Traffic State Information Research Articles

Personalized travel time estimation for urban road networks: A tensor-based context-aware approach

Urban travel time estimation is of significant importance at many levels of traffic operation and transportation management. This paper develops a tensor-based context-aware approach to dynamically provide personalized travel time estimation from a citywide perspective, using sparse and large-scale GPS trajectories. This novel model is comprised of four major components: map matching, travel time tensor construction, context-aware feature extraction, and travel time tensor factorization. First, GPS trajectories are map-matched onto the road network. Then, travel times of different drivers on different road segments in different time slots are modeled with a 3-order tensor. Following these, three categories of context features, i.e., historical, geographical and spatial-temporal features, are extracted to capture the contextual information of travel time and traffic condition in the road network. Finally, an objective function is devised to factorize travel time tensors with context features collaboratively. In addition, a gradient-based algorithm is developed to find an optimal solution for the context-aware estimation model. The novel model incorporates both the spatial correlation between different road segments and the deviation between different drivers, as well as the fine-grain temporal correlation between different time slots and the coarse-grain temporal correlation between recent and historical traffic conditions. The proposed model is applied in a real case on the urban road network of Beijing, China, based on the sparse and large-scale GPS trajectories collected from over 32,000 drivers in a period of 2 months. Empirical results on extensive experiments demonstrate that the proposed model provides an effective and robust approach for citywide personalized travel time estimation, and outperforms the competing methods.

A Smart Peri-Urban I2V Architecture for Dynamic Rerouting

Traffic congestion increases CO2 emissions, fuel consumption and travel times. These increases are related to the evolution of the growth in urban areas. Urban areas are being transforming into smart cities, that is, they use massive Infrastructure and Communication Technologies (ICT) to improve all the services with which citizens are provided. One objective of smart cities is to reduce traffic congestion. One approach to reaching this complex objective is by using Intelligent Transport Systems (ITS) to reroute vehicles in cities or in their periurban areas. Although, rerouting is a complex task, ICT have evolved a lot and they are currently being applied to the development of more powerful ITS. New ITS cooperative systems are commonly denoted as combinations of X2X (with X = I and/or X = V). In this paper, a new smart system based on I2V communications for peri-urban areas is described and implemented with the overall goal of improving rerouting, which implies reducing congestion. A demonstrator has been developed to evaluate the architecture and the impact that the use of public and up-to-date traffic information can have on rerouting. A real model of the peri-urban area of Castellon de la Plana has been used to evaluate the architecture. Using this model, several tests have been performed. Results show that traffic status information is valuable, but its impact on rerouting depends on the number of drivers that use this information appropriately.

Sequential data assimilation for a Lagrangian Space LWR model with error propagations

Decision support systems are of paramount importance to propose traffic control strategies and reliable information to road users. They usually take benefits from data-driven methods which represent a reliable way to gather real time traffic information and make predictions for recurring situations. Data Assimilation (DA) consists in considering both observed data and a traffic flow model to monitor and forecast traffic state. Traditionnaly, Kalman filtering methods with macroscopic traffic models have been widely used. More recently, mesoscopic approaches proved to be relevant for large scale network applications. However, errors on traffic state is a key information for DA methods. The paper proposes a DA framework that accounts for two types of error: (i) errors from the data collection system and (ii) errors that are propagated and amplified in time and space by the dynamic traffic flow model. When applied on a basic network, the proposed framework demonstrates its ability to track errors and the traffic state information is enriched with a level of uncertainty. It paves the way for new traffic indicators and new solutions for traffic forecast applications.

Study of traffic characteristics based on internet web real-time traffic conditions by image identification technology

ABSTRACTWith the rapid development of Internet, it is increasingly convenient to obtain real-time traffic condition information, which has greatly stimulated the improvement of urban traffic guidance. Traffic conditions are generally divided into four grades in the existing network platform, which are expressed in different colours. The understanding of traffic condition is still at the level of abstract senses. Therefore, it is difficult to grasp the characteristics of urban traffic. To this end, a new idea is proposed in this paper, and the new idea is to study the urban traffic characteristics based on real-time traffic condition information extraction with image identification technology. With this method, we can not only quantify the abstract traffic condition information, but also solve the loss of traffic condition information. In addition, an instance is analysed in this paper, it shows that it can provide references for urban traffic organization management very well.

Distributed Cooperative Reinforcement Learning-Based Traffic Signal Control That Integrates V2X Networks’ Dynamic Clustering

With the acceleration of urbanization in the world, urban traffic congestion has become an urgent challenge in most cities. Adaptive traffic signal control is the most approved control method to solve the problem, and accurate real-time traffic information is critical to this solution. This paper presents distributed cooperative reinforcement learning-based traffic control that integrates V2X networks’ dynamic clustering algorithm. To obtain traffic flow information accurately and instantaneously, it is important to improve the cluster stability in V2X networks. A dynamic clustering algorithm is proposed based on the enhanced affinity propagation. The proposed clustering algorithm introduces the initial cluster partition to maintain a proper cluster size and adds the lane and destination factors to improve the cluster's stability. The algorithm can provide efficient and accurate traffic state information to traffic signal controls. By integrating the clustering algorithm, a cooperative reinforcement learning control scheme is proposed to balance the traffic load. To address the tough dimensionality curse of reinforcement learning, a distributed mechanism for intersection cooperation is introduced, and a fast gradient-descent function approximation method is proposed to improve the controls’ real-time performance. The proposed intelligent traffic control scheme that integrates the stable clustering algorithm can effectively improve the traffic throughput, reduce the average waiting time, and avoid congestion. Numerical simulations on real scenarios validate the performance of the proposed approach.

State of the art on road traffic sensing and learning based on mobile user network log data

Abstract With the improvement of the storage and big data processing technology, mobile operators are able to extract and store a large amount of mobile network generated user behavior data, in order to develop various intelligent applications. One interesting application based on these data is traffic sensing, which uses techniques of learning human mobility patterns from updated location information in network interaction log data. Mobile networks, under which a huge amount of frequently updated location information of mobile users are tracked,can provide complete coverage to estimate traffic condition on roads and highways. This paper studies potential challenges and opportunities in intelligent traffic sensing from the data science point of view with mobile network generated data. Firstly, we classify the data resources available in the commercial radio network according to different taxonomy criteria. Then we outline the broken-down problems that fit in the framework of traffic sensing based on mobile user network log data. We study the existing data processing and learning algorithms on extracting traffic condition information from a large amount of mobile network log data. Finally we make suggestion on potential future work for traffic sensing on data from mobile networks. We believe the techniques and insights provided here will inspire the research community in data science to develop the machine learning models of traffic sensing on the widely collected mobile user behavior data.

Dynamic responses of asphalt concrete slab under cyclic wheel loading using acceleration spectrum analysis

The dynamic response of pavement, especially in term of vibration, provides information on pavement damage prediction, noise control and so forth. The vibration of pavement is also a potential energy which can be collected by energy harvester and utilized by sensing technology to obtain information of traffic and pavement conditions. In this research, both laboratory Model Mobile Load Simulator (MMLS) tests and Finite Element (FE) simulation were performed to assess the effects of several factors influencing the dynamic response of Hot Mix Asphalt (HMA) slabs that include material properties, wheel loading speed, top-down cracking and thickness of the slab. The parametric analysis was performed using the validated finite element model based on laboratory MMLS test results to investigate the effects of each parameter. Frequency spectrums of vertical acceleration for the HMA slabs under different loading scenarios were obtained through Fast Fourier Transform (FFT), and analyzed to help formulate recommendations for an actual pavement structure from dynamic responses in the field.

Integration of Traffic Information into the Path Planning among Moving Obstacles

This paper investigates the integration of traffic information (TI) into the routing in the presence of moving obstacles. When traffic accidents occur, the incidents could generate different kinds of hazards (e.g., toxic plumes), which make certain parts of the road network inaccessible. On the other hand, the first responders, who are responsible for management of the traffic incidents, need to be fast and safely guided to the incident place. To support navigation in the traffic network affected by moving obstacles, in this paper, we provide a spatio-temporal data model to structure the information of traffic conditions that is essential for the routing, and present an extended path planning algorithm, named MOAAstar–TI (Moving Obstacle Avoiding A* using Traffic Information), to generate routes avoiding the obstacles. A speed adjustment factor is introduced in the developed routing algorithm, allowing integration of both the information of vehicles and traffic situations to generate routes avoiding the moving obstacles caused by the incidents. We applied our system to a set of navigation scenarios. The application results show the potentials of our system in future application in real life.

Quantitative Analysis of Urban Regional Traffic Status

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

Route Choice Model Based on Game Theory for Commuters

The traffic behaviours of commuters may cause traffic congestion during peak hours. Advanced Traffic Information System can provide dynamic information to travellers. Due to the lack of timeliness and comprehensiveness, the provided information cannot satisfy the travellers’ needs. Since the assumptions of traditional route choice model based on Expected Utility Theory conflict with the actual situation, a route choice model based on Game Theory is proposed to provide reliable route choice to commuters in actual situation in this paper. The proposed model treats the alternative routes as game players and utilizes the precision of predicted information and familiarity of traffic condition to build a game. The optimal route can be generated considering Nash Equilibrium by solving the route choice game. Simulations and experimental analysis show that the proposed model can describe the commuters’ routine route choice decisionexactly and the provided route is reliable.

Improvement of Search Strategy With K-Nearest Neighbors Approach for Traffic State Prediction

Having access to the future traffic state information is crucial in maintaining successful intelligent transportation systems (ITS). However, predicting the future traffic state is a challenging research subject involving prediction reliability issues. Predictive performance measures, including the accuracy, efficiency, and stability, are generally considered as the most important priorities in the evaluation of prediction modules. Researchers have developed various K-nearest-neighbors-based searching algorithms that find the future state from the historical traffic patterns. Interestingly, there has not been sufficient effort made for improving the performance. For the emerging big data era, incorporating an efficient search strategy has become increasingly important since the applicability of the prediction module in ITS heavily relies on the efficiency of the searching method used. This paper develops a novel sequential search strategy for traffic state predictions. The proposed sequential strategy is found to be outperforming the conventional single-level search approach in terms of prediction measures, which are prediction accuracy, efficiency, and stability. Compared with the conventional approach, the proposed sequential method yields significantly more accurate results via internal hierarchical improvements across sublevels while maintaining excellent efficiency and stability.

Quality of Private Sector Travel-Time Data on Arterials

AbstractAccurate traffic state information is essential for both travelers and transportation agencies. In the past, traffic condition data were usually collected by a government agency using its own sensors. Recently, a number of private sector companies have started selling travel-time and speed data collected using probe vehicles, which provides a viable opportunity to outsource traffic data collection. Because these data sources and their related algorithms are proprietary, the reliability and accuracy of this private sector data is often an important issue for transportation agencies. Previous studies have examined the accuracy of private sector data on freeways, but arterials have not been examined extensively. Arterials represent a fundamentally more challenging environment for probe vehicle data given the larger variance in travel times created by traffic signals and other intermediate access points. In the research, the quality of private sector data on arterials is evaluated by utilizing Bluetoo...

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods.

Automatic Incident Detection Based on Bluetooth Detection in Northern Bavaria

This work describes an approach to determine the current travel times on freeways based on the detection and re-identification of Bluetooth devices onboard of vehicles using stationary roadside Bluetooth detection technology. It also aims at using this information for the traffic state determination of a whole freeway network with the goal of a fast and reliable dynamic net control in incident situations. Based on a four year experience in a Bluetooth detector test bed in Northern Bavaria, Germany, and after the evaluation of hundreds of millions of single detections, the technology as well as the developed algorithms for validation and evaluation of the data show their feasibility in practical use, especially in areas with a low density of stationary detectors like inductive loops. The data-driven part of the approach is divided into three subsequent steps. These steps are the determination of travel times, the data filtering and validation of plausible travel times and the automatic incident detection. The determination of travel time is based on the time stamp of the detection of a Bluetooth device with the shortest estimated distance to the position of the Bluetooth detector. For the data filtering the “Time Dependent Comparison to Neighbor Values Filter” will be applied. This filter allows for a fast and reliable differentiation between unrealistic (due to stops, detours, back and return trips etc.) and plausible travel time values for a certain segment of the freeway and is based on a method to validate if the determined travel time is in a plausibility threshold corridor defined by the values of the previous and the next neighboring travel times. The outcome is a detailed travel time information for the whole freeway network which is used for an automatic incident detection, which was developed and calibrated within this research project. This includes the detection of the start of an incident as well as the end of an incident. The result is continuous information about the prevailing travel times and a fast and reliable traffic state information for all segments, which allows for a dynamic large-scale re-routing in the Bavarian freeway network in case of congestions or other disturbances of the traffic flow.

A car-following model with the consideration of vehicle-to-vehicle communication technology

Recently, the research on traffic flow system based on some classical models, such as cellular automata and car-following models, has attracted much attention. Some meaningful achievements have been obtained in the past few years by scholars from various fields. This paper starts with literature review on traffic flow theory studies. Car-following models, including the initial model proposed by Newell in 1961 (Newell G F 1961 Oper. Res. 9 209) and some later modified ones (e.g. full velocity difference model, or FVD model for short) have been deeply investigated. Based on Newell's car-following model, an extension of car-following model with consideration of vehicle-to-vehicle (V2V) communication is then developed. The vehicle-to-vehicle communication technology, which was proposed in the early 2000s, enable vehicles to collect traffic condition information from other vehicles (e.g. speed, headway, position, acceleration, etc.) and provide them for drivers in almost real time. Compared with those without V2V devices, drivers with information from V2V devices can react to traffic flow fluctuation timelier and more precisely. To represent the pre-reaction of drivers to traffic flow information provided by V2V devices, a parameter, , is newly introduced into Newell's car-following model. Then by second-order Taylor series expansion, a new car-following model with the influence of V2V (called V2V model) is proposed. Neutral stability condition of V2V model as well as phase diagram is derived theoretically with linear analysis method. The phase diagram of linear stability condition is divided into stable and unstable regions. By analyzing stability performance of the proposed model, it is evident that V2V communication technology can improve the stability of traffic flow system. Numerical simulation is demonstrated to study the influence of V2V devices on traffic flow on the one hand, and to acquire density waves as well as hysteresis loops under different values of parameter on the other hand. The sensitive analysis method are adopted as well.The numerical simulation results indicate that: 1) when compared with FVD model, V2V model can make vehicles react to traffic flow fluctuation earlier and reduce the speed changes under start-up, brake and incident conditions; this indicates that the consideration of V2V devices can improve the safety and ride comfort of traffic flow system; 2) the V2V model is sensitive to the value changes of parameter and T; the stability of traffic flow can be improved if the value of parameter increases, or parameter T decreases; this outcome precisely agrees with the above theoretical analysis; 3) the characteristics of traffic flow can influence the performance of V2V technology: compared with under low density condition, V2V communication technology can significantly increase the average speed of traffic flow under high density condition.

Analysis of the Effectiveness of Providing National Highway Detour Information via Variable Message Signs on Expressway Operations

PURPOSES : Expressways experience chronic and recurring congestion, especially during weekends and holidays, because of the increased demands for leisure-related travel. The alternatives to solve chronic and recurring congestion may be three-fold: (1) physical expansion of expressway capacities, (2) road pricing, and (3) temporal and spatial distribution of traffic demands. Among these, the third alternative may be the most cost-effective method for the Korea Expressway Corporation (KEC) that can be achieved by using the existing ITS infrastructure. METHODS : KEC initiated a pilot study in which the traffic on congested expressways was managed by providing traffic condition information (i.e., travel times) of neighboring national highways for taking detours via variable message signs (VMS). This study aimed to estimate the detour rate, and the two pilot studies on Seohaean and Yeongdong expressways yielded many benefits. RESULTS : It was revealed that the total length of congestion segments decreased by 7.8 km, and the average travel speed increased by 5.3 km/h. CONCLUSIONS : Based on these findings, it was concluded that the propagation of detour information via VMSs during congestion hours can help reduce congestion on expressways and increase the benefits of the entire network.

Traffic big data prediction and visualization using Fast Incremental Model Trees-Drift Detection (FIMT-DD)

Information extraction using distributed sensors has been widely used to obtain information knowledge from various regions or areas. Vehicle traffic data extraction is one of the ways to gather information in order to get the traffic condition information. This research intends to predict and visualize the traffic conditions in a particular road region. Traffic data was obtained from Department of Transport UK. These data are collected using hundreds of sensors for 24 h. Thus, the size of data is very huge. In order to get the behavior of the traffic condition, we need to analyze the huge dataset which was obtained from the sensors. The uses of conventional data mining methods are not sufficient to use, due to the process of knowledge building that should store data temporary in the memory. The fact that data is continuously becoming larger over time, therefore we need to find a method that could automatically adapt to process data in the form of streams. We use method called FIMT-DD (Fast Incremental Model Trees-Drift Detection) to analyze and predict the very large traffic dataset. Based on the prediction system that we have developed, we also visualize the prediction of traffic flow condition within generated sensor point in the real map simulation.

Multipartner Demonstration of BGP-LS-Enabled Multidomain EON Control and Instantiation With H-PCE [Invited

The control of multidomain elastic optical networks (EONs) is possible by combining Hierarchical Path Computation Element (H-PCE)-based computation, Border Gateway Protocol with Extensions for Traffic Engineering Link State Information (BGP-LS) topology discovery, remote instantiation via Path Computation Element Communication Protocol (PCEP), and signaling via Resource Reservation Protocol with Extensions for Traffic Engineering (RSVP-TE). Two evolutionary architectures are considered, one based on stateless H-PCE, PCEP instantiation, and end-to-end RSVP-TE signaling (SL-E2E), and a second one based on stateful active H-PCE with perdomain instantiation and stitching. This paper presents the first multiplatform demonstration that fully validates both control architectures achieving multiprotocol interoperability. SL-E2E leads to slightly faster provisioning but needs to keep the state of the stitching of the end-to-end label-switched paths in the parent PCE.

Investigating Women’s and Men’s Propensity to Use Traffic Information in a Developing Country

Congestion problems and their vast negative effects on transportation networks are at the center of transportation providers’ attention. Considering the high costs associated with extending highway networks, using transportation demand management (TDM) strategies to alleviate congestion is a more cost-effective approach; however, planning and implementing TDM policies and strategies, in particular, necessitate careful examination and detailed analysis of commuter behavior and tendencies. Providing traffic condition information via radio to guide drivers through less congested paths is one common method of low-cost TDM in developing countries. The objective of this paper is to study the different behaviors between men and women in responding to traffic information that they receive by radio, as a part of advanced in-vehicle systems. In order to conduct this study, a random sample of drivers was surveyed to investigate their travel behaviors and responses while they were exposed to traffic information obtained through radio. Each gender response was studied separately to examine any possible differences in their propensity to use traffic information. In doing so, the ordered logit model was designated and NLOGIT package was used. The final results showed that age, driving time, listening to radio traffic information, preferred arrival time at workplace, and delay time variables in both men’s and women’s models were in common, but education and occupation were identified as significant in the females’ behavior only, and income and car ownership were significant for males. It is expected that appropriate decisions in recognition of these study results should be made to develop more effective traffic information for different users.

Dynamics of heterogeneity in urban networks: aggregated traffic modeling and hierarchical control

Real traffic data and simulation analysis reveal that for some urban networks a well-defined Macroscopic Fundamental Diagram (MFD) exists, which provides a unimodal and low-scatter relationship between the network vehicle density and outflow. Recent studies demonstrate that link density heterogeneity plays a significant role in the shape and scatter level of MFD and can cause hysteresis loops that influence the network performance. Evidently, a more homogeneous network in terms of link density can result in higher network outflow, which implies a network performance improvement. In this article, we introduce two aggregated models, region- and subregion-based MFDs, to study the dynamics of heterogeneity and how they can affect the accuracy scatter and hysteresis of a multi-subregion MFD model. We also introduce a hierarchical perimeter flow control problem by integrating the MFD heterogeneous modeling. The perimeter flow controllers operate on the border between urban regions, and manipulate the percentages of flows that transfer between the regions such that the network delay is minimized and the distribution of congestion is more homogeneous. The first level of the hierarchical control problem can be solved by a model predictive control approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant (reality) is formulated by the subregion-based MFDs, which is a more detailed model. At the lower level, a feedback controller of the hierarchical structure, tries to maximize the outflow of critical regions, by increasing their homogeneity. With inputs that can be observed with existing monitoring techniques and without the need for detailed traffic state information, the proposed framework succeeds to increase network flows and decrease the hysteresis loop of the MFD. Comparison with existing perimeter controllers without considering the more advanced heterogeneity modeling of MFD highlights the importance of such approach for traffic modeling and control.