Congested Road Segments Research Articles

Real-time rear-end conflict prediction on congested highways sections using trajectory data

Predicting rear-end conflicts in advance can avoid potential crashes and significantly improve road safety, especially in congested road sections. Many existing studies adopt macroscopic aggregated traffic flow state features and or environment features for rear-end conflicts prediction, which seems to overlook the impact of the temporal trends of various features during the conflict process on the outcomes. Thus, this paper uses microscopic trajectory data of front and rear vehicles for conflict prediction and explored the impact of trajectory changes trend on conflicts formation. A Gated Recurrent Unit (GRU) is employed to learn and encode conflict and non-conflict trajectory data and perform binary classification. The model has a 93 % recall and a 1.41 % false alarm rate. The Local Interpretable Model-agnostic Explanations (LIME) tool also explains the relationships between predicted conflict probability and input microscopic trajectory data. From the time analysis of the input trajectory using LIME, the following conclusions can be drawn. In congested road segments, when the speed of the leading vehicle is below 3 m/s and the speed of the following vehicle is above 4 m/s, it has a significant positive effect on the occurrence of conflicts. And some aggressive acceleration behaviors of drivers have the positive effect also. In addition, the reasons for conflicts among most vehicles are identical Because their feature distributions are similar. These findings can provide targeted insights for the management of ATM in congested road segments.

Supporting Immersive Video Streaming via V2X Communication

With the rapid advancement of autonomous driving and network technologies, future vehicles will function as network nodes, facilitating information transmission. Concurrently, in-vehicle entertainment systems will undergo substantial enhancements. Beyond traditional broadcasting and video playback, future systems will integrate immersive applications featuring 360-degree views and six degrees of freedom (6DoF) capabilities. As autonomous driving technology matures, vehicle passengers will be able to engage in a broader range of entertainment activities while on the move. However, this evolution in video applications will significantly increase bandwidth demand for vehicular networks, potentially leading to bandwidth shortages in congested traffic areas. This paper presents a method for bandwidth allocation for vehicle video applications within the landscape of vehicle-to-everything (V2X) networks. By utilizing a millimeter-wave (mmWave), terahertz (THz) frequency band, and cell-free (CF) extremely large-scale multiple-input multiple-output (XL-MIMO) wireless communication technologies, we provide vehicle passengers with the necessary bandwidth resources. Additionally, we address bandwidth contention issues in congested road segments by incorporating communication methods tailored to the characteristics of vehicular environments. By classifying users and adjusting according to the unique requirements of various multimedia applications, we ensure that real-time applications receive adequate bandwidth. Simulation experiments validate the proposed method’s effectiveness in managing bandwidth allocation for in-vehicle video applications within V2X networks. It increases the available bandwidth during peak hours by 32%, demonstrating its ability to reduce network congestion and ensure smooth playback of various video application types.

Centrality-based lane interventions in road networks for improved level of service: the case of downtown Boise, Idaho

Recent advancements in network science showed that the topological credentials of the elements (i.e., links) in a network carry important implications. Likewise, roadway segments (i.e., links) in a road network should be assessed based on their network position along with traffic conditions at a given geographic scale. The goal of this study is to present a framework that can identify and select critical links in a road network based on their topological importance such as centrality, and the effects of systematic interventions conducted on such links in improving overall system performance (vehicle delay, travel time) to provide an adequate level of service (LOS). A real-world road network (Boise downtown) is investigated by applying lane interventions on roadways experiencing high congestion. Microscopic traffic simulation and analyses are conducted to estimate the traffic flow parameters hence the performance of the road segments. The findings of this study show that interventions applied to critical and congested road segments improve the serviceability from LOS F to LOS E as well as from LOS D to LOS C. Besides, reduced travel time and vehicular delay (after applying intervention on critical components) are also observed for high demand OD pairs of the road network. As such the proposed framework has the potential to incorporate the topological credentials with traffic flow parameters and improve the performance of the road network. This systematic approach will help traffic managers and practitioners to develop strategies that enhance road network performance.

Distributed Urban Freeway Traffic Optimization Considering Congestion Propagation

Traffic optimization strategies are imperative for improving the performance of transportation networks. Most traffic optimization strategies only depend on traffic states of congested road segments, where congestion propagation is neglected. Therefore, we propose a distributed traffic optimization strategy for urban freeways considering the potential congested road segments caused by congestion propagation, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">potential-homogeneous-area</i> (PHA). Utilizing the historical traffic density data, we first quantify the effect of congestion propagation and identify PHA by applying the proposed spatiotemporal lambda-connectedness method. Meanwhile, dynamic capacity constraints of PHA are determined and are integrated with the cell transmission model (CTM) in a centralized traffic optimization problem. To reduce computational complexity and improve scalability, we then propose a double-consensus-based alternating direction method of multipliers algorithm (DC-ADMM) to handle the neighbor coupling constraints and global coupling constraints for solving the problem in a fully distributed way. We prove that the proposed DC-ADMM algorithm converges to the optimal solution in the condition of a convex objective function. Finally, simulations based on real traffic density data, collected in Inner Ring Road, Shanghai, China, reveal the effectiveness of our proposed strategy.

Turn-level network traffic bottleneck identification using vehicle trajectory data

Identifying traffic bottlenecks is a prerequisite to alleviate traffic congestion in urban networks. However, the state-of-the-art methods for bottleneck identification stay at the segment level which assume bottlenecks only result from congested road segments (CRSs). The possibility that a turning direction in a road segment could be a bottleneck has not been thoroughly investigated. In addition, most existing techniques only focus on the congestion degree of road segments, however ignoring the correlations between CRSs and their underlying congestion propagations. This study proposed a framework for turn-level bottleneck identification in large-scale road networks using vehicle trajectory data. First, filtered GPS trajectory data was used to identify CRSs and their congestion start time. Then, congestion correlation graphs and corresponding spanning trees were constructed by investigating forward and backward correlations between CRSs. These directional correlations were later modeled via a Bayesian inference approach. Last, congestion correlation cost models were built up to identify turn-level bottlenecks considering the probability of congestion correlation. Both simulation and field experiments were conducted to evaluate the performance of the proposed framework and compare with the state-of-the-art methods. Results reveal that our framework can effectively capture directional correlations between CRSs, and successfully identify turn-level bottlenecks in large-scale networks.

Massively Parallel Discovery of Loosely Moving Congestion Patterns from Trajectory Data

The efficient discovery of significant group patterns from large-scale spatiotemporal trajectory data is a primary challenge, particularly in the context of urban traffic management. Existing studies on group pattern discovery mainly focus on the spatial gathering and moving continuity of vehicles or animals; these studies either set too many limitations in the shape of the cluster and time continuity or only focus on the characteristic of the gathering. Meanwhile, little attention has been paid to the equidirectional movement of the aggregated objects and their loose coherence moving. In this study, we propose the concept of loosely moving congestion patterns that represent a group of moving objects together with similar movement tendency and loose coherence moving, which exhibit a potential congestion characteristic. Meanwhile, we also develop an accelerated algorithm called parallel equidirectional cluster-recombinant (PDCLUR) that runs on graphics processing units (GPUs) to detect congestion patterns from large-scale raw taxi-trajectory data. The case study results demonstrate the performance of our approach and its applicability to large trajectory dataset, and we can discover some significant loosely moving congesting patterns and when and where the most congested road segments are observed. The developed algorithm PDCLUR performs satisfactorily, affording an acceleration ratio of over 65 relative to the traditional sequential algorithms.

DETERMINATION OF THE FASTEST ROUTE FOR FIRE TRUCKS IN CIREBON CITY BASED ON DISTANCE, TIME, CONGESTION AND LAND USE

Fire fighter performance can be represented by the speed of handling the fire case, where greatly influenced by the travel speed of the fire trucks to the fire location. This study aims to determine the fastest route for fire trucks in Cirebon City based on the travel distance, time, congestion and land use. The method used in this study is by applying the Geographic Information System (GIS) model in identifying several variables that affect the travel speed of fire trucks. In this study, several alternative routes were determined from the fire station (Harjamukti fire station and Bima fire station) to the location of fire (Harjamukti Market) and then calculated the travel time on each alternative route by considering travel distance, time, congestion and land use. The results showed that the fastest travel time for fire trucks in Cirebon City was largely influenced by the travel distance. However, in conditions of high traffic flow, a route with a longer distance but does not cross a congested road segment can produce a faster travel time when compared to a shorter route but crosses congested roads. The fastest route from the Bima fire station to Harjamukti market is route 1 (2,854 m) in the morning, route 3 (3,019 m) in the afternoon and evening. Meanwhile, the fastest route from the Harjamukti fire station to Harjamukti Market is Route 1 (2,069 m) in the morning, afternoon and evening because it has the shortest distance.

Traffic characteristics and pollutant emission from road transport in urban area

In this work, the traffic characteristics and emissions of air pollutants were predicted for two vehicle classifications (passenger cars and trucks) at the national highway in Kyoto City, Japan. Traffic characteristic information (traffic volume, travel speed, and degree of congestion) was estimated based on the digitised data collected by the Ministry of Land, Infrastructure, Transport, and Tourism (MLIT) of Japan. A vehicle emission model, known as the computer program to calculate emissions from road transport (COPERT), was utilised to compute the emission factors (EFs) and total emissions of air pollutants in terms of exhaust particulate matter (PMExh), benzene (C6H6), carbon monoxide (CO), and nitrogen oxide (NOx). Input variables, such as fuel data, activity data, driving conditions, and meteorological conditions, are needed. The findings revealed that the pollutant emissions reached the higher values over the slower travel speed phase. Road no. 1 with the most congested road segment has intensified vehicle numbers, and the slowest traffic flow movement exposed a greater magnitude of pollutant emissions. C6H6 and CO emissions are obviously more emitted from the passenger cars whereas the trucks are responsible for the greater emission of NOx and PMExh. The EFs of pollutants were compared with the Japanese Emission Standards through JE05 and JC08 chassis dynamometer test cycles. The estimated EFs showed inconsistency with the EFs derived from the test cycles. These results may be deployed as the input in air quality dispersion modelling in urban areas for designing the air pollution abatement strategy.

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.

BaroSense

Traffic congestion on urban roadways is a serious problem requiring novel ways to detect and mitigate it. Determining the routes that lead to the traffic congestion segment is also vital in devising mitigation strategies. Further, crowdsourcing this information allows for use of these strategies quickly and in places where infrastructure is not available. In this work, we present an unconventional method, using the barometer sensor of mobile phones to (a) detect road traffic congestion and (b) estimate the paths that lead to the congested road segment. We make the observation that roads are not completely flat and very often, altitude varies along the road. The barometer sensor chips are sensitive enough to measure these variations and consume very little energy of the mobile phone, compared to other sensors such as the GPS or accelerometer. We devise a feature set to map the rate of change of this altitude as the user moves into activities characterized as “still” and “motion,” which are further used by the traffic congestion detection algorithm (RoadSphygmo) to classify the group of users as being in “moving,” “congestion,” or “stuck” states. To estimate the paths that lead to the congested road segment, we compare the user’s barometer sensor readings with a pre-stored road signature of barometer values using Dynamic Time Warping (DTW). We show that by using correlation of barometer sensor values, we can determine if users are in the same vehicle. We crowdsource this information from multiple mobile phones and use majority voting technique to improve the accuracy of traffic congestion detection and path estimation. We find a significant increase in the accuracies using crowdsourced information as compared to individual mobile phones. Further, we show that we can use barometer sensor for other applications such as bus occupancy, boarding/deboarding of a vehicle, and so on. The validation of the state determined by RoadSphygmo is done by comparing it with average GPS speed calculated during the same time period. The path estimation is validated over different intersections and considering various cases of commuter travel. The results obtained are promising and show that the traffic state determination and the estimation of the path taken by the commuter can achieve high accuracy.

An efficient multi‐destinations trip planning protocol for intelligent transport system

AbstractTargeting several destinations on the same trip is commonly seen over the downtown areas. Several drivers leave their home or work everyday targeting multiple end‐destinations for shopping or entertaining purposes. The sequence of visiting destinations can be selected randomly by the drivers, especially if they are less familiar with the area of interest or with multi‐destinations trips. However, expert drivers that travel more frequently towards several destinations should be more cautious planning their trips. They consider the relative locations of these end‐destinations and use their historical knowledge regarding the traffic over the connected road segments there. This paper proposes an efficient multi‐destinations trip planning protocol (MDPP) that recommends drivers with the best sequence of destinations to visit. The proposed protocol considers the location of each destination and the relative distance between all located destinations. It also considers the traffic distribution over the investigated areas of interest to recommend drivers to avoid highly congested road segments or road segments that suffer traffic issue such as accident or emergency existence. From the experimental results, we can see that the MDPP protocol has selected the best path towards the targeted destinations compared with other common selected sequences. It has decreased the travel time, fuel consumption, and the gas emissions for different investigated scenarios.

Dynamic-model-based switched proportional-integral state observer design and traffic density estimation for urban freeway

In the face of the frequent occurrence of urban traffic congestion, only part traffic sensors can be utilized in the urban freeway, in order to more accurately estimate traffic densities and timely identify the congested road segments, a novel estimated algorithm of proportional integral state observer is proposed. Firstly, by embedding the Cell Transmission Model (CTM) into the Dynamic Graph Hybrid Automata (DGHA) framework, we model traffic flow over the urban freeway network. In modeling procedure, an urban freeway network is partitioned in term of three hierarchical structures which are freeway networks, links and cells, respectively. On the one hand, both the dual digraph of road segment structure and hybrid automata respectively are adopted to describe the freeway topology and the multi-modes of dynamic densities in road segments. On the other hand, a deterministic and reasonable constraint is used to reduce the number of combination modes, and thus the modeling procedure is modularized, rule-based and easily-extensible with the help of a combination algorithm. Therefore, the Piecewise Affine System (PWAS) model of the freeway network can be obtained. Secondly, In order to estimate the traffic densities of the urban freeway network, based on the dynamic model, we design a switched proportional integral state observer, and the observer design problem is transferred into the stability problem of the switching system. So the proportional gains and integral gains can be computed via introducing the Lyapunov function and using LMI technique, and thus the traffic densities can be estimated by the designed observer. Especially, the traffic congestion can be identified by checking whether the estimated densities are greater than the critical ones. Finally, a section of Beijing south third ring freeway as an example to verify the proposed approach, and the effectiveness and the feasibility are shown in the experiment results.

Discovering Urban Traffic Congestion Propagation Patterns With Taxi Trajectory Data

Traffic congestion has gradually become a focal issue in people’s daily life. When the traffic flow on a road segment exceeds its actual capacity, congestion takes place. During rush hours, a congested road segment must carry heavy loads for a long time and is very likely to spread traffic congestion to this road’s adjacent segments via the spatial structure of the road. The new infected road segments continue propagating congestion in the same way. In this paper, we attempt to model the congestion propagation phenomenon with a space-temporal congestion subgraph (STCS). To this end, we detect each segment regardless of whether it is congested during consecutive time intervals and build the connection of two segments in terms of their spatio-temporal properties. Due to the sparseness of the trajectory data, two strategies of filling missing congestion edges from both temporal and spatial viewpoints are also proposed. Since STCSes are constructed from the same time interval over different days, we design a specific algorithm to discover the frequent congestion subgraphs. Finally, we evaluate the solution on Shanghai taxicab data and the corresponding road network. The experiment shows that the frequent congestion subgraph can reveal an urban congestion propagation pattern.

A Sequential Decomposition Framework and Method for Calibrating Dynamic Origin—Destination Demand in a Congested Network

This paper presents a two-stage model to calibrate the time-dependent, dynamic origin-destination (O–D) demand under congested traffic conditions. The first-stage model estimates O–D trip rates by minimizing link demand deviation with a one-norm formulation approach, so that over the calibration time period, the traffic demand on calibration links matches with the link demand from the field data. Due to its linear model structure, the first-stage model is more computationally effective and solvable on large real-life networks compared with the commonly seen least-square formulation. Then, a time-dependent user equilibrium traffic assignment model is formulated at the second stage to adjust the departure time profile iteratively, aiming to match the calibrated result with the field observed dynamic traffic condition, i.e., time-dependent speed profile. The second-stage model starts from the concept of a demand–capacity–volume relationship at a congested road segment, where demand exceeds supply, and utilizes shockwave theory to capture the differences between true demand and volume output, together with the idea of using travel time propagation between origin and bottleneck locations to infer real demand at the origin location. The two-stage model was implemented and tested in a case study in Tucson, AZ, USA, as an experimental proof of concept, which demonstrated the effectiveness of the proposed calibration framework and method under circumstances, in which the departure time profile was systematically distorted and initial demand solutions deviated from the true O–D matrices.

Simultaneous estimation of states and parameters in Newell’s simplified kinematic wave model with Eulerian and Lagrangian traffic data

The traffic state estimation process estimates various traffic states from available data in a road network and provides valuable information for travelers and decision makers to improve both travel experience and system performance. In many existing methods, model parameters and initial states have to be given in order to estimate traffic states, which limits the accuracy of the results as well as their transferability to different locations and times. In this paper, we propose a new framework to simultaneously estimate model parameters and traffic states for a congested road segment based on Newell’s simplified kinematic wave model (Newell, 1993). Given both Eulerian traffic count data and Lagrangian vehicle reidentification data, we formulate a single optimization problem in terms of the initial number of vehicles and model parameters. Then we decouple the optimization problem such that the initial number of vehicles can be analytically solved with a closed-form formula, and the model parameters, including the jam density and the shock wave speed in congested traffic, can be computed with the Gauss-Newton method. Based on Newell’s model, we can calculate individual vehicles’ trajectories as well as the average densities, speeds, and flow-rates inside the road segment. We also theoretically show that the optimization problem can have multiple solutions under absolutely steady traffic conditions. We apply the proposed method to the NGSIM datasets, verifying the validity of the method and showing that this method yields better results in the estimation of average densities than existing methods.

Collecting road traffic information using vehicular ad hoc networks

Road traffic information helps a driver in selecting an appropriate route to the travel destination by avoiding congested road segments. A vehicular ad hoc network (VANET) offers an alternative approach to collect road traffic information without a wired infrastructure and centralized storage. This paper proposes the road traffic collecting (RTC) protocol to gather road traffic information in a VANET on city roads. The key protocol operation is the query dissemination that steers a query message over a set of predefined routes towards the destination location. To collect road traffic information, nodes participating in the query dissemination include their own velocity to the query message so that congestion information on different positions of road segments is gathered. A node nearest to the destination that receives the query message returns the query reply containing road traffic information of the forward route back to the source. Unlike existing broadcasting and geocasting protocols, our proposed protocol uses the message exchange pattern that increases the reliability of query delivery under a collision channel. The simulation on a large network with high vehicle densities and simultaneous query sessions is conducted to evaluate the protocol performance in terms of the percentage of road segment coverage and the query response time under the presence of MAC layer collisions. The results show that RTC can collect 100 % of road traffic information over a set of predefined routes under a few number of parallel query sessions while an existing protocol that mainly uses a broadcast message can achieve only 40–70 %. For a large number of query sessions, RTC achieves 65–80 % of target road segments depending on the vehicle density with the average query time bounded to a few seconds.

Road traffic management based on self-load-balancing approach

Traffic congestion is one of the most challenging problems for nowadays cities. Several contributions mainly based on V2V (Vehicle-to-Vehicle) communication have been published, but most of them have never been applied due to their communication related problems and costs. In this article, a novel cost-effective approach is introduced inspired by social life of insects where direct (V2V) communication does not exist anymore. Vehicles are equipped with devices that perform simple tasks, but their interactions with the environment through RSUs (Road Side Units) allow the creation of an intelligence which notifies drivers about congested road segments to avoid them. We call this emerging behavior self-load balancing. Description of the fundamentals of this approach and its performance are detailed in this work.

A Clustering Scheme for Discovering Congested Routes on Road Networks

On road networks, the clustering of moving objects is important for traffic monitoring and routes recommendation. The existing schemes find out density route by considering the number of vehicles in a road segment. Since they don’t consider the features of each road segment such as width, length, and directions in a road network, the results are not correct in some real road networks. To overcome such problems, we propose a clustering method for congested routes discovering from the trajectories of moving objects on road networks. The proposed scheme can be divided into three steps. First, it divides each road network into segments with different width, length, and directions. Second, the congested road segments are detected through analyzing the trajectories of moving objects on the road network. The saturation degree of each road segment and the average moving speed of vehicles in a road segment are computed to detect the congested road segments. Finally, we compute the final congested routes by using a clustering scheme. The experimental results showed that the proposed scheme can efficiently discover the congested routes in different directions of the roads.

Traffic balancing-based path recommendation mechanisms in vehicular networks

In this paper, we propose both reactive and proactive balancing traffic path recommendation mechanisms, which we refer to as Bal-Traf and Abs-Bal, respectively. Bal-Traf is initiated when a certain output road segment located at any road intersection is detected in an overloaded situation. In the event that the existing traffic density of any output road exceeds its optimal capacity, Bal-Traf recommends that those vehicles that plan to pass over this road segment as next hop choose another, less congested output road segment. On the other hand, Abs-Bal is a proactive balancing traffic mechanism. Its main purpose is to distribute input traffic completely even among all output road segments at intersections. Moreover, Abs-Bal considers the best travel time of vehicles in addition to the goal of balancing traffic. From the experimental results, we can see that Bal-Traf eliminates the number of overwhelmed road segments over the road network in scenarios with only partial network congestion. It also decreases the number of congested road segments in scenarios with complete network congestion. However, it increases the density drastically over the remaining congested road segments in these scenarios. Abs-Bal performs well in decreasing the percentage of congested road segments and balancing traffic among road segments located throughout the road network, in the event of complete network congestion.

Discovering Congested Routes Using Vehicle Trajectories in Road Networks

The popular route recommendation and traffic monitoring over the road networks have become important in the location-based services. The schemes to find out the congested routes were proposed by considering the number of vehicles in a road segment. However, the existing schemes do not consider the features of each road segment such as width, length, and direction in a road network. Furthermore, the existing schemes fail to consider the average moving speed of vehicles. Therefore, they can detect the incorrect density routes. To overcome such problems, we propose a new discovering scheme of congested routes through the analysis of vehicle trajectories in a road network. The proposed scheme divides each road into segments with different width and length in a road network. And then, the congested road segment is detected through the saturation degree of the road segment and the average moving speed of vehicles in the road segment. Finally, we compute the final congested routes by using a clustering scheme. The experimental results have shown that the proposed scheme can efficiently discover the congested routes in the different directions of the roads.