НОВИЙ ПІДХІД ДО ВИЗНАЧЕННЯ ПРОПУСКНОЇ ЗДАТНОСТІ НЕРЕГУЛЬОВАНИХ ПЕРЕХРЕСТЬ

Traffic congestion has become a serious social problem in many countries and cities. In recent years, with the popularization of car navigation systems and the advancement of traffic information provision technologies, there has been an increasing demand for more accurate congestion prediction. In particular, studies have shown that the use of machine learning for congestion prediction is effective, as it can take into account various factors affecting traffic volume, such as weather, events, and days of the week. In addition, existing research has confirmed that it is possible to predict congestion patterns in advance based on past traffic data. However, studies have pointed out that existing machine learning models struggle to ensure sufficient prediction accuracy for abrupt changes in traffic flow caused by sudden events such as traffic accidents. Since the occurrence of accidents is accompanied by discontinuous fluctuations, general time-series prediction models cannot appropriately reflect their impact, leading to a tendency for larger prediction errors. Therefore, there is an urgent need to develop prediction methods that can adapt to sudden changes in traffic conditions. To address this issue, this study aims to construct a machine learning model using multivariate LSTM (Long Short-Term Memory) to achieve more accurate congestion prediction. LSTM excels at capturing long-term dependencies in time-series data and is adept at making predictions based on past data. Thus, it is highly effective in learning the occurrence trends of congestion with certain patterns. Specifically, the purpose of this study is to analyze the trends of prediction errors when traffic accidents occur and to construct a congestion prediction framework that considers the impact of accidents. In this way, we strive to realize a congestion prediction model that can adapt to sudden changes in traffic conditions, contributing to the optimization of traffic flow and the alleviation of congestion.

The metro station can form a new commercial district.The residential development in the outskirts has a major impact on the value of real estate.However, the area near the metro station can be wrongfully affected due to the structure and function of the railway station, noise or congestion, etc.This study aims to analyze how changes in traffic conditions affect the apartments.The opening of the urban road is considered as traffic change.Regarding it, the price trend will be investigated.There is an assumption that preference will grow when opened.Daegu Subway Line 3 in Korea, which opened in 2015, is selected.It is the relatively recent case.Sales price of apartment houses included in the station area and apartment houses outside the station area is obtained, from which we calculate the rate of change.The difference between station area and non-station area was analyzed under two hypotheses whether it existed or not.The 2015 case and after-one-year case were compared.The subway station proved to raise housing price.T-test was utilized to confirm the reliability of the results.They were significant at the 95% confidence level of the two-sided test.Consequently, the station apparently tends to increase public housing price.

Previous work was done to compare the static resource allocation (StRA) scheme and scalable resource allocation (ScRA) scheme within an integrated GSM and GPRS network (see Tang, S. and Tafazolli, R., Vehicular Technology Conference - Spring, 2003). In order to maintain the QoS for both, the ScRA scheme assumed that the load history database was a credible estimate of the present state of traffic intensity in the cell area. In cases when there is a sudden change in traffic conditions, the ScRA scheme may not be able to perform well. Hence, in order to cope with sudden changes of traffic in the network, we propose to incorporate prediction schemes into the ScRA algorithm. For comparison purposes, we present and evaluate three prediction strategies, namely linear regression prediction, exponential regression prediction, and current value prediction, in six various traffic conditions. In addition, resource utilisation gains between the StRA scheme and the proposed ScRA schemes are also studied. The findings show that ScRA exponential regression prediction scheme can cope best in all the tested traffic scenarios and experiences an average of 0.5 percent of resource utilisation gain over the StRA scheme.

This study investigates the factors affecting estimation accuracy of queue length at signalized intersections under low penetration of connected vehicles. A shockwave-based algorithm is proposed to estimate the maximum queue length and residual queue on a cycle-by-cycle basis. Simulation data collected from three consecutive signalized intersections were used to extract trajectories of connected vehicles under five different market penetration rates and two different traffic conditions (under-saturated and moderate). The results confirm that the queue length estimation process is probabilistic and affected by the stochastic changes in traffic conditions. This probabilistic nature is defined by a queue formation coverage index (QI) that proved to significantly affect the queue length estimation accuracy. Overall, the results show that the accuracy of the queue estimates is acceptable when a QI value of at least 50% is achieved. In such limited data environments, the QI showed potential to help as an assessment tool to evaluate the obtained queue estimates.

Forecasting future traffic flows from previous ones is a challenging problem because of their complex and dynamic nature of spatio-temporal structures. Most existing graph-based CNNs attempt to capture the static relations while largely neglecting the dynamics underlying sequential data. In this article, we present dynamic spatio-temporal graph-based CNNs (DST-GCNNs) by learning expressive features to represent spatio-temporal structures and predict future traffic flows from surveillance video data. In particular, DST-GCNN is a two stream network. In the flow prediction stream, we present a novel graph-based spatio-temporal convolutional layer to extract features from a graph representation of traffic flows. Then several such layers are stacked together to predict future flows over time. Meanwhile, the relations between traffic flows in the graph are often time variant as the traffic condition changes over time. To capture the graph dynamics, we use the graph prediction stream to predict the dynamic graph structures, and the predicted structures are fed into the flow prediction stream. Experiments on real datasets demonstrate that the proposed model achieves competitive performances compared with the other state-of-the-art methods.

Emergency response time often makes difference between life and death in cases that man-made or natural disasters happen. In this paper, we propose a real-time route guidance strategy to reduce the traveling time of emergency vehicles. Our strategy consists of path planning and reverse path planning for emergency vehicles. Since the traffic condition changes at any time, we use current and historical traffic information to plan a path. Furthermore, we propose a lane reservation scheme for emergency vehicles to minimize the crash risks. Before an emergency vehicle arrives at each road segment, Road Side Units (RSUs) will at suitable time start to disseminate the warning message so that other drivers have enough time to take proper actions such as speed deceleration or changing lane. Simulation results show that the proposed approach can reduce more than 20% traveling time of emergency vehicles comparing to the shortest path approach with sirens and lights to alert other drivers. By reducing the traveling time, the emergency rescue teams have higher probability to rescue lives and property in time.

To help with the planning of inter-vehicular communication networks, an accurate understanding of traffic behavior and traffic phase transition is required. We calculate inter-vehicle spacings from empirical data collected in a multi-lane highway in California, USA. We calculate the correlation coefficients for spacings between vehicles in individual lanes to show that the flows are independent. We determine the first four moments for individual lanes at regular time intervals, namely the mean, variance, skewness and kurtosis. We follow the evolution of these moments as the traffic condition changes from the low-density free flow to high-density congestion. We find that the higher moments of inter-vehicle spacings have a well defined dependence on the mean value. The variance of the spacing distribution monotonously increases with the mean vehicle spacing. In contrast, our analysis suggests that the skewness and kurtosis provide one of the most sensitive probes towards the search for the critical points. We find two significant results. First, the kurtosis calculated in different time intervals for different lanes varies smoothly with the skewness. They share the same behavior with the skewness and kurtosis calculated for probability density functions that depend on a single parameter. Second, the skewness and kurtosis as functions of the mean intervehicle spacing show sharp peaks at critical densities expected for transitions between different traffic phases. The data show a considerable scatter near the peak positions, which suggests that the critical behavior may depend on other parameters in addition to the traffic density.

Increasing traffic volumes leads to changes in traffic conditions, especially at intersections. These changes affect the ability of Vulnerable Road Users (VRUs) such as pedestrians and bicyclists to cross the road safely, especially at un-signalized crossings (without traffic lights), where many road users are conflicting at the same point of the road and neglecting 'in some cases' the priority issue. Although the emphasis on pedestrian safety has recently increased, there is still a need to analyze the causal factors of VRUs accidents and define their relationship to road design characteristics. This paper presents a study bout vehicle – pedestrian and bicyclist conflicts analysis using Swedish conflict technique at three un-signalized pedestrian crossings in the city of Győr- Hungary, where some pedestrian accidents were happened between 2014 and 2018, and reported based on accidents database in Hungary. The aim is to analyse vehicle-pedestrian and bicyclist interactions, and evaluate the severity of conflicts with the help of the Swedish technique graph. The findings concluded that there is a compatibility between Swedish conflict technique and accident records with regard to conflicts severity and the probability of accidents occurrence.

Short-term traffic volume prediction by ensemble learning in concept drifting environments

The Izu Islands comprise nine inhabited islands ranging from Izuôshima to Aogashima. Geologically volcanic with the sea-eroded bluffs generally unfavorable for port facilities, each of these islands has long remained a typical specimen of isolated existence. With the recent consolidation of airport and port facilities, however, the features of these islands have been rapidly undergoing substantial changes, supported by their advantage of location not far distant from the metropolitan province.Regional transformation incidental to the change in traffic conditions is conceived to appear most conspicuously in hitherto forlorn isolated islands. On such islands, even without quantitative analysis, it is certainly of deep interest to trace how, as a way of changing the self-supporting nature of the traditional economy, specific production and tourist industries have been introduced and developed in harmony with their old industries. The present study was designed to scrutinize the changes, mainly in its industrial structure, of an isolated island with the peculiarity of a regionally segregated existence. For this purpose, out of the nine islands, Kôzushima Isle, which is regarded as typical of the modernizing Izu islands with its improved port facilities and markedly developed tourist industry was examined, and an analysis was attempted of this isle's industrial structure and its changes, with on-the-spot surveys conducted to integrate a series of investigations.1) Formerly, the industrial structure of Keszushima Isle was composed mainly of conventional marine products industry, boat-fishing by men and seaweed gathering by women, complemented by small-scale livestock raising and sweet potato cultivation, and also by the government's unemployment relief public works. This minor-scale industrial makeup, essentially on a self-supporting basis, has undergone a drastic change, with the features specifically showing a decreasing weight of dependence on marine products, fish and sea-weed, and compensated for by an increasing weight of sugar pea cultivation, specialized on a commerical basis, and a rapidly extended sight-seeing business.In these radical changes in the industrial structure, which forced female employment to be widely reshuffled, the transition is considered to have been made with an appropriate labor allocation between men and women accompanied by a definitve pattern of specialization. It is certainly worthy of note that in this isolated island, with a narrow and weak economic base, all units of industry exist with close interrelations, including those in seasonally fluctuating work fields.2) It has been generally believed that the segregated existence of an isolated island is attributable to its inferiority in transportation links. This preoccupation is now facing a doubt raised by the fact that the industrial structure alteration appeared in Kôzushima Isle prior to the actual improvement of transportation conditions. Improvement in transportation is important in the industrial structure and people's life on an isolated island, but is it always an indispensable factor? It seems more adequate to think that the requirements from outside the island, which have become strong enough to overcome the poor transportation conditions, have brought great pressure to bear on the isolated island in changing its narrow and feeble economy.

In the contemporary landscape of global logistics, the capacity to dynamically optimize delivery routes in real time is a critical determinant of operational efficiency, customer satisfaction, and environmental sustainability. Conventional routing algorithms, while effective in static or semi-dynamic contexts, often fail to adapt to rapid changes in traffic conditions, weather disruptions, road closures, and last-minute delivery requests. This paper proposes a novel Deep Learning-based architecture that leverages Long Short-Term Memory (LSTM) networks for real-time traffic forecasting and Deep Q-Networks (DQNs) for adaptive route decision-making. The system processes live inputs from GPS sensors, weather APIs, and traffic feeds to dynamically generate optimal delivery paths. Extensive simulations using synthetic and real-world datasets from urban logistics providers demonstrate substantial improvements in delivery time (up to 21%), fuel consumption (13%), and vehicle utilization rates (17%) compared to traditional heuristics-based methods. This research provides both theoretical contributions and practical implementation guidelines for logistics operators seeking intelligent, AI-driven solutions for route optimization.

Expected changes in the energy performance of traffic flows as penetrated by electrified vehicles

An asynchronous transfer mode ATM network is a high-speed multimedia network that handles various kinds of traffic with different bit rates and different qualities of services QoS. To maintain QoS for each traffic source and to avoid a possible congestion problem, an ATM network requires highly sophisticated and flexible controllers to insure that the demanding performance can be achieved under unexpected changes in traffic conditions. In this article we propose an intelligent architecture using neural networks for traffic congestion control in an ATM network. The congestion control using neural networks is suitable for an ATM because neural networks can learn the offered traffic characteristics and the dynamic changes of the traffic. The proposed mechanism is based on the adaptive prediction of the future value of the offered traffic and the flow rate for each traffic source. At every given time slot, the controllers in the proposed architecture predict whether the congestion will happen or not and regulate the volume of input traffic for each traffic source before the congestion happens, maintaining the user-required QoS for each traffic source based on the predefined rules. Consequently, the mechanism guarantees the QoS for each traffic source and efficiently prevents congestion.

SummaryLow‐diameter networks require non‐minimal adaptive routing to deal with varying traffic characteristics and avoid pathological performance. Such routing is based on local estimations of network congestion, based on link‐level flow control credits. Dragonfly networks based on the extensions of commodity Ethernet networks using OpenFlow have been proposed for large HPC deployments with low power consumption. However, this network technology does not implement credit‐based flow control. This work explores a range of routing solutions based on exploiting explicit congestion notification messages (in particular, 802.1Qau) to adapt the number of packets using non‐minimal paths. The design (denoted QCN‐Switch) associates a probability value to each output port. This value is updated to reflect downstream congestion and used to statistically divert traffic away from congested areas when the load is uneven, as in the case of adversarial traffic. A feedback comparison variant is designed to separate the cases of uniform traffic at saturation and adversarial traffic at low loads. Evaluation results show that QCN‐Switch is a competitive design for both the uniform traffic and adversarial traffic. Furthermore, it is able to react to changes in traffic conditions in 0.4 ms or less. A sensitivity analysis identifies the best configuration and shows its performance trade‐offs.

An asynchronous transfer mode (ATM) network is a high-speed multimedia network which handles various kinds of traffic with different bit-rates and different qualities of service (QoS). In order to maintain the QoS for each traffic source and to avoid possible congestion problems, an ATM network requires highly sophisticated and flexible controllers to insure that this demanding performance can be achieved under unexpected changes in traffic conditions. In this paper, we propose an intelligent architecture using recurrent neural networks and an expert system for traffic control in ATM networks. This traffic control using neural networks is suitable for ATM because neural networks can learn the traffic characteristics and the dynamic changes in the traffic. The proposed mechanism is based on the adaptive prediction of the future values of the traffic and the flow rate for each traffic source. At every given time slot, the controllers in the proposed architecture predict whether the congestion will happen or not and regulate the volume of input traffic for each traffic source before the congestion happens, maintaining the user-required QoS for each traffic source based on the predefined rules. Consequently, the mechanism guarantees the QoS for each traffic source and efficiently prevents congestion.