Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.     

Alternatives in assigning short‐term counts to seasonal adjustment factor groupings

The 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.     

An intersection turning movement estimation procedure based on path flow estimator

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin–destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd.     

Rationalizing Reliable Imputation Durations of Genetically Designed Time Delay Neural Network and Locally Weighted Regression Models

Abstract

Estimating missing values is known as data imputation. Previous research has shown that genetic algorithms (GAs) designed locally weighted regression (LWR) and time delay neural network (TDNN) models can generate more accurate hourly volume imputations for a period of 12 successive hours than traditional methods used by highway agencies. It would be interesting and important to further refine the models for imputing larger missing intervals. Therefore, a large number of genetically designed LWR and TDNN models are developed in this study and used to impute up to a week-long missing interval (168 hours) for sample traffic counts obtained from various groups of roads in Alberta, Canada. It is found that road type and functional class have considerable influences on reliable imputations. The reliable imputation durations range from 4–5 days for traffic counts with most unstable patterns to over 10 days for those with most stable patterns. The study results clearly show that calibrated GA-designed models can provide reliable imputations for missing data with ‘block patterns’, and demonstrate their further potentials in traffic data programs.     

Estimating vehicle miles traveled combined with ITS data

‘Vehicle miles traveled’ (VMT) is an important performance measure for highway systems. Currently, VMT [or ‘annual average daily traffic’ (AADT)] is estimated from a combination of permanent counting stations and short-term counts done at specified locations as part of the Highway Performance Monitoring System (HPMS) mandated by the US Federal Highway Administration. However, on some roadway sections, Intelligent Transportation Systems (ITS) such as detectors and cameras also produce traffic data. The question addressed in this paper is whether and under what conditions ITS systems data could be used instead of HPMS short-term counts (called ‘coverage counts’)? This paper develops a methodology for determining a threshold number of missing daily traffic counts, or alternatively, the number of valid ITS data observations needed, in order to confidently replace the HPMS coverage counts with ITS data.

Because ITS counts, coverage counts, and actual ground counts (e.g. continuous counts) cannot be found coexisting on a roadway section, it is hard to compare them directly. In this paper, the Monte Carlo simulation method is employed to generate synthetic ITS counts and coverage counts from a set of relatively complete traffic counts collected at a continuous count station. Comparisons are made between simulated ITS counts, coverage counts, and actual ground counts. The simulation results indicate that when there are<330 daily traffic counts missing in a set of ITS counts in a year, that is, when there are at least 35 days of valid data, ITS counts can be used to derive a better AADT than using coverage counts. This result is applied to calculate the VMT for the Hampton Roads region in Virginia. The comparison between the VMTs derived with using and not using the threshold number indicates that these two VMTs are significantly different.     

Improving the estimation of total and direction-based heavy-duty vehicle annual average daily traffic

Abstract

The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4–13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four cluster-based methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings – which are consistent with previous total volume studies – further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.     

Data dependent input control for origin–destination demand estimation using observability analysis

In this paper, we address the observability issue of static O–D estimation based on link counts. Unlike most classic observability analyses that relied only on network topological relationships, our analysis incorporates the actual values of input parameters, thus including network operational relations as well. We first analyze possible mathematical properties of an O–D estimation problem with different data input. We then propose a modeling approach based on mixed-integer program for selecting model input that ensures observability and estimation quality. Through establishing a stronger connection between observability analysis and the corresponding estimation problem, the proposed method aims to improve estimation quality while reducing reliance on erroneous data.     

Cellpath: Fusion of cellular and traffic sensor data for route flow estimation via convex optimization

A new convex optimization framework is developed for the route flow estimation problem from the fusion of vehicle count and cellular network data. The issue of highly underdetermined link flow based methods in transportation networks is investigated, then solved using the proposed concept of cellpaths for cellular network data. With this data-driven approach, our proposed approach is versatile: it is compatible with other data sources, and it is model agnostic and thus compatible with user equilibrium, system-optimum, Stackelberg concepts, and other models. Using a dimensionality reduction scheme, we design a projected gradient algorithm suitable for the proposed route flow estimation problem. The algorithm solves a block isotonic regression problem in the projection step in linear time. The accuracy, computational efficiency, and versatility of the proposed approach are validated on the I-210 corridor near Los Angeles, where we achieve 90% route flow accuracy with 1033 traffic sensors and 1000 cellular towers covering a large network of highways and arterials with more than 20,000 links. In contrast to long-term land use planning applications, we demonstrate the first system to our knowledge that can produce route-level flow estimates suitable for short time horizon prediction and control applications in traffic management. Our system is open source and available for validation and extension.     

A model for national freight flows,distribution centers,empty trucks and urban truck movements

Trucks travel both short distances for local deliveries and long distances for transporting goods across the country. Often their travel behavior is tour-based, they run under tight schedules and under curfew on selected roads. Despite these differences from personal travel, in practice truck models largely follow person travel methods. To overcome this shortcoming, a two-layer truck model is developed for the Chicago Metropolitan Area. Long-distance trucks are driven by commodity flows, with distribution centers, rail yards, marine ports and airports being represented explicitly. Empty trucks are accounted for as well. For the short-distance truck model, a novel parameter estimation method makes use of limited data to derive region-specific parameters. The model is fully operational and validates reasonably well against traffic counts.     

Estimation of missing traffic counts using factor,genetic, neural,and regression techniques

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases.     

The treatment of uncertainty in the dynamic origin–destination estimation problem using a fuzzy approach

Regardless of existing types of transportation and traffic model and their applications, the essential input to these models is travel demand, which is usually described using origin–destination (OD) matrices. Due to the high cost and time required for the direct development of such matrices, they are sometimes estimated indirectly from traffic measurements recorded from the transportation network. Based on an assumed demand profile, OD estimation problems can be categorized into static or dynamic groups. Dynamic OD demand provides valuable information on the within-day fluctuation of traffic, which can be employed to analyse congestion dissipation. In addition, OD estimates are essential inputs to dynamic traffic assignment (DTA) models. This study presents a fuzzy approach to dynamic OD estimation problems. The problems are approached using a two-level model in which demand is estimated in the upper level and the lower level performs DTA via traffic simulation. Using fuzzy rules and the fuzzy C-Mean clustering approach, the proposed method treats uncertainty in historical OD demand and observed link counts. The approach employs expert knowledge to model fitted link counts and to set boundaries for the optimization problem by defining functions in the fuzzification process. The same operation is performed on the simulation outputs, and the entire process enables different types of optimization algorithm to be employed. The Box-complex method is utilized as an optimization algorithm in the implementation of the approach. Empirical case studies are performed on two networks to evaluate the validity and accuracy of the approach. The study results for a synthetic network and a real network demonstrate the robust performance of the proposed method even when using low-quality historical demand data.     

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.     

Simplified transport models based on traffic counts

Having accepted the need for the development of simpler and less cumbersome transport demand models, the paper concentrates on one possible line for simplification: estimation of trip matrices from link volume counts. Traffic counts are particularly attractive as a data basis for modelling because of their availability, low cost and nondisruptive character. It is first established that in normal conditions it may be possible to find more than one trip matrix which, when loaded onto a network, reproduces the observed link volumes. The paper then identifies three approaches to reduce this underspecification problem and produce a unique trip matrix consistent with the counts. The first approach consists of assuming that trip-making behaviour can be explained by a gravity model whose parameters can be calibrated from the traffic counts. Several forms of this gravity model have been put forward and they are discussed in Section 3. The second approach uses mathematical programming techniques associated to equilibrium assignment problems to estimate a trip matrix in congested areas. This method can also be supplemented by a special distribution model developed for small areas. The third approach relies on entropy and information theory considerations to estimate the most likely trip matrix consistent with the observed flows. A particular feature of this group is that they can include prior, perhaps outdated, information about the matrix.These three approaches are then compared and their likely areas for application identified. Problems for further research are discussed and finally an assessment is made of the possible role of these models vis-a-vis recent developments in transport planning.     

Toll Roads in Australia: An Overview of Characteristics and Accuracy of Demand Forecasts

Abstract

A primary motivation of this paper is to draw together, in one source, information on the nature, extent and performance of Australia's evolving toll road network which is currently spread across many disparate published and unpublished sources. This paper provides key information (e.g. length, toll rates, year opened, operator(s) and payment alternatives) on all of the fully interoperable toll roads in Australia that are present in Sydney (e.g. the M2, M4, M5, etc.), Melbourne (CityLink and EastLink) and Brisbane (the Gateway Bridge, the Logan Motorway and the Gateway Extension). Where available, we compare and discuss actual traffic levels and forecasts, revealing the sizeable gap or ‘error’ in forecasts, especially during the first year of operation. Ordinary least squares regression and panel random effects regression models are developed to identify potential sources of explanation of differences in error forecasts between the Australian toll roads at various points post the opening date. A separate analysis of a sample of toll roads in the USA was also undertaken that supports the main findings from the Australian toll road network. Key influences on errors in forecasts are the capacity of a toll road, the elapsed time that the toll road has been in place, the specific period of time in which a tolled road is introduced into the network (which influences the complexity of route options including multiple tolled routes and hence toll saturation), the length of the tolled route, the presence of cash payment and the charging regime (i.e. fixed vs. distance‐based or variable user tolls).     

Assessment of the effects of highway geometric design features on the frequency of truck involved crashes using bivariate regression

Given the enormous losses to society resulting from large truck involved crashes, a comprehensive understanding of the effects of highway geometric design features on the frequency of truck involved crashes is needed. To better predict the occurrence probabilities of large truck involved crashes and gain direction for policies and countermeasures aimed at reducing the crash frequencies, it is essential to examine truck involved crashes categorized by collision vehicle types, since passenger cars and large trucks differ in dimensions, size, weight, and operating characteristics. A data set that includes a total of 1310 highway segments with 1787 truck involved crashes for a 4-year period, from 2004 to 2007 in Tennessee is employed to examine the effects that geometric design features and other relevant attributes have on the crash frequency. Since truck involved crash counts have many zeros (often 60–90% of all values) with small sample means and two established categories, car-truck and truck-only crashes, are not independent in nature, the zero-inflated negative binomial (ZINB) models are developed under the bivariate regression framework to simultaneously address the above mentioned issues. In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation. Goodness of fit of the investigated models is evaluated using AIC, SBC statistics, the number of identified significant variables, and graphs of observed versus expected crash frequencies. The bivariate ZINB (BZINB) models have been found to have desirable distributional property to describe the relationship between the large truck involved crashes and geometric design features in terms of better goodness of fit, more precise parameter estimates, more identified significant factors, and improved predictive accuracy. The results of BZINB models indicate that the following factors are significantly related to the likelihood of truck involved crash occurrences: large truck annual average daily traffic (AADT), segment length, degree of horizontal curvature, terrain type, land use, median type, lane width, right side shoulder width, lighting condition, rutting depth (RD), and posted speed limits. Apart from that, passenger car AADT, lane number, and indicator for different speed limits are found to have statistical significant effects on the occurrences of car-truck crashes and international roughness index (IRI) is significant for the predictions of truck-only crashes.     

Bayesian committee of neural networks to predict travel times with confidence intervals

Short-term prediction of travel time is one of the central topics in current transportation research and practice. Among the more successful travel time prediction approaches are neural networks and combined prediction models (a ‘committee’). However, both approaches have disadvantages. Usually many candidate neural networks are trained and the best performing one is selected. However, it is difficult and arbitrary to select the optimal network. In committee approaches a principled and mathematically sound framework to combine travel time predictions is lacking. This paper overcomes the drawbacks of both approaches by combining neural networks in a committee using Bayesian inference theory. An ‘evidence’ factor can be calculated for each model, which can be used as a stopping criterion during training, and as a tool to select and combine different neural networks. Along with higher prediction accuracy, this approach allows for accurate estimation of confidence intervals for the predictions. When comparing the committee predictions to single neural network predictions on the A12 motorway in the Netherlands it is concluded that the approach indeed leads to improved travel time prediction accuracy.     

Simultaneous estimation of an origin–destination matrix and link choice proportions using traffic counts

A procedure for the simultaneous estimation of an origin–destination (OD) matrix and link choice proportions from OD survey data and traffic counts for congested network is proposed in this paper. Recognizing that link choice proportions in a network change with traffic conditions, and that the dispersion parameter of the route choice model should be updated for a current data set, this procedure performs statistical estimation and traffic assignment alternately until convergence in order to obtain the best estimators for both the OD matrix and link choice proportions, which are consistent with the survey data and traffic counts.Results from a numerical study using a hypothetical network have shown that a model allowing θ to be estimated simultaneously with an OD matrix from the observed data performs better than the model with a fixed predetermined θ. The application of the proposed model to the Tuen Mun Corridor network in Hong Kong is also presented in this paper. A reasonable estimate of the dispersion parameter θ for this network is obtained.     

Four-step travel demand model implementation for estimating traffic volumes on rural low-volume roads in Wyoming

This study develops a four-step travel demand model for estimating traffic volumes for low-volume roads in Wyoming. The study utilizes urban travel behavior parameters and processes modified to reflect the rural and low-volume nature of Wyoming local roads. The methodology disaggregates readily available census block data to create transportation analysis zones adequate for estimating traffic on low-volume rural roads. After building an initial model, the predicted and actual traffic volumes are compared to develop a calibration factor for adjusting trip rates. The adjusted model is verified by comparing estimated and actual traffic volumes for 100 roads. The R-square value from fitting predicted to actual traffic volumes is determined to be 74% whereas the Percent Root Mean Square Error is found to be 50.3%. The prediction accuracy for the four-step travel demand model is found to be better than a regression model developed in a previous study.     

Characteristics of truck traffic in Singapore

This paper presents the results of a project conducted to study the characteristics of truck traffic in Singapore. Detailed traffic surveys recording counts of vehicles by axle-configuration were performed at 219 sites over a period of nearly two years. The surveys covered 5 different road classes, namely expressways, arterials, collectors, industrial roads and local roads. It was found that the time distribution of truck travel were not the same among the five road classes. The peaking characteristics of truck traffic were less pronounced compared to passenger car traffic. The peak hour truck volume varied from 67.0% to 9.7% of the daily truck traffic as compared to 13.8% for passenger car traffic. The lane distribution pattern of truck traffic was studied in detail by road class, and was found to be a function of total directional traffic volume, total directional truck volume and the number of traffic lanes. Composition analysis was also carried out to study the lane use characteristics of single- and multiple-unit trucks.