Real-time Traffic Conditions Research Articles

Signal Consensus in TSP of the Same Grid in Road Network

In this paper, we propose a consensus algorithm with input constraints for traffic light signals in transit signal priority (TSP). TSP ensures control strategy of traffic light signals can be adjusted and applied according to the real-time traffic status, and provides priority for buses. We give the convergence conditions of the consensus algorithms with and without input constraints in TSP respectively and analyze the convergence performance of them by using matrix theory and graph theory, and PTV-VISSIM is used to simulate the traffic accident probability of three cases at intersections. Simulation results are presented that a consensus is asymptotically reached for all weights of priority; the algorithm with input constraints is more suitable for TSP than the algorithm without input constraints, and the traffic accident rate is reduced.

Connected Vehicular Transportation: Data Analytics and Traffic-Dependent Networking

With onboard operating systems becoming increasingly common in vehicles, the real-time broadband infotainment and Intelligent Transportation System (ITS) service applications in fast-motion vehicles become ever demanding, which are highly expected to significantly improve the efficiency and safety of our daily on-road lives. The emerging ITS and vehicular applications, e.g., trip planning, however, require substantial efforts on the real-time pervasive information collection and big data processing so as to provide quick decision making and feedbacks to the fast moving vehicles, which thus impose the significant challenges on the development of an efficient vehicular communication platform. In this article, we present TrasoNET, an integrated network framework to provide realtime intelligent transportation services to connected vehicles by exploring the data analytics and networking techniques. TrasoNET is built upon two key components. The first one guides vehicles to the appropriate access networks by exploring the information of realtime traffic status, specific user preferences, service applications and network conditions. The second component mainly involves a distributed automatic access engine, which enables individual vehicles to make distributed access decisions based on access recommender, local observation and historic information. We showcase the application of TrasoNET in a case study on real-time traffic sensing based on real traces of taxis.

Safety effects of dynamic speed limits on motorways

Dynamic speed limits (DSL) are limits that change according to real-time traffic, road or weather conditions. In DSL-schemes road users are typically informed of speed limit changes by electronic signs that are housed within gantries situated above lanes. Dynamic speed limit systems are increasingly applied worldwide, usually on motorways. One of the objectives of dynamic speed limits is to improve traffic safety through reductions in speed variations within and across lanes and between upstream and downstream flows.This paper shows the results of an empirical evaluation of the effects on traffic safety of a dynamic speed limit system on motorways in Flanders, Belgium. The evaluation was done by means of a before-after analysis of crashes, completed with a cost-benefit analysis.The results show that the number of injury crashes decreased significantly (−18%) after the introduction of the system. A separate analysis for serious and fatal injury crashes revealed a non-significant decrease of 6%. A distinction according to crash type showed an almost significant decrease of 20% in the number of rear-end crashes whereas the number of single-vehicle crashes decreased by 15% (ns). However, no effect was found for side crashes.In addition to the analysis of the effects, a cost-benefit analysis was applied. The costs of the implementation of these systems were compared with the benefits of crash prevention. The cost-benefit analyses of the crash effects showed a benefits-to-costs ratio of approximately 0.7, which means that the costs tend to exceed the benefits. Taking into account the important margins of uncertainty with respect to both costs and benefits, we have also explored how the net benefits are affected by some key assumptions. The general conclusion is that there is no convincing evidence that the costs of the system currently outweigh the expected benefits in terms of crash prevention.

Real-Time Traffic State Estimation With Connected Vehicles

A novel framework is developed in this paper, to increase the real-time roadway traffic condition assessment accuracy, which integrates connected vehicle technology (CVT) with artificial intelligence (AI) paradigm forming a CVT-AI method. Traffic density is a major indicator of traffic conditions. In this paper, the traffic operational condition is assessed based on traffic density. A simulated network of Interstate 26 in South Carolina is developed to investigate the effectiveness of the method. The assumption is that the vehicle onboard units will forward the CV generated data to the edge devices (e.g., roadside units) for further processing. CV generated distance headway and number of stops, and speed data are used to estimate traffic density. This paper reveals that, with 20% and greater CV penetration levels, the accuracy of the density information with the AI-aided CVT is a minimum of 85%. Moreover, this paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels. Level of service (LOS) is the indicator of traffic congestion level on highways and is described with traffic density in terms of passenger car/mile/lane for a specific free flow speed. LOS estimated using the CVT-AI density estimation method is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors. With a 10% or more CV penetration, higher accuracy is achieved using the CVT-AI algorithm compared with the PeMS density estimation algorithm.

A Unified Framework for Vehicle Rerouting and Traffic Light Control to Reduce Traffic Congestion

As the number of vehicles grows rapidly each year, more and more traffic congestion occurs, becoming a big issue for civil engineers in almost all metropolitan cities. In this paper, we propose a novel pheromone-based traffic management framework for reducing traffic congestion, which unifies the strategies of both dynamic vehicle rerouting and traffic light control. Specifically, each vehicle, represented as an agent, deposits digital pheromones over its route, while roadside infrastructure agents collect the pheromones and fuse them to evaluate real-time traffic conditions as well as to predict expected road congestion levels in near future. Once road congestion is predicted, a proactive vehicle rerouting strategy based on global distance and local pheromone is employed to assign alternative routes to selected vehicles before they enter congested roads. In the meanwhile, traffic light control agents take online strategies to further alleviate traffic congestion levels. We propose and evaluate two traffic light control strategies, depending on whether or not to consider downstream traffic conditions. The unified pheromone-based traffic management framework is compared with seven other approaches in simulation environments. Experimental results show that the proposed framework outperforms other approaches in terms of traffic congestion levels and several other transportation metrics, such as air pollution and fuel consumption. Moreover, experiments over various compliance and penetration rates show the robustness of the proposed framework.

Development and Evaluation of an Evolutionary Algorithm-Based OnLine Energy Management System for Plug-In Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) have been regarded as one of several promising countermeasures to transportation-related energy use and air quality issues. Compared with conventional hybrid electric vehicles, developing an energy management system (EMS) for PHEVs is more challenging due to their more complex powertrain. In this paper, we propose a generic framework of online EMS for PHEVs that is based on an evolutionary algorithm. It includes several control strategies for managing battery state-of-charge (SOC). Extensive simulation testing and evaluation using real-world traffic data indicates that the different SOC control strategies of the proposed online EMS all outperform the conventional control strategy. Out of all the SOC control strategies, the self-adaptive one is the most adaptive to real-time traffic conditions and the most robust to the uncertainties in recharging opportunity. A comparison to the existing models also employing short-term prediction shows that the proposed model can achieve the best fuel economy improvement but requiring less trip information.

Modeling lane-change-related crashes with lane-specific real-time traffic and weather data

ABSTRACTThis study investigates the relationship between lane-change-related crashes and lane-specific, real-time traffic factors. It is anticipated that the real-time traffic data for the two lanes—the vehicle's lane (subject lane) and the lane to which that a vehicle intends to change (target lane)—are more closely related to lane-change-related crashes, as opposed to congregated traffic data for all lanes. Lane-change-related crash data were obtained from a 62-mile long freeway in Southeast Wisconsin in 2012 and 2013. One-minute traffic data from the 5- to 10-minute interval prior to the crashes were extracted from an immediately upstream detector station and two immediately downstream stations from the crash location. Weather information was collected from a major historical weather database. A matched case-control logistic regression was used for analysis. Results show that the following factors significantly affect the probability of a lane-change-related crash: average flow into the target lane at the first downstream station, the flow ratio at the second downstream station, and snow conditions. Additionally, the average speed in the target lane at the first downstream station contributes to the occurrence of lane-change crashes during snowy conditions. According to the model, the probability of a lane-change-related crash under real-time traffic conditions can aid in flagging potential crash-prone conditions. The identified contributing factors can help traffic operators select traffic control and management countermeasures to proactively mitigate lane-change-related crashes.

AgBM-DTALite: An integrated modelling system of agent-based travel behaviour and transportation network dynamics

Advanced modelling methods and products, such as an integrated advanced travel demand model and a fine-grained time-sensitive network that can operate at statewide, metropolitan and subarea/corridor levels, are required by a number of transportation planning agencies to meet their objectives and address various key challenges. This research develops an application-ready integrated transportation model that can predict, in a future-year scenario or in a hypothetical scenario, both the changes in travel behavioural adjustments and the dynamics in traffic conditions. The integrated framework embeds theoretically sound behavioural foundation by incorporating agent-based searching, information acquisition, learning, knowledge updating and decision-making. Multidimensional travel behaviour, including mode choice, route choice, departure time choice and en-route diversion, is considered. Behavioural user equilibrium is defined without assuming perfect rationality. A dynamic traffic simulation engine is employed to model and simulate real-time traffic conditions. Data exchanges between the travel demand model and the traffic simulation are explained in detail. The integration is demonstrated using a real-world case study. Future applications should cover a wide spectrum of scenarios in transportation planning/policy and traffic operations/control analyses.

The implementation of a cloud city traffic state assessment system using a novel big data architecture

In order to store and analyze the increasing data in recent years, big data techniques are applied to many fields such as healthcare, manufacturing, telecommunications, retail, energy, transportation, automotive, security, environment, etc. This work implements a city traffic state assessment system in cloud using a novel big data architecture. The proposed system provides the real-time busses location and real-time traffic state, especially the real-time traffic state nearby, through open data, cloud computing, bid data technology, clustering methods, and irregular moving average. With the high-scalability cloud technologies, Hadoop and Spark, the proposed system architecture is first implemented successfully and efficiently. Next, we utilize irregular moving average and clustering methods to find the area of traffic jam. Finally, three important experiments are performed. The first experiment indicates that the computing ability of Spark is better than that of Hadoop. The second experiment applies Spark to process bus location data under different number of executors. In the last experiment, we apply irregular moving average and clustering methods to efficiently find the area of traffic jam in Taiwan Boulevard which is the main road in Taichung city. Based on these experimental results, the provided system services are present via an advanced web technology.

Fuzzy Group-Based Intersection Control via Vehicular Networks for Smart Transportations

Vehicular network has been recently used to achieve high efficient and flexible traffic scheduling at intersection roads for smart transportation systems. Different from existing works, where traffic signal is used to schedule waiting vehicles at each lane, we propose to divide vehicles in the same lane into small groups and schedule vehicle groups via wireless communication rather than traffic lights. Such direct scheduling of vehicles can reduce waiting time and improve fairness, especially when the traffic volume in different lanes is imbalanced. The key challenge in such a design lies in determining appropriate size of groups with respect to real-time traffic conditions. To cope with this issue, we propose a neuro-fuzzy network-based grouping mechanism, where the network is trained using reinforcement learning technique. Also, vehicle groups are scheduled via a neuro-fuzzy network. Simulations using ns3 are conducted to evaluate the performance of our algorithm and compare it with similar works. The results show that our algorithm can reduce waiting time and at the same time improve fairness in various cases, and the advantage against traffic light algorithms can be up to 40%.

To analyse real-time traffic conditions with the help of GPS

To analyse real-time traffic conditions with the help of GPS

User Selection and Power Allocation in Full-Duplex Multicell Networks

Full duplex (FD) communications has the potential to double the capacity of a half duplex (HD) system at the link level. However, in a cellular network, FD operation is not a straightforward extension of half duplex operations. The increased interference due to a large number of simultaneous transmissions in FD operation and realtime traffic conditions limits the capacity improvement. Realizing the potential of FD requires careful coordination of resource allocation among the cells as well as within the cell. In this paper, we propose a distributed resource allocation, i.e., joint user selection and power allocation for a FD multi-cell system, assuming FD base stations (BSs) and HD user equipment (UEs). Due to the complexity of finding the globally optimum solution, a sub-optimal solution for UE selection, and a novel geometric programming based solution for power allocation, are proposed. The proposed distributed approach converges quickly and performs almost as well as a centralized solution, but with much lower signaling overhead. It provides a hybrid scheduling policy which allows FD operations whenever it is advantageous, but otherwise defaults to HD operation. We focus on small cell systems because they are more suitable for FD operation, given practical self-interference cancellation limits.With practical self-interference cancellation, it is shown that the proposed hybrid FD system achieves nearly two times throughput improvement for an indoor multi-cell scenario, and about 65% improvement for an outdoor multi-cell scenario compared to the HD system.

Traffic‐aware routing protocol with cooperative coverage‐oriented information collection method for VANET

A traffic-aware routing protocol (TARCO) that considers integrated real-time traffic conditions for constructing delivery paths over a vehicular environment is presented. This goal was achieved through three mechanisms: cooperative traffic information collection, lightweight real-time road scoring and adaptable data route construction. An accurate overall view of the real-time traffic conditions was obtained from the cooperation between information aggregators at junctions and information collectors on road segments. Each road segment was then assigned a weight according to the overall view of the traffic conditions and updated periodically to reflect traffic variations. Finally, the road segments providing efficient and reliable data routes were used to construct a routing path with guaranteed connectivity and a short delivery delay to the destination. Simulation results showed that the use of TARCO leads to high network performance in terms of the packet delivery ratio, end-to-end delay and communication overhead.

A nonlinear algorithm for traffic estimation with state constraints

Abstract We present a real-time traffic state estimation algorithm for motorways. Natural constraints on the variables, like practical bounds on densities and velocities, are incorporated in the estimation process aiming to obtain better estimation results. The dynamic equation for the evolution of the traffic is defined by a second order macroscopic model which computes the density, the flow and the mean speed according to several nonlinear equations, but nothing avoids the results being out of those practical bounds. Different extensions of the Kalman method were already applied to this problem, but none of them consider natural constraints in the variables. On the other hand, general filter methods have been designed to cope with a constrained state. In order to incorporate the natural constraints of the traffic model, we adapt one of those methods based on the Unscented Kalman Filter. To validate the approach, many simulation cases over a freeway section were made using a microscopic simulation tool and comparing the Extended Kalman Approach with the proposed one.

Real-time Monitoring of Dynamic Traffic States by State-Space Model

Abstract: We propose real-time traffic state estimation using a state-space model that takes account of variability in the fundamental diagram (FD) and sensing data. In free flow situations, for instance, the FD regulating driving behavior may vary among drivers who possess differing characters. In addition, FD is affected by external factors such as interactions with pedestrians and vehicles. Variational theory (VT) was used as the system model, and measurement data were taken from probe vehicles and traffic detectors. VT is a static model, making real time estimation of traffic state changes difficult. We applied VT to a state-space model. Our proposal showed better agreement between simulated and benchmark traffic states than deterministic VT. For model validation, we applied the model to Komazawa Street in Tokyo, Japan.

Analysis and Operational Challenges of Dynamic Ride Sharing Demand Responsive Transportation Models

Abstract: There is a wide evidence that sustainable mobility is not only a technological question, automotive technology will be part of the solution as a necessary but not sufficient condition, sufficiency is emerging as a combination of a paradigm shift from car ownership to vehicle usage consequence of socio-economic changes, with the application of Information and Communication Technologies (ICT) that make possible for a user to have access to a mobility service from anywhere to anywhere at any time. Among the many emergent mobility services Multiple Passenger Ridesharing and its variants look the more promising. However, implementations of these systems accounting specifically for time dependencies, and time windows reflecting users' needs raise challenges in terms of real-time fleet dispatching and dynamic route calculation. On the other hand the feasibility and impacts analysis in terms of the many factors influencing the behavior of the system, as for example the service demand, the size of the service fleet, the capacity of the shared vehicles, the time windows requirements, soft or tight. This paper analyzes both aspects. The first is approached in terms of a Decision Support System whose solutions are computed in terms of ad hoc heuristics of variants of Pick Up and Delivery Problems with Time Windows and Feasibility and Profitability criteria rooted on Dynamic Insertion Heuristics. For the evaluation of the applications a Simulation Framework is proposed based on a microscopic simulation model that emulates real-time traffic conditions and a real traffic information system, and interacts with the Decision Support System feeding it with the required data to make the decisions that are implemented in the simulation to emulate the behavior of the shared fleet. The proposed simulation framework has been implemented in a model of Barcelona's Central Business District. The paper is completed with the discussion of the achieved results.

Real-time traffic network state estimation and prediction with decision support capabilities: Application to integrated corridor management

This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data.

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

Cost-efficient traffic-aware data collection protocol in VANET

Data from running vehicles are invaluable to numerous ITS and urban computing applications. This paper studies the issue of collecting data from multiple vehicles to a roadside base station via vehicle-to-vehicle and vehicle-to-infrastructure communications. Existing data collection approaches in VANET have mainly focused on the network problems, such as packet loss, network clustering or data aggregation, but the impact of real-time traffic condition is barely considered. In this paper, we investigate the data collection problem in VANETs under rapid evolving traffic conditions. Our approach can adaptively choose to carry or forward the data packet, based on current traffic information. The objective is to minimize the network communication overhead while satisfying the data collection time constraint. We formulate the data collection problem as a scheduling optimization problem and prove it is NP-complete. An optimal dynamic programming solution and a genetic algorithm based heuristic solution are developed to solve the problem under different application scenarios. Extensive evaluations validate that our proposed solution outperforms some existing ones in terms of effectiveness and efficiency.

Population exposure to ultrafine particles: Size-resolved and real-time models for highways

Prior research on ultrafine particles (UFP) emphasizes that concentrations are especially high on-highway, and that time on highways contribute disproportionately to total daily exposures. This study estimates individual and population exposure to ultra-fine particles in the Minneapolis – St. Paul (Twin Cities) metropolitan area, Minnesota. Our approach combines a real-time model of on-highway size-resolved UFP concentrations (32 bins, 5.5–600nm); individual travel patterns, derived from GPS travel trajectories collected in 144 individual vehicles (123h at locations with UFP estimates among 624 vehicle-hours of travel); and, loop-detector data, indicating real-time traffic conditions throughout the study area. The results provide size-resolved spatial and temporal patterns of exposure to UFP among freeway users. On-highway exposures demonstrate significant variability among users, with highest concentrations during commuting peaks and near highway interchanges. Findings from this paper could inform future epidemiological studies in on-road exposure to UFP by linking personal exposures to traffic conditions.