Traffic Management Schemes Research Articles

Highway traffic planning and management from the perspective of sustainable development

Highway traffic system is an important infrastructure of the city and one of the key elements of urban sustainable development. However, for a long time, the concepts and methods of highway traffic planning and management are relatively lagging behind, leading to the serious traffic congestion, environmental pollution, energy consumption and land use conflict problems in the highway traffic system. Therefore, it is necessary to re-examine the traditional concepts and methods of highway traffic planning and management, and formulate a more scientific and reasonable highway traffic planning and management scheme from the perspective of sustainable development.

Stability of regional traffic networks employing maximum throughput demand management

This paper considers the stability and optimality properties of traffic demand management schemes, motivated by the integration of smart monitoring and control technologies in traffic networks. First, a suitable optimization problem is formulated that aims to obtain demand input values that maximize the throughput within traffic networks adhering to regional traffic dynamics with triangular macroscopic fundamental diagrams. We show that optimal solutions to this problem may lead to unstable behaviour, revealing a trade-off between stability and optimality. To address this issue, we analytically study the stability properties of traffic networks at the presence of constant demand input and provide suitable local conditions that guarantee stability when the system’s equilibrium densities are strictly within the free-flow region, but not at the critical density. The latter case is significant, since the maximum throughput behaviour coincides in many cases with the local critical density. We resolve this by proposing a decentralized proportional demand control scheme and suitable local design conditions such that stability is guaranteed. Our analytic results are validated with numerical simulations in a 3-region system that demonstrate the effectiveness and practicality of the proposed approach.

Modeling of traffic at a road crossing and optimization of waiting time of the vehicles

Traffic management is a critical activity, the population is increasing day by day and so the traffic on the road is also increasing. Traffic jams and long waiting queues of vehicles at the road crossing are now part of everyone's life. The traffic lights used at the crossing to regulate the traffic play a vital role in the smooth functioning of traffic movement. At a crossing of four roads, it has been observed that giving an equal amount of green light to all roads is meaningless since the arrival of traffic on different paths is different. Importantly, the arrival rate is responsible for all traffic jams, long queues, and increased waiting time. Therefore, this paper suggests a green light allocation scheme for all paths i depending on the arrival rate of the vehicles. Thus, the allocation of green light will be dynamic. Further, weight is also computed, where more arrival rate means more weight, thereby assigning more time to the green signal. This will help in reducing the long queue length, residual traffic, and long waiting times. On simulating the traffic with the traffic data, the proposed optimized green light allocation scheme to path i reduces the residue traffic to negligible, allowing smooth traffic flow even during peak hours. The work also provides a proficient optimization of the waiting time of vehicles accumulated during the red light. According to the simulation, the maximum time assigned for the green signal during the peak hour of 9:30 AM to 10:00 AM for paths i, where 1≤i≤4 is 39.96, 33.36, 26.64, and 20.04 seconds respectively. Similarly, during the second rush hour of 5:00 PM to 6:00 PM, the simulation assigns a green signal time of 41.4, 37.2, 24.84, and 16.56 seconds for corresponding paths 1–4. Thus, the proposed work suggests an effective traffic management scheme at the four-road crossing.

Stability of Traffic Equilibria in a Day-to-Day Dynamic Model of Route Choice and Adaptive Signal Control

Adaptive traffic signal control has the potential to promote the efficient use of road intersections, thus contributing to the effectiveness of urban traffic management schemes. However, the reaction of drivers to repeatedly updated signal settings and the ensuing route choice dynamics may trigger the emergence of various kinds of network instability. In this study, the joint evolution of traffic flows and adaptive signal settings in a road network is investigated at the level of day-to-day dynamics with an explicit focus on the stability issue. We show how a Logit form signal control policy can be used, in interaction with route choice, to counter the emergence of instabilities possibly arising as a consequence of various behavioral factors and network conditions. After providing a general formulation of the model as a discrete time, deterministic nonlinear dynamical system, an explicit analysis of fixed-point stability is carried out for a simple network. Numerical results obtained from the implementation of the model on two example networks are presented in order to support the analytical findings of this study. We conclude that, in an integrated traffic management and information system, a properly calibrated adaptive signal control policy has the potential to offset the destabilizing effect of highly accurate driver information supplied by navigational aids. Our findings also suggest that the Logit-like control policy performs better than the Equisaturation signal setting method, in terms of average intersection delay at equilibrium, for all levels of driver information and travel demand tested in the experiment.

Study on the Management and Planning Methods of Transportation to and from Primary and Secondary Schools in Urban and Rural Areas

The transportation of urban and rural primary and secondary schools leads to traffic congestion in some sections, and the traffic order is chaotic, which seriously affects the travel of surrounding residents and greatly increases the additional traffic delay of pickup. How to effectively manage and plan the transportation of urban and rural primary and secondary schools and rationally plan the channelization of transportation roads have become a topic of great concern to the society. This paper will take several primary and secondary schools with representative traffic environment as the research object, analyze the behavior mechanism of the pick-up process, propose coping strategies for the school pick-up congestion problem under different road conditions, and plan a reasonable road channelization scheme and intersection design scheme. Finally, through the VISSIM simulation software, the proposed scheme and the original traffic environment are simulated and analyzed, and the vehicle delay before and after the improvement is compared, so as to find out whether the traffic planning and management scheme given in response to the school address in different traffic environments is reasonable. Through the simulation results, it is found that it is beneficial to reduce vehicle delay and avoid traffic jam by changing bidirectional to unidirectional on road section. In the vicinity of the intersection, one-way is the main way, and planning multiple drop-off points is helpful to solve the traffic congestion caused by school pick-up.

Real-time Automated Traffic Management Scheme Using Blockchain Based on Unmanned Aerial Vehicles

Real-time Automated Traffic Management Scheme Using Blockchain Based on Unmanned Aerial Vehicles

Open data on road traffic accidents accumulation, processing and analysis

Introduction. An urgent task for any municipality is the development of an Integrated Traffic Management Scheme, which is based on an analysis of the current situation. At the same time, the most important element is the analysis of road traffic accidents (RTA). The purpose of this study is to develop a methodology for accumulating, processing and analyzing the data on road accidents from the main official source - the State Inspectorate for Road Safety (GIBDD).Description of the problem. The data can be uploaded from the traffic police portal in two formats, but none of them allows for automated analysis of road accidents and presentation of its results using geographic information systems (GIS).Proposed methodology. The paper proposes a methodology for analyzing traffic accidents, including the following steps: uploading data from the site, converting data into convenient formats, entering data into a geographic information system, analyzing data using geographic information systems.Methodology approbation. Using the proposed methodology, an analysis of traffic accidents in the Cheboksary agglomeration was made and its visualization was made using GIS.Conclusions. The use of information about road accidents in the formats offered on the traffic police website is impossible either for analysis or for visualization in a GIS. The proposed method for accumulating, processing and analyzing accidents based on data from an official source using GIS makes it possible to automate the process and improve the quality of accident analysis.

Parallel-Computing-Based Calibration for Microscopic Traffic Simulation Model

Microscopic traffic simulation is vital to assess the performances of various traffic operation and management schemes. Microscopic traffic simulation is usually not parameter-free, and it relies on independent parameters to predict traffic evolution. Thus, parameter calibration is indispensable to conveying trustworthy simulation results. Heuristic algorithms are widely used for parameter calibration. Its logic is for achieving iterative optimization through continuous trial-and-error simulations. This process is time-consuming and usually takes several hours, making the calibration unable to meet the requirements of speed and efficiency. In recent years, parallel computing technology has been gradually applied in the engineering realm, which makes rapid calibration possible. Following the three steps of parallel framework selection, algorithm bottleneck identification, and subtask load balancing, this paper designs and implements the parallelization of genetic algorithm and particle swarm optimization (PSO) calibration algorithms. Finally, the proposed parallel framework is applied to simulation parameter calibration of a section of a 5 km long highway in Australia, and the effectiveness of parallel computing is evaluated from the two dimensions of reduction in calibration computational time and scalability. The results show that the proposed parallel calibration algorithm can shorten the 5 h calibration process to less than 1 h, reducing the calibration time by 80%. The parallel PSO calibration algorithm has better scalability, and its acceleration effect is better when more processors are used.

A non-additive path-based reward credit scheme for traffic congestion management

This study investigates the potential of non-additive path-based pricing for congestion management in urban transportation networks. We propose a novel path-based reward credit scheme to provide commuter incentives with the goal of reducing traffic congestion. We consider that a known proportion of commuters subscribe to this reward credit scheme and may earn credits when traveling in the network. We introduce a bilevel optimization formulation to determine optimal non-additive, path-based reward credits under traffic equilibrium conditions. In this formulation, the follower problem is a parameterized user equilibrium traffic assignment problem with two classes of users and non-additive path costs. We develop a single-level reformulation based on its first-order optimality conditions and derive theoretical properties of the reward credit scheme. Customized branch-and-bound algorithms are designed to solve the problem. We also introduce a heuristic approach that repeatedly solves parameterized follower problems to enhance scalability. We report numerical results that demonstrate the computational efficiency of the proposed methods over a benchmarking approach. We conduct a comprehensive evaluation of this path-based reward credit scheme compared with a link-based subsidy pricing scheme. We find that, on average, under a limited budget and a user participation level of at least 40%, the proposed path-based incentive mechanism yields larger reductions in traffic congestion over link-based approaches.

METHODOLOGY FOR USING A GENETIC ALGORITHM TO SOLVE URBAN PLANNING PROBLEMS

With the development of computer technology, algorithms for solving problems based on artificial intelligence are gaining popularity. The genetic algorithm is one of them. The article describes using of this algorithm in the context of urban planning problems. 
 The genetic algorithm is primarily created to search for extremes of complex functions, where the variable can be described as a vector. A significant number of urban planning problems can be represented as follows. The article describes the methodology for solving urban planning problems using this algorithm. The main advantage of the proposed algorithm is its relative ease of implementation and a wide range of issues that can be solved with its help. The main disadvantage of this algorithm is that it is obtained the result, with a certain probability, can reflect the local minimum of the function, as well as the need to change the parameters of the genetic algorithm individually for each individual task. 
 The article gives an example of using the proposed methodology to solve the problem of optimizing the location of work place. It is also proposed to apply a genetic algorithm to optimize the traffic light cycle, automate the process of building planning and develop traffic management schemes. 
 The implementation of the proposed methodology can significantly increase the quality of urban planning projects, as well as reduce the time spent on their implementation.

Blockchain-Assisted Privacy-Preserving Traffic Route Management Scheme for Fog-Based Vehicular Ad-Hoc Networks

Traffic route management is essential for reducing traffic jams and enhancing driving safety becauese of the growing number of vehicles and frequent occurrence of traffic accidents. However, in vehicular ad-hoc networks (VANETs), real-time messages are transmitted via wireless channels, which can result in security and privacy concerns. Existing proposals for traffic route management exist security vulnerabilities, as well as high calculation and communication costs. Encouraged by this fact, we design a lightweight traffic route management scheme for fog-based VANETs. In this scheme, vehicles utilize homomorphic encryption to encrypt their driving routes and then send the encrypted information to a fog node. The traffic management center (TMC) decrypts the received ciphertexts that are aggregated by the fog node and performs traffic management according to the decrypted data, without knowing individual route of each vehicle. Furthermore, blockchain is used in the scheme to conduct public keys management of vehicles. Our detailed security proof and analysis indicate that our proposal can meet the security objectives of VANETs. Further, to demonstrate the feasibility of the scheme, we also implement it in the Ethereum test network (i.e.,Rinkeby). Significantly, the performance analysis demonstrates that our proposal achieves a better performance than other relevant representative schemes.

Unprotected Left-Turn Behavior Model Capturing Path Variations at Intersections

Path dispersion (the spatial distribution of vehicular paths) is an important feature of traffic flow inside intersections and differs from traffic flow running along traffic lanes at road segment, especially under conflicting movements. The path dispersion reflects the operational features of traffic flow and is related to driving behaviour, arrival flow patterns, layout design, and the traffic control and management scheme. This study aims to improve the understanding of the overall path dispersion of unprotected left-turns and the opposing through movement. A behavioural simulation model was established to represent the overall path dispersion. Human behaviours regarding vehicle trajectory planning with and without conflicting vehicles were modelled based on optimal control and integrated into the proposed discrete event simulation framework. The descriptive power and accuracy of the proposed simulation model were validated using empirical data. The effects of the spatial size of the intersection, crossing angle, and traffic volume on the path dispersion of the left-turn and through movement were explored based on numerical experiments. The results show that the proposed simulation model can represent the path dispersion of left-turn and opposing through movement well for both the calibrated intersections and newly added intersections without model parameter recalibration with an average error of 8.92%.

From Corridor to Network Macroscopic Fundamental Diagrams: A Semi-Analytical Approximation Approach

The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost. Funding: G. Tilg acknowledges support from the German Federal Ministry for Digital and Transport (BMDV) for the funding of the project LSS (capacity increase of urban networks). S. F. A. Batista and M. Menéndez acknowledge support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award [CG001]. L. Ambühl acknowledges support from the ETH Research Grant [ETH-27 16-1] under the project name SPEED. L. Leclercq acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program [Grant 646592 - MAGnUMproject]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0402 .

Collaborate or Compete? Time-Varying Incentives versus Tolling in Parallel Bottleneck

Incentive-based traffic demand management (IBTDM) schemes redistribute traffic demand across space and time. The relationship between IBTDM and traditional tolling programs impacts financial sustainability and decision making of both programs. This study aims to examine if an IBTDM and a tolling program can coexist and should collaborate or compete to establish a financially sustainable mobility ecosystem. We investigate a duopoly case with an IBTDM and a tolling program in a parallel bottleneck model with fixed demand. It is assumed that the IBTDM program has a user acquisition cost and is constrained by a total budget. Then, the collaboration and competition cases between two programs are investigated, respectively. We found that both programs can attract substantial amounts of commuters and thus can coexist with each other, while the IBTDM program could attract more commuters. Overall, the tolling agency will not cooperate with the IBTDM company if its goal is to raise revenue. Additionally, the IBTDM company held an advantage as it can determine an optimal incentive scheme with incomplete information when competing. This study fills the knowledge gap on the coexistence of incentive and tolling programs, which helps to optimize the profiles of incentive and tolling, as well as to make strategic decisions on competition and cooperation.

Understanding Influencing Factors of Travel Mode Choice in Urban-Suburban Travel: A Case Study in Shanghai

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.

Modeling the Combined Effect of Travelers’ Contrarian Behavior, Learning and Inertia on the Day-to-Day Dynamics of Route Choice

Understanding the many facets of repeated route choice behavior in traffic networks is essential for obtaining accurate flow forecasts and enhancing the effectiveness of traffic management measures. This paper presents a model of the day-to-day evolution of route choices incorporating travelers’ contrarian behavior, learning and inertia. The model is formulated as a discrete-time nonlinear dynamical system, and its properties are investigated analytically and numerically with a focus on the effect of the fraction of individuals adopting a contrarian route choice behavior. The findings of the study indicate that the extent of contrarian behavior may have significant impacts on the attractiveness and stability of network equilibria as well as on global system performance. We show that a properly balanced combination of direct and contrarian subjects can protect the system from instabilities triggered by other behavioral and network features. Our results also suggest that the fixed point stability range may depend to a considerable extent on travelers’ inertia and memory of previous experiences, as well as on the form of the travel cost functions used in the model. The occurrence of contrarian behavior should be explicitly taken into account in the design of traffic management schemes involving the deployment of Advanced Traveler Information Systems (ATISs), as it may act as a mitigating factor against the concentration of choices on the recommended routes. The analytical framework proposed in this paper represents a novel contribution, since contrarian behavior in repeated route choice has been investigated mainly by means of empirical or simulation approaches thus far.

Non-divergent traffic management scheme using classification learning for smart transportation systems

Smart transportation is an autonomous arrangement that embodies technologically enabled intelligent vehicles and traffic systems to assist diverse real-time roadside applications and navigation. Intelligent Pervasive computing techniques with a common shared platform, such as the cloud, are employed to ease operations. This article introduces a Non-Divergent Traffic Management Scheme (NDTMS) to improve roadside driving for users and vehicles. This scheme recognizes and classifies traffic, neighbors, and environmental navigation data. Combining two inputs into the ecological data, the Pervasive computing systems assist in non-deviating seamless application support. The input data gives information regarding the traffic on roads and traffic density. The extracted data from the neighbors help in matching the data with features and reduces traffic. The problem of data mishandling for traffic management is addressed by classifier learning links to the application-specific requirements. This Pervasive computing technique operates on combinational data for precise traffic management through navigation/ alerts/ communications.

Оценка эффективности спиралевидной разметки на кольцевых пересечениях автомобильных дорог

The results of a number of foreign studies recommend the application of spiral markings to ensure a higher level of safety at roundabouts of medium and large diameter highways. In a number of cities of the Russian Federation, these recommendations were implemented with the introduction of new traffic management schemes using spiral marking of traffic lanes. However, there is no objective data confirming the effectiveness of these measures in Russia. The aim of the study was to evaluate the effectiveness of spiral markings according to two criteria – the number of road accidents and traffic violations at roundabouts before and after the reorganization of traffic. The information was obtained from the traffic police and from video surveillance of individual sections of roundabouts. As a result of the study: 1) the hypothesis of a decrease in the number of accidents and the assertion that new traffic management schemes (spiral markings) by themselves provide greater traffic safety at roundabouts are refuted; 2) it is revealed that drivers tend to violate the requirements of spiral markings, which increases the likelihood of accidents; 3) it is concluded that new traffic management schemes can have a positive impact on road safety only when engineering measures are implemented to physically separate traffic flows on the ring roadway and reduce the speed of vehicles at the entrances and exits of roundabouts. The results of the work revealed promising areas for further research: to study the relationship between the trajectory of vehicles, their location from the point of view of certain intersection sites, spiral markings, characteristics of traffic flow (speed, density, congestion, flow composition), as well as weather condi¬tions. The identification of such links will allow us to evaluate the effectiveness of spiral marking and draw conclusions about the expediency of its further use at roundabouts in the territory of the Russian Federation.

Adaptive Area-Based Traffic Congestion Control and Management Scheme Based on Fog Computing

How to globally construct the effective traffic congestion control and management method on large-scale urban road network is an important research challenge. Since an urban region can be divided into many areas to be managed, grid management has great potential to improve traffic management on large-scale urban road network. In this paper, we propose an adaptive area-based traffic congestion control and management scheme, which is based on fog computing-based internet of vehicles. In our proposed scheme, an urban region is divided to many traffic management areas. Based on the real-time dynamic traffic tightness degrees and the static correlation degrees between these divided management areas, we can obtain the complete real-time regional traffic correlations of these divided management areas. Further, when a traffic congestion occurs, the fog servers use the constructed measures to control the signal lights in real time and effectively guide the traffic flow through the cooperation between these divided management areas. The experimental results show our proposed scheme can effectively implement the road guidance and traffic light control to alleviate or eliminate traffic congestion when a traffic congestion occurs.

A Scheme to Improve Network Performance Based on Traffic Restriction and Pricing in the Presence of Carpooling

For the purpose of optimizing the traffic network in the presence of carpooling, a traffic demand management scheme was proposed with the consideration of traffic restriction, carpooling, and road pricing. We establish a variational inequality describing the travelers’ mode and route choice following the logit mode split and multimodal user equilibrium principle in the condition of elastic demand. A bilevel programming was designed with the variational inequality as the lower level and optimization objective function taking into account traffic congestion and social welfare as the upper level. The results show that this scheme is possible to be effective in improving network performance. The higher average occupancy of carpooling vehicles and the carpooling demand may not bring better network performance due to the topology, parameters of the network, and travel demand. The fare of carpooling travelers is possible to impact the network very slightly with this scheme. Besides, the group of travelers who is able to afford more than one vehicle is hardly affected by this scheme.