Automatic Traffic Recorder Research Articles

Innovative Approach to Convert Geographic Information System-Based Highway Models into Multidimensional Building Information Modeling Platforms Using Dynamo

This study proposes an innovative process of transforming a geographic information system (GIS)-based highway model into a robust building information modeling (BIM) platform using Dynamo visual programming tool. The existing GIS system used by the Indiana Department of Transportation for highway network representation has significant limitations of data storage and handling capacity, representation of complex entities, and inclusion of crucial traffic data from weigh-in-motion and automatic traffic recorder stations. The developed process involves converting poly-curves from the GIS model into model-curves compatible with BIM, classifying attributes into shape families, and integrating traffic data from traffic recorder stations. The resulting multidimensional BIM platform, extending from three to eight dimensions, encompasses essential project details and traffic data. To ensure interoperability, an Open BIM process involving the generation of an industry foundation classes (IFC) file is utilized, allowing efficient data exchange among different software platforms and stakeholders.

Imputing Missing Data in Hourly Traffic Counts

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.

Traffic sensor health monitoring using spatiotemporal graphical modeling

Accurate traffic sensor data is essential for traffic operation management systems and acquisition of real-time traffic surveillance data depends heavily on the reliability of the traffic sensors (e.g., wide range detector, automatic traffic recorder). Therefore, detecting the health status of the sensors in a traffic sensor network is critical for the departments of transportation as well as other public and private entities, especially in the circumstances where real-time decision is required. With the purpose of efficiently determining the sensor health status and identifying the failed sensor(s) in a timely manner, this paper proposes a graphical modeling approach called spatiotemporal pattern network (STPN). Traffic speed and volume measurement sensors are used in this paper to formulate and analyze the proposed sensor health monitoring system and historical time-series data from a network of traffic sensors on the Interstate 35 (I-35) within the state of Iowa is used for validation. Based on the validation results, we demonstrate that the proposed approach can: (i) extract spatiotemporal dependencies among the different sensors which leads to an efficient graphical representation of the sensor network in the information space, and (ii) distinguish and quantify a sensor issue by leveraging the extracted spatiotemporal relationship of the candidate sensor(s) to the other sensors in the network.

Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders

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.

Speed Characteristics in Relation to Speed Limit Increase and Its Influence on Driver’s Speed Selection Behavior

Speed is a quality measurement for travel, since it is related to traffic, safety, time, and economics. The speed limit on selected freeways in Kansas changed from 70 mph to 75 mph in the summer of 2011. In this study, the driver’s speed selection behavior was analyzed by considering average speed and 85th percentile speed in the before and after periods. Data from Automatic Traffic Recorders (ATRs) on the sections affected by speed limit increase and sections with no speed limit increase were analyzed. The t-test was applied to investigate if there was any significant difference in the speed of drivers on both treated and control sections. The Kolmogorov-Smirnov (K-S) test was also conducted to see if the distribution of speed data in the before period was different than after the period. The results showed that for the majority of the sections affected by speed limit change, there was a statistically significant difference in the 85th percentile speed of drivers during after period. Additionally, the K-S test results showed that the distribution of speed data in the before period was different than after the period for the majority of treated sections. The results indicated how drivers’ behavior was influenced by the speed limit increase.

Newcastle Traffic Classification Using Clustering Algorithms

The urban road traffic network evolution is complex and varies depend on road type, zoning types and social activities. Typical traffic pattern variation of road network could be examined by considering the daily human travel activities. Thus, factor and cluster analysis is carried out. This paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads based on traffic profile. The analysis was carried out using data available from 45 Automatic Traffic Recorder (ATR) sites in Newcastle, UK. Factor and cluster analysis were applied on the road traffic data so that roads could be classified, allowing diurnal traffic profiles to be assigned a group to roads with similar attributes. These groups could be classify based on road traffic characteristics. Five road classifications were found.

Analiza wpływu zmienności natężenia ruchu na poziom hałasu na drogach

The paper presents the results of analyses concerning the annual variability of the road traffic noise level calculated in the reference point. Because of the large differences between the average noise values and the maximum ones, scaling factors enabling one to convert annual average values into the highest annual averages have been developed. The time range favourable for conducting sampling measurements ensuring the reliable estimation of annual average and highest annual average of the road traffic noise level has been determined. Moreover, factors for scaling the annual average daily traffic (for all vehicles and heavy vehicles, respectively), with a division into two daily periods, have been developed. The scaling factors make it possible to determine the noise level when no direct measurement of traffic intensity can be carried out. The analyses were made using data from 25 automatic traffic recorders (ATRs), equipped with state-of-the-art SICK and Golden river vehicle counters, located on national interurban expressways (class A and S roads).

Development and Evaluation of Recurrent Neural Network-Based Models for Hourly Traffic Volume and Annual Average Daily Traffic Prediction

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.

Estimating Traffic Volume for Local Streets with Imbalanced Data

Annual average daily traffic (AADT) is an important measurement used in traffic engineering. Local streets are major components of a road network. However, automatic traffic recorders (ATRs) used to collect AADT are often limited to arterial roads, and such information is, therefore, often unavailable for local streets. Estimating AADT on local streets becomes a necessity as local street traffic continues to grow and the capacity of arterial roads becomes insufficient. A challenge is that an under-represented sample of local street AADT may result in biased estimation. A synthetic minority oversampling technique (SMOTE) is applied to oversample local streets to correct the imbalanced sampling among different road types. A generalized linear mixed model (GLMM) is employed to estimate AADT incorporating various independent variables, including factors of roadway design, socio-demographics, and land use. The model is examined with an AADT dataset from Seattle, WA. Results show that: (1) SMOTE helps to correct imbalanced sampling proportions and improve model performance significantly; (2) the number of lanes and the number of crosswalks are both positively associated with AADT; (3) road segments located in areas with a higher population density or more mixed land use have a higher AADT; (4) distance to the nearest arterial road is negatively correlated with AADT; and (5) AADT creates spatial spillover effects on neighboring road segments. The combination of SMOTE and GLMM improves the estimation accuracy on AADT, which contributes to better data for transportation planning and traffic monitoring, and to cost saving on data collection.

Development of Statewide Annual Average Daily Traffic Estimation Model from Short-Term Counts: A Comparative Study for South Carolina

Annual Average Daily Traffic (AADT) is an important parameter for traffic engineering analysis. Departments of Transportation continually collect traffic count using both permanent count stations (i.e., Automatic Traffic Recorders or ATRs) and temporary short-term count stations. In South Carolina, 87% of the ATRs are located on interstates and arterial highways. For most secondary highways (i.e., collectors and local roads), AADT is estimated based on short-term counts. This paper develops AADT estimation models for different roadway functional classes with two machine learning techniques: Support Vector Regression (SVR) and Artificial Neural Network (ANN). The models predict AADT from short-term counts. The results are first compared against each other, using the 2011 ATR data, to identify the best models. Then, the results of the best models are compared against both the regression-based model and factor-based model. The comparison reveals the superiority of the SVR model for AADT estimation for different roadway functional classes over all other methods. Among models for different roadway functional classes, developed with the 2011 ATR data, the SVR-based models show minimum errors in estimating AADT compared to the ANN-based, regression-based, and factor-based models, depicting the superiority of the SVR-based model for all roadway functional classes over other models in terms of AADT estimation accuracy. SVR models are validated for each roadway functional class using the 2016 ATR data and short-term count data collected by the South Carolina Department of Transportation (SCDOT). The validation results show that the SVR-based AADT estimation models can be used by the SCDOT as a reliable option to predict AADT from the short-term counts.

Estimating historical hourly traffic volumes via machine learning and vehicle probe data: A Maryland case study

This paper focuses on the problem of estimating historical traffic volumes between sparsely-located traffic sensors, which transportation agencies need to accurately compute statewide performance measures. To this end, the paper examines applications of vehicle probe data, automatic traffic recorder counts, and neural network models to estimate hourly volumes in the Maryland highway network, and proposes a novel approach that combines neural networks with an existing profiling method. On average, the proposed approach yields 24% more accurate estimates than volume profiles, which are currently used by transportation agencies across the US to compute statewide performance measures. The paper also quantifies the value of using vehicle probe data in estimating hourly traffic volumes, which provides important managerial insights to transportation agencies interested in acquiring this type of data. For example, results show that volumes can be estimated with a mean absolute percent error of about 21% at locations where average number of observed probes is between 30 and 47 vehicles/h, which provides a useful guideline for assessing the value of probe vehicle data from different vendors.

Data-driven analysis on the effects of extreme weather elements on traffic volume in Atlanta, GA, USA

Severe weather events pose a significant threat to transportation networks. This research analyzes and discusses the impact of precipitation, temperature, visibility and wind speed on hourly weekday traffic flow volume in Atlanta, Georgia. The study involves the following: determine weather variables that affect traffic volume, develop a machine learning technique to derive decision rules based on weather and traffic volume, and create a web-based decision support visualization tool using the analyzed results. The relationship between extreme weather events and traffic volume was investigated by comparing traffic volume between a base case scenario and an extreme weather scenario. Data from 48 Automatic Traffic Recorder (ATR) sites around Atlanta, GA, USA and hourly precipitation data from 4 climate measurement stations were used to conduct this study. The spatiotemporal relationships between traffic volume and weather variables were analyzed individually and evaluated using a non-parametric statistical test. A machine learning technique is applied to derive decision rules that result in reduction in traffic volume. Results show significant impacts on traffic volume from visibility, precipitation and temperature and helps in isolating hours in a typical weekday when such impacts are felt. A decision support tool was also developed to visualize traffic volume and weather interactions. The data-driven insights from this analysis is applicable to transportation planners, centralized traffic control rooms and urban infrastructure decision makers.

Lifespan Evaluation of Traffic Detector for Automated Traffic Recorders Based on Weibull Distribution

AbstractAn automatic traffic recorder (ATR) makes it easy to detect a vehicle’s speed and the number of axles because it produces highly precise data; thus, it can be used to obtain major traffic c...

Predicting Day and Night Traffic Volumes on Rural Roads for Statistical Road Safety Modeling

Statistical road safety modelers have commonly used some combination of segment length and traffic volume as measures of exposure. Traffic volume is usually represented in statistical road safety models with annual average daily traffic (AADT), which turns out to be a highly influential right-hand-side variable for regression models of expected crash frequency. Models that use AADT alone do not explicitly capture differences in traffic volume patterns throughout the 24-h day; this factor can have significant effects on safety performance. This study adds to the existing literature by developing more disaggregated estimates of traffic volumes for day and night conditions in rural areas and modeling road safety using those estimates. The proposed approach is demonstrated with the data from all automatic traffic recorder stations in Utah, with subsequent safety analysis focused on rural two-lane horizontal curve segments. Universal kriging, along with multiple covariates, proved to be an effective spatial technique for predicting day and night traffic volumes at unmeasured locations using data from permanent traffic-recording stations. Predicted day and night traffic volume estimates were incorporated into statistical road safety models of the expected number of crashes on rural two-lane horizontal curves to determine how this new information affected safety model estimation results. The parameter estimate for the predicted ratio of night-to-day traffic volume was positive and statistically significant and verified the hypothesis that horizontal curves with higher proportions of traffic at night were expected to experience more crashes than similar curves with higher proportions of traffic during the day.

Road safety performance measures and AADT uncertainty from short-term counts

ObjectiveThe objective of this paper is to enable better risk analysis of road safety performance measures by creating the first knowledge base on uncertainty surrounding annual average daily traffic (AADT) estimates when the estimates are derived by expanding short-term counts with the individual permanent counter method. BackgroundMany road safety performance measures and performance models use AADT as an input. While there is an awareness that the input suffers from uncertainty, the uncertainty is not well known or accounted for. MethodThe paper samples data from a set of 69 permanent automatic traffic recorders in Manitoba, Canada, to simulate almost 2 million short-term counts over a five year period. These short-term counts are expanded to AADT estimates by transferring temporal information from a directly linked nearby permanent count control station, and the resulting AADT values are compared to a known reference AADT to compute errors. The impacts of five factors on AADT error are considered: length of short-term count, number of short-term counts, use of weekday versus weekend counts, distance from a count to its expansion control station, and the AADT at the count site. ResultsThe mean absolute transfer error for expanded AADT estimates is 6.7%, and this value varied by traffic pattern group from 5% to 10.5%. Reference percentiles of the error distribution show that almost all errors are between −20% and +30%. Error decreases substantially by using a 48-h count instead of a 24-h count, and only slightly by using two counts instead of one. Weekday counts are superior to weekend counts, especially if the count is only 24h. Mean absolute transfer error increases with distance to control station (elasticity 0.121, p=0.001), and increases with AADT (elasticity 0.857, p<0.001). ImplicationsThese results can support evidence-based risk analysis of road safety performance measures that use AADT as inputs. Analytical frameworks for such analysis exist but are infrequently used in road safety because the evidence base on AADT uncertainty is not well developed.

COMPARATIVE ANALYSIS OF MECHANICAL AND MANUAL MODES OF TRAFFIC SURVEY FOR TRAFFIC LOAD DETERMINATION

Traffic survey was carried out to obtain data that are necessary for traffic study and evaluation. One of the most important characteristics of a traffic stream is its volume which can be defined as the number of vehicles passing through a section of a road per unit time. The road project which is approximately 75 km in length consists of a number of traversed roads, but the traffic study is focused only on the 6 major intersections. The surveys of traffic were conducted using volume count from 7am to 7pm each day because working at night is very unsafe in this area, by using an automatic traffic recorder (Trax 1 Plus) and road tube installation for collecting accurate data with manual counting for comparison. This study aims to analyze the current road traffic data collection methods - comparing both mechanical and manual mode of Traffic Survey simulation - in terms of capabilities and limitations on Nigeria roads using Ogoja (Mbok) Junction and Mfum road in Cross River State as a case study. The tendency of the mechanical counting machine to withstand nonstop24hrs-continuous counting was noted during the exercise compared to manual counting, since security at night cannot be predicted on manual. The design equivalent standard axle load (ESAL) generated from the result shows that Ikom-Cameroon Junction has the highest value with a difference of (0.999x10^ 6 ) between the mechanical and manual, this value shows a downward effect using manual data for the pavement structural thickness evaluation, this can further degenerate to the structural failure at the earlier stage, tensional fatigue, cracks etc. However, traffic study for the period 2015 to 2035 was considered in the analysis, the equivalent standard axle load (ESAL) generated along the section of the corridor and the pavement thickness shows that automatic counter machine is more accurate than the manual. Nonetheless the mechanical counter is more advantageous compared to the manual counter to this reasons the mechanical counter should therefore be employed during the raw data traffic survey. Manual on the other hand less useful for traffic survey in predicting the feature traffic analysis. http://dx.doi.org/10.4314/njt.v35i2.1

Evaluation of Traffic Data Collection Method

Traffic counts are one of the fundamental data sources for the Highway Performance Monitoring System (HPMS). Automatic Traffic Recorders (ATRs) are used to provide continuous traffic count coverage at selected locations to estimate annual average daily traffic (AADT). However, ATR data is often unavailable. This paper investigated the feasibility of using Video Detection System (VDS) technology when ATR data is not available. An Android Tablet-based manual traffic counting application was developed to acquire manual count based ground truth data. The performance of VDS was evaluated under various conditions including mounting styles, heights, and roadway offsets. The results indicated that VDS data presents reasonably accurate data, although the data exhibits more variability compared to ATR data.

Determining the optimal number of seasonal adjustment factor groupings when estimating annual average daily traffic and investigating their characteristics

Although cluster analysis is recommended by the US Traffic Monitoring Guide (TMG) to supplement the development of seasonal adjustment factor groupings (SAFGs), the relationships among SAFGs' characteristics remain undiscovered, while the determination of the optimal number of clusters is an ambiguous task exposed to great subjectivity. Statistical indicators provide a mathematical solution by removing engineering judgment without taking into consideration any guidelines or other criteria, necessary for transportation planners to generate ‘practical and sensible’ groupings. The method examined in this study aims to overcome the above weaknesses incorporating into the methodology a series of statistics, recommendations, and previous research findings. The investigation of the relationships among (1) the within-group variation, (2) the total number of sites, (3) the minimum number of stations within a cluster, (4) the optimal number of clusters, and (5) the geographical size of the groups constitutes the main objectives of this research. According to the results, the cluster variability declines as the available number of stations increases. When the minimum number of stations within a cluster increases, the weighted coefficient of variation inflates as well, with the rate of increase depending on sample size. The average number of automatic traffic recorders per cluster is analogous to the sample size, while the optimal number of clusters varies conversely with the minimum number of stations within a cluster. The application developed for the conduct of the analysis minimizes the computational time needed, while it can be easily implemented by engineers to automate the process recommended by the TMG, enhancing the current state of practice.

Estimation of Seasonal Daily Traffic Flow of Agricultural Products and Implications for Implementation of Automatic Traffic Recorders

Reliable traffic counts on a highway system are critical for sound decision making about the maintenance, operation, and expansion of the system. Portable short-term automatic traffic recorders (ATRs) are a cost-efficient way to complement traffic counts from permanent ATR sites by performing temporary traffic counts on the highway system. Complicating the collection of traffic data with these short-term devices is the seasonal variation in vehicle operations throughout the year. This work focused on predicting the spatial distribution of seasonal traffic resulting from agricultural activities by using a new method that combines geographic information system spatial functions and the four-step travel demand model. This research collected information about township grids for Montana (as proxies for trip origins), grain elevators (trip destinations), agricultural ground cover, and crop yield estimates to estimate flows in tonnage at the grid level on the road network. Results suggest that the proposed method using the location of major crops and the locations of grain elevators can be used to predict tonnage of product that will be added to individual routes. The predicted values can then be compared with reported heavy-truck traffic to locate sites that may have underrepresented traffic flows. Although this work considered specifically three crops, the method can be applied to any resource flow that has known origin and destination information. The method can be enhanced by refining assumptions of the composition of heavy trucks transporting agricultural products and by field measurements of vehicle flows to better test the validity of the model.

Comparison of Clustering Methods for Road Group Identification in FHWA Traffic Monitoring Approach: Effects on AADT Estimates

Defining road groups is the first step in the Federal Highway Administration (FHWA) factor approach procedure for annual average daily traffic (AADT) estimation and is one of the main sources of errors in AADT estimates. This paper focuses on a comparative analysis of cluster analysis methods to identify road groups with similar traffic patterns according to different combinations of seasonal adjustment factors calculated for passenger vehicles and trucks. The aim is to highlight the differences among methods and input variables in the AADT estimation process, optimizing information commonly available to analysts. The analysis made use of traffic data from 50 automatic traffic recorder (ATR) sites in the Province of Venice, Italy. The estimation accuracy of the clustering methods was assessed and compared by considering the values of mean absolute percent error in AADT estimates. The performance of clustering methods was found to differ, depending on data sets and traffic patterns. Particularly significant for the accuracy of AADT estimates was the choice to use seasonal adjustment factors disaggregated by vehicle type as input variables.