Average Traffic Volume Research Articles

Holistic life cycle cost analysis of road bridges with non-metallic reinforcement

Corrosion-related damage due to exposure to environmental conditions is the main cause of costs in the maintenance of transport infrastructure. Because of its high corrosion resistance and the associated higher durability, non-metallic reinforcement offers great potential for preventing such damage, thus reducing maintenance costs. In this article, potential savings for an integral road bridge are studied through a holistic life cycle cost (LCC) analysis considering four different reinforcement materials (steel, glass, basalt, carbon). A fair comparison is enabled by a material-specific design, the calculation of maintenance and user costs and the consideration of the respective disposal scenario. Moreover, environmental costs are recognised by carbon pricing based on life cycle analysis (LCA). The influence of individual parameters is quantified by means of a sensitivity analysis and the probability of savings is studied by Monte Carlo simulation. It is shown that higher investment costs for non-metallic reinforcement can be compensated by lower user costs. This is mainly due to shorter maintenance periods, as less time is required for repair action, whereby potential savings in user costs are particularly evident if the traffic route below the bridge is not disrupted. It is concluded that even from today's perspective, the use of glass and basalt fibre-reinforced polymer (FRP) reinforcement in highway bridges with average traffic volumes very likely offers an economic advantage over corrosion-prone reinforcing steel.

Analysis of Accident Risk Points (Black Spots) on the Bubutan – Perak Barat Road Section Surabaya

According to the traffic accident data from the Traffic Accident Unit of the Tanjung Perak Port Police and the Traffic Accident Unit of the Dukuh Pakis Police in Surabaya City in 2020-2022, traffic accidents that occurred on the Bubutan Road - West Perak Road section experienced an increase in the number of traffic accidents. The total details in 2021 were 13 incidents, in 2022 there was an increase to 17 incidents, besides that, the material losses incurred in 2022 had the highest figure, which amounted to IDR 46,000,000. So it is necessary to analyze the accident-prone points on the road section. The method used are AEK (Accident Equivalent Number), IPA (Importance Performance Analysis) method, and calculation of free flow speed, traffic volume analysis, and side obstacle analysis. Based on the analysis of accident-prone areas using the AEK method, it is known that the upper control limit value is 36.07 where Jalan Perak Barat has an AEK value above the BKA value in 2021 of 37, then in 2022 of 40. Jalan Perak Barat has an average traffic volume of 434.47 smp / hour, with a moderate level of side obstacles, but has a free flow speed that exceeds the planned speed. One of the factors causing traffic accidents on Jalan Perak Barat is the driver of a vehicle traveling at high speed, so rumble strip planning and road facility rejuvenation are recommended.

Freeway crash risk prediction considering unobserved heterogeneity: A random effect negative binomial regression approach

Unobserved heterogeneity of crashes remains a significant issue for freeways that influence crash prediction, and therefore deserves much attention. Using a fusion data set of crash data, driving behavior data, and traffic flow data, this study explores the spatiotemporal heterogeneity of crash determinants for different freeway segments (e.g. Yixing section and Liyang section of Ning-Hang freeway of China) and then predict the crash probability. A random effect negative binomial regression model is built to investigate the contributing factors of the crashes. Remarkable differences are observed in the crash determinants for Yixing section (include average vehicle speed, hourly average traffic volume, average free speed, road segment length, and number of left lane-merging) and Liyang section (include average intensity of aggressive driving behavior, average kilometer traffic volume). The results found the traffic flow has a more significant impact on crashes than the driving behaviors. It is found that the crash probability is a monotone decreasing function when the predicted number of crash is 0. With the increase of the number of predicted crash, the crash probability gradually converges from a large value to 0. Then the probability of other predicted number of crashes (e.g. crash = 1, crash = 2, crash = 3) presents a quadratic parabola trends. The model comparison demonstrates that the proposed model outperforms conventional model, and the prediction performance for Liyang section is better than that of Yixing section. The research findings are interesting and important for preventing crashes.

Pengaruh Karakteristik Lalu Lintas Terhadap Kebisingan Jalan Raya

The availability of transportation facilities in urban areas strongly supports community activities and mobility from one land use to another. The increase in the number of motorized vehicles causes an increase in noise level on the highway.
 The purpose of this study was to analyze the traffic characteristics on Ahmad Yani street Kendari City, noise level, and partial effect of traffic characteristics on the noise level. The research method uses descriptive and parametric statistical approaches.
 The results showed that the average traffic volume on Ahmad Yani street was 1179 pcu/hour, with a composition of motorcycles 59%, light vehicles 37%, and heavy vehicles 4%. The average vehicle speed is 48 km/hour and average traffic density is 51 pcu/km. The highest noise level occurred in the school area of 76.1 dB, while the lowest occurred in the trading area of 70.5 dB. The average noise level on the Ahmad Yani street is 73.4 dB. The effect of vehicle volume on the noise level is 56.7%, vehicle speed on the noise level is 53.4%, and the traffic density on the noise level is 68.7%. This shows that the traffic density variable has the greatest influence on the noise intensity on the highway, followed by vehicle volume variable, and the lowest is vehicle speed variable.

Evaluasi Kinerja Jalan Pada Jalur Kritis Jalan Temanggung Tilung (Sta 1+300 Sampai Sta 1+500)

Temanggung Tilung road, a class 4/2 D road with 5.5-meter lanes, joins RTA Milono road and G. Obos road in Palangka Raya. This road sees more traffic every year. The research assessed the performance of Temanggung Tilung Palangkaraya sections during peak traffic hours and road section factors. Field surveys collected primary data. For performance analysis, it refers to the 1997 (MKJI) for urban roads and from the results of data processing, it shows that the degree of saturation that occurs is still optimal (DS = 0.22 pcu/hour) direction of G.Obos-RTA Milono while the direction of RTA Milono-G.obos with (DS = 0.22 pcu/hour) indicates that the road section is included in the standard class. Parameters on the Temanggung Tilung road section (existing) obtained the highest average traffic volume at peak hours on the Temanggung Tilung road section of 709.15 pcu/hour, the highest average speed of traffic space of 36.8 km/hour, the highest side resistance of 1026.43 side friction that occurs on the Temanggung Tilung road section is categorized as very high, both for parked or stopped vehicles, pedestrians and slow vehicles. Thus causing congestion at a certain point.

Performance of Road Sections on the Road by Adding Traffic Lanes by Utilizing Roadside Drainage

Along with the rapid development in the Jalan Benteng Pancasila area, Mojokerto City, it has resulted in the emergence of problems, namely the problem of traffic jams along the road. One of the factors that contribute to the problem of traffic jams that occur along Jalan Benteng Pancasila is the presence of side barriers. Studies on the performance of Jalan Benteng Pancasila are needed to find solutions to existing problems. In this study, what was carried out was to analyze the capacity and degree of saturation in the existing and planned conditions. The analysis process was carried out using the Indonesian Highway Capacity Guidelines (IHCG) method. Based on the results of the analysis, the average traffic volume value at peak hours was 1396.90 pcu/hour, while the side resistance class was Medium. The capacity value of the Benteng Pancasila road section is 2480.41 pcu/hour, the Degree of Saturation value is 0.56, and the travel speed value is 39.0 km/hour. Thus the Jalan Benteng Pancasila section of Mojokero City can be stated to be performing well with traffic flow in a stable condition.

Development of Regression Model on the Impact of Rainfall Intensity on Traffic Congestion in Port Harcourt City, Nigeria

Severe weather (rainfall intensity) impacts significantly on transportation network. This research evaluates the impact of precipitation on vehicular congestion in the city of Port Harcourt, Nigeria. Traffic and rainfall data was obtained for the month of June, July and August, 2018 at Location Junction, Port Harcourt. The data was analysed in excel software to understand the trends and patterns, and the rainfall impacts on road traffic through traffic volume and vehicle speed. For the period under study, it was observed that during the month of June, average traffic volume due to rainfall was 2584, while in the month of July, the average traffic volume due to rainfall was 2177, and for the month of August, the average traffic volume was 1446. The gradual decrease in average traffic volume was obviously as a result of increase in rainfall during the study. It also shows that rainfall affects traffic speed as drivers tends to slow down during rainfall, and this was used to develop a regression model that could be used to forecast traffic congestion (with respect to speed) during rainfall condition in the city of Port Harcourt. This model was tested in MATLAB, and the result validated the data analysis which was carried out through statistical tool (excel) which shows that when rainfall intensity increases, traffic volume and traffic speed decrease. Keywords: Excel Software, MATLAB, Rainfall intensity, Regression model, Traffic congestion.

Multi-Objective Optimal Deployment of Road Traffic Monitoring Cameras: A Case Study in Wujiang, China

This study presents a multi-objective optimal framework for deploying traffic monitoring cameras at road networks. Compared with previous studies that focused on addressing single traffic problem such as OD estimation, link flow observation, path flow reconstruction, and travel time estimation, this study aims to address a comprehensive traffic management problem, including crash prevention, traffic violation governance, and traffic efficiency improvement. First, a potential principle for selecting the location of traffic monitoring deployment is determined, taking into account the key signalized intersections, areas prone to traffic congestion, crash-prone spots, and areas prone to traffic violations. Then, a multi-objective optimal model is developed to minimize the ATFM (i.e., average traffic volume of each five minutes), TCF (i.e., traffic crash frequency), and TVF (i.e., traffic violation frequency) while adhering to cost constraints. Finally, RVEA and NSGA-II algorithms are used to solve the multi-objective optimal model, respectively, and a comprehensive metric is proposed to evaluate the deployment schemes. The case study results demonstrate that the solutions obtained by the RVEA algorithm outperform those of the NSGA-II algorithm, and the best traffic monitoring deployment rate is 62.79%, under cost constraints. In addition, the comparison using the FAHP method also illustrates that the RVEA scheme is superior to the NSGA-II scheme. The above research results could potentially be used to optimize the locations of traffic cameras in road networks, which help to improve traffic management.

Can We Detect Urban‐Scale CO2 Emission Changes Within Medium‐Sized Cities?

AbstractThe COVID‐19 pandemic resulted in a widespread lockdown during the spring of 2020. Measurements collected on a light rail system in the Salt Lake Valley (SLV), combined with observations from the Utah Urban Carbon Dioxide Network observed a notable decrease in urban CO2 concentrations during the spring of 2020 relative to previous years. These decreases coincided with a ∼30% reduction in average traffic volume. CO2 measurements across the SLV were used within a Bayesian inverse model to spatially allocate anthropogenic emission reductions for the first COVID‐19 lockdown. The inverse model was first used to constrain anthropogenic emissions for the previous year (2019) to provide the best possible estimate of emissions for 2020, before accounting for emission reductions observed during the COVID‐19 lockdown. The posterior emissions for 2019 were then used as the prior emission estimate for the 2020 COVID‐19 lockdown analysis. Results from the inverse analysis suggest that the SLV observed a 20% decrease in afternoon CO2 emissions from March to April 2020 (−90.5 tC hr−1). The largest reductions in CO2 emissions were centered over the northern part of the valley (downtown Salt Lake City), near major roadways, and potentially at industrial point sources. These results demonstrate that CO2 monitoring networks can track reductions in CO2 emissions even in medium‐sized cities like Salt Lake City.

Early Friction and Texture Evolution After an Asphalt Overlay

Recent studies have shown that friction and, more notably, macrotexture reduce markedly when pavements are newly overlaid. However, past studies have not identified whether these effects are temporary and, if so, how long they may last. These effects must be quantified to inform strategies for best monitoring and managing friction and the surface characteristics of a pavement network. This paper uses a group of sites that received a surface overlay at some point between the end of 2019 and the end of 2021. At each site, friction and texture were measured sequentially after construction in the center of the lane (CL) and in the right-wheel path (RWP). These observations were used to evaluate the early friction and texture evolution after an asphalt overlay. First, seasonal effects were evaluated using the CL measurements, presumed to be unaffected by traffic, which indicated friction is affected by seasonality, whereas texture is not. A friction seasonal effect model was calibrated and then used to remove the seasonal variation from the RWP observations. The results showed that friction initially increases after an overlay but then, after reaching a maximum, starts to decrease. The average traffic volume needed to reach the point of maximum friction was 15.5 million repetitions. Four parameters, computed from the texture profile, were used to represent macrotexture. The macrotexture was found to generally increase after overlay construction, but in some sites the magnitude and number of peaks decayed with time.

Impact Assessment of Particulate Pollutants in Selected Traffic Congested Areas in Zaria Metropolis, Kaduna State, Nigeria

Abstract It has been noticed that the concentration of particle pollutants in major nodal points in Zaria metropolis has continued to grow as the traffic volume increases. The concentration of particulate matter and meteorological data was measured within the Zaria metropolis and was limited to major ways within the town which include Kwangila, PZ, Kasua Mata, Agoro and Kofar Doka. The concentrations of the Particulate Matter in different particle sizes (P0.3, P0.5.P1.0, P2.5 and P5.0) were measured using particle counter, at each sampling point for 3hrs during the morning peak hours (7:00am to 10.00am), for 3hrs during the afternoon off peak hours (11.00am to 2:00pm) and for 3hrs for the evening peak hours (4:00 to 7:00pm) after a comprehensive traffic volume of vehicles plying the sampling points had been done for one month. The average traffic volume for the peak period (morning) across the five sampling points ranged from 2701-3060 while that of the evening peak hours ranged from 2676-3651. The average traffic volume for the off peak (afternoon) ranged from 1777-2255 with kwangila recording the highest traffic volume for both peak and off peak periods. The impact of traffic density on the concentration of particulate matter measured showed that increase in traffic led to a corresponding increase in the concentration of particulate matter recorded for both peak and off- peak periods. The P2.5 was used as bases for comparison with the AQI standard for peak and off peak periods. For peak hour session, the air quality at Kwangila, Kasua Mata and Agoro was classified as unhealthy and hazardous while that of PZ and Kofar Doka was classified unhealthy to people with heart and lungs diseases. The concentration of PM obtained for off-peak period for all the sampling points was classified moderate and safe when compared with AQI standard. In this regard, there is a need to not only promote public transportation with larger capacity of passengers but to also improve the public transportation system. This will reduce greenhouse gas emissions by providing a convenient option to reduce unnecessary driving and by lowering emissions per passenger kilometer travelled. The use of non-motorized means of transportation would also help in curbing excessive release of pollutants into the atmosphere. Planting of Trees along major ways to trap and absorb pollutants is another option of reducing the amount of pollutants in the atmosphere. The use of older vehicles should be discouraged. The older a vehicle is the more the tendency to emit more pollutants.

Concentration and inorganic elemental analysis of particulate matter in a road tunnel environment (Žilina, Slovakia): Contribution of non-exhaust sources

Particulate matter (PM) is present in the surrounding air. The tunnel environment is no exception, where the PM source is road traffic. In a broader sense, the tunnel can be described as a separate point source of air pollution from which PM pollutants spread to the portal parts and the external environment. PM originates from the exhaust and non-exhaust processes of road traffic (brake wear, tire wear, road surface wear, and road dust re-suspension). This study deals with the specification of non-exhaust PM emissions in a tunnel environment where the primary source is road traffic. PM measurements took place in the “Považský Chlmec” highway tunnel with a length of 2,118 m directly in the tunnel tube and near the tunnel portal. PM measurements were performed using gravimetric and optical methods. PM chemical analyses were performed using energy-dispersive X-ray fluorescence (EDXRF). The concentration of PM in the tunnel was on average: PM10 = 30.76 μg/m3 and PM2.5 = 15.66 μg/m3 and near the tunnel portal PM10 = 14.38 μg/m3 and PM2.5 = 8.74 μg/m3. The average traffic volume in the tunnel tube was 2,274 veh/24 h. Using EDXRF, the main chemical elements Al, Br, Ca, Cl, Cr, Cu, Fe, K, Mg, Mn, Na, P, Si, S, Ti, and Zn were identified in the PM. Chemical element concentrations in PM10 and PM2.5 were subjected to factor analysis (FA) and principal component analysis (PCA) to determine the origin of PM. Two sources were identified for PM10 and three for PM2.5. Absolute principal component scores (APCS) in conjunction with multiple regression analysis (MRA) were used to determine the source contribution to the production of PM10 and PM2.5.

Proximity to fatal accidents predicts police citation rates on urban and rural roads

Objectives: Police enforcement can effectively deter risky driving behaviors and reduce traffic fatalities, including speed-related fatalities. Unlike other areas of data-driven policing, spatial methods to improve road safety are not well-described. The objectives of this study were as follows: (1) determine if proximity to a prior roadway fatality increases the risk of a traffic citation being issued after adjusting for relevant roadway variables; (2) determine if this effect varies between rural and urban roads. Methods: The study region included a rural county and adjacent small city (City of Charlottesville, Albemarle County, Virginia). Fatality locations were obtained from the Fatality Analysis Reporting System (FARS) from 2008 to 2018. Police citation data were obtained from the State of Virginia for 2020. Data on fatalities and roadway features were used to create a model to predict traffic citation density. Traffic stop locations were analyzed as a point pattern on a linear network, assuming a Poisson process with varying intensity. The model adjusted for average traffic volume, distance to the nearest fatal crash along the road network, rural vs urban roadway, posted speed limit, and interstate vs non-interstate road. To account for over-dispersion, quasi-Poisson model was used. Results: There were 138 fatalities and 651 traffic citations during the time periods examined. After adjusting for other covariates, the expected number of citations/km was higher with increasing proximity to prior fatal crashes, RR = 1.34 (95% CI: 1.04, 1.72) per km. The effect of proximity did not vary significantly between urban and rural roads (p = 0.2707). However, citation intensity was significantly higher on urban roads vs. rural roads, RR = 2.65 (1.09, 6.45). Predicted citation intensity reflected anticipated enforcement clusters inside the city limits and on major county roads, suggesting satisfactory model fit. Conclusions: This study demonstrated a novel approach to quantify the impact of road fatalities on police activity, measured by traffic citations. Proximity to fatal crashes was found to affect police citation rates, and this effect is consistent between urban and rural areas. Future work will aim to identify areas of under enforcement based on proximity to fatal crashes and other roadway variables.

Traffic operation analysis for underground and ground roads using microscopic traffic simulation

This study analyzed traffic congestion scenarios in underground roads diverging and merging sections and underground sections that run parallel to ground roads using a microscopic traffic simulation model. The appropriate ratio of the ramp inflow or outflow of traffic volume to the entry traffic volume at the starting section of the underground road network was analyzed. Furthermore, the effect of traffic operation such as adequate throughput, average traffic speed, occupancy, and volume versus capacity (V/C) ratios before the occurrence of traffic congestion in all three road networks was properly produced. To maintain a minimum level of service of all underground sections at the D–E level, the underground inflow traffic volume should be equal to approximately 70% of the 4000 pcph inflow traffic volume from an open road to the underground. The results of this study are a useful reference for establishing traffic control application standards to minimize underground traffic congestion. They also provide useful insights for underground road design.

Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment.

Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method.

A hybrid machine learning model for predicting Real-Time secondary crash likelihood

Secondary crashes usually occur within the spatio-temporal impact ranges of primary crashes, which could cause traffic disturbance and increase traffic safety problems. However, existing studies only focused on predicting the likelihood of crashes leading to secondary crashes without considering the likelihood of the occurrence of secondary crashes. In addition, previous studies did not consider the real-time implementation of secondary crash likelihood prediction models and included too many features that were not available in real-time. A real-time secondary crash likelihood prediction model aims to predict the likelihood of secondary crashes in a short period (e.g., 5–10 min) and update every minute. The main objective of this paper is to develop a machine learning model to predict secondary crash likelihood in real-time. Two XGBoost models were developed for predicting the likelihood of crashes leading to secondary crashes and the likelihood of the occurrence of secondary crashes, respectively. A hybrid model was proposed to combine the results from the two developed models. Results indicated that the proposed hybrid model significantly improved the accuracy of secondary crash likelihood prediction. The proposed model has the potential to be applied in proactive traffic safety management system and prevent the occurrence of secondary crashes. Moreover, experimental results suggested that several features related to real-time traffic flow conditions were crucial for predicting secondary crash likelihood, such as the average traffic volume and average occupancy.

Impact of the COVID-19 lockdown on the chemical composition and sources of urban PM2.5

The lockdown measures caused by the COVID-19 pandemic substantially affected air quality in many cities through reduced emissions from a variety of sources, including traffic. The change in PM2.5 and its chemical composition in downtown Toronto, Canada, including organic/inorganic composition and trace metals, were examined by comparing with a pre-lockdown period and respective periods in the three previous years. During the COVID-19 lockdown, the average traffic volume reduced by 58%, whereas PM2.5 only decreased by 4% relative to the baselines. Major chemical components of PM2.5, such as organic aerosol and ammonium nitrate, showed significant seasonal changes between pre- and lockdown periods. The changes in local and regional PM2.5 sources were assessed using hourly chemical composition measurements of PM2.5. Major regional and secondary PM2.5 sources exhibited no clear reductions during the lockdown period compared to pre-lockdown and the previous years. However, cooking emissions substantially dropped by approximately 61% due to the restrictions imposed on local businesses (i.e., restaurants) during the lockdown, and then gradually increased throughout the recovery periods. The reduction in non-tailpipe emissions, characterized by road dust and brake/tire dust, ranged from 37% to 61%, consistent with the changes in traffic volume and meteorology across seasons in 2020. Tailpipe emissions dropped by approximately 54% and exhibited even larger reductions during morning rush hours. The reduction of tailpipe emissions was statistically associated with the reduced number of trucks, highlighting that a small fraction of trucks contributes disproportionally to tailpipe emissions. This study provides insight into the potential for local benefits to arise from traffic intervention in traffic-dominated urban areas and supports the development of targeted strategies and regulations to effectively reduce local air pollution.

Evaluating the effect of ramp metering on freeway safety using real-time traffic data

Ramp metering relieves traffic congestion, reduces delay, and maintains the capacity flow on freeways. Due to its operational mechanism, ramp metering can also improve freeway safety. While the operational benefits of ramp metering have extensively been quantified, research on its safety effects is sparse. This study focused on evaluating the effects of ramp metering on the safety performance of the freeway mainline. It developed a crash risk prediction model for segments downstream of the entrance ramps when ramp metering is activated. The study was based on a corridor with system-wide ramp metering along I-95 in Miami, Florida. Real-time traffic, crash, and ramp metering operations data collected from 2016 to 2018 were used in the analysis. The study adopted a matched crash and non-crash case approach to evaluate the crash risk when ramp meters were activated and deactivated. A penalized logistic regression model was developed using a bootstrap resampling technique to estimate the effects of ramp metering activation and select important variables that could predict crash risk when ramp meters were activated. Results indicated that ramp metering improves safety along the freeway corridor by reducing the crash risk downstream of the entrance ramps. During ramp metering activation, the crash risk on segments downstream of the entrance ramps 5 min later can be predicted using the difference in the average lane speeds between upstream and downstream detectors, the average traffic volume in the lanes at the downstream and upstream detectors, and the coefficient of variation of speed between lanes in the upstream detectors. Also, the coefficient of variation of occupancy downstream could predict the crash risk 15 min later. The study results could be used by transportation agencies when evaluating the deployment of ramp meters. Moreover, the developed crash risk prediction model could be used in real-time to help agencies identify the increased crash risk and provide appropriate warning information to the upstream traffic.

OBSŁUGA TRANSPORTOWA MAŁYCH MIAST

The main problem connected with transport in small towns is an efficient passage of the transit route through the town. Road traffic planning is considered to be vital, which in many cases is not possible due to high costs. Another effective solution is calming the traffic along the route through towns, especially in the central areas, with lowering the speed limit to 30-40 kph (exceptionally allowing 50 kph). In this way, a consensus is achieved between the needs of traffic and town’s functioning. In consequence, the status of “priority for the town” is thus given. Ample experience confirms that there is a significant improvement in traffic safety on the main road, including: decrease in the number of deaths, reduction of traffic speed, improvement in the safety of vulnerable road users, improvement in the aesthetics and appearance of public space, improving parking conditions and reducing air pollution. Effects are achieved by reducing the speed and rebuilding of street sections. In the cross-sections of streets, new solutions for pedestrian and bicycle traffic develop the public space, which in this way becomes friendly to residents. Linear infrastructure for bicycles may be limited only to a road with a high car traffic density. The planned road solution must be integrally connected with spatial development. The threshold of road nuisance perceptible on the street in a small town is lower than in medium-sized and large cities. The average annual traffic daily volume should not exceed 5000 vpd. In the aspect of protection of living environment conditions, traffic intensity on small town streets should be by half lower (<2000 vpd), which means a moderate traffic function of the street.

Influence of Wider Longitudinal Road Markings on Vehicle Speeds in Two-Lane Rural Highways

Longitudinal road markings are a valuable aid in driving guidance. An increase in their width may influence driving and, therefore, road safety. Wider road markings generate a perception of a narrowing lane, which may induct drivers to reduce speed. The present study tries to verify if an increased width of longitudinal road markings actually helps one to drive more slowly, and consequently leads to enhanced road safety. For this purpose, three curves with reduced visibility were selected and driving speed was measured with normal and modified (wider) longitudinal road markings. The results showed a speed reduction effect of around 3.1% with wide road markings. The speed-reducing effect of wide marks was greater during weekends and with more intense traffic volume, while it was slightly attenuated by night. Finally, the calculation of some standard cases on a working day, and considering average traffic volume, gave the following speed reductions during the day and at night, respectively: for light vehicles, 2.24% and 1.96%; for heavy vehicles, 2.46% and 2.15%. In view of the results obtained, it may be said that using wide road markings can help reduce vehicle speed, thereby contributing to reduced traffic accidents and making road transport more sustainable.