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In recent times, congestion in traffic is bit cost effective and several traffic management methods are utilized to balance the traffic as per the capacity of roads. However, many methods fails in pre-analyzing the network events to regulate the traffic flow. Also, the dynamic movement of vehicles fails in adapting the vehicle units to follow the changing topology. Hence, it is necessary for packet forwarding between source and destination nodes in a precise manner, where the packet loss and communication overhead should be in control. In this paper, we utilize the concept of Connected Dominating Set (CDS) approach in traffic management for reducing the flow of traffic streaming in main routes. The CDS approach dynamically adapts itself with the changing topology and acts as a virtual backbone to reduce traffic overhead. It restricts the increased flow of vehicles to the dominators, since traffic congestion in such areas increases. The CDS forwarding (CDSF) finds the dominating or high congestion routes and diverts the traffic to other areas based on the level of traffic in sub-routes. The CDSF method works on an assumption that edges on road segments has multiple vehicles, where it increases the network efficiency. However, we make careful consideration of choosing the other road segments to traverse the required packets from source to destination with reduced packet loss. The construction of a virtual backbone network with series of neighboring backbone nodes traverses efficiently the packets. Further, choosing vehicles with low velocity improves the probability of connectivity in a specific area and increases the network throughput. This improves the re-routing flexibility and makes the packets to reach the destination without traffic congestion in VANETs. The CDSF method and control strategies are tested in NS2 and SUMO simulator under different traffic conditions. The results of simulation show that CDSF traffic management system reduces significantly the traffic congestion and overall traffic cost that includes CDF, average latency, average end-to-end delay, Hop count, packet error rate, percentage delivery ratio, data delivery rate, throughput. The result shows that the proposed method 0.345 times improved packet delivery ratio, 0.35 times improved throughput and 0.7 times reduced delay than the existing methods. Further, the CDSF method controls the traffic density by reducing the maximum queue length and traffic controlling time. [ABSTRACT FROM AUTHOR]

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice. [ABSTRACT FROM AUTHOR]

Unforeseeable developments will accompany progressive COVID-19 recovery globally. Similarly, science will inform changes amidst its own progress. Social isolation and distancing imposed by the pandemic are likely to result in changed habits, behavior, and thinking paradigms. Inevitably, this should affect the tremendous confusion inhibiting automated urban mobility's evolution. While mobility often seems magnanimously resistant to change, using international data, this analysis shows road traffic, the largest net contributor to global warming, is responsible for even greater damages. The core claim justifies replacing private cars (PCs) by existing and future public transit (PT) vehicles. In testing 17 major cities globally, 94% of the scenarios proved PT superior or equivalent to PCs for reducing travel time. As a result, a foreseeable, future scenario shows potential reduction in car traffic by approximately two-thirds compared with the current situation. In two arenas, proactive government can promote such sustainable urban mobility: (1) developing autonomous vehicles for PT only; (2) coordinating standardization for seamless urban mobility. These global decisions for improving our lives in the future are likely to be better received and understood subsequent to COVID-19, as the focus of our concerns changes from what preoccupied us under the circumstances prior to the pandemic. [ABSTRACT FROM AUTHOR]

A statistical model of traffic flows designed for modeling the motion of vehicles on long highways is proposed. The model simulates the motion of groups of vehicles on the road using the fundamental flow–density diagram on a selected segment of the road with the purpose of calculating the speed of the group; it is assumed that the group size linearly depends on its speed. The proposed approach combines the advantages of macroscopic and microscopic simulation. That is, it allows one to simulate the motion of vehicles in megalopolises to a high accuracy and with low requirements for computational resources. The principles of simulation are described, algorithms for recalculating the model states are presented, and computational experiments confirming the validity and workability of the simulation results for various configurations of the road network are discussed. In the experiments, the fundamental flow diagram is constructed on the basis of sensors of the traffic control center. [ABSTRACT FROM AUTHOR]

When facing some emergency situations, such as fires, traffic accidents, etc., it is necessary for the network to use the available information to provide the best and fastest access routes. In these cases, it is important to centrally manage all devices throughout the network, so a software-defined network (SDN) -based architecture is used to manage and control emergency service actions and evacuation plans. At this time, differentiated services are provided to customers in emergency and non-emergency situations through network slicing, and resource allocation is performed for network slicing such as delay and bandwidth parameters. In this paper, the first resource management plan is the merit of quick converging to the global optimal solution which was used to design the evaluation function of the network resource management; the second is monitors and analyzes access traffic and centralizes control and management to meet the bandwidth requirements of tenants to counter the tidal effect. The ability of fast random search of particle swarm optimization was used to realize the update and optimization. The simulation results show that can reduce the energy consumption of the network and improve the utilization rate of cyber source at the same time. [ABSTRACT FROM AUTHOR]

With the recent significant growth of virtualization and cloud services, the data center network (DCN) as the underlying infrastructure is more important. The increasing and changing volume of workloads highlights critical issues such as load balancing and energy efficiency in data centers. Large path diversity in DCNs introduces multipath forwarding as a promising approach to improve load distribution. However, the over-provisioned DCNs consume large amounts of power while the network is under full capacity most of the time. Accordingly, this paper proposes BiTE, a dynamic bi-level traffic engineering (TE) scheme in a hierarchical Software Defined Networking (SDN)-based DCN to strike a balance between load balancing and energy efficiency objectives. BiTE consists of decision-making problems at two levels modeled as a multi-period bi-level optimization problem, where each decision maker optimizes one of objectives. According to the inherent complexity of bi-level programming, a co-evolutionary metaheuristic algorithm is proposed for solving BiTE. BiTE performance is evaluated in comparison to NSGA-II algorithm and four previously proposed TE schemes in terms of several load balancing and energy saving metrics under different scenarios. The results show that BiTE performs well in traffic load balancing while preserves the energy efficiency. We apply the Analytic Hierarchy Process (AHP) method to multi-criteria analyze and rank the performance of studied TE mechanisms. AHP results for different scenarios indicate that BiTE is in first or second place in terms of the overall performance score among six studied approaches. [ABSTRACT FROM AUTHOR]

COVID-19 has affected every sector of our society, among which human mobility is taking a dramatic change due to quarantine and social distancing. We investigate the impact of the pandemic and subsequent mobility changes on road traffic safety. Using traffic accident data from the city of Los Angeles and New York City, we find that the impact is not merely a blunt reduction in traffic and accidents; rather, (1) the proportion of accidents unexpectedly increases for "Hispanic" and "Male" groups; (2) the "hot spots" of accidents have shifted in both time and space and are likely moved from higher-income areas (e.g., Hollywood and Lower Manhattan) to lower-income areas (e.g., southern LA and southern Brooklyn); (3) the severity level of accidents decreases with the number of accidents regardless of transportation modes. Understanding those variations of traffic accidents not only sheds a light on the heterogeneous impact of COVID-19 across demographic and geographic factors, but also helps policymakers and planners design more effective safety policies and interventions during critical conditions such as the pandemic. [ABSTRACT FROM AUTHOR]

This work provides a comprehensive review of the development and applications of bridge rotation construction methods (BRMs) and related critical construction technologies in China. A brief history of BRMs and an outline of their categories, including the rotating structural system, are given first. Subsequently, a discussion on the recent practice of BRMs in China is carried out from three types of bridges: (1) continuous beam and rigid frame bridges; (2) arch bridges; (3) cable-stayed bridges. For each group, a survey of the vital bridge cases that have adopted BRMs in China in the past two decades is provided. Key parameters, including the structure weight of rotation, maximum cantilever span, and rotation duration, are also compared and discussed. Afterward, the principles and applications of the weighing test and the spherical hinge stability, which are both related to the safety of the horizontal rotation construction process, are analyzed in the paper. Additionally, the limitations of BRMs are presented and discussed, and the article concludes with a summary of the current use and future trend of BRMs. The information and experience of BRMs provided in this work can be referenced by engineers and researchers who are devoted to the construction of bridges. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]

As the pace of life accelerates, people are troubled by spending too much time on commuting, and people's waiting time for travel can be reduced by reasonable strategy of traffic control. Previous research on traffic control mainly focused on timing and ignored the study of phase. For asymmetric or small traffic flow, the common timing project based on single-loop phase structure cannot achieve optimal control. Therefore, this paper proposed a fuzzy control method of traffic light under time-varying universe and the phase structure have been optimized. For asymmetric traffic flow, the phase structure is optimized by adding additional phases to form a double-loop phase structure. In slack hour, the phase structure is optimized by merging phase. Taking an intersection in Jing-Hong city as an example, the feasibility of the method is verified. The results show that the delay of vehicles can be reduced and the traffic capacity of the intersection can be improved by this method. [ABSTRACT FROM AUTHOR]

Occlusion removal is a significant problem to be resolved in a remote traffic control system to enhance road safety. However, the conventional techniques do not recognize traffic signs well due to the vehicles are occluded. Besides occlusion removal was not performed in existing techniques with a less amount of time. In order to overcome such limitations, Non-linear Gaussian Bilateral Filtered Sorenson–Dice Exemplar Image Inpainting Based Bayes Conditional Probability (NGBFSEII-BCP) Method is proposed. Initially, a number of remote sensing images are taken as input from Highway Traffic Dataset. Then, the NGBFSEII-BCP method applies the Non-Linear Gaussian Bilateral Filtering (NGBF) algorithm for removing the noise pixels in input images. After preprocessing, the NGBFSEII-BCP method is used to remove the occlusion in the input images. Finally, NGBFSEII-BCP Method applies Bayes conditional probability to find operation status and thereby gets higher road safety using remote sensing images. The technique conducts the simulation evaluation using metrics such as peak signal to noise ratio, computational time, and detection accuracy. The simulation result illustrates that the NGBFSEII-BCP Method increases the detection accuracy by 20% and reduces the computation time by 32% as compared to state-of-the-art works. [ABSTRACT FROM AUTHOR]

Peroxisomes are organelles that perform a wide range of essential metabolic processes. To ensure that peroxisomes are optimally positioned in the cell, they must be transported by both long- and short-range trafficking events in response to cellular needs. Here, we review our current understanding of the mechanisms by which the cytoskeleton and organelle contact sites alter peroxisomal distribution. Though the focus of the review is peroxisomal transport in mammalian cells, findings from flies and fungi are used for comparison and to inform the gaps in our understanding. Attention is given to the apparent overlap in regulatory mechanisms for mitochondrial and peroxisomal trafficking, along with the recently discovered role of the mitochondrial Rho-GTPases, Miro, in peroxisomal dynamics. Moreover, we outline and discuss the known pathological and pharmacological conditions that perturb peroxisomal positioning. We conclude by highlighting several gaps in our current knowledge and suggest future directions that require attention. [ABSTRACT FROM AUTHOR]

This article proposes the use of agent-based simulation to analyze complex design trade-offs that involve value tensions. This is illustrated by a case study in the domain of train traffic control. In this case, agent-based simulation supported the involvement of stakeholders and increased understanding of a design trade-off. [ABSTRACT FROM AUTHOR]

Although the number of traffic accidents in China has been declining year by year, the proportion of major traffic accidents in mountainous areas has continued to rise, reaching 60% in recent years. This shows that a mountain road safety system has not been effectively established. The majority of the methods used in mountain road safety assessments are statistical analysis methods based on accident causation theory. However, these expert methods greatly depend on massive amounts of traffic environmental data, which are very difficult and time-consuming to obtain. This study had the goal of reducing the environmental monitoring data required for this assessment and increasing analysis efficiency. Based on the research of road traffic accident data and traffic flow characteristics in mountainous areas, an attribute reduction algorithm was developed to weigh the significance of the data and construct a systemic analysis mothed. The proposed risk analysis model, which considers the accidents, roadside environment, and traffic safety facilities, is based on Shaanxi province data. The results from the actual implementation of this model indicate that the approach has good operability and holds great promise for mountain road safety assessment. [ABSTRACT FROM AUTHOR]

We propose a stochastic approximation (SA) based method with randomization of samples for policy evaluation using the least squares temporal difference (LSTD) algorithm. Our proposed scheme is equivalent to running regular temporal difference learning with linear function approximation, albeit with samples picked uniformly from a given dataset. Our method results in an O(d) improvement in complexity in comparison to LSTD, where d is the dimension of the data. We provide non-asymptotic bounds for our proposed method, both in high probability and in expectation, under the assumption that the matrix underlying the LSTD solution is positive definite. The latter assumption can be easily satisfied for the pathwise LSTD variant proposed by Lazaric (J Mach Learn Res 13:3041–3074, 2012). Moreover, we also establish that using our method in place of LSTD does not impact the rate of convergence of the approximate value function to the true value function. These rate results coupled with the low computational complexity of our method make it attractive for implementation in big data settings, where d is large. A similar low-complexity alternative for least squares regression is well-known as the stochastic gradient descent (SGD) algorithm. We provide finite-time bounds for SGD. We demonstrate the practicality of our method as an efficient alternative for pathwise LSTD empirically by combining it with the least squares policy iteration algorithm in a traffic signal control application. We also conduct another set of experiments that combines the SA-based low-complexity variant for least squares regression with the LinUCB algorithm for contextual bandits, using the large scale news recommendation dataset from Yahoo. [ABSTRACT FROM AUTHOR]

Over the last few years, energy optimization in wireless sensor networks (WSNs) has drawn the attention of both the research community and actual users. Sensor nodes are powered by attached batteries that are considered as a critical aspect of sensor nodes design. Besides, the constraint of the limited battery capacity is associated with the concern on how to reduce the energy consumption of nodes to extend the network lifetime. In this context, the purpose of this study is to implement an adaptive medium access control (MAC) for energy saving and traffic control enhancement. This program was designed to arrange nodes into two priority groups according to their traffic rate and data transmission packet delay. This fuzzy algorithm depends on their queue length where it is implemented into the carrier sense multiple access with collision avoidance (CSMA/CA) algorithm. However, other types of nodes should send their data during the contention-free period with a GTS reallocation scheme. Those nodes are classified as low priority access to the medium, and their data transmission is scheduled using time division multiple access methods. Moreover, this proposed scheme dynamically adjusts the contention access period length to ensure that nodes can complete their data transmission during the same super-frame. Simulation results are done using the network simulator tool (NS-2), and it has improved efficiency regarding the IEEE-802.15.4 standard. [ABSTRACT FROM AUTHOR]

The ceaseless increase in the number of the wireless Internet of Things (IoT) devices has resulted in the need of different traffic engineering techniques to manage the massive network traffic. Wireless Mesh Networks (WMNs) are an important constituent part of the wireless IoT networks, and are helpful to route the IoT networks' traffic over long distances. The WMN devices are powerful in comparison to the IoT sensor devices, and are suitable to run the traffic engineering algorithms. To further improve the performance of the WMNs, Software Defined Networking can be used. Its unique features like global visibility, agility, etc., guarantee the optimal network management. As granularity plays an important role in data analysis and none of the existing works has discussed a time granularity based network data analysis, this work tries to offer a time granular analysis of Software Defined Wireless Mesh based IoT (SDWM-IoT) network's traffic using supervised learning approaches. A time granular analysis helps to explore the functional traits of the data at the Coarse, Medium, and Fine granularity levels. This assists in divulging and understanding the hidden characteristics and behaviour of the SDWM-IoT network's data based on varying time granularity, respectively. Some well known supervised learning algorithms are used to offer an in-depth analysis of the traffic, and to draw the relevant conclusions. Different variants of Decision Tree, Support Vector Machine and K-Nearest Neighbour (KNN) are used to analyze the traffic and achieve a reliable accuracy rate of more than 90%. Among all the variants, fine-KNN produces the best accuracy for most of the traffic classes with a rate of more than 98%. In addition to this, a tenfold cross-validation technique is also used to prevent the the chances of over-fitting. [ABSTRACT FROM AUTHOR]

Intelligent transportation brings huge benefits to humans' life and Industrial production in terms of vehicle control and traffic management. Now, the development of edge-cloud computing has once again promoted intelligent transportation into a new era. However, the development of intelligent transportation inevitably produces a large amount of data, which brings new challenges to data privacy protection and security. In this paper, we propose to develop an improved trajectory prediction framework based on the self-adaptive trajectory prediction model (SATP), which could significantly enhance traffic safety in transportation systems. The proposed framework is capable of guaranteeing the accurate trajectory prediction of moving target under different application scenarios. In particular, to reduce the size of original trajectory point data collected by sensors, the angle change and minimum description length (MDL) principle are first combined to remove the redundant points in raw trajectories. The obtained points can then be reduced for model using the two-step clustering method. To further enhance the prediction performance, we add the "self-transfer" to the original model to solve the problems that the state of original SATP model may be discontinuous. Furthermore, we propose to develop a trajectory complementation method based on Bezier curve to improve the prediction accuracy. Finally, by comparing the two-step clustering method with the commonly-used SinglePass and density-based clustering method (DBCM) algorithms, the proposed two-step clustering policy greatly reduce the time cost of clustering. At the same time, by comparing the improved SATP model with the original model, the results show that the improved SATP method can greatly improve the speed of prediction model. [ABSTRACT FROM AUTHOR]

Understanding spatial interactions such as human mobility has been one of the main analytical themes in geography, spatial economics, and traffic engineering for a long time. The intervening opportunities models, including the radiation model, provide a framework to elucidate spatial interactions generated by an individual's distance-ordered decision-making process. However, such classical definitions of intervening opportunities have often failed to predict realistic flow volumes, particularly for short-distance flows. To overcome this problem, we have proposed a new formulation of intervening opportunities with a kernel function to introduce a fuzziness in spatial search behaviours of destinations, to develop a new variant of the radiation model. The mobility patterns resulting from the modified radiation model that included kernel-based intervening opportunities outperformed the original radiation model when fitted to four datasets of inter-regional flows. [ABSTRACT FROM AUTHOR]

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number of learning-based methods have been used in traffic signal control, they suffer from poor performance and slow learning convergence. In this paper, a transfer learning-based method for traffic signal control has been proposed. Multi-agent system has also been used for modelling the traffic network and transfer learning has been used to make reinforcement learning agents transfer their experience to each other. Furthermore, a classifier has been utilized to classify the transferred experiences. The results show that using the proposed method leads to a significant improvement on average delay time and convergence time of the learning process. [ABSTRACT FROM AUTHOR]

In this paper we address unbalanced spatial distribution of hub-level flows in an optimal hub-and-spoke network structure of median-type models. Our study is based on a rather general variant of the multiple allocation hub location problems with fixed setup costs for hub nodes and hub edges in both capacitated and uncapacitated variants wherein the number of hub nodes traversed along origin-destination pairs is not constrained to one or two as in the classical models.. From the perspective of an infrastructure owner, we want to make sure that there exists a choice of design for the hub-level sub-network (hubs and hub edges) that considers both objectives of minimizing cost of transportation and balancing spatial distribution of flow across the hub-level network. We propose a bi-objective (transportation cost and hub-level flow variance) mixed integer non-linear programming formulation and handle the bi-objective model via a compromise programming framework. We exploit the structure of the problem and propose a second-order conic reformulation of the model along with a very efficient matheuristics algorithm for larger size instances. [ABSTRACT FROM AUTHOR]

VANET is an emerging area of wireless ad-hoc networks to contribute in the success of connected vehicles projects. The extremely changeable number of mobile nodes and high mobility are challenging issues. Furthermore, this particular network has several problems in term of defining suitable schemes and protocols like rate adaptation mechanisms. The overall performance of diverse applications in VANET such as traffic control and multimedia delivery is based on the achievement ratio these networks can offer and the network throughput. Rate adaptation is an essential technique to evade the performance network degradation and to maximize the throughput by using the estimation of the present channel characteristics and determining the optimal bitrate for subsequent transmissions. Despite there are several available rate control algorithms for 802.11 WLANs standards, there are few works devoted to the rate adaptation for the standard of vehicular networks. In this paper, we compare and evaluate the existing data rate adaptation schemes in numerous vehicular environments to recognize their behavior and analyze their performance in diverse scenarios. Six algorithms were chosen for comparison using the NS-3 simulator: AARF, AARF-CD, AMRR, CARA, Onoe, and Minstrel. The simulation outcomes demonstrate that Minstrel algorithm outperforms the remaining five mechanisms in dense and dynamic situations. [ABSTRACT FROM AUTHOR]

Short-term traffic flow prediction is an important basis of intelligent transportation systems. Its accuracy directly affects the performance of traffic control and induction. To improve prediction accuracy, in this paper, a traffic flow prediction model is proposed by combining hierarchical agglomerative clustering (HAC), standardized Euclidean distance (SED), and a long short-term memory network (LSTM). The proposed model is called the CHS-LSTM model. In this model, HAC is used to carry out cluster analysis on the original traffic flow data sample set of target detection sections. Based on the results of cluster analysis, traffic flow data are divided into several categories. For different categories of traffic flow, SED is used to calculate the spatial correlation of the road network where the target sections are located, and the K sections most relevant to the target detection sections are used in the construction of an input data matrix for LSTM. The prediction result with the minimum root-mean-square error is regarded as the final prediction result. The electronic toll collection data of Taiwan are used as the fundamental data in this paper to verify the performance and effectiveness of the CHS-LSTM model. The experimental results indicate that the CHS-LSTM model can effectively improve the prediction accuracy of LSTM. Moreover, compared with well-known models generally used for predicting the intensity of traffic flow, our proposed model also shows its superiority. [ABSTRACT FROM AUTHOR]

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. [ABSTRACT FROM AUTHOR]

Predicting traffic disturbances is a challenging problem in urban cities. Emergency vehicles (EV) is one of the biggest disturbances that affect traffic fluidity. The goal of this paper is to provide a machine learning application to deal with emergency cases in traffic networks. Particularly, we investigate the use of deep learning techniques coupled with Artificial Immune System to tackle the issue of EV guidance at signalized intersections. To accomplish this goal, we develop a traffic signal control system capable to estimate traffic status, guide EV to reach their destinations while assuming better traffic condition, control traffic signals, and adapt to new disturbances. For traffic forecasting, the suggested system inherits the advantages of convolutional neural networks, classification, and long short term memory. To control traffic signals, the suggested system uses the immune memory algorithm. To enhance and adapt control decisions to traffic disturbances, the suggested system uses a continuous learning approach assumed by an adapted reinforcement learning algorithm. Assessments using well-known algorithms from the literature are detailed in this work. The benchmarking algorithms are the preemptive longest queue first matching weight matrix system, the pre-emptive immune memory algorithm inspired case-based reasoning, and the preemptive optimized stage based fixed time algorithm. Experiments show a competitive performance of the suggested system compared to benchmarking algorithms. [ABSTRACT FROM AUTHOR]

Traffic is a major challenge for electronic cities and coping with it requires analyzing traffic congestion in the city road network. In this paper, the performance index of vehicle speed was estimated to evaluate the conditions of road networks. This study analyzes the traffic density for the main network of Hamedan communication routes based on the collected data of Speed performance of Hamedan traffic control system. According to this analysis, the congestion index and traffic peak hours were determined. Also the relationship between vehicle speed and traffic congestion was predictably predictable by neural network and the genetic algorithm. In this study areas of traffic were identified using Hamedan traffic control center due to the speed of vehicles. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) methods learn optimal decisions in the presence of a stationary environment. However, the stationary assumption on the environment is very restrictive. In many real world problems like traffic signal control, robotic applications, etc., one often encounters situations with non-stationary environments, and in these scenarios, RL methods yield sub-optimal decisions. In this paper, we thus consider the problem of developing RL methods that obtain optimal decisions in a non-stationary environment. The goal of this problem is to maximize the long-term discounted reward accrued when the underlying model of the environment changes over time. To achieve this, we first adapt a change point algorithm to detect change in the statistics of the environment and then develop an RL algorithm that maximizes the long-run reward accrued. We illustrate that our change point method detects change in the model of the environment effectively and thus facilitates the RL algorithm in maximizing the long-run reward. We further validate the effectiveness of the proposed solution on non-stationary random Markov decision processes, a sensor energy management problem, and a traffic signal control problem. [ABSTRACT FROM AUTHOR]

Advantages of Software Defined Networking are unquestionable and are widely described in numerous scientific papers, business white papers and press articles. However, to achieve full maturity, crucial impediments to this concept and its shortcomings must be overcame. One of the most important issues regards significant setup latency of a new flow. To address this issue we propose PARD: a hybrid proactive and reactive method to manage flow table entries. Additional advantages of the proposed solution are, among the others, its ability to preserve all capabilities of Software Defined Networking, utilization of multiple flow tables, a possibility to employ fine-grained traffic engineering and, finally, compatibility with existing protocol and hardware design. It is shown that the proposed solution is able to significantly reduce latency of first packets of a new flow, which directly impacts packet loss and perceived throughput. Thus, our solution is expected to enable a wide deployment of Software Defined Networking concept without any need for protocol changes or, what is extremely important, hardware modifications. [ABSTRACT FROM AUTHOR]

In the context of an Internet-of-Things (IoT) ecosystem, this paper discusses two necessary stages for managing federations of things. The first stage defines things in terms of duties and non-functional properties that define the quality of these duties. And, the second stage uses these properties to assign appropriate things to future federations. Specialized into ad hoc and planned, federations are expected to satisfy needs and requirements of real-life situations like traffic control that arise at run-time. A set of experiments using a mix of real and simulated datasets, demonstrate the technical doability of thing assignment to federations and are presented in the paper, as well. [ABSTRACT FROM AUTHOR]

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions. [ABSTRACT FROM AUTHOR]

Due to the rapid growth of the number of passengers over the few recent decades, air traffic forecasting has become a crucial tool for digital transportation systems, playing a fundamental role in the planning and development of traffic management and control systems. The main goal of forecasting in air transport is to predict traffic conditions in a network, on the basis of its past behavior, in order to improve safety and reduce airspace congestion. Nevertheless, air traffic time series often present an intricate behavior because of their irregular trends and strong seasonalities. In this paper, the methodology based on time series decomposition and artificial neural networks (ANNs) is thus reviewed and reconsidered within this framework of air traffic management. In this respect, a hybrid approach coupling feedforward neural networks with a nonlinear least squares-based regression curve fitting is developed for the multistep-ahead prediction. Empirical experiments are conducted in order to demonstrate the effectiveness of the proposed model on passenger traffic real datasets. The results show that, despite its simplicity, the base model is capable of generating accurate forecasts, with a performance comparable with that of powerful state-of-the-art forecasting models. In addition, there is evidence that trend pretreatment (wholly or partially) would rather degrade the forecasting accuracy. [ABSTRACT FROM AUTHOR]

The article discusses the issues of improving the collection of traffic information using video cameras and the statistical processing of collected data. The aim of the article was to identify the main patterns of traffic at intersections in traffic congestion and to develop an analysis technique to improve traffic management at intersections. In modern conditions, there is a sharp increase in the number of vehicles, which leads to negative consequences, such as an increase in travel time, additional fuel consumption, increased risk of traffic accidents and others. To solve the problem of improving traffic control at intersections, it is necessary to have a reliable information collection system and apply modern effective methods of processing the collected information. The purpose of this article is to determine the most important traffic characteristics that affect the throughput of intersections. As a criterion for the cross-pass ability of the intersection, the actual number of passing cars during the permission signal of the torch light is taken. Using multivariate regression analysis, a model was developed to predict intersection throughput taking into account the most important traffic characteristics. Analysis of the throughput of intersections using the fuzzy logic method confirmed the correctness of the developed model. In addition, based on the results of processing information collected at 20 intersections and including 597 observations, a methodology was developed for determining the similarity of traffic intersections. This allows us to identify homogeneous types of intersections and to develop typical traffic management techniques in the city, instead of individually managing each node of the city's transport network individually. The results obtained lead to a significant reduction in costs for the organization of rational traffic flows. [ABSTRACT FROM AUTHOR]

Recent research in communication moves towards cyber space with modern applications such as multimedia surveillance, smart autonomous traffic control, advanced health care monitoring, industrial automation, and continuous remote environmental and structural monitoring. Such applications are extensively generates multimedia information and converse through Wireless Multimedia Sensor Networks (WMSNs). Path selection plays vital role in transmitting the multimedia data over wireless networks. Single path selection incurs routing failure due to the energy hole because of power shortage and traffic congestion. Due to energy hole in any particular node the packet gets dropped in the route from source to destination. The multipath routing algorithm selects more than one path to increase the rate of the packet delivery than the single path routing. The prediction of energy deficiency in sensor nodes is becoming over head in WSMNs. In order to improve the packet delivery and performance of the network, in this paper a Priority Based Multipath Routing Algorithm (PBMRA) is proposed. It consists of three modules: The Neighbourhood Selection identifies the neighbours; Multipath Construction constructs multiple paths from the indentified neighbours; and Priority Selection selects the high energized routes based on the priority of node attributes such as liveliness, path delay and hop count. The proposed algorithm is experimentally compared with other existing algorithms such as AODV, DSR, DSDV and OLSR. The experimental results show that the PBMRA algorithm increases the overall throughput, packet received rate and provides the reliable route than existing algorithms. [ABSTRACT FROM AUTHOR]

Roundabouts are a form of circular intersections adopted to secure road safety of the vehicles. However, collisions are not prevented entirely even when using roundabouts, since accidents may happen at specific intersection points leading to either traffic congestions or serious accidents. In this paper, a visible light communication (VLC)-based collision avoidance scheme is proposed to coordinate autonomous vehicles in traversing roundabouts, which has high effectiveness in vehicular environments. In particular, roadside units (RSUs) are deployed at the roundabout entrances to coordinate the vehicles in a vehicle-to-infrastructure (V2I) mode. By adopting the synchronization approach, vehicles can pass the roundabout simultaneously if their paths are concurrent with each other. Otherwise, vehicles are prioritized according to their arrival time and reasonable decelerations are applied to waive potential conflicts. Simulation results showed that our proposed approach satisfies the roundabout traffic demands in terms of concurrency, safety, and time utilization as vehicles are strongly recommended to decelerate in only 22% of the cases studied in different scenarios. [ABSTRACT FROM AUTHOR]

This paper presents a formal model of the case study proposed for the ABZ2018 conference, which concerns the Hybrid ERTMS/ETCS Level 3 Standard. This standard allows trains to communicate with a train supervisor to report their integrity and positions, thanks to an onboard train integrity monitoring system. The supervisor assigns trains a movement authority to control traffic and to avoid collisions. The standard also provides for trains that cannot communicate with the supervisor; these trains are detected by sensors on tracks and obey traffic signals set by the supervisor along the trackside. Using communication allows for a finer grain control of the tracks. Our model is derived using stepwise refinement with the Event-B method. We take into account the main features of the case study (VSS management, timers, ERTMS and non-ERTMS trains). Our model is decomposed into four refinements. All proof obligations have been discharged using the Rodin provers, except those related to the computation of the VSS state machine, which was found to be ambiguous (nondeterministic). Our model has been validated using ProB. The main safety property, which is that ERTMS trains do not collide, is proved. Our model focuses on the discrete control logic aspects of the case study. [ABSTRACT FROM AUTHOR]

Zero-inflated Poisson (ZIP) regression is widely applied to model effects of covariates on an outcome count with excess zeros. In some applications, covariates in a ZIP regression model are partially observed. Based on the imputed data generated by applying the multiple imputation (MI) schemes developed by Wang and Chen (Ann Stat 37:490–517, 2009), two methods are proposed to estimate the parameters of a ZIP regression model with covariates missing at random. One, proposed by Rubin (in: Proceedings of the survey research methods section of the American Statistical Association, 1978), consists of obtaining a unified estimate as the average of estimates from all imputed datasets. The other, proposed by Fay (J Am Stat Assoc 91:490–498, 1996), consists of averaging the estimating scores from all imputed data sets to solve the imputed estimating equation. Moreover, it is shown that the two proposed estimation methods are asymptotically equivalent to the semiparametric inverse probability weighting method. A modified formula is proposed to estimate the variances of the MI estimators. An extensive simulation study is conducted to investigate the performance of the estimation methods. The practicality of the methodology is illustrated with a dataset of motorcycle survey of traffic regulations. [ABSTRACT FROM AUTHOR]

In order to improve the traffic efficiency of official vehicles in the traffic road network, a backpressure routing control strategy for multi-commodity flow (official traffic flow) using official vehicle network environmental data information is proposed. Firstly, the road network composed of official service vehicle-mounted wireless network nodes is used to collect information on road conditions and official service vehicles. In order to improve the real-time and forward-looking route control, an official service vehicle flow forecasting method is introduced to construct a virtual official service vehicle queue. A multi-commodity flow (official service vehicle flow) backpressure route method is proposed, and an official service vehicle control strategy is designed to improve the self-adaptive route of K-means algorithm. In addition, the weight of backpressure strategy is improved according to traffic pressure conditions, and the adaptability of backpressure route algorithm is improved by using optimized parameters. Finally, the simulation results show that the proposed method can effectively control traffic vehicles and improve traffic smoothness. [ABSTRACT FROM AUTHOR]

In this paper, in line with the general framework of value-sensitive design, we aim to operationalize the general concept of "Meaningful Human Control" (MHC) in order to pave the way for its translation into more specific design requirements. In particular, we focus on the operationalization of the first of the two conditions (Santoni de Sio and Van den Hoven 2018) investigated: the so-called 'tracking' condition. Our investigation is led in relation to one specific subcase of automated system: dual-mode driving systems (e.g. Tesla 'autopilot'). First, we connect and compare meaningful human control with a concept of control very popular in engineering and traffic psychology (Michon 1985), and we explain to what extent tracking resembles and differs from it. This will help clarifying the extent to which the idea of meaningful human control is connected to, but also goes beyond, current notions of control in engineering and psychology. Second, we take the systematic analysis of practical reasoning as traditionally presented in the philosophy of human action (Anscombe, Bratman, Mele) and we adapt it to offer a general framework where different types of reasons and agents are identified according to their relation to an automated system's behaviour. This framework is meant to help explaining what reasons and what agents (should) play a role in controlling a given system, thereby enabling policy makers to produce usable guidelines and engineers to design systems that properly respond to selected human reasons. In the final part, we discuss a practical example of how our framework could be employed in designing automated driving systems. [ABSTRACT FROM AUTHOR]

We show an application of the methods of mathematical logic to the design of a control system for group movement of trains for a railway station. We develop a typical lifecycle of a route (route design, assembly, control over the movement of a train along the route, disassembling the route) that guarantees safe group movement of trains at a station. We construct the model of a route in the form of a logical passability function and the model of a railway station in the form of a logical diagram. Based on these models, we find the set of potentially feasible station routes, states of railroad switches and route traffic lights. We define different relationships between routes: compatible, incompatible, alternative. [ABSTRACT FROM AUTHOR]

Because of the hierarchical significance of traffic sign images, the traditional methods do not effectively control and extract the brightness and features of layered images. Therefore, an automatic recognition algorithm for traffic signs based on a convolution neural network is proposed in this paper. First, the histogram equalization method is used to pre-process the traffic sign images, with details of the images being enhanced and contrast of the images improved. Then, the traffic sign images are recognized by a convolution neural network and the large scale structure of information in the traffic sign images are obtained by using a hierarchical significance detection method based on graphical models. Next, the area of interest in the traffic sign images are extracted by using the hierarchical significance model. Finally, the Softmax classifier is selected to classify the input feature images to realize the automatic recognition of traffic signs. Experimental results show that the proposed algorithm can control the brightness of traffic sign images, which can accurately extract image regions of interest and complete the automatic recognition of traffic signs. [ABSTRACT FROM AUTHOR]

In this paper, we propose enhanced fundamental diagrams based on different water film depths by considering the effects of hydroplaning using a physical method. Various factors are calculated to describe the total safe distance headway of main vehicle components. These factors include the driver reaction times, reaction distances, vehicle braking times, and vehicle braking distances corresponding to different water film depths. An excellent match is found between the computed braking distance, the braking time calculated using the proposed numerical model, and the results published in other papers. These calculations are performed to estimate the distance headway and quantitatively analyse the relationships between the speed, density, and water film depth. By using three road-specific parameters estimated by our proposed model, namely, the free-flow speed, jam density, and capacity flow, a link transmission model is developed to analyse the dynamic impact of the water film depth. [ABSTRACT FROM AUTHOR]

The spread of traffic jams in urban networks has long been viewed as a complex spatio-temporal phenomenon that often requires computationally intensive microscopic models for analysis purposes. In this study, we present a framework to describe the dynamics of congestion propagation and dissipation of traffic in cities using a simple contagion process, inspired by those used to model infectious disease spread in a population. We introduce two macroscopic characteristics for network traffic dynamics, namely congestion propagation rate β and congestion dissipation rate μ. We describe the dynamics of congestion spread using these new parameters embedded within a system of ordinary differential equations, similar to the well-known susceptible-infected-recovered (SIR) model. The proposed contagion-based dynamics are verified through an empirical multi-city analysis, and can be used to monitor, predict and control the fraction of congested links in the network over time. Predicting and controlling traffic congestion propagation is an ongoing challenge in most urban settings. Here, Seberi et al. apply a contagion model describing epidemic spread in population to model traffic jams, and verify its validity using large-scale data from six different cities around the world. [ABSTRACT FROM AUTHOR]

Fingerprint positioning technology is among the most promising choices for seamless localization and is anticipated to be the future of seamless-locating services. The convenience of deployment and the high density signal source of wireless sensor networks (WSN) make them an ideal infrastructure for fingerprint positioning. In related researches, most WSN based fingerprint positioning systems are experimental demos that focus on the algorithm effectiveness and ignore the system reliability. This work proposes a practical WSN based fingerprint localization system. The system covers both indoor and outdoor scenarios and fulfills the demand for seamless localization. This paper work presents four measures that improve fault tolerance and system efficiency: a traffic regulation based radiomap (TRRM) establishing method, a full-overlapping clustering strategy, an adaptive feature space (AFS) algorithm, and a praxeological tracking algorithm. The proposed system is verified by hardware experiments on smart phones. Positioning accuracy is within 5 m in pedestrian tests and 10 m in driving tests. [ABSTRACT FROM AUTHOR]

Control of continuous and hybrid systems using discrete abstractions often suffers from scalability issues, due to the use of state space partitions as symbolic states. In this paper, for incrementally stable switched systems, we introduce a class of abstractions that do not rely on state space partitions but use mode sequences as symbolic states. Our approach differs from existing works by the possibility of considering sequences of varying length, giving the possibility to adjust locally the resolution of the abstraction. Temporal constraints on the switching signal can also be taken into account. We thus define multi-resolution bisimilar abstractions that enjoy interesting properties that can be used to design specific algorithms to synthesize safety controllers. These algorithms need not compute the full abstraction that is built incrementally during controller synthesis, exploring finer resolutions only when the specification cannot be enforced at the coarser level. We illustrate the approach by a numerical example inspired by road traffic regulation. [ABSTRACT FROM AUTHOR]

We study the dynamical process of congestion formation for large-scale urban networks by exploring a unique dataset of taxi movements in a megacity. We develop a dynamic model based on a reaction and a diffusion term that properly reproduces the cascade phenomena of traffic. The interaction of these two terms brings the values of the speeds on road network in self-organized patterns and it reveals an elegant physical law that reproduces the dynamics of congestion with very few parameters. The results presented show a promising match with an available real data set of link speeds estimated from more than 40 millions of GPS coordinates per day of about 20,000 taxis in Shenzhen, China. [ABSTRACT FROM AUTHOR]

Key message: Trafficking protein particle (TRAPP) complexes subunit gene AtTrs33 plays an important role in keeping apical meristematic activity and dominance in Arabidopsis. TRAPP complexes, composed of multimeric subunits, are guanine–nucleotide exchange factors for certain Rab GTPases and are believed to be involved in the regulation of membrane trafficking, but the cases in Arabidopsis are largely unknown. Trs33, recently proposed to be a component of TRAPP IV, is non-essential in yeast cells. A single copy of Trs33 gene, AtTrs33, was identified in Arabidopsis. GUS activity assay indicated that AtTrs33 was ubiquitously expressed. Based on a T-DNA insertion line, we found that loss-of-function of AtTrs33 is lethal for apical growth. Knock-down or knock-in of AtTrs33 affects apical meristematic growth and fertility, which indicates that AtTrs33 plays an important role in keeping apical meristematic activity and dominance in Arabidopsis. Analysis of auxin responses and PIN1/2 localization indicate that impaired apical meristematic activity and dominance were caused by altered auxin responses through non-polarized PIN1 localization. The present study reported that AtTrs33 plays an essential role in Arabidopsis cell growth and organization, which is different with its homologue in yeast. These findings provide new insights into the functional divergence of TRAPP subunits. [ABSTRACT FROM AUTHOR]

A Mobile ad hoc Network (MANET) is an infrastructure less network that permits nodes to make a wireless network with mobility. In MANETs, the node linkages breaks are frequent due to all nodes are in movement. When neighbor nodes travel out of other node communication range connection among both mobile nodes breaks. Thus it makes delay and packet loss in the network. In the present Traffic Control method, several times, since the highest traffic, heavy rain, accidents, or other reasons, there may be probabilities of jam on roads. Thus, people could get delay owing to the highest traffic. Therefore, it is highly preferred to have an effective way to track the traffic on roads and thus select the suitable road that is traffic free. To resolve these problems, the volunteer Nodes of Ant Colony Optimization Routing (VNACO) is proposed. The primary objective of VNACO is to reduce both the delay and routing overhead in Peer to Peer (P2P) MANETs. Here, peer node act as a volunteer node and mobile node for transmitting data from source to destination. During data transmission, the relay node moving out of communication range when the volunteer node overheard the dropped data packet then deliver the data to the corresponding relay node. As a result, minimizes both the delay and packet losses. In VNACO, the volunteer's nodes are elected based on node aptitude. Node aptitude is calculated by iterating node connectivity, transmission processing time, node energy and available bandwidth. In addition, an ant colony optimization algorithm to finds the optimal route and reduce the routing overhead in P2P MANETs. The significant application of this mechanism is utilized for the traffic management system. Network simulation results indicate that the VNACO mechanism is minimizing 22% delay and reducing 14% packet losses compared to the baseline protocol. [ABSTRACT FROM AUTHOR]

A system to automatically recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers. However, the license plate recognition task is generally computationally intensive, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed for each segmented character. This paper presents a methodology for engineering a system to detect and recognize Brazilian license plates using convolutional neural networks (CNN) that is suitable for embedded systems. The resulting system detects license plates in the captured image using Tiny YOLOv3 architecture and identifies its characters using a second convolutional network trained on synthetic images and fine-tuned with real license plate images. The proposed architecture has demonstrated to be robust to angle, lightning, and noise variations while requiring a single forward pass for each network, therefore allowing faster processing compared to other deep learning approaches. Our methodology was validated using real license plate images under different environmental conditions reached a detection rate of 99.37% and an overall recognition rate of 98.43% while showing an average time of 2.70 s to process 1024 × 768 images with a single license plate in a Raspberry Pi3 (ARM Cortex-A53 CPU). To improve the recognition accuracy, an ensemble of CNN models was tested instead of a single CNN model, which resulted in an increase in the average processing time to 4.88 s for each image while increasing the recognition rate to 99.53%. Finally, we discuss the impact of using an ensemble of CNNs considering the accuracy-performance trade-off when engineering embedded systems for license plate recognition. [ABSTRACT FROM AUTHOR]

Since the current Internet is only able to provide best-effort services, the quality of service (QoS) for many emerging businesses cannot be well guaranteed, especially for delay-sensitive services. Meanwhile, due to the privatization of the network management, it is difficult to provide on-demand QoS services in large-scale heterogeneous networks. In addition, attacks from unauthorized access users also threaten the security of the whole network. Therefore, heterogeneous networks bring new challenges to the flexible control of different network equipment. Software-defined network (SDN) is an emerging paradigm which separates the network's control logic from the underlying different network elements. Moreover, it promotes the centralization of network control to improve end-to-end QoS services, and introduces the program ability to mitigate threats. In this paper, we design a monitoring loop of link latency by using both LLDP and Echo probing modules. Then, a dynamic routing algorithm is proposed to select optimized transmission paths based on the information of link latency. Next, we develop a routing application assorted with the monitoring mechanism by extending the RYU controller. More specifically, we implement our solution in a semi-practical SDN testbed. Finally, the overall feasibility and efficiency of the proposed solution are experimentally verified and evaluated. [ABSTRACT FROM AUTHOR]

Pedestrian detection is one of the challenging tasks in the urban traffic environments. A natural urban traffic environments include different objects like buildings, vehicles, pedestrians and so on. The conventional approach only used for a particular traffic scenario and it does not suitable for different scenarios. A novel approach is required to model these traffic scenarios. Multiresolution Morlet Decomposition Based Iterative Learning Deformable Part Model (MMD-ILDP) is proposed for improving the performance of multiresolution pedestrian detection to control the traffic in the urban area with higher accuracy. The MMD-ILDP Model uses Morlet wavelet transformation for decomposing the image into subbands with multiple resolutions. After wavelet decomposition, histogram of oriented gradients (HOG) feature pyramid is generated. Then, feature matching is performed between the pedestrian objects in the image and the feature pyramid generated with HOG and the root and part scores are computed. Finally, the root and part scores are combined to compute the final score of objects in the image. The performance measures used in evaluating the proposed algorithm are detection accuracy, time and space complexity. The simulation results show that the MMD-ILDP Model gives improved pedestrian detection in urban traffic environment where healthcare systems find more difficult to reach to the people and also reduces the time complexity on detecting the pedestrians in road traffic scenes when compared to the existing DPM and RealBoost methods. [ABSTRACT FROM AUTHOR]