Traffic Monitoring Programs Research Articles

Use of axle load spectra (ALS) for estimating calibration drift in weigh-in-motion (WIM) systems

The road agencies collect and submit weigh-in-motion (WIM) data to the Federal Highway Administration as part of their traffic monitoring program. Therefore, the WIM data should be precise and accurate. One way to evaluate WIM measurement errors is by using the test truck data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. This paper presents an approach for estimating WIM system accuracy based on axle load spectra attributes (normalized axle load spectra (NALS) shape factors). This alternative approach allows for characterizing temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to Class 9 NALS shape factors in the proposed methodology. This paper aims to determine WIM system errors based on axle loading without physically performing WIM equipment performance validation using test trucks. The presented methodology can be used to estimate systematic errors (drift) in the WIM system at any point in time after the equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the single axle (SA) and tandem axle (TA) can be estimated with SA and TA NALS shape factors with an acceptable degree of error for bending plate to quartz piezo sensors. Examples are included to demonstrate the application and significance of the developed models.

Quality Assurance Methods for Hourly Nonmotorized Traffic Counts

Growing numbers of public transportation and recreation agencies have implemented automated bicycle and pedestrian traffic monitoring programs. To support these initiatives, researchers and agency analysts have developed programs, methods, and measures to ensure data quality. These methods include statistical tests for identifying and flagging outliers and other invalid counts and procedures for imputing or managing missing and censored counts. Because statistics such as annual average daily bicyclists are used frequently in planning and engineering, analysts have focused on validation of daily counts using data from permanent monitors. Procedures for ensuring the validity of hourly counts have not been standardized. This paper presents quality assurance (QA) methods for hourly nonmotorized traffic counts at 115 short-duration and nine permanent monitoring sites on multiuse trails monitored by the Minnesota Department of Natural Resources (MnDNR) and the Minnesota Department of Transportation (MnDOT) between 2016 and 2021. These methods include statistical tests for flagging and censoring invalid hourly counts and procedures for imputing estimates for censored or missing counts. Differences in traffic flow estimates with and without QA are documented, and hourly patterns and factors are presented. Use of quality assurance methods for hourly counts can increase the validity of estimates of nonmotorized traffic.

A flexible approach to select road traffic counting locations: System design and application of a fuzzy Delphi analytic hierarchy process

Although the diffusion of Information and Communication Technologies provided transportation analysists with new data sources (such as Floating Car and mobile devices), the use of permanent sensors installed on roads is still of paramount importance for traffic analysis. Continuous count stations are one of the main components of a road traffic monitoring program, which could fulfil several aims: (1) planning of a transportation system, (2) traffic management and operations, (3) improvement of road safety, (4) special monitoring for planned/emergency events, and (5) estimation of environmental impacts. In this research activity a Fuzzy Delphi Analytic Hierarchy Process (FDAHP) was applied to select the best locations for a fixed number of permanent count stations, as the basis of a traffic monitoring program that can reach the previously described targets. A real case study was developed in the Città Metropolitana di Venezia (Italy), where budget limitations imposed the need for a scientific and sound framework to decide where new sensors should be installed. Specific objectives and criteria were defined, as well as proper indicators to quantify them using available data. Their relative importance was evaluated by a panel of experts belonging to several agencies, therefore with different perspectives. The information obtained was analysed following the FDAHP approach to produce a ranking of the potential locations. The proposed holistic approach can be considered as a reference framework for a system to support local authorities in creating a traffic monitoring system and can be easily applied to other study areas.

Examination of the Temporal Stability of Daily and Monthly Adjustment Factors of Bicycle Traffic

Monthly adjustment factors (MFs) and daily adjustment factors (DFs) are typically calculated using complete annual data from permanent count stations. These factors are applied at other locations where short-term counts are collected using temporary counters. In this research, the long-term temporal stability of MFs and DFs for bicycle traffic was investigated. Annual daily count data collected from permanent count stations in Vancouver, Canada between 2010 and 2019 were used in the study. A longitudinal analysis of MFs and DFs in the 10 years was undertaken. The coefficient of variation (CoV) of the MFs of the 10 years was found to be less than 12% for the months between April and September, indicating the potential temporal transferability of these factors from one year to a future year. The other 4 months of the year showed a higher CoV with December being the highest (i.e., 25%). As for the DFs, it was found that, depending on the day of week, weather, and month, substantial variation could exist in the calculated factors. Nevertheless, factors developed for dry weekdays in the summer months were shown to exhibit the lowest variability over the 10 years and as such these factors were deemed transferable from one year to another. The results presented in this paper could be beneficial for transportation planners and traffic engineers managing traffic monitoring programs of cycling traffic for which the potential for transferring historical adjustment factors to future years was demonstrated.

Advances in pedestrian travel monitoring: Temporal patterns and spatial characteristics using pedestrian push-button data from Utah traffic signals

In this study, we advanced pedestrian travel monitoring using a novel data source: pedestrian push-button presses obtained from archived traffic signal controller logs at more than 1,500 signalized intersections in Utah over one year. The purposes of this study were to: (1) quantify pedestrian activity patterns; (2) create factor groups and expansion/adjustment factors from these temporal patterns; and (3) explore relationships between patterns and spatial characteristics. Using empirical clustering, we classified signals into five groups, based on normalized hourly/weekly counts (each hour’s proportion of weekly totals, or the inverse of the expansion factors), and three clusters with similar monthly adjustment factors. We also used multinomial logit models to identify spatial characteristics (land use, built environment, socio-economic characteristics, and climatic regions) associated with different temporal patterns. For example, we found that signals near schools were much more likely to have bimodal daily peak hours and lower pedestrian activity during out-of-school months. Despite these good results, our hourly/weekday patterns differed less than in past research, highlighting the limits of existing infrastructure for capturing all kinds of activity patterns. Nevertheless, we demonstrated that signals with push-button data are a useful supplement to existing permanent counters within a broader pedestrian traffic monitoring program.

Development and attribution of a linear referencing system for managing and disseminating traffic volume data on rural highway networks

Developing and disseminating system‐wide traffic volume data are critical objectives of traffic monitoring programs. Jurisdictions commonly use maps to disseminate traffic volume data and visualize spatial traffic patterns throughout a highway network. A linear referencing system is essential in this process, yet limited research is available on the methods of sequencing (segmenting) a highway network and attributing traffic volume data to those sequences. This paper aims to fill this knowledge gap by describing the development and application of a generic three‐phase methodology: (1) to subdivide a rural highway network into sequences assumed to have uniform or homogeneous traffic volume by applying objective criteria; (2) to attribute traffic volume data to the sequenced highway network based on a set of attribution principles; and (3) to evaluate and refine the results. Application of the methodology in Manitoba resulted in the development and attribution of a new linear referencing system comprising 2,164 sequences. This provided the foundation for mapping spatial fluctuations in traffic volume at the network level. More generally, it revealed the importance of periodically evaluating the data visualization and dissemination impacts that arise when changes are made to the sampling procedure within a traffic monitoring program and when physical modifications occur on the highway network.

Diagnosing the Effects of Weather Factors on Cold Region Highway Traffic Operation with Two Layers of Nonparametric Statistical Analysis

Highway agencies use significant financial resources to operate traffic monitoring programs. Conventional traffic monitoring practices are operated only during weather-permissible periods, leading to inefficient traffic monitoring caused by restricted sample sizes. None of the previous studies considered the possibility of executing a traffic monitoring program throughout the entire year including wsevere winter weather conditions. The objective of this research was to diagnose the influence of snowfall and temperature on traffic volume changes according to vehicle class by employing two phases of statistical approaches and to propose unique traffic investigation programs for a different type of vehicle. This research used huge vehicular records archived for 6 years at the weigh-in-motion site operated in Leduc City, Alberta, Canada. Three vehicle classes classified from the raw data were used in the analysis. Three classes were total traffic, passenger cars, and truck traffic. Both layers of the statistical approach reported that passenger vehicles are impacted by climate elements. However, the truck was not significantly affected by snowfall and temperature. The results of this investigation will support highway agencies in developing evidence-based programs for traffic counting and road maintenance in winter.

Assessing the effect of snowfall and cold temperature on a commuter highway traffic volume using several layers of statistical methods

Presented in this paper is an application of the parametric and non-parametric statistical modelling technique to assess and diagnosis the impact of snowfall and cold temperature on classified vehicle volume variations with five years of Weigh-in-Motion (WIM) data recorded at the commuter type highway nearby the City of Leduc in Alberta, Canada. The influence of the winter driving conditions on classified vehicle volumes travelling in mixed traffic stream is examined. The first phase involves developing parametric dummy-variable regression models to relate and then quantify the variations of daily traffic volumes associated with snowfall and temperature. The second phase involves utilizing a non-parametric Chi-square test combined with the Binomial and the Friedman test. Two levels of statistical tests commonly revealed that the total traffic and passenger car volumes are influenced by both snowfall and temperature, but the truck traffic volumes are not affected significantly. The results of this study may contribute to presenting a new statistical methodology for observing changes in highway traffic in winter. It can also provide important insights for highway agencies to develop efficient traffic monitoring programs throughout the year.

Alternative Methods for Estimating Seasonal Factors and Accuracy of Daily Volumes They Yield

AbstractThe Federal Highway Administration requires each state department of transportation to have a traffic monitoring program for estimation of among others annual average daily traffic (AADT), ...

Minimizing Annual Average Daily Nonmotorized Traffic Estimation Errors: How Many Counters Are Needed per Factor Group?

Accurate estimates of bicycle and pedestrian volume inform safety studies, trend monitoring, and infrastructure improvements. The Federal Highway Administration’s Traffic Monitoring Guide advises current practice for estimation of nonmotorized traffic. While methodologies have been developed to minimize error in estimation of annual average daily nonmotorized traffic (AADNT), challenges persist. This study provides new guidance for monitoring and volume estimation of nonmotorized traffic. Using continuous count data from 102 sites across six cities, the findings confirm that mean absolute percent error (MAPE) in estimated AADNT is minimized when seven-day short duration counts are collected in June through September and for 24-h counts, when data are collected Tuesdays through Thursdays (except for pedestrian-only counts). MAPE across all days (except holidays) and seasons was 34% for 24-h and 20–22% for seven-day short duration counts. The magnitude of bicycle and pedestrian volumes did not significantly affect estimation errors. For factor groups larger than one counter, the length of short duration samples may influence accuracy of AADNT estimates more than the number of counters per group, all else equal. To maximize precision of estimates of AADNT, four or more counters per factor group for bicycle and five or more for pedestrian travel monitoring are recommended. These findings provide guidance for practitioners seeking to establish or improve nonmotorized traffic monitoring programs.

Investigating and validating methods of monitoring foot-traffic in night-time entertainment precincts in Australia

BackgroundAssaults occur frequently in night-time entertainment precincts (NEPs), with rates typically reported using estimated resident population. However, this form of reporting does not accurately represent the number of people within the NEP at the time of an assault or potential fluctuations in density throughout the course of the night. As such, the aim of this study was to assess multiple methods of obtaining an accurate estimate of hourly foot-traffic within NEPs. MethodsThe validity and reliability of three types of foot traffic counters were assessed. A passive-infrared sensor and two different types of smartphone sensor were installed at two sites in Australia from 2016 to 2018, ongoing (pilot phase: 2016–2017; validation phase: 2018). Researchers also manually counted the number of people walking past through the range of two of these sensors across the course of Friday, Saturday and Sunday nights between 8 pm to 2am. ResultsResults show a similar trend between the smartphone counts, the sensor counts, and the manual counts; however there was notable variability (43%–267% compared with manual counts). Analysis showed that all measures were significantly positively correlated. ConclusionReliable counting of the number of people attending nightlife precincts is an important element of ongoing studies into nightlife settings and associated rates of harm. There are multiple methods of estimating fluctuations in foot traffic within a NEP, however, determining the most appropriate method to use requires consideration of the proximity of pathways in the area, budget constraints, and project aims. Of the methods tested, laptop WiFi traffic monitoring programs functioned the least consistently. Specifically designed smartphone sensors overcame this issue; however, they required dedicated power sources. The current study found infrared scanners appeared to be the most accurate across sites; additionally they functioned consistently, and were the simplest method to setup and maintain.

Incorporating the Effect of Special Events into Continuous Count Site Selection for Pedestrian Traffic

This paper presents results from pedestrian monitoring research conducted in a dense urban environment in Winnipeg, Canada. Pedestrian counts were conducted in downtown Winnipeg using infrared pedestrian counters. Count sites were assigned to traffic pattern groups (TPGs) based on their response to special events occurring in the study area. Once these groups were established, eight continuous count sites were installed to initiate an ongoing pedestrian traffic monitoring program for the city. Traffic monitoring efforts have primarily focused on motorized travel. As more jurisdictions prioritize active transportation, addressing the need for network-level pedestrian data is essential to optimize engineering decisions. The first step to developing any system-wide traffic monitoring program is to define TPGs. These groups enable the spatial variation of short-duration counts to be adjusted to annual statistics by the temporal variation of similarly behaving continuous counts. Short-duration count sites were characterized by daily and hourly trends consistent with existing pedestrian traffic monitoring practices. Recognizing the influence of large evening events on pedestrian traffic, a metric was developed called the evening proportion ratio (EPR) to quantify the effect of special events. Based on the spatial distribution of EPR values, two TPGs were developed for downtown Winnipeg. These are the “urban utilitarian” and “urban utilitarian – event” groups. These groups were used to select continuous count locations for ongoing pedestrian traffic data collection. The importance of this research lies in its future applicability to other jurisdictions in developing a standard approach for urban transportation authorities to strategically implement pedestrian traffic monitoring programs.

Statistical Investigation of Truck Type Distribution on Cold Region Highways During Winter Months

Recent research on impact of weather interaction on classified traffic volume variations on provincial highways in cold regions showed that the total truck volume is not affected with severity of snowfall and cold temperature. However, no research is conducted to analyze the variations in truck distributions, despite of its importance for truck counting and monitoring program. Described in this paper is the statistical investigation of association of truck type distribution on cold region highways during severe winter months and seasons in a year. The investigation is based on weigh-in-motion data collected from six sites located on five provincial highways in Alberta, Canada. Trucks were classified into three types such as straight unit, single trailer, and multi trailer using the FHWA vehicle classification scheme. Two statistical tests namely Chi-squared test and Binomial probability test are applied to analyze the distributional change in three different truck classes during high snowfall and low temperature conditions. The analysis suggested that the truck type distribution does not change from winter to non-winter season for regional commuter road (Highway 2A) and long distance roads (Highway 2 and Highway 16). Also, no change in truck distribution from month to month was noticed during sever winter months. Consistent results were not found for special roads such as Highway 44 due to difference in road user characteristics. The study findings have practical implications for rationalization of the length and frequency of traffic counts including classified traffic monitoring programs throughout the year. The knowledge about independency of truck type distribution with various seasons is likely to help in effective traffic monitoring and estimation of the highway planning and design parameters like Truck Annual Average Daily Traffic (TAADT), Truck Average Daily Traffic (TADT) and Design Hour Truck Volume etc.

Creating a Pedestrian Count Program in Downtown Winnipeg ( breakout presentation )

Background In the wake of an increasing number of North American jurisdictions moving to prioritize active modes of travel, research into traffic data collection has progressed beyond its infancy to a point where cities have begun network wide active transportation monitoring programs. This trend motivated by shifting societal values towards environmental sustainability, public health, and urban vibrancy; requires accurate and accessible data to best inform investment decisions and network improvements. Most pedestrian data collection of this variety has been conducted in an ad hoc manner on multi-use paths or simple sidewalk networks and often as a supplement to existing motorized traffic monitoring. In light of this, there is a clear need for a standard approach to collecting and reporting pedestrian volume data in highly urbanized areas. Method Documenting work performed for the City of Winnipeg, this research will provide a summary of actions taken and lessons learned in producing annual pedestrian volume statistics from Winnipeg’s downtown area. Using automated pedestrian counting devices, fourteen-day counts at 45 unique locations were conducted throughout the downtown area to characterize pedestrian activity. Following this, eight locations were selected to be monitored continuously throughout 2016. This enabled a detailed analysis of pedestrian trends throughout the year as well as the opportunity to extrapolate the results of existing short duration counts. Aim This data is currently being used to develop an ongoing pedestrian traffic-monitoring program for the City of Winnipeg. Once complete, this program will be used to estimate annual pedestrian volumes at locations where short duration counts are performed. This will serve as a vital tool to provide insights for transportation professionals, city planners, as well as community and activist groups to support design decisions. Results/Conclusion The two studies performed resulted in over 4.2 million pedestrians counted in 3480, 24-hour counts. This data has been used to distinguish two types of pedestrian activity in Winnipeg’s downtown area which can be used to estimate annual statistics. Additionally, summaries have been produced and presented to the City of Winnipeg, which describe yearly trends in pedestrian activity that will be used for engineering, planning, and policy design purposes.

Designing a bicycle and pedestrian traffic monitoring program to estimate annual average daily traffic in a small rural college town

Cycling and walking are commonly recognized as energy-efficient alternatives to motorized transport. Research and practice lack a comprehensive set of methods to assess spatiotemporal patterns of traffic volumes across an entire transportation network. Current non-motorized traffic monitoring programs are primarily implemented in urban areas and for singular components of the network (e.g., off-street trails, specific corridors). Our approach synthesizes ongoing efforts in non-motorized traffic monitoring to estimate Annual Average Daily Traffic (AADT), across an entire network in Blacksburg, VA – a small, rural college town. We selected count sites across the network, stratified by street functional class (e.g., major roads, local roads), centrality of the link relative to origins and destinations, and planned bicycle facilities. We collected 45,456h of pedestrian and cyclist counts using three types of automated counters: pneumatic tube (n=12), passive infrared (n=10), and radio beam (n=3) at both reference locations (n=4; 1-year) and short-duration locations (n=97; 1-week) during 2015. We found a strong correlation between manual validation counts and automated counts. We used day-of-year scaling factors to estimate AADT for bicycles and pedestrians and found that temporal and spatial patterns differed between modes. Pedestrian volumes were higher and more variable than bicycle volumes (median [interquartile range] AADT for pedestrians: 135 [89–292]; bicycles: 23 [11–43]); both modes were positively correlated with street functional class, presence of facilities, and proximity to campus. Our approach provides insight for planners or policymakers interested in comprehensive monitoring programs to track performance measures or for use in environmental and health impact studies.

Evaluating Annual Average Daily Traffic Calculation Methods with Continuous Truck Traffic Data

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.

Characterization of crash-prone drivers in Saudi Arabia – A multivariate analysis

This study conducted a survey of traffic crashes with the data collected from police stations in the three cities of Saudi Arabia involving different features related to crashes, drivers, vehicles, and understanding of traffic signs. Among the chauffeurs, drivers at fault and not at fault were separated and investigated through factor analysis for 19 parameters related to their background and knowledge of traffic signs. The data show that a particular age group and time of the day may provide more insights to characterize the overall crashes in these cities. The factor analysis shows that the drivers at fault and not at fault may have distinguishable profile. Logit models were developed to quantify the effects of these variables. The models show that driver’s experience and knowledge of traffic signs for chauffeurs has positive impact on reducing faulty behavior of drivers. Approximately, 68%–74% of the original variables are required to characterize chauffeurs, indicating the possibility of data reduction in traffic safety monitoring program. This study may assist in profiling the chauffeurs involved in crashes and reducing the parameters to be monitored for traffic safety program. The recommendations of this study may be considered beneficial in making policy for licensing and hiring of chauffeurs.

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

There are ongoing efforts to leverage the large volume of data collected to support real-time traffic operations for various non-real-time uses. However, adapting data streams to a different purpose can be challenging, especially if different applications use the data in different ways. Although traffic operations and traffic monitoring programs often use similar technology for vehicle detection, they may have different sensitivities to potential errors. Although small errors in traffic counts may not be critical in traffic operations, for which data are refreshed every few seconds, the same error levels could become much more significant in traffic monitoring, for which data, and the corresponding errors, are typically aggregated over longer periods of time. This study investigates if and how traffic volume data from detectors at signalized intersections could be appropriately used in a traffic monitoring program that supports the Highway Performance Monitoring System (HPMS). For this purpose, this study evaluates both the accuracy and representativeness of these traffic signal detector data in comparison to standard HPMS-type traffic count data obtained by portable pneumatic counters under different traffic flow conditions and intersection geometries. The results, although varying by site, indicate that these traffic signal detectors can produce 15-min aggregate traffic counts of comparable quality to results from portable pneumatic tube counters and provide 90% accuracy at a 95% level of confidence for 15-min aggregate counts. Through an examination of the representativeness of the data under different conditions, the study develops a set of eligibility criteria that can be used to identify intersections that are suitable for performance monitoring data collection.

Using Knowledge-automation Expert Systems to Enhance the Use and Understanding of Traffic Monitoring Data in State DOTs

Traffic monitoring is the process of observing and collecting data that describes the use and performance of the roadway systems. Strong and effective traffic monitoring program not only improves the mobility on roadways and reliability of decisions based on traffic data, but also ensures states are receiving appropriate federal funding. Rapidly evolving traffic data collection technologies; changing computing and communication technologies; receiving, processing and understanding of big data sets; economic, environmental and geographic constraints for data collection; and many other general or state specific limitations increased the complexity of traffic monitoring programs. This study aims to investigate the use of knowledge-automation expert systems in states’ traffic monitoring programs. This proposed approach is expected to increase the understanding and use of traffic data by providing situation specific advice based on user input and requests.

Automated Turning Movement Counts for Shared Lanes: Leveraging Vehicle Detection Data

Turning movement count data (i.e., vehicle volumes broken down by movement, approach, and time periods) are the foundation of signal performance evaluations and a crucial component of a data-driven decision-making process used by transportation agencies. In this paper, the authors show how data available from intersections equipped with radar-based vehicle detection can be used to produce turning movement counts. A classification algorithm developed and discussed by the authors is capable of producing turning movement counts regardless of lane configuration and without the need for definition of detection zones. The algorithm works by using the data produced by vehicle detection systems that go unused and are never communicated to the signal controller. The nature of the data collection process makes the algorithm independent of the controller used. Results from the algorithm are promising; an average error of −0.26 vehicles per 15-min count period (absolute error of 2.31 vehicles) was obtained with the algorithm. Furthermore, the application of the algorithm provides an opportunity beyond signal operations. Processed trajectory data and results from the algorithm could be used to break the boundary that often exists between operations, planning, and safety, and thus show how a monitoring system that relies on the algorithm could help a traffic monitoring program meet the different—and sometimes competing—interests of agencies.