Gridlock Models with the IBM Mega Traffic Simulator: Dependency on Vehicle Acceleration and Road Structure

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.

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 estimation and traffic simulation are essential parts of the intelligent transportation system. In recent years, massive traffic data has brought many data-based traffic estimation methods, but few are utilized in traffic simulation. The cause is that the data-based traffic simulation has high requirements for data quality, needing the trip information of all vehicles, not met by common traffic data. Fortunately, the electronic registration identification (ERI) data of the vehicle can satisfy. Therefore, we utilize ERI data to study the traffic estimation and simulation for the road network. The core of this research is the travel time estimation model, which is constructed by combining traffic theory models and the deep neural network. The traffic theory models are a group of linear models, which represent the relationship between traffic flow or traffic density and travel time. The deep neural network can extract the temporal and spatial characteristics of the road network traffic by Moving Average Convergence-Divergence (MACD) and Graph Convolutional Network (GCN), respectively. We named the travel time estimation model MGCN. Then, we employed MGCN for traffic simulation. In the experiment section, we employed Chongqing ERI data to verify the research content. Compared with some baseline methods, our method is superior in travel time estimation and traffic simulation.

In large cities, a situation often arises when roundabout intersections of highways no longer cope with the passage of traffic flows. The main reasons for this problem are the inconsistency of the potential traffic capacity of the roundabout with real traffic flows, as well as the lack of knowledge and skills of drivers on the roundabout carriageway. To reduce the influence of the human factor, various solutions are proposed, but, as practice has shown, ducted roundabouts or «turbo rings» are the most effective. A feature of this configuration of the carriageway is the predictability of driver behavior due to the physical separation of lanes for traffic. In a number of countries, turbo ring intersections have already been put into operation, which made it possible to confirm the alleged advantages of this configuration: the absence of changeover maneuvers on the ring carriageway, the need to give way to a maximum of two traffic flows, and a decrease in the average travel speed along the ring intersection. The aim of this study was to compare the performance of the existing threelane intersection, characterized by a predominance of lefthand turn flow during rush hours, and the developed design of the turbo ring interchange. Empirical data on traffic flows and length of queues, as well as a matrix of correspondences, were used to calibrate the computer micromodel. Micromodeling was carried out by means of: specifying the matrix of correspondences, taking into account the distribution of the traffic flow in time; setting the rules of priority, speed of approach to the intersection and speed reduction zones; development of scenarios for the analysis of morning and evening rush hours, fluctuations in traffic flows in time. Performance comparisons were made for the node as a whole, as well as for the selected busiest routes in terms of average and maximum congestion lengths, average latency and downtime. To assess the performance of the intersections, it was proposed to use foreign methods for assessing the capacity based on the mathematical models of Bowie, Hagring and Fortine. It was revealed that the transformation of the existing circular intersection into a turbo ring made it possible not only to increase the throughput of the node, but also to reduce congestion at the entrances and exits, which determines the practical significance of the study. To study the possibility of integrating the methodology for assessing the throughput, considered in the article, into the domestic method for choosing the type of intersections, which is the basis of regulatory and technical documents. The ultimate goal of the study is to create a new methodology for assessing the throughput of circular intersections of various types, including taking into account the peculiarities of the functioning of circular intersections on the territory of the Russian Federation.

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.

OPTIMIZATION OF TRAFFIC LIGHTS AT CROSSROADS

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

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.

The ability to predict traffic flow over time for crowded areas during rush hours is increasingly important as it can help authorities make informed decisions for congestion mitigation or scheduling of infrastructure development in an area. However, a crucial challenge in traffic flow forecasting is the slow shifting in temporal peaks between daily and weekly cycles, resulting in the nonstationarity of the traffic flow signal and leading to difficulty in accurate forecasting. To address this challenge, we propose a slow shifting concerned machine learning method for traffic flow forecasting, which includes two parts. First, we take advantage of Empirical Mode Decomposition as the feature engineering to alleviate the nonstationarity of traffic flow data, yielding a series of stationary components. Second, due to the superiority of Long-Short-Term-Memory networks in capturing temporal features, an advanced traffic flow forecasting model is developed by taking the stationary components as inputs. Finally, we apply this method on a benchmark of real-world data and provide a comparison with other existing methods. Our proposed method outperforms the state-of-art results by 14.55% and 62.56% using the metrics of root mean squared error and mean absolute percentage error, respectively.

This paper proposes a system to analyze urban traffic through the using of artificial vision, in order to get reliable information about the traffic flow in cities with severe traffic jam, as in Bogota, Colombia. It was proposed a method efficient enough to be implemented in an embedded system, in order to process the images captured by a local camera and send the synthesized information to the cloud. This approach would allow spending fewer data transference, because it would not be necessary to send the video of each camera in the city via streaming, instead, each camera would send only the relevant traffic information. The system is able to calculate traffic flow, classified in motorbikes, buses, microbuses, minivans, sedans, SUVs and trucks.

&lt;p&gt;Heavy car traffic on main streets is nowadays a major problem of modern cities. In order to improve safety of pedestrians and cyclists and at the same time maintain fluent car traffic flow, a separation of those two traffic types may be desirable in some cases. This paper presents different possible solutions to this problem implemented in Polish cities.&lt;/p&gt;&lt;p&gt;Underpasses or footbridges across busy streets in urbanized areas can be an alternative to pedestrian crossings. Footbridges apart from ensuring safe communication, can be attractive architectural elements of urban space. The cable stayed footbridge in Jaworzno will not only facilitate safe pedestrian communication between a large housing estate and a sports hall, but is likely to become a landmark of the city.&lt;/p&gt;&lt;p&gt;An attractive proposal for pedestrians and cyclists moving along crowded and polluted streets are routes marked out on riverside areas. The footbridges connecting islands on the Odra River in Wrocław not only have enabled efficient communication, but also have changed the islands into easily accessible places of relaxation. Free communication along the river embankments intersected by busy routes can be facilitated by building additional passages under existing bridges. The subject of the competition in Poznań was a footbridge over the Warta River, located close to the historical part of the city. Its implementation, apart from the main function of connecting the university campus with the city centre, will create a great recreational place for students and all citizens.&lt;/p&gt;&lt;p&gt;An unusual supplement for traditional means of transport in urbanized area can be an aerial tramway line. “Polinka” cable car that connects both parts of the campus of Wrocław University of Science and Technology separated by the Odra River have turned out to be an unconventional and attractive alternative to a typical footbridge.&lt;/p&gt;

This paper proposes a novel method of verifying comprehensive driver model used for the evaluation of driving safety systems, which is achieved by coupling the traffic simulation and the driving simulator (DS). The method consists of three-step procedure. In the first step, an actual driver operates a DS vehicle in the traffic flow controlled by the traffic simulation. Then in the next step, the actual driver is replaced by a driver model and the surrounding vehicle maneuvers are replayed using the recorded data from the first step. Then, the maneuver by the driver model is compared directly with the actual driver's maneuver along the simulation time steps. For example, while turning right (in front of the oncoming traffic), the proposed verification method was applied to our driver-agent model of traffic simulation system ASSTREET (The Advanced Safety System & Traffic REaltime Evaluation Tool) to demonstrate that the proposed method provides a straightforward way of verifying the driver-agent model capability to reproduce the driving behavior including the subtle driving behaviors, such as safety confirmation, that can have significant impact on driving safety. The scheme of interactive connection of ASSTREET and a driving simulator is also described. Language: en

The prevailing roadway and traffic conditions on expressways in India are vastly different when compared with the other roads in India and also, there is no perfect lane-discipline. The knowledge of roadway capacity is an important basic input required for planning, design, analysis, and operation of roadway systems. Hence, this work aims to model traffic flow on Indian urban expressways with specific reference to Delhi–Gurgaon expressway and estimate its capacity using the micro-simulation model using VISSIM 5·40. For this purpose, the field data collected on traffic-flow characteristics on expressways was used in calibration and validation of the simulation model. The validated simulation model was then used to develop fundamental traffic–flow relationships, namely, speed–flow, speed–area occupancy, and flow–area occupancy for the traffic-flow levels, starting from near-zero until the capacity of the facility. The capacity of an eight-lane divided urban expressway in level terrain with 14·0-m wide road space (one direction of traffic flow) was found to be in the range of 9700–10 000 vehicles/h. Moreover, through sensitivity analysis using simulation model, the following relationships, (i) percentage trucks in the traffic stream and capacity-level flow and (ii) percentage of trucks in the traffic stream and capacity-level traffic-stream speed, were developed. The results and the overall methodology followed in this study for modeling traffic flow on expressways in India to determine the capacity estimates under varying traffic compositions may be very useful for the practitioners and also for the continuing efforts to prepare an Indian Highway Capacity Manual.

Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.