Traffic Arrival Patterns Research Articles

One-stage estimation of cyclic arrival rates using license plate recognition data

Traffic arrival on urban roads usually reveals time-varying rates due to the effects of signal control, motivating us to investigate the distribution of arrival rates within the signal cycle. However, such information is not readily available as only a portion of arrival traffic can be observed in the field. Previous studies have utilized different data sources and proposed probability-based models to solve the estimation problem. However, these studies rely heavily on the assumption that the observed vehicles are evenly distributed in the arrival traffic flow and exact signal timings are known. This study uses license plate recognition (LPR) data collected at adjacent signalized intersections to estimate cyclic arrival rates in a historical period. A probability-based model is formulated by parameterizing the vehicle arrival distribution as a mixture of circular distributions. This facilitates us to describe similar traffic arrival patterns over different signal cycles and capture multiple arrival platoons from different signal phases within the signal cycle. The proposed model is validated in a field experiment with different arrival patterns and traffic conditions. Model performance under both full and partial monitoring of LPR sensors at the upstream intersection is analyzed. The robustness of the proposed model with unevenly sampled observations in arrival traffic flow is also investigated.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Stochastic capacity analysis for a distributed connected automated vehicle virtual car-following control strategy

Capacity analysis of the pure connected automated vehicle (CAV) traffic remains a challenging problem due to the high-dimensional factors involved in the control design. Especially, the communication loss and communication topology greatly impact the headway variation of CAVs and hence capacity with stochastic properties. This study provides a stochastic framework to mathematically derive multiple factors’ impact including free-flow speed, control gains, communication loss, and traffic arrival pattern on the pure CAV traffic capacity based on a virtual car-following control strategy targeting a single-lane highway and merging section. To begin with, we first mathematically derive the stochastic capacity for a single-lane highway based on a multi-predecessor-based linear feedback and feedforward car-following model for generic stochastic communication loss models. For a further illustration, a detailed analysis is conducted based on a well-known Signal-to-Interference-plus-Noise Ratio (SINR) communication loss model. We then extend the derivation to a merging section by a virtual car-following concept considering traffic arrival pattern’s stochasticity. Numerical sensitivity analyses have been conducted to systematically evaluate the impact of multiple factors mentioned. As the result indicated, the stochastic communication loss and traffic arrival pattern do have a significant impact on the pure CAV traffic capacity of the above scenarios.

A Study of Delay Estimation Methods at Signalized Intersections for Mixed Traffic Condition

An effective traffic control measure is the one that can significantly reduce delays incurred due to rising traffic congestion and improves travel time reliability. To achieve this, an accurate estimation of delay is very critical. The method of delay estimation varies with the type of data source available, and the type of data collected. This paper aims at studying various methods of delay estimation at an intersection for data types from three data sources—location-based data (videography), Wi-Fi sensor data, and GPS based probe data. The challenges associated with executing each of these methods are also discussed. Besides, a scalable and reliable data source among the three was chosen to calibrate and validate the delay equation suggested by Highway Capacity Manual 2000 (HCM 2000) to suit Indian traffic conditions. A linear regression model was fitted for the progression factor (PF) of the HCM delay equation with an R squared value of 0.85. Validation of the calibrated model yielded an average Mean Absolute Percentage Error (MAPE) of 12.59%. The calibrated model can be used for the estimation of delay based on historic traffic arrival patterns and signal timings.

Lane-Based Traffic Arrival Pattern Estimation Using License Plate Recognition Data

Understanding the traffic arrival process and its patterns is of vital importance for delay and queue analysis at intersections. The installation of advance loop detectors to sense vehicle arrivals could be costly and biased. Utilizing sampled vehicle trajectory data to reconstruct the traffic arrival flow might suffer from small sample sizes. License plate recognition (LPR) data commonly available at intersections in cities in China are promising for overcoming such limitations. This study aims to estimate a lane-based traffic arrival pattern by using LPR data collected at downstream and upstream intersections. The proposed method develops a probability model with an assumption of a two-stage piecewise arrival process for upstream merge movements. Given observations of vehicle arrivals provided by matched cars and trucks in LPR data, the model estimates second-based mean arrival rates for each lane at the downstream intersection. The proposed method is validated using actual LPR data collected at two adjacent intersections in Kunshan City, China. The results demonstrate that the proposed method can describe the traffic arrival patterns of upstream merge movements with either two-stage or uniform arrival processes in different traffic scenarios. In addition, the proposed method is more robust and reliable than an average-based benchmark method in terms of revealing actual traffic arrival patterns under different match rates.

Adaptive DRX Method for MTC Device Energy Saving by Using a Machine Learning Algorithm in an MEC Framework

Machine-type communication (MTC) is recognized as an enabling building block for constructing the Internet of Things (IoT) through cellular networks. Since MTC devices (MTCDs) are usually powered by batteries, reducing the energy consumption of an MTCD is an important requirement in the design of an IoT system. The third-generation partnership project (3GPP) specified discontinuous reception (DRX) to save energy in user equipment (UE) by making a UE device sleep whenever it is not involved in data transmission or reception. However, UE is used for human-type communications (HTCs), and MTCs are different from HTCs in many ways, namely, in terms of traffic arrival patterns, mobility, and information priority. Thus, using the DRX developed for UE to save the energy of an MTCD leads to inefficiency in terms of the amount of power saved and the data transmission delay. In the literature, many efforts have been made to dynamically vary the DRX cycle based on traffic activities to reduce the energy consumption of an MTCD. These assume a specific distribution for the traffic arrival process and predict the next packet arrival time to control the DRX cycle. However, since the characteristics of the MTC traffic arrival pattern vary widely according to the types of applications and the network status, previous approaches are limited in the achievable energy efficiency and the range of MTC applications that they can support. To address these issues, we devise adaptive DRX (ADRX) by adopting a machine learning algorithm called bank-of-experts. Without making any assumption regarding the underlying traffic process, ADRX predicts the next packet arrival time using only the observed packet arrival times. Using the predicted next packet arrival time, ADRX dynamically adjusts the sleep interval. In terms of implementation, ADRX is executed in an eNB by following the Multi-access Edge Computing (MEC) framework. Thus, an MTCD avoids consuming the energy required to execute a sophisticated energy-saving algorithm while obtaining the benefits provided by ADRX. Since different IoT services have different requirements for the amount of saved energy and data transmission delay, we design a loss function by combining these two performance metrics so that ADRX can support a wide range of application services. We first analyze the bound of the cumulative regrets of ADRX, and then through simulations, we compare the performance of ADRX with the standard DRX with optimal sleep duration (OSDRX) and a generalized and autonomous DRX (GDRX) scheme. The results show that ADRX outperforms GDRX in terms of the loss function and has almost the same performance as OSDRX.

Analysis of Self-similar Nature Vehicle Arrival Data Pattern on Arterial Roads

Traffic congestion is a main criterion of increasing the disturbance of the movements of traffic on roads. In various metropolitan areas managing the traffic congestion is a difficult task, it was investigated that busy traffic reveals self-similarity, influenced by this aspect, realtime road traffic arrival data pattern proven as self-similar paradigm by means of disparate Hurst index methods. The current article furnishes various proficiency used to measure Hurst index which is popular demarcate of self-similar behavior traffic. In this paper, real time data is used to investigate the pattern of vehicular arrival data traffic flow at Mancherial Chowrasta intersection of Karimnagar City. Traffic arrival data patterns are recorded from location is processed and analyzed using Hurst index method named as correlogram. Quantitative results finally reveal that analysis shown in this study is supportive to manage the traffic signals at that junction, and to discover methods to avoid congestion traffic streams.

Analysis of discontinuous reception (DRX) on energy efficiency and transmission delay with bursty packet data traffic

Discontinuous reception (DRX) is a way for user equipment (UE) to save energy. DRX forces a UE to turn off its transceivers for a DRX cycle when it does not have a packet to receive from a base station, called an eNB. However, if a packet arrives at an eNB when the UE is performing a DRX cycle, the transmission of the packet is delayed until the UE finishes the DRX cycle. Therefore, as the length of the DRX cycle increases, not only the amount of UE energy saved by the DRX but also the transmission delay of a packet increase. Different applications have different traffic arrival patterns and require different optimal balances between energy efficiency and transmission delay. Thus, understanding the tradeoff between these two performance metrics is important for achieving the optimal use of DRX in a wide range of use cases. In this paper, we mathematically analyze DRX to understand this tradeoff. We note that previous studies were limited in that their analysis models only partially reflect the DRX operation, and they make assumptions to simplify the analysis, which creates a gap between the analysis results and the actual performance of the DRX. To fill this gap, in this paper, we present an analysis model that fully reflects the DRX operation. To quantify the energy efficiency of the DRX, we also propose a new metric called a real power-saving (RPS) factor by considering all the states and state transitions in the DRX specification. In addition, we improve the accuracy of the analysis result for the average packet transmission delay by removing unrealistic assumptions. Through extensive simulation studies, we validate our analysis results. We also show that compared with the other analysis results, our analysis model improves the accuracy of the performance metrics.

A Novel Medium Access Control Algorithm for Ad Hoc Networks Based on Ising Model

Medium Access Control (MAC) scheduling in ad hoc networks is a challenging task due to the trade-offs between fairness, delay and throughput. In this work, we propose a distributed link scheduling algorithm based on the Ising Model from statistical mechanics, by associating a novel Hamiltonian measure with the network such that its optimization yields a feasible schedule. This work overcomes the shortcomings of previous Ising Model based algorithms by incorporating queuing and servicing dynamics as well as fairness measures for improving aggregate throughput and network latency. Our simulations show considerable improvement in performance compared to existing benchmarks over a variety of traffic arrival patterns and network topologies.

Modeling and Performance Assessment of Dynamic Rate Adaptation for M2M Communications

With the advance of Internet of Things and the support of a diverse array of smart-world applications, the number of Machine-to-Machine (M2M) devices has continued to grow at an accelerated rate. This significant and unchecked increase poses enormous challenges to both M2M infrastructure and the coexistence of M2M applications. In this paper, we model the traffic arrival patterns of time-driven, event-driven and loop-driven M2M applications, and propose a novel dynamic rate adaptation (DRA) scheme to obtain an optimized service rate distribution among a mixture of diverse M2M applications. DRA introduces real-time monitoring of M2M traffic arrival rate, building on which a service rate distribution between M2M applications can be incrementally adjusted moment to moment, through the use of the mean value theorem of integrals (MVTI) and generalized processor sharing (GPS). Via a combination of both theoretical analysis and extensive performance evaluation, we have validated the effectiveness of our proposed DRA scheme. Our experimental results demonstrate that DRA can significantly improve M2M communications performance with respect to throughput, delay, and energy consumption. In addition, we extend our proposed DRA scheme from the perspective of Device-to-Device (D2D) communications and further discuss the resilience of DRA.

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

Performance analysis of dual connectivity in control/user-plane split heterogeneous networks

To ensure seamless mobility, logical separation between control plane and data plane has been evolved as a promising solution for the forthcoming fifth generation (5G) cellular networks. Here, macro-cells provide control coverage using a low frequency band signal, whereas small-cells provide high data rates to the mobile terminals (MTs) over high frequency band signals. While performing frequent handovers over small-cells, throughput perceived by an MT may fall below the requested data rate. To deal with the high data rate demand of the forthcoming 5G networks, recently dual connectivity (DC) technology has been proposed for long term evolution (LTE) networks in Release 12. However, analyzing the performance of DC in control/user-plane (C/U) split LTE heterogeneous networks is quite limited in the preceding literature. In this work, we propose an analytical framework to compare the performances of DC with hard handover in C/U split architecture in terms of system throughput and saturation probability, i.e., the probability that the total demand for resources exceeds the total capacity of the serving cells. Our proposed framework explicitly considers the data rate demands of the MTs, traffic arrival pattern and channel conditions. The analytical results have also been validated against simulation results. Our analyses reveal that the performance gain of DC over traditional hard handover is actually conditional on underlying traffic load density and call arrival rates.

<italic>TrafficShaper:</italic> Shaping Inter-Datacenter Traffic to Reduce the Transmission Cost

The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet service providers with the widely adopted $q$ th percentile charging model . In such a charging model, the time slots with top ( $100-q$ ) percent of data transmission do not affect the total transmission cost and can be viewed as “free.” This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, a very little work has been done to exploit those “free” time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to “free” time slots, or inevitably relies on prior knowledge of future traffic arrival patterns. In this paper, we present TrafficShaper , a new scheduler that shapes the inter-DC traffic to exploit the “free” time slots involved in the $q$ th percentile charging model , so as to reduce or even minimize the transmission cost. When shaping traffic, TrafficShaper advocates a simple principle: more traffic peaks should be scheduled in “free” time slots, while less traffic differentiation should be maintained among the remaining time slots. To this end, TrafficShaper designs a pricing-aware control framework, which makes online decisions for inter-DC transfers without requiring a prior knowledge of traffic arrivals. To verify the performance of TrafficShaper , we conduct rigorous theoretical analysis based on Lyapunov optimization techniques, large-scale trace-driven simulations, and small-scale testbed implementation. Results from rigorous mathematical analyses demonstrate that TrafficShaper can make the transmission cost arbitrarily close to the optimum value. Extensive trace-driven simulation results show that TrafficShaper can reduce the transmission cost by up to 40.23%, compared with the state-of-the-art solutions. The testbed experiments further verify that TrafficShaper can realistically reduce the transmission cost by up to 19.38%.

PD-MAC: Design and Implementation of Polling Distribution-MAC for Improving Energy Efficiency of Wireless Sensor Networks

Reducing the energy consumed by MAC layer in Wireless Sensor Networks (WSNs) can lead to major performance improvements. A number of previous scholars have focused on optimizing the energy usage in WSN through adjusting the wake-up intervals, duty cycles and polling intervals. Some of the protocols focusing on dynamically adjusting the duty-cycle or channel polling intervals based on the traffic arrival patterns include SCP-MAC, RIMAP, AS-MAC, MCAS-MAC, LAD and self-adaptive wakeup scheduling scheme. This paper aims at improving the energy efficiency of MAC protocols by studying the influence of using ‘polling interval distributions’ rather than ‘polling intervals’ for different traffic types. The influence of using deterministic and exponential polling interval distributions is illustrated by developing an asynchronous MAC protocol PD-MAC (Polling Distribution-MAC). Two types of traffic: deterministic and exponential has been used for evaluating the performance of PD-MAC. TinyOS platform for mica2 motes has been used for implementation. The impact of varying message generation intervals and polling intervals, over the delay and energy performance of PD-MAC has been studied. Results have revealed that PD-MAC with exponentially distributed polling intervals results in improved energy performance in contrast to PD-MAC with deterministic polling intervals. Similarly, PD-MAC with exponentially distributed polling intervals is shown to outperform established MAC protocol SCP-MAC. Hence, the major contribution of PD-MAC is to introduce an energy efficient MAC solution for WSN applications operating under different traffic patterns without the need of extensive memory usage or computational complexity.

Impacts of traffic flow arrival pattern on the necessary queue storage space at metered on-ramps

ABSTRACTThis paper compares queue lengths at the two different on-ramp configurations: arterial-to-freeway ramp and freeway-to-freeway connector. Mesoscopic queue length simulation models are developed based on the input-output method. The input-output method aims to model the traffic flow arrival profile; then, the mesoscopic simulation model generates random traffic volume to simulate the stochasticity of traffic flow. The simulation models are validated using field-collected data. Simulation results indicated that the traffic flow arrival pattern has significant impacts on ramp queue length; vehicle platoons released from the upstream signalized intersection tend to exacerbate ramp queue length. At metered ramps, the required queue storage length is found to be 5.7% of demand when demand is less than 500 vehicles per hour per lane (vphpl), or 3.9% when demand is greater than 500 vehicles per hour per lane (vphpl). In comparison, at metered connectors, the percent numbers are 4.7 and 2.1%, respectively.

Linear Regression Model Fitting and Implication to Self Similar Behavior Traffic Arrival Data Pattern at Web Centers

Linear Regression Model Fitting and Implication to Self Similar Behavior Traffic Arrival Data Pattern at Web Centers

On Sample-Path Optimal Dynamic Scheduling for Sum-Queue Minimization in Trees Under the $K$-Hop Interference Model

We investigate the problem of minimizing the sum of the queue lengths of all the nodes in a wireless network with a tree topology. Nodes send their packets to the tree's root (sink). We consider a time-slotted system and a $K$ -hop interference model. We characterize the existence of causal sample-path optimal scheduling policies in these networks, i.e., we wish to find a policy such that at each time-slot, for any traffic arrival pattern, the sum of the queue lengths of all the nodes is minimum among all policies. We provide an algorithm that takes any tree and $K$ as inputs, and outputs whether a causal sample-path optimal policy exists for this tree under the $K$ -hop interference model. We show that when this algorithm returns FALSE, there exists a traffic arrival pattern for which no causal sample-path optimal policy exists for the given tree structure. We further show that for certain tree structures, even noncausal sample-path optimal policies do not exist. We provide causal sample-path optimal policies for those tree structures for which the algorithm returns TRUE. Thus, we completely characterize the existence of such policies for all trees under the $K$ -hop interference model. The nonexistence of sample-path optimal policies in a large class of tree structures implies that we need to study other (relatively) weaker metrics for this problem.

Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: Computational issues and optimization

Advanced connected and automated vehicle technologies enable us to modify driving behavior and control vehicle trajectories, which have been greatly constrained by human limits in existing manually-driven highway traffic. In order to maximize benefits from these technologies on highway traffic management, vehicle trajectories need to be not only controlled at the individual level but also coordinated collectively for a stream of traffic. As one of the pioneering attempts to highway traffic trajectory control, Part I of this study (Zhou et al., 2016) proposed a parsimonious shooting heuristic (SH) algorithm for constructing feasible trajectories for a stream of vehicles considering realistic constraints including vehicle kinematic limits, traffic arrival patterns, car-following safety, and signal operations. Based on the algorithmic and theoretical developments in the preceding paper, this paper proposes a holistic optimization framework for identifying a stream of vehicle trajectories that yield the optimum traffic performance measures on mobility, environment and safety. The computational complexity and mobility optimality of SH is theoretically analyzed, and verifies superior computational performance and high solution quality of SH. A numerical sub-gradient-based algorithm with SH as a subroutine (NG-SH) is proposed to simultaneously optimize travel time, a surrogate safety measure, and fuel consumption for a stream of vehicles on a signalized highway section. Numerical examples are conducted to illustrate computational and theoretical findings. They show that vehicle trajectories generated from NG-SH significantly outperform the benchmark case with all human drivers at all measures for all experimental scenarios. This study reveals a great potential of transformative trajectory optimization approaches in transportation engineering applications. It lays a solid foundation for developing holistic cooperative control strategies on a general transportation network with emerging technologies.

Estimation of Traffic Arrival Pattern at Signalized Intersection using ARIMA Model

Traffic arrival pattern is an important parameter in delay estimation at signalized intersection. Usually, arrival pattern is assumed to be Poisson distribution. However, it varies widely under different volume-capacity ratio at signalized intersection of an arterial road. Therefore, conventional Poisson model cannot predict traffic pattern properly. A time series ARIMA model was proposed in this study to compare with Poisson model. A Paramics simulation model of Route 18 arterial road located in New Brunswick, New Jersey, USA was studied for the research purpose. Three intersections in Route 18—Naricon Place intersection, South Woodland Avenue intersection and West Ferris Street intersection— were considered where traffic arrivals were under dispersed, random and over dispersed under different simulation scenarios respectively. Later, traffic arrival patterns obtained from simulation were compared with Poisson and ARIMA model using SAS statistical software. Arrival headway, vehicle counts per signal cycle, variance to mean ratio were considered and statistical analysis were performed between two candidate models. Study found that, ARIMA model predicts arrival pattern more accurately than Poisson model. General Terms Traffic Engineering, Computer Simulation, Times Series Model.

An Efficient Battery Lifetime Aware Power Saving (EBLAPS) Mechanism in IEEE 802.16e Networks

The IEEE 802.16e standard is an emergent broadband wireless access technology that added the mobility feature to the original standard. This feature made battery life of an operated mobile subscribe station (MSS) a bigger challenge because an MSS is powered by a rechargeable battery. The battery lifetime mechanism has to be created in order to prolong the battery-life of an MSS. The battery lifetime-aware power saving (BLAPS) scheme has been created to prolong the battery life of an MSS by adaptively adjusting the three-sleep parameters named idle threshold, initial sleep window, and final sleep window according to the residual energy and the traffic load. However, the scheme minimized the energy consumption of the MSS at the expense of the average response delay due to the effect of the remaining energy. It also used the standard sleep mode algorithm where the MSS frequently goes to listening mode when the traffic is low which leads to the high-energy consumption. In this paper, a new energy mechanism called efficient battery life-aware power saving scheme has been proposed to enhance the parameters of BLAPS. The three-sleep parameters in BLAPS mechanism are analytically enhanced according to the downlink stochastic traffic arrival pattern of an MSS. Moreover, an improved sleep mode control algorithm has been introduced to reduce the frequent transition to listening mode in case of low traffic. The simulation has been extensively used to evaluate the proposed scheme. The results have shown that the proposed scheme outperforms the BLAPS significantly in terms of both the average response delay and the average energy consumption.