Traffic Demand Research Articles

Electric vehicle drivers’ choices of expressway usage and peak avoidance: an empirical analysis considering the random effects among individuals

ABSTRACT Traffic congestion is a persistent challenge in urban areas, particularly with increased private car ownership post-COVID-19. Traditional administrative measures to manage traffic demand have proven unsustainable, necessitating more effective strategies. This study examines trajectory data from 3,064 commuting (CMT) and 2,690 non-commuting (Non-CMT) electric vehicles in Shanghai to analyze how travel purposes, distances, directions, and departure times influence expressway usage and peak avoidance decisions. It identifies significant differences in choices between CMT and Non-CMT users, highlighting the need for personalized travel management methods based on vehicle usage patterns. Route management strategies should focus on commuting trips for CMT vehicle users, while spatial control measures considering the trip distance and direction can reduce Non-CMT vehicle users’ dependence on expressways during peak hours. This research contributes to enhancing our understanding of the heterogeneity of travelers’ behaviors and offers personal and practical insights for managing traffic congestion sustainably in metropolitan cities.

Unlocking mode programming with multi-plane light conversion using computer-generated hologram optimisation

Abstract Programmable optical devices provide performance enhancement and flexibility to spatial multiplexing systems enabling transmission of tributaries in high-order eigenmodes of spatially-diverse transmission media, like multimode fibre (MMF). Wavefront shaping with spatial light modulators (SLMs) facilitates scalability of the transmission media by allowing for channel diagonalization and quasi-single-input single-output operation. Programmable mode multiplexing configurations like multi-plane light conversion (MPLC) utilise the SLM and offer the potential to simultaneously launch an arbitrary subset of spatial tributaries in any N -mode MMF. Such programmable optical processor would enable the throughput of space-division multiplexing (SDM) systems to be progressively increased by addressing a growing number of tributaries over one MMF and in this way meet a growing traffic demand – similarly to the wavelength-division multiplexing evolution path. Conventionally, MPLC phasemasks are calculated using the wavefront matching algorithm (WMA). However, this method does not exploit the full potential of programmable mode multiplexers. We show, that computer-generated hologram algorithms like direct search enable significant improvement compared to the traditional WMA-approach. Such gains are enabled by tailored cost functions with dynamic constraints concerning insertion loss as wellas mode extinction ratio. We show that average mode extinction ratio can be greatly improved by as much as ≈ 15 dB at the expense of a small insertion loss35
deterioration of < 3 dB. One particular feature of programmable mode multiplexers is the adaptability to optimised transmission functions. Besides conventional LP modes transmission, we employ our approach on Schmidt modes, which are spatial eigenchannels with minimum crosstalk derived from a measured transmission matrix

Collaborative control framework at isolated signalized intersections under the mixed connected automated vehicles environment

AbstractThis study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane. Based on the delay analysis, the spatiotemporal resource collaborative optimization model of lane setting and signal timing is established to minimize the average delay. Subsequently, in the buffer zone, a graph‐theoretic‐based sorting and platooning model provides a clear and concise representation of the transformation process from the initial state to the target state of vehicles, enabling the platoon formation. Additionally, trajectory optimization is integrated into the collaborative control framework by the optimal control model and car‐following model in the passing zone. Simulation experiments and sensitivity analyses demonstrate the effectiveness of the proposed framework in reducing average vehicle delay, improving fuel consumption, and coping with changing traffic demand at intersections.

Do the sparser access points have less impact on arterial traffic? A microscopic simulation-based study

A dynamic microscopic traffic flow simulation within a ring arterial context was developed to investigate the effects of access point spacing on urban arterial flow under a right-in-right-out access management system. The microscopic traffic flow model, centered on car-following and lane-changing behaviors, was established based on vehicle interactions. The car-following aspect encompasses free driving, car-following behavior, and deceleration and braking states while lane-changing considerations include decision-making and acceptable gap assessment. Experimental scenarios account for arterial traffic density, access traffic demand intensity, average access point spacing, and variation coefficient of access point spacing. The traffic flow and speeds within the ring arterial were evaluated across 5040 operational conditions (equating to 5880 simulation hours). The traffic flow trends and speed variations with density across different access spacing scenarios were analyzed. We made an intriguing discovery: the impact on arterial traffic flow increases with larger average access point spacing, challenging conventional traffic planning recommendations that advocate for greater spacing. Additionally, access traffic minimally affects the overall arterial flow when arterial traffic volume is low. By highlighting these critical insights, this study introduces novel considerations for designing and managing access points.

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption. The max-min planning investigates energy efficiency only from the links/nodes perspective. The max-min planning for energy-efficient networking, if not carefully designed from the network-wide standpoint, can lead to lower energy efficiency for the whole network due to lack of global planning, which in turn results in the degraded performance due to network un-connectivity after closing the nodes/links. In this paper we rethink the max-min planning framework on energy-efficient software-defined networking for intelligent networking of 5G networks, which takes in account combining network connectivity and maximum network flow with minimum energy consumption. Our framework aims at how to deliver dynamic end-to-end traffic demands with the appropriate network topology by building data forwarding plane with maximum network flow and control plane with network connectivity. We discuss the associated challenges and implementation issues. A dynamic max-min planning framework depending on dynamic end-to-end traffic demands is presented to achieve network-wide energy efficiency. Numerical results show the improved energy efficiency performance for the whole network.

Impact of stochastic vehicle generation on traffic microsimulation model output

One of the most important characteristics of microsimulation is the ability to model the temporal and spatial nature of traffic demand. Every traffic microsimulation has a vehicle generation model that determines how and when the driver–vehicle units are introduced in the simulation. While microsimulation use has become increasingly popular, it is unclear how the vehicle generation options affect the final result. The purpose of this paper is to examine the stochastic component of the vehicle generation model and how it may affect the simulation output. Three scenarios, including the passenger car equivalent (PCE) model of the Highway Capacity Manual (HCM-7), were used for assessing stochastic volumes and their impacts on performance measures. Results indicate a statistically significant impact of the variability of stochastic volumes on the estimation of performance measures at the breakdown flow phase and synchronized flow phase for interrupted and uninterrupted flow conditions, respectively. This finding highlights the importance of reporting vehicle generation as a calibration parameter, enabling others to replicate the experiments. When utilizing microsimulations for the assessment of roadway/system performance, it is crucial for users to have a thorough understanding of demand generation.

The interchange stations siting model of urban rail transit network

The "node anchor weaving method" is commonly used in urban rail transit network planning, which emphasizes identifying the important nodes of the network first, and then using the key nodes as the "anchor nodes" to construct the network. Interchange stations are the most crucial network nodes in terms of both service function and network function. As a result, when planning the network, it is critical to identify the interchanges ahead of time. A key node identification model is initially established in this article, and then a network interchange stations location model is built on top of it. Based on the urban road network, the key node identification model takes into account the location of the road network nodes, the number of neighboring nodes, the influence of the neighboring nodes, and the path information between the nodes, and the key nodes in the network are effectively identified through the improvement of the gravitational model and the neighboring core model, and the model's validity is verified through the SIR contagion model. The online network interchange stations siting model considers the matching degree of residents' traffic demand, the interchange stations coverage to the city, the intensity of land use, and the interchange stations' duplicate coverage to the urban space as evaluation indices for the interchange stations; the objective of solving the interchange stations is the comprehensive optimality of the indexes; in order to avoid the problem of perturbation in the optimization and solution algorithm, this paper presents an improved genetic-simulated annealing optimization algorithm that introduces the concept of community, and avoids the problem of perturbation in the optimization and solution algorithms. The effectiveness of the model is verified by using examples of the Mitte-Center and Friedrichshain networks.

Asynchronous decentralized traffic signal coordinated control in urban road network

AbstractThis study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between decision‐making and state prediction among different agents in decentralized coordinated decision‐making problems, enabling agents to proceed with collaborative decision‐making without waiting for other agents’ decisions. Within the proposed ADCSC framework, each controller dynamically optimizes its signal timing strategy with a unique rolling horizon scheme. The scheme's individualized parameters for each controller are determined based on the vehicle travel time between the adjacent intersections, ensuring that controllers can make informed control decisions with accurate arrival flow information from upstream intersections. The signal optimization problem is formulated as a mixed integer linear program model, which adopts a flexible signal scheme without a fixed phase structure and sequence. Simulation results demonstrate that the proposed ADCSC strategy significantly outperforms the benchmark signal coordination methods in terms of average delay, travel speed, stop numbers, and energy consumption. Experimental analysis on computation time validates the applicability of the proposed optimization model for real‐time implementation. Sensitivity analysis on key parameters in the framework is conducted, offering insights for parameter selection in practice. Furthermore, the ADCSC framework is extended to a road network in Qinzhou City, China, with 45 signalized intersections, demonstrating its effectiveness and scalability in the real‐world road network.

Research on the Impact and Strategies of Rail Transit Network Integration in the Context of Metropolitan Area Development: A Case Study of the Wuhan Metropolitan Area

The metropolitan area is one of the key focal points in the construction and development of China’s new urbanization. Urban integration is an emerging trend in metropolitan areas. This paper explores the traffic demand characteristics and economic aspects of rail transit within metropolitan regions and argues that the construction of an integrated urban rail transit network is an effective approach to support their development. Rail transit in metropolitan areas offers both technical and economic advantages, improving the efficiency of time and space resource utilization, fostering economic cooperation, and ultimately contributing to an integrated development model. However, the integration of rail transit networks faces several challenges, including road network planning, technical standards, and operational organization. Using the Wuhan metropolitan area as a case study, this paper analyzes the challenges of rail transit network integration and proposes strategic solutions for development.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Data transmission optimization based on multi-objective deep reinforcement learning

Abstract Simultaneously reducing network energy consumption and delay is a hot topic today. This paper addresses this issue by designing a novel multi-objective data transmission optimization algorithm based on deep reinforcement learning. A three-layer back propagation (BP) neural network is designed to improve the accuracy of environmental state prediction, by learning from historical state and action sequence data, which can help the agent make better decision for routing selection in complex network environment. Based on this, we use Q-Learning to find routing for transmission demands, aggregating more traffic through less links and routers, to reduce energy consumption and delay. To enhance the efficiency and robustness of the algorithm, a new reward mechanism is designed based on the traffic demand and the link state. The algorithm divides candidate links into three levels for path selection so that a better solution can be obtained on the basis of ensuring feasible solutions are obtained. Continuous updating of the Pareto set through multiple state steps approximates the optimal solution. We leverage the Euclidean distance to the reference point to measure the optimization effect of the two objectives. The simulation results show that this algorithm outperforms existing algorithms in reducing energy consumption and network delay.

Analysis of mixed traffic flow characteristics based on cellular automata model under lane management measures

In the future, mixed traffic flow comprising Human-Driven Vehicles (HDVs), Connected and Autonomous Vehicles (CAVs), and CAV platoons will coexist for an extended period. Exploring the operational characteristics of mixed traffic flow under different lane management measures is essential for effective management and control. Initially, we analyze the motion characteristics of HDVs, CAVs, and CAV platoons and identify the car-following types within mixed traffic. Based on vehicle motion characteristics and the variance in maximum desired speeds among HDV drivers, we establish longitudinal motion rules for HDVs using a safety distance model. For CAVs and their platoons, we develop longitudinal motion rules by considering platoon merging and splitting behaviors, as well as speed and spacing error requirements for platoon driving. Subsequently, we formulate lateral lane-change rules based on the Symmetric Two-lane Cellular Automata (STCA) model, considering differences in reaction times and following distances between HDVs and CAVs. Finally, we conducted simulation experiments on a unidirectional three-lane highway using the multi-lane mixed traffic flow cellular automata model, analyzing the characteristics of mixed traffic under various lane management measures, such as mixed lane, CAV-dedicated lane, and CAV-priority lane. The results show that as the proportion of CAV (denoted as p) increases, the traffic flow capacity, optimal density, and jam density also increase. When p≤0.6, the k−q diagram exhibits a triangular shape, whereas for p>0.6, it assumes a trapezoidal shape. Implementing CAV-dedicated lane can reduce Cooperative Adaptive Cruise Control (CACC) degradation rates but only enhances traffic flow capacity when the CAV proportion reaches a certain threshold. Compared to the mixed lane scheme, when p≤0.3 and k≤0.3, the CAV-priority lane schemes not only meet traffic demands but also reduce CACC degradation rates. The vehicle speed in CAV-priority lane surpasses that in HDV lanes, facilitating improved traffic efficiency for CAVs. The distribution of maximum speeds among HDV drivers affects the fundamental diagram of mixed traffic flow and the performance of CAV-priority lane, with greater impacts observed as the standard deviation of the HDVs' maximum speed increases.

A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites.

Toward the vision of seamless global connectivity in the 6G era, the non-terrestrial network (NTN) in space-air-ground integrated networks (SAGINs) network architecture is one of the highly promising solutions. From the perspective of relay nodes, NTN includes satellite nodes and space-based platform nodes. As a resource management technology in satellite communication, beam-hopping has garnered significant attention from researchers due to its effectiveness in ad-dressing the disparity between offered capacities and uneven terrestrial traffic demands. Recognizing that the larger beams offer broader coverage but the smaller ones provide better an-ti-interference capabilities and higher throughput, this paper introduces an adaptive cluster-ing-based approach. It provides large, medium, and small user beams to target ground users. The proposed algorithm aims to minimize total system latency and enhance system throughput. Sim-ulation results show that employing the proposed algorithm in the baseline model results in a 3.44% increase in system throughput and a 35.5% reduction in system latency. Furthermore, simulation results based on alternative models indicate that while the proposed algorithm may lead to a slight decrease in system throughput, it brings significant improvements in system latency.

An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization

AbstractSurrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.

Spatial Negative Influence of Cairo Mega Movement corridors on adjacent Local Areas Land Uses and Movement

The research is focused on discussing the importance of transportation in metropolitan areas and analyzing the positive and negative spatial impacts of the major movement corridors in Greater Cairo on the surrounding local areas. It aims to highlight the regional benefits of these corridors while also addressing the spatial challenges faced by the local areas. The study emphasizes the significance of movement circulation for pedestrians and vehicles in the urban functionality of local movement, serving as a connection between land uses and daily activities. While the mega movement corridors have significantly improved access between Greater Cairo's main districts, they have also introduced challenges at the local level. The research examines the impact of the Ain Shams corridor as an example of a new movement corridor introduced by the government in the last 5 years, in two major areas, and additionally, identifies the resulting challenges in land uses and movement circulation in these areas. The findings underscore the importance of segregating movement circulation between the regional and local levels to enhance accessibility for pedestrians and vehicles in local areas. Furthermore, the study reinforces the importance of comprehensive measures to enhance the efficiency of the corridor. Implementing solutions such as dedicated service roads, improved parking infrastructure, and optimized traffic management strategies can play a pivotal role in mitigating congestion and addressing the specific needs of local traffic. By addressing these challenges, the corridor can achieve improved functionality, accommodate the diverse traffic demands, and enhance the overall transportation experience for commuters and urban activities.

Investigation of Traffic System with Traffic Restriction Scheme in the Presence of Automated and Human-Driven Vehicles

In the context of transportation development, the simultaneous emergence of automated vehicles (AVs) and human-driven vehicles (HDVs) can lead to varied traffic system performance. For the purpose of improving traffic systems, this paper proposes a traffic restriction scheme only for HDVs. We develop a variational inequality (VI) model to describe travel mode and route choices under this restriction scheme and design an algorithm to solve the model. The proposed model and algorithm are applied to a Sioux Falls network example to evaluate the effects of the traffic restriction scheme. Our findings indicate that the scheme improves overall social welfare, with a higher proportion of restricted travelers leading to greater social welfare as well as increased travel demand due to changes in capacity. However, some lower exogenous monetary factors lead to negative social welfare, as the presence of AVs may exacerbate road congestion. Additionally, advancements in technology are needed to adjust the weightings of travel time and congestion level estimates to further enhance social welfare. These results offer valuable insights for traffic demand management in traffic systems with a mix of AVs and HDVs.

Review on road congestion pricing: a long-term land use effect perspective

Road congestion pricing (RCP) is an important way of controlling traffic congestion from the perspective of traffic demand management. This paper introduces the concept and four main types of applications of RCP, summarises the development of RCP theory, reveals the first- and second-order effects of RCP on transportation systems and land use, and analyses the long-term impact of RCP on the spatial location structure of land use. Relevant research progress considering land use effects is systematically and comprehensively reviewed from three research perspectives: demand modelling (pre-RCP), monitoring and analysis (post-RCP) and modelling methods (integration models and other models). The results showed that RCP will affect residents’ travel behaviour in the short term and will lead to changes in spatial location decisions in the long run, thus creating new travel demands and travel modes, affecting the transportation system and urban spatial patterns again. Implementing cordon charges has an agglomeration effect on the population in city centres. Cordon charges also have a significant negative impact on businesses in the toll area and a dispersive effect on jobs. Combined with appropriate strategies for shared autonomous vehicles, RCP can simultaneously improve transportation efficiency and regional accessibility.

Modelling of wake-induced vibration of a long-span bridge with separated parallel nonidentical decks

The separated parallel decks design for long-span bridges can meet higher traffic demands, offering advantages in urban bridge design. However, when exposed to wind, the periodic wake shedding from the front bridge deck significantly affects the rear deck, leading to wake-induced vibrations (WIV). To address this issue, this study proposes and compares four models to comprehensively predict the wake-induced vibration of a long-span bridge with separated parallel nonidentical decks. The predictive capabilities of the four models for WIV are meticulously compared, and the reasons for the observed errors are analyzed. Ultimately, a model capable of accounting for the concurrent influences of wake-induced forces, instantaneous structural motion feedback, and non-linear self-excited forces is obtained. A series of wind tunnel tests are conducted to validate and evaluate the proposed models. Besides, this study encompasses an analysis of how structural damping and the deck layouts influence WIV. These findings bear substantial relevance to engineering practices confronted with similar challenges, thereby contributing to a deeper understanding of the intricate dynamics at play in such complex structural configurations.

Artificial Intelligence-Based Adaptive Traffic Signal Control System: A Comprehensive Review

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.