to convert the time-mean speed to space-mean speed that can represent the actual operating speed along segment using the average technique. A total of 18 data sets of time-mean speed and space-mean speed in six segments of uninterrupted two-lane single carriageway road were collected using automatic traffic counter and moving car observer, respectively. Two sets of automatic traffic counter were installed along the segment to record the spot speed and then converted to the time-mean speed, while the moving car observer were conducted concurrently to get the space-mean speed. The data sets were analysed using linear regression analysis. The results of summary of model show a strong relationship with coefficient of determination, R2=0.808, while ANOVA test results show the coefficient of constant and time-mean speed is not zero with p-value is less than 0.05 at confident level of 95%. The error obtained from this alternative equation are lesser than the established equation and it recommends that the alternative equation is acceptable.

Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model

In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.

A simple algorithm for the estimation of road traffic space mean speeds from data available to most management centres

The control of the evolution of road traffic streams is highly related to productivity, safety, sustainability and, even, comfort. Although, nowadays, the findings from research efforts and the development of new technologies enable accurate traffic forecasts in almost any conditions, these calculations are usually limited by the data and the equipment available. Most traffic management centres depend on the data provided, at best, by double-loop detectors. These loops supply time means over different aggregation periods, which are indiscriminately used as the bases for subsequent estimations. Since space mean speeds are those needed in most applications (note the fundamental relationship between flow and density in traffic flow theory), most current practice begins with an error. This paper introduces a simple algorithm that the allows estimation of space mean speeds from the data provided by the loops without the need for any additional financial outlay, as long as the traffic in each time interval of aggregation is stationary and its speed distribution is log-normal. Specifically, it is focused on the calculation of the variance of the speeds with regard to the time mean, thus making possible to use the relationship between time mean speeds and space mean speeds defined by Rakha (2005). The results obtained with real data show that the algorithm behaves well if the calculation conditions help fulfil the initial hypotheses. The primary difficulties arise with transient traffic and, in this case, other specific methodologies should be used. Data fusion seems promising in this regard. Nevertheless, it cannot be denied that the improvement provided by the algorithm turns out to be highly beneficial both when used alone in the case of stationarity or as a part of a fusion.

Tidal stream characteristics on the coast of Cape Fuguei in northwestern Taiwan for a potential power generation site

Sites of high tidal current power potential are mostly found at narrow tidal passages or channels. Not many coastal sites with unbounded ocean with high tidal current speed have been reported. The coastal sea near the Cape Fuguei in northwestern Taiwan is such a site with a high tidal power potential. The purpose of this study is to investigate the tidal current speed at this location for assessing the tidal current power potential. In-situ and remote sensing instruments were deployed simultaneously to measure the temporal and spatial distributions of tidal currents. An Acoustic Doppler Current Profiler (ADCP) was deployed at a 26m depth site and an X-band marine radar was installed at a coastal building. Two measurements lasting over one month were done. Both instruments showed the maximum current speed at the surface was more than 3.5m/s in the measurement period. ADCP measurements showed that the time mean speed in the upper layer (−1.7 to −6m) was 1.5m/s. The current speed decreased with depth slightly. It was also found from both ADCP and radar measurements that in the surface layer, the probability of duration for current speed exceeding 1m/s was more than 70%. In addition, radar measurement showed that the average surface current speed at the area from 2km offshore out to 4km (radar offshore limit) was more than 1m/s. The size of this area is more than 15km2. Our investigations indicated that this area has a high tidal power potential for current turbine deployments.