Real-time Traffic Conditions Research Articles

A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems

ABSTRACTConsiderable effort has been devoted to developing real-time traffic network management systems for congestion mitigation in large metropolitan areas. These systems usually adopt high-resolution simulation models to provide real-time traffic network state estimation and short-term prediction capabilities. If the simulation results deviate from their corresponding real-world observations, online adjustment to the parameters of the simulation model is recommended to maintain its consistency. For that purpose, several consistency checking and online adjustment modules could be integrated with the simulation model and activated periodically to maintain the model consistency. This paper presents a multi-agent learning methodology for consistency checking and online calibration of real-time traffic network simulation models. The methodology allows multiple online adjustment modules to learn based on their historical performance. Consequently, an adjustment module is activated only if its activation is expected to reduce any detected model inconsistency. The methodology eliminates computation burden associated with the unnecessary activation of these adjustment modules, which might affect the system’s real-time execution requirement. The performance of the methodology is examined using real-world data. The results show that the methodology is promising as an efficient mechanism for maintaining model estimation consistency.

Real-time estimation of secondary crash likelihood on freeways using high-resolution loop detector data

This study aimed to develop a secondary crash risk prediction model on freeways using real-time traffic flow data. The crash and traffic data were collected on the I-880 freeway for five years in California, United States. The secondary crashes were identified by a method based on speed contour plot. The random effect logit model was used to link the probability of secondary crashes with the real-time traffic flow conditions, primary crash characteristics, environmental conditions, and geometric characteristics. The results showed that real-time traffic variables significantly affect the likelihood of secondary crashes. These traffic variables include the traffic volume, average speed, standard deviation of detector occupancy, and volume difference between adjacent lanes. In addition, the primary crash characteristics, environmental conditions and geometric characteristics also significantly affect the risks of secondary crashes. The model evaluation results showed that the predictive performance of the developed model was deemed satisfactory. The inclusion of traffic flow variables and random effect increases prediction accuracy by 16.6% and 7.7%, respectively. These results have the potential to be used in advanced traffic management systems to develop proactive traffic control strategies to prevent the occurrences of secondary crashes on freeways.

Fusing Incomplete Multisensor Heterogeneous Data to Estimate Urban Traffic

Today, data-driven intelligent transportation systems must address data quality challenges, such as the missing data problem. For example, is it possible to improve the performance of traffic state estimation using incomplete data? In this article, an incomplete traffic data fusing method is proposed to estimate traffic state accurately. It improves missing data estimation by extracting data correlations and applying incomplete data fusion, implementing the two approaches in parallel. The main research focus is on extracting the inherent spatio-temporal correlations of traffic states data from road segments based on a multiple linear regression (MLR) model. Computational experiments for accuracy and efficiency demonstrate that this method can use data correlations to implement accurate and real-time traffic state estimation. This article is part of a special issue on quality modeling.

A Traffic congestion judgement Algorithm development for signal control using taxi gps data

COSMOS system which was developed in Seoul for real-time signal control was designed to judge traffic condition for practicing signal operation. However, it occurs efficiency problem that stop line detection and queue length detection could not judge overflow saturation of street. For that reason, following research process GPS of Seoul city's corporationowned taxi to calculate travel speed that excluded existing system of stop line detection and queue length detection. Also, Research of calculating queue length by GPS data which was progressed with following research expressed queue length. It is based on establishing algorithm of judging congestion situation. The algorithm was applied to a few areas where appeared congestion situation consistently to confirm real time traffic condition with established network. [Entrance of the National Sport Institute Gangnam station Intersection, Yuksam station intersection National Sport Institute.

A traffic flow state transition model for urban road network based on Hidden Markov Model

Traffic guidance and prompt information could induce the change of traffic states on road sections, and in turn the effects of these changes will be transited to their relative upstream and downstream sections, which lead to dynamic variations in traffic states of urban regional road networks. In this paper, the rule of dynamic transition in traffic state of urban road networks under the effect of traffic information is studied. Specifically, the hidden Markov model is selected to represent the dynamic transition process. Then Expectation Maximization (EM) Algorithm is presented for potential traffic state estimation. Finally, verification is carried out through simulation with Variable Message Sign (VMS) selected as the information release terminal and then a certain regional road network in Beijing is chosen as the study object. The results show that the model in this paper can describe transition process of road traffic state under the effect of VMS information, and also can be used for real-time traffic state estimation of urban road networks. The study has both theoretical and practical values in evaluation of service quality of traffic information and in making traffic dispersion and control strategies for traffic management department.

Crowdsourcing in ITS: The State of the Work and the Networking

In the last decade, crowdsourcing has emerged as a novel mechanism for accomplishing temporal and spatial critical tasks in transportation with the collective intelligence of individuals and organizations. This paper presents a timely literature review of crowdsourcing and its applications in intelligent transportation systems (ITS). We investigate the ITS services enabled by crowdsourcing, the keyword co-occurrence and coauthorship networks formed by ITS publications, and identify the problems and challenges that need further research. Finally, we briefly introduce our future works focusing on using geospatial tagged data to analyze real-time traffic conditions and the management of traffic flow in urban environment. This review aims to help ITS practitioners and researchers build a state-of-the-art understanding of crowdsourcing in ITS, as well as to call for more research on the application of crowdsourcing in transportation systems.

Continuous Reverse k-Nearest-Neighbor Query in Dynamic Road Network

Traditional location-based query measures the distance of all data points in various methods, but it is not applicable for the condition in which the weight of all sections in the road network are altered dynamically. In order to solve the new application problems brought by the real-time traffic status, a dynamic monitoring algorithm is designed in this paper, and it mainly updates the weight changes of road network with temporary network extension tree incrementally. On this basis, a continuous reverse K-nearest-neighbor (RKNN) query method is proposed, and it is proved to be efficient by the experimental results.

Maximum Green Time Settings for Traffic-Actuated Signal Control at Isolated Intersections Using Fuzzy Logic

Traffic Actuated Control (TAC) is widely used for signal control in isolated intersections. TAC is a responsible method for online traffic data. In TAC, green durations are dynamically determined based on online traffic data. These determinations are subject to predefined static parameters which do not respond to sudden traffic flow changes. One type of these parameters in TAC is maximum green time. In this paper, maximum green times are dynamically determined by means of fuzzy control. At first, it monitors the whole intersection traffic conditions and then dynamically adjusts maximum green times. The main advantage of the proposed approach is its capability to adjust maximum green times which are responsive to real time traffic condition in isolated intersections. The efficiency of the proposed method has been evaluated by AIMSUN 7 microscopic traffic simulation tool. The evaluation results show the efficiency and the robustness of the proposed method compared to maximum green times provided by SYNCHRO8.

Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach

The real-time traffic state estimation we propose uses a state-space model considering the variability of the fundamental diagram (FD) and sensing data. Serious congestion was caused by vehicle evacuation in many Sanriku coast cities following the great East Japan earthquake on March 11, 2011. Many of the vehicles in these congested queues were caught in the enormous tsunami after the earthquake [1]. Safe, efficient evacuation and rescue and restoration require that dynamic traffic states be monitored in real time especially in natural disasters. Variational theory (VT) based on kinematic wave theory is used for the system model, with probe vehicle and traffic detector data used to for measurement data. Our proposal agrees better with simulated benchmark traffic states than deterministic VT results do.

교통안전성을 고려한 고속도로 오르막차로 동적운영 알고리즘 개발

본 연구는 다양한 교통상황에서의 오르막차로의 안전성 측면을 고려하여 운영기준 수립 및 운영 알고리즘을 개발하고자 하였다. 교통량과 중차량 비율에 따른 시나리오를 설정하고, 미시적 교통류 시뮬레이션으로 구현하여 분석을 수행하였으며, 오르막차로 개폐여부에 따른 안전성을 평가하였다. 안전성을 평가하기 위하여 교통상충건수와 상충심각도를 평가지표(SSM:Surrogate Safety Measure)로 사용하였다. 분석결과, 서비스 수준이 낮고 중차량 비율이 증가할수록 교통상충건수가 증가하였으며, 서비스수준이 높고 중차량 비율이 증가할수록 상충 심각도가 높아짐을 보였다. 이를 바탕으로 교통 안전성 측면을 고려한 오르막차로 개 폐 기준 및 탄력적 운영을 위한 알고리즘을 제시하였다. 본 연구결과를 활용할 경우, 기존의 운영효율성 측면과 안전성측면 동시에 고려할 수 있는 효율적인 오르막차로 운영이 가능할 것이며, 교통사고 감소 효과도 기대할 수 있을 것이다. 또한 갓길차로, 공사구간관리 등 LCS 운영시 안전성 측면을 고려한 전략적 운영을 지원할 수 있을 것이다. Interest in freeway truck traffic has increased largely due to greater safety concerns regarding truck-related crashes. The negative interactions between slow-moving trucks and other vehicles are a primary cause of hazardous conditions, which lead to crashes with larger speed variations. To improve operational efficiency and safety, providing a climbing lane that separates slow-moving trucks from higher performance vehicles is frequently considered when upgrading geometrics. This study developed an operations strategy for freeway climbing lanes based on traffic conditions in real time. To consider traffic safety when designing a dynamic strategy to determine whether a climbing lane is closed or open, various factors, including the level of service (LOS) and the percentage of trucks, are investigated through microscopic simulations. A microscopic traffic simulator, VISSIM, was used to simulate freeway traffic streams and collect vehicle-maneuvering data. Additionally, an external application program interface, VISSIM's COM-interface, was used to implement the proposed climbing lane operations strategies. Surrogate safety measures (SSM), including the frequency of rear-end conflicts and, were used to quantitatively evaluate the traffic safety using an analysis of individual vehicle trajectories obtained from VISSIM simulations with various operations scenarios. It is expected that the proposed algorithm can be the backbone for operating the climbing lane in real time for safer traffic management.

On Reducing Power Consumption of Transmitting Stations in 802.11 MIMO Networks

We propose an online algorithm for energy minimization of transmitting stations in IEEE 802.11 wireless local area networks (WLANs). Our algorithm configures radio frequency (RF) parameters such as the number of transmitting antennas based on the real-time traffic demand and channel condition so that the power consumption of the device is reduced, and the minimum data rate requirements of the stations at a target error rate are satisfied. The process of the transition between different RF configurations is investigated by employing a two-dimensional Markov chain model. Using an IEEE 802.11ac hardware testbed, we experimentally demonstrate that with our algorithm the power consumption of a transmitting station can be reduced upto 33%. We also validate that the experimental result and the simulation result obtained with our Markov model closely match.

RAQ-A Random Forest Approach for Predicting Air Quality in Urban Sensing Systems.

Air quality information such as the concentration of PM2.5 is of great significance for human health and city management. It affects the way of traveling, urban planning, government policies and so on. However, in major cities there is typically only a limited number of air quality monitoring stations. In the meantime, air quality varies in the urban areas and there can be large differences, even between closely neighboring regions. In this paper, a random forest approach for predicting air quality (RAQ) is proposed for urban sensing systems. The data generated by urban sensing includes meteorology data, road information, real-time traffic status and point of interest (POI) distribution. The random forest algorithm is exploited for data training and prediction. The performance of RAQ is evaluated with real city data. Compared with three other algorithms, this approach achieves better prediction precision. Exciting results are observed from the experiments that the air quality can be inferred with amazingly high accuracy from the data which are obtained from urban sensing.

A Novel Freeway Traffic Speed Estimation Model with Massive Cellular Signaling Data

With the growing popularity of cell phones, using massive cellular signaling data as probe to track the vehicles movement trajectory and obtain the real-time traffic condition has become one of the most attractive candidate techniques. However, traditional approaches may offer a poor performance in removing noisy data and minimizing deviation of traffic speed in adjacent time intervals. In this paper, a novel approach is proposed to solve these two issues. The authors move noisy data by comparing the cellular signaling data with the trained data set, and adopt a modified Kalman filter algorithm to minimize the deviations. The experiment results show that the accuracy of the approach performs better in comparison to other two traffic speed estimation approaches.

Analysis of safety factors for urban expressways considering the effect of congestion in Shanghai, China

Urban expressways are the key components of the urban traffic network. The traffic safety situation on expressways directly influences the efficiency of the whole network. A total of 48,325 crashes were recorded by Shanghai Expressway Surveillance System in a three-year period. Considering the different crash mechanisms under different congestion levels, models for the total crashes, non-congested-flow crashes and congested-flow crashes were respectively formulated based on the real-time traffic condition corresponding to each crash. Moreover, considering the potential spatial correlation among segments, the adjacent-correlated spatial and distance-correlated spatial models were formulated and compared to the traditional non-spatial-correlated model. A Bayesian approach was employed to estimate the parameters. The results showed that the congestion index, merging ratio, ramp density, and average daily traffic significantly affect the crash frequency. The safety factors in non-congested flow and congested flow are different; diverging behavior is more risky in non-congested flow, more lanes tend to increase the risk of crashes in congested flow, and horizontal curves tend to decrease the crash risk in congested flow but cause high risk in non-congested flow. In addition, the distance-correlated spatial model is found to be the best-fitting model. The results of this study suggested that dedicated safety countermeasures can be designed for different traffic situations on urban expressways.

A methodology for traffic-related Twitter messages interpretation

This paper addresses the problem of interpreting tweets that describe traffic-related events and that are distributed by government agencies in charge of road networks or by news agencies. Processing such tweets is of interest for two reasons. First, albeit phrased in natural language, such tweets use a much more regular and well-behaved prose than generic user-generated tweets. This characteristic facilitates automating their interpretation and achieving high precision and recall. Second, government agencies and news agencies use Twitter channels to distribute real-time traffic conditions and to alert drivers about planned changes on the road network and about future events that may affect traffic conditions. Hence, such tweets provide exactly the kind of information that proactive truck fleet monitoring and similar applications require. The main contribution of the paper is an automatic tweet interpretation tool, based on Machine Learning techniques, that achieves good performance for traffic-related tweets distributed by traffic authorities and news agencies. The paper also covers in detail experiments with real traffic-related tweets to access the precision and recall of the tool.

Balanced traffic routing: Design, implementation, and evaluation

Navigators based on real-time traffic conditions achieve suboptimal results since, in face of congestion, they greedily shift drivers to currently light-traffic roads and cause new traffic jams. This article presents Themis, a participatory system navigating drivers in a balanced way. By analyzing time-stamped position reports and route decisions collected from the Themis mobile app, the Themis server estimates both the current traffic rhythm and the future traffic distribution. According to the estimated travel time and a popularity score computed for each route, Themis coordinates the traffic between alternative routes and proactively alleviates congestion. Themis has been implemented and its performance has been evaluated in both a synthetic experiment using real data from over 26,000 taxis and a field study. Results from both experiments demonstrate that Themis reduces traffic congestion and average travel time at various traffic densities and system penetration rates.

Energy-Optimal Speed Control for Electric Vehicles on Signalized Arterials

Electrification of passenger vehicles has been viewed by many as a way to significantly reduce carbon emissions, operate vehicles more efficiently, and reduce oil dependence. Due to the potential benefits of electric vehicle (EV), many federal and local governments have allocated considerable funding and taken a number of legislative and regulatory steps to promote EV deployment and adoption. With this momentum, it is not difficult to see that in the near future, EVs could gain a significant market penetration, particularly in densely populated urban areas with systemic air quality problems. We will soon face one of the biggest challenges: how to improve the efficiency for the EV transportation system? This research aims to contribute to this field by proposing an analytical model that determines a time-dependent optimal velocity profile for an EV in order to minimize the electricity usage along a chosen route by systematically considering road characteristics and real-time traffic conditions. In particular, the proposed multistage optimal control model uniquely considers the impact of the presence of intersection queues in both temporal and spatial dimensions, which has been ignored in most traditional speed control models even for internal combustion engine vehicles. In addition, to facilitate the real-time operations, an approximation model, which simplifies the optimal speed profile, is further developed to increase the computation efficiency. The testing using the field data collected from a six-intersection signalized arterial corridor shows that the optimal velocity profile can significantly save energy for an EV, and the computational efficiency of the proposed approximation model is suitable for real-time applications.

Studying Driving Risk Factors using Multi-Source Mobile Computing Data

ABSTRACT Traffic congestion can largely be attributed to the issues related with driving behavior, which may cause vehicle crash, stop-and-go traffic due to frequent lane changing behaviors, etc., and makes the driving behavior research also of significance in the realm of traffic management and demand management. The emergence and subsequent rapid advances with new information and communication technologies (ICT) now offers the capability of collecting high-fidelity and highresolution trajectory data in a cost-effective manner. In this research, we use a smartphone app to collect data for the purpose of studying driving risk factors. What’s unique about the data in this research is its backend server also estimates traffic speed and volume for each link that the vehicle traverses. In order words, the data collected with build-in GPS modules in the smartphone include not only the vehicle spatial-temporal dimension location, which could be used to correlate the network geography attributes and/or real-time traffic condition, but also the detailed information about the vehicle dynamics including speed, acceleration, and deceleration, whereby a driver’s control and maneuver of a vehicle can be analyzed in detail. Such type of dataset combining both user trajectory and link speed/volume information is rarely seen in prior research, permitting a unique opportunity to link critical traffic congestion factors leading to driving behavior and crash potential. In this paper, the overall research framework used in this research is presented, which mainly includes data collection, data processing, calibration and analysis methodology. A preliminary case study — including data summary statistics and correlation analysis — is also presented. The results of our study will further existing knowledge about driving exposure factors that are closely linked to crash risk, and provide the foundation for advanced forms of Usage Based Insurance.

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.

Prediction of traveller information and route choice based on real-time estimated traffic state

Accurate depiction of existing traffic states is essential to devise effective real-time traffic management strategies using intelligent transportation systems. Existing applications of dynamic traffic assignment (DTA) methods are mainly based on either the prediction from macroscopic traffic flow models or measurements from the sensors and do not take advantage of the traffic state estimation techniques, which produce an estimate of the traffic states which has less uncertainty than the prediction or measurement alone. On the other hand, research studies which highlight the estimation of real-time traffic state are focused only on traffic state estimation and have not utilised the estimated traffic state for DTA applications. In this paper we propose a framework which utilises real-time traffic state estimate to optimise network performance during an incident through the traveller information system. The estimate of real-time traffic states is obtained by combining the prediction of traffic density using the cell transmission model (CTM) and the measurements from the traffic sensors in extended Kalman filter (EKF) recursive algorithm. The estimated traffic state is used for predicting travel times on alternative routes in a small traffic network, and the predicted travel times are communicated to the commuters by a variable message sign (VMS). In numerical experiments on a two-route network, the proposed estimation and information method is seen to significantly improve travel times and network performance during a traffic incident.