Traffic Agents Research Articles

Vehicle Trajectory Prediction Using Generative Adversarial Network With Temporal Logic Syntax Tree Features

In this work, we propose a novel approach for integrating rules into traffic agent trajectory prediction. Consideration of rules is important for understanding how people behave-yet, it cannot be assumed that rules are always followed. To address this challenge, we evaluate different approaches of integrating rules as inductive biases into deep learning-based prediction models. We propose a framework based on generative adversarial networks that uses tools from formal methods, namely signal temporal logic and syntax trees. This allows us to leverage information on rule obedience as features in neural networks and improves prediction accuracy without biasing towards lawful behavior. We evaluate our method on a real-world driving dataset and show improvement in performance over off-the-shelf predictors.

Probabilistic trajectory prediction of heterogeneous traffic agents based on layered spatio-temporal graph

In order to safely and comfortably navigate in the complex urban traffic, it is necessary to make multi-modal predictions of autonomous vehicles for the next trajectory of various traffic participants, with the continuous movement trend and inertia of the surrounding traffic agents taken into account. At present, most trajectory prediction methods focus on prediction on future behavior of traffic agents but with limited, consideration of the response of traffic agents to the future behavior of the ego-agent. Moreover, it can only predict the trajectory of single-type agents, which make it impossible to learn interaction in a complex environment between traffic agents. In this paper, we proposed a graph-based heterogeneous traffic agents trajectory prediction model LSTGHP, which consists of the following three parts: (1) layered spatio-temporal graph module; (2) ego-agent motion module; (3) trajectory prediction module, which can realize multi-modal prediction of future trajectories of traffic agents with different semantic categories in the scene. To evaluate its performance, we collected trajectory datasets of heterogeneous traffic agents in a time-varying, highly dynamic urban intersection environment, where vehicles, bicycles, and pedestrians interacted with each other in the scene. It can be drawn from experimental results that our model can improve its prediction accuracy while interacting at a close range. Compared with the previous prediction methods, the model has less prediction error in the trajectory prediction of heterogeneous traffic agents.

Drug use and driving behaviors among drivers with and without alcohol-related infractions.

Introduction Brazil is one of the countries with the highest rates of alcohol-related traffic infractions, but little is known about the profile of the drivers who commit them. Identifying the characteristics of impaired drivers is essential for planning preventive actions.Objective To compare drug use and driving behavior profiles of drivers with and without alcohol-related infractions.Methods 178 drivers stopped at routine roadblocks were assessed by traffic agents who conducted standard roadblock procedures (document verification; request of a breathalyzer test [BT]). Drug use and driving behavior data were collected through semi-structured interviews. Subjects were divided into three groups: drivers who refused the BT (RDs, n = 72), drivers who tested positive on the BT (PDs, n = 34), and drivers who had committed other infractions (ODs, n = 72).Results The proportion of alcohol use in the last year was higher among RDs (100%) than in the PD and OD groups (97.1% and 72.2% respectively, p < 0.001). Lifetime prevalence of cannabis and cocaine use for the overall sample was 44.3% and 18.2%, respectively. Fewer individuals in the OD group (31.5%) reported having been stopped at roadblocks in the previous year compared to the PDs (55.9%) and RDs (48.6%, p = 0.03). However, a higher proportion of RDs reported drunk driving in the same period (87.5%; PD 69.7%; OD 26.9%; p < 0.001).Conclusion Essential differences among groups were observed. RDs had a higher proportion of alcohol use and drunk driving in the previous year; drivers who fit into this particular group may be unresponsive or less responsive to social deterrence and enforcement actions.

Analysis of accident duration on urban arterials roads

Acidentes de trânsito são o principal fator para a formação de congestionamentos não recorrentes em vias urbanas e responsáveis por gerar atrasos inesperados aos usuários do sistema. Entender o comportamento da duração desses acidentes e identificar os seus fatores explicativos pode conduzir para um gerenciamento mais eficiente do tráfego urbano. Buscando contribuir com essa problemática, este trabalho teve como objetivo analisar a duração de acidentes de trânsito ocorridos em vias urbanas da cidade de Fortaleza-CE. Inicialmente, um método para detectar a duração dos acidentes a partir de dados de tráfego de equipamentos de fiscalização eletrônica foi proposto, dada a indisponibilidade dessa informação no banco de dados de acidentes de trânsito. Como resultados, a duração dos 324 acidentes detectados teve uma média de 72 minutos e uma dispersão de 48 minutos. A função densidade de probabilidade Gamma apresentou o melhor ajuste à duração dos acidentes, indicando que essa variável é influenciada por eventos que ocorrem ao longo do tempo, aumentando a probabilidade de o acidente ser resolvido. A análise mostrou que fatores relacionados ao atendimento dos acidentes, ao comportamento dos indivíduos, ao número de veículos envolvidos e ao local de ocorrência (meio de quadra ou interseção) podem estar influenciando a duração dos acidentes. Entretanto, não foram encontrados indícios de que a duração dos acidentes é influenciada pela sua gravidade (acidentes com ou sem vítimas) ou pela sua natureza.

Passenger Flow Prediction Based on Land Use around Metro Stations: A Case Study

High-density land uses cause high-intensity traffic demand. Metro as an urban mass transit mode is considered as a sustainable strategy to balance the urban high-density land uses development and the high-intensity traffic demand. However, the capacity of the metro cannot always meet the traffic demand during rush hours. It calls for traffic agents to reinforce the operation and management standard to improve the service level. Passenger flow prediction is the foremost and pivotal technology in improving the management standard and service level of metro. It is an important technological means in ensuring sustainable and steady development of urban transportation. This paper uses mathematical and neural network modeling methods to predict metro passenger flow based on the land uses around the metro stations, along with considering the spatial correlation of metro stations within the metro line and the temporal correlation of time series in passenger flow prediction. It aims to provide a feasible solution to predict the passenger flow based on land uses around the metro stations and then potentially improving the understanding of the land uses around the metro station impact on the metro passenger flow, and exploring the potential association between the land uses and the metro passenger flow. Based on the data source from metro line 2 in Qingdao, China, the perdition results show the proposed methods have a good accuracy, with Mean Absolute Percentage Errors (MAPEs) of 11.6%, 3.24%, and 3.86 corresponding to the metro line prediction model with Categorical Regression (CATREG), single metro station prediction model with Artificial Neural Network (ANN), and single metro station prediction model with Long Short-Term Memory (LSTM), respectively.

BASTA: BDI-based architecture of simulated traffic agents

ABSTRACT It is expected that connected and autonomous vehicles (CAVs) will soon become a regular means of transportation. To fully leverage the potential of this new technology, it is necessary to equip such cars with efficient algorithms permitting them to drive in a safe and optimal manner. Thereby we aim to design and implement tools for convenient evaluation of strategies for driving and interactions in various settings. In this paper, we present the results of the first stage of our bigger research program on a simulation framework of CAVs. A search for a balance between complexity and comprehensibility of the solution led us to the field of multiagent systems. Beliefs-Desires-Intentions (BDI) systems offer useful abstractions for activities of a single self-driving car and collective intelligence of such vehicles. Indeed, the BDI framework helps to combine two distinct natures of a self-driving car: its reactiveness and proactiveness. Moreover, the modularity of the resulting architectures for an individual CAV and urban traffic induced by these cars makes the design intelligible and flexible. Our prototype verifies the feasibility of this concept.

STRESS BETWEEN PRACTICING AND NON­PRACTICAL TRANSIT AGENTS OF PHISICAL EXERCISES

Nowadays stress has become more and more present in workplaces, due to such phenomenon, this study aims to compare the level of stress between practicing and non-practicing traffic agents of the Traffic and Patos - PB Transport Superintendence. -PB. The sample consisted of 35 subjects of both sexes aged 31 to 46 years divided into two groups G1 = 22 exerciser and G2 = 13 non-exerciser. The instruments used were: a socio demographic questionnaire and job perception data and the LIPP (ISS test). The results indicate that 54.54% of the subjects belonging to G1 do not fit in any phase, being characterized by the absence of stress; in relation to G2 it can be observed that 69.23% of the subjects had some stress symptom. As evidenced in the literature that the practice of physical exercise plays an essential role in the prevention and treatment of physical and psychological diseases, this study confirms that the practice of physical exercise is linked to lower stress rates in traffic agents.

Learning Generative Socially Aware Models of Pedestrian Motion

Among the biggest challenges faced when integrating robots and intelligent vehicles in crowded environments is the difficulty encountered by artificial agents in anticipating people's moves, which is indeed a critical machine reasoning task in order to enable safe and comfortable interaction with humans. In general terms, this is a multi-agent prediction problem, whose solution, from a Bayesian perspective, involves on one hand inferring the joint latent dynamical state of all agents conditioned on observed data and on the other hand learning the corresponding system dynamics. Among the advantages of such a probabilistic approach is the fact that it naturally allows to account for the intrinsic stochasticity of both human behavior and sensor data in a principled manner. In this letter, we propose a novel Bayesian generative model, which generalizes a popular class of deterministic multiagent motion models and is capable of encoding traffic agents' interactions in a latent probabilistic space. We demonstrate, using real pedestrian data-sets, how it can be exploited for inference, prediction, and trained for learning of agent interaction dynamics. Our experimental evaluations indicate that the proposed approach outperforms both probabilistic latent variable models that neglect multi-agent interactions as well as deterministic physics-inspired models of human motion in crowds.

Impacts of gradual automated vehicle penetration on motorway operation: a comprehensive evaluation

Gradual penetration of automated vehicles (AVs) into current motorway systems will usher the stage of mixed traffic in which AVs will coexist with human driven vehicles. Thus, there is an urgent need to identify the possible impacts of this mixed traffic on motorway operation. To investigate the potential benefits or losses due to introducing AVs into existing motorway systems, this study conducts a comprehensive evaluation based on simulation using a 5.3 km stretch on Auckland Motorway and traffic data provided by New Zealand Traffic Agent (NZTA). We analyze the impacts of different AV shares on mobility, safety, emissions and fuel consumption. The motorway with and without traffic control are tested under four scenarios of traffic conditions, namely, heavily congested traffic (> 0.95*capacity), lightly congested traffic (≈0.7*capacity), free-flow traffic (≈0.5*capacity), and future traffic (3*heavily congested traffic volume). The outcomes of the research can provide motorway designers and operators a reasonable range of influences contributed by AV penetration so as to better prepare for AVs’ arrival.

User experience and operational feasibility of four point-of-collection oral fluid drug-testing devices according to Brazilian traffic agents

Objective: Traffic fatalities in Brazil still rank among the highest worldwide, with an overall rate of 23.4 deaths/100,000 inhabitants/year. Although alcohol and drug use play an important role in traffic accidents, national data about their relative influence are scarce. Drug screening is not routinely performed by traffic agents because alcohol is the only substance regularly investigated in roadblocks. Therefore, we aimed to describe the initial traffic agent user experience for 4 handheld point-of-collection oral fluid drug testing devices used in routine roadblocks in Brazil, focusing on usage perceptions in hopes of generalizing this approach for other developing countries.Methods: Four different oral fluid collection devices were evaluated: The DDS2, the DOA MultiScreen, the Dräger DrugTest 5000, and the Multi-Drug Multi-Line Twist Screen Device. Fourteen trained traffic agents obtained oral fluid from 164 drivers and performed 37 qualitative evaluations of the devices. Traffic agents filled out a questionnaire focusing on 9 feasibility criteria: Overall simplicity for roadside operation; operational success; saliva sample collection time; sample analysis time; ease of sample preparation and analysis; agreement with observed clinical signs; overall hygiene and safety; sufficient operating instructions; and hygiene of saliva collection. These were weighted based on an expert panel and yielded an overall composite device experience score that ranged from 1 (poor) to 100 (excellent).Results: Ease of use, operational success, and acceptable collection and analysis time were considered the most important criteria by the expert panel. The results ranged from 27.3 to 88.9% for simplicity of use; 45.5 to 100.0% for operational success; 27.3 to 100% for acceptable collection time; and 36.4 to 100.0% for acceptable analysis time. The final device scores, based on the agents’ user experiences, ranked as follows: DOA MultiScreen: 49.3/100; Dräger DrugTest 5000: 82.4/100; Multi-Drug Multi-Line Twist Screen Device: 84.3/100; DDS2: 88.4/100.Conclusion: Based on the selected criteria, 3 of the 4 devices were considered useful by traffic agents in routine roadblock operations. The weighted evaluations suggest that their ease of use (handling, sampling analysis, and reliability), as well as their agreement with findings obtained by other means, defined their utility to traffic agents, although such appraisals must be further analyzed in future studies.

Comparative Analysis of Variants of Token Bucket Traffic Meter Algorithms for QoS Router using UDP as Traffic Agent

Traffic meter algorithms serve as a means of examining traffic stream’s conformance with service level agreement between customers (traffic sources) and Internet Service provider at the edge router of a differentiated service domain for proper quality of service admission control. This paper presented comparative analysis of variants of token bucket meter algorithms for QoS router using user datagram protocol (UDP) as traffic agents and exponential ON/OFF as traffic generator. The research adopted simulation technique to carry out the design of network models or topologies using the same parameter setting to implement the algorithm of token bucket variants of traffic meter. The following metrics were used for the evaluation: throughput, fairness rate, loss rate and one-way packet delay. The evaluated results were ranked and further subjected to 2-way analysis of variance (ANOVA) model to indicate the significant differences among the traffic meter algorithms. Based on ranking system, TRTCM was ranked first in terms of throughput (with 67117) and fairness rate (with 0.2586) and TBM was ranked first in terms of loss rate (with 74.003) and one-way packet delay (with 0.09304). The 2-way ANOVA model showed the significant differences among the traffic meter algorithms considered for the simulation.

Microscopic Road Traffic Scene Analysis Using Computer Vision and Traffic Flow Modelling

This paper presents the development of a vision-based system for microscopic road traffic scene analysis and understanding using computer vision and computational intelligence techniques. The traffic flow model is calibrated using the information obtained from the road-side cameras. It aims to demonstrate an understanding of different levels of traffic scene analysis from simple detection, tracking, and classification of traffic agents to a higher level of vehicular and pedestrian dynamics, traffic congestion build-up, and multi-agent interactions. The study used a video dataset suitable for analysis of a T-intersection. Vehicle detection and tracking have 88.84% accuracy and 88.20% precision. The system can classify private cars, public utility vehicles, buses, and motorcycles. Vehicular flow of every detected vehicles from origin to destination are also monitored for traffic volume estimation, and volume distribution analysis. Lastly, a microscopic traffic model for a T-intersection was developed to simulate a traffic response based on actual road scenarios.

Design and Implementation of an Improved Two Rate Three Color Meter (Itrtcm) Algorithm for Quality of Service Admission Control Using Udp as Traffic Agent

Design and Implementation of an Improved Two Rate Three Color Meter (Itrtcm) Algorithm for Quality of Service Admission Control Using Udp as Traffic Agent

A Multisecret Value Access Control Framework for Airliner in Multinational Air Traffic Management

When been threatened by hijacking or suicide-by-pilots, the airliner may either crash itself or be shot down due to the potential of the suicide attack. There exist some solutions that allow air traffic controllers or federal agents to take over pilots’ authority in the emergency. Though rarely, an air traffic controller may abuse this privilege to mishandle airliners that leads to catastrophic events. In this paper, to mitigate such risks, we propose a multisecret value access control framework based on new designed and existing cryptographic techniques such as xor-based secret sharing schemes (SSSs). It not only satisfies the efficiency requirement but also assures that each nation owns a unique secret value. We further develop and implement the xor-based SSS on Linux system. Both experimental results and performance evaluation demonstrate that our solution is not only efficient and bust also secure by design for the multinational air traffic management.

Performance Analysis of AODV Protocol with TCP/FTP Traffic and Improved AODV Protocol with UDP/CBR Traffic for Optimizing Routing and QoSin MANET

The transmission and reception of information between sourceand destination in a MANET relies on the performance of the traffic scenario (application traffic agent and data traffic) used in a mobile ad-hoc network. The traffic scenario basically defines the reliability and capability of information transmission, which formulates its performance analysis. The objective of this paper is to compare the performance of TCP/FTP on Normal AODV and UDP/CBR traffic on Improved AODV routing protocol generally implemented in a mobile ad hoc environment. An empirical study has been done using NS-2. Exhaustive simulations have been done to analyze the results, which are evaluated for performance metrics, such as throughput, packet delivery ratio and average end to end delay. The effect of variations in, number of nodes, on the network performance is analyzed over a wide range of their values. It is observed that the UDP/CBR on IMPROVED AODV offers a far better performance for throughput than TCP/FTP on Normal AODV; in case of PDR, it offers great PDR for CBR traffic on Improved AODV than TCP on NORMAL AODV.The results follow these trends over a wide range of simulation parameters. Keywords: AODV, Average End To End Delay MANET, NS-2, Packet Delivery Ratio, Routing Protocol TCP/FTP, Throughput, UDP/CBR

Prevalence and consequences of sleep disorders among traffic agents: A case study

This study came up with the main objective to inspect the sleep quality and sleep disorders among traffic agents in Rio de Janeiro, Brazil. A cross-sectional and descriptive study was carried out in which 118 traffic agents were surveyed. To this end, the Pittsburgh Quality of Sleep Index (PSQI) questionnaire was administered. The results demonstrate that the mean PSQI global score was 8.5 ± 2.4. Thirty four subjects had high sleep quality and 84 had low sleep quality. Global PQSI scores had a mean of 3.5 ± 1.37. Those categorized as low sleep quality ranged from 6-12 with a mean of 7.52 ± 1.64. Related to the usual medications, drugs most commonly used are those intended for the treatment of anxiety (9.32%), sleep inducers (10.17%), hypertension (24.58%), anti-depressants (16%),and analgesics (30.51%). The relationship of insomnia severity to age was significant from a statistical point of view (p = 0.001). Poor sleep quality has a negative impact on general health and wellbeing and can produce signs and symptoms associated with concussions. The relatively moderate sleep deficits experienced by subjects in this study did not affect, at first, the performance on professional activities.

Estudio comparativo sobre las redes de apoyo social en médicos familiares y agentes de tránsito, México

El apoyo social es el proceso que se da entre las personas que componen la red social de un sujeto. Acciones como escuchar, estimar, valorar, etc., son conductas que se dan entre los sujetos que componen la red. El objetivo del presente trabajo fue analizar la situación del apoyo social y las redes sociales de apoyo en los médicos familiares y los agentes de tránsito, de una ciudad del país de México. Participaron de manera voluntaria y bajo consentimiento informado 197 médicos y 875 agentes de tránsito. La información se recabó por medio de un cuestionario con ítems sociodemográficos y laborales además del Inventario de Recursos Sociales de Díaz Veiga modificado, al que se les realizaron tanto análisis descriptivos como inferenciales. Entre los resultados más importantes encontramos que la mayoría de la población cuenta con una adecuada red de apoyo social y que las únicas asociaciones significativas fueron entre la red objetiva y subjetiva familiar con los amigos, y, entre la red subjetiva familiar y subjetiva laboral con los hijos. Las redes de apoyo juegan un papel importantísimo como factores protectores ante cualquier situación crítica, con la finalidad de contribuir a elevar la calidad de vida.

An Overview of Staged-bus Operation in Cities

In developing countries, demand for passenger travel is higher, due to economic growth. Government policy encourages people to use public transportation instead of their private carsto reduce of traffic congestion and air pollution. In the city areas, bus networks are the major part of the public transportation system because they are easily accessible and cheaper compared to other types of public transportation. In this study revelation of details of bus operation included different agents is studied. Bus operations, during provision of their service, have interaction with three main agents: bus agent, passenger agent and traffic agent. Study of the influence of each agent in bus operation is presented in this study, using the macroscopic way.

A comprehensive distributed architecture for railway traffic control using multi-agent systems

In this paper, we propose a Distributed Architecture for Railway Traffic Control using Multi-Agent Systems called DARTCMAS. It has a hierarchical organizational model that consists of three main layers of diverse agents: “Train Agents” at the bottom, “Station Agents” in the middle and “Center of Traffic Control Agent” in the top layer. Any train in the railway traffic system is considered as an intelligent agent that has reactive, proactive and interactive properties, simultaneously. DARTCMAS considers local traffic control centers that are embedded in some selected stations as “Station Agents”. Lastly, “Center of Traffic Control Agent” acts as a comprehensive monitoring and supporting reference. We define an internal architecture for each of the agents. Furthermore, two types of interactions among agents are developed: first, inter-layer interactions that represent communications between two adjacent layers in the organizational model of DARTCMAS; and second, intra-layer interactions that represent relations between the same agents type in a specific layer. Simulations have been performed using MATLAB and the “Java Agent DEvelopment framework” whose results show the effectiveness of the model.

Assessment of Traffic Noise Pollution in Traffic Agents

Authors are indebted to Municipal Institute of Traffic Engineering and Control (MANAUSTRANS).