Dynamic Traffic Characteristics Research Articles

Distributed coordinated control of mixed expressway and urban roads: a sensitivity-based approach

The inherent interdependency between mixed expressway and urban road networks calls for an effective coordinated traffic control approach. This study introduces a sensitivity-based distributed method to coordinate ramp metering and traffic signal control. Firstly, the quantitative relationships between network nodes are established through a developed traffic flow model for mixed networks. The network-level sensitivity information is analyzed to assess the influence of controllers on the traffic state. Additionally, a dynamic subproblems decomposition algorithm is proposed, considering the network’s spatial structure and dynamic traffic characteristics. By employing community detection techniques, tightly connected controllers are clustered to enable efficient and adaptive distributed control. The optimization problem is then decomposed into several subproblems, each formulated as a bi-level programming problem within a model predictive control framework. Numerical experiments demonstrate that the proposed strategy enhances the traffic efficiency of mixed networks with a 14.5% reduction in total travel time during peak hours, which shows better performance compared with the other three existing control strategies.

Two-layer adaptive signal control framework for large-scale dynamically-congested networks: Combining efficient Max Pressure with Perimeter Control

Traffic-responsive signal control is a cost-effective, easy-to-implement, network management strategy, bearing high potential to improve performance in heavily congested networks with dynamic traffic characteristics. Max Pressure (MP) distributed control gained significant popularity due to its theoretically proven ability of throughput maximization under specific assumptions. However, its effectiveness is questionable in over-saturated conditions, while network-scale implementation is often practically limited due to high instrumentation cost, which increases proportionally to the number of controlled intersections. Perimeter control (PC) based on the concept of Macroscopic Fundamental Diagram (MFD) is a state-of-the-art aggregated control strategy that regulates exchange flows between homogeneously congested regions, with the objective of maximizing traffic system performance and prevent over-saturation. However, homogeneity assumption is hardly realistic under congested conditions, which can compromise PC effectiveness. In this paper, network-wide parallel application of PC and MP strategies embedded in a two-layer control framework is evaluated in a macroscopic simulation environment. With the aim of reducing implementation cost of network-wide MP without significant performance drop, we propose a critical node identification algorithm that is based on node traffic characteristics and assess partial MP deployment to the most critical nodes. An enhanced version of Store-and-forward dynamic traffic paradigm incorporating finite queues and spill-back consideration is used to test different configurations of the two-layer framework, as well as each layer individually, for a real large-scale network, in moderate and highly congested conditions. Results show that: (i) combination of MP and PC outperforms individual layer application in almost all cases for both demand scenarios tested; (ii) MP control in critical node sets formed by the proposed strategy leads to similar or even better performance compared to full-network implementation, thus allowing for significant cost reduction; iii) the proposed control schemes improve system performance even under stochastic demand fluctuations of up to 20% of mean.

Joint computation offloading and parallel scheduling to maximize delay-guarantee in cooperative MEC systems

The growing development of the Internet of Things (IoT) is accelerating the emergence and growth of new IoT services and applications, which will result in massive amounts of data being generated, transmitted and processed in wireless communication networks. Mobile Edge Computing (MEC) is a desired paradigm to timely process the data from IoT for value maximization. In MEC, a number of computing-capable devices are deployed at the network edge near data sources to support edge computing, such that the long network transmission delay in cloud computing paradigm could be avoided. Since an edge device might not always have sufficient resources to process the massive amount of data, computation offloading is significantly important considering the cooperation among edge devices. However, the dynamic traffic characteristics and heterogeneous computing capabilities of edge devices challenge the offloading. In addition, different scheduling schemes might provide different computation delays to the offloaded tasks. Thus, offloading in mobile nodes and scheduling in the MEC server are coupled to determine service delay. This paper seeks to guarantee low delay for computation intensive applications by jointly optimizing the offloading and scheduling in such an MEC system. We propose a Delay-Greedy Computation Offloading (DGCO) algorithm to make offloading decisions for new tasks in distributed computing-enabled mobile devices. A Reinforcement Learning-based Parallel Scheduling (RLPS) algorithm is further designed to schedule offloaded tasks in the multi-core MEC server. With an offloading delay broadcast mechanism, the DGCO and RLPS cooperate to achieve the goal of delay-guarantee-ratio maximization. Finally, the simulation results show that our proposal can bound the end-to-end delay of various tasks. Even under slightly heavy task load, the delay-guarantee-ratio given by DGCO-RLPS can still approximate 95%, while that given by benchmarked algorithms is reduced to intolerable value. The simulation results are demonstrated the effectiveness of DGCO-RLPS for delay guarantee in MEC.

Dynamic Routing Algorithm for Hazmat Transportation Problems

After a series of gas pipeline explosions in Kaohsiung City, Taiwan, in 2014, the Environmental Protection Administration (EPA) issued regulations in 2017 requiring all chemical tank trucks to be equipped with GPS for real-time monitoring and control. Hazmat transportation problems, along with solution algorithms and applications, have been studied extensively. However, most of the research has focused on the route planning level. Dynamic characteristics should be incorporated to reflect possible dynamic traffic situations with more advanced information and communication capabilities for chemical tank trucks. This research formulates a bi-objective dynamic model and proposes a solution algorithm based on a genetic algorithm (GA) for dynamic hazmat route optimization. A dynamic bi-objective model, including transportation cost and risk, is developed to reflect time-varying traffic conditions. Dynamic traffic characteristics are reflected through traffic volume and travel time, simulated from simulation models. Numerical experiments are conducted in the Kaohsiung City network. The results show that dynamic routes for hazmat transportation vary with respect to traffic flow conditions. The discussions of this research are expected to provide some insights into policy implications.

Determination of Passenger Car Unit for Urban Roads: A Case Study in Addis Ababa

In Ethiopia, specifically on Addis Ababa city roads operating, vehicles are mixed with varying static and dynamic traffic characteristics that are very challenging for the traffic analyst to come up with a sole solution to the traffic malady. Under heterogeneous traffic, each vehicle type cannot be compared with other vehicle types since it shows different effects on the road. It could be observed that the majority of the drivers in Addis Ababa road networks do not follow proper lane discipline to use any available space on the road sections. Currently, Ethiopia is using the passenger car unit values from other countries manual, which doesn't represent the local traffic and roadway conditions. This study was initiated to establish the PCU values that will cater the local settings. Hence, this study was focused on estimating the PCU values of multilane highways in Addis Ababa city and determining the influence of traffic volume and width of the road on the values of PCU. The PCUs were estimated for four vehicle types based on the variation of traffic volume and carriageway width. Dynamic PCU method was used to determine PCU values, and a linear regression analysis was performed for model development. The developed model is used to estimate PCU values having the same traffic conditions and roadway conditions found in other countries. The study results indicated that as the traffic volume and carriageway width increases, the passenger car unit value also increases. Also, it was found out that the results of PCU values in this study showed higher values than those provided in Highway Capacity Manual (HCM). Hence, the PCU values obtained are recommended to reflect the existing traffic condition in the locality.

Determination of Passenger Car Unit for Urban Roads: A Case Study in Addis Ababa

In Ethiopia, specifically on Addis Ababa city roads operating, vehicles are mixed with varying static and dynamic traffic characteristics that are very challenging for the traffic analyst to come up with a sole solution to the traffic malady. Under heterogeneous traffic, each vehicle type cannot be compared with other vehicle types since it shows different effects on the road. During the field observations, most of the drivers in Addis Ababa road networks did not follow proper lane discipline to use any available space on the road sections. Currently, Ethiopia is using the passenger car unit values from other countries' manuals, which don't represent the local traffic and roadway conditions. This study was initiated to establish the passenger car unit (pcu) to cater to the regional settings. Hence, the study's focus was to estimate the pcu values of multilane highways in Addis Ababa city and determine the influence of traffic volume and width of the road on the importance of pcu. The passenger car units were estimated for four vehicle types based on traffic volume and carriageway width variation. Dynamic PCU method was used to determine values and performed a Linear Regression analysis for model development. The developed model was used to estimate pcu values having the same traffic and roadway conditions in other countries. Results indicated that as the traffic volume and carriageway width increase, the passenger car unit also increases. Also, this study found out that pcu values were higher than those provided in Highway Capacity Manual. Hence, the pcu values obtained are recommended to reflect the existing traffic condition in the locality.

CORE: Prediction-Based Control Plane Load Reduction in Software-Defined IoT Networks

In this paper, we propose a scheme to address the problem of load management in the control plane of Software-Defined Internet of Things (SDIoT) networks. In SDIoT, multiple controllers are deployed to enhance network scalability. With the growth of IoT, the number of devices is increasing rapidly. The management of control plane load is an essential issue for IoT networks because of the dynamic traffic characteristics. IoT traffic is highly dynamic due to the heterogeneity of IoT devices in terms of mobility, activation model, Quality of Service (QoS) demand, and flow generation rate. The challenge is to prevent controller overload and distribute traffic optimally under the consideration of heterogeneous IoT devices. The proposed scheme estimates control plane load based on the mobility and activation model of IoT devices. For mobility prediction, we use Order- m fallback Markov Predictor as it consumes less space and performs efficiently even for small values of m. Based on the prediction results, we implement a traffic-aware rule-caching mechanism and a master controller assignment scheme to reduce the control plane load. Simulation results show that the proposed scheme reduces the peak intensity of the control traffic by 23.08% and 16.67%, as compared to the considered benchmark schemes.

Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.

Relationship Between Urban Road Traffic Characteristics and Road Grade Based on a Time Series Clustering Model: A Case Study in Nanjing, China

With the increasing number of vehicles in large- and medium-sized cities challenges in urban traffic management, control, and road planning are being faced. Taxi GPS trajectory data is a novel data source that can be used to study the potential dynamic traffic characteristics of urban roads, and thus identify locations that show a notable lack of road planning. Considering that road traffic characteristics on their own are insufficient for a comprehensive understanding of urban traffic, we develop a road traffic characteristic time series clustering model to analyze the relationship between urban road traffic characteristics and road grade based on existing taxi trajectory data. We select the main urban area of Nanjing as our study area and use the taxi trajectory data of a single month for evaluating our method. The experiments show that the clustering model exhibit good performance and can be successfully used for road traffic characteristic classification. Moreover, we analyze the correlation between traffic characteristics and road grade to identify road segments with planning designs that do not match the actual traffic demands.

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbour (kNN) method is widely used for short-term traffic forecasting. However, the self-adjustment of kNN parameters has been a problem due to dynamic traffic characteristics. This study proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that the DP-kNN can perform better than the manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

A cross-layer QoS management framework for ZigBee cluster-tree networks

Wireless sensor networks show great potential to successfully address the timeliness and energy-efficiency requirements of different cyber-physical system applications. Generally, these requirements span several layers of the stack and demand an on-line mechanism capable of efficiently tuning several parameters, in order to better support highly dynamic traffic characteristics. This work presents a cross-layer QoS management framework for ZigBee cluster-tree networks. The proposed framework carries out an on-line control of a set of parameters ranging from the MAC sub-layer to the network layer, improving the successful transmission probability and minimizing the memory requirements and queuing delays through an efficient bandwidth allocation at the network clusters. Through extensive simulations in a real datacenter monitoring application scenario, we show that the proposed framework improves the successful transmission probability by 10 %, and reduces the end-to-end delay by 94 %.

A kinematic wave approach to traffic statics and dynamics in a double-ring network

Recently there has been much interest in understanding macroscopic fundamental diagrams of stationary road networks. However, there lacks a systematic method to define and solve stationary states in a road network with complex junctions. In this study we propose a kinematic wave approach to defining, analyzing, and simulating static and dynamic traffic characteristics in a network of two ring roads connected by a 2×2 junction, which can be either an uninterrupted interchange or a signalized intersection. This study is enabled by recently developed macroscopic junction models of general junctions. With a junction model based on fair merging and first-in-first-out diverging rules, we first define and solve stationary states and then derive the macroscopic fundamental diagram (MFD) of a stationary uninterrupted network. We conclude that the flow-density relationship of the uninterrupted double-ring network is not unique for high average network densities (i.e., when one ring becomes congested) and unveil the existence of infinitely many stationary states that can arise with a zero-speed shockwave. From simulation results with a corresponding Cell Transmission Model, we verify that all stationary states in the MFD are stable and can be reached, but show that randomness in the retaining ratio of each ring drives the network to more symmetric traffic patterns and higher flow-rates. Furthermore we model a signalized intersection as two alternate diverge junctions and demonstrate that the signalized double-ring network can reach asymptotically periodic traffic patterns, which are therefore defined as “stationary” states in signalized networks. With simulations we show that the flow-density relation is well defined in such “stationary” states, and asymptotic traffic patterns can be impacted by signal cycle lengths and retaining ratios. But compared with uninterrupted interchanges, signalized intersections lead to more asymmetric traffic patterns, lower flow-rates, and even gridlocks when the average density is higher than half of the jam density. The results are consistent between this study and existing studies, but the network kinematic wave model, with appropriate junction models, is mathematically tractable and physically meaningful. It has offered a more complete picture regarding the number and type of stationary states, their stability, and MFD in freeway and signalized networks.

An empirical study of simulation-based dynamic traffic assignment procedures

Under Intelligent Transportation Systems (ITS), real-time operations of traffic management measures depend on long-term planning results, such as the origin–destination (OD) trip distribution; however, results from current planning procedures are unable to provide fundamental data for dynamic analysis. In order to capture dynamic traffic characteristics, transportation planning models should play an important role to integrate basic data with real-time traffic management and control. In this paper, a heuristic algorithm is proposed to establish the linkage between daily OD trips and dynamic traffic assignment (DTA) procedures; thus results from transportation planning projects, in terms of daily OD trips, can be extended to estimate time-dependent OD trips. Field data from Taiwan are collected and applied in the calibration and validation processes. Dynamic Network Assignment-Simulation Model for Advanced Road Telematics (DYNASMART-P), a simulation-based DTA model, is applied to generate time-dependent flows. The results from the validation process show high agreement between actual flows from vehicle detectors (VDs) and simulated flows from DYNAMSART-P.

A dynamic impairment-aware networking solution for transparent mesh optical networks

Core networks of the future will have a translucent and eventually transparent optical structure. Ultra-high-speed end-to-end connectivity with high quality of service and high reliability will be realized through the exploitation of optimized protocols and lightpath routing algorithms. These algorithms will complement a flexible control and management plane integrated in the proposed solution. Physical layer impairments and optical performance are monitored and incorporated in impairment-aware lightpath routing algorithms. These algorithms will be integrated into a novel dynamic network planning tool that will consider dynamic traffic characteristics, a reconfigurable optical layer, and varying physical impairment and component characteristics. The network planning tool along with extended control planes will make it possible to realize the vision of optical transparency. This article presents a novel framework that addresses dynamic cross-layer network planning and optimization while considering the development of a future transport network infrastructure.

TAP: Traffic-aware topology control in on-demand ad hoc networks

Energy efficiency is crucial to achieve satisfactory network lifetime in ad hoc networks. In order to reduce the energy consumption significantly, a node needs to turn off its transceiver. Many existing energy-saving algorithms are based on constructing a simplified routing backbone for global connectivity. In this paper some problems involved with node sleep in on-demand ad hoc networks are addressed firstly. Then we propose a distributed, cross-layer Traffic-Aware Participation (TAP) algorithm, where nodes make decisions on whether to sleep or not based on both the traffic pattern and local connectivity. Nodes get dynamic traffic characteristics as well as active neighbors within two hops via routing control and data packets periodically. We further present a lightweight algorithm to avoid network partition resulted from node sleeping. Simulation results show that, compared to current sleep-based topology control algorithms, TAP achieves better network service quality and lower delay while allowing comparable energy conservation.