Local Traffic Congestion Research Articles

Empirical analysis of congestion spreading in Seoul traffic network.

Understanding how local traffic congestion spreads in urban traffic networks is fundamental to solving congestion problems in cities. In this work, by analyzing the high-resolution data of traffic velocity in Seoul, we empirically investigate the spreading patterns and cluster formation of traffic congestion in a real-world urban traffic network. To do this, we propose a congestion identification method suitable for various types of interacting traffic flows in urban traffic networks. Our method reveals that congestion spreading in Seoul may be characterized by a treelike structure during the morning rush hour but a more persistent loop structure during the evening rush hour. Our findings suggest that diffusion and stacking processes of local congestion play a major role in the formation of urban traffic congestion.

Heterogeneous impacts of local traffic congestion on local air pollution within a city: Utilizing taxi trajectory data

This study estimates the causal effects of traffic congestion on air quality in local areas within a city, highlighting the spatial and temporal variation in local traffic, local air pollution, and health risks to residents due to local pollutant exposure. We employ a large-scale taxi driving trajectory data to construct measures of traffic congestion in local areas surrounding air quality monitoring stations in Beijing, on an hourly basis. The station-by-hour panel data enables the estimation of both contemporaneous and lagged marginal effects of local traffic on local air pollution. We find that local traffic congestion increases concentrations of NO2 and CO contemporaneously, but concentrations of PM2.5 and PM10 in a 2-h lagged manner. The magnitude and significance of the estimates vary with local traffic and pollution conditions, implying heterogeneous marginal effects on local air pollution of local traffic across locations, hours of the day, months and seasons. These results stress the importance of designing traffic policies that are both spatially and temporally fine-grained to attain efficiency and equity.

Dynamic collaborative optimization coverage algorithm of UAV based on trajectory prediction in Internet of Vehicles

This research presents a dynamic pre-deployment strategy based on vehicle trajectory prediction information to address the issues of coverage gaps of base stations and local traffic congestion in urban vehicle networking. Under the architecture of distributed federated learning and blockchain, numerous UAVs equipped with edge computing servers eliminate the central aggregation node and use an enhanced Raft method to train a unified Seq2Seq-GRU trajectory prediction model. In the training cycle, the scheme selects the nodes to perform parameter aggregation and model update based on the quantity of data given. Second, based on the model’s prediction findings, this study offers an enhanced virtual force guide deployment method that directs the UAV to dynamically deploy across each virtual force in order to increase the vehicle’s access rate and communication quality.

The need for sustainable local management to solve the reality of increasing traffic congestions in Iraq

The need to find solutions based on sustainable methods for the transportation sector in Iraq has become an urgent necessity due to the ever-increasing volume of traffic congestion caused mainly by the increase in the number of vehicles and the total dependence on them for transportation in the absence of other alternatives. This increase in traffic congestion drains all of the fossil energy in addition to losses in time, health and comfort of users and those close to the roads. Also, it increases the costs in maintaining roads and vehicles, as well as the harmful effects on the environment. A number of literatures have been studied to explain the effects and causes of traffic congestion, especially in countries whose conditions are similar to Iraq. The roads and intersections in the city of Baghdad were monitored, and a number of experts were requested to discuss the causes. A sample questionnaire was conducted that included several levels and ages of Iraqi society to determine the sample's impression on a number of points affecting the local traffic congestion in Baghdad city. It was revealed through the questionnaire that there are four main causes for traffic congestion and there is a strong desire among Iraqis to own and drive vehicles, which led to a significant and continuous increase in their numbers, and this is one of the main causes of congestion.

Spatial-Temporal Conv-Sequence Learning With Accident Encoding for Traffic Flow Prediction

In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations. Current state-of-the-art methods for predicting traffic flow are based on graph architectures and sequence learning models, but they do not fully exploit dynamic spatial-temporal information in the traffic system. Specifically, the temporal dependencies in the short-range are diluted by recurrent neural networks. Moreover, local spatial information is also ignored by existing sequence models, because their convolution operation uses global average pooling. Besides, accidents may occur during object transition, which will cause congestion in the real world and further decrease prediction accuracy. To overcome these challenges, we propose Spatial-Temporal Conv-sequence Learning (STCL), where a focused temporal block uses unidirectional convolution to capture short-term periodic temporal dependencies effectively, and a spatial-temporal fusion module is responsible for extracting dependencies of interactions and decreasing the feature dimensions. Moreover, as the accidents features have an impact on local traffic congestion, we employ position encoding to detect anomalies in complex traffic situations. We have conducted a large number of experiments on real-world tasks and verified the effectiveness of our proposed method.

From Search-for-Parking to Dispatch-for-Parking in an Era of Connected and Automated Vehicles: A Macroscopic Approach

The advantage of self-relocation of connected and automated vehicles (CAVs) can eliminate heavy searching-for-parking traffic in areas with limited parking availability. However, the floating trips will exacerbate local traffic congestion and parking competition if relocated CAVs are not well distributed in the network. To address these issues, this paper proposes a centralized dispatching-for-parking system to dynamically dispatch CAVs between different regions to optimize parking resource utilization and traffic distribution. A macroscopic modeling approach is presented with the consideration of mixed traffic flows of human-driven vehicles (HDVs) and CAVs. The system dynamics are modeled with the representation of the macroscopic fundamental diagram (MFD) in a multiregion road network. The objective of the system is to minimize the total network delay, which is formulated by the framework of model predictive control (MPC). Results of the numerical experiments in a two-region network show that the approach improves the performance of system operation and alleviates traffic congestion and imbalance between parking supply and demand in downtown areas. The sensitivity analysis on the level of CAV penetration reveals that the total network delay gradually decreases with the penetration increase, and HDVs benefit more from the MPC controller. The study demonstrates the applicability and implication of the dispatching-for-parking system in an era of CAVs.

Heterogeneous Impacts of Local Traffic Congestion on Local Air Pollution within a City: Utilizing Taxi Trajectory Data

Heterogeneous Impacts of Local Traffic Congestion on Local Air Pollution within a City: Utilizing Taxi Trajectory Data

Impact of COVID-19 Social Distancing Policies on Traffic Congestion, Mobility, and NO2 Pollution

Lockdowns implemented during the COVID-19 pandemic were utilized to evaluate the associations between “social distancing policies” (SDPs), traffic congestion, mobility, and NO2 air pollution. Spatiotemporal linear mixed models were used on city-day data from 22 US cities to estimate the associations between SDPs, traffic congestion and mobility. Autoregressive integrated moving average models with Fourier terms were then used on historical data to forecast expected 2020 NO2. Time series models were subsequently employed to measure how much reductions in local traffic congestion were associated with lower-than-forecasted 2020 NO2. Finally, the equity of NO2 pollution was assessed with community-level sociodemographics. When cities’ most stringent SDPs were implemented, they observed a 23.47 (95% CI: 18.82–28.12) percent reduction in average daily congestion and a 13.48 (95% CI: 10.36–16.59) percent decrease in average daily mobility compared to unrestricted days. For each standard deviation (8.38%) reduction in local daily congestion, average daily NO2 decreased by 1.37 (95% CI: 1.24–1.51) parts per billion relative to its forecasted value. Citizenship, education, and race were associated with elevated absolute NO2 pollution levels but were not detectibly associated with reductions in 2020 NO2 relative to its forecasted value. This illustrates the immediate behavioral and environmental impacts of local SDPs during the COVID-19 pandemic.

Optimization Model and Method of Variable Speed Limit for Urban Expressway

The urban expressway network is the main part of the urban traffic network carrying most of the city’s traffic pressure for its continuity and rapidity, but the control method of the traffic flow was too simple to other control methods in application in addition to the ramp control and the fixed speed control. In this paper, the theory of variable speed limit (VSL) was used to develop an optimal control model based on the improved traffic flow simulation model according to the characteristics of urban expressway traffic flow. The objective of the proposed model is to minimize the delay and maximize the traffic flow. It can adjust the traffic flow on the network in space time so that the whole network is in a state of equilibrium which not only is conducive to the control of the local traffic congestion and avoids the spread of congestion but also improves the traffic safety. The SPSA-based solution algorithm was proposed by taking into account the needs of real-time online applications. It can not only ensure the accuracy of the solution but also meet the requirements of the simulation time. The simulation results show that the variable speed limit can be optimized in moderate demand, and the proposed model and algorithm are effective and feasible in this paper. The conclusions are useful to help the traffic management department to formulate reasonable traffic control strategies.

Optimization Strategy of Electric Vehicles Charging Path Based on “Traffic-Price-Distribution” Mode

According to the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles is proposed, which is under the “traffic-price-distribution” mode. Moreover, this strategy builds an electric vehicle charging and navigation system on the basis of the road traffic network model, real-time electricity price model and distribution network model. Based on the Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, it gets the optimal charging path navigation with the goal of minimizing the charging cost of electric vehicles. The simulation results in MATLAB and MATPOWER (MATLAB R2018a, MATPOWER3.1b2, PSERC, Cannell University) show that the electric vehicle charging path optimization strategy can solve the local traffic congestion problem better and improve the safety and stability of the distribution network because of the fully considering the convenience of electric vehicle charging.

Coordination Optimization of VSL Strategy on Urban Expressway and Main Road Intersection Signal

With the rapid urbanization during the past three decades in China, the urban expressway network has arisen with large numbers in many cities. However, the traffic conditions are becoming worse and worse. One of the main reasons is that the connected sections of main road and expressway do not have a coordination by the intersection signal and variable speed limits (VSL) on expressway. In this paper, a new cooperation optimization model of VSL on urban expressway and the intersection signal timing are proposed based on the mesoscopic traffic flow model. Its objective is to minimize the total delay and the number of parking. Its essence is adjusting traffic flow on the network in space-time so that the whole network can be in a state of equilibrium. It is effective to the control of local traffic congestion and can avoid the spread of congestion, thus improving traffic safety. Additionally, the optimal VSL strategy is proposed. And then a solution algorithm based on the simultaneous perturbation stochastic approximation (SPSA) is designed for the proposed model. In the end, a practical example of Yanshan overpass in Ji’nan elevated expressway is used to validate the effectiveness of the proposed method. The simulation results are satisfactory. The conclusions are very useful for traffic management department to implement reasonable traffic control strategy.

Development of a Multimodal Microsimulation-Based Evacuation Model

This study presents a multimodal evacuation microsimulation modeling framework. The paper first determines optimum marshal point locations and transit routes, then examines network conditions through traffic microsimulation of a mass evacuation of the Halifax Peninsula, Canada. The proposed optimization modeling approach identifies marshal point locations based on transit demand obtained from a Halifax Regional Transport network model. A mixed integer linear programming (MILP) technique is used to formulate the marshal point location and transit route choice problem. The study proposes a novel approach to solving the MILP problem, using the “branch and cut” algorithm, which demonstrates superiority in computation time and production of quality solutions. The optimization model determines 135 marshal points and 12 transit routes to evacuate approximately 8,400 transit-dependent individuals. Transit demand and marshal point locations are found to be concentrated at the core of the peninsula. The microsimulation modeling takes a dynamic traffic assignment-based approach. The simulation model predicts that it takes 22 h to evacuate all auto users but just 7 h for the transit-dependent population. The study reveals that the transit system has excess capacity to assist evacuees who switch from auto and other modes. Local traffic congestion prolongs the evacuation of a few densely-populated zones in the downtown core of the peninsula. The findings of this research help policy-makers understand the impacts of marshal point locations and transit route choice decisions on multimodal evacuation performance, and provide insights into emergency planning of multimodal evacuations under "mode switch" and transit-based evacuation scenarios.

V2V‐based method for the detection of road traffic congestion

The traffic congestion detection based on the internet of vehicles is gaining enormous research interest. A vehicle-to-vehicle (V2V)-based method for the detection of road traffic congestion is proposed. Firstly, a fuzzy controller was constructed based on the vehicle speed, traffic density, and traffic congestion rating system, and the level of local traffic congestion was evaluated. Then, the level of local traffic congestion of neighbouring vehicles was queried based on V2V communication, and the level of regional traffic congestion was obtained based on a large sub-sample hypothesis test. Finally, a simulation test platform was built based on vehicles in network simulation, and the back-off time slots and received packets of vehicle nodes were calculated. The accuracy of the proposed method for detecting road traffic congestion was compared to the cooperative traffic congestion detection (CoTEC) method and the geomagnetic coil method. The results show that the detection accuracy of the proposed method increased by 5.5 and 7.5%, respectively, compared to the geomagnetic coil method and CoTEC method. The V2V communication network overhead of the proposed traffic congestion detection method is reduced by 90.8% compared to the adopted CoTEC method. The communication overhead of the vehicle node using the proposed method is significantly decreased when there is no traffic congestion.

Adaptive Pinning Synchronization of Complex Networks with Negative Weights and Its Application in Traffic Road Network

As local traffic congestion and uncertainty factors existing on roads may lead to cascading failures or even large area traffic network congestion, a pinning control method is proposed to divert the traffic and then restore the smooth flow of traffic. To eliminate the impacts of uncertainties and negative weights for the traffic network performance, the adaptive pinning control and coupling adjustment strategies are designed to estimate controller parameters and adjust coupling strength to compensate for the impacts on the pinned nodes and unpinned nodes. Based on Lyapunov stability theory, adaptive pinning controllers and network adjusters are developed to guarantee the achievement of network synchronization even in the presence of the uncertainties and negative weights. In addition, we investigate the effects of the type of nodes on pinning synchronization performance. Numerical simulations show that if the network’s degree and the single node energy index are considered, better synchronization performance can be obtained by comparing with the pervious pinning schemes.

Evaluating the impact of a workplace parking levy on local traffic congestion: The case of Nottingham UK

A Workplace Parking Levy (WPL) scheme raises a levy on private non-domestic off street parking provided by employers. In April 2012 Nottingham became the first UK City to implement such a scheme with the revenue generated hypothecated for funding transport improvements.The lag between the introduction of the WPL and the opening of related public transport improvements represents an opportunity to study the impact of a WPL on congestion as a standalone measure. In order to achieve this it is necessary to consider changes to variables external to the WPL, which also impact on congestion, which may obscure any beneficial impact of the scheme. An autoregressive time series model which accounts for the impact of these exogenous variables is used to evaluate the impact of the introduction of the WPL on congestion. Delay per Vehicle Mile is used as the dependent variable to represent congestion while the number of Liable Workplace Parking Places (LWPP) is used as a continuous intervention variable representing the introduction of the WPL. The model also contains a number of economic, transportation and climatic control variables.The results indicate that the introduction of the WPL as measured by the number of LWPP has a statistically significant impact on traffic congestion in Nottingham. Additionally, external explanatory variables are also shown to impact on congestion, suggesting that these may be masking the true impact of the scheme. This research represents the first statistical analysis of the link between the introduction of a WPL and a reduction in congestion.

Steady-State Traffic Signal Control With Variable Phase Combinations and Sequences

Abstract: The space distribution of traffic flows is often unbalanced, resulting in regional congestion, and even oversaturation of the network. In this paper, the steady-state traffic signal control design approach, with consensus-based control objective of network states, is proposed. A traffic flow model is first introduced based on store-and-forward modeling procedures. Furthermore, a link-based distributed signal control law is proposed with the consensus of link occupancies as control objective, and moreover control approach for phase combinations and sequences is introduced, especially in case of higher network demands. At last, simulation investigations for a topological network in Beijing are conducted in VISSIM software, which shows that the proposed approaches can improve performance indices of the network and then reduce local traffic congestion.

Temporal characteristics of black carbon concentrations and its potential emission sources in a southern Taiwan industrial urban area

This study investigates the temporal characteristics of black carbon and its potential emission sources, as well as the fractions of BC in PM2.5 levels in Kaohsiung urban area, which is an industrial city in southern Taiwan. Concentrations of BC and PM2.5 are monitored continuously from March 2006 to February 2010, using an aethalometer and a tapered element oscillating microbalance monitor. Additionally, the presence of organic compounds (or UV enhanced species) in particles at the sampling site is determined using the Delta-C (UVBC-BC) value. According to long-term measurement results, BC and PM2.5 concentrations are 3.33 and 34.0 μg m(-3), respectively, in the Kaohsiung urban area. The ratio of BC/PM2.5 is approximately 11 %. Low concentration of BC and PM2.5 in the summer of this study period is mostly likely owing to meteorological conditions that favored dispersion of local air pollutants. Nevertheless, BC concentrations peaked markedly during morning hours (7:00-11:00), likely owing to local traffic congestion. Measurement results suggest that BC is released from local traffic activities and emitted from industrial activities at this sampling site. Additionally, Delta-C values are significantly higher than zero during January-March and November-December periods in this industrial urban area, implying that UV enhanced species can be observed. At this sampling site, these UV enhanced species do not only originate from household activity and solid waste burning but also release from industrial activities. The elevated Delta-C values during nighttime (18:00-6:00) in the autumn and winter seasons are likely related to those UV enhanced species in the atmosphere, which can be condensed on particle surface under low temperature conditions. According to long-term measurement results, significantly positive Delta-C values can be observed under temperatures <20 °C and relative humidity of 60-75 % in this study. Despite the household activity and solid waste burning, the major sources of particles that are bound with UV enhanced species in this sampling site are industrial parks and a coal-fired power plant.

Integrating the Military into the Metropolitan Transportation Planning Process

According to the TRB Military Transportation Committee, most U.S. metropolitan planning areas with military installations currently have a disconnect between Department of Defense military bases, metropolitan planning organizations (MPOs), departments of transportation, and local communities. The Hampton Roads (Virginia) Transportation Planning Organization (HRTPO), however, has a long-standing relationship with the military community and has taken steps to increase related efforts in recent years. HRTPO has advanced the cause of planning in Hampton Roads—and, by example, in the United States—through a coordinated approach to meeting the transportation needs of the military located in the Hampton Roads region. In response to military concerns about local traffic congestion and delays, the HRTPO board placed greater emphasis on military transportation planning in the region by endorsing annual military briefings by military representatives to the MPO board and by including a military needs study in its work program. The Hampton Roads Military Transportation Needs Study, an ongoing effort by HRTPO, is reportedly the first and only attempt of an MPO to identify solutions to the transportation needs of the local military. The purpose of this paper is to inform other metropolitan areas about the integration of the military into the transportation planning process in Hampton Roads and to provide a summary of key findings from the region's Military Transportation Needs Study. Other MPOs can apply the methods, results, successes, and lessons learned from Hampton Roads to their respective regions.

How Employers View Traffic Congestion

This paper summarizes a national representative survey of 1,000 employers about the impact of traffic congestion on their business activities. About one third of those surveyed viewed traffic congestion as a moderate or major problem. Most surveyed believed that traffic congestion had increased in the past 5 years. Most employers received or shipped materials regularly, required some workers to drive on the job, and hosted customers. The average employer handled about 28 shipments per week, but large employers often handled several hundred per week. About 12% of shipments were thought to be delayed by local traffic congestion, which cost about $5.3 billion nationally in annual lost time. Some workers drove regularly as part of their jobs; this task amounted to about 23 billion hours nationally in annual transit. About 16% reported congestion delays, which totaled 3.8 billion hours annually and cost about $76 billion, estimates that were similar to those for commuter delay. To reduce these impacts, employers used e-mail, third-party carriers, consolidated shipments, driver assistance, flexibility in work and meeting schedules, and work at home days. Few employers provided transit passes. Access was a key, valued feature of location, and employers deemed it the top advantage of current sites. Only 16% of employers reported that they would consider relocation. However, of those employers that had experienced major congestion problems, 27% had considered relocation, and so had 75% of smaller employers whose workers drove extensively on the job. The study concluded that the impact of traffic congestion on employers was substantial and similar to that on commuters. As an often overlooked dimension of congestion, the impact of traffic congestion on employers needs more attention in transportation plans.

Improving the performance of P2P networks using SPIS with Query Filtering

The need of simple storage access, bandwidth and processing power of computers located at the edges of the network is highly required in advanced computing. Peer to Peer P2P satisfies the functional requirements of several applications. The searching of the content and file sharing is perhaps the most popular application of P2P networks. In this application, flooding is used as the prevailing resource location method for content searching and file sharing. Larger volume of unnecessary traffic in the network is created due to flooding mechanism. It is because a peer may receive the same queries multiple times through different paths. In this paper, the performance of the searching is analyzed and improved using the proposed method namely Super-peer based Innovative Search SPIS. In this method, the super-peer maintains the local peer's information; in addition it maintains other super-peer's information for reducing the unnecessary search in the P2P networks. During the searching, the number of query packets creates a local traffic inconsistency and congestion. Flooding a query to every peer significantly diminishes the performance and effectiveness of the system. Hence, the new method namely SPIS with Query Filtering is proposed. Here, the query is filtered based on the file availability in the forwarding super-peer for reducing the number of duplication query which reduces the query processing time. Hence, the performance of the P2P networks can be improved and thereby reduces traffic.