Traffic Evolution Research Articles

Resilience Analysis of Transport Networks by Combining Variable Message Signs With Agent-Based Day-to-Day Dynamic Learning

To date the resilience of transport networks has not been effectively modelled by taking into account the traffic dynamics along with individual drivers' learning process and irrational behaviours. This study proposes an agent-based day-to-day dynamic model with bounded rationality to capture traffic evolution and drivers' inertial behaviours when transport networks suffer from local capacity degradation, and variable message signs are incorporated into the proposed model to improve the resilience, which is indicated by the rapidity of recovering to a new approximation equilibrium after disruptions. We employ a small network as a numerical study to conduct resilience analysis, and variable message signs with different compliance rates are utilized to induce traffic flows for alternative routes when a given link of the network is subject to mild (25%), moderate (50%), severe (75%) capacity reduction. The results show that variable message signs can apparently improve the resilience of the network in most of cases, and a larger compliance rate of variable message signs does not necessarily lead to better rapidity of recovery for approximation equilibrium. This study may provide an insight into the resilience analysis and improvement of transport networks under different levels of disruptions, which fully takes into account the individual drivers' day-to-day learning process, behavioural inertial and the control mechanism of variable message signs with different compliance rates.

Macroscopic Model on Driver Physiological and Psychological Behavior at changes in Traffic

A model is presented which can accurately characterize macroscopic traffic evolution. This model is based on analogies with the ideal gas law. A traffic constant is proposed which considers both physiological and psychological driver behavior. Physiological behavior is the time taken to observe and process local traffic conditions and then initiate actions while psychological behavior includes the attitude and awareness of a driver. Thus, the traffic constant encompasses the perception, awareness, attitude, and reaction of a driver. The proposed model is evaluated for changes in traffic flow caused by an inactive bottleneck. The results are compared with those for the Payne-Whitham (PW) model. This shows that the temporal and spatial evolution of traffic with the proposed model is more realistic.

Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions

This study investigates the dynamical evolution of travelers’ departure time choice in a bottleneck-constrained roadway system with stochastic capacity. When considering day-to-day traffic evolution, this study explores two scenarios: travelers have long-term historical knowledge and real-time information provision; and travelers have short-term travel experience and real-time information provision. In this context, this study models boundedly rational travelers. This study sheds light on the trade-off between daily (short-term) equilibrium and statistical (long-term) equilibrium.

Clustered robust routing for traffic engineering in software-defined networks

One of the key advantages of Software-Defined Networks (SDN) is the opportunity to integrate Traffic Engineering modules able to optimize network configuration according to traffic. Ideally, the network should be dynamically reconfigured as traffic evolves, so as to achieve remarkable gains in the efficient use of resources with respect to traditional static approaches. Unfortunately, reconfigurations cannot be too frequent due to a number of reasons related to route stability, forwarding rules instantiation, individual flows dynamics, traffic monitoring overhead, etc.In this paper, we focus on the fundamental problem of deciding whether, when and how to reconfigure the network during traffic evolution. We propose a new approach to cluster relevant points in the multi-dimensional traffic space taking into account similarities in multiple domains and not only in traffic values. Moreover, to provide more flexibility to the decisions on when to apply a reconfiguration, we allow some overlap between clusters that can guarantee a good-quality routing even in case of smooth transitions.We compare our algorithm with state-of-the-art approaches in realistic network scenarios. Results show that our method significantly reduces the number of reconfigurations with a negligible deviation of the network performance with respect to the continuous update of the network configuration.Moreover, we present an experimental platform where our solution is implemented in a production-ready SDN controller.

Mixed Traffic of Connected and Autonomous Vehicles and Human-Driven Vehicles: Traffic Evolution and Control using Spring-Mass-Damper System

This paper sheds light on mixed-traffic dynamics considering the differences in driving characteristics, namely acceleration/deceleration rate, desired speed, and response time, between connected and autonomous vehicles (CAVs) and human-driven vehicles (HDVs). In light traffic, these differences were found to induce platoon formations, headed by vehicles with a lower acceleration rate and propensity not to exceed the desired speed (HDV in this study). Platoon formations lead to large inter-platoon spacing that can be utilized to accommodate cut-in vehicles. In a near-capacity condition, however, the differences in driving characteristics can induce voids and undermine traffic throughput when traffic is disturbed by merging vehicles. Based on these findings, a simple CAV control method is proposed based on the spring-mass-damper (SMD) system approach that directly considers the HDV behavior to mitigate disturbance propagation and throughput reduction. The main principle is to adjust the control parameters (lower spring coefficient and higher damping coefficient in the SMD control model) with an aim to control CAVs to absorb the cut-in impact (i.e., spacing shortage) before it reaches the first upstream HDV. A simulation experiment suggests the feasible region of the control parameters, subject to the recovery time, the number of controllable CAVs, and the cut-in impact.

Bi-level route guidance method for large-scale urban road networks

This paper proposes a bi-level route guidance method based on macroscopic fundamental diagram (MFD) for urban road network, which is a combination of central route guidance and distributed route guidance and it considers the intentions of both traffic management and traveler. It is a compromise of system optimum and user optimum. In upper-level route guidance, traffic dynamic evolution model of route guidance sub-region based on MFD is constructed and system optimal dynamic traffic assignment method on traffic guidance sub-region level is proposed. In lower-level route guidance, the traffic guidance paths are generated by solving the optimal path problem of the reactive users. Through analysis of the data gathering from Changchun City, China, it is verified that the proposed method not only meets the real-time requirements of dynamic traffic guidance but also provides benefits for the whole traffic system and individual traveler.

Learning and managing stochastic network traffic dynamics with an aggregate traffic representation

This study estimates and manages the stochastic traffic dynamics in a bi-modal transportation system, and gives hints on how increasing data availability in transport and cities can be utilized to estimate transport supply functions and manage transport demand simultaneously. In the bi-modal system, travelers’ mode choices are based on their perceptions of the two travel modes: driving or public transit. Some travelers who have access to real-time road (car) traffic information may shift their mode based on the information received (note that real-time information about public transit departures/arrivals is not considered here). For the roadway network, the within-day traffic evolution is modeled through a Macroscopic Fundamental Diagram (MFD), where the flow dynamics exhibits a certain level of uncertainty. A non-parametric approach is proposed to estimate the MFD. To improve traffic efficiency, we develop an adaptive pricing mechanism coupled with the learned MFD. The adaptive pricing extends the study of Liu and Geroliminis (2017) to the time-dependent case, which can better accommodate temporal demand variations and achieve higher efficiency. Numerical studies are conducted on a one-region theoretical city network to illustrate the dynamic evolution of traffic, the MFD learning framework, and the efficiency of the adaptive pricing mechanism.

MFC Free-Flow Model: Introducing Vehicle Dynamics in Microsimulation

Free-flow movement of vehicles in microsimulation software is usually defined by a set of equations with no explicit link to the instantaneous dynamics of the vehicles. In some cases, the car and the driver are modeled in a deterministic way, producing a driving behavior, which does not resemble real measurements of car dynamics or driving style. Depending on the research topic, the interest in microsimulation is to capture traffic dynamics phenomena, such as shockwave propagation or hysterisis. Existing car-following models are designed to simulate more the traffic evolution, rather than the vehicle motion, and consequently, minimal computational complexity is a strong requirement. However, traffic-related phenomena, such as the capacity drop are influenced by the free-flow acceleration regime. Furthermore, the acceleration pattern of a vehicle plays an essential role in the estimation of the energy required during its motion, and therefore in the fuel consumption and the CO2 emissions. The present work proposes a lightweight microsimulation free-flow acceleration model (MFC) that is able to capture the vehicle acceleration dynamics accurately and consistently, it provides a link between the model and the driver and can be easily implemented and tested without raising the computational complexity. The proposed model is calibrated, validated, and compared with known car-following models on road data on a fixed route inside the Joint Research Centre of the European Commission. Finally, the MFC is assessed based on 0–100 km/h acceleration specifications of vehicles available in the market. The results prove the robustness and flexibility of the model.

Compositional data techniques for the analysis of the container traffic share in a multi-port region

The statistical techniques based on compositional data are applied to investigate the evolution of the traffic share of the container throughput in a multi-port system. Compositional vectors are those which contain relative information of parts of some whole. The application of conventional statistical techniques to compositional data may lead to erroneous conclusions and spurious correlations. Therefore, compositional data (CoDa) should be treated taking into account their own mathematical structure. The so-called log-ratio approach provides a set of transformations that allow to apply conventional statistical techniques to the transformed compositional data samples. Thus, the objective of this paper is double. As a first stage it aims to introduce the CoDa formalism and highlight its potentiality in the port container throughput analysis as example of transport system providing an applied example: the container throughput evolution in the Spanish Mediterranean Ports system during the period 1976–2015. Second, based on the previous analysis, the aim is to characterize the container throughput in SpanishMed ports and its temporal evolution. The CoDa analysis clarifies the interpretation and data association of the container traffic throughput evolution in function of some selected change points: boom of containerization in 1990s and 2008 crisis. This contribution proves that the CoDa methodology is useful to investigate the complexity of the transport disciplines in order to understand and to manage the spatial integration that results from the movement of people and freight.

A Boundedly Rational Day-to-Day Rerouting Model with Dynamic Indifference Band and Net Marginal Cost Preference

This paper suggests factoring both dynamic indifference band (DIB) and marginal decision rule into boundedly rational day-to-day traffic dynamics modeling. To this purpose, a relative indifference band based DIB is employed to capture the variation of indifference band against dynamical network traffic conditions, and the net marginal cost preference (NetMCP) is newly introduced and quantified to replace the routine marginal cost preference as an amended behavior criterion for rerouting decision. To concentrate on the essence, DIB and NetMCP are mathematically formulated into the traffic evolution equation of the classical proportional-switch adjustment process with a new revision protocol, leading to the present dynamically bounded rational swapping process with NetMCP (DBRSP-NetMCP), of which the stationary state is defined and proved. It is also proved that DBRSP-NetMCP is a bounded rational behavior swapping process; and given separable link travel times, DBRSP-NetMCP is globally stable. It is numerically demonstrated that i) an object-oriented modelling approach may cause obvious deviation in rerouting behavior from the behavior-oriented one, and thus is inadvisable to model the day-to-day traffic dynamics; and ii) both DIB and NetMCP show striking effect on traffic evolution, and NetMCP helps to shorten the evolution path towards the stationary states, which may contribute to better the performance of a swapping algorithm to solve the deterministic traffic equilibrium.

From a feeder port to a hub port: The evolution pathways, dynamics and perspectives of Ningbo-Zhoushan port (China)

This paper analyses the spatio-temporal evolution of Ningbo-Zhoushan port growing from a feeder port to a hub port finding the historical pathways followed by its expanding in terms of container throughput capacity and total traffic. The dynamic mechanism of evolution is the results of economic globalization, containerization and its natural endowments in channel and terminal depths. Analysis of the traffic evolution and its underlying dynamics suggest 3 periods in the development processes of container transport in Ningbo-Zhoushan: (1) adoption period (1986–2000) in which the main dynamics is the impact of the Chinese ‘Open Door policy’; (2) acceleration period (2001–2008) in which the dynamics is related of the mainland China's entry into the World Trade Organization; (3) peak growth period (2009-now) in which the dynamics is impacted by the anti-crisis strategy against the financial and economic crisis in 2008. We analyse the perspectives of Ningbo-Zhoushan port. ARIMA model is employed to forecast the container traffic in the coming future; about after 2026, the throughput in Ningbo-Zhoushan port would reach about 49 million TEU which would be approximately equal to that of Shanghai port. The resultant port development would exemplify a model of spatial distribution such as a multi-layered gateway hub. In the respect of growth potential, Ningbo-Zhoushan port possesses excellent coastline resource suitable for deep-water berthing, bonded port policy and free trade zone policy. Geographical position, service level, hinterland economic level and government will support its perspectives.

Distance-Based Congestion Pricing with Day-to-Day Dynamic Traffic Flow Evolution Process

This paper studies the distance-based congestion pricing in a network considering the day-to-day dynamic traffic flow evolution process. It is well known that, after an implementation or adjustment of a new congestion toll scheme, the network environment will change and traffic flows will be nonequilibrium in the following days; thus it is not suitable to take the equilibrium-based indexes as the objective of the congestion toll. In the context of nonequilibrium state, prior research proposed a mini–max regret model to solve the distance-based congestion pricing problem in a network considering day-to-day dynamics. However, it is computationally demanding due to the calculation of minimal total travel cost for each day among the whole planning horizon. Therefore, in order to overcome the expensive computational burden problem and make the robust toll scheme more practical, we propose a new robust optimization model in this paper. The essence of this model, which is an extension of our prior work, is to optimize the worst condition among the whole planning period and ameliorate severe traffic congestions in some bad days. Firstly, a piecewise linear function is adopted to formulate the nonlinear distance toll, which can be encapsulated to a day-to-day dynamics context. A very clear and concise model named logit-type Markov adaptive learning model is then proposed to depict commuters’ day-to-day route choice behaviors. Finally, a robust optimization model which minimizes the maximum total travel cost among the whole planning horizon is formulated and a modified artificial bee colony algorithm is developed for the robust optimization model.

Analyzing Random Access Collisions in Massive IoT Networks

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

Impact of personalised route recommendation in the cooperation vehicle-infrastructure systems on the network traffic flow evolution

ABSTRACTInspired by the prevailing recommendation system application, personalised travel factors are introduced into route recommendation in order to provide more human-oriented travel service. With real-time information provided by the cooperation vehicle-infrastructure systems (CVIS), four real travel factors including distance, grade, time and toll are adopted to construct a route feature vector and an individual traveler preference feature vector, respectively. A novel route recommendation model based on Pearson’s correlation coefficient is formulated. A searching algorithm of all feasible routes is designed that achieves a better balance of time and space complexity. Considering that the traveler has heterogeneity in the numerous ways of using route recommendation information and choosing a satisfactory route, individual compliance with the route recommendation is creatively proposed and used to imitate a day-to-day route choice. A specific simulation with Monte Carlo method is conducted on a test network to show the dynamic evolution features of network traffic flow.

Network Traffic Flow Evolution Model Based on Disequilibrium Theory

The disequilibrium theory in economics is used to depict the network traffic flow evolution process from disequilibrium to equilibrium. Three path choice behavior criteria are proposed, and the equilibrium traffic flow patterns formed by these three criteria are defined as price regulation user equilibrium, quantity regulation user equilibrium, and price-quantity regulation user equilibrium, respectively. Based on the principle of price-quantity regulation user equilibrium, the method of network tatonnement process is used to establish a network traffic flow evolution model. The unique solution of the evolution model is proved by using Picard’s existence and uniqueness theorem, and the stability condition of the unique solution is derived based on stability theorem of nonlinear system. Through numerical experiments, the evolution processes of network traffic flow under different regulation modes are analyzed. The results show that all the single price regulation, single quantity regulation, and price-quantity regulation can simulate the evolution process of network traffic flow. Price-quantity regulation is the combination of price regulation user equilibrium and quantity regulation user equilibrium, which thus can simulate the evolution process of network traffic flow with multiple user class.

Optical Power Control in Translucent Flexible Optical Networks With GMPLS Control Plane

The continuously increasing traffic of Internet services (cloud services, video streaming, social networks, and recently, Internet of Things services) is leading to huge traffic growth in core optical networks. This traffic evolution is pushing network operators to efficiently exploit their infrastructures in order to postpone, as much as possible, the expensive deployment of new infrastructures. In this respect, the migration from fixed-to flex-grid optical networks was triggered in order to efficiently use optical network capacity, taking benefits from the improved spectral efficiency of flexible transponders. In our previous work [J. Opt. Commun. Netw., vol. 8, no. 8, p. 553, Aug. 2016], we demonstrated that migrating towards flexible networks while keeping in use existing optical amplifiers will cause a power saturation problem over highly loaded links due to the increase in the number of optical channels. To overcome this problem, we proposed in that work a power adaptation process that consists of converting transmission performance margins into optical power attenuation over optical links. However, the realized work considered only a transparent optical network controlled by the generalized multiprotocol label switching (GMPLS) protocol suite. In this paper, we consider the case of a translucent optical network where optical regeneration is required, and thus, the power adaptation process is adapted to this kind of network. A new routing algorithm and protocol extensions are proposed to take into account power and regeneration information in the GMPLS control plane of translucent networks.

Short-Term Traffic State Prediction Based on the Spatiotemporal Features of Critical Road Sections.

Recently, short-term traffic prediction under conditions with corrupted or missing data has become a popular topic. Since a road section has predictive power regarding the adjacent roads at a specific location, this paper proposes a novel hybrid convolutional long short-term memory neural network model based on critical road sections (CRS-ConvLSTM NN) to predict the traffic evolution of global networks. The critical road sections that have the most powerful impact on the subnetwork are identified by a spatiotemporal correlation algorithm. Subsequently, the traffic speed of the critical road sections is used as the input to the ConvLSTM to predict the future traffic states of the entire network. The experimental results from a Beijing traffic network indicate that the CRS-ConvLSTM outperforms prevailing deep learning (DL) approaches for cases that consider critical road sections and the results validate the capability and generalizability of the model when predicting with different numbers of critical road sections.

Effects of vehicle gap changes on fuel economy and emission performance of the traffic flow in the ACC strategy.

This paper focuses on the effects of vehicle gap changes on fuel and emission performance of the simulated traffic flow in the adaptive cruise control (ACC) strategy. Firstly, the close correlation of vehicle gap changes and the host car's behaviors was explored with the measured car-following data. Secondly, the correlation between the host car's velocity and vehicle gap changes with different memory steps was also explored to develop the nth car’s optimal velocity function. Thirdly, a microscopic traffic simulation program was created for analyzing the traffic flow evolution process and approximately estimating the fuel consumptions and exhaust emissions. As a result, it was seen that vehicle gap changes with memory significantly affect fuel economy and emission performance of the simulated traffic flow in the ACC strategy, which can result in low fuel consumptions and exhaust emissions. This study is an incremental step forward for designing the control strategy of the ACC system.

Exponential MLWDF (EXP-MLWDF) Downlink Scheduling Algorithm Evaluated in LTE for High Mobility and Dense Area Scenario

Nowadays, with the advent of smartphones, most of people started to make voice and video conference calls continuously even in a high mobility scenario, the bandwidth requirements have increased considerably, which can cause network congestion phenomena. To avoid network congestion problems and to support high mobility scenario, 3GPP has developed a new cellular standard based packet switching, termed LTE (Long Term Evolution). The purpose of this paper is to evaluate the performance of the new proposed algorithm, named Exponential Modified Largest Weighted Delay First ‘EXP-MLWDF’, for high mobility scenario and with the presence of a large number of active users, in comparison with the well-known algorithms such as a proportional fair algorithm (PF), Exponential Proportional Fairness (EXP/PF), Logarithm Rule (LOG-Rule), Exponential Rule (EXP-Rule) and Modified Largest Weighted Delay First (MLWDF). The performance evaluation is conducted in terms of system throughput, delay and PLR. Finally, it will be concluded that the proposed scheduler satisfies the quality of service (QoS) requirements of the real-time traffic in terms of packet loss ratio (PLR), average throughput and packet delay. Because of the traffic evolution, some key issues related to scheduling strategies that will be considered in the future requirements are discussed in this article.

Managing Day-to-Day Network Traffic Evolution via an Altering Ex-Post Information Release Strategy

AbstractProviding travelers with full ex-post travel time information from day to day can only make a disequilibrium network evolve to the user equilibrium, rather than the system-optimal or other ...