Low Traffic Volume Research Articles

Effects of vehicle traffic on space use and road crossings of caribou in the Arctic.

Assessing the effects of industrial development on wildlife is a key objective of managers and conservation practitioners. However, wildlife responses are often only investigated with respect to the footprint of infrastructure, even though human activity can strongly mediate development impacts. In Arctic Alaska, there is substantial interest in expanding energy development, raising concerns about the potential effects on barren-ground caribou (Rangifer tarandus granti). While caribou generally avoid industrial infrastructure, little is known about the role of human activity in moderating their responses, and whether managing activity levels could minimize development effects. To address this uncertainty, we examined the influence of traffic volume on caribou summer space use and road crossings in the Central Arctic Herd within the Kuparuk and Milne Point oil fields on the North Slope of Alaska. We first modeled spatiotemporal variation in hourly traffic volumes across the road system from traffic counter data using gradient-boosted regression trees. We then used generalized additive models to estimate nonlinear step selection functions and road-crossing probabilities from collared female caribou during the post-calving and insect harassment seasons, when they primarily interact with roads. Step selection analyses revealed that caribou selected areas further from roads (~1-3 km) during the post-calving and mosquito seasons and selected areas with lower traffic volumes during all seasons, with selection probabilities peaking when traffic was <5 vehicles/h. Using road-crossing models, we found that caribou were less likely to cross roads during the insect seasons as traffic increased, but that response dissipated as insect harassment became more severe. Past studies suggested that caribou exhibit behavioral responses when traffic exceeds 15 vehicles/h, but our results demonstrate behavioral responses at much lower traffic levels. Our results illustrate that vehicle activity mediates caribou responses to road infrastructure, information that can be used in future land-use planning to minimize the behavioral responses of caribou to industrial development in sensitive Arctic landscapes.

A Meta‐analysis of the effects of ground‐based extraction technologies on fine roots in forest soils

AbstractFine roots are an important component of forest soil as they play a key role in fundamental processes like plant nutrition and water supply. As with all the features of forest soil, the compaction related to the forest operations and, in particular, to the wood extraction via ground‐based technologies could lead to a significant impact on the presence of fine roots in the soil affected by the passage of the machines. Considering the lack of a review, we used a meta‐analytic approach to synthesise effect sizes of ground‐based extraction technologies affecting the presence of fine roots in the soil, using a multivariate mixed‐effects meta‐analytic model. The obtained results revealed that the presence of fine roots in the soil affected by the passage of the machines was significantly reduced by both skidding (g = −1.23, 95%CI ‐1.87, −0.60) and forwarding (g = −1.37, 95%CI ‐2.01, −0.74). Due to the higher soil compaction caused by forwarding, this method had a marginally but statistically significant greater impact than skidding. We further confirmed the hypothesis that soil compaction and the presence of fine roots were strongly correlated, with the latter being greatly reduced in compacted soils characterised by higher bulk density. What is more, even more than 20 years after a harvesting intervention, the presence of fine roots was significantly lower in both strip roads (forwarding) and skid trails (skidding) as compared to areas which were not impacted by the machine passage. This shows that fine roots are particularly vulnerable to forest operations. On the other hand, the majority of the trails in the database used for the meta‐analysis were created in countries that favour the creation of a small number of widely used trails. Therefore, it would be scientifically valuable to do a comparative evaluation in various forestry contexts, such as in the Mediterranean area, where the development of the forest trails network is oriented on creating a large number of trails with low traffic volumes. Because machinery‐induced soil compaction is the major driver of the decrease in fine roots in skid trails and strip roads, both the application of best management practices as well as of a smarter planning of the trail network to limit soil compaction are strongly recommended. Both applications are highly recommended to be used in the planning phase and in the practical implementation of logging activities.

Peningkatan Tebal Lapis Perkerasan Kaku pada Jalan Yos Sudarso Kumbe dengan Metode AASHTO

Yos Sudarso Road is a connecting road heavily traveled by heavy vehicles. The number of heavy vehicles that often pass through this road is one of the supporting factors for the damage to the road, in addition to the absence of maintenance and improvement carried out, resulting in a decrease in the level of serviceability of the road. This condition is the main objective of this research, namely to determine the design of increasing the thickness of the rigid pavement layer on Jalan Yos Sudarso Kumbe. The method used in this improvement is the American Association Of State Highway And Transporting official 1993. The research conducted on Yos Sudarso Kumbe road includes collecting CBR data using DCP tools, surveying traffic volume to find the LHR value, then observing how long the puddle of rainwater on the road surface will disappear to determine the drainage quality. From the results of the analysis and calculations carried out using the 1993 AASHTO method with 15% CBR, the thickness of the pavement layer is 135 mm (5.2 inches) because the thickness of the pavement layer does not meet the minimum standard of rigid pavement thickness with low traffic volume. The minimum standard of rigid pavement thickness with low traffic volume is 150 mm (6 inches), with D10 mm reinforcement and 30 cm spacing between support, D19 mm dowel, 45 cm length, 30 cm spacing between bars, and D16 mm tie bars, 70 cm length, 75 cm spacing between bars.

Examining Self-Reported Young Novice and Young Experienced Drivers’ Speeding Behavior: An Exploratory Study

Road related fatalities are a global problem. Speed management remains an elusive skill for many young drivers. The main aim of the study is to understand self-reported speeding behavior, as well as the situations in which young novice and young experienced drivers exceed the speed limit. Three hundred and thirty-four young drivers completed the online driving survey. All participants were grouped based on experience (i.e., novice vs. experienced) and type of drivers (i.e., conformer, dettered, manipulator and defier). Overall, the results showed similarities between responses about their speeding behavior for both young novice and young experienced drivers. The results illustrate that the most common situations in which young drivers exceed the posted speed limit are road design, poor signage, rush, human error, and low traffic volume areas. These results are valuable in developing training to ‘calibrate’ young drivers’ actual and perceived behavior and improve their speed compliance.

Mixed-Effects Zero-Inflated Negative Binomial Crash Predictive Models for Unsignalized Intersections along Two-Lane Highways with Minor Roads Operating with Very Low Traffic Volumes

Current and past investigations into road accidents have mainly focused on high-volume roads which have experienced a significant number of collisions. However, low-volume roads also record crash data which merit analysis to understand the causes of accidents and find potential countermeasures. This study focuses on unsignalized intersections linking major highways and minor stop-controlled local roads that operate with very low traffic volumes. In this scenario, an over-abundance of zero observations has to be accounted for. To address this issue, zero-inflated Poisson and negative binomial regression models have been calibrated and analyzed to find the geometric and operational factors which contribute most to the crash rate. Random effects have also been considered to account for unobserved heterogeneity inherent in the grouping of intersections within the database. The results indicate that the mixed-effect negative binomial model had the best prediction performance. Developing models for subgroups of crashes, for example, failure-to-yield and other types of crash, resulted in better prediction performance. The major road’s average annual daily traffic and the surface type (i.e., paved or unpaved) of minor road approaches had a significant effect on the frequency of all crash types. The type of intersection (three- or four-legs), the skew angle of intersection, deviation angle of near curves, and operating speed differences also had statistically significant effects on the frequency of crashes that do not include the failure-to-yield.

Cold mix asphalt with polymeric stone for low traffic volume roads

The use of polymer-based household waste in the wearing course of flexible pavements has been analysed, either as an asphalt bitumen modifier or as a dry additive. However, there are not enough records in which this waste is used to make up the aggregate to be used in an asphalt mix. This article presents the development carried out at LEMaC, consisting of pieces made from the mixture of household waste polymers and soils, which has been called Polymeric Stone. This material is used to design a cold-mix asphalt, and its use has been validated after evaluating a series of properties.

Accurate detection of vehicle, pedestrian, cyclist and wheelchair from roadside light detection and ranging sensors

Accurate detection plays a critical role in improving the safety situation of vulnerable road users. This study extends infrastructure-based LiDAR application to all three major vulnerable road user groups including pedestrians, cyclists, and wheelchair users. Two critical problems for accurate detection of small-sized road users are scanning angle variability and feature fluctuation. To address these issues, a feature-based classification method combined with prior LiDAR trajectory information is developed. Effective dimension-related features are proposed and five classifiers including artificial neural network (ANN), random forest (RF), adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and long short-term memory (LSTM) are tested with a novel feature engineering process. A total of seven features are selected from the point cloud of clusters for vehicle/pedestrian/cyclist/wheelchair classification. By updating these significant features based on prior information of the entire trajectory, the performance of road user classification (imbalanced datasets) has been significantly improved. Experimental study is conducted to examine the recall rate, F1-score, and AUC of vehicles, pedestrians, cyclists, and wheelchairs before and after integration with prior trajectory information. The result shows the trained AdaBoost, RUSBoost, and LSTM classifiers with prior trajectory information can achieve recall/F1-score/AUC: (1) Low traffic volumes – vehicles (100%/99.96%/99.96%), pedestrians (99.96%/99.96%/99.97%), cyclists (99.74%/99.45%/99.67%), and wheelchairs (99.22%/99.68%/99.01%) and (2) Moderate traffic volumes – vehicles (99.39%/99.44%/99.69%), pedestrians (98.33%/97.99%/98.64%), and cyclists (95.41%/94.29%/94.40%), using 32-laser LiDAR sensors (10 Hz).

An Incident Detection Model Using Random Forest Classifier

Traffic incidents have adverse effects on traffic operations, safety, and the economy. Efficient Automatic Incident Detection (AID) systems are crucial for timely and accurate incident detection. This paper develops a realistic AID model using the Random Forest (RF), which is a machine learning technique. The model is trained and tested on simulated data from VISSIM traffic simulation software. The model considers the variations in four critical factors: congestion levels, incident severity, incident location, and detector distance. Comparative evaluation with existing AID models, in the literature, demonstrates the superiority of the developed model, exhibiting higher Detection Rate (DR), lower Mean Time to Detect (MTTD), and lower False Alarm Rate (FAR). During training, the RF model achieved a DR of 96.97%, MTTD of 1.05 min, and FAR of 0.62%. During testing, it achieved a DR of 100%, MTTD of 1.17 min, and FAR of 0.862%. Findings indicate that detecting minor incidents during low traffic volumes is challenging. FAR decreases with the increase in Demand to Capacity ratio (D/C), while MTTD increases with D/C. Higher incident severity leads to lower MTTD values, while greater distance between an incident and upstream detector has the opposite effect. The FAR is inversely proportional to the incident’s location from the upstream detector, while being directly proportional to the distance between detectors. Larger detector spacings result in longer detection times.

Developing a Data-Driven Network Screening Procedure for Systemic Safety Approach

Systemic analysis is considered an important safety analysis approach that is complementary to the Highway Safety Manual hotspot analysis. The network screening step in systemic analysis is to identify sites with characteristics that are associated with specific types of severe crashes. Traditionally, determining the risk scores involves subjective criteria. This research aims to develop a data-driven approach to replace the subjective methods used in the past. To achieve the research objective, this study collected roadway and crash data from the Connecticut Department of Transportation. A data-driven crash risk factor categorization methodology is proposed to estimate accurately the performance measures indicating crash risks. Moreover, this study proposes and compares four different risk scoring matrices to identify an optimal risk scoring method that is attuned with the principles of the systemic approach to safety as well as providing additional insights on justifying the systemic safety analysis results. The proposed methodology is implemented to conduct network screening for severe roadway departure crashes and later validated using severe aggressive-driving related crashes. Risk-based network screening results indicate that risk scores derived from normalized crash over-representation provide additional emphasis on sites with low traffic volume that are associated with high severe crash counts. The highest modified crash rate was obtained using normalized crash over-representation based risk scores indicating that the proposed network screening methodology can not only identify roadway attributes that are correlated with severe crashes but also account for low-volume roadway sites with severe crashes. The validation analysis indicated proposed method is transferrable to different emphasis area related crashes.

Motivational and deterrent effects of route attributes in cyclists’ route choice

Cyclists’ route choice is influenced by various route attributes as well as by individual characteristics or the purpose of cycling. Previous studies examined the extent to which those route attributes either motivate or deter to choose a certain route. However, one attribute, e.g. traffic volume, can both motivate and deter depending on the specific level, e.g. high vs. low traffic volume, and the effect of the levels may not be equal. Previous studies usually focused on either the motivational or the deterrent level. This study aims to compare both levels of several attributes, considering also individual factors and different cycling purposes. In an online questionnaire, (N = 169) participants were firstly separated into two trip purpose conditions by instruction (work vs. shopping). They were then presented with two routes representing the motivational and deterrent aspect of one attribute, and were asked for the maximum acceptable travel time for one route given the travel time of the alternative route and vice versa. Results indicate that commuter cyclists are more sensitive to deterrent aspects than to motivational aspects. In general, the gradient and the surface of a route have the highest impact, whereas advisory cycle lanes and traffic lights have the smallest. However, the impact of route attributes was influenced by individual aspects. This study is one of few that examined the impact of various route attributes by considering both the motivational and deterrent level of each attribute. It, thus, exceeds the existing literature by results of these effects and help to obtain a deeper understanding of cyclists’ decision to choose or avoid certain routes.

Leveraging probe data to model speeding on urban limited access highway segments: Examining the impact of operational performance, roadway characteristics, and COVID-19 pandemic

Stay-at-home orders - imposed to prevent the spread of COVID-19 - drastically changed the way highways operate. Despite lower traffic volumes during these times, the rate of fatal and serious injury crashes increased significantly across the United States due to increased speeding on roads with less traffic congestion and lower levels of speed enforcement. This paper uses a mixed effect binomial regression model to investigate the impact of stay-at-home orders on odds of speeding on urban limited access highway segments in Maine and Connecticut. This paper also establishes a link between traffic density and the odds of speeding. For this purpose, hourly speed and volume probe data were collected on limited access highway segments for the U.S. states of Maine and Connecticut to estimate the traffic density. The traffic density was then combined with the roadway geometric characteristics, speed limit, as well as dummy variables denoting the time of the week, time of the day, COVID-19 phases (before, during and after stay-at-home order), and the interactions between them. Density, represented in the model as Level of Service, was found to be associated with the odds of speeding, with better levels of service such as A, or B (low density) resulting in the higher odds that drivers would speed. We also found that narrower shoulder width could result in lower odds of speeding. Furthermore, we found that during the stay-at-home order, the odds of speeding by more than 10, 15, and 20 mph increased respectively by 54%, 71% and 85% in Connecticut, and by 15%, 36%, and 65% in Maine during evening peak hours. Additionally, one year after the onset of the pandemic, during evening peak hours, the odds of speeding greater than 10, 15, and 20 mph were still 35%, 29%, and 19% greater in Connecticut and 35% 35% and 20% greater in Maine compared to before pandemic.

The Smart City and Healthy Walking: An Environmental Comparison Between Healthy and the Shortest Route Choices

Walking is a means of health promotion, which is one of the main features of smart cities. A smart city’s built environment can help people choose a healthy walking route instead of the shortest one. Our study investigated which environmental factors pedestrians who select healthy routes prefer and favored environmental factors in pedestrian navigation mobile applications. Survey data were collected from 164 residents in Daegu, South Korea, from October 12 to October 25, 2022. &lt;em&gt;t&lt;/em&gt; and chi-square tests were used to compare perceptual differences between the healthy route and the shortest route preference groups. The results indicate that 56.7% of respondents preferred a healthy walking route over the shortest route. Pedestrians who chose the healthy route preferred to have less noise and more greenery along their commute and feel safer from traffic accidents and crimes than those who chose the shortest route. Moreover, people who favored healthy routes also considered the following environmental factors in pedestrian navigation mobile applications: (a) greenery and waterfront areas, (b) low traffic volume, and (c) safety from traffic accidents and crimes. The results suggest that urban planning and design policies support healthier and more active walking in smart cities.

Feasibility of Siliceous Sandstone Utilization in Porous Asphalt Mixture

In this paper, the feasibility of using siliceous sandstone aggregate in porous asphalt mixture porous asphalt concrete (PAC)-16 was studied. The gradations of siliceous sandstone and limestone PAC-16 mixtures were calculated by the backward regression method and asphalt contents were obtained by the Schellenberg leakage test and Cantabro scattering test. The void rate attenuation, gradation refinement, mechanical properties, and road performances of sandstone and limestone PAC-16 mixtures were tested and compared. Result shows that under the same load, the attenuation of total and connected void rates of sandstone mixture are less, and the mass percentage variation of aggregate in each grade in a sandstone mixture under repeated load is smaller. A sandstone mixture has similar mechanical properties as a limestone mixture with a lower void rate, and it has better mechanical properties compared with limestone mixture with a similar void rate. Compared with a limestone mixture, a sandstone mixture has better permeability and high-temperature performance, close low-temperature performance, and slightly worse moisture stability. Therefore, a sandstone mixture could not be applied in winter frost areas to reduce freeze-thaw cycling, and it could be applied in single-layer drainage pavement with low traffic volume or in the lower layer of double-layer drainage pavement to avoid direct contact with traffic loads. The addition of an antistripping agent is strongly recommended.

Bicycling comfort at roundabouts: Effects of design and situational factors

As roundabouts replace traditional intersections, there are concerns regarding the safety of roundabouts for bicycling. Given the limitations of crash data, this study informs an understanding of bicycle safety at roundabouts in the US through an analysis of cyclists’ perceptions of comfort regarding different roundabout design and operational attributes and different bicycling situations (e.g., entering, exiting, circulating) at roundabouts. In an online questionnaire, we presented 568 US adult bicyclists with renderings of a hypothetical roundabout and asked a series of questions about perceived comfort. We then used an integrated set of ordered probit regression models to analyze comfort outcomes (overall and in five situations) against roundabout attributes, while controlling for personal socio-demographics and cycling characteristics (including type of cyclist). Although most current cyclists (71%) reported some degree of comfort bicycling at roundabouts, around a third (29%) felt somewhat or very uncomfortable. Roundabouts perceived to be more comfortable for bicycling had: one (rather than two) lanes, lower traffic volumes, more bicycle facilities-especially separated bicycle lanes (a “protected roundabout”)-and a larger central island. The most comfortable situations were entering and exiting the roundabout, while the least comfortable situations were riding on the sidewalk or in the crosswalk. Circulating within the roundabout was the situation rated most similar to ratings of overall comfort. “Strong and fearless” cyclists were generally more comfortable at roundabouts than “interested but concerned” cyclists, except for sidewalk riding. These results offer implications for how to make roundabouts more comfortable for people bicycling of all abilities and levels of confidence.

A Comparison of Contributing Factors between Young and Old Riders of Motorcycle Crash Severity on Local Roads

This study aims to identify the factors that influence the severity of motorcycle crashes on local roads, particularly given the high speeds often observed for motorcycles on these roads with low traffic volumes and numerous multi-leg intersections. Previous research has shown that a rider’s age can impact their speed behavior. To explore this issue, data on motorcycle crashes from 2015 to 2020 in Thailand—a middle-income developing country—were analyzed using a random parameter logit model with unobserved heterogeneity in means and variances, comparing young (&lt;30-year-old) and older (&gt;50-year-old) riders. The contributing factors were divided into four groups: driver, crash, environmental, and road factors. The transferability test yielded different results for the young rider and old rider models, indicating that it is appropriate to analyze these models separately. A constant value revealed that old riders were more likely to die in a crash than young riders. In terms of the random parameter, the local address and road surface variables were found to be significant in both models. The results of unobserved heterogeneity in means and variances identified significant variables in both models, including gender, exceeding the speed limit, lit roads, unlit roads, mobile phone use, and road surface. These findings were used to develop policy recommendations for reducing the severity of motorcycle crashes on local roads.

Investigation of durability properties of grouted concrete pavement as alternative pavement for low traffic roads

This Study presents an experimental investigation on the durability properties mainly Initial surface absorption test (ISAT) and Electrical resistivity (ER) of construction of grouted concrete rigid pavement as a low cost and labour-intensive concrete pavement to be used in interior parts of the country catering to low traffic volume. Initially a optimum grout mix is designed based on its cost, flowability, compressive strength and flexural strength. Then 6 different grade proportion of coarse aggregate are taken for further investigation, ensuring minimum of 20% void ratio to a maximum of 35% void ratio after hand compaction, for allowing the proper flow of the grout mix. Then the resulted grouted concrete mix were tested for ISAT and ER after curing period of 28 days and 28–56 days respectively. Based on the test results it can be concluded that mix 4 show most favourable durability properties to be used as a pavement to be used in low traffic volume areas.

Travel time, delay and CO2 impacts of SAE L3 driving automation of passenger cars on the European motorway network

Impacts of driving automation on traffic flow and emissions are usually studied with traffic simulations using only few speed limits and traffic volumes. Without considering the real-world prevalence of simulated scenarios, it is unknown how the results translate to real-world conditions, such as a regional motorway network. The present study assessed the potential impacts of conditionally automated driving, described by stable vehicle motion control and longer time gaps, on the European motorway network assuming no changes in other influential factors, such as travel demand or vehicle fleet. Traffic simulations provided estimates of the effect magnitude per vehicle kilometre travelled (VKT) in representative conditions, and results were scaled up using map-, traffic- and weather-related data, accounting for the VKT per condition. Overall, the impacts of automated vehicles (AVs) on the European motorway network are likely small. Travel times and delay are estimated to increase by 0.8% and 1.3% respectively at a 100% AV penetration rate among passenger cars, and CO2 emissions to drop by 0.5%. While large reductions of average travel time (up to 8.0–10.4%), delay (up to 17.5–34.8%) and emissions (up to 13.5–15.0%) were found at high traffic volumes, most (86%) of the VKT accumulate at low traffic volumes, with small estimated effects. Thus, although beneficial in some conditions, the AVs considered in this study are not likely to support Europe’s sustainability goals. Findings advocate a comprehensive approach: Whereas impacts are likely greatest in heavy traffic, the prevalence of conditions must be considered in network level assessment.

Research on vehicle driving characteristics in narrow lanes based on different vehicle-following states

Drivers could be more careful when vehicles move in the non-standard narrow lane. This might cause the drivers to behave differently from other normal-size lanes. To investigate the characteristics of the traffic flow in narrow lanes, this paper collected the vehicle trajectory data on four-lane road segments with 2.8m width and 3.25m width respectively. Firstly, the spatial distribution of wheel tracks in the lane under low traffic volume is studied, and the results show that the wheel trails’ distribution is obviously affected by three factors: speed, lane width, and lane in cross-section position, which are significantly different from normal-size lanes. Secondly, statistical analysis shows that vehicle-following status in narrow lane environment can be divided into three types: staggered following, parallel following, and transitional following. Finally, a multivariate logistic regression model is constructed to predict the driver’s following strategy under different driving micro-environment parameters. The research results can support the fine management of lanes and help to improve the driving strategy of autonomous vehicles.

Comprehensive Automated Driving Maneuvers under a Non-Signalized Intersection Adopting Deep Reinforcement Learning

Automated driving systems have become a potential approach to mitigating collisions, emissions, and human errors in mixed-traffic environments. This study proposes the use of a deep reinforcement learning method to verify the effects of comprehensive automated vehicle movements at a non-signalized intersection according to training policy and measures of effectiveness. This method integrates multilayer perceptron and partially observable Markov decision process algorithms to generate a proper decision-making algorithm for automated vehicles. This study also evaluates the efficiency of proximal policy optimization hyperparameters for the performance of the training process. Firstly, we set initial parameters and create simulation scenarios. Secondly, the SUMO simulator executes and exports observations. Thirdly, the Flow tool transfers these observations into the states of reinforcement learning agents. Next, the multilayer perceptron algorithm trains the input data and updates policies to generate the proper actions. Finally, this training checks the termination and iteration process. These proposed experiments not only increase the speeds of vehicles but also decrease the emissions at a higher market penetration rate and a lower traffic volume. We demonstrate that the fully autonomous condition increased the average speed 1.49 times compared to the entirely human-driven experiment.

Eco-oriented signal control of intersections with vehicle type considerations using integrated estimation of driving behavior

Significant effects at signalized intersections include the cost associated with extra travel time, fuel consumption, and gas emissions due to the stop-and-go or idle process of vehicles waiting for traffic lights. Based on vehicle instantaneous states, this paper firstly develops a mathematical function of ecological cost to quantify the monetary impact of the signal control scheme on users and the environment. And then, this paper contributes to developing a vehicle kinematic model to describe the vehicle movement process and estimate the instantaneous states (i.e., speed, acceleration/deceleration) by second, and correspondently proposing a new eco-oriented signal control framework at isolated intersections. Finally, the proposed travel time cost and ecological cost are taken as measures of effectiveness to identify four key factors of the eco-oriented model in traffic volume in key signal phases, directional volume ratio, electric vehicle ratio, and bus ratio. This study found the eco-oriented signal method led to a significant difference in ecological cost reduction of 5% or more compared to the delay-oriented control group under the low traffic volume in key phases, the electric vehicles dominate and no less than 5% of bus ratio.