Преобразование обычного кольцевого пересечения автомобильной дороги в турбокольцевое: сравнение пропускной способности

As the population increases and the growth of vehicles especially in urban areas causes traffic crowds. Roads are a very important means of land transportation to consider in order to support the progress of an urban area. Increasing the volume of traffic on urban roads, especially in the city of Meulaboh precisely deviated from the range caused by the increasing growth of traffic vehicles, especially during rush hour. Kisaran intersection connects Sisimangaraja road to Gajah Mada street, from Gajah Mada road heading Manekro road, and from Imam Bonjol road toward Sisimangaraja road. This intersection is one of the roads that is always crowded and very strategic and is used as access to get to the city center. Re-evaluate the performance of the signal intersection to find out the capacity, degree of saturation, delay, length of the queue and the number of stalled vehicles, which is based on the current traffic volume. The problem to be raised and also the purpose of this study is to determine the performance of the intersection of the Intersection Junction based on current traffic flow conditions. The method used is the Indonesian Road Capacity Manual Method 1997. Based on the results of the analysis of calculations, the volume of traffic flow of Manekro 3218 pcu / hour with a capacity of 1963 pcu / hour, Imam Bonjol road 2855 pcu / hour with a capacity of 1741 pcu / hour, Sisimangaraja road 2981 pcu / hour with a capacity of 1818 pcu / hour and the Gajah Mada road. Keywords : Evaluation, Signalized Intersection, Intersection Performance

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first the traffic volume and congestion (travel time delay) relation is applied to model the 24-hour traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning-evening rush hours. We estimated an annual emission of 1.82 Gg for PME (Exhaust particulate matter), 0.94 Gg for BC (Black Carbon), 0.75 Gg for OM (Organic matter), 221 Gg for CO (Carbon monoxide), 56 Gg for NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (> 50 %) across all pollutants except PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first the traffic volume and congestion (travel time delay) relation is applied to model the 24-hour traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning-evening rush hours. We estimated an annual emission of 1.82 Gg for PME (Exhaust particulate matter), 0.94 Gg for BC (Black Carbon), 0.75 Gg for OM (Organic matter), 221 Gg for CO (Carbon monoxide), 56 Gg for NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (> 50 %) across all pollutants except PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

Traffic congestion is increasingly becoming a severe problem in many large cities around the world. The problem is more complex in developing countries where cities are growing at a much faster rate than those in the developed world. Advanced Traffic Management Systems (ATMS) are one of the Intelligent Transport Systems (ITS) technologies that have been recommended and used as a tool to ease congestion problems in many large cities in the developing world. However, it is unknown how specific local conditions commonly observed in these cities, such as poor lane discipline and complex road user interactions, affect the performance of these systems. GETRAM (Generic Environment for Traffic Analysis and Modeling) was used in this research as a tool to develop microscopic traffic simulation models for the city of Bandung in Indonesia. The field data in this research, comprising throughputs, queue lengths and travel times, were collected during peak and off peak periods from all 90 signalised intersections connected to SCATS (Sydney Co-ordinated Adaptive Traffic System). This field data is believed to comprise one of the largest sets of “real world” data available for the development and validation of microscopic traffic simulation models. Two data sets were collected for this research: the first was used to develop and calibrate the simulation model and the second was used for validation. A number of statistical tests were used to determine the adequacy of the model in replicating traffic conditions. The results of statistical tests clearly showed that all of the calibrated and validated models reproduced field conditions with an acceptable degree of confidence. Therefore, the models were accepted as accurate and valid replications of the “real world”. The validated models were then used to evaluate the performance of SCATS which was implemented in Bandung in June 1997 as a pilot project. The results of comparative evaluation of the models under SCATS and under the Fixed Time control (without SCATS) demonstrated that SCATS did not necessarily always produce better results than the Fixed Time control. Furthermore, the performance of SCATS was strongly influenced by specific local conditions in the city. The multiple regression method was used to investigate the relationship between the traffic performance measures and significant basic variables. Based on this analysis, the main findings were: first, throughput was found to increase at intersections with higher v/c (volume to capacity) ratios. Second, throughput was found to decrease at intersections with higher numbers of phases and movements, longer widths of leg intersections, and farther distances to adjacent intersections. Third, queue length was found to increase at intersections with higher numbers of phases and movements. Based on the above findings, a number of improvements were recommended to enhance the performance of SCATS. This research also used traffic simulation to evaluate the impacts of these recommended improvements in increasing the performance of SCATS. The main findings from this evaluation were: first, restricted number of phases and movements at selected intersections substantially increased the traffic flow (78%) and decreased the queue length (by 55 to 67%) at the intersection. Second, making leg intersections wider—without physically building additional road capacity but by changing the stream with higher road hierarchy and higher v/c ratio from a two-way road into a one-way road—has a great impact on enhancing the performance of SCATS. Traffic flows were found to increase between 7 and 106%, and queue lengths were found to markedly decrease between 77 and 100% at all the suggested intersections. Third, the application of SCATS at intersections which are not closely spaced was not effective. Therefore, it is recommended that intersections which are not closely spaced remain under the Fixed Time control. The results and findings from this study provide road authorities in developing countries with an appreciation and enhanced understanding of the factors that influence the performance of traffic management systems in cities with similar characteristics to those in Bandung. These findings will also assist traffic engineers determine the best practices for the implementation of advanced traffic control systems in their cities.

This paper presents an empirical investigation into performance indicators on two-lane rural highways. Field data from four study sites in the state of Montana were used in this investigation. Six performance indicators were investigated: average travel speed, average travel speed of passenger cars, average travel speed as a percent of free-flow speed, average travel speed of passenger cars as a percent of free-flow speed of passenger cars, percent followers, and follower density. The study examined the level of association between the selected performance indicators and major platooning variables, namely, traffic flow in the direction of travel, opposing traffic flow, percent heavy vehicles, standard deviation of free-flow speeds, and percent no-passing zones. Among all platooning variables investigated, traffic flow in the direction of travel was found to have the highest correlation with performance indicators. Other platooning variables exhibited only weak correlations with performance indicators. Also, follower density followed by percent followers exhibited the highest correlations with platooning variables.

This study addresses the traffic signal optimization problem at a median-west median intersection. Based on 36 days of time-segmented traffic volume data, it combines the NS traffic flow model, Webster signal timing model, and genetic algorithm to propose a dynamic phase control scheme. By analyzing the traffic characteristics during morning rush hour, evening rush hour, off-peak hours, and nighttime, differentiated signal phases are designed: high-flow turning maneuvers (such as U-turns and left turns) are prioritized during peak hours, combined phases are added during off-peak hours, and induction control is used at night. After optimization, the average speed on the main road during morning rush hour increases from 28 km/h to 42 km/h, with delays reduced by 35%, and off-peak traffic capacity improves by 25%. The scheme achieves maximum traffic flow speed on the main road through phase conflict avoidance, green wave coordination, and dynamic parameter adjustments, providing a data-driven and model-coordinated solution for optimizing complex urban intersections.

Summary form only given. With the rapid urbanization of developing country, the harm effect to environment of urban carbon emission in traffic has attracted wide attention, under the background of global climate change. The widely used methods, such as remote sensing and monitor station, are unable to get carbon emission on urban road network accurately, due to poor spatial resolution and coverage. In this context, the article discusses the method to extract carbon emission of commuting using urban form information. The research uses the TRANUS integrated land use and transport model to simulate commuting traffic flow on road network. Urban form information is used as input parameter, then transformed into residents' travel requirement, and finally into traffic flow covered all road network. The urban form information includes land use, population, income, block size, potential traffic mode, land price, traffic facilities et al. The spatial distribution of carbon emission on road network is derived from traffic flow information, using the standard car emission factors from IPCC. This paper takes Xiamen Island as study area, simulates residents' commuting travel during rush hour of an ordinary workday. By comparison with 11 sample point data of actual traffic monitor stations, accuracy evaluation shows that system error of traffic flow simulation is 6.56%, and relative mean error is 18.43%. The spatial distribution of commuting travel carbon emission shows variability on different road sections. Carbon emission on most road sections is less than 1.0 tCO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> e/km, some sections is higher than 6.0 tCO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> e/km which mainly distribute on central and north part of the island. The urban form information is showed connection to the characteristics of carbon emission. The result shows that higher income people have a higher total carbon emission and emission intensity. Emission intensity of high income people on Xiamen Island is nearly 30 times than that of low income people. Private cars totally emit 94.17 tCO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> e during rush hour, comparing to the emission of public transportation is 22.23 tCO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> e. Within all public transport forms, bus, BRT and TAXI take up about 38.1%, 3.45% and 58.45% separately. However, the emission intensity of TAXI highly exceeds the other two modes. This study is expected to find an effective method to evaluate the carbon emission with spatial details, and we hope that it can help people to construct a low-carbon city.

Phase diagram in multi-phase heterogeneous traffic flow model integrating the perceptual range difference under human-driven and connected vehicles environment

The growth in economic activities has led to an increase in car ownership, contributing significantly to traffic congestion in modern urban areas. The study investigated the impact of multi-story parking facilities on traffic congestion and environmental performance at three critical intersections in Baghdad: Al-Khulafaa, Al-Ameen, and Al-Maidan. Under existing conditions, all intersections suffered from high congestion levels, long delays, low travel speeds, and high emissions. Field data were collected using Metro Count devices and analyzed through SIDRA Intersection v9 software under two scenarios: the existing conditions and an alternative scenario incorporating structured parking and improved traffic control measures. Comparisons were made using key performance measures, including average vehicle delay, queue length, travel speed, fuel consumption, and pollutant emissions. In the alternative scenario, the findings showed significant improvements: travel speeds virtually doubled, queue lengths decreased by over 70%, and vehicle delays decreased by up to 87%. Additionally, all sites had a greater than 70% decrease in CO₂, CO, HC, and NOₓ emissions as well as annual fuel usage. The results show that drivers seeking open spots and on-street parking are the main causes of traffic congestion. The study concludes that incorporating multi-story parking facilities into urban traffic planning is a very successful way to increase mobility, reduce environmental impact, and boost the overall effectiveness of the road network in crowded central regions.

Rush hour and sustained traffic flows in eight cities are studied using the IBM Mega Traffic Simulator to understand the importance of road structures and vehicle acceleration in the prevention of gridlock. Individual cars among the tens of thousands launched are monitored at every simulation time step using live streaming data transfer from the simulation software to analysis software on another computer. A measure of gridlock is the fraction of cars moving at less than 30% of their local road speed. Plots of this fraction versus the instantaneous number of cars on the road show hysteresis during rush hour simulations, indicating that it can take twice as long to unravel clogged roads as fill them. The area under the hysteresis loop is used as a measure of gridlock to compare different cities normalized to the same central areas. The differences between cities, combined with differences between idealized models using square or triangular road grids, indicate that gridlock tends to occur most when there are a small number of long roads that channel large fractions of traffic. These long roads help light traffic flow but they make heavy flows worse. Increasing the speed on these long roads makes gridlock even worse in heavy conditions. City throughput rates are also modeled using a smooth ramp up to a constant vehicle launch rate. Models with increasing acceleration for the same road speeds show clear improvements in city traffic flow as a result of faster interactions at intersections and merging points. However, these improvements are relatively small when the gridlock is caused by long roads having many cars waiting to exit at the same intersection. In general, gridlock in our models begins at intersections regardless of the available road space in the network

"Marine Traffic Simulation System" has been developed by the first author and others for long years. It has three simulation modes. First mode is normal navigation mode. It reproduces realistic traffic flows according to the predefined planed route. Second mode is collision avoidance mode. It has the feature of automatic collision avoidance for all ships besides the normal navigation mode. Third mode is external data mode. It reproduces traffic flow according to the data given by such as AIS. With collision avoidance mode, more realistic traffic flow can be reproduced. However, the validation of the simulation result is not yet done, because it was difficult to get the real traffic flow data before AIS data acquisition system established. This is one of the main purposes of this paper. The system is applied for southward area from Tokyo Bay. Assuming external mode as a real traffic flow, other two modes are evaluated. Normal navigation mode overestimates the navigational risk, while collision avoidance mode reproduces it fairly well in terms of several indices to represent the risk of the area. Three indices; number of occurrence of CR≧0.9, near-miss, and collision respectively, are proposed to assess the navigational risk and discussed. The normalization of these indices, traffic density and gaming area is also proposed and verified including the simulation results of two other areas.

In recent studies on traffic flow, cellular automata (CA) have been efficiently applied for simulating the motion of vehicles Since each vehicle has an exclusion volume and moves by itself not being ruled by the Newton's laws of motion, CA is quite suitable for modelling traffic flow In the present paper, we propose a stochastic CA model for traffic flow and show the availability of CA modelling for the complex phenomena that occur in real traffic flow.

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

A hybrid short-term traffic flow forecasting method based on spectral analysis and statistical volatility model

With the rapid growth of transportation, all kinds of traffic data show explosive growth, and accurate and timely traffic forecasting is also particularly important. Among them, traffic flow prediction is the basis for realizing reasonable traffic guidance and control, and it is also a prerequisite for intelligent transportation. Traffic flow data belongs to a typical chaotic time series with strong non-linearity. In the application of neural network, a two-layer neural network can theoretically approach any continuous function infinitely, so as to achieve very accurate prediction. This paper proposes a predictive traffic flow model based on the combination of GRU network and BP neural network, and uses the US Twin Cities traffic data experiment to verify that the model is feasible in traffic flow prediction. The experimental results show that the combined model has high prediction accuracy and can capture the volatility of traffic flow at rush hour. At the same time, the model has strong robustness.