Highway Segments Research Articles

Enhancement of Highway Conditions during Winter Weather Operations through Coupling Raised Pavement Markers with Rumble Strips

The use of snowplows in northern Texas frequently results in the loss of retroreflective raised pavement markers (RPMs). The loss of RPMs is not only costly but also creates unsafe driving conditions during inclement weather. Pavement sections in many regions across the state of Texas often use a centerline rumble strip for safety. Traditionally, these rumble strips have precluded the use of metal-encased RPMs (i.e., snowplowable RPMs). This paper is focused on determining the efficacy of inserting commercially available RPMs into the trough regions of the rumble strips, and thus enabling the rumble strips to be multifunctional by not only providing a sound warning to drivers passing over the centerline but also giving nighttime lane delineation visibility to drivers by preventing the loss of RPMs because of snowplows. Field screening studies on small roadway segments and pilot implementation in a couple of highway segments in northern Texas were conducted. The studies reveal that embedding RPMs in existing rumble strips can be a viable approach for centerline delineation of highways that mitigates the issues of dislodgement of RPMs as a result of snowplow operations.

Weather-Based Lane-Change Microsimulation Parameters for Safety and Operational Performance Evaluation of Weaving and Basic Freeway Segments

It is well recognized that adverse weather conditions have significant negative impacts on safety and mobility of transportation systems. Microsimulation modeling has emerged as a cost-effective tool to quantify the safety and operational effects arising from adverse weather. Developing a realistic microsimulation model necessitates adjusting driving behavior models through trajectory-level data. This study contributes a methodology to update lane-change parameters to develop weather-specific microsimulation models based on different freeway facilities, and ultimately evaluate the safety and operational performance of the roadways. Representative parameters in various weather and facility types were extracted using an automated process. As part of the comprehensive assessment of the adjusted parameters, a weaving section and a basic freeway segment on Interstate 80 in Wyoming were identified as potential candidates. The safety and operational analyses were conducted using VISSIM. Various simulation scenarios were designed based on the field traffic flow data. The safety analysis using three surrogate measures of safety including time-to-collision, deceleration rate to avoid collision, and post encroachment time revealed that adverse weather generated a higher number of conflicts than did clear weather for both facilities. The operational analysis suggested that adverse weather produced lower average speed and higher total travel time and delay than clear weather. The demonstrated methodology could be used in assessing various connected vehicle applications associated with lane change in microsimulation from safety and operational perspectives and could be adopted by transportation agencies to develop weather-based microsimulation models.

Shared vs. dedicated lanes for automated vehicle deployment: A simulation-based assessment

• In shared lanes (SL), CAVs reduce delay and emissions by 38% and 52%, respectively. • An optimum percentage of CAV dedicated lanes (DL) is calculated for each MPR level. • SLs perform better at low traffic volumes, while DLs perform better at high volumes. • Emissions increase slightly below 40% MPR but are quickly reduced at higher MPRs. • Delay and throughput improvements occur mostly at MPRs above 30%. Connected and automated vehicles (CAV) will change the transportation world at an increasingly substantial pace. To realize the full potentials of CAV technologies, we need to investigate the transition period during which CAVs will share the roads with manually driven vehicles (MDV). This study provides a detailed assessment of the impacts of CAVs on freeways in the near future, which could serve as a basis for transportation officials and authorities to compare the different approaches for CAV deployment. The analysis was performed by modeling a hypothetical freeway segment using microsimulation. Three measures of effectiveness were used to quantify the impacts of CAVs, namely, average delay, throughput, and greenhouse gas emissions. The results reveal that at the baseline scenario of shared lanes (SL) with medium traffic volume, replacing MDVs with CAVs reduces 28.4 seconds (38%) in average delay. Additionally, throughput increases by 30%, and over 6,900 gCO 2 equivalent emissions (52%) are saved. Following the baseline analysis, an optimum percentage of dedicated lanes (DL) is derived for each market penetration rate (MPR) during the transition period. Finally, a comparison between DLs and SLs is conducted across multiple traffic volume scenarios. The findings indicate that SLs generally perform better at light traffic volumes, while DLs surpass them in congested conditions. When CAVs are deployed, both delay and throughput follow steady improvement in performance that is more evident at higher MPRs, i.e., above 30%. On the other hand, emissions experience a significant reduction in efficiency at 0 to 40% MPR but quickly make up for these losses at higher MPRs.

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this paper, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers’ clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles’ abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%–15% of all moving vehicles in the Dallas–Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle’s actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials—a common visualization of arterial signal performance for transportation planning and operations.

Modelling animal-vehicle collision counts across large networks using a Bayesian hierarchical model with time-varying parameters

Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify high-risk locations are valuable to AVC prevention, yielding economic, social, and environmental cost savings. However, many challenges exist in the study of AVC datasets. These include seasonality of animal activity, unknown exposure (i.e., the number of animal crossings), very low AVC counts across most sections of extensive roadway networks, and computational burdens that come with discrete response analysis using large datasets. To overcome these challenges, a Bayesian hierarchical model is proposed where the exposure is modeled with nonparametric Dirichlet process, and the number of segment-level AVCs is assumed to follow a binomial distribution. A Pólya-Gamma augmented Gibbs sampler is derived to estimate the proposed model. By using the AVC data of multiple years across about 85,000 segments of state-controlled highways in Texas, U.S., it is demonstrated that the model is scalable to large datasets, with a preponderance of zeros and clear monthly seasonality in counts, while identifying high-risk locations and key explanatory factors based on segment-specific factors (such as changes in speed limit). This can be done within the modelling framework, which provides useful information for policy-making purposes.

Data-Driven Computation of State-Dependent Passenger Car Equivalency for Multiple Truck Lengths

Passenger car equivalency (PCE) has been used for decades to convert mixed vehicle traffic into equivalent pure passenger car traffic streams for transportation planning, design, and operational analysis purposes. The term PCE was defined by the Highway Capacity Manual (HCM) in 1965 as an adjustment factor, and many approaches have continued to be developed for refining PCE values for different types of freeway geometry and vehicle distributions since then. This paper proposes an improved data-driven state-dependent passenger car equivalency (PCE–SD) approach, initially for level terrain basic freeway segments that consider average vehicle lengths for different types of vehicles. The term “state-dependent” refers to the space headway ratio at the same speed ranges between long vehicles (trucks), that fall into the same clustered category, and passenger cars. The relatively unique data stream used in this study comes from the Portland Oregon Regional Transportation Archive Listing (PORTAL) online datahub. This rich continuous data stream provides valuable information about traffic-related data at a 20-s aggregation level. Most importantly for this effort, ranges of categorical vehicle lengths are also available. According to analysis results, PCE–SD values estimate the actual passenger car distribution at different speeds and levels of operational performance (levels of service) more accurately compared with the HCM-6 PCEs on level terrain basic freeway segments. The authors propose that a state-dependent passenger car equivalency approach bridges the conversion gap from different types of trucks to passenger vehicles and could be used as an improved supplementary methodology.

Model-based dynamic toll pricing scheme for a congested suburban freeway with multiple access locations

In this paper we propose a novel approach for alleviating traffic congestion in freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, with the underlying model derived using a combination of both traffic flow modeling and driver behavior. The traffic segment we focus on is a suburban freeway with multiple access locations. A model derived from the cell transmission method was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized for determining the model parameters. The behavior of the driver with respect to the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different hypothesis on the driver’s VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm developed here provides an opportunity to improve on existing toll policy by guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput.

Traffic sign extraction using deep hierarchical feature learning and mobile light detection and ranging (LiDAR) data on rural highways

The application of deep learning techniques on point cloud data holds significant promise for efficient data segmentation and classification of traffic signs. This study proposes modifications to the PointNet++ neural network to improve performance on outdoor scenes. In addition, the method leverages the use of local geometric features in the training process. Several models with different combinations of geometric features and proposed changes were trained using labeled data from seven highway segments in Alberta, Canada. The results indicate that the proposed models have improved performance in accuracy and processing times compared to previous studies on sign detection using point cloud data. The overall per sign detection performance shows a 99.2% recall (98% per point) and a 98% F1-score (97% per point). Overall, the inclusion of z-gradient significantly increased sign detection in terms of precision, recall, and F1-score, by 9%, 4.9%, and 7.1%, respectively, allowing the model to yield notable performance improvements for outdoor scene recognition. Ablation tests were performed to validate the performed PointNet++ modifications. The modified PointNet++ was compared with SqueezeSegV2, a state-of-the-art neural network designed for road-object segmentation, and showed improved performance. A comparison was also made with existing sign detection methods on the Paris-Lille-3D benchmark, finding higher recall rates than existing studies. The proposed approach suggests that with adjustments, the PointNet++ neural network architecture can achieve remarkable results on large metric scale scenes for sign extraction using point cloud data.

Autonomous Vehicles for Enhancing Expressway Capacity: A Dynamic Perspective

With rapidly developing communication and autonomous-driving technology, traffic flow on road networks will change from homogeneous human-driven vehicle (HDV) traffic flow to heterogeneous mixed traffic flow (MTF) comprising HDVs, autonomous vehicles (AVs), and connective-and-autonomous vehicles (CAVs). To understand the changes in the MTF of transportation engineering, we investigated the reserved capacity (RC) and right-of-way (ROW) reallocation policy that should be utilized under MTF scenarios. We established an MTF-based theoretical model to calculate the expressway segment capacity, theoretically analyzed the influence of the market penetration rate (MPR) on capacity and validated the model through numerical analysis. The results showed that the MPR of AVs and CAVs can enhance the MTF RC that is within 0–200% and that the platooning rate of CAVs positively influences the MTF RC. CAV popularization does not necessarily lead to a rapid increase in the transportation system efficiency when the MPR is <40% but significantly improves the efficiency of existing urban transportation facilities. When the MPR is >40%, the greatest enhancement is 4800 pcu/h/lane in terms of RC. A ROW reallocation policy that equips CAV-dedicated lanes according to the MPR of AVs and CAVs can enhance the capacity of expressway systems by 500 pcu/h/lane in terms of RC.

One Approach to Quantifying Rainfall Impact on the Traffic Flow of a Specific Freeway Segment

Spatial constraints in urban areas very often lead to the application of traffic management measures to meet transport demands. Accordingly, it is very important to identify all potential impacts that could lead to reductions in the street network’s capacity. One such impact is weather conditions. The main motivation of this research is to analyze the impacts of rainfall on one of the most important segments of Belgrade’s street network that represents part of a freeway passing the city center. Our focus is on quantifying the impact of rainfall on speed and capacity. This paper proposes a new approach to analyzing the rainfall impact at the traffic lane level that provides additional possibilities to apply traffic management measures on the traffic lane level instead of at the direction level only. Functional dependences and differences in capacity and speed on traffic lanes were found under the influence of different rainfall intensities. Reductions in free-flow speed depend on the rain category and traffic lanes and vary from 4.5% to 11.58%, while reductions in traffic lane capacity range from 2.46% to 12.97%. We demonstrate the importance of considering the impact of rainfall at the traffic lane level, which could be quality input data for defining appropriate traffic management measures to mitigate the negative impacts.

Motorway Bottleneck Probability Estimation in Connected Vehicles Environment Using Speed Transition Matrices.

Increased development of the urban areas leads to intensive transport service demand, especially on urban motorways. To increase traffic flow and reduce congestion, motorway traffic bottlenecks caused by high traffic demand need to be efficiently resolved using Intelligent Transport Systems services. Communication technology development that supports Connected Vehicles (CVs), which act as an active mobile sensor for collecting traffic data, provides an opportunity to harness the large datasets to develop novel methods regarding traffic bottlenecks detection. This paper presents a speed transition matrix based model for bottleneck probability estimation on motorways. The method is based on the computation of the speed at the vehicle transition point between consecutive motorway segments, which forms a traffic pattern that is represented using transition matrices. The main feature extracted from the traffic patterns was the center of mass, whose position is used as an input to the fuzzy-based system for bottleneck probability estimation. The proposed method is evaluated on four different simulated motorway traffic scenarios: (i) traffic collision site, (ii) short recurring bottleneck, (iii) long recurring bottleneck, and (iv) moving bottleneck. The method achieves comparable bottleneck detection results on every scenario, with a total accuracy of 92% on the validation dataset. The results indicate possible implementation of the method in the motorway traffic environment with a high CVs penetration rate using them as the sensory input data for the control systems based on the machine learning algorithms.

Impact of Overloading Vehicle towards the Level of Service on Freeway Segment (Case Study: JORR KM 27 to KM 23)

Abstract The congestion problem that occurs on the road is one of the indicators to determine the level of service. Approximately 20% of all traffic congestion is due to the existence of shockwave jams or phantom jams. One of the factors causing the shock wave phenomenon is the speed differences between heavy vehicles and light vehicles (Garber & Hoel, 2009). On the other hand, the Over Dimension and Over Loading (ODOL) Vehicle worsened the situation by reducing operational speed due to the exceeding load. Indonesia Traffic Police stated that throughout 2019 there were 136.470 vehicles out of 1.376.956 offences that were ODOL vehicles. This research aims to identify the effect of overloading vehicles and the volume of heavy vehicles on creating shock wave phenomenon that led to the lower level of service. Microsimulation modelling is carried out to obtain the resulting level of service from heavy vehicles volume using the primary data from the data collection. The result shows that the large volume of heavy vehicles exacerbated by the presence of ODOL vehicles can cause a temporary change in road speed and lead to a decreased level of service.

Estimating mountainous freeway crash rate: Application of a spatial model with three-dimensional (3D) alignment parameters

The road alignment is a three-dimensional (3D) curve in nature. In this study, we quantitatively examine the effect of 3D road alignment on traffic safety on mountainous freeways. Geometric parameters of 3D curvature and torsion in mathematics are derived to characterize the 3D road curve. Based on the coordination of different horizontal and vertical elements, 3D road alignment is divided into twelve types of combined alignment. For each alignment combination, the 3D curvature and torsion are calculated according to the differential geometry theory. Regarding crash statistical modeling, the Bayesian spatial Tobit (BST) model is developed to accommodate possible spatial correlation of traffic crashes among adjacent freeway segments. The Bayesian Tobit (BT) model is also built for comparison.A 118-km mountainous freeway associated road geometric features, traffic volume with three years of crash data is used as a case study. The result from the model comparison shows the BST model outperforms the BT model in terms of goodness-of-fit. Parameter estimation result for the BST model shows that the differences of average 3D curvature (and torsion) between adjacent segments have statistically significant effects on the crash rate of the segment, indicating it is necessary to consider three-dimensional alignment parameters in estimating mountainous freeway crash rate. Moreover, by comparing the predicted crash rate calculated by the BST model and the observed crash rate, the result shows the proposed BST model can provide a reliable prediction for freeway crash rates of different combined alignments. This study provides new insight on the effect of road geometric design on traffic safety but also deepens our understanding of spatial correlations in freeway crash modeling.

Overcoming challenges in crash prediction modeling using discretized duration approach: An investigation of sampling approaches

Until recently, statistical approaches used for real-time crash prediction modeling were limited to case-control design and “sampling of alternatives” approaches. A recent study has developed a duration-based real-time crash prediction model capable of incorporating dynamic (time-varying) covariates within its framework. The modeling approach discretizes the duration between crashes into equal time intervals which can be modeled as alternatives in a multinomial logit framework. The approach, however, requires a reformulation of the original crash dataset to fit its modeling framework which results in considerably large data making model estimation computationally demanding. Additionally, validation of the model in the original study is based on crash data from just one interstate, I-405, assuming homogenous highway segments each 5 miles in length. This study improves upon the original study by investigating sampling techniques that can be applied to the reformulated data to reduce computational load using 2019 crash data from two interstates, I-40 and I-55, in Memphis, Tennessee. Furthermore, discretization of inter-crash duration is undertaken following non-homogenous segmentation of the interstates that is based on highway geometry, terrain, and posted speed limit. To accomplish the study objectives, a relatively small future window of 1 h with 15-minute time intervals is used to discretize the inter-crash duration and obtain the reformulated data. Sampling of crashes for model estimation is then done at the crash, epoch, and segment levels to answer the question of which sampling technique (by crash, epoch, or segment) would result in reasonable accuracy when compared with the complete (100%) data. Results show that 25% of samples drawn at the epoch level can result in a considerable reduction of computational load while providing reasonably consistent estimates.

Network traffic instability with automated driving and cooperative merging

Connected and/or automated vehicles (CAVs or AVs) have been shown to dampen stop-and-go waves in mixed autonomy traffic, thus improving string stability. However, their effects on network traffic instability due to turning and merging maneuvers are less known. In this paper, we characterize such effects using the macroscopic or network fundamental diagram (MFD or NFD). We first revisit and extend the theoretical two-ring network, and then develop an integrated modeling and simulation framework that explicitly accounts for different microscopic traffic models of human-driven vehicles (HVs), AVs, and CAVs. Results suggest that network traffic instability resulting from turning and merging maneuvers persists even if vehicles become automated and cooperative. When the turning probability is low, the presence of CAVs does not induce a significant change in the bifurcation point of the NFD. Scatter in both link fundamental diagrams (FDs) and NFDs, however, reduces resulting in higher and more stable network flows. When the turning probability is high, AVs without cooperation turn out to worsen network traffic stability, giving rise to an NFD that undergoes bifurcation long before the theoretical critical network density is reached. This is in contrast to the case with CAVs that perform cooperative merging. Results also suggest that, whenever the penetration rate of CAVs is too low or too high, making HVs connected is not as effective in delaying the bifurcation of the NFD as when the penetration rate is moderate. We further compare cooperative merging with adaptive signal control and adaptive driver routing to demonstrate its benefits in improving network flows. Simulation experiments on a real motorway segment in Sydney, Australia are also performed to confirm our findings.

Empirical Verification of Car-Following Parameters Using Naturalistic Driving Data on Freeway Segments

AbstractMicroscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential c...

Adjustment of key lane change parameters to develop microsimulation models for representative assessment of safety and operational impacts of adverse weather using SHRP2 naturalistic driving data

Introduction: Adverse weather has a considerable negative impact on safety and mobility of transportation networks. Microsimulation models are one of the potential tools that could be used to evaluate the safety and operational impacts of adverse weather. The development of a realistic microsimulation model requires the adjustment of driving behavior parameters with disaggregate trajectory-level data. This study presented a novel approach to update and adjust lane change model parameters for the development of realistic microsimulation models in different weather conditions by leveraging the trajectory-level data from SHRP2 Naturalistic Driving Study (NDS). Method: Representative key lane change parameters in various weather conditions were extracted from an automatic identification algorithm. These lane change parameters were used to develop microsimulation models in VISSIM in an attempt to assess the safety and operational impacts of adverse weather on a freeway weaving segment. Results: The evaluation of safety impacts of adverse weather with regard to three Surrogate Measures of Safety (SMoS) namely Time-to-Collision (TTC), Post Encroachment Time (PET), and Deceleration Rate to Avoid Collision (DRAC) suggested that extreme adverse weather (including heavy rain, heavy snow, and heavy fog) produced a higher total number of simulated conflicts compared to clear weather. The operational analysis results revealed that adjusted parameters in most of the adverse weather produced lower average speeds with higher total travel times and total delays than clear weather. Conclusions: The outcomes of safety and operational assessments for the adjusted parameters showed that the development of microsimulation models should be based on weather-specific, rather than default parameters. Practical Applications: The methodology presented in this study could be adopted by transportation agencies to develop weather-specific microsimulation models. Moreover, the demonstrated approach could be used to evaluate different Connected Vehicle (CV) applications related to lane change in terms of safety and operations in microsimulation platforms.

Monitoring of highway impact on fauna as a component of management of a protected area in the Brazilian Atlantic Forest

Although being conceived with economical and social significance, building roads is a high impact mechanism of landscape fragmentation, in which the involved environmental factors are not fully considered. The SP-613 highway segments the forest and divides in half the 34000 ha of Morro do Diabo State Park (MDSP), representing the major source of disturbance to the protected area’s wildlife. A random monitoring was performed between 1989-1999 and counted 158 animals and 23 wild species killed in collisions with vehicles. Among these, 16% were endangered species. Data compiled between 2002 and 2010, following the same previous methodology, counted 46 dead animals and 15 species, indicating the geographic localizations of the collisions. A new stage of the monitoring was performed, with systematic data collection throughout two different periods: the first one with 197 days of monitoring and 56 days of sampling; the second one with 292 days of monitoring and 83 days of sampling. The species, sex, when possible, date, place and carcass conditions were registered, resulting in 29 individuals and 16 wild species. The three rounds of monitoring totaled 233 registered animals among 15 orders, 28 families and 33 impacted species. In this work, we revisit the data from the first two monitorings and present the analysis of the last survey, the most impacted species and the mortality rates after the adoption of measures aiming to diminish the fauna mortality along the highway.

Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data

Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.

Citizen Science for Traffic Monitoring: Investigating the Potentials for Complementing Traffic Counters with Crowdsourced Data

Traffic counts are among the most frequently employed data to assess the traffic patterns and key performance indicators of next generation sustainable cities. Automatised counting is often based on conventional traffic monitoring systems such as inductive loop counters (ILCs). These are costly to install, maintain, and support. In this paper, we investigate the possibilities to complement and potentially replace the existing traffic monitoring infrastructure with crowdsourcing solutions. More precisely, we investigate the capabilities to predict the ILC-obtained data using Telraam counters, low-cost camera counters voluntarily employed by citizens and freely accessible by the general public. In this context, we apply different exploratory data analysis approaches and demonstrate a regression procedure with a selected set of regression models. The presented analysis is demonstrated on different urban and highway road segments in Slovenia. Our results show that the data obtained from low-cost and easily accessible counters can be used to replace the existing traffic monitoring infrastructure in different scenarios. These results confirm the prospective to directly apply the citizen engagement in the process of planning and maintaining sustainable future cities.