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This study contributes to our understanding of the changes in traffic volumes on major roadway facilities in Florida due to the COVID-19 pandemic from a spatiotemporal perspective. Three different models were tested in this study: (1) the linear regression model, (2) the spatial autoregressive model (SAR), and (3) the spatial error model (SEM). For the model estimation, traffic volume data for 2019 and 2020 from 3,957 detectors were augmented with independent variables, such as COVID-19 case information, socioeconomics, land-use and built environment characteristics, roadway characteristics, meteorological information, and spatial locations. Traffic volume data was analyzed separately for weekdays and holidays. SEM models offered a good fit and intuitive parameter estimates. The significant value of spatial autocorrelation coefficients in the SEM supports our hypothesis that common unobserved factors affect traffic volumes in neighboring detectors. The model results clearly indicate a disruption in normal traffic demand due to the increased transmission rate of COVID-19. The traffic demand for recreational areas, especially on holidays, was found to have declined after March 2020. In addition, change in daily COVID-19 cases was found to have a larger impact on South Florida (District 6)'s freeway demand on weekdays compared to other parts of the state. Further, the gradual increase of demand due to rapid vaccination was also demonstrated in this study. The model system will help transportation researchers and policy makers understand the changes in freeway volume during the COVID-19 pandemic as well as its spatiotemporal recovery. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) technology is a key component of an autonomous vehicle’s sensing system. It also has the potential to be used at the roadside as a major infrastructure-based detection for connected and autonomous traffic infrastructure systems, as well as for the general purpose of traffic data collection and performance evaluation. Lane and movement-based traffic volume data collection is a basic function of roadside traffic sensing systems. The accuracy of volume detection is mainly impacted by occlusion for most of the advanced traffic sensing technologies, such as LiDAR, video, and radar. This paper presents research results to quantify the influence of occlusion on LiDAR systems’ traffic volume detection in different traffic demand scenarios. A method for automatic identification and classification of LiDAR specific occlusion was first developed based on the inherent characteristics of LiDAR sensors, which can report occlusion ratios of roadside LiDAR data. Then, the study was extended to accommodate all traffic demand scenarios, traffic levels of service (LOS A to E), and different truck compositions (5% to 30%) by integrating the developed method and traffic simulation. Lastly, a comprehensive case study first verified the accuracy of the simulation results using field data collected from two testbeds, and then at the third testbed, a lane and movement-based traffic volume study was demonstrated. The practical significance of this paper is to help traffic engineers making informed decisions when considering LiDAR as their choice of sensing technology in the field from two aspects: (1) the quantitative relationship between expected occlusion rate and resulted detection accuracy under various traffic conditions; (2) lessons learned from the pilot field implementation on LiDAR, installation strategy, data storage, and communication. [ABSTRACT FROM AUTHOR]

This study develops various crash modification factors (CMFs) for combinations of traffic and roadway cross-sectional elements at noncurved (i.e., straight) and curved roadway sections using the cross-sectional method. Generally, a CMF estimates the expected changes in crash frequency after a single specific treatment is implemented on a roadway. The results indicate that increasing lane width, increasing shoulder width, and their combination are safety effective in reducing crash frequency in general. In particular, it was found that crash frequency decreases as the lane width increases until 3.51 m (11.5 ft) width and it increases as the lane width exceeds 3.51 m. The crash rates start to decrease again after 3.81 m (12.5 ft). The results also indicate that the developed nonlinearizing link function derived nonlinear relationship between crash rates and lane width, and this relationship was clearly reflected in the generalized nonlinear models (GNMs). Moreover, there is a significant interaction impact between lane and shoulder widths at segments with a horizontal curve whereas the interaction term is not significant at noncurved segments. It was also found that the CMFs for increasing lane and shoulder widths are decreasing as annual average daily traffic (AADT) level increases. Based on the findings from this study, it is recommended to apply the proposed methodology of utilizing the GNMs with interaction terms in the cross-sectional method to (1) estimate more accurate CMFs for single treatments and combinations of treatments and (2) reflect the nonlinearity of crash predictors and interaction impacts among variables. [ABSTRACT FROM AUTHOR]

Use of speed-prediction models sometimes appears as a feasible alternative to laborious field measurements, particularly when field data cannot fulfill designers' requirements. However, developing speed models is a challenging task, especially in the context of developing countries like India where vehicles with diverse static and dynamic characteristics use the same right of way. Here, the traffic composition plays a significant role in determining vehicular speed. Determination of passenger car units (PCU), which is required to convert heterogeneous traffic flow to its homogeneous equivalent, also requires speed of different types of vehicles at varying traffic conditions and traffic volumes on the road. In this context, the present research is carried out to examine the effects of traffic volume and its composition on speed and PCU factors for individual types of vehicles under mixed traffic conditions. Classified traffic-volume and speed data were collected at six-lane divided arterial sections in New Delhi and categorized speed models developed adopting a kriging approximation technique, an alternative for commonly used regression. A novel algorithm for selecting the optimal correlation function in kriging has also been proposed in this paper. Developed speed models are validated with the data set of another location kept aside for this purpose, and the predicted speeds show a good agreement to the observed ones. Predicted speeds are then used to estimate PCU factors for each vehicle category. Finally, the proposed models are utilized to evaluate the effects of traffic volume and its composition on speed and PCU of different vehicle categories. [ABSTRACT FROM AUTHOR]

Automated counting technologies are one of the fastest growing sources of data in the non-motorized transportation field. Although automated counts make it possible to collect data for longer time periods and to document temporal variations in volumes more effectively than manual counts, all of the technologies being used are subject to systematic miscount rates that must be accounted for to generate accurate volume estimates. In this paper, accuracy and precision rates are tested for six automated pedestrian and bicycle counting technologies: passive infrared, active infrared, radio beam, pneumatic tubes, inductive loops, and piezoelectric strips. For some technologies, multiple products are tested. Counting devices were installed at 13 sites in seven cities to introduce variation in environmental (weather) conditions and volume levels, and manual validation counts were conducted based on video footage taken at each of the test sites. Correction functions are developed for each technology to increase accuracy of volume estimates. Various environmental conditions including temperature, rain, and lighting are tested in the development of the correction functions. For most technologies, a net undercount effect was observed that appears to worsen at higher volumes. Average error rates (average percentage deviation) for the tested technologies range from 0.55% for inductive loops to -17.38% for pneumatic tubes. However, after applying correction functions accuracy improves for nearly all technologies. [ABSTRACT FROM AUTHOR]

Traffic performance of a highway is expressed in terms of level of service (LOS). It is a major input to important decisions on public fund investments. Because of its unique operational characteristics, evaluation of traffic performance on two-lane highways is a critical task. This research examines the applicability of various performance measures for two-lane highways under mixed traffic conditions and assigns threshold values for different levels of service based on the best performance measure observed. Graphical and statistical analyses are carried out among the six most popular performance measures and traffic volume, but none of the considered measures are found to be reliable. A new parameter, number of followers (NF), which is defined as the number of vehicles traveling with a gap less than 2.6 s, is found to have good correlation with traffic volume but it does not reflect the true congestion conditions of a roadway. Therefore, the NF is further modified to the number of followers as a proportion of capacity (NFPC), and this parameter demonstrates a very strong correlation with traffic volume. Threshold values are proposed for the different LOS based on NFPC for mixed traffic conditions. [ABSTRACT FROM AUTHOR]

The importance of reliable estimates of travel demand for effective planning, design, and management of roads and facilities is well known by transportation engineers. A review of current short-term traffic monitoring practices shows that most transportation agencies simply use road functional class as the criteria to assign short-term traffic counts (STTCs) to permanent traffic counter (PTC) factor groups, or they commit significant resources to implement other data intensive methods, such as regression analysis. The improved methods described in this study estimate average annual daily traffic (AADT) with higher accuracy using all historical counts collected to date for a short-term counting site to create its seasonal traffic pattern and assign it to a PTC or a PTC group without imposing additional data collection cost. Two pattern-matching methods, and their combination with Bayesian statistics, are proposed and tested using PTC data from Alberta, and their results are compared to the Federal Highway Administration (FHWA) method. Study results show that, compared to the FHWA method, the proposed methods reduce the 95th percentile of the absolute percent AADT estimation errors (P95) by 0.5 to 31.9 when applied to different testing sites. [ABSTRACT FROM AUTHOR]

This paper investigates the long-term trend of temporal distribution of roadway traffic using hourly traffic-count data collected in Seoul, South Korea, from 1995 to 2010. Interpolations using quadratic local polynomial regressions and a Gaussian mixture model were applied in order to identify changes in temporal distributions at a scale of less than one hour. The identified changes in distribution characteristics suggested that more attention should be given to outbound traffic, which appeared to be worsening because of the increased population and jobs outside of Seoul. Although this study did not attempt to thoroughly investigate the causal relationship between the changes of temporal distributions and the associated factors, the framework and methodology suggested are expected to play a foundational role in identifying the long-term trend of traffic flow using archived data. [ABSTRACT FROM AUTHOR]

Daily and monthly adjustment factors have traditionally been used to expand the estimation of daily traffic volumes into average monthly and average annual traffic. However, research efforts that are devoted to developing and evaluating these factors for nonmotorized traffic, especially for cycling, are currently rare. This is attributed mainly to the lack of extensive data that can enable reliable calibration and testing of such factors. This paper explores some issues related to the development and application of daily adjustment factors for bicycle traffic. Examples include the impact of grouping daily factors by weekday/weekend and developing weather-specific factors and factors for different road classes. The analysis made use of a large data set of 500 months of daily bicycle volume data that covered 74 links in the city of Vancouver, Canada, for the years 2010 and 2011. Monthly average daily cycling volumes were estimated using different sets of factors, and the estimation accuracy was assessed and compared. It was found that developing factors for each day of the week provided similar estimation errors to grouping the factors for weekdays and weekends. In general, the best estimation results of the monthly average cycling volumes were achieved when using daily factors that are disaggregated by weather conditions. The temporal transferability of the factors was also evaluated by applying the factors from 2010 and 2011 to data from 2009. Compared to the daily factors from 2011, it was shown that the factors from 2010 provided slightly better estimation accuracies of the monthly average of daily bicycle traffic for different months in 2009. This demonstrates the degradation in the reliability of the daily adjustment factors over time, which calls for an update of these factors every few years. [ABSTRACT FROM AUTHOR]

This paper focuses on the application of the principle of demographic gravitation to estimate link-level annual average daily traffic (AADT) based on land-use characteristics. According to the principle, the effect of a variable on AADT of a link decreases with an increase in distance from the link. The spatial variations in land-use characteristics were captured and integrated for each study link using the principle of demographic gravitation. The captured land-use characteristics and on-network characteristics were used as independent variables. Traffic count data available from the permanent count stations in the city of Charlotte, North Carolina, were used as the dependent variable to develop statistical and neural network models. Negative binomial count statistical models (with log-link) were developed as data were observed to be over-dispersed while neural network models were developed based on a multilayered, feed-forward, back-propagation design for supervised learning. The results obtained indicate that statistical and neural network models ensured significantly lower errors when compared to outputs from traditional four-step method used by regional modelers. Overall, the neural network model yielded better results in estimating AADT than any other approach considered in this research. The neural network approach can be particularly suitable for their better predictive capability, whereas the statistical models could be used for mathematical formulation or understanding the role of explanatory variables in estimating AADT. [ABSTRACT FROM AUTHOR]

A road bridge containing disused flatbed rail wagons as the primary deck superstructure was performance tested on a low-volume, high-axle load traffic road in Queensland, Australia, and some key results are presented in this paper. A fully laden truck of total weight 28.88% of the serviceability design load prescribed in the Australian bridge code was used; its wheel positions were accurately captured using a high-speed camera and synchronized with the real-time deflections and strains measured at the critical members of the flat rail wagons. The strains remained well below the yield and narrated the existence of composite action between the RC slab pavement and the wagon deck. A three-dimensional grillage model was developed and calibrated using the test data, which established the structural adequacy of the rail wagons and the positive contribution of the RC slab pavement to resist high-axle traffic loads on a single lane bridge in the low-volume road network. [ABSTRACT FROM AUTHOR]

Truck travel, normally expressed in terms of truck vehicle-distance travel (VDT), is a critical data requirement for the design, construction, operation, and management of rural highway systems. The need to estimate and characterize truck travel on a system-wide basis, yet retain a compatible, site-specific mining capability, challenges the traditional approach of traffic monitoring programs. This paper develops and applies a two-phase methodology to estimate truck VDT on rural highway systems. The first phase, which leverages the approach recommended for developing truck traffic data inputs for mechanistic-empirical pavement design, processes vehicle classification data obtained from continuous, sample, and manual counts into annual average daily truck traffic (AADTT) estimates by vehicle class. The second phase attributes these site-specific AADTT estimates to highway segments comprising a rural highway network. The methodology, which is transferrable across jurisdictions, integrates standard statistical procedures with engineering judgment and trucking industry intelligence by establishing transparent decision algorithms and criteria. Application of the methodology using real data reveals relevant limitations and illustrative results that have application to a wide range of transportation engineering decisions. [ABSTRACT FROM AUTHOR]

The development and decreased cost of technology and communications have brought about a huge increase in the availability of traffic data. With every passing day, traffic management centers must deal with an increased amount of detailed data. Once the real time use of these data is complete, they must be stored for long periods of time. In this long-term context, the vast amount of raw data is meaningless, which is a clear example of data asphyxiation. Traffic management centers must aggregate and synthesize the data to extract the maximum information from them. Pattern classification is a way to deal with this issue. Traditionally, traffic demand patterns have been easily constructed using ad hoc methods, where the experience and judgment of the analyst are their main attribute. These procedures lack the required rigor to support current needs in terms of planning and operational management. This paper proposes a quantitative method to systematically derive traffic demand patterns from historical data. The method is based on the cluster analysis technique and allows for the inclusion of preexisting knowledge, which eases the interpretation and practical use of the results. The proposed pattern classification procedure is applied to 5 years of hourly traffic volumes on a Spanish highway. The obtained results prove the validity and utility of the method in accurately summarizing the seasonal and daily characteristics of traffic demand. [ABSTRACT FROM AUTHOR]

Path flow estimator (PFE) is a single-level network observer proposed to estimate path flows and origin-destination flows from traffic counts in a transportation network. The PFE model handles the traffic count inconsistency problem by allowing user to specify appropriate error bounds (or confidence interval) on the traffic counts. This approach, although flexible, introduces a systematic bias in underestimating the total demand when improper error bounds are specified. This paper presents an L∞-norm PFE model that minimizes the systematic bias of the total demand estimate encountered in the PFE model by determining the least maximum absolute error needed to accommodate measurement errors and traffic count inconsistencies within the estimation process. A solution algorithm based on the dual formulation combined with a column generation procedure is developed for solving the proposed L∞-norm PFE model. Numerical results are presented to illustrate the features and applicability of the proposed L∞-norm PFE model and solution algorithm. [ABSTRACT FROM AUTHOR]

Data collected at four locations in a medium-sized city of India are analyzed for pedestrians flow characteristics under mixed traffic conditions. The data are presented in the form of mathematical and graphical relationships between speed and volume, speed and density, flow and area module, and flow and density. The results of analysis are compared with those given in literature. The free-flow speed of pedestrians is observed to be 80 m/min which is higher than that reported for China and Singapore, but slightly lower than that in Germany. It is comparable to free speeds reported for Philippines and the United States. The speed of the pedestrian was found to be influenced by the age and gender also. Male pedestrians move faster than female pedestrians. Pedestrians in the age group of 10–15 years had the highest speed, 82 m/min. the speed was reduced by about 85% when pedestrians move with their baggage (or luggage). [ABSTRACT FROM AUTHOR]

This study has three main objectives: (1) to develop new, theory-based, queuing relationships for the quality of flow on two-lane rural highways; (2) to estimate the relationships both from the empirical data that were collected on 15 two-lane rural highways and from the queuing models that were developed; and (3) to propose a new level-of-service variable that measures the quality of the flow both inside and between platoons. The paper presents five flow-characteristic measures for two-lane rural highways: the flow, the average platoon length, the traffic intensity, the percent-time-spent following, and the freedom of flow. It is shown that the five measures can be calculated from easily collectible data parameters and also from empirical models related to the two-way flow that are developed based on the field-data collected. It is proposed that the level of service be estimated from the freedom-of-flow parameter η, which is developed in the paper. The relationship between η and the two-way flow is calibrated from traffic observations. Level-of-service thresholds, based on the flow and on η are presented and discussed. [ABSTRACT FROM AUTHOR]

Chapman’s Peak Drive is a scenic coastal road in the Western Cape province of South Africa with a history of reoccurring rockfall incidents. This paper presents qualitative case study research that explores the competitive award of a concession to design, build, finance, and operate an upgrade of the road for a period of 30 years. The analysis focuses on the intensity of competition for the concession and its implications on value for money. The focus of the analysis is on rockfall risk which was the principal justification for the upgrade and which had the greatest financial significance for the project. The information on the case study project was obtained by interviewing some of the public and private sector individuals involved and by examining the documentation issued by the public sector. Analysis revealed that the award process supported the adoption of an innovative rockfall protection solution that is believed to be the optimal technical solution for Chapman’s Peak Drive. However, it also revealed that the value for money of the concession to the province may have been compromised by limited and unfocused competition between bidders. The paper hypothesizes that the amount and timing of information disclosed, use of bid bonds, level of output specification used, disclosure of the relative importance of the bid evaluation criteria, and timing of financier’s involvement, influenced the value for money of the project and that further quantitative research on their general effects on the value for money of concessions needs to be investigated. The primary contribution of the paper is that it allows more precise hypotheses to be devised for the purposes of quantitative research in the problem domain. [ABSTRACT FROM AUTHOR]

Tracking vehicles through roundabouts is laborious. Because of this, turning movement volumes are usually estimated using a variety of methods. This paper identifies and evaluates practical methods of estimating turning movement volumes at roundabouts. Thirty four experiments were used to test the effects of different estimation treatments: estimation procedure, time interval size, count location configuration, and turning movement sample procedure. A new economical sampling procedure was introduced and included in the experiment design. The experiments were evaluated using volume estimation and capacity estimation errors. Findings showed that the new sample procedure works well relative to a random sampling procedure. Other insights were gained regarding the four treatments included in the experiment design. [ABSTRACT FROM AUTHOR]

On most segments in a state highway system, annual average daily traffic (AADT) in the current year is estimated by applying an applicable growth factor to coverage count data obtained in an earlier year. The accuracy of AADT estimates could conceivably be improved by exploiting existing imagery of highway segments containing vehicles. When both earlier year coverage counts and a current year image containing traffic information are available, a weighted combination of both pieces of information is proposed for AADT estimation. The weights can be easily estimated from available information and data regularly collected by state departments of transportation. An empirical study was conducted using 122 highway segments to evaluate the improved accuracy in estimating AADT that would have resulted from using this weighted method with a single contemporary image for a 10-year period from 1994 and 2003. The accuracy was markedly improved and stable over a large range of important input values. The demonstrated improvements in accuracy and the ease of using this method with existing data are great enough that field testing should now be considered. [ABSTRACT FROM AUTHOR]

To assess highway system performance, states are required to submit compositional traffic count reports to the Federal Highway Administration (FHWA). A methodology was developed for obtaining compositional vehicle counts from low-cost portable video technology. Autoscope, an advanced video camera with machine vision processing, was used to capture, process, and store compositional counts based on vehicle length. A maximum of five length-based vehicle classes can be differentiated from video; therefore a methodology was developed that generates the FHWA 13-category vehicle classes. A disaggregation process, typically used in stochastic hydrology, is employed. Results from the disaggregation model are compared with actual FHWA classification data obtained from axle counters. It was observed that less than 1% of the vehicles were not included in the model output and 3.43% were misclassified. In addition, 95 and 99% confidence intervals calculated by the disaggregation model included 92.3 and 100% of the actual axle counter data. [ABSTRACT FROM AUTHOR]

A common travel analysis technique is to ascribe capacities to links in a planning network based on functional classification or facility type, under which a four-lane divided arterial, for example, would be assigned the same capacity everywhere, irrespective of the surrounding land use, the presence of traffic signals, and whether turning lanes are provided or not. While this practice has been used for many years and is typically adequate for the analysis of large road networks, there are penalties (in terms of lost accuracy) that accompany its use. The objective of this research was to evaluate different techniques to determine what information was necessary to reasonably estimate link capacity while decreasing the time, effort, and cost of data collection. It was found that assigning capacities on a link-specific basis produced significant differences when compared to the practice of assigning average capacities, suggesting that travel demand modeling could be improved by using link-by-link estimates of capacity. The results of this study also supported the concept that improvements gained by using all Highway Capacity Manual adjustment factors were marginal compared to the improvement achieved by including only the g/C ratio and number of lanes; suggesting further that ignoring other factors like lane widths, heavy vehicles, turning movements, etc. will still result in reasonably accurate estimates of volume, travel time, and travel speed. [ABSTRACT FROM AUTHOR]

Existing methods for estimating origin–destination (O–D) matrix from link traffic counts cannot consider multiple-vehicle information, which is easily obtained from a field survey. Those methods use a single-vehicle information and it may decrease the performance of an O–D matrix estimator. Up until now, there is little research regarding multiple-vehicle O–D matrix estimation from multiple-vehicle link counts. This paper suggests a multiple-vehicle O–D matrix estimation from traffic counts using a genetic algorithm (GAMUC). From the numerical tests, we found that the performance of GAMUC was better than those of the previous methods for the error of target O–D matrix and for the number of link counts. In this paper, we also found that the method provided good results on the error of target O–D matrix and on the varied O–D matrix structure, which occurred when the target O–D matrix was different from the true one. [ABSTRACT FROM AUTHOR]