Journal articles on the topic 'Hourly Traffic Volume'
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1
Sharma, Satish C., Jin Y. Oh, and Jon J. Wyatt. "Estimation of design hourly volume from seasonal traffic counts." Canadian Journal of Civil Engineering 14, no. 6 (December 1, 1987): 728–31. http://dx.doi.org/10.1139/l87-110.
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By considering the thirtieth highest hourly volume (30HV) as the design hour volume, this study reexamines a commonly used method of predicting the 30HV as a function of the annual average daily traffice (AADT) volume. Based on Alberta's highway system data, some common limitations of the traditional 30HV–AADT model are pointed out. Also included in the analysis presented is a proposed alternative model which utilizes July/August traffic data to estimate the design hour volume. The alternative model appears to provide more accurate prediction results and it also eliminates the need of subjectively classifying the roads into various groups as required by the 30HV–AADT method. Key words: annual average daily traffice, design hour volume, highway design, road classification, traffic volume counts, transportation.
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Oh, Ju Sam, Tae Young Heo, and Jin Ki Eom. "Hourly traffic volume estimation: radio frequency identification application." World Review of Intermodal Transportation Research 2, no. 2/3 (2009): 187. http://dx.doi.org/10.1504/writr.2009.023306.
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Shafique, Muhammad Awais. "Imputing Missing Data in Hourly Traffic Counts." Sensors 22, no. 24 (December 15, 2022): 9876. http://dx.doi.org/10.3390/s22249876.
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Hourly traffic volumes, collected by automatic traffic recorders (ATRs), are of paramount importance since they are used to calculate average annual daily traffic (AADT) and design hourly volume (DHV). Hence, it is necessary to ensure the quality of the collected data. Unfortunately, ATRs malfunction occasionally, resulting in missing data, as well as unreliable counts. This naturally has an impact on the accuracy of the key parameters derived from the hourly counts. This study aims to solve this problem. ATR data from New South Wales, Australia was screened for irregularities and invalid entries. A total of 25% of the reliable data was randomly selected to test thirteen different imputation methods. Two scenarios for data omission, i.e., 25% and 100%, were analyzed. Results indicated that missForest outperformed other imputation methods; hence, it was used to impute the actual missing data to complete the dataset. AADT values were calculated from both original counts before imputation and completed counts after imputation. AADT values from imputed data were slightly higher. The average daily volumes when plotted validated the quality of imputed data, as the annual trends demonstrated a relatively better fit.
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Kim, Seong Ho, Won Ho Suh, and Jun Gin Kim. "Airport Access Road Traffic Demand Estimation." Applied Mechanics and Materials 764-765 (May 2015): 1356–60. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1356.
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For most traffic engineering studies, traffic flows are usually analyzed and evaluated on hourly basis. However few studies have been performed that estimate the number of traffic volumes made to an airport as a function of air passengers by time of day. The objective of this paper is to develop a mathematical model which forecasts hourly traffic volume by using hourly airport operation data along with airport user characteristics data. An analytical model was developed. This model can be used to (1) predict the number of vehicles queued at airport entrances intersection or toll plaza, (2) predict optimum toll lane staffing, and (3) analyze the level of congestion on the roadway for different levels of air passenger demand in future.
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Khan, Zadid, Sakib Mahmud Khan, Kakan Dey, and Mashrur Chowdhury. "Development and Evaluation of Recurrent Neural Network-Based Models for Hourly Traffic Volume and Annual Average Daily Traffic Prediction." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (June 2, 2019): 489–503. http://dx.doi.org/10.1177/0361198119849059.
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The prediction of high-resolution hourly traffic volumes of a given roadway is essential for transportation planning. Traditionally, automatic traffic recorders (ATR) are used to collect these hourly volume data. These large datasets are time-series data characterized by long-term temporal dependencies and missing values. Regarding the temporal dependencies, all roadways are characterized by seasonal variations that can be weekly, monthly or yearly, depending on the cause of the variation. Traditional time-series forecasting models perform poorly when they encounter missing data in the dataset. To address this limitation, robust, recurrent neural network (RNN)-based, multi-step-ahead forecasting models are developed for time-series in this study. The simple RNN, the gated recurrent unit (GRU) and the long short-term memory (LSTM) units are used to develop the forecasting models and evaluate their performance. Two approaches are used to address the missing value issue: masking and imputation, in conjunction with the RNN models. Six different imputation algorithms are then used to identify the best model. The analysis indicates that the LSTM model performs better than simple RNN and GRU models, and imputation performs better than masking to predict future traffic volume. Based on analysis using 92 ATRs, the LSTM-Median model is deemed the best model in all scenarios for hourly traffic volume and annual average daily traffic (AADT) prediction, with an average root mean squared error (RMSE) of 274 and mean absolute percentage error (MAPE) of 18.91% for hourly traffic volume prediction and average RMSE of 824 and MAPE of 2.10% for AADT prediction.
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Capparuccini, David Mario, Ardeshir Faghri, Abishai Polus, and Robert E. Suarez. "Fluctuation and Seasonality of Hourly Traffic and Accuracy of Design Hourly Volume Estimates." Transportation Research Record: Journal of the Transportation Research Board 2049, no. 1 (January 2008): 63–70. http://dx.doi.org/10.3141/2049-08.
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Liu, Zhaobin, and Satish Sharma. "Predicting Directional Design Hourly Volume from Statutory Holiday Traffic." Transportation Research Record: Journal of the Transportation Research Board 1968, no. 1 (January 2006): 30–39. http://dx.doi.org/10.1177/0361198106196800104.
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Zhou, Min, and Virginia P. Sisiopiku. "Relationship Between Volume-to-Capacity Ratios and Accident Rates." Transportation Research Record: Journal of the Transportation Research Board 1581, no. 1 (January 1997): 47–52. http://dx.doi.org/10.3141/1581-06.
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The general relationships between hourly accident rates and hourly traffic volume/capacity ( v/c) ratios were examined. A 26 km (16 mi) segment of Interstate I-94 in the Detroit area was selected as the study segment. The v/c ratios were calculated from average hourly traffic volume counts collected in 1993 and 1994 from three permanent count stations. Accident rates were derived from hourly distributed number of accidents in the same 2 years. The correlation between v/c values and accident rates follows a general U-shaped pattern. The study of all observed accidents combined indicates that accident rates are highest in the very low hourly v/c range, decrease rapidly with increasing v/c ratio, and then gradually increase as the v/c ratio continues to increase. U-shaped models also explain the relationship between v/c and accident rates for weekdays and weekend days, multivehicle, rear-end, and property-damage-only accidents. On the other hand, single-vehicle, fixed-object, and turnover accidents, and accidents involving injury and fatality follow a generally decreasing trend with increasing v/c ratio. Traffic conflict is viewed as a major contributing factor to high accident rates observed in the high v/c range, whereas night conditions and driver inattention were identified as explanatory factors for the occurrence of high accident rates in the low v/c ranges.
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Ma, Fangchen, Jinliang Xu, Chao Gao, and Yufeng Bi. "Study on the Distribution of the Suburban Expressway Hourly Traffic Volume and Modification of the Design Hourly Volume under the Background of the Tourism Economy—Analysis on Design Factors of Normalized Congestion in Suburban Expressway." Sustainability 14, no. 17 (August 29, 2022): 10775. http://dx.doi.org/10.3390/su141710775.
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An unreasonable design hourly volume (DHV) greatly impacts road facility construction costs and traffic efficiency. With the rapid rise in the tourism economy and widespread emergence of holiday travel characteristics in China, DHV applicability for suburban expressways requires verification. This study collected annual traffic volume data over 8760 h along Xi’an Ring Expressway from 2017 to 2019. Traffic demand distribution patterns throughout the year and peak hours were analyzed on the basis of the descending order of the obtained hourly traffic volume (HV) and factor data. HV distribution characteristics, 30th highest hourly volume (30 HV) typicality, and DHV factor applicability were investigated. Due to travel characteristics under the background of the tourism economy, the peak HV distribution exhibits polarization characteristics. The recommended value of the design hour traffic factor in the specification corresponded to a number of hours greater than 200, with the 30 HV factor under the background of the tourism economy being 25% higher than the recommended value. Considering the tourism economy, the high level of traffic operation time increased significantly, and the characteristics of a sharp decline in the peak HV disappeared. The 30 HV factor no longer exhibited traditional characteristics. The design causes of traffic congestion are identified herein.
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Sekuła, Przemysław, Zachary Vander Laan, Kaveh Farokhi Sadabadi, Krzysztof Kania, and Sara Zahedian. "Transferability of a Machine Learning-Based Model of Hourly Traffic Volume Estimation—Florida and New Hampshire Case Study." Journal of Advanced Transportation 2021 (November 26, 2021): 1–15. http://dx.doi.org/10.1155/2021/9944918.
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This paper focuses on the problem of model transferability for machine learning models used to estimate hourly traffic volumes. The presented findings enable not only an increase in the accuracy of existing models but also, simultaneously, reduce the cost of data needed for training the models—making statewide traffic volume estimation more economically feasible. Previous research indicates that machine learning volume estimation models that leverage GPS probe data can provide transportation agencies with accurate estimates of hourly traffic volumes—which are fundamental for both operational and planning purposes—and do so with a higher level of accuracy than the prevailing profiling method. However, this approach requires a large dataset for model calibration (i.e., input and continuous count station data), which involves significant monetary investment and data-processing effort. This paper proposes solutions, which allow the model to be prepared using a much smaller dataset, given that a previously collected dataset, which may be gathered in a different place and time period, exists. Based on a broad selection of experiments, the results indicate that the proposed approach is capable of achieving similar model performance while collecting data for a 5 times shorter time period and utilizing 1/4 of the number of continuous count stations. These findings will help reduce the cost of preparing and maintaining the traffic volume models and render the traffic volume estimations more financially appealing.
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Siddique, Mohammed Saiful Alam, and Shamsul Hoque. "PREDICTING THE DAILY TRAFFIC VOLUME FROM HOURLY TRAFFIC DATA USING ARTIFICIAL NEURAL NETWORK." Neural Network World 27, no. 3 (2017): 283–94. http://dx.doi.org/10.14311/nnw.2017.27.015.
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Zahedian, Sara, Przemysław Sekuła, Amir Nohekhan, and Zachary Vander Laan. "Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 3 (March 2020): 272–82. http://dx.doi.org/10.1177/0361198120910737.
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Traffic volumes are an essential input to many highway planning and design models; however, collecting this data for all road network segments is neither practical nor cost-effective. Accordingly, transportation agencies must find ways to leverage limited ground truth volume data to obtain reasonable estimates at scale on the statewide network. This paper aims to investigate the impact of selecting a subset of available automatic traffic recorders (ATRs) (i.e., the ground truth volume data source) and incorporating their data as explanatory variables into a previously developed machine learning regression model for estimating hourly traffic volumes. The study introduces a handful of strategies for selecting this subset of ATRs and walks through the process of choosing them and training models using their data as additional inputs using the New Hampshire road network as a case study. The results reveal that the overall performance of the artificial neural network (ANN) machine learning model improves with the additional inputs of selected ATRs. However, this improvement is more significant if the ATRs are selected based on their spatial distribution over the traffic message channel (TMC) network. For instance, selecting eight ATR stations according to the TMC coverage-based strategy and training the ANN with their inputs leads to average relative reductions of 35.39% and 13.44% in the mean absolute percentage error (MAPE) and error to maximum flow ratio (EMFR), respectively. The results achieved by this study can be further expanded to create a practical strategy for optimizing the number and location of ATRs through transportation networks in a state.
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Cook, Mylan R., Kent L. Gee, Mark K. Transtrum, Shane V. Lympany, and Matthew F. Calton. "A physics-guided model for predicting spectral and temporal variability of road traffic noise." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A48. http://dx.doi.org/10.1121/10.0015497.
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The National Transportation Noise Map (NTNM) provides daily averaged A-weighted equivalent sound levels across the continental United States (CONUS) due to road traffic. The NTNM maps the spatial variability of road traffic noise, but not the temporal or spectral variability. A physics-guided model was developed to predict the temporal and spectral variability of road traffic noise across CONUS. Empirical models were developed to predict hourly road traffic volume and vehicle class mix across CONUS based on publicly available traffic volume measurements and geospatial data. The Federal Highway Administration’s Traffic Noise Model characterizes average spectral levels by vehicle class; by combining the empirical model with the Traffic Noise Model’s characteristic vehicle class spectra, hourly traffic noise predictions across CONUS are made which include temporal and spectral variability. This physics-based modeling approach improves upon nation-wide mapping of road traffic noise. [Work supported by U.S. Army SBIR.]
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Tang, Guo Lei, Ran Zhang, Wen Yuan Wang, and Zi Jian Guo. "Evaluation of Ship-Diversion Induced Emergency on Port Traffic Volumes." Applied Mechanics and Materials 505-506 (January 2014): 583–91. http://dx.doi.org/10.4028/www.scientific.net/amm.505-506.583.
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The worst ice blockages along the Yellow Sea coast and Bohai Sea had severe effects on maritime shipping, oil and gas mining and farming activities in the winter of 2009 and 2010. Ice blockages made some small ships divert to neighboring ports, which also affected these ports' operations. To evaluate the effect of ship-diversion induced emergency on port traffic, we propose a simulation model considering ship-diversion induced emergency, port transportation and operations. Then we carry out simulation experiments on a container terminal in Dalian Port. The experiment results show that ship-diversion induced emergency increased the peak hourly traffic volume, average daily traffic and thirtieth hourly volume, which provides a basis for port traffic planning and organization.
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Zandi, Kamran, Ali Tavakoli Kashani, and Atsuyuki Okabe. "Influence of Traffic Parameters on the Spatial Distribution of Crashes on a Freeway to Increase Safety." Sustainability 15, no. 1 (December 28, 2022): 493. http://dx.doi.org/10.3390/su15010493.
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Significant research has been conducted in recent years to determine crash hotspots. This study focused on the effects of various traffic parameters, including average traffic speed and traffic volume, on the spatial distributions of freeway crashes. Specifically, this study analyzed the spatial distributions of crashes on the Qazvin–Abyek freeway in Iran using four-year crash records. Spatial crash clustering analysis was performed to identify hotspots and high cluster segments using global Moran’s I, local Moran’s I, and Getis-Ord Gi*. The global Moran’s I indicated that clusters were formed under the low range of hourly traffic volume (less than 1107 veh/h) and the high range of traffic speed (more than 97 km/h), which increased the number of heavy vehicle crashes in the early morning (time 03–06) around the 52 km segment. The results obtained from kernel density estimation (KDE), local Moran’s I, and Getis-Ord Gi* revealed similar crash hotspots. The results further showed different spatial distributions of crashes for different traffic hourly volumes, traffic speed, and crash times, and there was hotspot migration by applying different traffic conditions. These findings can be used to identify high-risk crash conditions for traffic managers and help them to make the best decisions to enhance road safety.
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Singh, Nishant, Nitin Chauhan, Nishant Pande, and Narad Muni Prasad. "TRAFFIC VOLUME STUDY AND CONGESTION SOLUTIONS." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 307–21. http://dx.doi.org/10.33564/ijeast.2021.v06i07.049.
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Traffic Engineering is used to accomplish secure and time bound commutation of persons and freights on road network which in turn relies on traffic flow. Due to an increase in the standard of living of middle-class families in the last decade, traffic volume has considerably increased. Thus, lack of proper management of roads can result in non-sustainable development. This work analyses traffic properties of Shivaji Marg, Janakpuri district centre, Delhi, India by performing direct manual counting method of survey. Objective of this study is to record traffic volume of the site for the weekdays from 8AM to 2PM, determine the morning peak hour and the morning peak hour factor using the standard passenger car unit (PCU) values. Also, this study attempts to determine the practical PCU values for different automobiles running on the site and calculate the corresponding morning peak hour factor. Lastly, this work comments on the congestion level of the site by comparing the recorded hourly traffic volume with the permissible volume per hour. The study found out that the practical PCU values differ from their corresponding standard values as it depends on the speed and space occupied by the automobiles which can vary from site to site. Also, it was found that the hourly traffic volume is within the permissible limit implying no congestion in the current scenario. There are, however, certain limitations of this work like absence of advanced technology, variation in the PCU values, time constraints. Nevertheless, this study will help the stakeholders in the construction sector to prioritise their resources towards factors which need to be improved to enhance the serviceability of the highway for present as well as for the future generation.
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Spławińska, Malwina. "Methodology For Determining Reliable Traffic Parameters For Current Analysis Of Performance Of Motorways And Expressways." Baltic Journal of Road and Bridge Engineering 14, no. 1 (March 28, 2019): 104–23. http://dx.doi.org/10.7250/bjrbe.2019-14.435.
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In this paper, the results of analyses concerning selected traffic characteristics typical for Polish motorways and expressways are presented. The input data were collected automatically by stations located on various highways. In the first place, with the use of the coefficient of variability, periods with the lowest traffic volume variability in the year and the day were determined. On this basis, the most favourable time scope of random measurements was determined to allow reliable estimation of traffic parameters for road performance analyses. Then, based on model relationships between the characteristics of traffic volume variability over time and constant volume (regression relationships, a model of Artificial Neural Networks), correction factors were developed enabling direct conversion of the obtained measurement results into Design Hourly Volume. In addition, the rules for determining the share of heavy vehicles meeting the conditions at peak hours of the year were developed. The presented approach is in line with the current research trend on a global scale and allows for improving the accuracy of estimating Design Hourly Volume by 20 per cent concerning the method currently recommended in Poland.
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Mascio, Paola Di, and Laura Moretti. "Hourly Capacity of a Two Crossing Runway Airport." Infrastructures 5, no. 12 (December 4, 2020): 111. http://dx.doi.org/10.3390/infrastructures5120111.
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At the international level, the interest in airport capacity is growing in the last years because its maximization ensures the best performances of the infrastructure. However, infrastructure, procedure, human factor constraints should be considered to ensure a safe and regular flow to the flights. This paper analyzed the airport capacity of an airport with two crossing runways. The fast time simulation allowed modeling the baseline scenario (current traffic volume and composition) and six operative scenarios; for each scenario, the traffic was increased until double the current volume. The obtained results in terms of average delay and throughput were analyzed to identify the best performing and operative layout and the most suitable to manage increasing hourly movements within the threshold delay of 10 min. The obtained results refer to the specific examined layout, and all input data were provided by the airport management body: the results are reliable, and the pursued approach could be implemented to different airports.
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Spławińska, M. "Factors Determining Seasonal Variations in Traffic Volumes." Archives of Civil Engineering 63, no. 4 (December 1, 2017): 35–50. http://dx.doi.org/10.1515/ace-2017-0039.
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Abstract The characteristics of seasonal variations in traffic volumes are used for a variety of purposes, for example to determine the basic parameters describing annual average daily traffic – AADT, and design hourly volume – DHV, analyses of road network reliability, and traffic management. Via these analyses proper classification of road sections into appropriate seasonal factor groups (SFGs) has a decisive influence on results. This article, on the basis of computational experiments (models of artificial neural networks, discriminatory analysis), aims to identify which factors have the greatest impact on the allocation of a section of road to the corresponding SFG, based on short-term measurements. These factors are presented as qualitative data: the Polish region, spatial relationships, functions of road, cross-sections, technical class; and quantitative data: rush hour traffic volume.
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Dutta, Nancy, and Michael D. Fontaine. "Assessment of the Effects of Volume Completeness and Spatial and Temporal Correlation on Hourly Freeway Crash Prediction Models." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 9 (July 28, 2020): 1097–109. http://dx.doi.org/10.1177/0361198120934470.
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Traditional traffic safety analyses of crash frequency usually use highly aggregated cross-sectional data and ignore the time-varying nature of some critical factors. This research used 7 years of hourly data from 110 rural four-lane segments and 80 urban six-lane segments to develop hourly level crash prediction models and contrasted them with traditional annual average daily traffic (AADT)-based models. To account for the overdispersion of data and unobserved heterogeneity, generalized linear mixed-effect models were contrasted with negative binomial models. The models used average hourly volume as a measure of exposure, and the quantity of volume data available for the sites ranged from continuous counts to locations where only a couple of weeks of data were available every other year (short counts). While developing disaggregated models, the difference in data availability from these sources can be a potential source of error, so evaluating the change in performance of prediction models with changes in volume data availability was examined. The results showed that the best models include a combination of average hourly volume, selected geometric variables, and speed related parameters. Hourly models that included speed parameters consistently outperformed AADT models. Further investigation revealed that the positive effect of using a more inclusive and larger dataset was larger than the effect of accounting for data correlation. This showed that using short count stations as a data source does not diminish the quality of the developed models, thus indicating that these methods could be applied broadly across agencies, even when volume data is relatively sparse.
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Grande, Giuseppe, Steven Wood, Auja Ominski, and Jonathan D. Regehr. "Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data." Transportation Research Record: Journal of the Transportation Research Board 2644, no. 1 (January 2017): 30–38. http://dx.doi.org/10.3141/2644-04.
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Traffic volume, often measured in relation to annual average daily traffic (AADT), is a fundamental output of traffic monitoring programs. At continuous count sites, unusual events or counter malfunctions periodically cause data loss, which influences AADT accuracy and precision. This paper evaluates five methods used to calculate AADT values from continuous count data, including the use of a simple average, the commonly adopted method developed by AASHTO (the AASHTO method), and methods that incorporate adjustments to the AASHTO method. The evaluation imposes data removal scenarios designed to simulate real-life causes of data loss to quantify the accuracy and precision improvements provided by these adjustments. Truck traffic data are used to reveal issues arising when volumes are low or when they exhibit unusual temporal patterns. Unlike the AASHTO method, which incorporates a weighted average and an hourly base time period, the FHWA method provides the most accurate and precise results in all data removal scenarios, according to the evaluation. Specifically, when up to 15 days of data are randomly removed, application of the FHWA method can be expected to produce errors within approximately é1.4% of the true AADT value, 95% of the time. Results also demonstrate that including a weighted average improves AADT accuracy primarily, whereas the use of hourly rather than daily count data influences precision. If possible, practitioners contemplating the adoption of the FHWA method should assess its relative advantages within their local context.
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Iqbal, Shahadat, Taraneh Ardalan, Mohammed Hadi, and Evangelos Kaisar. "Developing Guidelines for Implementing Transit Signal Priority and Freight Signal Priority Using Simulation Modeling and a Decision Tree Algorithm." Transportation Research Record: Journal of the Transportation Research Board 2676, no. 4 (November 28, 2021): 133–44. http://dx.doi.org/10.1177/03611981211057528.
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Transit signal priority (TSP) and freight signal priority (FSP) allow transportation agencies to prioritize signal service allocations considering the priority of vehicles and, potentially, decrease the impact signal control has on them. However, there have been no studies to develop guidelines for implementing signal control considering both TSP and FSP. This paper reports on a study conducted to provide such guidelines that employed a literature review, a simulation study, and a decision tree algorithm based on the simulation results. The guideline developed provides recommendations in accordance with the signal timing slack time, the proportion of major to minor street hourly volume, hourly truck volume per lane for the major street, hourly truck volume per lane for the minor street, the proportion of major to minor street hourly truck volume, the proportion of major to minor street hourly bus volume, the volume-to-capacity ratio for the major street, and the volume-to-capacity ratio for the minor street. The guideline developed was validated by implementing it for a case study facility. The validation result showed that the guideline works correctly for both high and low traffic demand.
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Retallack, Angus Eugene, and Bertram Ostendorf. "Relationship Between Traffic Volume and Accident Frequency at Intersections." International Journal of Environmental Research and Public Health 17, no. 4 (February 21, 2020): 1393. http://dx.doi.org/10.3390/ijerph17041393.
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Driven by the high social costs and emotional trauma that result from traffic accidents around the world, research into understanding the factors that influence accident occurrence is critical. There is a lack of consensus about how the management of congestion may affect traffic accidents. This paper aims to improve our understanding of this relationship by analysing accidents at 120 intersections in Adelaide, Australia. Data comprised of 1629 motor vehicle accidents with traffic volumes from a dataset of more than five million hourly measurements. The effect of rainfall was also examined. Results showed an approximately linear relationship between traffic volume and accident frequency at lower traffic volumes. In the highest traffic volumes, poisson and negative binomial models showed a significant quadratic explanatory term as accident frequency increases at a higher rate. This implies that focusing management efforts on avoiding these conditions would be most effective in reducing accident frequency. The relative risk of rainfall on accident frequency decreases with increasing congestion index. Accident risk is five times greater during rain at low congestion levels, successively decreasing to no elevated risk at the highest congestion level. No significant effect of congestion index on accident severity was detected.
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Hernández-Vega, Henry, and Carolina Matamoros-Jiménez. "Clustering Approach to Generate Pedestrian Traffic Pattern Groups." Ciencia e Ingeniería Neogranadina 31, no. 2 (December 31, 2021): 41–60. http://dx.doi.org/10.18359/rcin.4403.
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This study shows the development of patterns of temporal hourly volume distributions in an urban area in Costa Rica, based on a cluster analysis of pedestrian data. This study aims to establish specific pattern groups for the temporal variation of weekday pedestrian volumes applying cluster analysis in the central business district of Guadalupe in San José. For 46 counting sites, vectors with the weekday hourly factors, the proportion of the daily pedestrian traffic, were estimated. A hierarchical cluster method was implemented to group the vectors of hourly factors from the different counting sites. This method groups elements by minimizing the Euclidean distance between elements of the same group and, at the same time, maximizing the distances from elements of other groups. In addition, the groups found through this analysis are related to land use through buffers of different radios. Eight temporal pattern groups were obtained through cluster analysis. Two pattern groups account for more than two-thirds of the sites included in the study. Fisher’s exact independence test shows that banks and public services could explain some of the patterns observed. The classification of 46 counting sites based on temporal distribution patterns, and the relation with the establishments in the area, allows a simplification of the information and facilitates an understanding of the pedestrian mobility in the area. Further research is required that leads towards geographical elements that could explain the differences in temporal and mobility patterns.
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Baek, Jongdae. "Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment." Sensors 22, no. 1 (December 23, 2021): 86. http://dx.doi.org/10.3390/s22010086.
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Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method.
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Dai, Lei Lei, Zheng Liang Sun, Dong Bo Liu, and Ya Li. "An Improved Method of Traffic Control Period Division for Intersection Based on Signal Cycle Calculation." Applied Mechanics and Materials 253-255 (December 2012): 1731–35. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1731.
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A simple and practical method of intersection control period division was proposed from the urban traffic control perspective. According to the historical statistics of the traffic volume, considering the differences of traffic flows, and based on the process of average hourly traffic volume, the method could calculate the cycle length of signalized intersection, set determination threshold value of control period, and divide 24h a day into several control periods, which provided a scientific basis for the establishment of signal timing programs with multiple periods a day. The calculation results showed that this method was agreed with the actual traffic flow compared with the traditional method.
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HYODO, Satoshi, and Toshio YOSHII. "AN ANALYSIS OF THE IMPACT OF HOURLY TRAFFIC VOLUME ON TRAFFIC ACCIDENT RISK ON CENSUS HIGHWAYS." Journal of Japan Society of Civil Engineers, Ser. D3 (Infrastructure Planning and Management) 72, no. 5 (2016): I_1283—I_1291. http://dx.doi.org/10.2208/jscejipm.72.i_1283.
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Zhong, Ming, and Satish Sharma. "Matching Hourly, Daily, and Monthly Traffic Patterns to Estimate Missing Volume Data." Transportation Research Record: Journal of the Transportation Research Board 1957, no. 1 (January 2006): 32–42. http://dx.doi.org/10.1177/0361198106195700106.
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Yi, Zhiyan, Xiaoyue Cathy Liu, Nikola Markovic, and Jeff Phillips. "Inferencing hourly traffic volume using data-driven machine learning and graph theory." Computers, Environment and Urban Systems 85 (January 2021): 101548. http://dx.doi.org/10.1016/j.compenvurbsys.2020.101548.
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Ma, Fangchen, Jinliang Xu, Chao Gao, and Yufeng Bi. "Study on the Applicability and Modification of the Design Hourly Volume on Rural Expressways Considering Holiday Traffic Polarization." International Journal of Environmental Research and Public Health 19, no. 16 (August 11, 2022): 9897. http://dx.doi.org/10.3390/ijerph19169897.
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The design hourly volume (DHV) of traffic based on the 30th highest hourly volume (30 HV) of the year has been widely applied in expressway design in various countries to balance the benefit and economy of expressway engineering. However, this design method has barely changed since it was first adopted in China, which may be contrary to the rapidly changing traffic macroenvironment. In this study, annual hourly traffic volume (HV) data pertaining to expressways in East China, Southwest China and Northwest China were collected. Based on the descending order of the obtained HV and HV factor data, the distribution patterns of the traffic demand throughout the year and peak hours were analyzed. The distribution characteristics of the HV, typicality of 30 HV and applicability of the DHV factor were investigated. It was found that severe polarization occurred in the HV distribution in China. The actual 30 HV factor is more than 0.5 times the recommended value in the specification. Continued use of the current DHV would result in more than 200 h of inefficient travel time, 5.7 times more than expected, with the DHV factor is currently no longer applicable in China. Furthermore, the annual 30 HV value loses its typical status. Depending on the level of local economic development, using 10 HV factor or 80 HV factor as the new DHV factor can better alleviate the congestion problem. This study determines the reasons for the widespread congestion issues in China from the perspective of expressway design, which is beneficial to adjust the basis of expressway design in China.
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Knapp, Keith K., and Leland D. Smithson. "Winter Storm Event Volume Impact Analysis Using Multiple-Source Archived Monitoring Data." Transportation Research Record: Journal of the Transportation Research Board 1700, no. 1 (January 2000): 10–16. http://dx.doi.org/10.3141/1700-03.
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Data collection and information management systems typically are implemented to improve decision-making efficiency and capabilities. Results of these systems sometimes are used in real-time scenarios; however, in other instances, the data are collected and summarized, then archived or possibly destroyed. Data from several information management systems in Iowa were used to analyze the traffic volume impacts of winter storm events. Roadway and weather condition data were acquired from a roadway weather information system, and hourly traffic volumes were obtained from automatic traffic recorders. Daily snowfall data were acquired from the Iowa Department of Agriculture and Land Stewardship and the National Weather Service. Winter storm events with at least a 4-h duration and snowfall at 0.51 cm/h (0.20 in./h) or more were evaluated. Overall, 64 winter storm events were defined and analyzed for seven interstate locations. The analysis indicated that winter storm events generally decrease traffic volumes, although the impact can vary greatly. The calculated average winter storm event volume reduction was approximately 29 percent, but ranged (by data collection location) from approximately 16 to 47 percent. Regression analysis revealed a statistically significant positive relationship between winter storm event percent volume reduction, total snowfall, and the square of maximum gust wind speed. This research can be used to determine the potential volume impacts of winter storm events and provide additional data for the eventual development of a winter-weather level of service system based on traffic flow characteristics.
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Sarraj, Yahya R. "Hourly and Daily Traffic Expansion Factors on Selected Roads in Gaza, Palestine." Open Civil Engineering Journal 12, no. 1 (November 14, 2018): 355–67. http://dx.doi.org/10.2174/1874149501812010355.
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Background: The shortage of sufficient, reliable and continuous traffic data in many developing countries makes it difficult for traffic engineers and researchers. Traffic data are essential for both planning and design of transportation facilities. Objective: This work tries to provide data that in order to help local experts in Gaza city to provide better estimates of the average daily traffic (ADT) and hence better transportation facilities. Methods: The analysis and discussion are based on continuous traffic flow counts conducted on three selected main streets in Gaza. Data were collected for 24 hours on seven consecutive days on each street. Results: The results indicate that the average hourly expansion factors(HEFs) have a margin of error for the period between 7:00 and 16:00 that does not exceed ±3%. The results also indicate that the average daily expansion factors (DEFs) on the three streets have a maximum margin of error of 3.2% on both Sunday and Monday. On the other hand, the analysis proved that the maximum peak hour volume was 2864 vehicles/hour on Al Jalaa Street between 7:00 and 8:00 and the average peak-to-daily ratio (p/d) of the three streets was found to be 7.18%. The maximum directional traffic split (D) was found to be 60% in the heaviest direction of traffic flow during the peak period. Conclusion: The HEFs and DEFs produced in this work can be used with a 95% confidence on the main streets of Gaza city.
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Hasan, Tanweer, and Robert W. Stokes. "Guidelines for Right-Turn Treatments at Unsignalized Intersections and Driveways on Rural Highways." Transportation Research Record: Journal of the Transportation Research Board 1579, no. 1 (January 1997): 63–72. http://dx.doi.org/10.3141/1579-08.
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Guidelines for right-turn treatments at unsignalized intersections and driveways on rural two-lane and four-lane highways are presented. Two types of treatments, full-width lane and taper, were considered over the do-nothing radius treatment. The guidelines indicate the design hourly traffic volumes for which the benefits of right-turn treatments exceed their costs. The benefits used in the economic analysis were the operational and accident cost savings provided by right-turn treatments. The costs used in the development of the guidelines were the costs of constructing full-width right-turn lanes and tapers. The operational effects were estimated in terms of delay and excess fuel consumption experienced by through traffic due to right-turning vehicles. To account for the safety effects, the relationship between speed differential and accidents was used to estimate the reduction in right-turn, same-direction, rear-end accidents that would be expected to result from the provision of a right-turn treatment. The guidelines indicate the right-turn design hourly volume required to justify a right-turn treatment as a function of the following factors: ( a) directional design hourly volume, (b) highway operating speed, and ( c) number of lanes on the highway. Comparisons with other guidelines indicate that the range of guidelines developed are reasonable. In addition, they are more definitive than other guidelines because they account for highway operating speed and address taper treatments as well as full-width turn lanes.
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Al-Bayati, Amjad H., Ahmad S. Shakoree, and Zahraa A. Ramadan. "Factors Affecting Traffic Accidents Density on Selected Multilane Rural Highways." Civil Engineering Journal 7, no. 7 (July 1, 2021): 1183–202. http://dx.doi.org/10.28991/cej-2021-03091719.
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Estimations of average crash density as a function of traffic elements and characteristics can be used for making good decisions relating to planning, designing, operating, and maintaining roadway networks. This study describes the relationships between total, collision, turnover, and runover accident densities with factors such as hourly traffic flow and average spot speed on multilane rural highways in Iraq. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. Three highways are selected in Wassit governorate as a case study to cover the studied locations of the accidents. The selection includes Kut–Suwera, Kut–ShekhSaad, and Kut–Hay multilane divided highways located in the south of Iraq. The preliminary presentation of the studied highways was performed using Geographic Information System (GIS) software. Data collection was done to obtain crash numbers and types over five years with their locations, hourly traffic flow, and average spot speed and define roadway segments lengths of crash locations. The cumulative speed distribution curves introduce that the spot speed spectrum for each highway's whole traffic extends over a relatively wide range, indicating a maximum speed of 180 kph and a minimum speed of 30 kph. Multiple linear regression analysis is applied to the data using SPSS software to attain the relationships between the dependent variables and the independent variables to identify elements strongly correlated with crash densities. Four regression models are developed which verify good and strong statistical relationships between crash densities with the studied factors. The results show that traffic volume and driving speed have a significant impact on the crash densities. It means that there is a positive correlation between the single factors and crash occurrence. The higher volumes and the faster the driving speed, the more likely it is to crash. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also extended. Doi: 10.28991/cej-2021-03091719 Full Text: PDF
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Spławińska, Malwina. "DEVELOPMENT OF MODELS FOR DETERMINING THE TRAFFIC VOLUME FOR THE ANALYSIS OF ROADS EFFICIENCY." Archives of Transport 33, no. 1 (March 31, 2015): 81–91. http://dx.doi.org/10.5604/08669546.1160929.
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The article presents different methods of estimating DHV, including traditional Factor Approach, developed Regression Models and Artificial Neural Networks models. As explanatory variables: quantitative variables (AADT and the share of heavy vehicles) as well as qualitative variables (the cross-section, roads class, nature of the area, the profile of seasonal variations, region of Poland and the nature of traffic patterns) were used. In addition, the results of preliminary analyses of the DHV estimates based on the maximum hourly volume derived from a few hours traffic measurement on weekdays where there is the greatest share of hours with the highest traffic volume in the year were presented. On the basis of comparisons of the presented methods, Multiple Regression Model was identified as the most useful.
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Sahu, Prasanta, Leela Bayireddy, and Hyuk-Jae Roh. "A New Approach to Exploring the Relationship between Weather Phenomenon and Truck Traffic Volume in the Cold Region Highway Network." Modelling 1, no. 2 (October 15, 2020): 122–33. http://dx.doi.org/10.3390/modelling1020008.
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Weather events are arbitrary, and this makes it difficult to incorporate weather parameters into transportation models. Recent research on traffic weather interaction analysis conducted at the University of Regina, Canada reported traffic variations with cold temperatures and snowfall. The research team at the University of Regina proposed a linear association between snowfall and temperature to analyze the traffic variation on provincial highways during winter months. The variations were studies with the inclusion of the expected daily volume factor as an independent variable in the model structure. However, the study did not analyze the nature of the association between daily truck traffic volume and snowfall. Based on these drawbacks of the past studies, in this research, the objective is to focus on the effects of snow and temperature on traffic volume changes with a methodological help of Maximal Information Coefficient (MIC), which stems from the maximal information-based nonparametric exploration (MINE) statistics. The results obtained from the analysis indicate that the relationship between snow and truck traffic is non-linear. However, the study could not establish any functional relationship between snowfall and daily truck volume. It is desired to further conduct an hourly analysis to explore a new relationship between snowfall and truck volume.
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Tarko, Andrew P., and Rafael I. Perez-Cartagena. "Variability of Peak Hour Factor at Intersections." Transportation Research Record: Journal of the Transportation Research Board 1920, no. 1 (January 2005): 125–30. http://dx.doi.org/10.1177/0361198105192000115.
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A peak hour factor (PHF) is used to convert hourly traffic volume into the flow rate that represents the busiest 15 min of the rush hour. Past research indicated that PHF had a strong impact on traffic analysis results. The common practice is to use a default value recommended by national or local guidelines or to use limited field observations. This paper investigates the variability of PHF over time and across locations. The day-to-day variability of PHF was found to be as strong as the site-to-site variability. This finding prompts estimation of the PHF on the basis of multiple field measurements or, when it is not possible to obtain measurements, for the use of a model that returns the average value of PHF. This paper presents such a model, which links PHF with the hourly volume, population, and time of day. The paper demonstrates that a large portion of the variability in the sample of observations either can be explained with the model or can be attributed to the day-to-day fluctuation.
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Lin, Lei, Weizi Li, and Lei Zhu. "Data-Driven Graph Filter-Based Graph Convolutional Neural Network Approach for Network-Level Multi-Step Traffic Prediction." Sustainability 14, no. 24 (December 13, 2022): 16701. http://dx.doi.org/10.3390/su142416701.
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Accurately predicting network-level traffic conditions has been identified as a critical need for smart and advanced transportation services. In recent decades, machine learning and artificial intelligence have been widely applied for traffic state, including traffic volume prediction. This paper proposes a novel deep learning model, Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF), for network-wide multi-step traffic volume prediction. More specifically, the proposed GCNN-DDGF model can automatically capture hidden spatiotemporal correlations between traffic detectors, and its sequence-to-sequence recurrent neural network architecture is able to further utilize temporal dependency from historical traffic flow data for multi-step prediction. The proposed model was tested in a network-wide hourly traffic volume dataset between 1 January 2018 and 30 June 2019 from 150 sensors in the Los Angeles area. Detailed experimental results illustrate that the proposed model outperforms the other five widely used deep learning and machine learning models in terms of computational efficiency and prediction accuracy. For instance, the GCNN-DDGF model improves MAE, MAPE, and RMSE by 25.33%, 20.45%, and 29.20% compared to the state-of-the-art models, such as Diffusion Convolution Recurrent Neural Network (DCRNN), which is widely accepted as a popular and effective deep learning model.
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Ugwuanyi, HK, FO Okafor, and JC Ezeokonkwo. "ASSESSMENT OF TRAFFIC FLOW ON ENUGU HIGHWAYS USING SPEED DENSITY REGRESSION COEFFICIENT." Nigerian Journal of Technology 36, no. 3 (June 30, 2017): 749–57. http://dx.doi.org/10.4314/njt.v36i3.13.
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In an attempt to estimate the operating speeds and volume of traffic on highway lanes as a function of predicted demands, speed-density models were estimated using data from highway sites. Speed, flow and volume are the most important elements of the traffic flow. In this study, the speed-density regression models are compared using five highways in relation to their correlation coefficient based on the daily traffic flow data obtained from the roads. The traffic flow data were collected by hourly traffic count on each road. The coefficient of correlation (R) proved to have the best fit with a higher confidence and less variation for a two-lane highway than a one-lane highway. The space-mean speed (u) and density (k) relationship for the two-lane highways are; u,  and u whereas the space-mean speed (u) and density (k) relationship for the one-lane highways are; u =  respectively. This research provides practical application for speed estimation, construction, maintenance and optimization of the highways using the speed-density models which will enhance traffic monitoring, traffic control management, traffic forecasting and model calibration. http://dx.doi.org/10.4314/njt.v36i3.13
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Liu, Y. L., Y. E. Ge, and H. Oliver Gao. "Improving estimates of transportation emissions: Modeling hourly truck traffic using period-based car volume data." Transportation Research Part D: Transport and Environment 26 (January 2014): 32–41. http://dx.doi.org/10.1016/j.trd.2013.10.007.
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Lee, Stephen C., and Joe Lee. "Consideration of 24-hr Volumes in Selection of Traffic Signal Control Strategies for Isolated Intersections." Transportation Research Record: Journal of the Transportation Research Board 1553, no. 1 (January 1996): 18–27. http://dx.doi.org/10.1177/0361198196155300103.
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Selection of the most appropriate traffic signal control strategy for isolated intersections is a difficult and complicated process. TRAF-NETSIM was used to evaluate the operational performance of an isolated intersection under pretimed, semiactuated, and actuated control for continuous 24-hr traffic volumes. Guidelines were developed for selecting the most effective control strategy. Findings include the following: either pretimed or actuated control is the most effective strategy for isolated intersections without flashing for the 24-hr and peak 8-hr traffic volumes. The conventional three-dial pretimed controller is still a valuable control strategy and should not be eliminated from consideration. The most effective strategy for the peak 8-hr operation of an isolated intersection is probably the most effective one for overall 24-hr operation, as well. The combined control strategy (which consists of one or more of the pretimed, actuated, and semiactuated controls dependent on the hourly volumes) without flashing is the most effective strategy for the 24-hr and peak 8-hr traffic volumes. The signal flashing mode is very effective during the night when the total intersection critical lane volume falls below 500 vehicles per hour. Advanced pretimed controllers are generally more effective than conventional three-dial pretimed controllers. There is no direct, universal method to determine the most effective combined traffic signal control strategy for isolated intersections.
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Gunawardena, Nishantha R., Kumares C. Sinha, and Jon D. Fricker. "Development of Peak-Hour and Peak Directional Factors for Congestion Management Systems." Transportation Research Record: Journal of the Transportation Research Board 1552, no. 1 (January 1996): 8–18. http://dx.doi.org/10.1177/0361198196155200102.
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A statewide congestion management system involves the application of large amounts of traffic data to a large number of roadway links at a detailed level. A procedure has been developed to identify congestion on roadway links at a macroscopic level using daily volume counts. Daily volume counts are used to determine a.m. and p.m. peak-hour ( K) and peak-directional (D) factors. These factors are then used with average daily traffic data to determine whether a link is congested at a preliminary level and in need of further analysis. Links identified as congested can then be subjected to a more detailed microscopic study using hourly volume counts to determine extent, duration, and severity of congestion. Development of a.m. and p.m. K and D factors for five different classes of roadways is discussed and values that can be used in congestion management systems are recommended.
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Miranda-Moreno, Luis F., Thomas Nosal, Robert J. Schneider, and Frank Proulx. "Classification of Bicycle Traffic Patterns in Five North American Cities." Transportation Research Record: Journal of the Transportation Research Board 2339, no. 1 (January 2013): 68–79. http://dx.doi.org/10.3141/2339-08.
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This study used a unique database of long-term bicycle counts from 38 locations in five North American cities and along the Route Verte in Quebec, Canada, to analyze bicycle ridership patterns. The cities in the study were Montreal, Quebec; Ottawa, Ontario; and Vancouver, British Columbia, in Canada and Portland, Oregon, and San Francisco, California, in the United States. Count data showed that the bicycle volume patterns at each location could be classified as utilitarian, mixed utilitarian, mixed recreational, and recreational. Study locations classified by these categories were found to have consistent hourly and weekly traffic patterns across cities, despite considerable differences between the cities in their weather, size, and urban form. Seasonal patterns across the four categories and in the cities also were identified. Expansion factors for each classification are presented by hour and day of the week. Monthly expansion factors are presented for each city. Finally, traffic volume characteristics are presented for comparison purposes.
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Sołowczuk, Alicja. "Effect of Traffic Calming in a Downtown District of Szczecin, Poland." Energies 14, no. 18 (September 15, 2021): 5838. http://dx.doi.org/10.3390/en14185838.
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The increasing use of road vehicles has caused a number of transport and environmental issues throughout the world. To cope with them, traffic calming schemes are being increasingly implemented in built-up areas. An example of such schemes are Tempo-30 zones. The traffic calming measures applied as part of this scheme must be carefully planned in terms of location and design details in order to obtain the desired reduction in speed, traffic volume and exhaust emissions and, last but foremost, to increase the safety and facilitate the movement of vulnerable road users. The coexistence and combined effect of these measures and their design details must also be taken into account. The purpose of this study was to investigate whether the applied traffic calming measures had a considerable bearing on the reduction in speed to the desired level, as assumed in the traffic calming plan. Three street sections starting and ending with different intersection types were chosen to examine the synergy of the applied traffic calming measures. The numbers and speeds of vehicles were measured in three day-long continuous surveys. As it was expected, the amount of speed reduction depended on the hourly traffic volume on a one-way street and various other traffic engineering aspects. The obtained results may be used to modify the existing speed profile models and can guide traffic engineers in choosing the most effective traffic calming measures.
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Crivellari, Alessandro, and Euro Beinat. "Forecasting Spatially-Distributed Urban Traffic Volumes via Multi-Target LSTM-Based Neural Network Regressor." Mathematics 8, no. 12 (December 17, 2020): 2233. http://dx.doi.org/10.3390/math8122233.
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Monitoring the distribution of vehicles across the city is of great importance for urban traffic control. In particular, information on the number of vehicles entering and leaving a city, or moving between urban areas, gives a valuable estimate on potential bottlenecks and congestions. The possibility of predicting such flows in advance is even more beneficial, allowing for timely traffic management strategies and targeted congestion warnings. Our work is inserted in the context of short-term forecasting, aiming to predict rapid changes and sudden variations in the traffic volume, beyond the general trend. Moreover, it concurrently targets multiple locations in the city, providing an instant prediction outcome comprising the future distribution of vehicles across several urban locations. Specifically, we propose a multi-target deep learning regressor for simultaneous predictions of traffic volumes, in multiple entry and exit points among city neighborhoods. The experiment focuses on an hourly forecasting of the amount of vehicles accessing and moving between New York City neighborhoods through the Metropolitan Transportation Authority (MTA) bridges and tunnels. By leveraging a single training process for all location points, and an instant one-step volume inference for every location at each time update, our sequential modeling approach is able to grasp rapid variations in the time series and process the collective information of all entry and exit points, whose distinct predicted values are outputted at once. The multi-target model, based on long short-term memory (LSTM) recurrent neural network layers, was tested on a real-world dataset, achieving an average prediction error of 7% and demonstrating its feasibility for short-term spatially-distributed urban traffic forecasting.
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Barka, Roza E. "Fitting Volume Delay Functions under interrupted and uninterrupted flow conditions at Greek urban roads." European Transport/Trasporti Europei, no. 83 (September 2021): 1–16. http://dx.doi.org/10.48295/et.2021.83.7.
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This paper presents the calibration of the most commonly used Volume Delay Functions (VDF): BPR, Conical, Akcelik and Modified Davidson, for an urban area populated by over 1 million inhabitants, the city of Thessaloniki in Greece. The estimation of the unknown coefficients was carried out for a typical freeway, the ring road of the city, and selected arterial and collected roads of the city center, through recent data of hourly observed vehicle speeds and volumes obtained from video recordings and loop detectors. The BPR function yielded the highest accuracy across all the examined road sections and was characterized as the most suitable to simulate and interpret the existing traffic conditions. The estimated coefficients differed significantly from the values proposed in the pertinent literature, which highlights the importance of using locally derived data for the calibration of the VDFs.
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Shavyraa, Ch D. "RESULTS OF PASSENGER RESOURCES EXAMINATIONS IN KYZYL CITY OF THE TYVA REPUBLIC." Russian Automobile and Highway Industry Journal 15, no. 5 (November 11, 2018): 718–24. http://dx.doi.org/10.26518/2071-7296-2018-5-718-724.
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Introduction.The paper considers the results of surveys taking into account the efficiency increase of passenger traffic in a small town. The need to regulate the work of carriers of various ownership forms, taking into account the characteristics of urban routes, determines the relevance of the research in this area. Therefore, the purpose of the survey is to clarify the total size of the movement of the zones and the city as a whole for the further projects’ development of the road network planning and for the passenger traffic development.Materials and methods.The paper considers alternatives to passenger surveys, i.e. application of the most optimal variants of the transportation plan. The characteristics of the route and load on the routes of Kyzyl are also illustrated. The author uses the methodology of surveying the population transport needs in a small town, in particular, the counting of passengers.Results.As a result, the author conducts the survey of passenger traffic, taking into account the city specific. The characteristics of the route and the load on the routes of Kyzyl are studied. It is important to use the methodology of the transport needs and transport services for population in a small town.Discussion and conclusions. On the basis of the conducted surveys of passenger traffic, the average hourly volume of transportation on routes is revealed and the unevenness of traffic volume is calculated.
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Gu, Jian, Miaohua Li, Linghua Yu, Shun Li, and Kejun Long. "Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data." Journal of Advanced Transportation 2021 (April 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/8876626.
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In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.
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Sharma, Tarun, and Sandeep Singh. "Traffic Study and Analysis of Highway (NH5) from Balongi to Kharar." IOP Conference Series: Earth and Environmental Science 889, no. 1 (November 1, 2021): 012054. http://dx.doi.org/10.1088/1755-1315/889/1/012054.
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Abstract Evaluation of in service pavements is very vital for keeping them in good serviceable condition because pavements deteriorate with age and traffic loading. To get a complete idea of the existing condition of any pavement both structural and functional evaluation are necessary. This study aims to investigate the accidental spots, traffic volume, and pavement condition. For this survey, the location of Kharar was chosen i.e. Balongi to Kharar Bus-Stand of 7 km stretch. The road initiates with intersection near Kharar bus stand and passes through many in between intersections near Sunny Enclave, VR Punjab Mall which are prime locations in that area. This road also connects with T junction and connects to NH5 / NH7 via Airport road. The data collected was processed, categorized and analyzed to generate reports for vehicle classification, hourly traffic variation, accidental black spots, pavement condition and origin & destination of trips.
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Liu, Huasheng, Haoran Deng, Yu Li, Yuqi Zhao, and Xiaowen Li. "School Surrounding Region Traffic Commuting Analysis Based on Simulation." International Journal of Environmental Research and Public Health 19, no. 11 (May 27, 2022): 6566. http://dx.doi.org/10.3390/ijerph19116566.
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Student commuting is an important part of urban travel demand and private car commuting plays an important role in urban traffic, especially in areas near schools. Since parents, especially the parents of elementary and junior high school students, prefer to drive rather than take public transport, there will be a negative effect on traffic management. To address the challenge, a simulation model is established based on schools’ surrounding regions to analyze traffic status. Specifically, the model focuses on urban construction and transportation near the entrance of schools and neighborhoods. In addition, four variable parameters consisting of the directional hourly volume, the parking demand of delivery vehicles, the distance between the school and intersection, and the average parking time for pick-up vehicles are set as influence factors, while traffic efficiency, energy consumption, and pollutant emissions are considered as the evaluation criteria of our model. Extensive simulated experiments show that comparing different scenarios, the traffic state of schools’ surrounding areas can achieve much better performance when the distance between entrances and intersections is 400 m under the 1000 pcu/h condition. This research can provide a scientific basis for school regional traffic management and organization optimization.