Traffic Index Research Articles

ЕКОНОМІЧНЕ ОБҐРУНТУВАННЯ ЗАСТОСУВАННЯ МОДУЛЬНОГО ПРИНЦИПУ ВИКОРИСТАННЯ ЕЛЕКТРОБУСІВ У ПАСАЖИРСЬКОМУ СПОЛУЧЕННІ МЕГАПОЛІСІВ

The article analyzes congestion of the urban transport network in Kyiv and its impact on air pollution in the city and suggests ways to solve problems that occur because of it. The object of the study is public transport, specifically electric buses. The purpose of the work is to propose an innovative approach to the use of electric buses using analysis of air pollution indicators, the traffic index of urban congestion and the non-stationarity of the passenger flow. Research methods are generally scientific. The city of Kyiv ranks number 7 in the world in terms of traffic congestion and the amount of added time that people spend due to congestion. The transport network overload leads not only to congestion, but also to air pollution in the city. The authors of the article analyze the air pollution and the state of congestion on the roads of Kyiv. In order to improve the current situation in the city, the concept of modular use of electric buses is proposed, which envisions production of electric trucks and electric trailers for passenger transportation (modules) to it. The use of electric buses reduces environmental pollution, but does not solve the problem of congestion in megapolises. The solution to the problem must use comprehensive approach. The authors came to conclusion that it is not possible to significantly influence the non-stationary flow of passengers, but it is possible to release a fleet with different passenger capacity at different times of the day. During rush hours the proposal is to limit the total number of cars and release an electric bus with the module of the largest capacity, in the late evening and at night release fleet with modules designed for a small number of passengers, in other hours release electric buses with a medium capacity module. The modular principle of formation of electric bus routes will optimize the general expenses for transportation and will take into consideration seasonal, weekly and daily non-stationary flow of passengers. Development of this modular electric vehicles will be the task for automotive industry. KEY WORDS: ELECTRIC BUS, MEGAPOLIS, ECOLOGY, PASSENGER TRANSPORTATION, BUS TRANSPORTATION, MODULAR PRINCIPLE, MANAGEMENT.

Integrating Pattern Features to Sequence Model for Traffic Index Prediction

Integrating Pattern Features to Sequence Model for Traffic Index Prediction

Traffic Index Prediction and Classification Considering Characteristics of Time Series Based on Autoregressive Integrated Moving Average Convolutional Neural Network Model

Traffic Index Prediction and Classification Considering Characteristics of Time Series Based on Autoregressive Integrated Moving Average Convolutional Neural Network Model

EXPLORING THE POTENTIAL OF TRAFFIC INDEX DATA TO ANALYZE ESSENTIAL TRAFFIC IMPACT IN DEVELOPING CITIES

Abstract. In developing countries, metropolitan cities, due to their economic activities, attract an increasing amount of commuters on a daily basis. This has led to major freeways and roads experiencing high levels of congestion and consequently high pollution levels. In 2020, due to a global pandemic of an outbreak of Corona Virus (COVID-19), the national government declared a national shutdown with only essential traffic being allowed to operate. Given the scenario of the national lock-down this allows for the statistical analysis of the impact of essential traffic on the overall transportation system. Consequently the aim of the paper was to assess the congestion and CO2 emission impact of essential traffic for the City of Johannesburg. Using an exploratory approach, we monitored and collected traffic congestion data from the Tomtom traffic index for the metropolitan city of Johannesburg, South Africa. We develop a relationship between congestion and pollution to visualise the daily variations in pollution and congestion levels. We demonstrate this by comparing variations in congestion levels in two epochs, viz the period without movement restrictions and the period whereby movement is restricted. The results reveal essential traffic on the congestion index to be below 22 percent for both weekends and weekdays. A scenario common only during weekends in 2019. Whilst for the emission index, CO2 levels are approximately less than 45 percent throughout the week. The paper concludes the investment into mining and analysing traffic data has a significantly role for future mobility planning in both the developed and developing world and, more generally, improving the quality of commuting trips in the city.

Landscape geometry-based percolation of traffic in several populous cities around the world

In this study, we propose an alternative concept for describing the average traffic congestion in several populous cities around the world, namely landscape percolation. The ratio of the residential area size to road width is a fundamental parameter that controls the traffic congestion. We have compared the model with data extracted from several populous cities around the world (directly from Google Earth images) and demonstrated very consistent results. The criterion for a city landscape that makes a city is considered as congested or less congested has been identified. The model also explains remarkably well the consistency of the measured data with various reports on congestion levels (such as the recognized Tomtom congestion level or Numbeo traffic index) of some populous cities around the world. These findings may help in designing new cities or redesigning the infrastructure of congested cities, for example, for deciding what the maximum size of the residential area is and how width the roads are. This work also shows the similarity of the problem in conducting composite (electric current flow), brine transport between icebergs (fluid flow), and traffic (vehicle flow).

A graph convolutional network model for evaluating potential congestion spots based on local urban built environments

AbstractAutomatically identifying potential congestion spots in cities has significant practical implications for efficient urban development and management. It requires the ability to examine the relationships between urban built environment features and traffic congestion situations. This article presents a novel and effective approach for achieving the task based on a machine‐learning technique and publicly available street‐view imagery and point‐of‐interest (POI) data. The proposed multiple‐graph‐based convolutional network architecture can: (a) extract essential urban built environment features from street‐view imagery and neighboring POIs; (b) model the spatial dependencies between traffic congestion on road networks via graph convolution; and (c) evaluate the risk level of road intersections to emerging congestion situations based on local built environment features. We apply the model to Wuhan in China, and predict the potential congestion spots across the city. The results confirm that the model prediction is highly consistent (about 85.5%) when compared to the ground‐truth data based on traffic indices derived from a big taxi GPS trajectory dataset. This research enhances the understanding of traffic congestion situations under various geographic, societal, and economic contexts based on easily accessible road, street‐view, and POI datasets at large spatiotemporal scales.

Monitoring 2011–2020 Traffic Patterns in Wuhan (China) with COSMO-SkyMed SAR, Amidst the 7th CISM Military World Games and COVID-19 Outbreak

Vehicle detection from satellite imagery can support different applications, such as security and situational awareness. In the civilian domain, it can provide quantitative evidence to investigate urban mobility and traffic patterns in cities. Satellite synthetic aperture radar (SAR) can help in detecting vehicles in (almost) all weather conditions and during the day and night. In this study, the capability of SAR StripMap imaging mode data to monitor traffic is analyzed using the case study of Wuhan, China. In ordinary times, the bridges crossing the Yangtze river are the key infrastructure allowing for urban mobility in Wuhan. More recently, the city has been the first in the world to be put in lockdown due to the outbreak of the Coronavirus Disease of 2019 (COVID-19). Using a very long time series of 294 COSMO-SkyMed StripMap HIMAGE mode scenes collected from 2011 to 2020, we detected vehicles on seven bridges, estimated their speed, and analyzed the traffic pattern over time. Vehicles are detected based on their azimuth shift caused by their across-track motion. Our goal is to monitor the variations in traffic instead of single-car detection. The results from 2011 to 2019 show a general increase in the number of vehicles crossing the bridges, as new infrastructure was built over the years. Variations in detected vehicle numbers were especially found during the two events of the 7th International Military Sports Council (CISM) Military World Games in October 2019, and the COVID-19 lockdown in early 2020. These events were therefore used for internal validation of our assessment of traffic patterns. On the other side, TomTom traffic index data were used for external validation. The results and their comparison with TomTom data prove the effectiveness of our method in detecting traffic patterns, but also demonstrate that mostly large vehicles (e.g., trucks or buses) are detected. Future work should be carried out to improve the detection rate of smaller vehicles.

Traffic peak period detection using traffic index cloud maps

Traffic peak period detection is one key issue in ITS research area, which can afford time information for traffic flow guidance. Classical methods devote themselves to detect the peak period of road segmentations and small road network areas. Namely, these methods focus on traffic peak period detection in small space scale. However, the traffic peak periods of road segmentations and small road network areas cannot present the traffic peak periods of the whole city. In fact, the traffic peak periods of the whole city are more important for the traffic administration department. To solve this problem, a new method for detecting traffic peak periods of the whole city is proposed, which is based on the traffic index cloud maps. Experimental results on the GPS data show that the proposed method can recognize the traffic peak periods in a large space scale accurately.

Traffic index prediction based on sequence to sequence learning

The traffic index prediction plays an important role in many intelligent transportation applications. The challenge mainly comes from the strong nonlinearities in the changing of traffic index, which is caused by transitions of various traffic states including smooth traffic, congestion, breakdown and recovery. Deep learning performs well in capturing and describing the nonlinear relationship among variables. This paper uses a Sequence to Sequence (Seq2Seq) deep learning framework to predict the traffic index. In the proposed method, we use Long Short Term Memory (LSTM) as the basic circulating unit. And the LSTM units are stacked to extract the time changing characteristic and the periodic characteristic of the input traffic index sequence. Besides, the extracted feature vectors are decoded by another single layer LSTM to predict the traffic index of each time after inputting sequence. Experiments are conducted on Beijing traffic index datasets. The proposed method outperformed Autoregressive Integrated Moving Average Model (ARIMA) and LSTM under some commonly used evaluation metrics.

The Same Entrance Full-Pass (SEFP) Control Method for the Prevention of Over-Saturation at the Critical Intersection

Traffic congestion will lead to a series of negative effects. In order to alleviate congestion and evacuate queuing vehicles as soon as possible, traffic control at the critical intersection is particularly important. In this paper, the Same Entrance Full-Pass (SEFP) control method for the prevention of over-saturation at the critical intersection is proposed, which allows all vehicles coming from the same entrance to be released at the same time, avoiding the drawbacks of the traditional release mode. In the meantime, the three-turn traffic flows moving into the same downstream section of the adjacent intersection are controlled synchronously to limit the number of vehicles heading for the critical intersection effectively. By using the traffic wave theory, the signal offsets between the critical intersection and its adjacent intersections are calculated for signal coordinated control, which can ensure all the vehicles coming from the same entrance pass the stop line of the critical intersection directly with no stop, thus improving the operation efficiency of the critical intersection and preventing the traffic over-saturation. To avoid vehicle retention and accumulation on the entrance lanes of the critical intersection, ground sense coils are employed to detect the vehicle queuing and the closure control is carried out at the adjacent intersections according to the real queue length, making the traffic indexes (the total traffic volume, vehicle delay and queuing length) improved greatly. The simulation results of the actual critical intersection and its adjacent intersections confirm the effectiveness and practicability of the proposed SEFP method.

Solutions for Improving Transit through Intermodal Passenger Terminals

According to the transport White Paper “better modal choices will result from greater integration of the modal networks: airports, ports, railway, metro and bus stations, should increasingly be linked and transformed into multimodal connection platforms for passengers.”Transport terminals are the junction points of local, regional or inter-regional transport. The main objective of a transport terminal is to serve the passengers’ demand by maintaining their safety and comfort. Intermodal terminals have emerged as a response or solution to the demands of sustainable development (reducing congestion should be the main objective), Bucharest ranks 5th in the world at congestion (according to Tomtom Traffic Index). The use of intermodality in passenger transport significantly reduces congestion in cities.Intermodal passenger terminals lead to reduced congestion but impair passenger quality parameters such as time, comfort, convenience and even travel’s cost because each mode of transport has its own pricing principles.The problem of reducing the transit time through these intermodal terminals is the key in making the public transport more attractive; the way the terminals are designed and organized leads to their intensive use or not. The paper presents a model for designing intermodal passenger terminals that encourage modal transfer. The model’s framework suppose the correlation of the transport modes timetables, placing the platforms so that the movements between them are as small as possible, tariff integration and the use of an unique ticket, etc.The model presented within the paper has been tested on a hypothetical intermodal passenger terminal that has benefitted by transport modes schedules correlation through a linear programming with integer numbers model.

Assessment of Observed Building Structure Setback of Shops along an Arterial Road and Noise Intrusion Level

Roads irrespective of the type have specific standard horizontal distance measured at 90 degrees from a lot boundary to a development known as a setback. Non-observance of the recommended setbacks accommodated in any urban center’s master plan creates noise hazard to the public health and safety as the movement of vehicular traffic is not without the attendant noise. This study assessed noise intrusion level in shops along a section of Ibadan-Abeokuta road with due consideration to compliance with the recommended building structure setback. Analysis of noise descriptors evaluated in this study gave A-weighted equivalent sound pressure level average of 91.3 dBA, the daytime average sound level (LD) 92.27 dBA, traffic noise index (TNI) 41.63 dBA, the noise pollution level (LNP) 85.91 dBA and noise climate (NC) 5.38 dBA. Correlation analysis between the observed setbacks and the noise levels gave an “r” value of -.496 significant at p < .05. The paired t-test analysis showed a mean ± SD difference of 15.90 ± 7.08, and t-value of 32.99, corresponding to a two-tailed p < .05 with 215 degrees of freedom. This implies that there was a significant statistical difference between the noise levels measured at the observed and recommended setbacks

Integration of Remote Sensing and Social Sensing Data in a Deep Learning Framework for Hourly Urban PM2.5 Mapping.

Fine spatiotemporal mapping of PM2.5 concentration in urban areas is of great significance in epidemiologic research. However, both the diversity and the complex nonlinear relationships of PM2.5 influencing factors pose challenges for accurate mapping. To address these issues, we innovatively combined social sensing data with remote sensing data and other auxiliary variables, which can bring both natural and social factors into the modeling; meanwhile, we used a deep learning method to learn the nonlinear relationships. The geospatial analysis methods were applied to realize effective feature extraction of the social sensing data and a grid matching process was carried out to integrate the spatiotemporal multi-source heterogeneous data. Based on this research strategy, we finally generated hourly PM2.5 concentration data at a spatial resolution of 0.01°. This method was successfully applied to the central urban area of Wuhan in China, which the optimal result of the 10-fold cross-validation R2 was 0.832. Our work indicated that the real-time check-in and traffic index variables can improve both quantitative and mapping results. The mapping results could be potentially applied for urban environmental monitoring, pollution exposure assessment, and health risk research.

Evaluation of the mechanical stability of asphalt mixture using the gyratory compactor

In this study, four asphalt mixtures were produced using two different aggregate sources, three aggregate gradations, and six asphalt contents. Super Gyratory Compactor SGC was used to compact all mixtures. The SGC produced densification curves were used to determine volumetric properties of the mixture. It is necessary to evaluate the construction and the densification caused due to the traffic loading. Considering the Construction Densification Index CDI, (the area that is under the densification curve ranges from density at eight gyrations to a density of 92 % of the theoretical maximum specific gravity), that stands for work validation through the period of construction to achieve the percentage of air void of about 8 %. Since all the mixtures used in this study are Superpave designed mixtures, it is important to determine whether the Traffic Force Index (TFI) value can be identified with the stability of mixture as measured by the Marshall stability. The Superpave Gyratory results show very interesting indications that the SGC can be adopted as a tool for the estimation of the mechanical stability of mixtures. According to the presented results, the traffic index TFI, estimated from the SGC, is associated with the Marshall stability.

Coupling relationship between green production and green consumption: Case of the Yangtze River Delta area

AbstractGreen production and green consumption have always been topics of concern. This study explores the coupling relationship between green production and green consumption by setting up a coupling coordination degree model using 2006–2015 panel data for the Yangtze River Delta area. The results highlight that, first, the contribution rate of the tertiary industry to the gross domestic product index is the key factor influencing green production. Meanwhile, passenger volume in the public traffic index is the main factor impacting green consumption. Second, while both green production and consumption levels have somewhat improved in the area, the gap between them continues to widen. Furthermore, the improvement in green consumption lags behind that in green production. Finally, the coupling coordination degree between green production and consumption in the area shows an increasing trend, although at a low level. These findings suggest that governments should focus on improving green consumption to increase the coupling coordination degree.

Design and implementation of tunnel image mosaic system based on open CV

With the development of transportation, more and more tunnels have appeared. However, the monitoring system in the tunnel is not perfect. It is difficult to monitor the complete tunnel situation and increase the vehicle traffic risk index in the tunnel. Therefore, this paper proposes a tunnel image mosaic technology based on ORB algorithm. This technique splicing images captured by a scattering camera in a tunnel into a complete tunnel image. It also facilitates monitoring of conditions within the tunnel and reducing traffic accidents. The main processes include image preprocessing, image registration, feature point matching, and image fusion. Experiments show that this technology can be applied to the tunnel monitoring system and is widely recognized.

A real‐time traffic index model for expressways

SummaryIn this paper, a real‐time traffic index model of expressways is proposed by using a traffic index to evaluate the actual conditions of expressways. The model considers the actual situation of floating and nonfloating vehicles on expressways, in the context of massive floating car data. Included is the realization of the complete calculation model of real‐time traffic index estimation, including highway section division, spatial topology map matching, driving route calculation, and road congestion status judgment. For roads without floating car coverage, the weighted‐moving‐average time‐series prediction method is used to predict the traffic index, so that the running condition of all roads in the network can be analyzed completely. The simulation results show that the proposed highway traffic index can reflect not only the overall high‐speed operation but also real‐time congestion at specific high‐speed or specific highway intervals, providing an effective reference for travel.

A global dataset of CO2 emissions and ancillary data related to emissions for 343 cities

We present a global dataset of anthropogenic carbon dioxide (CO2) emissions for 343 cities. The dataset builds upon data from CDP (187 cities, few in developing countries), the Bonn Center for Local Climate Action and Reporting (73 cities, mainly in developing countries), and data collected by Peking University (83 cities in China). The CDP data being self-reported by cities, we applied quality control procedures, documented the type of emissions and reporting method used, and made a correction to separate CO2 emissions from those of other greenhouse gases. Further, a set of ancillary data that have a direct or potentially indirect impact on CO2 emissions were collected from other datasets (e.g. socio-economic and traffic indices) or calculated (climate indices, urban area expansion), then combined with the emission data. We applied several quality controls and validation comparisons with independent datasets. The dataset presented here is not intended to be comprehensive or a representative sample of cities in general, as the choice of cities is based on self-reporting not a designed sampling procedure.

Equilibrium between Road Traffic Congestion and Low-Carbon Economy: A Case Study from Beijing, China

China has allocated low-carbon targets into all regions and trades, and road traffic also has its own emission reduction targets. Congestion may increase carbon emissions from road traffic. It is worthwhile to study whether it is possible to achieve the goal of road traffic reduction by controlling congestion; that is, to achieve the equilibrium between traffic congestion and a low-carbon economy. The innovation of this paper is mainly reflected in the innovative topic selection, the introduction of a traffic index, and the establishment of the first traffic congestion and low-carbon economic equilibrium model. First, the relevant calculation method of the traffic index is introduced, and the traffic index is used to quantify the traffic congestion degree. Using the traffic index, GDP, and road passenger traffic volume, a nonlinear regression model of road traffic carbon emissions is constructed. Then, the calculation method of the carbon emission intensity of road traffic in the region is proposed. The equilibrium model of traffic congestion and a low-carbon economy is constructed to look for the degree of road traffic congestion that may occur under the permitted carbon emission intensity. Taking Beijing, where electric vehicles account for less than 3% of the total vehicles, as an example, it is difficult to achieve the equilibrium target between road traffic congestion and a low-carbon economy by alleviating traffic congestion in 2020. If the target of traffic carbon emission reduction in 2020 is adjusted from 40%–45% to 19.7% based on 2005, the equilibrium will be achieved. A negative correlation between road traffic carbon emissions and the reciprocal of the traffic index (1/TI) is found after eliminating the effects of GDP and PTV (road passenger traffic volume). As the traffic index decreases by units, the carbon emission reduction accelerates. The results show that carbon reduction targets cannot be simply allocated to various industries. The results of the research on the degree of the impact of traffic congestion on carbon emissions can be used as a basis for carbon reduction decisions of the traffic sector. The research method of this paper can provide a reference for the study of the equilibrium of traffic congestion and a low-carbon economy in other regions.

Comprehensive evaluation of green transportation in Chongqing main urban area based on sustainable development theory

ABSTRACTTaking the main urban area of Chongqing as the research area, this paper combines the content of urban green transportation and sustainable development, characteristics of mountain cities and factors affecting green transportation. The relevant indexes of urban green traffic evaluation index system are screened by taking into account the availability of each indicator data, while a comprehensive evaluation index system of green traffic suitable for mountain cities is established. Analytic Hierarchy Process (AHP) and Information Entropy Method are used to determine the weight of each factor and the combination weight of each index in the urban traffic green evaluation index system. Then, the weights of the evaluation factors and indicators are combined and empowered. Combining ArcGIS software with evaluation model, data collection and evaluation of green traffic indicators are carried out in nine districts of Chongqing. Finally, suggestions and improvement measures are put forward to promote the development of green traffic in Chongqing.