Changing Traffic Conditions Research Articles

Definição de segmentos homogêneos para o trecho da BR-040 que compreende a cidade de Congonhas/MG

As condições de trafegabilidade do pavimento de uma rodovia estão sujeitas às mais diversas variações ao longo de sua extensão; seja devido a características físicas; seja devido alterações no tráfego, no clima ou, em obras de revitalização. Dessa forma, muitos são os parâmetros considerados no desempenho do pavimento rodoviário, o que consequentemente, resultará em suas condições de trafegabilidade e, tornar-se-á, imprescindível, a consequente divisão da rodovia em segmentos homogêneos, os quais necessitam conter características estruturais e comportamentais semelhantes. Neste ínterim, este trabalho visa a elaborar uma nova proposta para segmentação homogênea da rodovia BR-040, trecho que compreende a cidade de Congonhas/MG, contribuindo assim com o processo de análise comportamental da rodovia. Para tanto, foram utilizadas informações do banco de dados da gerência de pavimentos da concessionária responsável pelo trecho entre Brasília/DF e Juiz de Fora/MG. A delimitação dos novos segmentos homogêneos foi determinada conforme o “Método das Diferenças Acumuladas” (Analysis Unit Delineation by Cumulative Differences), recomendado pela American Association of State Highway and Tranportation Officials – AASHTO (1993) e definida com base na estrutura do pavimento, bem como parâmetros que refletem as condições estruturais e funcionais dos pavimentos. Foi possível a identificação de 45 segmentos homogêneos, evidenciando que houve alterações significativas nas condições estruturais e funcionais do pavimento analisado.

Effects of vehicle restriction policies: Analysis using license plate recognition data in Langfang, China

To mitigate externalities of the urban transportation system, various travel demand management policies have been tested across the world in the past decades. Among them, vehicle restriction is considered the most straightforward way to reduce travel demands. The effects of this policy, however, are still inconclusive. Using the massive license plate recognition (LPR) data in Langfang, China, this study investigates the differences of two typical license-plate-number-based vehicle restriction strategies, i.e., One-Day-Per-Week (ODPW) and Odd-And-Even (OAE). This study compares ODPW and OAE from two perspectives: travel demand and traffic condition over the road network. In addition to the change of traffic volume, the analysis of travel demand also includes the investigations of “illegal” travel, which denotes the road use of restricted vehicles, and travel intensity, which represents the travel frequency of certain vehicles in a time period. The changes of traffic condition are assessed by travel speed. Findings confirm that traffic volume declines as ODPW shifts to OAE, but not as significantly as expected. This less-than-full adjustment can be explained by the increase of illegal travel and travel intensity. The policy shift from ODPW to OAE significantly improves the travel speed in most of the road segments, whether in peak hours or off-peak hours. The conclusions in this study provide in-depth insights into the influence of vehicle restriction policies and contribute to the effective design of travel demand management strategies.

Green Swarm: Greener routes with bio-inspired techniques

This article proposes a mobility architecture, called Green Swarm, to reduce greenhouse gas emissions from road traffic in smart cities. The traffic flow optimization of four European cities: Malaga, Stockholm, Berlin, and Paris, is addressed with new case studies importing each city's actual roads and traffic lights from OpenStreetMap into the SUMO traffic simulator, so as to find the best ways to redirect the traffic flow, and advise drivers. Additionally, the proposal is compared with three other strategies, which are also combined with Green Swarm in order to improve metrics such as travel times, gas emissions, and fuel consumption. This results in reductions in gas emissions as well as in travel times and fuel consumption in more than 500 city scenarios. The proposal has also been tested in scenarios where not all drivers are using it, to observe the change in traffic conditions when it is only in partial use, successfully paving the way for future sustainable cities.

ENVIRONMENTAL FACTORS AND CONSTRAINTS IN THE DEVELOPMENT OF THE NEW TERRITORY OF MOSCOW (SO-CALLED «NEW MOSCOW»)

The article considers the main trends in the environmental situation in the New Moscow in connection with the acquisition of capital status and rapid population growth. The New Moscow is the territory annexed to Moscow as part of a large-scale project to expand the territory of Moscow at the expense of the Moscow oblast in July 2012. Under the influence of both hereditary and transformational factors, the situation in Moscow new adjoint area is rapidly deteriorating, and for Moscow in the old borders inherited factors are mostly negative for the formation of the environmental situation, and the transformation is in the direction of slow and gradual improvement.In Moscow new adjoint area the structure of pollution sources is linked to the postindustrial type of cities (heating systems and other non-industrial sources, vehicles), as the most non-industrial part of the Moscow region was chosen to join the capital. The level of pollution is low, but the environmental efficiency is also low and the level of pollution is growing rapidly. The decline in production and structural changes in industry, as well as the growth of the car fleet, the change in traffic conditions along the roads, led to a change in the territorial proportions in atmospheric pollution in Moscow, both in the old and new boundaries, and the scale of housing construction in Moscow new adjoint area led to a pressure on water sources and changing natural landscapes.

Macroscopic traffic state estimation using relative flows from stationary and moving observers

This article presents a procedure to estimate the macroscopic traffic state in a pre-defined space-time mesh using relative flow data collected by stationary and moving observers. The procedure consist of two consecutive and independent processes: (1) estimate point observations of the cumulative vehicle number in space-time, i.e., N(x, t), based on relative flow data from the observers and (2) estimate flow and density in a pre-define space-time mesh based on the point observations of N. In this paper, the principles behind the first process are explained and a methodology (the Point-Observations N (PON) estimation methodology) is introduced for the second process. This methodology does not incorporate information in the form of a traffic flow model or historical data. To evaluate this performance and improve our understanding of the methodology, a microscopic simulation study is conducted. The estimation performance is effected by the homogeneity and stationarity of traffic in estimation area and in the sample area. In case of large changes in traffic conditions, e.g., from free-flow to congestion or stop-and-go waves, a better sampling resolution will improve localizing these changes in space and time and hence improve the estimation performance. In the simulation study, the proposed methodology is also compared with estimates based on loop-detector data. This indicates that the combination of the proposed methodology and data yields an alternative for existing combinations of methodology and data. Especially, in terms of density estimation the introduced methodology shows promising results.

Short-term traffic forecasting: An adaptive ST-KNN model that considers spatial heterogeneity

Accurate and robust short-term traffic forecasting is a critical issue in intelligent transportation systems and real-time traffic-related applications. Existing short-term traffic forecasting approaches adopt fixed model structures and assume traffic correlations between adjacent road segments within assigned time periods. Due to the inherent spatial heterogeneity of city traffic, it is difficult for these approaches to obtain stable and satisfying results. To overcome the problems of fixed model structures and quantitatively unclear spatiotemporal dependency relationships, this paper proposes an adaptive spatiotemporal k-nearest neighbor model (adaptive-STKNN) for short-term traffic forecasting. It comprehensively considers the spatial heterogeneity of city traffic based on adaptive spatial neighbors, time windows, spatiotemporal weights and other parameters. First, for each road segment, we determine the sizes of spatial neighbors and the lengths of time windows for traffic influence using cross-correlation and autocorrelation functions, respectively. Second, adaptive spatiotemporal weights are introduced into the distance functions to optimize the candidate neighbor search mechanism. Next, we establish adaptive spatiotemporal parameters to reflect continuous changes in traffic conditions, including the number of candidate neighbors and the weight allocation parameter in the predictive function. Finally, we evaluate the adaptive-STKNN model using two vehicular speed datasets collected on expressways in California, U.S.A., and on city roads in Beijing, China. Four traditional prediction models are compared with the adaptive-STKNN model in terms of forecasting accuracy and generalization ability. The results demonstrate that the adaptive-STKNN model outperforms those models during all time periods and especially the peak period. In addition, the results also show the generalization ability of the adaptive-STKNN model.

Real Time Short-term Forecasting Method of Remaining Parking Space in Urban Parking Guidance Systems

Short-term forecasting of the remaining parking space is important for urban parking guidance systems (PGS). The previous methods like polynomial equations and neural network methods are difficult to be applied in practice because of low accuracy or lengthy initial training time which is unfavourable if real-time training is carried out on adapting to changing traffic conditions. To forecast the remaining parking space in real-time with higher accuracy and improve the performances of PGS, this study develops an online forecasting model based on a time series method. By analysing the characteristics of data collected in Nanjing, China, an autoregressive integrated moving average (ARIMA) model has been established and a real-time forecasting procedure developed. The performance of this proposed model has been further analysed and compared with the performances of a neural network method and the Markov chain method. The results indicate that the mean error of the proposed model is about 2 vehicles per 15 minutes, which can meet the requirements for general PGS. Furthermore, this method outperforms the neural network model and the Markov chain method both in individual and collective error analysis. In summary, the proposed online forecasting method appears to be promising for forecasting the remaining parking space in supporting the PGS.

Anticipating emission-sensitive traffic management strategies for dynamic delivery routing

Traffic pollution is an increasing challenge for cities. Emissions such as nitrogen dioxides pose a major health threat to the city’s inhabitants. These emissions often accumulate to critical levels in local areas of the city. To react to these critical emission levels, cities start implementing dynamic traffic management systems (TMS). These systems dynamically redirect traffic flows away from critical areas. These measures impact the travel speeds within the city. This is of particular importance for parcel delivery companies. These companies deliver goods to customers in the city. To avoid long delivery times and higher costs, companies already adapt their routing with respect to changing traffic conditions. Still, a communication with the TMS may allow anticipatory planning to avoid potentially critical areas in the city. In this paper, we show how communication between TMS and delivery companies results in benefits for both parties. To exploit the provided information, we develop a dynamic routing policy anticipating potential future measures of the TMS. We analyze our algorithm in a comprehensive case study for the TMS of the city of Braunschweig, Germany, a city often used as reference for a typical European city layout. We show that for the delivery company, integrating the TMS’ information in their routing algorithms reduces the driving times significantly. For the TMS, providing the information results in less traffic in the polluted areas.

Daily scheduling of caregivers with stochastic times

This paper addresses a daily caregiver scheduling and routing problem arising in home health care or home care service providers. The problem is quite challenging due to its uncertainties in terms of travel and service times derived from changes in road traffic conditions and customer health status in practice. We first model the problem as a stochastic programme with recourse, where the recourse action is to skip customers without services if the caregiver arrives later than their latest starting service time (i.e. hard time window requirements). Then, we formulate the problem as a set partitioning model and solve it with a branch-and-price (B&P) algorithm. Specifically, we devise an effective discrete approximation method to calculate the arrival time distribution of caregivers, incorporate it into a problem-specific label algorithm, and use a removal-and-insertion-based heuristic and the decremental state-space relaxation technique to accelerate the pricing process. Finally, we conduct numerical experiments on randomly generated instances to validate the effectiveness of the discrete approximation method and the proposed B&P algorithm.

Utilizing data mining techniques to predict expected freeway travel time from experienced travel time

As the most important real-time traveler information, travel time can be either experienced or expected (i.e. to be experienced). When a vehicle completes a trip, the travel time refers to the experienced travel time. In contrast, when a vehicle starts its journey, the travel time is unknown but can be predicted, which is the expected travel time. Although the experienced travel time is termed as the real-time travel time, a traveler may encounter a somewhat different travel time (from expected travel time) due to the changing traffic conditions. Therefore, expected travel time needs to be predicted. In this study, the expected travel time was predicted from the experienced travel time using the data mining techniques such as k-nearest neighbor (k-NN), least squares regression boosting (LSBoost) and Kalman filter (KF) methods. After comparing the performances of KF to corresponding modeling techniques from both link and corridor perspectives, it is concluded that the KF method offers superior prediction accuracy in a link-based model. Moreover, the effect of different noise assumptions was examined and it is found that the steady noise computed from the full-dataset had the most accurate prediction. A data processing algorithm, which processed more than a hundred million records reliably and efficiently was also introduced.

Non‐minimal adaptive routing based on explicit congestion notifications

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.

ОСОБЛИВОСТІ АНАЛІТИЧНОГО МОДЕЛЮВАННЯ УПРАВЛІННЯ РУХОМ АВТОМОБІЛЬНОЇ ТЕХНІКИ СПЕЦІАЛЬНОГО ПРИЗНАЧЕННЯ

The article defines the driver's position in the driver-car-road-environment system relative to the movement of special-purpose vehicles. To clarify the changes in the control system, analytical models of interaction between the driver and military vehicles have been considered. The movement of special-purpose vehicles is diverse. To determine the changes in the control system, it makes sense to determine the likely models of interaction between the driver and the machine. Unlike traditional high-speed road vehicles, the movement of all-terrain vehicles across the terrain is at a low speed. When driving in a column where speed and direction of movement are the same for all cars, the driver's reaction is insignificant in relation to the surrounding environment. In most cases, the pace and direction of movement are set independently of the driver's involvement, but with the need for constant attention to even slight changes in traffic conditions. The use of steer ability and durability of special purpose vehicles depends to a large extent on the driver's capabilities. In complex conditions of movement there is a control with change of speed and position of the vehicle. Significance is the connection between the machine and the driver, which requires the development of research on the search and development of analytical models of automotive engineering in general, as well as special purpose wheeled vehicles. The analytical model of the driver's control system is proposed and considered. The speed of the machine allows us to find out and to combine the mathematical description of the interaction between the driver and the machine. The model is an example for further development of similar studies of control systems of the driver - a special purpose vehicle.

Influence of Attention on the Control of Speed and Steering in Older Drivers and Stroke Patients

Objective: Attention is important for driving an automobile. The aim of this study was to identify driving behaviors that are influenced by attention. Methods: This study included 49 healthy individuals (19 men and 30 women) and 10 stroke patients (8 men and 2 women). The subjects’ attention was assessed using the Trail Making Test and the Simple Reaction Time task of the Continuous Performance Test. In a driving simulator with driving conditions set in accordance with the traffic regulations in Japan, including two lanes on each side of the road and a speed limit of 60 km/h, the subjects performed four left turn tasks, six right turn tasks, and six changing lane tasks. Road edge excursion frequency, speeding frequency and duration, crash frequency, speed during driving, distance from the vehicle ahead after entering the passing lane, vehicle positioning, and braking reaction time for a suddenly crossing vehicle were recorded. Results: The results showed that attention deteriorated in the older subjects and that this affected driving behaviors, particularly for staying in one´s lane. We identified the characteristics of driving behaviors during right and left turns as well as changing lane. Elder subjects delayed corrections of steering and the control of speed. Conclusion: In this driving simulator study, deterioration of attention in older drivers was related to road edge excursion and a delay in the control of speed and steering required for responding to the external environment under changing traffic conditions. It would be predicted the characteristics of the driving behavior by evaluating the attention. Although driving behaviors were similar for the stroke patients and the healthy subjects, stroke patients´ attention while driving requires a more detailed evaluation.

An evaluation of public truck terminal implementation and truck ban policy in a medium-size city of Thailand: a case study of Khon Kaen city

This study aims at investigation the potential impacts of a public truck terminal implementation along with truck ban policy adaptation in Khon Kaen City, Thailand. A four-step model is developed and used for examining traffic condition changes due to the terminal relocation. Three different terminal locations are set. Truck traffic of each scenario are estimated and converted to total network cost, and then compared with the base-case scenario. The most suitable truck terminal location is suggested. Also, several concerns regarding the public truck terminal and truck ban policy in the study area are given.

Stochastic Flow Models with Delays and Applications to Multi-Intersection Traffic Light Control

We extend Stochastic Flow Models (SFMs), used for a large class of discrete event and hybrid systems, by including the delays which typically arise in flow movement. We apply this framework to the multi-intersection traffic light control problem by including transit delays for vehicles moving from one intersection to the next. Using Infinitesimal Perturbation Analysis (IPA) for this SFM with delays, we derive new on-line gradient estimates of several congestion cost metrics with respect to the controllable green and red cycle lengths. The IPA estimators are used to iteratively adjust light cycle lengths to improve performance and, in conjunction with a standard gradient-based algorithm, to obtain optimal values which adapt to changing traffic conditions. We introduce two new cost metrics to better capture congestion and show that the inclusion of delays in our analysis leads to improved performance relative to models that ignore delays.

Adaptive Packet Scheduling in IoT Environment Based on Q-learning

In the Internet of Things (IoT) environment consisting of various devices the arrival rate of data packets dynamically changes. Failure to process them in complying with the QoS requirement can significantly degrade the reliability and quality of the system. Therefore, the gateway collecting the data needs to quickly establish a new scheduling policy according to the changing traffic condition. The existing packet scheduling schemes are not effective for IoT since the data transmission pattern is not defined in advance. Q-learning is a type of reinforcement learning that can establish a dynamic scheduling policy according to the state of each queue without any prior knowledge on the network status. In this paper a novel Q-learning scheme is proposed which updates the Q-table and reward table based on the condition of the queues in the gateway and adjusts the reward value according to the time slot. Computer simulation reveals that the proposed scheme significantly reduces the scheduling time while allowing high accuracy compared to the existing Q-learning scheme based on Stochastic Learning Automaton (SLA).

A Novel Cell Reconfiguration Technique for Dynamic TDD Wireless Networks

In dynamic time division duplexing (DTDD) systems, the uplink (UL) and downlink (DL) resources can be configured to adapt to changing traffic conditions. Therefore, DTDD systems are advantageously deployed in scenarios in which the UL and DL traffic demands are asymmetric and time-varying. Unfortunately, multicell DTDD systems give rise to base station-to-base station and user equipment-to-user equipment interference, that can severely degrade the system performance. Previous works on DTDD either assumed that the UL/DL configurations are given, or they did not take into account the negative impact of multicell DTDD interference. In this letter, we propose a novel cell reconfiguration technique that considers both the prevailing traffic conditions and multicell interference levels. The proposed technique is based on an efficient solution of a mixed integer linear program, whose objective is to maximize the overall system throughput taking into account users’ traffic preferences. Realistic system level simulations indicate that the proposed scheme outperforms not only the static TDD system but also other reference schemes, that disregard the DTDD specific interference effects.

Employing Cyber-Physical Systems: Dynamic Traffic Light Control at Road Intersections.

Traffic lights (TLs) are used to control traffic at road intersections. Typically, the TL control mechanism on the road operates according to a fixed periodic schedule to change the light (red/yellow/green). A different schedule may be employed at late night or early morning hours. Such fixed light control does not react to changing traffic conditions, and is unaware of (unresponsive to) congestion. In this paper, we propose a novel framework dynamic TL control (DTLC) at road intersections. DTLC relies on a sensor network to collect traffic data and includes novel protocols to handle congestion and facilitate more efficient traffic flow. The proposed low-overhead algorithms are practical to employ in live traffic flow scenarios. Through analysis and simulations, we demonstrate the benefits of DTLC in optimizing traffic flow metrics, such as traffic throughput, vehicle waiting time, and waiting line length. This paper is a step toward employing smart TLs, which are part of the future envisioned "Smart City."

Green Link Weights for Disruption-Free Energy-Aware Traffic Engineering

Energy-aware traffic engineering (ETE) has been gaining increasing research attentions due to the cost reduction benefits that it can offer to network operators and for environmental reasons. While numerous approaches exist which attempt to provide energy reduction benefits by intelligently manipulating network devices and their configurations, most of them suffer from one fundamental shortcoming: however, minor adaptations to a given IP network topology configuration all lead to temporal service disruptions incurred by routing reconvergence, which makes these schemes less appealing to network operators. The more frequently the IP topology reconfigurations take place in order to optimize the network performance against dynamic traffic demands, the more frequently service disruptions will occur to end users. Motivated by the essential requirement for network operators to enable seamless service assurance, we put forward a framework for disruption-free ETE, which leverages on selective link sleeping and wake-up operations in a disruption-free manner. The framework allows for maximizing the opportunities for disruption-free reconfigurations based on intelligent IGP link weight settings, assisted by a dynamic scheme that optimizes the reconfigurations in response to changing traffic conditions. As our simulation-based evaluation show, the framework is capable of achieving significant energy saving gains while at the same time ensuring robustness in terms of disruption avoidance and resilience to congestion.

IMPLEMENTATION OF INCIDENT DETECTION ALGORITHM BASED ON FUZZY LOGIC IN PTV VISSIM

Traffic incident management is a major challenge in the management of movement, requiring constant attention and significant investment, as well as fast and accurate solutions in order to re-establish normal traffic conditions. Automatic control methods are becoming an important factor for the reduction of traffic congestion caused by an arising incident. In this paper, the algorithm of automatic detection incident based on fuzzy logic is implemented in the software PTV VISSIM. 9 different types of tests were conducted on the two lane road section segment with changing traffic conditions: the location of the road accident, loading of traffic. The main conclusion of the research is that the proposed algorithm for the incidents detection demonstrates good performance in the time of detection and false alarms