GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data

Annual average daily traffic (AADT) serves the important basic data in transportation sector. Despite of its importance, AADT is estimated through permanent traffic counts (PTC) at limited locations because of constraints in budget and so on. At most of locations, AADT is estimated using short-term traffic counts (STC). Though many studies have been carried out at home and abroad in an effort to enhance the accuracy of AADT estimate, the method to simplify average STC data has been adopted because of application difficulty. A typical model for estimating AADT is an adjustment factor application model which applies the monthly or weekly adjustment factors at PTC points (or group) with similar traffic pattern. But this model has the limit in determining the PTC points (or group) with similar traffic pattern with STC. Because STC represents usually 24-hour or 48-hour data, it`s difficult to forecast a 365-day traffic variation. In order to improve the accuracy of traffic volume prediction, this study used the geostatistical approach called co-kriging and according to their reports. To compare results, using 3 methods : using adjustment factor in same section(method 1), using grouping method to apply adjustment factor(method 2), cokriging model using previous year`s traffic data which is in a high spatial correlation with traffic volume data as a secondary variable. This study deals with estimating AADT considering time and space so AADT estimation is more reliable comparing other research.

SUMMARYThe precise estimation of the annual average daily traffic (AADT) is a task of significant interest for many transportation authorities and Departments of Transportation. In this study, three methods are developed to improve the assignment of short‐term counts to seasonal adjustment factor (SAF) groupings: the traditional functional classification, a discriminant analysis (DA), and a new statistical approach based on a weighted coefficient of variation (WCV). The data analyzed within this study are generated from all available continuous counters within the State of Ohio between 2002 and 2006. The analysis is conducted using SAFs that are separately calculated for the total volume and the directional specific volumes of a site. The results show that the directionally based assignment errors are statistically lower at a 95% confidence interval when compared with those generated by the total volume analysis. It is also found that the hourly time‐of‐day factors are more important in the assignment process than the average daily traffic. The directionally based WCV produces a decline in the average mean absolute percentage error (MAPE) over the roadway functional classification by 58% and in the standard deviation of the absolute error (SDAE) by 70%. On the contrary, the directionally based DA lowers the MAPE and the SDAE by 35% and 60%, respectively. Copyright © 2012 John Wiley & Sons, Ltd.

Reliable estimates of heavy-truck volumes in the United States are important in a number of transportation applications including pavement design and management, traffic safety, and traffic operations. Additionally, because heavy vehicles emit pollutants at much higher rates than passenger vehicles, reliable volume estimates are critical to computing accurate inventories of on-road emissions. Accurate baseline inventories are also necessary to forecast future scenarios. The research presented in this paper evaluated three different methods commonly used by transportation agencies to estimate annual average daily traffic (AADT), which is used to determine vehicle miles traveled (VMT). Traffic data from continuous count stations provided by the Iowa Department of Transportation were used to estimate AADT for single-unit and multiunit trucks for rural freeways and rural primary highways using the three methods. The first method developed general expansion factors, which apply to all vehicles. AADT, representing all vehicles, was estimated for short-term counts and was multiplied by statewide average truck volumes for the corresponding roadway type to obtain AADT for each truck category. The second method also developed general expansion factors and AADT estimates. Truck AADT for the second method was calculated by multiplying the general AADT by truck volumes from the short-term counts. The third method developed expansion factors specific to each truck group. AADT estimates for each truck group were estimated from short-term counts using corresponding expansion factors. Accuracy of the three methods was determined by comparing actual AADT from count station data to estimates from the three methods. Accuracy of the three methods was compared using n-fold cross-validation. Mean squared error of prediction was used to estimate the difference between estimated and actual AADT. Prediction error was lowest for the method that developed separate expansion factors for trucks. Implications for emissions estimation using the different methods are also discussed.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns. Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions. Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods' performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others. Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Background: The annual average daily traffic (AADT) data from road segments are critical for roadway projects, especially with the decision-making processes about operations, travel demand, safety-performance evaluation, and maintenance. Regular updates help to determine traffic patterns for decision-making. Unfortunately, the luxury of having permanent recorders on all road segments, especially low-volume roads, is virtually impossible. Consequently, insufficient AADT information is acquired for planning and new developments. A growing number of statistical, mathematical, and machine-learning algorithms have helped estimate AADT data values accurately, to some extent, at both sampled and unsampled locations on low-volume roadways. In some cases, roads with no representative AADT data are resolved with information from roadways with similar traffic patterns. Methods: This study adopted an integrative approach with a combined systematic literature review (SLR) and meta-analysis (MA) to identify and to evaluate the performance, the sources of error, and possible advantages and disadvantages of the techniques utilized most for estimating AADT data. As a result, an SLR of various peer-reviewed articles and reports was completed to answer four research questions. Results: The study showed that the most frequent techniques utilized to estimate AADT data on low-volume roadways were regression, artificial neural-network techniques, travel-demand models, the traditional factor approach, and spatial interpolation techniques. These AADT data-estimating methods’ performance was subjected to meta-analysis. Three studies were completed: R squared, root means square error, and mean absolute percentage error. The meta-analysis results indicated a mixed summary effect: 1. all studies were equal; 2. all studies were not comparable. However, the integrated qualitative and quantitative approach indicated that spatial-interpolation (Kriging) methods outperformed the others. Conclusions: Spatial-interpolation methods may be selected over others to generate accurate AADT data by practitioners at all levels for decision making. Besides, the resulting cross-validation statistics give statistics like the other methods' performance measures.

Traffic monitoring agencies collect traffic data samples to estimate annual average daily traffic (AADT) at short duration count sites. The steps to estimate AADT from sample data introduce error that manifests as uncertainty in the AADT statistic and its applications. Past research suggests that the assignment of a short duration count site to a traffic pattern group (TPG), characterized by known traffic periodicities, represents a significant but poorly quantified source of error. This paper presents an approach to quantify the range of errors arising from such assignments and to mitigate these errors using a novel data-driven assignment method. The approach uses simulated 48-hour short duration counts sampled from continuous count sites with known AADT to develop a benchmark of the total error expected when AADT is estimated from such samples. Likewise, the analysis produces a set of AADT estimates using temporal factors from pre-defined TPGs to quantify the range of assignment errors. The data-driven assignment method aims to mitigate these errors by minimizing the absolute mean deviation in AADT estimates produced from multiple short duration counts in a single year. The approach is applied to traffic data collected in Manitoba, Canada, as a case study. The results indicate that the mean absolute error from 48-hour short duration counts is 6.40% of the true AADT and that improper assignment can lead to a range in mean absolute errors of 9%. When applied to previously unassigned sites, the data-driven assignment method reduced mean absolute errors from 10.32%, using a conventional assignment method, to 7.86%.

**Traffic increases have made road infrastructure construction difficult for transport planners and operators, risking pavement collapse or resource waste. Egyptian roads have deteriorated due to traffic loads, requiring periodic maintenance and reconstruction. Traffic data is scarce in developing countries like Egypt, making these analyses difficult. This study estimates traffic growth rates across the country using 2008–2018 Egyptian data to address traffic forecasting difficulties. Annual average daily traffic (AADT), population density, and other socioeconomic variables are used in regression analysis to estimate traffic increases. The AADT socio-economic relationship was substantial, with adjusted R2 values from 0.74 to 0.99. Comparing predicted traffic to last year's traffic data at each station in the research showed appropriate results for the proposed models. AADT error rates for proposed models and true values were 0.00–0.025. Thus, traffic growth rate data can improve and develop new roads in such regions. These findings enhance planning accuracy for future projects.

Traffic volume data, especially average annual daily traffic (AADT), are important in transportation engineering. They are required in managing and maintaining existing facilities and in planning and designing new facilities. Many state highway agencies use the ramp counting procedure described in FHWA’s Traffic Monitoring Guide to estimate AADTs for freeways. The procedure involves counting all entrance and exit ramps between two established mainline counters (anchor points) and then reconciling the count data to estimate mainline AADT. The reconciling of count data includes three steps. First, AADTs for the ramps and the anchor points are estimated from the count data. Then AADT for each uncounted mainline link is calculated by addition or subtraction of ramp AADT to or from mainline AADT, starting from one anchor point. Finally, adjustments of the AADT are performed to achieve a match at the second anchor point if necessary. The process can be time-consuming and labor-intensive if it is done manually. A computer program to automate the process is required. The traffic count analysis system (TCAS) developed to automate the reconciling of count data in the ramp counting process is described. The TCAS was developed on the basis of data coaptation and data reconciliation techniques frequently used in the processing of network flow rate data. Data coaptation is used to calculate flow rates for uncounted links, and data reconciliation is used to adjust and balance the flow rates. The TCAS has been tested for the two longest freeways in Connecticut. The results are close to those from the ramp counting procedure. However, the TCAS significantly reduces the time and labor required for processing traffic volume data for freeways.

This report examines the experience of state and provincial departments of transportation (DOTs) with strategic planning and synthesizes current approaches to linking strategic planning with other decision-making processes, including operational and tactical planning, resource allocation, performance management, and performance measurement. It will be of interest primarily to chief executive officers, executive team members, and other officials who are responsible for developing, supporting, and using strategic management systems in state and provincial DOTs. The report is intended to help these industry leaders strengthen the overall performance of their organizations by examining exemplary practices in various DOTs. Case studies are also provided documenting one transportation agency that has used strategic planning over an extended period and one that recently implemented strategic planning. This synthesis report contains information drawn from survey responses from U.S. state and Canadian provincial transportation agencies. Follow-up telephone interviews were conducted with relevant personnel from many of those agencies that responded to the survey and some that did not to clarify responses and probe additional issues. A review of the relevant literature was also undertaken to provide background on the topic, help define the overall approach, and discuss the limitations of strategic planning.

The purpose of this research is to evaluate the environmental commitment tracking systems (ETSs) used by other state departments of transportation (DOTs) in an effort to determine which system would be the most beneficial for long-term implementation at the Colorado DOT. The authors developed a quantitative decision-making framework to identify and prioritize the features that the Colorado DOT prefers and evaluate ETSs used by other state DOTs with respect to those features to provide recommendations as to which ETS the Colorado DOT should adopt. ETSs from California, Florida, Kentucky, New York, Tennessee, Texas, Virginia, and Washington were evaluated. The quantitative decision-making framework consists of four steps: (a) conduct interviews to identify the features that the Colorado DOT prefers to have in its ETS, (b) assign weights to those features to establish their importance relative to each other on the basis of Colorado DOT preferences by using the analytic hierarchy process, (c) collect data from the above-mentioned eight state DOTs through surveys to identify which features their ETSs have, and (d) perform a quantitative evaluation of those ETSs with their respective weights according to Colorado DOT preferences. Application of this framework identified the ETSs that are most closely correlated with Colorado DOT preferences and resulted in a number of recommendations to the Colorado DOT. Although the findings presented are specific to the Colorado DOT, the quantitative decision-making framework can be used by any DOT to evaluate an ETS.

Seasonal factor is an important parameter for converting coverage counts to annual average daily traffic (AADT). There have been many studies on establishing seasonal factor (SF) categories, but there is a limited understanding of how to assign SF groups to short-term count sites. In the current practice, established factor groups are typically assigned based on function class, the physical proximity of short count sites to a permanent traffic count site, and other engineering judgment. There is no sound theoretical basis or rules to guide the seasonal factor assignment process, which results in inaccurate AADT estimates for coverage count sites. This paper describes a data-driven procedure for assigning a seasonal factor category to a given portable count site by taking into consideration the similarities between the characteristics of permanent count sites in the seasonal factor group and the portable count site. A fuzzy decision tree was constructed based on the known SF groupings of permanent count sites and their land use attributes. The decision tree was then applied to determine the SF category for a given portable count site in Broward, Miami-Dade, and Palm Beach counties in Florida.

Improving Group Assignment and AADT Estimation Accuracy of Short-term Traffic Counts using Historical Seasonal Patterns & Bayesian Statistics

P02 Promotion of Active Transportation among State Departments of Transportation in the US

Effects of Using Average Annual Daily Traffic (AADT) with Exogenous Factors to Predict Daily Traffic

As state departments of transportation (DOTs) and local transportation agencies make difficult funding decisions in these financially constrained times, access management improvements provide a cost-effective alternative for improving the safety and operation of roadways. Mindful of the benefits of implementing access management treatments, the Michigan DOT has conducted an access management program since 2002. The Michigan DOT contracted with the Texas Transportation Institute (TTI) to provide recommendations for adjustments to the Michigan access management program. As a result of the evaluation, TTI identified lessons learned related to the importance of (a) continuing support and funding for the access management program, (b) a clear access management policy statement, (c) coordination and cooperation between state and local transportation agencies, (d) continued training, (e) interagency coordination across state DOT divisions, (f) continued review and update of technical documentation and guidance, and (g) continued objective program evaluations. The lessons learned that are documented and described in this paper are invaluable reminders to practitioners in state DOTs with established programs and excellent points to consider for practitioners in state DOTs just beginning to foster access management implementation. Practitioners from local transportation agencies (e.g., cities, villages, counties, and townships) that are implementing access management or planning to implement access management will also find these lessons learned invaluable. The research community will also benefit from the lessons learned identified in this paper because the findings validate the procedural, political, and institutional considerations often touted as imperative to successful implementation of access management.