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Traffic operations at toll plazas are influenced by the merging and diverging behavior of vehicles near the toll plaza in the flare area. Proper guidelines for the design of merging and diverging sections at toll plazas under mixed traffic conditions are not specified by the Indian Highway Capacity Manual, the Indian Road Congress, or the American Association of State Highway and Transportation Officials. Thus, the present study attempts to develop design criteria for diverging and merging lengths. The field data were collected using the performance-box instrument at Ghoti toll plaza located at National Highway No. 60 in India, operating under mixed traffic conditions. The concept of discretization of speed profile is proposed and applied to segregate sections of the zone of influence into ten equal parts each of 50 m length. The statistical test shows that speed has a significant difference between the sections and also in between the different classes of vehicle. Results of the speed-distance plot show that the second-degree polynomial function is the best fit for zone of deceleration and power function for the zone of acceleration. The study revealed that, for 50 km/h approach speed, the minimum diverging and merging distances should be 125.67–308.98 m and 47.93–294.23 m, respectively. The proposed guidelines for merging and diverging length are useful for the design of the flare area and to improve operational efficiency and level of service at toll plazas operating under mixed traffic conditions.

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

Traffic speed deflection devices (TSDDs) have been developed since around 2000 to allow for safe and efficient structural evaluation of highway networks. One barrier to TSDD implementation is the inherent differences in deflections produced by moving truck loads and by falling weight deflectometer (FWD), the current deflection testing standard. To better understand the differences in data produced by the two devices, FHWA sponsored research into one particular TSDD, the rolling wheel deflectometer (RWD). The study utilized the finite layer program ViscoWave to model both FWD and RWD loads to demonstrate the effect of their inherent differences on pavement deflections and other simulated parameters. In addition, ViscoWave was used to generate theoretical FWD and RWD deflections for a diverse set of pavement structures and subgrade conditions. The resulting deflections were used to develop correlations between the two devices, which were validated with side-by-side FWD and RWD field tests performed on 23 sites. The research determined that the differences between FWD and RWD deflections vary depending on pavement factors and loading characteristics. The two devices produced similar deflections on thicker, stiffer, lower-deflection pavements, while the FWD produced relatively higher deflections on thinner, weaker, higher-deflection pavements. Therefore, use of common FWD data analysis programs will produce different results, such as layer moduli, for TSDD devices. Advanced analysis routines capable of modeling the TSDD’s moving load and loading configurations are needed.

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

The introduction of deep learning (DL) models and data analysis may significantly elevate the performance of traffic speed prediction. Adverse weather causes mobility and safety concerns because of varying traffic speeds with poor visibility and road conditions. Most previous modeling approaches have not considered the heterogeneity of temporal and spatial data, such as traffic and weather conditions. This paper presents a framework, consisting of two DL models, to predict traffic speed under normal conditions and during adverse weather, considering prevailing traffic speed, wind speed, traffic volume, road capacity, wind bearing, precipitation intensity, and visibility. To ensure the accuracy of speed prediction, different DL models were assessed. The results indicated that the proposed one-dimensional convolutional neural network model outperformed others in relation to the least root mean square error and the least mean absolute error. Considering real-time weather data feeds on a 15-min basis, a tool was also developed for displaying predicted traffic speeds on New Jersey freeways. Application of the proposed framework models for predicting spatio-temporal hot-spot congestion caused by adverse weather is discussed.

Accurate and timely estimation of freeway traffic speeds by short segments plays an important role in traffic monitoring systems. In the literature, the ability of machine learning techniques to capture the stochastic characteristics of traffic has been proved. Also, the deployment of intelligent transportation systems (ITSs) has provided enriched traffic data, which enables the adoption of a variety of machine learning methods to estimate freeway traffic speeds. However, the limitation of data quality and coverage remain a big challenge in current traffic monitoring systems. To overcome this problem, this study aims to develop a hybrid machine learning approach, by creating a new training variable based on the second-order traffic flow model, to improve the accuracy of traffic speed estimation. Grounded on a novel integrated framework, the estimation is performed using three machine learning techniques, that is, Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). All three models are trained with the integrated dataset including the traffic flow model estimates and the iPeMS and PeMS data from the Utah Department of Transportation (DOT). Further using the PeMS data as the ground truth for model evaluation, the comparisons between the hybrid approach and pure machine learning models show that the hybrid approach can effectively capture the time-varying pattern of the traffic and help improve the estimation accuracy.

Backcalculation analysis of pavement layer moduli is typically conducted based on falling weight deflectometer (FWD) deflection measurements; however, the stationary nature of the FWD requires lane closure and traffic control. In recent years, traffic speed deflection devices such as the traffic speed deflectometer (TSD), which can continuously measure pavement surface deflections at traffic speed, have been introduced. In this study, a mechanistic-based approach was developed to convert TSD deflection measurements into the equivalent FWD deflections. The proposed approach uses 3D-Move software to calculate the theoretical deflection bowls corresponding to FWD and TSD loading configurations. Since 3D-Move requires the definition of the constitutive behaviors of the pavement layers, cores were extracted from 13 sections in Louisiana and were tested in the laboratory to estimate the dynamic complex modulus of asphalt concrete. The 3D-Move generated deflection bowls were validated with field TSD and FWD data with acceptable accuracy. A parametric study was then conducted using the validated 3D-Move model; the parametric study consisted of simulating pavement designs with varying thicknesses and material properties and their corresponding FWD and TSD surface deflections were calculated. The results obtained from the parametric study were then incorporated into a Windows-based software application, which uses artificial neural network as the regression algorithm to convert TSD deflections to their corresponding FWD deflections. This conversion would allow backcalculation of layer moduli using TSD-measured deflections, as equivalent FWD deflections can be used with readily available tools to backcalculate the layer moduli.

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

The traffic speed deflectometer (TSD) has proven a valuable tool for network level structural evaluation. At the project level, however, the use of TSD data is still quite limited. An obstacle to the use of TSD at the project level is that the standard approaches to back-calculation of pavement properties are based on the falling weight deflectometer (FWD). The FWD experiment is similar, but not equivalent, to the TSD experiment, and therefore it is not straightforward to apply the traditional FWD back-calculation procedures to TSD data. In this paper, a TSD-specific back-calculation procedure is presented. The procedure is based on a layered linear visco-elastic pavement model and takes the driving speed of the vehicle into account. This is in contrast to most existing back-calculation procedures, which treat the problem as static and the pavement as purely elastic. The developed back-calculation procedure is tested on both simulated and real TSD data. The real TSD measurements exhibit significant effects of damping and visco-elasticity. The back-calculation algorithm is able to capture these effects and yields model fits in excellent agreement with the measured values.

The rolling wheel deflectometer (RWD) and traffic speed deflectometer (TSD) are continuous deflection measuring devices used for structural evaluation of in-service pavements. Although recent studies have demonstrated the benefits of these devices, state agencies are inclined to believe that surface indices are generally sufficient to describe in-service pavement conditions (functional and structural conditions) and to make cost-effective maintenance and rehabilitation decisions. The main objective of this study was to assess whether the use of surface indices only or the declining rates of these indices to identify structurally damaged sections is feasible instead of relying on RWD and TSD estimated pavement structural indices. Results of the analysis showed that structural deficiency, rates of deterioration, and surface indices were correlated to a certain extent. These results were expected, as pavements that are structurally deteriorated will exhibit surface deficiencies over time. Yet, surface indices cannot be used as a reliable predictor of structural capacity. For RWD, the most accurate surface index, which was the alligator cracking surface index, erroneously identified 35% of structurally sound sections as structurally deficient and 51.5% of structurally deficient sections as structurally sound. Similar results were obtained for the TSD; in this case, the most accurate surface index, which was the random cracking index, erroneously identified 16.7% of structurally sound sections as structurally deficient and 51.0% of structurally deficient sections as structurally sound. The cost implication associated with misinterpreted sections from functional indices was investigated. The incorporation of structural indices is expected to provide significant savings to state agencies.

Structural number (SN) represents the structural capacity of a flexible pavement system to sustain anticipated traffic and is among the structural indices most commonly used by pavement design engineers in the U.S. Effective structural number (SNeff) is an indicator of structural capacity of in-service pavement sections and is conventionally estimated from nondestructive testing (NDT) device data such as falling weight deflectometers (FWDs) using methods such as suggested by AASHTO. In addition to pavement design, structural condition is a critical input for the selection of maintenance and rehabilitation strategies in pavement management system (PMS) application. However, use of SN in network level application has not been practical because of limitations of FWD such as stop-and-go operation, lane closures, and low testing frequency. The traffic speed deflectometer (TSD), a continuous deflection device, has recently been gaining worldwide application as a reliable NDT device for network level PMS applications. The objective of this study is to develop a practical approach to compute and utilize SN of in-service flexible pavements from TSD data for network level PMS applications. The study is based on the fundamental that, for the same pavement, SNeff from the TSD using the proposed method should be in good agreement with SNeff from the FWD using AASHTO method. The developed method was field validated with TSD and FWD data collected at in-service pavement sections. In addition, the use of structural number ratio, defined as a ratio of SNeff to required SN, in network level prioritization of structural capacity improvements was illustrated.

In this study, experimental studies that the authors have previously performed on a test road for fixed observer conditions to determine the visibility levels are extended to the moving observer position. As a result of the measurements and tests, when the vehicle speed is constant at the current speed limits for urban roads, it has been seen that the visibility level values in the calculation area remain within the acceptable limits and changing the luminous fluxes will not significantly change the visual performance of the drivers up to two lower lighting classes. It has been shown that the light level can be reduced to create two additional classes besides the current one when the traffic density decreases, at the times when vehicle speeds do not decrease. The results are applied as a case study on the Cendere Street. Road lighting classes are determined according to the EN 13201-1 Technical Report. Road lighting classes that can be applied at different times in the Cendere Street change between M3 and M5 according to the average speed of the passing vehicles, traffic density, road and ambient conditions. It is estimated that about 40% energy saving can be achieved when a lighting automation system is installed.

In the Highway Capacity Manual (HCM), the passenger car equivalent (PCE) of a truck is used to account for the impacts of trucks on traffic flow. The 2010 HCM PCE values were estimated by the equal-density method using a FRESIM simulation. It was determined that the truck PCE for level freeway segments was 1.5 for all conditions. In the 2016 HCM, the PCE values were based on VISSIM simulation output at 1 min intervals along a three-lane, 13 mile (8 mile level and 5 mile graded) section of a roadway. It was determined that the truck PCE for level freeway segments was 2.0. It is hypothesized in this paper that the HCM PCE values are not appropriate for the western United States, which consistently experiences truck percentages higher than 25%, the maximum truck percentage published in the HCM PCE table. The HCM PCE procedure assumes that truck and passenger cars travel at the same average free-flow speed on level terrain. However, many heavy trucks in the western United States have speed limiters to improve fuel economy, and therefore travel slower than the speed limit. The interaction of high truck percentages and large speed differences may result in moving bottlenecks when trucks pass other trucks at low speed differentials. This may lead to an increased delay for the following passenger car vehicles. The 2016 HCM PCEs are based on three-lane simulations where the PCE is calculated based on near-capacity flows. This approach might not be appropriate for western states where these conditions rarely exist. This paper examines these effects using data from I-80 in western Nebraska. The paper develops new PCE values based on the 2010 HCM equal-density approach using calibrated CORSIM and VISSIM simulation models. It was found that the PCE values in the HCM 2010 and HCM 2016 underestimate the effect of heavy trucks on level terrain freeways that experience high truck percentages, and where different vehicle types have large differences in average free-flow speeds.

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

The importance of incorporating pavement structural conditions in the selection of maintenance and rehabilitation strategies along with functional indices has been recognized by state agencies. To measure in-service pavement structural capacity, surface deflection under a defined load has been typically used. The traffic speed deflectometer (TSD) has emerged as a continuous deflection-measuring device as it operates at traffic speed and reduces lane closure and user delays. The present study developed a nonlinear regression model to predict pavement structural number (SN) based on surface deflections measured by the TSD along with the total pavement layer thickness and traffic volume. The proposed model was successfully developed and validated with SN calculated based on TSD and falling weight deflectometer deflection data obtained from two testing programs in Louisiana and Idaho. The model was further validated with respect to its prediction of in-service structural capacity loss and deficiency. Based on statistical measures and the model’s ability in identifying structurally deficient sections, results showed satisfactory accuracy of the model and supports its use for network-level decision-making processes in the pavement management system.

In this paper, the traffic speed deflectometer (TSD), a device used for network level structural evaluation, is assessed. TSD testing was performed in nine states on a total of 5,928 miles (some repeated) during three time periods: November 2013, May to July 2014, and June to September 2015. This paper presents (1) the results of repeatability and comparison of the TSD with the falling weight deflectometer (FWD), (2) the results of the comparison of TSD measurements with typical pavement management system (PMS) data, and (3) an approach that can be implemented by State Highway Agencies (SHAs) to incorporate indices derived from TSD data into their PMS decision-making process. The results show that repeated TSD measurements follow similar trends and the TSD measurements and FWD measurements on the same pavement sections follow similar trends as well. Comparing TSD measurements with PMS surface condition data confirmed that the TSD provided valuable information about the structural condition of the tested pavement sections that cannot be derived from the already available pavement surface condition as part of an agency’s PMS. An example of how TSD information can be used to refine the triggered maintenance treatment category as part of a network-level PMS analysis is presented for a roughly 75-mile section of I-81 south in Virginia.

The project has focused on the East Idaho Loop Corridor (EILC), representing 518 mi of primary roadways covering a wide range of geographic conditions. The Idaho Transportation Department (TD) has pursued this effort to support future project planning and design efforts and advance the management of its assets into a more efficient, best-first set of priorities. The EILC was surveyed with a traffic speed deflectometer (TSD) and with ground-penetrating radar (GPR). After preliminary review of the TSD data, segments were selected for falling weight deflectometer (FWD) testing to confirm patterns observed in the TSD data and to adapt FWD analysis methods to the TSD data. Ninety-nine borings were taken to confirm the pavement layer structure and verify thickness calculations. The Idaho TD data were analyzed at a 10-m interval with the GPR layer thickness data to determine subgrade modulus, pavement modulus, and structural number. These values were then used to estimate overlay requirements and, given traffic projections, to calculate the remaining life as a continuous function of roadway position. The data were incorporated into a spatial geodatabase that provides the Idaho TD with a convenient means to visualize and evaluate the overall condition of the network down to the detail of its individual segments. The segments were subdivided into homogenous subsections on the basis of remaining life, and these subsegments were used for identifying and programming rehabilitation projects. The level of condition detail available at the subsegment level allows for pavement design and for scoping restoration projects.

In the past few years, state transportation departments in the United States have been investigating the potential use of the traffic speed deflectometer (TSD) in routine network-level pavement structural evaluation, including efforts to compute parameters typically obtained with the falling weight deflectometer (FWD). However, differences in loading configuration (single plate versus dual tires) and mechanism (impact versus moving load) between the FWD and the TSD necessitate modification of conventional analysis methodologies for use with TSD data. The TSD is a trailer truck that uses Doppler lasers to measure the pavement deflection velocities at normal traffic speed. Deflection velocities are then converted to surface deflections with a computational algorithm. This study proposes two approaches to backcalculating layer moduli from the TSD data. The first method uses 3D-Move software and deflection velocities measured directly by the TSD. The 3D-Move software can closely simulate TSD loading by considering moving load, nonuniform contact pressure of the dual tires, and viscoelastic material properties of asphalt layers. The second method uses TSD-computed surface deflections in a linear elastic analysis backcalculation program modified to incorporate a dual-tire configuration. The two methods were evaluated with field data from the MnROAD test facility and two in-service pavement sections in Pennsylvania and Idaho. The results show that the second method can be reliably used in network-level applications until the first method becomes computationally practical. The limitations of use of the TSD deflection measurement in the conventional FWD backcalculation program was also demonstrated.

Automated vehicles (AVs) have received great attention in recent years, and an automated road transportation sector may become reality in the next decades. Many benefits of AVs have been optimistically predicted, although some benefits may be overestimated because of a lack of thinking from a holistic point of view. From a future perspective, this study investigated the potential consequences to the long-term service performance of practical physical road infrastructure after the advent of the implementation of AVs on a large scale. Specifically, the pavement rutting performance by the possibly changed behaviors, such as the vehicle’s wheel wander, lane capacity, and traffic speed, was examined carefully with the finite element modeling approach. With the use of AVs, the decreased wheel wander and increased lane capacity could bring an accelerated rutting potential, but the increase in traffic speed would negate this effect, which was shown by the simulation results of rut depth. Therefore the influence cannot be judged as positive or negative in general; judgment actually depends much on the practical road and traffic conditions. In the future the physical roads not only might serve for the mobility of the vehicles but also might be capable of enabling other new functions. An early consideration of how to lead the future development of physical road infrastructure toward multifunctionality is emphasized.

This paper presents empirical findings on car-following and lane-changing behavior involving heavy vehicles; trajectory data from the next generation simulation program were used in the study. It was found that when following passenger cars, heavy vehicles tended to reduce speed variations caused by traffic disturbances and thereby dampened traffic oscillations. In contrast, passenger cars following heavy vehicles tended to amplify traffic disturbances, although with lower probability and magnitude compared with the dampening effect. Moreover, heavy vehicles tended to discourage lane changes, especially behind them. This finding has convoluted implications: although reduced lane changes can improve traffic stability by preventing or reducing disturbances, large gaps can persist behind heavy vehicles and contribute to underutilization of road capacity.

AASHTO's functional classification system specifies that arterials should be designed to provide high mobility and low access; however, this narrow view does not fully represent the vitality of many urban streets, which need to maintain high access and high mobility across a variety of modes. Webster Avenue (US-1) in the Bronx, New York City, was an arterial that was focused on providing vehicular mobility but did not support the mobility and accessibility goals of public transit users and pedestrians. This paper describes results of the multimodal redesign of Webster Avenue by the New York City Department of Transportation (DOT) and the Metropolitan Transit Authority's New York City Transit (NYCT) as part of implementing bus rapid transit (BRT) in the corridor. The new arterial street design improves the organization of traffic: buses, which make frequent stops, now travel exclusively in the right travel lane, while general traffic is able to use the left travel lane. Key project results include a 19% to 23% improvement in bus speeds for the new BRT–select bus service route (compared with the former limited-stop route), 11% to 16% improvement in local bus speeds, maintenance of traffic speeds and volumes, and significant pedestrian safety improvements along the corridor. These results were achieved through extensive outreach and coordination with community stakeholders, careful use of different priority treatments on different parts of the corridor, and the strong working partnership between the New York City DOT and NYCT.

The purpose of this study was to examine the travel speed characteristics of passenger vehicles, trucks, and buses in states with different speed limit policies, primarily to ascertain the effects of differential speed limits (DSLs) versus those of uniform speed limits (USLs) for large vehicles. Spot-speed studies were conducted in the neighboring states of Indiana, Michigan, and Ohio; these studies permitted examination of speed characteristics under several speed limit settings along the same freeways. The sites included urban and rural locations, with speed limits at individual locations that varied from 55 to 70 mph. These sites included USLs and DSLs, with 5 mph differentials on rural freeways in Indiana and 10 mph differentials at rural locations in Michigan. Spot-speed data were collected at 157 freeway sites in the three states along flat, tangent segments. Regression models were estimated to ascertain differences in the mean speeds, the 85th percentile speeds, and the standard deviation in speeds across locations. The results showed passenger vehicle speeds to be quite consistent across the three states where a common 70 mph limit was in effect. Speeds varied more at locations with lower posted limits and between trucks and buses. Speeds were most consistent in Ohio at locations with higher USLs. The variability in travel speeds for all vehicles was found to be highest on freeways with DSLs, followed by urban freeways with USLs of 55 mph.

Accurate and timely predictions of traffic conditions are required for congestion avoidance and route guidance in real-time freeway traffic operations. Special attention to winter operations is needed because prediction error could be amplified under severe weather conditions involving snow. This study employed a vehicle detection system to propose a speed prediction methodology that used the k–nearest neighbors algorithm. The speed prediction was further evaluated under different weather conditions with a road weather information system. Cross-comparisons of the mean absolute percentage error (MAPE) between three weather conditions (normal, light snow, and heavy snow) revealed that the MAPE tended to increase with increases in the forecasting time step (T) and snow intensity. The marginal MAPE over the time step was larger during heavy snow conditions than under normal and light snow conditions. These findings indicate that for winter freeway operations, the time step should be selected dynamically, depending on the weather conditions rather than with a static strategy for all conditions. To this end, this study proposes a framework to determine a dynamic forecasting T that is associated with weather conditions.

Recently, there has been increased interest in quantifying and modeling the impact of inclement weather on transportation system performance. One problem that the majority of research studies on the topic have faced was the great dependence on weather data merely from atmospheric weather stations, which lack information about road surface condition. The emergence of social media platforms, such as Twitter and Facebook, provides a new opportunity to extract more weather-related data from such platforms. This study had two primary objectives: (a) examine whether real-world weather events can be inferred from social media data and (b) determine whether including weather variables extracted from social media data can improve the predictive accuracy of models developed to quantify the impact of inclement weather on freeway traffic speed. To achieve those objectives, weather data, Twitter data, and traffic information were compiled for the Buffalo–Niagara, New York, metropolitan area as a case study. A method called the Twitter Weather Events Observation was then applied to the Twitter data, and the sensitivity and false alarm rate for the method was evaluated against real-world weather data. Then, linear regression models for predicting the impact of inclement weather on freeway speed were developed with and without the Twitter-based weather variables incorporated. The results indicated that Twitter data have a relatively high sensitivity for predicting inclement weather (i.e., snow), especially during the daytime and for areas with significant snowfall. The results also showed that the incorporation of Twitter-based weather variables could help improve the predictive accuracy of the models.

This paper presents a planning-level methodology for the analysis of freeway facilities. The proposed approach is based on and compatible with the operational method of the Highway Capacity Manual 2010 (HCM 2010). The approach is specifically constructed with the intent to minimize input data requirements. The method covers both under- and oversaturated flow conditions and produces estimates of travel time, speed, density, and level of service. The underlying methodology relies on developing a relationship between a basic freeway segment's delay rate per unit distance and its demand-to-capacity ratio. For weaving segments, the study develops capacity adjustment factors on the basis of volume ratio and segment length. With these factors, demand-to-capacity ratios on weave segments were adjusted and the segment was treated similarly to a basic freeway segment. For merge and diverge segments, a novel methodology is proposed to estimate their capacity on the basis of demand level, free-flow speed, and space mean speed. Subsequently, capacity adjustment factors are calculated on those segments and their demand-to-capacity ratios are adjusted accordingly. The proposed approach is applied to two examples in the HCM 2010 and produced very promising results. For undersaturated flow conditions, facility travel time is at most 3.4% and density was at most 1.1% at variance from the results found by applying the HCM 2010 operational methodology. The corresponding differences for oversaturated conditions are 6.7% and 13.0%, respectively.

Pavement management systems of highway agencies in the various states are primarily based on surface condition data. Surface cracking is used as the main indicator of pavement structural condition. However, with effective pavement treatment that intervenes early to preserve and extend the life of pavements and increasingly thicker long-life pavements, surface cracks no longer tell the true structural condition, or health, of the pavement structure. In addition, surface cracks lack an indicator of pavement deterioration. Knowledge of the true pavement structural condition and the rate of deterioration is needed not only for planning of optimal structural rehabilitation activities and future budget needs but also for implementing a performance-based federal-aid program. This paper presents a methodology for interpreting measurements from traffic speed deflection devices (TSDDs) to track flexible pavement structural condition over time and for assessing rehabilitation needs at a network level to address both structural adequacy and surface condition. The paper also demonstrates a methodology for interpreting TSDD measurements to estimate remaining pavement structural capacity. Curvature indexes measured from TSDD were found to be reasonable estimators of pavement structural condition and were used in the demonstration. Horizontal tensile strain, a primary initiator of fatigue damage and cracking, can be estimated from periodic TSDD measurements and used as a leading indicator of pavement deterioration and structural performance. Any differences in pavement structural performance arising from the pavement's as-designed versus as-constructed state and its assumed versus actual traffic and climate effects can be assessed and future treatments modified as necessary.

In recent years, resonant cycles have emerged as a new principle in signal coordination. Resonant cycles were originally associated with cycle lengths that resulted in good progression over a range of traffic volumes. Several studies documented the potential benefits of this new principle. In this research, “resonant cycles” are defined as cycle lengths that are robust over a range of traffic volumes on two-way arterials. However, resonant cycles may not always exist on traffic corridors, depending on their operational and geometric factors, and the impact of these factors is not well understood. To examine these conditions more closely, this paper provides a comprehensive analysis of resonant cycles. Geometric and operational traffic data obtained from two corridors were used. Resonant cycles were observed on both corridors; the benefits resulted in reductions of approximately 8% in the total delay and 19% in the number of stops when compared with the critical intersection method. The geometric and operational variables that permitted resonant cycles were identified through a macroscopic model. The results indicated that resonant cycles could easily be found on these two corridors under low-volume scenarios. When moderate-volume conditions occurred, certain combinations of geometric and operational traffic variables supported resonant cycles. In contrast, cross-street volumes that were similar in magnitude to the volumes of the main street tended to remove the cycle resonance for moderate traffic volumes. When a microscopic model was used to estimate optimal cycle lengths, the range of traffic volumes under a resonant cycle was reduced.

Traffic noise has a negative influence on society, and minimum acoustic comfort standards must be maintained for individuals to be able to perform their daily activities. When traffic speeds exceed 40 km/h, the predominant noise source is tire–pavement interaction. This study assessed noise generated in the tire–pavement interface at different traffic speeds (40, 60, and 80 km/h), for different types of asphalt coatings applied in low-volume roads in the state of Sergipe, Brazil. Coatings included conventional asphalt mix and polymer-modified hot-mix asphalt concrete, cold-mix asphalt, and double bituminous surface treatment. An increase in noise was directly proportional to increased traffic speed and also varied with changing parameters such as voids, surface texture, and roughness. Because of the upper microtexture, a double bituminous surface treatment can be used as a good solution for quiet pavements in low-volume roads.

Most previous algorithms for variable speed limit and variable speed advisory (VSL-VSA) for bottleneck flow maximization are local and occupancy measurement–based. Some algorithms for freeway networks with multiple bottlenecks were proposed on the basis of the second-order METANET model, but these were rather complicated. This paper proposes ( a) a novel simple VSL-VSA control algorithm for bottleneck flow improvement based on speed measurements and ( b) a strategy to expand to a freeway network with multiple bottlenecks by using a distance-based interpolation. The algorithm was validated with a well-calibrated traffic network model for I-66 inside the Capital Beltway around Washington, D.C. Simulation results showed that overall system performance was improved even with a 10% compliance rate, and the improvement was not sensitive to the compliance rate if the latter was greater than 10%. The performance parameters included total travel time, total travel distance, total number of stops, average speed variation, total delays, and flow at the bottlenecks. The VSL-VSA algorithm can be implemented by using infrastructure-to-vehicle communication to a display or to determine the set speed for an adaptive cruise control system or variable message signs on the roadside. The algorithm was conceptually tested with three vehicles that used the VSL as a set speed for their infrastructure-to-vehicle cooperative adaptive cruise control systems.

Vehicle weight is one of several factors that affect vehicle emissions. Vehicle weight is especially important when modeling emissions from heavy-duty trucks (HDTs). The motor vehicle emission simulator (MOVES) model provides a way to account for vehicle weight during the construction of vehicle emissions inventories. To date, vehicle weight has not received much attention, although reliable vehicle weight data have become increasingly available in the past several years with the deployment of weigh-in-motion (WIM) technology. This study developed a method for fusing vehicle weight data from WIM stations and traffic data from vehicle detector stations (VDS) to obtain HDT activity data for input in MOVES as vehicle operating mode distributions. The study identified trucks recorded by a WIM station that were likely to travel over a VDS during a specified time period. The measured weight data of the trucks were fused with the second-by-second speed and acceleration values in truck trajectories that were created based on the knowledge of truck traffic speed at the VDS. The study used freeways in Los Angeles County, California, as a case study. The case study showed that the distributions of vehicle operating mode were quite different between the proposed method and the existing method, which assumed an average weight value for all HDTs in the same class. In the example case illustrated in this paper, the proposed method resulted in 78% higher nitrogen oxide emissions and 30% higher particulate matter emissions than the existing method.

The Texas A&M Transportation Institute's Urban Mobility Report (UMR) is acknowledged to be the most authoritative source of information about traffic congestion and its possible solutions. As policy makers from the local to national levels devise strategies to reduce greenhouse gas (GHG) emissions, the level of interest in the environmental impact of urban congestion has increased. To this end, the researchers developed and applied a methodology to determine carbon dioxide (CO2) emissions caused by congestion for inclusion in the UMR. The methodology also estimated fuel consumption on the basis of the CO2 emissions estimates. The researchers developed a five-step methodology with data from three primary data sources: (a) FHWA's Highway Performance Monitoring System, (b) INRIX traffic speed data, and (c) the U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator model. Results were intuitive and reasonable when emission rates (pounds of CO2 per mile) were compared with the emissions inventories in selected cities. The researchers incorporated the new methodology for all urban areas into the 2012 UMR and plan to include the same measures in future releases of the report. The researchers reported that, in 2011, 56 billion pounds of additional CO2 were produced in all 498 urban areas during congestion only; this amount equated to 2.9 billion gallons of wasted fuel. The amount of CO2 produced under free-flow conditions (i.e., absent congestion) was 1.8 trillion pounds in 2011 in all 498 urban areas.

Although the correlation between traffic variables and weather appears to be intuitive, quantifying the effects that weather, especially rain, has on driver response in travel speeds and traffic demands is needed to evaluate practical aspects of traffic operations. Previous studies have researched driver responses to inclement weather on freeways located in northern regions of the United States and Canada. However, driver familiarity with local weather conditions is a factor that should be considered in determining inclement weather effects on traffic variables. The focus of this research was to examine driver response to rain precipitation on freeways located in the southeastern regions of the United States to determine whether results from previous studies were general indicators or location specific in nature. To study the impacts of rain precipitation on hourly mean speeds and traffic volumes, hourly weather data and traffic sensor data were collected for two freeway segments in Jacksonville, Florida. The study investigated conditions such as wet versus dry (rain or no rain) and dry versus rain intensity (no rain or light, moderate, or heavy rain) for each segment. The results indicated that mean travel speeds decreased during rainfall events and speed reductions increased with increasing rain intensity. Reductions found for light rainfall events were within the range of previous studies; however, speed reductions during moderate to heavy rains varied widely. The results also indicated that the hour of the day was a factor in the degree of motorists’ speed reduction. Traffic volumes also declined during rainy conditions, with significant reductions during peak hours.

Although pedestrian crashes account for only 1% of reported motor vehicle crashes in the United States, pedestrian crashes account for 14% of fatal crashes. Pedestrians are at a higher risk of fatalities than are automobile operators when crashes per traveling distance are considered. Because of the high societal costs of pedestrian crashes, measures designed to reduce their frequency and severity should receive high priority. Additional countermeasures such as crosswalks and signs need to be devised to decrease pedestrian-automobile collisions. The main objective of this study was to investigate the effects of yield signs on yielding for pedestrians and on traffic speed and the implication of these effects for pedestrian safety. The city of Grand Forks, North Dakota, and the University of North Dakota campus were used as test sections. Data were collected on yielding for pedestrians and on traffic speed without and with a yield sign. The two-proportion z-test and an independent t-test were used to invest...

This paper presents an alternative approach for interpreting data collected with the traffic speed deflectometer (TSD). Although the idea of measuring pavement deflections at traffic speeds in a continuous manner is not new, worldwide interest is recent, in part because of the availability of a commercial TSD device. As with any new device, there is a need for robust, scientifically sound, yet practical interpretation procedures. This paper describes the details of such a procedure recently developed at the Road and Bridge Research Institute in Poland. In addition to a detailed description of the proposed algorithm and its unique features, this study demonstrates an example application that uses data collected by the TSD operated by the Road and Bridge Research Institute. The proposed approach can be successfully applied to the TSD data and allows for deep customization for a user's and customer's needs and applications.

This paper explores the “lane-changing preparation process,” defined as the synchronization process, from a new, integrated perspective. A comprehensive study was conducted to analyze the behavioral characteristics of the speed synchronization process of merging vehicles from entrance ramps by tracking their trajectories in the merge lanes. On the basis of gap selection, merging vehicles were classified as either original gap type or overtaking type, and the existence of speed synchronization was proved by comparing the speed differences of merging vehicles in relation to the putative leader (PL) and the putative follower (PF) vehicles. Several rules of merging vehicles during speed synchronization were established in the behavioral study. The merging vehicles tended to maintain a speed 5 to 7 m/s higher than the speed of the PL so as to overtake unsatisfied gaps in the adjacent through lane. When those vehicles intended to accept a gap, they first adjusted their speeds to be within a 2-m/s difference from their PL and PF vehicles and then merged into the targeted gap within 2 to 3 s. Factors affecting drivers' tolerance of their speed with PL and PF vehicles were modeled with the multiregression method. Significant effects in speed synchronization include the number of rejected gaps, acceleration or deceleration, the speed difference between the PL and the PF, the time headway, the remaining distance in the auxiliary lane, and whether the PL is a merged vehicle. Results from this study may contribute to the understanding of the complex lane-changing behavior in microscopic traffic flow modeling.

This paper presents the results of a study to develop improved design guidance for high- to low-speed transition zones on rural highways. The primary steps of the research included a literature review and state-of-practice review on speed reduction treatments used in transition zones and observational field studies of several transition zone treatments in the United States. The principal findings from the observation field studies are that roundabouts and transverse pavement markings increase the rate of compliance of vehicles traveling at or below the speed limit at the end of a transition zone by 15% and 20%, respectively, as compared with no treatment. Roundabouts increase the rate of compliance of vehicles traveling within 5 mph (8 km/h) of the speed limit at the end of a transition zone by 11%, compared with no treatment. The findings support previous research that indicates the need to provide additional measures through the community to maintain speed reduction downstream of the transition zone through the community. This paper also addresses several issues to be considered in the design of high- to low-speed transition zones, including definitions and site characteristics of the geographical limits or boundaries of the transition zone study area; a methodology for assessing whether a high- to low-speed transition zone has speed limit compliance or safety issues to support the need for, and the selection of, an appropriate treatment to address the issues; and guiding principles and design concepts to be considered in the design of a transition zone.

According to the TRB Military Transportation Committee, most U.S. metropolitan planning areas with military installations currently have a disconnect between Department of Defense military bases, metropolitan planning organizations (MPOs), departments of transportation, and local communities. The Hampton Roads (Virginia) Transportation Planning Organization (HRTPO), however, has a long-standing relationship with the military community and has taken steps to increase related efforts in recent years. HRTPO has advanced the cause of planning in Hampton Roads—and, by example, in the United States—through a coordinated approach to meeting the transportation needs of the military located in the Hampton Roads region. In response to military concerns about local traffic congestion and delays, the HRTPO board placed greater emphasis on military transportation planning in the region by endorsing annual military briefings by military representatives to the MPO board and by including a military needs study in its work program. The Hampton Roads Military Transportation Needs Study, an ongoing effort by HRTPO, is reportedly the first and only attempt of an MPO to identify solutions to the transportation needs of the local military. The purpose of this paper is to inform other metropolitan areas about the integration of the military into the transportation planning process in Hampton Roads and to provide a summary of key findings from the region's Military Transportation Needs Study. Other MPOs can apply the methods, results, successes, and lessons learned from Hampton Roads to their respective regions.

The objective of this research was to estimate the effects that a specific pattern of illegal parking (double-parking) had on traffic conditions and the environment by using microscopic simulation. Through a sensitivity analysis, the effects of illegal double-parking on average speed, delay, and stopped time were estimated. Results showed that the existence of the phenomenon entailed a severe decrease in average speed and an important increase in delay and stopped time. Through a case study, the effects that a reduction or an elimination of the phenomenon would have in a real network were evaluated: all traffic indicators would be improved if double-parking were suppressed partially (e.g., through an intensification of enforcement) or, even better, completely. Results showed that limiting double-parking could result in an increase in speeds of about 10% to 15% and a decrease of about 15% and 20% in delay and stopped time, respectively. However, even greater improvements may be achieved if double-parking is eliminated: average speed can increase by up to 44%, while delay and stopped time can decrease by up to 33% and 47%, respectively. On the basis of the results extracted from the case study, the effects for the whole district of the municipality of Athens, as well as for the entire Athens region, were assessed by using several indexes and performance measures. Savings from decreased lost time and reduced carbon dioxide, carbon monoxide, and hydrocarbon emissions were calculated. Directions for future research are also proposed.

The estimation of traffic speed based on loop detector occupancy is a long-standing problem that is critical to traffic management and control. Single-loop detector stations are still popular in highway traffic systems in practice. Although dual loop stations could generate more accurate speed estimation with event data, updating single loops to dual loops is costly. Most previous work, such as the g-factor method on speed estimation with a single-loop system, used aggregated data and assumed a known effective vehicle length. However, those methods did not always produce good results, particularly in traffic transition phases. This paper proposes a new approach using event data and mode occupancy within a moving window of variable length to estimate the effective vehicle length, or g-factor. Berkeley Highway Laboratory dual loop data with a 1-Hz update rate and 60-Hz information were used for algorithm validation and reliability tests. The speed estimated from the corrected dual loop data was used as the ground truth for comparison. Root mean square error was used to quantify the discrepancy. Results showed that the proposed approach generates very satisfactory speed estimations compared with estimations from dual loop stations. This algorithm can be run at the control-cabinet level, in which event data of an individual loop from a 170 or 2070 controller are available without the need of any hardware modification. These controllers have the processor capabilities to handle data in real time.

Across the nation, one work zone fatality occurs every 10 h (about 2.3 per day) and one injury, every 13 min (110 per day). Speed limit compliance in and around work zones concerns transportation agency officials because excessive speeds have proved to be primary causes of crashes in work zones. As much as 25% of fatal crashes in work zones may have involved high speeds. Research was conducted with the use of a changeable message sign with radar (CMSR) at one work zone location on a four-lane, divided highway in Arizona. The CMSR, which was triggered by radar-detected speed, provided feedback to drivers about the amount of their potential traffic fine. A review of the literature indicated that this type of feedback has received only limited study. The CMSR alternated one speed feedback message, “Your Speed XX mph,” with another, “Possible Fine $XXX.” The dollar amount varied according to the speed of the vehicle. The coupling of speeding-fine feedback with alternating feedback on vehicle speed led to an observed reduction of speeds in the tested work zone. Reductions in mean speeds were modest, at approximately 4 mph. However, the impact on excessive speeders was significant. The number of speeders that traveled 5 and 10 mph, or more, above the speed limit decreased by one-fourth. At the extreme speeds of 15, 20, and 25 mph over the speed limit, the number of speeding vehicles was reduced in each case by half.

The Safe Routes to School (SRTS) program, through a variety of tools, seeks to enable children to walk and bicycle safely to school. One such tool recently implemented through a program of the North Carolina Department of Transportation (DOT) is the use of speed feedback signs (“Your Speed” signs) to reduce vehicle speeds in school zones. The North Carolina DOT has no policy or standard for the use of these signs within school zones, and research illustrating the value of using permanently installed “Your Speed” signs in school zones is sparse. The Eastern Carolina Injury Prevention Program (ECIPP) applied for SRTS funding to install these signs as part of its education, encouragement, and enforcement project. The North Carolina DOT therefore initiated a study on the use of the signs in conjunction with ECIPP's larger SRTS project. Significant findings of this study include a 3.0 mph (p < .0001) to 4.5 mph (p < .0001) reduction in speed sustained over a 12-month postinstallation period, suggesting that responses to “Your Speed” signs may not diminish as drivers become accustomed to the signs’ presence. On the basis of these promising results, the North Carolina DOT is considering additional research to inform policy considerations fully for the future use of such signs. This research may be useful for organizations seeking innovative SRTS program tools to improve speed compliance in school zones, contemplating the use of speed feedback signs in school zones but needing information on their effectiveness, and having interest in a different way to evaluate their own SRTS projects.

Continuous deflection-measuring devices, or continuous deflectometers, are increasingly being used to support project-level and network-level pavement management decisions. Continuous deflectometers are nondestructive pavement evaluation devices that measure pavement deflections caused by a moving load. Some continuous deflectometers can measure with little to no traffic control; this feature makes them more advantageous to use than stationary devices, such as the falling weight deflectometer. The current technologies implemented in different types of deflectometers are discussed, and the most promising devices for supporting network-level pavement management decisions are identified. In that respect, two devices (the rolling wheel deflectometer and the traffic speed deflectometer) have shown promising results and are being evaluated further under Project R06 (F) of SHRP 2.

Decisions to run traffic on freeway shoulders during peak periods are motivated by the need to relieve congestion. Practicing traffic engineers generally believe that the decreased congestion resulting from running traffic on hard shoulders (i.e., hard shoulder running) is associated with some unspecified degree of improved safety, yet the majority of researchers agree that accident rates increase as the number of lanes increase, even if full shoulders are provided. Despite many years of modern road building, these conflicting views have not been reconciled. This paper first examines the relationship of traffic flow parameters, such as volume, density, and speed, to safety with calibrated performance functions of corridor-specific safety. On the basis of an understanding of this relationship, a possible explanation of the effect on safety of hard shoulder running is formulated. Empirical examination of the relationship of flow, density, and speed to the crash rate on selected freeways in Colorado suggests that as flow increases, the crash rate initially remains constant until a certain critical threshold combination of speed and density is reached. Once this threshold is exceeded, the crash rate rises rapidly. This rapid rise in crash rate may be caused by an increase in density without a notable reduction in speed and the resultant small headways that make it difficult or impossible for drivers to compensate for error. This model suggests that during hard shoulder running, crash rates decline because of the lower traffic volume or density per lane and that the safety benefits of a reduced volume or density per lane outweigh the adverse effects of the lack of provision of a full shoulder.

Existing geometric design guidance related to interchange ramp spacing in the Texas Roadway Design Manual and AASHTO's Policy on Geometric Design of Highways and Streets is not speed dependent even though intuition indicates that spacing and speed are related. Understanding the relationship between interchange ramp spacing, speed, and freeway operations is important, especially in developing potential design values for higher speeds (e.g., 85 to 100 mph). The objectives of this Texas Department of Transportation project were (a) to investigate relationships between weaving length, speed, and overall vehicle operations for successive ramps on Texas freeways and (b) to propose updates to current guidance on recommended distances between ramps. Several methods were used to assist in developing guidance on ramp spacing lengths. The methods and resources used to generate potential lengths included (a) guidance provided in the Design Manual for Roads and Bridges published by the Highways Agency in England, (b) minimum deceleration and acceleration lengths for freeway conditions, (c) decision sight distance, (d) sign spacing needs, (e) NCHRP Project 3–75 findings, (f) findings from field studies at seven sites in Texas, (g) findings from simulation conducted as part of this research, and (h) safety relationships identified in the literature. Suggested ramp spacings were developed for the entrance–exit and exit–exit ramp scenarios.

Mopeds, experiencing faster growth of use than bicycles, have been swarming urban streets in China in recent years. The mixture of mopeds and bicycles in the same road space has led to great change in the composition and the traffic flow characteristics of nonmotorized traffic. It is impractical to measure the nonmotorized traffic flow by simply aggregating the vehicle counts of mopeds and bicycles because of their distinct temporal–spatial traffic features. This paper proposes a methodology to measure the normalized volume of a nonmotorized traffic mix of mopeds and bicycles. A bicycle equivalent (BE) concept is introduced to measure the moped in equivalent bicycle units and then to normalize the traffic flow mix. Two BE analysis methods that express the relationship between speeds, densities, and occupied spaces for mopeds and bicycles have been developed. A case study estimates the BE from these methods by using field data collected at a midblock section of an arterial in Shanghai. Statistical analysis of observed data shows that the estimated equivalent unit of moped to bicycle is variable under different traffic conditions. Specifically, the equivalent value of moped to bicycle is less than one under lower-density conditions. The value is, however, more than one under higher-density conditions. These findings are valuable to support the measurement of a nonmotorized traffic mix of mopeds and bicycles.

Research was done to quantify the impact of icy roadway conditions on driver car-following behavior. The data used in the study were gathered by a group of researchers at Japan's Hokkaido University in a controlled environment under dry and icy roadway conditions. The data were used to calibrate the Van Aerde steady-state car-following model, along with vehicle acceleration and deceleration constraints. The impact of icy roadway conditions on five driver-specific car-following parameters [driver perception–reaction time (PRT), free-flow or desired speed, speed-at-capacity, capacity, and jam density] was conducted with one-way analysis of variance and Kruskal–Wallis tests. The results demonstrate that icy roadway conditions produce statistically significant differences at a 5% significance level. Specifically, icy roadway conditions reduce the mean free-flow speed, speed at capacity, and capacity by 28%, 13%, and 46%, respectively, compared with driving on dry roadways. The mean PRT for icy conditions is found to take 13% longer than driving under dry conditions. The longer PRTs can be attributed to drivers driving at lower speeds and larger spacing on icy surfaces compared with dry conditions.

This paper summarizes a research project in which observers recorded the speeds and distances of vehicles decelerating in the through lanes before turning right into a driveway. All data were collected on multiline urban arterial roadways with posted speeds of 35 mph, 40 mph, or 45 mph. The main objective was to develop speed–distance models for the decelerating vehicles; deceleration was not linear, and was best modeled in three stages. In addition, the models developed during this project were compared with previous models developed by others, the effects of deceleration on the through traffic stream were examined, and the implications for designing the lengths of right-turn deceleration lanes were considered.

This paper discusses road safety issues and the necessity for safety enhancements, especially on rural roads. Points of discontinuity in infrastructure consistently lead to serious accidents. The major causes of these accidents are inattentiveness and excessive speed. A possible measure to increase attention and reduce speed is installation of transversal rumble strips (TRS). The German government funded a research project to evaluate the use of these strips in the approaches to hazardous bends. The design of rumble strips was optimized on the basis of literature research and metrological preexaminations. The resulting design is expected to create a maximal impact on the driver. The effect of these optimized rumble strips on speed choice was tested by field studies. In the approaches to hazardous bends on rural roads, rumble strips were installed according to the design of the pretests. The impact on the drivers’ behavior, especially on speed choice, was investigated by before-and-after analyses with radar measurements and video surveying. In addition to statistical analyses of the effect on the driving speed, detailed investigations on the impact to special driver groups and car types were performed. According to the analysis made in this research project, TRS are cost-efficient and have a positive effect on driving behavior and hence on road safety.

The objectives of this study are twofold: to quantify nonrecurrent congestion delay caused by traffic accidents that occur on freeways and to analyze the causal factors by different types of accidents. Unlike previous studies, this study proposes an analytical procedure to estimate congestion impact by using the difference between speed reduced by a traffic accident and accident-free normal flow speed. The proposed method is demonstrated by a case study using accident data derived from 27 freeways in South Korea in 2008. Ultimately, the method can develop a performance measure for evaluating transportation policies and planning level analyses associated with the design of transportation systems and preparation of operating plans for safety, as well as for quantitatively evaluating deployed transportation projects or technologies. Moreover, the results from statistical analyses can potentially be useful in making strategic plans and formulating mitigation measures for reducing nonrecurrent congestion delay caused by accidents on the South Korean freeway system.

Acceleration lanes provide access to freeways with the aim of improving traffic flow conditions and safety. Therefore, provision of an appropriate entrance ramp and acceleration lane geometry that allows the entering vehicle to accelerate to a speed closer to the main flow speed is crucial for comfortable and safe merging maneuvers. This paper presents an analysis of driver behavior on different acceleration lanes in various traffic conditions performed on an interactive driving simulator. Three different traffic volumes combined with two lengths of acceleration lane were simulated and their effects on driving performances have been studied. It has been observed that driver behavior during merging maneuvers is significantly influenced by traffic volume on the main lane and that the acceleration lane length does not show any significant effect on drivers’ speed, trajectories, and accelerations. In particular, as the traffic volume increases, so does the merging length of the driver; the acceleration oscillations and the number of gaps rejected also increase. The general results show that the advanced techniques of driving simulation can disclose the relationships between road design parameters and behavioral aspects important to create safer road infrastructure. Further simulation studies are planned to confirm the findings and to strengthen and generalize the results.