Traffic Congestion Relief Research Articles

An urban short-term traffic flow prediction model based on wavelet neural network with improved whale optimization algorithm

To solve the problem of urban traffic congestion is a major challenge to modern society. Short-term urban traffic flow prediction is the key to realizing traffic control and vehicle guidance on the relief of traffic congestion to make a better city experience. Short-term urban traffic flow is characterized by a high level of time-variation, nonlinearity and randomness. To tackle this problem and to improve the performance of short-term traffic flow prediction, the author of this paper comes up with an improved whale optimization algorithm (IWOA) and tries to introduce it into the wavelet neural network (WNN) so as to optimize its initial weights and wavelet factors via iteration and to establish a short-term traffic flow prediction model based on Wavelet Neural Network with Improved Whale Optimization Algorithm (IWOA-WNN). This model appears to predict traffic flow in a more effective manner and it can remedy the defects of wavelet neural network which usually leads to low prediction accuracy and slow response. The experimental results show that compared with the pure WNN model and WOA-WNN model, the IWOA-WNN model has turned out to be a great improvement in terms of absolute error and percentage of absolute error.

Pooled versus private ride-hailing: A joint revealed and stated preference analysis recognizing psycho-social factors

Pooled mobility services hold substantial promise as a means to provide better accessibility to those who may find it difficult to drive themselves, while also promoting sustainable transportation efforts. In this paper, we develop a joint revealed preference-stated preference model for the choice between pooled versus private ride-hailing that (a) accommodates a suite of individual-level socio-demographics, individual-level psycho-social attributes, built environment variables, and trip-level variables, and (b) explicitly recognizes the importance of considering familiarity with pooled ride-hailing (RH) as an integral element of the pooled RH choice process. The primary source of data for the analysis is drawn from a 2019 survey of Austin, Texas residents. Our results underscore the value of using psycho-social latent constructs in the adoption of current and emerging mobility services, both in terms of improved prediction fit as well as in terms of designing proactive strategies to promote pooled RH service adoption. Women, older adults, and non-Hispanic/non-Latino Whites have a low propensity to choose the pooled RH mode, while employed individuals, highly educated individuals, and those living in high density urban areas have a high propensity. Overall, the average VTT estimate is $27.80 per hour for commute travel, $19.40 per hour for shopping travel, and $10.70 per hour for leisure travel, while the willingness to pool (that is, the willingness to pay to not pool a ride or WTS) averages about 62 cents for commute travel, $1.70 for shopping travel and $1.32 for leisure travel. These estimates can be used by TNCs and cities to consider new integrated pooled RH-fixed transit service designs, position traffic congestion alleviation strategies and new mobility services, and customize information campaigns to promote pooled RH mode use.

Developing the Optimal Dynamic Operation Method of Pace-Maker Light for Traffic Congestion Relief

Developing the Optimal Dynamic Operation Method of Pace-Maker Light for Traffic Congestion Relief

Pedestrian-Safety-Aware Traffic Light Control Strategy for Urban Traffic Congestion Alleviation

Conflicts between pedestrians and vehicles are one of the common safety issues at signalized intersections. Pedestrian Flashing GREEN (FG), a time interval for pedestrians on crosswalks to safely finish crossing before the next phase occurs, may fail to clear the crosswalk in the allotted time, due to significant pedestrian non-compliant behavior. In this manner, probability of pedestrian-vehicle exposures increases when non-compatible vehicle flows are released at the next immediate phase. This paper seeks to address this issue by presenting a traffic signal control strategy for urban traffic networks that aims to minimize vehicle traveling delay (increase efficiency) as well as pedestrian crossing risk (increase safety). First, a macroscopic model for pedestrian-vehicle mixed-flow networks is proposed. Considering the high-incidence rate of pedestrian violations during FG, an additional Dynamic All RED (DAR) phase is introduced at the end of each FG period, whose duration is adaptively adjusted according to the number of non-compliant pedestrians. With computational complexity being a concern for our model, an evolutionary algorithm with repairing mechanism (EARM), is proposed to solve our problem. Case studies are provided to illustrate the potential impact of the pedestrian movement to the vehicle traffic networks when pedestrian safety is considered in the system, as well as the efficacy of our traffic light control strategy for pedestrians and vehicles on risk reduction.

RETRACTED: Optimal logistics transportation and route planning based on fpga processor real-time system and machine learning

The shared bus's development requirements, relief of traffic congestion in urban areas, and improved utilization of the road resources providing the transport mode of the excellent user experience neotype are very urgent. To predict precise travel needs, the key for planning a dynamic routing lie a lie of the shared bus implementation. However, the shared bus data's sparse and high volatility will require a lot of resistance to predict travel accurately. Based on the user experience, very different from the traditional public transportation that is far more challenging to the relatively high number of optimization goals is because passengers of the shared bus route planning and shared bus route planning. This article, based on the shared bus data from different audiences sources, travel demand prediction and dynamic route planning in "the last mile", and a two-step process that consists of the shared bus dynamic routing (sub-bus), proposed and your scene. First of all, such traffic, time, week, location, and five of the prediction function such as a bus, to analyze residents' travel behavior to prepare the travel demand based on them precisely the machine learning model used to predict. Secondly, dynamically and predict the results of multiple operations bus optimal routing, designed to generate a fixed based on the shared bus destinations' operating characteristics, a dynamic programming algorithm wants below. Several experiments, based on the shared subway shuttle bus of evidence that people of the data and the reality has been purchased, the sub-bus is better than the method of dynamic route planning, etc. for the scene of such a "last mile".

Modeling and simulation of intersection quasi-moving block speed guidance based on connected vehicles

Purpose This study aims to propose a speed guidance model of the CV environment to alleviate traffic congestion at intersections and improve traffic efficiency. By introducing the theory of moving block section for high-speed train control, a speed guidance model based on the quasi-moving block speed guidance (QMBSG) is proposed to direct platoon including human-driven vehicles and connected vehicles (CV) through the intersection coordinately. Design/methodology/approach In this model, the green time of the intersection is divided into multiple block intervals according to the minimal safety headway. Connected vehicles can pass through the intersection by following the block interval using the QMBSG model. The block interval is assigned dynamically according to the traveling relation of HV and CV, when entering the communication range of the intersection. To validate the comprehensive guidance effect of the proposed model, a general evaluation function (GEF) is established. Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Findings Compared to CVs without speed guidance, the simulation results show that the GEF of QMBSG model has an obvious improvement. Also, compared to the single intersection speed guidance model, the GEF value of the QMBSG model improves over 17.1%. To further explore the guidance effect, the impact of sensitivity factors of the CVs’ environment, such as intersection environment, communication range and penetration rate (PR) is analyzed. When the PR reaches 75.0%, the GEF value will change suddenly and the model guidance effect will be significantly improved. This paper also analyzes the impact of the length of block interval under different PR and traffic demands. It is found that the proposed model has a better guidance effect when the length of the block section is 2 s, which facilitates traffic congestion alleviation of the intersection in practice. Originality/value Based on the aforementioned discussion, the contributions of this paper are three-fold. Based on the traveling information of HV/CV and the signal phase and timing plans, the QMBSG model is proposed to direct platoon consisting of HV and CV through the intersection coordinately, by following the block interval assigned dynamically. Considering comprehensively the indexes of mobility, safety and environment, a GEF is provided to evaluate the guidance effect of vehicles through the intersection. Sensitivity analysis is carried out on the QMBSG model. The key communication and traffic parameters of the CV environment are analyzed, such as path attenuation, PR, etc. Finally, the effect of the length of block interval is explored.

Induced Vehicle Travel in the Environmental Review Process

If we expand roadway capacity, more drivers will come, or so economic theory suggests and a substantial body of empirical research now shows. Despite strong evidence, the “induced travel” effect is often ignored, underestimated, or misestimated in the planning process, particularly in the assessment of the environmental impacts of roadway capacity expansions. Underestimating induced travel will generally lead to overestimation of the traffic congestion relief benefits a highway expansion project might generate, along with underestimation of its environmental impacts. A major reason that induced travel tends to be underplayed in environmental analyses is that travel demand models do not typically include all of the feedback loops necessary to accurately predict the induced travel effect. We developed an online tool, based on elasticities reported in the literature, to facilitate the estimation of the induced vehicle travel impacts of roadway capacity expansion projects in California, with potential future expansion to other geographies. We describe the tool, apply it to five case study highway capacity expansion projects, and then compare the results with the induced travel estimates reported in the environmental impact analyses for those projects. Our results suggest that environmental analyses frequently fail to fully capture the induced vehicle travel effects of highway capacity expansion projects.

Congestion Propagation Based Bottleneck Identification in Urban Road Networks

Due to the rapid development of vehicular transportation and urbanization, traffic congestion has been increasing and becomes a serious problem in almost all major cities worldwide. Many instances of traffic congestion can be traced to their root causes, the so-called traffic bottlenecks, where relief of traffic congestion at bottlenecks can bring network-wide improvement. Therefore, it is important to identify the locations of bottlenecks and very often the most effective way to improve traffic flow and relieve traffic congestion is to improve traffic situations at bottlenecks. In this article, we first propose a novel definition of traffic bottleneck taking into account both the congestion level cost of a road segment itself and the contagion cost that the congestion may propagate to other road segments. Then, an algorithm is presented to identify congested road segments and construct congestion propagation graphs to model congestion propagation in urban road networks. Using the graphs, maximal spanning trees are constructed that allow an easy identification of the causal relationship between congestion at different road segments. Moreover, using Markov analysis to determine the probabilities of congestion propagation from one road segment to another road segment, we can calculate the aforementioned congestion cost and identify bottlenecks in the road network. Finally, simulation studies using SUMO confirm that traffic relief at the bottlenecks identified using the proposed technique can bring more effective network-wide improvement. Furthermore, when considering the impact of congestion propagation, the most congested road segments are not necessarily bottlenecks in the road network. The proposed approach can better capture the features of urban bottlenecks and lead to a more effective way to identify bottlenecks for traffic improvement. Experiments are further conducted using data collected from inductive loop detectors in Taipei road network and some road segments are identified as bottlenecks using the proposed method.

Traffic congestion relief associated with public transport: state-of-the-art

Public transport (PT) influences the urban road system in many ways, including traffic congestion, environment, society, safety and land use impacts. While there are many studies focusing on the benefits of PT, research on congestion impacts, a fundamental component of any analysis of transport performance, associated with PT has received little attention. This paper aims to review the traffic congestion impacts of PT and how they are assessed. Traffic congestion is most commonly related to vehicle travel; yet, the real measure of congestion in transport systems is people travel. This paper looks at the appropriateness of existing traffic congestion measures and how suitable they are for measuring the impact of an existing PT system in the short-term. The literature review indicates that most studies relating to the congestion impacts of PT have used vehicle-based congestion measures. People-based measures may be more appropriate in assessing PT impacts. The paper also proposes a new framework for looking at the short-term effects of an existing PT system on traffic congestion. It suggests a few areas where further work can be undertaken to improve our understanding of traffic congestion incorporating PT such as exploring the mode shift from PT to car, estimating network-wide PT congestion creation impacts and determining the net congestion impact of PT.

Suppressing the effects of induced traffic in urban road systems: impact assessment with macrosimulation tools - results from the city of Krakow (Poland)

This paper presents results from simulation analysis of road network development scenarios in the city of Krakow (Poland), accounting for potential impact of traffic demand elasticity. These envisage a completion of a suburban ring road, which would improve network travel conditions and could therefore induce additional traffic volumes (and eventually lead to further traffic congestion). The objective was to investigate how this effect might be inhibited by introducing complementary changes in the city road network, ranging from individual road narrowing schemes in the city centre to “bolder” measures comprising network-wide traffic calming and road closure scenarios. Our macrosimulation-based analysis reveals that, once accounting for demand elasticity phenomena, city-wide benefits are roughly similar for all the considered scenarios, but a durable traffic congestion relief could be only achieved with a consequent reduction of inner-city network capacity. Our findings indicate how the appraisal process of urban road projects could be facilitated with an estimate of potential demand elasticity effects, as effectiveness of such schemes needs to be assessed not just in terms of level-of-service improvements, but also their exposure (or resilience) to erosion in scheme benefits due to probable induced traffic phenomena.

A Critical Appraisal of Mode Choice Model of Work Trips

In the present scenario in world, many mode choice models have been developed to predict the travelers mode choice in the available modes of transportation system .The mode choice model is one of a very significantcomponent in the urban transportation planning and policies,specifically in countries that are toward development and urbanization, likeSouth Asian countries(India, Bangladesh, Nepal,etc.)the increasing horizontal spread of cities that led to increased travel demand.The aim of this review is to study the developing of mode choice model for various transportation modes .The developed models cover different modes of transportation currently employed in cities,which are Private car,Taxi,Publicbus,Auto-rickshaw,Motorcycles,Shared car,Bicycles,Walking.They have been tried to be estimated for work, education,shopping and other trips .There are some factors that considerablyinfluence the choice of transport modes are:Socio economics variables such as age,gender, car ownership,and family monthly income.Network variables are such as travel time,travel cost,comfort,reliability, employment, driving licenseweather,and dust &noise. The data was collected for eachof the alternative modes through questionnaire by face to face interview or by using Google form. There are different methods that can be applied for the developing a mode choice model,multinomial logitmodel is the easiest method with simple mathematical calculations; this method was also used by many authors for analysis and for checking the validation the likelihood ration test .the application of mode choice model is significant to mode user to have best choice, and also such models can assist in the alleviation of traffic congestion and air pollution in the city.

A novel dynamic pricing scheme for a large-scale electric vehicle sharing network considering vehicle relocation and vehicle-grid-integration

With promising benefits such as traffic emission reduction, traffic congestion alleviation, and parking problem solving, Electric Vehicle (EV)-sharing systems have attracted large attentions in recent years. Different from other business modes, customers in sharing economy systems are usually price sensitive. Therefore, it is possible to shift the usage of shared EVs through a well-designed Dynamic Pricing Scheme (DPS), with the objective of maximizing the system operator's total profit. In this study, we propose a novel DPS for a large-scale EV-sharing network to address the EV unbalancing issue and satisfy the vehicle-grid-integration (VGI) service based on accurate station-level demand prediction. The proposed DPS is formulated as a complex optimization problem, which includes two Price Adjustment Level (PAL) decision variables for every origin-destination pair of stations. The two PALs are employed to affect the EV-sharing demand and travel time between each station pair, respectively. Physical and operational constraints from both EV demand and VGI service aspects are also included in the proposed model. Two case study are conducted to validate the effectiveness of the proposed method.

Evaluation of Shared Space to Reduce Traffic Congestion

Pedestrian and vehicle interactions often lead to conflicts that bring about safety, traffic congestion, and priority or right of way issues. Common methods used in the past to combat said issues have largely relied on the principle of separating the motions of pedestrians and vehicles by means of bridges, tunnels, signals, and access restrictions. A different approach known as shared space aims to solve the same problems with a less structured and defined environment which instead places more reliance on human interaction and perception. Although it has been used in multiple scenarios across Europe with success, instances of shared spaces in the United States are few. In the past, the success of shared space has mainly focused on safety, aesthetic, and pedestrian use metrics, with little quantitative knowledge regarding the traffic congestion relief benefits. This research focuses on evaluating and quantifying the traffic congestion relief abilities of shared space designs utilizing Vissim traffic microsimulation software and the economic impact these changes can make. A major pedestrian crossing location on West Virginia University’s Downtown campus along a major urban arterial was chosen as the case location upon which the model was to be built. This location posed unique aspects, which made it a prime choice for this research as the major concern for years has been traffic congestion, in addition to pedestrian safety and aesthetic appeal. The results of the analysis show that shared space can reduce vehicle travel time by up to 50% and delays by 66%.

Integrated Traffic Control for Freeways using Variable Speed Limits and Lane Change Control Actions

The wide deployment of vehicle automation and communication systems (VACS) in the next decade is expected to influence traffic performance on freeways. Apart from safety and comfort, one of the goals is the alleviation of traffic congestion which is a major and challenging problem for modern societies. The paper investigates the combined use of two feedback control strategies utilizing VACS at different penetration rates, aiming to maximize throughput at bottleneck locations. The first control strategy employs mainstream traffic flow control using appropriate variable speed limits as an actuator. The second control strategy delivers appropriate lane-changing actions to selected connected vehicles using a feedback-feedforward control law. Investigations of the proposed integrated scheme have been conducted using a microscopic simulation model for a hypothetical freeway featuring a lane-drop bottleneck. The results demonstrate significant improvements even for low penetration rates of connected vehicles.

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

AbstractTraffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning.

The Signal Control Optimization of Road Intersections with Slow Traffic Based on Improved PSO

The slow traffic in cities with pedestrians and bicycles is becoming more and more important due to their environmental protection and low carbon. However, the driving at the road intersections is seriously mixed and easy to jam. Traffic delays mainly occur here. Improving the traffic efficiency of the intersections plays an important role for the alleviation of traffic congestion. In this paper, a multi-objective signal timing optimization model is established about the vehicle average delay, slow traffic delay, vehicle stopping times and traffic capacity. Then an improved particle swarm optimization algorithm (PSO) is used to solve it. The experimental results show that the model is optimized to reduce the vehicle delay, slow traffic delay and stopping times of an intersection, and then increase the traffic capacity. So the multiple indicators of an intersection are improved, which have significant effects on the increase of operational efficiency of an intersection.

Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach

Trip purpose inference is critical in transportation demand management (TDM) as well as traffic congestion alleviation. However, destination choice can be affected by a variety of factors, many of which are difficult to determine (e.g. socio-demographics). Besides, the spatio-temporal variation and correlation inherent in travel patterns further intensify the difficulty of understanding destination choice behavior. To this end, this research proposes a Bayesian hierarchical approach for modeling the destination choice behavior through time and space. The proposed method can take into account both the unavailable factors and spatio-temporal correlations by introducing random fields. Moreover, the implementation of the Integrated Nested Laplace Approximations (INLA) combined with the Stochastic Partial Differential Equation (SPDE) makes it computationally feasible to model large-scale spatio-temporal correlation structures. The model is further applied to two-week data from more than 8000 taxis in Harbin. The empirical results indicate that the proposed approach is capable to capture spatio-temporal variability in destination distribution, and the inclusion of spatial and temporal random effects is of great help to improve the model performance. The case study also examines how the land-use types influence the destination choice. It is believed that the modeling method and the exploratory spatial–temporal analysis of destination distribution in this study can enriches the methodologies for travel demand modeling as well as decision support for transport policy development.

Simulation analysis of the western 3rd ring road project in Krakow and its forecast impact upon the urban transport network performance

The paper presents results from simulation analysis of the western 3rd ring road project in the city of Krakow (i.e. Trasa Lagiewnicka, Trasa Pychowicka and Trasa Zwierzyniecka), performed in the macroscopic model of transport system in the Krakow metropolitan area. Simulation works reveal shifts in traffic flows and changes in urban transport network performance which would likely take place after the stage-wise construction of consecutive 3rd ring road western sections. Results indicate that the analysed road schemes would indeed have a relevant impact upon travel conditions, though in initial stages these would be rather limited and confined to the southern part of the city. Therefore, completion of the whole western section of the 3rd ring road is crucial to its overall effectiveness and would only then bring substantial benefits on a city-wide scale. Simulation works seem to confirm that the future 3rd ring road would become an essential and highly-utilised link in the urban road network and would provide a much more efficient connection between the northern and southern parts of the Krakow city. However, apart from the projected benefits, it is also important to underline potential negative implications – and consequently, further changes in city transport system should envisage reduction of the road network capacity within the inner-city area. This would then provide the best possible chance of fostering the improvements achieved with the 3rd ring road scheme: i.e., positive changes in accessibility and travelling conditions across the whole city and long-term, sustainable traffic congestion relief in the inner-city Krakow area.

DyETC: Dynamic Electronic Toll Collection for Traffic Congestion Alleviation

To alleviate traffic congestion in urban areas, electronic toll collection (ETC) systems are deployed all over the world. Despite the merits, tolls are usually pre-determined and fixed from day to day, which fail to consider traffic dynamics and thus have limited regulation effect when traffic conditions are abnormal. In this paper, we propose a novel dynamic ETC (DyETC) scheme which adjusts tolls to traffic conditions in realtime. The DyETC problem is formulated as a Markov decision process (MDP), the solution of which is very challenging due to its 1) multi-dimensional state space, 2) multi-dimensional, continuous and bounded action space, and 3) time-dependent state and action values. Due to the complexity of the formulated MDP, existing methods cannot be applied to our problem. Therefore, we develop a novel algorithm, PG-beta, which makes three improvements to traditional policy gradient method by proposing 1) time-dependent value and policy functions, 2) Beta distribution policy function and 3) state abstraction. Experimental results show that, compared with existing ETC schemes, DyETC increases traffic volume by around 8%, and reduces travel time by around 14:6% during rush hour. Considering the total traffic volume in a traffic network, this contributes to a substantial increase to social welfare.

How do public transport users adjust their travel behaviour if public transport ceases? A qualitative study

Mode shift from public transport (PT) to private car when a PT shut-down occurs results in an increase in the number of car trips on the road network which may contribute to traffic congestion. Policies aimed at reducing the mode shift to car can be designed through a better understanding of PT users’ mode shift in the event of a PT disruption. Furthermore, the share of car mode shift is also an important parameter for assessing traffic congestion relief associated with day to day PT provision in cities. This study sought to uncover factors influencing the mode shift to car among PT users in the event of a PT disruption. Semi-structured interviews were conducted with 30PT users from Melbourne, Australia. Grounded theory was used to derive categories and subcategories of behavioural responses. Factors affecting mode shift to car if PT ceases in the short term were classified into three main themes with several subcategories: individual-specific factors, context-specific factors and journey-specific factors. In the long term, the analysis reveals that only context-specific factors have an influence on mode shift. Findings show that car access, travel time, travel cost, trip importance, non-central business district (CBD) trips, weather, flexibility and accessibility to PT stations are the most important factors favouring use of the car if PT ceases in the short term. This acts to increase traffic congestion due to the mode shift to car. However, in the long term, removing PT is expected to have an impact on land use, leading to individuals changing their residential and workplace location.