Adjacent Intersections Research Articles

Graph-Based Dynamic Modeling and Traffic Prediction of Urban Road Network

Based on various on-road sensor observations, dynamic modeling and analysis of urban road networks becomes an important task of an intelligent transportation system. The major difficulty of this task is that traffic states vary dynamically in both spatial and temporal domains. In this paper, we propose a novel urban road network modeling approach. A road network is described by a weighted undirected graph composed of vertices and edges denoting, respectively, traffic intersections and their pairwise connections. Given the topology of the network, an effective weight estimation algorithm is proposed to extract spatial correlations among adjacent traffic intersections from physical sensor observations. Graph weights can be regularly updated to capture the dynamic essence of traffic states over time. On the basis of weight estimation, we further develop a dynamic spatio-temporal traffic prediction model by using the spatio-temporal autoregressive integrated moving average (STARIMA) model. The effectiveness of the proposed graph-based STARIMA is validated by a series of numerical experiments.

Minimization of traffic delay in traffic flows with coordinated control

The method and results of transport research, carried out by field research method, on the determination of the main indicators of traffic flows with significant unevenness of the movement on the arterial street in conditions of coordinated control is reviewed in the paper. Time parameters of traffic light control for which a reduction in traffic delay is achieved in direct and opposite traffic flow by the change of permissive signal depending on traffic intensity are determined using the simulation method. Change (increase) of the duration of the permissive signal provides uninterrupted movement of vehicles` group during their passage of stop-line at traffic light objects. The proposed method can be used on sections of transport networks with coordinated control, where there is significant heterogeneity of traffic flow, and it prevents the dissipation of groups that consist of vehicles with different dynamic characteristics. Such a result is being performed in the case when in the system of automated control, which combines adjacent intersections on an arterial street, fixed-time program control of traffic light signalization is operating. In this condition, there is a possibility to adjust the duration of signals of traffic light groups by correcting the width (permissive signal duration) and angle of inclination (speed of movement) of the timeline in coordination graphs. The scientific novelty of this research is that the method of traffic delay minimization in conditions of coordinated control acquired further development. The essence of the method is in the controlled change of the range of permissive signal duration in conditions of simultaneous control of the speed of movement between adjacent intersections. Practical value is the application of different programs of traffic light control on sections of arterial streets in transport districts where a significant difference of values of traffic intensity by directions is in morning and evening peak periods.

Prevalence and dynamics of distracted pedestrian behaviour at railway level crossings: Emerging issues

Recent increases in pedestrian collisions have led to several studies investigating the effects of distraction on pedestrian behaviour at road intersections. Although distraction has been identified as a contributing factor to pedestrian crashes at railway crossings, only limited research is available regarding the prevalence of this behaviour occurring at railway level crossings. It is, therefore, essential to better understand distraction prevalence at railway crossings to support the use of countermeasures to improve safety outcomes. We conducted field observations at a railway crossing in Brisbane, Australia and its adjacent road intersection to gauge the prevalence of distracted pedestrians. Overall, 585 pedestrians were observed and video recorded during the daytime. The video recordings were coded to estimate the prevalence of distraction behaviour that road users engaged in, factors that affected these proportions, and dynamic changes in behaviour. Compliance with signals was also analysed. We found distraction behaviours such as talking and looking at the mobile screen (41.9%) while walking to be prevalent and affected by age. Highly distractive tasks were found to be less commonplace at the railway crossing, accounting for 3% of the observations. Still, pedestrians at the railway crossing engaged in these highly distractive tasks on their phones for a much longer period of time. While most non-compliances (with traffic lights) occurred among attentive pedestrians and are likely to be intentional, non-compliances by distracted pedestrians were also observed, highlighting that distraction can lead to unsafe decisions or lack of decisions that result in unsafe behaviours. Finally, distraction was found to be a dynamic phenomenon as a few pedestrians stopped engaging in distractive tasks once they reached the crossing, while others engaged in more distractive tasks once they were on the road or crossing. Our study shows that pedestrian distraction is a prevalent issue at railway crossings and future research is required to further understand and mitigate this changing behaviour.

A Collaborative Road Traffic Regulation Approach Using a Wireless Sensor Network

In this paper, we detail and evaluate a coordinated approach to determining the sequence and duration of green lights at several intersections as part of the Intelligent Transportation System. We present the architecture of a wireless network used to track variations in adjacent intersections. Our algorithm exploits the collected data to determine the sequence of the green lights based on three objectives: (i) reduce the length of queues in the intersection, (ii) prioritize sending vehicle flows to intersections with lower traffic density than the most congested, (iii) synchronize traffic signals between adjacent intersections to create green waves. Traffic simulations have been simulated by the SUMO traffic simulator, they show that our solution manages to react to traffic change and reduce waiting time compared to isolated control strategies.

Decentralized arterial traffic signal optimization with connected vehicle information

This paper proposes a decentralized signal control algorithm that leverages connected vehicle information to improve traffic operations along arterials. The proposed algorithm obtains real-time vehicle locations and speeds, as well as information on pedestrians waiting to cross individual intersections, to optimize signal phasing and timing plans. Signal timing is optimized at individual intersections; however, information about vehicle platoons passing a given intersection is shared with neighboring intersections to facilitate natural coordination between adjacent intersections. The decentralized algorithm is compared to a centralized algorithm that optimizes signal timing at all intersections simultaneously, as well as a traditional coordination strategy. The proposed algorithm is shown both to be more computationally efficient than the centralized approach and provide better operational performance (in terms of person, vehicle and pedestrian delay) than both the centralized algorithm and the traditional strategy. The algorithm is robust to a range of demand patterns and can be applied under scenarios in which not all vehicles are connected or full information about pedestrian arrivals is not available.

A novel and robust contact detection algorithm for arbitrarily shaped blocks

A novel 3D contact detection algorithm for arbitrarily shaped, concave and convex blocks is presented. The algorithm comprises a new neighbouring search technique and a novel method to detect the true contact type between two neighbouring blocks, regardless of their shape. The new neighbouring search technique uses a convenient geometrical plane to reduce the number of searches and an efficient boundary covering to improve the accuracy and efficiency of the solution. The algorithm to detect the true contact type includes three sub-algorithms. The first sub-algorithm uses the geometry of concave faces to find the true contact type between a vertex and face. The second sub-algorithm finds multiple intersections and overlaps between an edge and a concave face using the location of the midpoint created by two adjacent intersections or overlapping vertices. The third sub-algorithm uses both current and new vertices made by the intersection between the edges of two faces to create a new edge and vertex configuration, which is used to determine the single or multiple overlapping areas between two concave faces. This novel algorithm is shown to be more accurate and efficient for detecting neighbours and contact types than other commonly used algorithms.

Evaluating the Operational Efficiency of Two Versions of Super Diverging Diamond Interchange Design: A Case Study in Denver, Colorado

This paper aims to advance the current research on the new super diverging diamond interchange (super DDI) design by evaluating the operational efficiency using real-world locations. As part of a comprehensive research effort on improving the performance of failing service interchanges in the mountain-plains region, the study identified three interchanges (Interstate 225 and Mississippi Avenue, Interstate 25 and 120th Avenue, and Interstate 25 and Hampden Avenue) at Denver, Colorado as the potential candidates to model for future retrofit. Four interchange designs (i.e., existing CDI [conventional diamond interchange], DDI, super DDI-1, and super DDI-2) were tested in this study. The operational analysis was conducted using VISSIM and Synchro. Several microsimulation models (120 scenarios with 600 runs in total) were created with three peak hours (a.m., noon, and p.m.) for existing (the year 2020) and projected (the year 2030) traffic volumes. The study considered two simulation networks: (1) when no adjacent traffic signal exists, to determine how the four interchange designs would perform if there were no adjacent signals or they were far away from the interchange; and (2) when there are two adjacent traffic signals, to evaluate the performance of the four interchanges in a bigger corridor with signal coordination needed. An important finding is that super DDI designs outperformed DDI with adjacent signals and higher traffic demand, while DDI performed similarly to or sometimes insignificantly better than super DDI if no adjacent intersections were located in the vicinity and if the demand was lower than the DDI’s capacity.

Regional flow control in peak travel period based on fuzzy algorithm

The existing methods cannot accurately output the state of traffic lights at multi moments, which leads to a long average waiting time and a large number of stops in the peak travel period. Therefore, a regional flow control method of peak travel period based on fuzzy algorithm is proposed. Firstly, the multi-scale prediction method was used to predict the flow in peak travel period. Then, a two-level fuzzy controller for the regional coordination of peak travel period was constructed, and the traffic flow information between adjacent intersections and the predicted remaining time period were set as the input of the fuzzy controller. Next, the two-level controller was used to output the green light phase of the next moment, so as to adjust the traffic flow and integration degree between the upstream and downstream of the adjacent intersections in the control sub-region of the peak travel period. Finally, the regional coordinated control of the flow in the peak travel period was realized. The simulation results showed that the proposed method could comprehensively reduce the average waiting time, average driving time, average number of stops and intersection saturation, and better realize the flow control during peak travel period.

Dynamic Control Subarea Division Based on Node Importance Evaluating

In order to optimize the existing control subarea division methods, a new dynamic subarea division method based on node importance evaluating is proposed for regional coordinated control. Firstly, considering the characteristics of road network and traffic flow between adjacent intersections, correlation degree model is established by calculating the correlation coefficients of traffic flow, signal cycle, and traffic state. Then, road traffic network is abstracted into a network topology structure graph. From the global perspective of the road network, intersection position information and the importance contribution between adjacent intersections are taken into account, and the correlation degrees between adjacent intersections are taken as the edge weights to construct intersection importance evaluation matrix. Finally, an actual road network is selected, and an improved Newman algorithm is employed to verify and analyze the method proposed in this paper. Results show that, compared with other methods, the control subarea division result of the new proposed method is more elaborate and more in line with the actual traffic flow characteristics. Moreover, dynamic subarea division can be realized according to the traffic characteristics of different periods, which can provide a good basis for the formulation of the signal control schemes in the next stage.

Towards improving sustainability of rail transport by reducing traffic delays at level crossings: A case study for the State of Florida

Rail transport makes a significant contribution to the economy of many geographical locations around the globe. Along with numerous merits, rail transport has some demerits as well, such as traffic delays and safety issues at level crossings. Traffic delays at level crossings have various negative impacts, including economic losses, increasing number of violations because of delays, queue spillbacks at the adjacent intersections, and environmental impacts. This study reviews a number of level crossing delay estimation approaches found in the literature. Based on the state-of-the-art review and practical considerations, a new approach for estimation of the overall traffic delay per day at level crossings with the existing warning devices is proposed to improve the sustainability of rail transport. Moreover, in order to assist with the selection of countermeasures for level crossing upgrades, the proposed approach can be also used to estimate the overall traffic delay per day at level crossings after implementation of countermeasures. A case study is conducted for the public and private level crossings in the State of Florida, considering the countermeasures commonly used in the United States. It was revealed that traffic delays were higher at the public level crossings than at the private level crossings, both before and after implementation of the candidate countermeasures. The case study also indicated that low-cost countermeasures could be more advantageous in terms of safety effectiveness and traffic delays as compared to many high-cost conventional countermeasures. Furthermore, certain attributes of the level crossings in Florida with the highest traffic delays were investigated.

An edge computing based data detection scheme for traffic light at intersections

The reliability and validity of traffic data plays an important role in intelligent transportation systems. Most of the data detection schemes target the network level, and rarely discuss the impact of vehicle data detection at intersections on the intelligent control of traffic lights. Moreover, most data detection is processed in a centralized cloud center, which is not suitable for complex and changeable intersections. In this regard, we propose an edge computing based data detection scheme for traffic light intersections. In our scheme, traffic lights act as edge nodes to detect vehicles data. We first consider a single intersection scenario. We collect vehicle data through V2E communication through base station, and use the quotient filter to detect and verify the validity of the data. We further consider the multiple intersections scenario. We merge the vehicle data of the two adjacent intersections, and then detect the validity of the data by the quotient filter. In addition, the scheme uses mmh3 hash functions in the QF filter to reduce the computing resource occupation of edge nodes and the bit error rate. The experimental results show that the data detection solution is effective in detecting data quickly even there is a large number of vehicles and complex vehicle data. Our proposed scheme can also verify the reliability and effectiveness of vehicles with a smaller delay.

Dual temporal gated multi-graph convolution network for taxi demand prediction

Taxi demand prediction is essential to build efficient traffic transportation systems for smart city. It helps to properly allocate vehicles, ease the traffic pressure and improve passengers’ experience. Traditional taxi demand prediction methods mostly rely on time-series forecasting techniques, which cannot model the nonlinearity embedded in data. Recent studies start to combine the Euclidean spatial features through grid-based methods. By considering the spatial correlations among different regions, we can capture how the temporal events have impacts on those with adjacent links or intersections and improve prediction precision. Some graph-based models are proposed to encode the non-Euclidean correlations as well. However, the temporal periodicity of data is often overlooked, and the study units are usually constructed as oversimplified grids. In this paper, we define places with specific semantic and humanistic experiences as study units, using a fuzzy set method based on adaptive kernel density estimation. Then, we introduce dual temporal gated multi-graph convolution network to predict the future taxi demand. Specifically, multi-graph convolution is used to model spatial correlations with graphs, including the neighborhood, functional similarities and landscape similarities based on street view images. As for the temporal dependencies modeling, we design the dual temporal gated branches to capture information hidden in both previous and periodic observations. Experiments on two real-world datasets show the effectiveness of our model over the baselines.

Identification of Optimal Left-Turn Restriction Locations using Heuristic Methods

Restricting left turns throughout a network improves overall flow capacity by eliminating conflicts between left-turning and through-moving vehicles. However, doing so requires vehicles to travel longer distances. Implementing left-turn restrictions at only a subset of locations can help balance this tradeoff between increased capacity and longer trips. Unfortunately, identifying exactly where these restrictions should be implemented is a complex problem because of the many configurations that must be considered and interdependencies between left-turn restriction decisions at adjacent intersections. This paper compares three heuristic solution algorithms to identify optimal location of left-turn restrictions at individual intersections in perfect and imperfect grid networks. Scenarios are tested in which restriction decisions are the same for all intersection approaches and only the same for approaches in the same direction. The latter case is particularly complex as it increases the number of potential configurations exponentially. The results suggest all methods tested can be effectively used to solve this problem, although the hybrid method proposed in this paper appears to perform the best under scenarios with larger solution spaces. The proposed framework and procedures can be applied to realistic city networks to identify where left-turn restrictions should be implemented to improve overall network operations. Application of these methods to square grid networks under uniform demand patterns reveal a general pattern in which left turns should be restricted at central intersections that carry larger vehicle flows but allowed otherwise. Such findings can be used as a starting point for where to restrict left turns in more realistic networks.

ANALISA KOORDINASI SINYAL ANTAR SIMPANG PADA RUAS JALAN RADEN PANJI SUROSO KOTA MALANG

There are two adjacent intersections on Jl. Raden Panji Suroso get stopped at two intersections. It is a need to provide a proper traffic signal coordination aiming at improving the performance of those two intersections and managing the vehicle operating cost efficiently. The data were taken from the estimation of geometric shape at every intersection and the intersection capacity at the peak hours. The data, then, were integrated with the Indonesian Highway Capacity Manual (MKJI) methodology. Signal coordination makes the vehicles does not need to stop at every intersection. Increased adjacent performance with 0.813 degree of saturation from 0.814; 79,222m queue from 132,00m, and 39,919 seconds delay from 377,855; service level index from F into D; IDR . 2.850.274/ 1,000 km operating cost reduction for vehicle class I; IDR 4.236.430/1,000 km for vehicle class IIA; and IDR 5.385.889/1,000 km for vehicle class IIB respectively

Distributed Kuramoto Self-Synchronization of Vehicle Speed Trajectories in Traffic Networks

This article presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is a classical model for the self-synchronization of coupled oscillators. The mapping between phase and location for vehicles on different roads is designed such that Kuramoto synchronization ensures safe intersection navigation. Each vehicle uses a constrained optimal control policy to achieve its desired target Kuramoto phase at the upcoming intersection. The overall outcome is a distributed traffic synchronization algorithm that simultaneously tackles two challenges traditionally addressed independently, namely: coordinating crossing at an individual intersection, and harmonizing traffic flow between adjacent intersections. Simulation studies highlight the positive impact of this strategy on fuel consumption and traffic delay time, compared to a network with traditional traffic light timing.

Improvement of methods for assessing the effectiveness of dedicated lanes for public transport

There are different configurations of street and road networks in cities, which is why those transportation models that determine how effectively a public transport network is operated are different. Along with this, some transport areas may have characteristic features predetermined by the density of a street network, the intensity of individual and public traffic. The special feature of the current study is determining the operational effectiveness of dedicated lanes for public transport given a significant density of the main street and road network. Significant density is characterized by its value for the distance between adjacent intersections in the range of 150‒200 m. With such planning patterns, there is a mutual influence of the conditions of individual and public transport between adjacent intersections. An increase in the distance between intersections disrupts the stability of traffic flow through its disintegration into separate groups based on the dynamic characteristics of vehicles. A characteristic feature of the proposed procedure for evaluating the operational effectiveness of dedicated lanes is that the use of a GPS monitoring system makes it possible to relatively quickly determine the areas of the network where there are the greatest delays in movement in real time. After that, attention is focused on investigating the main factors of influence and their parameters followed by modeling. The reported results would in the future contribute to devising a clear sequence of transport-related research based on a set of their methods in order to acquire representative data and define adequate patterns. An important practical result is the use of not only established normative approaches to the design of dedicated lanes, which are common for all types of street and road networks but taking into consideration the peculiarities characteristic of their individual sections.

Lane-Based Traffic Arrival Pattern Estimation Using License Plate Recognition Data

Understanding the traffic arrival process and its patterns is of vital importance for delay and queue analysis at intersections. The installation of advance loop detectors to sense vehicle arrivals could be costly and biased. Utilizing sampled vehicle trajectory data to reconstruct the traffic arrival flow might suffer from small sample sizes. License plate recognition (LPR) data commonly available at intersections in cities in China are promising for overcoming such limitations. This study aims to estimate a lane-based traffic arrival pattern by using LPR data collected at downstream and upstream intersections. The proposed method develops a probability model with an assumption of a two-stage piecewise arrival process for upstream merge movements. Given observations of vehicle arrivals provided by matched cars and trucks in LPR data, the model estimates second-based mean arrival rates for each lane at the downstream intersection. The proposed method is validated using actual LPR data collected at two adjacent intersections in Kunshan City, China. The results demonstrate that the proposed method can describe the traffic arrival patterns of upstream merge movements with either two-stage or uniform arrival processes in different traffic scenarios. In addition, the proposed method is more robust and reliable than an average-based benchmark method in terms of revealing actual traffic arrival patterns under different match rates.

Signal adaptive cooperative control of two adjacent traffic intersections using a two-stage algorithm

It is obvious that the traffic light plans of two intersections interact with each other, and the key problem for the system adjacent intersections is how to calculate of the arrival rates of the connection lanes of each phase. To treat the interaction of the two intersections, in this paper, the transition probabilities of the connection lanes of intersections 1 and 2 are introduced, which are utilized to compute the arrival rates of the connection lanes. In addition, since the green times of connection lanes interact with each other, a corresponding model of the phases of the connection lanes is proposed, which is employed to search for the optimal phase of the connection lanes. Moreover, the traffic time of the vehicles on the connection lanes is considered in the corresponding model. hen, an optimization model of the traffic signal of the two adjacent intersections is constructed. To solve the model, a novel two-stage algorithm is proposed. The first stage matches the arrival rates and the phase of the connection lanes of the two adjacent intersections. The second stage optimizes the model. Four numerical experiments are utilized to test the model and algorithm, and three other algorithms are chosen for comparison with our algorithm. The results of the numerical experiments demonstrate that the model and the algorithm are effective and that the model and algorithm can find more reasonable plans for the plans of two adjacent intersections.

Leveraging Multiagent Learning for Automated Vehicles Scheduling at Nonsignalized Intersections

Recent advancements of Vehicle-to-Everything (V2X) communication combined with artificial intelligence (AI) technologies have shown enormous potentials for improving traffic management efficiency and intelligence. To provide innovative and effective data-driven traffic management solution for the coming automated vehicle era, we present a vehicle-road collaboration-enabled nonsignalized intersection management architecture in this paper. First, by dividing the intersection zone into the central section (CS) and the waiting section (WS), a vehicle regulation scheme involved with communication and computation planes is developed for V2X-enabled nonsignalized intersection management. Specifically, in order to guarantee vehicle safety, the definition of no overlapping occupation time in CS and the fastest crossing time point (FCTP) algorithm are employed for vehicle collision avoidance. Second, considering the relative coordination between adjacent intersections, a multiagent-based deep reinforcement learning scheduling (MA-DRLS) algorithm is proposed to realize cooperative multiple intersection management. Through information exchange with different intersection agents, each agent can obtain an optimal scheduling strategy using independent deep reinforcement learning (DRL) network. The features of fixed Q-targets and experience replay are leveraged to improve the reliability of neural network during the training process. Finally, simulation performances in terms of intersection throughput and vehicle waiting time have been provided to validate the effectiveness and demonstrate the superiority of the proposed nonsignalized intersection management solution.

Impact of the Link Length on the Delay in Two-Way Signal Coordination

Signal coordination plays an important role in urban traffic control systems. Studying the influence of some factors such as the link length on the potential benefits of signal coordination can result in more efficient traffic control. Researchers have observed a highly fluctuating or damped sine-wave type relationship between the link length and the delay in two-way coordinated control, but a clear explanation of this relationship is still lacking. This paper presents a thorough analysis of the mechanism by which link length affects the delay in two-way coordination. The impact of the link length involves two aspects. We first derived formulas for the delay using shockwave queuing profiles that included the impact of the offsets in the two directions. We then conducted numerical experiments employing Robertson’s platoon dispersion model to incorporate the impact of platoon dispersion. By comparing the results from the derived formulas and the numerical experiments, we concluded that the periodicity is due to the mutual relation between the offsets in the two directions, whereas the attenuation is due to platoon dispersion. The findings should provide valuable insights for developing a more reasonable correlation degree model for the coordination of adjacent intersections. In addition, we found that platoon dispersion has a negligible influence on the critical link lengths at which the delay cannot be reduced by signal coordination. This means that whether or not platoon dispersion is considered, it will not affect the choice between the simultaneous and the alternative progressions to minimize the delay under a given link length, which is very meaningful in practical work.