Distribution Of Traffic Demand Research Articles

Double-Timescale Multi-Agent Deep Reinforcement Learning for Flexible Payload in VHTS Systems

With the expansion of the very-high-throughput satellite (VHTS) system, the uneven distribution of traffic demands in time and space has become increasingly significant and cannot be ignored. It is a significant challenge to efficiently and dynamically allocate scarce on-board resources to ensure capacity and demand matching. The advancement of flexible payload technology provides the possibility to overcome this challenge. However, computational complexity is increasing due to the unsynchronized resource adjustment and the time-varying demands of the VHTS system. Therefore, we propose a double-timescale bandwidth and power allocation (DT-BPA) scheme to effectively manage the available resources in the flexible payload architecture. We use a multi-agent deep reinforcement learning (MADRL) algorithm aiming to meet the time-varying traffic demands of each beam and improve resource utilization. The simulation results demonstrate that the proposed DT-BPA algorithm enhanced the matching degree of capacity and demand as well as reduced the system’s power consumption. Additionally, it can be trained offline and implemented online, providing a more cost-effective solution for the VHTS system.

CQDFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation

Due to the inherent difficulty in direct observation of traffic demand (including generation, attraction, and assignment), the estimation of origin–destination (OD) poses a significant and intricate challenge in the realm of Intelligent Transportation Systems. As the state-of-the-art methods usually focus on a single traffic demand distribution, accurate estimation of OD in the face of diverse traffic demand and road structures remains a formidable task. To this end, this study proposes a novel model, Cyclic Quasi-Dynamic Transformers (CQDFormer), which leverages forward and backward neural networks for effective OD estimation and traffic assignment. The employment of quasi-dynamic assumption and self-attention mechanism enables CQDFormer to capture the diverse and non-linear characteristics inherent in traffic demand. We utilize calibrated simulations to generate traffic count-OD pairwise data. Additionally, we incorporate real prior matrices and traffic count data to mitigate the distributional shift between simulation and the reality. The proposed CQDFormer is examined using Simuation of Urban Mobility (SUMO), on a large-scale downtown area in Haikou, China, comprising 2328 roads and 1171 junctions. It is found that CQDFormer shows satisfied convergence performance, and achieves a reduction of RMSE by 46.98%, MAE by 45.40% and MAPE by 29.76%, in comparison to the state-of-the-art method with the best performance.

Urban Resident Travel Survey Method Based on Cellular Signaling Data

A low-cost, timely, and durable long-term approach to resident travel surveys is crucial for authorities to understand the city’s transportation systems and formulate transportation planning and management policies. This paper summarizes commonly used wireless positioning technologies and uses the STDBSCAN method to identify travel endpoints based on the characteristics of trajectory location information. It uses Shenzhen cellular signaling data to visually analyze the spatial and temporal distribution of urban traffic demand, traffic correlation, and asymmetry of traffic flow between different traffic zones. The results confirm that mobile internet information represented by cellular signaling information can effectively reflect the traffic status of urban areas, which, compared to traditional travel survey methods, has the advantages of lower cost, more timely feedback, and can be durably carried out in the long term.

A Stochastic Network Calculus (SNC)-Based Model for Planning B5G uRLLC RAN Slices

Radio Access Network (RAN) slicing involves several challenges. In particular, the Mobile Network Operator (MNO) must ensure —before deploying each slice—that corresponding requirements can be met throughout its lifetime. For ultra-Reliable Low Latency Communication (uRLLC) slices, the MNO must guarantee the packet transmission delay within a delay budget with a certain probability. Most existing solutions focus on allocating dynamically radio resources to maximize the number of packets, whose transmission delay is within the delay budget. However, these solutions do not ensure the violation probability is below a target value in the long term. In this paper, we focus on slicing from a planning perspective. Specifically, we propose a Stochastic Network Calculus (SNC)-based model, which given the amount of radio resources allocated for a uRLLC slice, the target violation probability and the traffic demand distribution, provides the delay bound for such conditions. Additionally, we propose heuristics for planning uRLLC slices. Interestingly, such heuristics benefit from the proposed SNC-based model to compute the amount of radio resources to be assigned to each slice while its delay bound, given a target violation probability, is within the delay budget. We validate the SNC-based model and demonstrate the effectiveness of the heuristics.

Flight Training Syllabus Structure Impact on Proactive Planning of High-Performance Military Aircraft Pilot Training Operations in Flexible Airspace Structures

European airspace is marked by fragmentation, and from fragmentation originated congestion. As an answer to congestion, flexible use of airspace is created to fulfil all users’ requirements optimally, and at its very core lies civil-military cooperation and the coordination of airspace use. Such an approach is enabled by collaborative decision-making, which presumes timely and complete exchange of complete relevant information between airspace users. To participate in the process, the military should be transparent regarding its restrictions and capabilities of adaptation to other airspace users’ demands. Flight training syllabus structure and its specific impact on proactive planning of high-performance military aircraft pilot training operations in flexible airspace structures is the subject of this research. Significant diversity in the hourly distribution of civil traffic demand in flexible airspace structures in Zagreb FIR proves them applicable for a proactive approach to high-performance aircraft pilot training operations planning and management. Noticeable reduction in the number of civil aircraft affected by high-performance aircraft pilot training operations in flexible airspace structures at congested flight levels is corroborated. Simulations of proactive planning of high-performance aircraft pilot training operations reveal indications of flight syllabus structure’s impact on pilot flight training duration, as well as the possibility to manage its restrictions.

Construction of One-Way Car-Sharing System Considering Coalition Rationality Based on Mechanism Design

This article proposes a control algorithm for one-waycar sharing. The problem with one-way car sharing is that the origin and the destination parking lots tend to be biased because of user traffic demands, and the cost of relocating the vehicle to increase the operating rate is high. To solve this problem, we propose providing incentives to users in the form of discounts and changing the parking lots and departure times to prevent bias in the number of vehicles in the parking lots. In this study, the optimum parking lot was determined using a queue to incorporate the user traffic demand distribution. Furthermore, based on the Vickrey–Clarke–Groves mechanism design, an incentive design was created to satisfy desirable properties, such as strategy-proofness, individual rationality, and coalition rationality. In the proposed one-way car-sharing system, the system manager decides the proposal based on the information reported by the users. Therefore, satisfying these properties not only guarantees that the users will benefit from agreeing to the proposal, but also that even in case of a false declaration or shill bidding, the discount will not be unreasonably profitable. Finally, the increase in demand acceptance by applying the proposed method is confirmed by numerical simulation.

Study on the Distribution of the Suburban Expressway Hourly Traffic Volume and Modification of the Design Hourly Volume under the Background of the Tourism Economy—Analysis on Design Factors of Normalized Congestion in Suburban Expressway

An unreasonable design hourly volume (DHV) greatly impacts road facility construction costs and traffic efficiency. With the rapid rise in the tourism economy and widespread emergence of holiday travel characteristics in China, DHV applicability for suburban expressways requires verification. This study collected annual traffic volume data over 8760 h along Xi’an Ring Expressway from 2017 to 2019. Traffic demand distribution patterns throughout the year and peak hours were analyzed on the basis of the descending order of the obtained hourly traffic volume (HV) and factor data. HV distribution characteristics, 30th highest hourly volume (30 HV) typicality, and DHV factor applicability were investigated. Due to travel characteristics under the background of the tourism economy, the peak HV distribution exhibits polarization characteristics. The recommended value of the design hour traffic factor in the specification corresponded to a number of hours greater than 200, with the 30 HV factor under the background of the tourism economy being 25% higher than the recommended value. Considering the tourism economy, the high level of traffic operation time increased significantly, and the characteristics of a sharp decline in the peak HV disappeared. The 30 HV factor no longer exhibited traditional characteristics. The design causes of traffic congestion are identified herein.

Joint optimization algorithm of user association and power control based on spatial traffic distribution

Abstract5G networks must reduce network overhead while meeting user needs. Based on the flow model, this paper jointly optimizes the user association and power control in cellular networks, aiming at achieving a reasonable compromise between user delay and network energy consumption. Unlike previous snapshot models, flow models focus on the spatial distribution of traffic demand instead of the specific location of users. We consider a nonuniformity of spatial traffic distribution and load coupling among base stations (BSs). Firstly, an iterative algorithm is proposed to solve the optimal user association strategy under certain power of BSs. Then, a greedy power adjustment and user association algorithm (GPA‐UA) is proposed to further optimize the objective function. The simulation results show that the proposed algorithm can reduce both user delay and network energy consumption in most cases, and the performance of the proposed algorithm is approximate to the Optimal scheme while GPA‐UA has lower complexity.

Post-earthquake assessment model for highway bridge networks considering traffic congestion due to earthquake-induced bridge damage

Reliable post-earthquake modeling of a highway bridge network (HBN) is essential for seismic reliability, risk, and resilience assessment. This requires the accurate modeling of the effects of earthquake-induced bridge damage on traffic congestion. Accordingly, a novel post-earthquake HBN simulation method, wherein each bridge is defined as a link instead of as a part of a highway link, was proposed herein. In addition, the feasibility of implementing machine learning methods for fragility modeling of regular highway bridges was explored as well. The developed artificial neural network (ANN) fragility model can accurately capture the seismic damage of highway bridges at trivial computation costs compared to time-history analysis methods. Finally, the proposed methods were combined with probabilistic seismic hazard analysis, traffic demand assessment and distribution, etc., and applied to an HBN that connects several cities in Arizona and New Mexico to validate their efficacy and scalability. The results revealed that compared to the proposed HBN simulation method, conventional HBN modeling methods may underestimate the seismic resilience of HBNs.

Revealing spatiotemporal travel demand and community structure characteristics with taxi trip data: A case study of New York City.

Urban traffic demand distribution is dynamic in both space and time. A thorough analysis of individuals’ travel patterns can effectively reflect the dynamics of a city. This study aims to develop an analytical framework to explore the spatiotemporal traffic demand and the characteristics of the community structure shaped by travel, which is analyzed empirically in New York City. It uses spatial statistics and graph-based approaches to quantify travel behaviors and generate previously unobtainable insights. Specifically, people primarily travel for commuting on weekdays and entertainment on weekends. On weekdays, people tend to arrive in the financial and commercial areas in the morning, and the functions of zones arrived in the evening are more diversified. While on weekends, people are more likely to arrive at parks and department stores during the daytime and theaters at night. These hotspots show positive spatial autocorrelation at a significance level of p = 0.001. In addition, the travel flow at different peak times form relatively stable community structures, we find interesting phenomena through the complex network theory: 1) Every community has a very small number of taxi zones (TZs) with a large number of passengers, and the weighted degree of TZs in the community follows power-law distribution; 2) As the importance of TZs increases, their interaction intensity within the community gradually increases, or increases and then decreases. In other words, the formation of a community is determined by the key TZs with numerous traffic demands, but these TZs may have limited connection with the community in which they are located. The proposed analytical framework and results provide practical insights for urban and transportation planning.

Collaborative control of traffic signal and variable guiding lane for isolated intersection under connected and automated vehicle environment

AbstractExisting signalized intersection control methods allocate intersection resources to each phase from the time dimension based on traffic conflict. Solving the traffic control issues caused by the unbalanced traffic demands at intersections is always a challenge. Recently developed connected and automated vehicle (CAV) technologies render it possible to collect detailed real‐time traffic information from vehicles for the optimal control and management of time‐space resources at signalized intersections. In this paper, we propose a mixed integer quadratic programming to jointly optimize signal timings and variable guide lane settings for a typical four‐arm intersection in the CAV environment. From the time dimension, restrictions of the conventional concept of the signal cycle are overcome. Phase sequences, green start, and duration are freely assigned for each CAV platoon based on the movement‐based signal timing. From the space dimension, lane resources can be reasonably allocated in consideration of the dynamic traffic demand distribution. In this study, a demand‐response control is realized for the signalized intersection through time‐space resources cooperated optimization. Furthermore, vehicle trajectory control is integrated into the collaborative control framework to reduce or eliminate wasted green time. At last, we propose the dynamic control process using the collaborative control framework. Numerical simulation results show that the collaborative control method outperforms over fixed‐time and signal optimization control modes in terms of travel time with both under‐saturated and over‐saturated conditions.

Exploring Taxi Demand Distribution of Comprehensive Land-Use Patterns Using Online Car-Hailing Data and Points of Interest in Chengdu, China

With the rapid advance of urbanization, land-use intensity is increasing, and various land-use forms gather to form comprehensive land-use patterns. Traffic demand shows variability and complexity under comprehensive land-use patterns. Accurate analysis of traffic demand in urban transportation is the key to active traffic control and road guidance. Researchers have widely studied the relationship between traffic demand and land-use patterns, while land-use intensity is ignored when classifying land-use patterns, and the traffic demand distribution in each land-use pattern is not studied specifically. Taxi is a flexible public mode in urban areas, and taxi demand is an important component in analyzing traffic demand and identifying traffic hotspots in cities. This paper explores taxi demand distribution of comprehensive land-use patterns using online car-hailing data and points of interest (POI) in Chengdu, China. The demand-driven traffic analysis zones are developed by clustering origin–destination points of online car-hailing services. Using POI data, comprehensive land-use patterns are classified with land-use forms and land-use intensity. The K-shape algorithm is adopted to extract the typical taxi demand distribution in each comprehensive land-use pattern. Finally, two indicators, total taxi demand (TTD) and taxi demand difference (TDD), are computed and further analyzed. Results show that taxi demand distribution is still differential even under the same land-use pattern. Three land-use patterns whose average hourly taxi demand reaches about 300 vehicles per square kilometer have the largest TTD and most uneven TDD. The findings can support traffic management, land-use combination, and land-use adjustment to avoid concentrated taxi demand and mismatched TDD.

Managing ship lock congestion in an inland waterway: A bottleneck model with a service time window

This paper proposes a bottleneck model for managing the ship lock congestion in an inland waterway caused by the uneven temporal distribution of ship traffic demand. Due to safety checks and regular maintenance, the lock only works within a specified time window during a day. A bottleneck model with a service time window is presented to describe the dynamic scheduling process of the ships passing through the lock. The properties of the proposed model are investigated. To alleviate ships' delays at the lock area, two congestion tolling schemes (i.e., first-best and second-best) and a novel administrative scheme of moving the service time window (MST) are explored together with a combined scheme of tolling and MST. The results show that the ship's preferred departure time and the value of time for schedule delay matter in the choice of the counter-measures against ship traffic congestion. MST alone can be an effective substitute for the tolling schemes in most cases and it even outperforms the first-best tolling under certain conditions. This finding is appealing given the academic debate and public controversy raised by congestion tolling. Besides, combining MST with either tolling scheme can always yield higher efficiency. However, the benefits of MST can be marginal or even zero when the difference between the ship's preferred departure time and lock's initial service start time is around a certain value. In such cases, the authority should be cautious about combining any tolling scheme with MST since the implementation costs might outweigh the additional benefits brought by MST.

Multimodal transportation network centrality analysis for Belt and Road Initiative

As the Belt and Road Initiative (BRI) takes root, the Air Transportation Network (ATN) is gaining prominence, as evidenced by the Air Silk Road Initiative proposed in 2017. However, the ATN of the BRI countries and its integration with maritime and rail transport have not been studied. This paper analyzes the network structure of the ATN of the BRI countries. A multi-layer weighted betweenness metric is used to rank the centrality of the hubs in the network based on the connectivity, distance, and traffic between the airports. Using the centrality ranking of the airports to reflect the distribution of traffic demand in the BRI countries, a gravity model is then applied to quantify the centrality of the maritime and rail hubs. Mapping these hubs to their home cities, this paper assesses the current state of the multimodal transport hubs and recommends new hubs to increase the international trading reach of the BRI.

Quantifying the impact of COVID–19 preventive measures on traffic in the State of Qatar

Ever since the beginning of 2020, the mobility of people and goods all over the world has been extremely limited as a result of movement restrictions imposed by local authorities as part of several other efforts to stop the wide spread of COVID–19 pandemic. The Supreme Committee for Crisis Management in the State of Qatar adopted the policy of incremental preventive measures that were adjusted based on the pandemic status. These actions involved several restrictions, aiming a balance between reducing the pandemic spread and the typical daily activities disturbance. This paper assesses the impact of pandemic response measures on traffic mobility by quantifying the holistic impact of the incremental measures at different stages on traffic volumes and traffic safety. Daily traffic counts from 24 intersections were collected every 15-min for several days, representing the traffic before and after implementing each preventive measure. Besides, a screenline was used to represent the traffic entering and leaving the Central Business District (CBD) in the City of Doha. The results show that the daily traffic demand distribution over the course of day was not affected by those preventive measures. However, an overall demand reduction of 30% in baseline traffic was observed for all studied intersections and the screenline when all preventive measures were active. Moreover, the analysis of traffic violations and the total crashes indicated a drop of 73% and 37% respectively. The results from this assessment will assist decision and policy makers, and planners to prioritize traffic management actions for future needs. Further, the findings can also be utilized for mega–event traffic management in the post-COVID era, such as FIFA World Cup 2022 and 2030 Asian Games.

Resource Allocation for LEO Beam-Hopping Satellites in a Spectrum Sharing Scenario

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

Radio Resource Management Techniques for Multibeam Satellite Systems

Next–generation of satellite communication (SatCom) networks are expected to support extremely high data rates for a seamless integration into future large satellite-terrestrial networks. In view of the coming spectral limitations, the main challenge is to reduce the cost (satellite launch and operation) per bit, which can be achieved by enhancing the spectral efficiencies. In addition, the capability to quickly and flexibly assign radio resources according to the traffic demand distribution has become a must for future multibeam broadband satellite systems. This letter presents the radio resource management problems encountered in the design of future broadband SatComs and provides a comprehensive overview of the available techniques to address such challenges. Firstly, we focus on the demand-matching formulation of the power and bandwidth assignment. Secondly, we present the scheduling design in practical multibeam satellite systems. Finally, a number of future challenges and the respective open research topics are described.

Scheduling preferences, parking competition, and bottleneck congestion: A model of trip timing and parking location choices by heterogeneous commuters

This study examines the effects of a time-varying congestion toll and a location-dependent parking fee on the behavior of heterogeneous commuters and their commuting costs. To this end, we develop a model of departure time and parking location choices by heterogeneous commuters and characterize its equilibrium. By comparing the equilibrium with and without pricing policies, we obtain the following results: (1) imposing a parking fee and expanding parking capacity may concentrate the temporal distribution of traffic demand, thereby exacerbating traffic congestion; (2) the expansion of parking capacity does not necessarily lead to a Pareto improvement when a parking fee is not imposed; (3) the social optimum is achieved by combining a parking fee with a congestion toll; and (4) the revenue obtained from pricing of roads and parking exactly equals the costs for optimal bottleneck and parking capacities, respectively; that is, the self-financing principle holds separately for bottleneck capacity and parking capacity.

Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network

Precise traffic demand prediction could help government and enterprises make better management and operation decisions by providing them with data-driven insights. However, it is a nontrivial effort to design an effective traffic demand prediction method due to the spatial and temporal characteristics of traffic demand distributions, dynamics of human mobility, and impacts of multiple environmental factors. To handle these problems, a Dynamic Transition Convolutional Neural Network (DTCNN) is proposed for the purpose of precise traffic demand prediction. Particularly, a transition network is first constructed according to the citiwide historical departure and arrival records, where the nodes are virtual stations discovered by a density-peak based clustering algorithm and the edges of two nodes correspond to transition flows of two stations. Then, a dynamic transition convolution unit is designed to model the spatial distributions of the traffic demands, and to capture the evolution of the demand dynamics. Last, a unifying learning framework is provided to incorporate the spatiotemporal states of the traffic demands with environmental factors. Experiments have been conducted on NYC taxi and bike-sharing data, and the results validate the effectiveness of the proposed method.

Robust Signal Control of Exit Lanes for Left-Turn Intersections With the Consideration of Traffic Fluctuation

Traffic throughput at intersections can be improved by using exit lanes for left-turning (EFL), tidal flow lanes near an intersection, which has been recently introduced. This paper considers the operational robustness of the EFL intersection in relation to the control scheme applied to the traffic light setting. For safety and efficiency, it is important that the tidal lanes are emptied before traffic in the opposing direction uses these lanes. Hence, the signal control should not only be optimized for the mean value but be robust for all kinds of fluctuations. This paper formulates a traffic control scheme using robust optimization, i.e. an optimization scheme which explicitly accounts for extreme events. The fluctuation of traffic is considered from three aspects: the distribution of traffic demand, the distribution of base saturation flow rate, and the distribution of actual travel speed. Via a case study and extensive numerical analysis, we find that the established robust optimization method produces an efficient design of signal control and design speed at the EFL intersection under traffic demand and supply fluctuations. Though the optimization method is now applied to intersections with an EFL, it is considered useful for all intersections with high fluctuation of traffic demand and saturation flow rate.