Congestion In Urban Road Networks Research Articles

Strategic flood impact mitigation in developing countries’ urban road networks: Application to Hanoi

Due to climate change, the frequency and scale of flood events worldwide are increasing dramatically. Flood impacts are especially acute in developing countries, where they often revert years of progress in sustainable development and poverty reduction. This paper introduces an optimization-based decision support tool for selecting cost-efficient flood mitigation investments in developing countries’ urban areas. The core of the tool is a scenario-based, multi-period, bi-objective Mixed Integer Linear Programming model which minimizes infrastructure damage and traffic congestion in urban road networks. The tool was developed in collaboration with Vietnamese stakeholders (e.g., local communities and government authorities), and integrates data and inputs from other disciplines, including social science, transport economics, climatology and hydrology. A metaheuristic, combining a Greedy Randomized Adaptive Search Procedure with a Variable Neighborhood Descent algorithm, is developed to solve large scale problem instances. An extensive computational campaign on randomly generated instances demonstrates the efficiency of the metaheuristic in solving realistic problems with hundreds of interdependent flood mitigation interventions. Finally, the applicability of the interdisciplinary approach is demonstrated on a real case study to generate a 20-year plan of mitigation investments for the urban area of Hanoi. Policy implications and impacts of the study are also discussed.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

AN ASSESSMENT OF TRAFFIC CONGESTION IN TAMAN SRI SERDANG, SELANGOR, MALAYSIA

The issue of traffic congestion has emerged as a significant and multifaceted challenge in numerous cities worldwide, encompassing substantial economic and environmental implications. Numerous experts have proposed that the foremost measure to address the congestion issue is to identify the characteristics of traffic congestion. This study aimed to assess the traffic congestion in urban road networks. Thus, the main methodological approach for conducting the study was the traffic volume study in Taman Sri Serdang, which was observed to examine the traffic flow during weekdays and weekends to examine the traffic flow patterns. Accordingly, conducting a traffic volume and Level of Service (LOS) analysis is necessary to enhance accessibility and road capacity. A comparison of the traffic trends and LOS during weekdays and weekends was also discussed. The findings concerning peak-hour traffic patterns are highlighted, and the conclusions are derived regarding urban traffic flow patterns.

Congestion in cities: Can road capacity expansions provide a solution?

Road network congestion; a traffic state characterised by slower speeds, longer trip times, and increased vehicular queuing; is a major issue in most urban areas around the globe. Building more roads is a commonly employed policy intervention to reduce congestion. This strategy, however, is controversial because under certain conditions road capacity expansions may induce growth in traffic volumes. A crucial precursor to understanding whether road capacity expansions provide a solution to congestion is to quantify the technology driving congestion in urban road networks. This congestion technology describes the variation in performance of the network, often represented by traffic flow through the road network, over its intensity of use given by the number of vehicles in the network. However, obtaining empirical estimates of congestion technology from data on traffic variables is challenging due to statistical biases that emerge via the complex interactions between traffic flow, traffic controls, and capacity. To adjust for such biases, this paper presents an approach based on causal statistical modelling to quantify the nature and form of congestion technology in road networks in twenty-four cities worldwide. Our results suggest that increasing network capacity is in general not an efficient solution to manage congestion, in the sense that the average travel speed in the network does not increase substantially with an increase in capacity. This result and our congestion technology estimates have important implications for optimal urban transportation strategies.

Vehicular Visible Light Communication for Intersection Management

An innovative treatment for congested urban road networks is the split intersection. Here, a congested two-way–two-way traffic light-controlled intersection is transformed into two lighter intersections. By reducing conflict points and improving travel time, it facilitates smoother flow with less driver delay. We propose a visible light communication system based on Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) and Infrastructure-to-Vehicle (I2V) communications able to safely manage vehicles crossing through an intersection, leveraging Edge of Things (EoT) facilities. Headlights, street lamps, and traffic signals are used by connected vehicles to communicate with one another and with infrastructure. Through internally installed Driver Agents, an Intersection Manager coordinates traffic flow and interacts with vehicles. For the safe passage of vehicles across intersections, request/response mechanisms and time and space relative pose concepts are used. A virtual scenario is proposed, and a “mesh/cellular” hybrid architecture used. Light signals are emitted by transmitters by encoding, modulating, and converting data. Optical sensors with light-filtering properties are used as receivers and decoders. The VLC request/response concept uplink and downlink communication between the infrastructure and the vehicles is tested. Based on the results, the short-range mesh network provides a secure communication path between street lamp controllers and edge computers through neighbor traffic light controllers that have active cellular connections, as well as peer-to-peer communication, allowing V-VLC ready cars to exchange information.

Bi-objective optimization models for mitigating traffic congestion in urban road networks

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world. In this context, this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation. Four congestion mitigation strategies are identified that are based on deviation and relative deviation of link volume from the corresponding capacity. Consequently, four bi-objective mathematical programming optimal flow distribution (OFD) models are proposed. The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volume-to-capacity links compared to UE and SO models. Among the models, the system optimality with minimal sum and maximum absolute relative-deviation models (SO-SAR and SO-MAR) showed superior results for different performance measures. The SO-SAR model yielded 50% and 30% fewer links at higher link utilization factor than UE and SO models, respectively. Also, it showed more than 25% improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of 1.04 compared to the other OFD and UE models. Conversely, the SO-MAR model yielded the least total distance and total system travel time, resulting in lower fuel consumption and emissions, thus contributing to sustainability. The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.

Dynamic Trajectory-Based Traffic Dispersion Method for Intersection Traffic Accidents in an Intelligent and Connected Environment

Intersection traffic accidents (ITAs) are one of the major causes of nonrecurrent congestion in urban road networks because the intersection is the bottleneck of road traffic. Although there has been a proliferation of studies that confirm connected automated vehicle (CAV) technology demonstrates the potential to improve traffic mobility and safety performance at intersections, studies that handle ITAs using CAV technology are scarce. To fill this gap, this study proposes an intersection control system to coordinate vehicle trajectories and ensure operational efficiency and safety under the condition of typical ITAs. A route allocation judgment method based on the position of obstacles is proposed to avoid the accident vehicle. A spatiotemporal grid-based trajectory coordination model is proposed by formulating the route allocation. We utilize the .NET Framework to secondarily develop a Simulation of Urban Mobility-based simulation environment that examines the proposed method. The experimental results indicate that the presented method outperforms the signal control method in terms of reducing delay per vehicle and rescue time and increasing throughput. Consequently, we concluded that the proposed method could handle ITAs under an intelligent and connected environment and improve the safety and robustness of intelligent intersections. It is reasonably believed that the technique has potential applicability in autonomous intersection management.

Urban traffic light scheduling for pedestrian–vehicle mixed-flow networks using discrete sine–cosine algorithm and its variants

This study addresses the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks. A macroscopic model, which strikes an appropriate balance between pedestrians’ needs and vehicle drivers’ needs, is employed to describe the traffic light scheduling problem in a scheduling framework. The objective of this problem is to minimize the total network-wise delay time of vehicles and pedestrians within a given finite-time window, which is crucial to avoid traffic congestion in urban road networks. To achieve this objective, the present study first uses a well-known optimization solver called GUROBI to obtain the optimal solution by converting the problem into mixed-integer linear programming. The obtained results indicate the computational inefficiency of the solver for large network sizes. To overcome this computational inefficiency, three novel metaheuristic methods based on the sine–cosine algorithm are proposed. These methods are denoted by discrete sine–cosine algorithm, discrete sine–cosine algorithm with local search operator, and discrete sine–cosine algorithm with local search operator and memory utilization inspired by harmony search. Each of these methods is developed hierarchically by taking the advantages of previously developed method(s) in terms of a better search process to provide more accurate solutions and a better convergence rate. To validate all these proposed metaheuristics, extensive computational experiments are carried out using the real traffic infrastructure of Singapore. Moreover, various performance measures such as statistical optimization results, relative percentage deviation, computational time, statistical analysis, and convergence behavior analysis have been employed to evaluate the performance of algorithms. The comparison of the proposed SCA variants is done with GUROBI solver and other metaheuristics namely, harmony search, firefly algorithm, bat algorithm, artificial bee colony, genetic algorithm, salp swarm algorithm, and harris hawks optimization. Overall comparison analysis concludes that the proposed methods are very efficient to solve the traffic light scheduling problem for pedestrian–vehicle mixed-flow networks with different network sizes and prediction time horizons.

Community detection in large scale congested urban road networks.

Traffic congestion in large urban networks may take different shapes and propagates non-uniformly variations from day to day. Given the fact that congestion on a road segment is spatially correlated to adjacent roads and propagates spatiotemporally with finite speed, it is essential to describe the main pockets of congestion in a city with a small number of clusters. For example, the perimeter control with macroscopic fundamental diagrams is one of the effective traffic management tools. Perimeter control adjusts the inflow to pre-specified regions of a city through signal timing on the border of a region in order to optimize the traffic condition within the region. The precision of macroscopic fundamental diagrams depends on the homogeneity of traffic condition on road segments of the region. Hence, previous studies have defined the boundaries of the region under perimeter control subjected to the regional homogeneity. In this study, a cost-effective method is proposed for the mentioned problem that simultaneously considers homogeneity, contiguity and compactness of clusters and has a shorter computational time. Since it is necessary to control the cost and complexity of perimeter control in terms of the number of traffic signals, sparse parts of the network could be potential candidates for boundaries. Therefore, a community detection method (Infomap) is initially adopted and then those clusters are improved by refining the communities in relation to roads with the highest heterogeneity. The proposed method is applied to Shenzhen, China and San Francisco, USA and the outcomes are compared to previous studies. The results of comparison reveal that the proposed method is as effective as the best previous methods in detecting homogenous communities, but it outperforms them in contiguity. It is worth noting that this is the first method that guarantees the connectedness of clusters, which is a prerequisite of perimeter control.

Congestion in cities: evidence on the form and causality of the macroscopic fundamental relationship of traffic flow

This paper quantifies the technology driving congestion in urban road networks. To do so, we estimate macroscopic fundamental relationships for homogeneously congested sub-networks (reservoirs) in thirty-four cities worldwide. We adopt a causal approach based on non-parametric instrumental variables to estimate the form of the reservoir-level flow-density relationship using large-scale traffic sensor data. Specifically, we apply a Bayesian non-parametric spline-based regression model with instrumental variables to adjust for potential confounding/endogeneity biases due to simultaneity and omitted variables such as vehicle interactions and traffic controls. Our estimates suggest that the provision of vehicular travel in cities is subject to decreasing returns to density and network size. Importantly, we find that increasing road network capacity is not an efficient solution to manage congestion because average travel speed does not change substantially with increase in capacity. As a by-product of the estimation, we also deliver estimates of important traffic control inputs such as capacity and critical occupancy for these reservoirs. Our results have implications for traffic flow modelling used by both economists and traffic engineers.

Decentralized store-and-forward based strategies for the signal control problem in large-scale congested urban road networks

Signal control strategies for congested urban road networks designed in a centralized framework require many communication links, serious processing power, and infrastructure for the centralized coordination. As a result, strategies based on a centralized framework are not scalable. The use of decentralized signal control strategies for large-scale urban traffic networks is a solution to this problem, since it allows for the implementation of such strategies on networks whose centralized solution is not easily scalable. This paper addresses the problem of designing a decentralized traffic-responsive signal control solution, proposing two methods based on different formulations of the store-and-forward model: (i) the Decentralized Traffic-responsive Urban Control (DTUC) method; and (ii) the Decentralized Decoupled Traffic-responsive Urban Control (D2TUC). The decentralized configuration is such that each intersection is associated with one computational unit, with limited computational power and memory, which controls the traffic signals of the incoming links. Sufficient conditions for the controllability of the considered store-and-forward models are also presented. Both methods are validated resorting to numerical simulations of the urban traffic network of Chania, Greece, for two demand scenarios, and their performance is compared with the performance of the Traffic-responsive Urban Control (TUC) centralized strategy. One of the proposed decentralized methods, D2TUC, is shown to match the performance of TUC.

Air taxi service for urban mobility: A critical review of recent developments, future challenges, and opportunities

Expected to operate in the imminent future, air taxi service (ATS) is an aerial on-demand transport for a single passenger or a small group of riders, which seeks to transform the method of everyday commute. This uncharted territory in the emerging transportation world is anticipated to enable consumers bypass traffic congestion in urban road networks. By adopting an electric vertical takeoff and landing concept (eVTOL), air taxis could be operational from skyports retrofitted on building rooftops, thus gaining advantage from an implementation standpoint. Motivated by the potential impact of ATS, this study provides a review of air taxi systems and associated operations. We first discuss the current developments in the ATS (demand prediction, air taxi network design, and vehicle configuration). Next, we anticipate potential future challenges of ATS from an operations management perspective, and review the existing literature that could be leveraged to tackle these problems (ride-matching, pricing strategies, vehicle maintenance scheduling, and pilot training and recruitment). Finally, we detail future research opportunities in the air taxi domain.

Multi-level information fusion to alleviate network congestion

While increasing urban traffic can be an indicator of development, this inevitably results in traffic congestion in urban road networks. Is there a way to manage traffic flow through the control of traffic signals such that the overall network congestion is improved? Traffic light signals can be represented as two states of an Ising model. It is possible for traffic lights to ”communicate” with each other through a fusion process from a remote management control system. This requires collection of information which can be fed to a centralized decision-making control mechanism. We first explore the fusion process between traffic signals and show that it is possible for traffic flow in a city to follow the phase transition as exhibited in the 2D Ising model. The model will be extended to show that a random switching between signalling control mechanisms can result in congested traffic being susceptible to transit out of congestion.

Determinants of the congestion caused by a traffic accident in urban road networks

Non-recurrent congestion is frustrating to travelers as it often causes unexpected delay, which would result in missing important meetings or appointments. Major causes of non-recurrent congestion include adverse weather conditions, natural hazards, and traffic accidents. Although there has been a proliferation of studies that investigate how adverse weather conditions and natural hazards impact road congestion in urban road networks, studies that look into determinants of the congestion caused by a traffic accident are scarce. This research fills in this gap in the literature. When a traffic accident occurs on an urban link, the congestion would propagate to and affect adjacent links. We develop a modified version of the Dijkstra's algorithm to identify the set of links in the neighborhood of the accident. We first measure the level of congestion caused by the traffic accident as the reduction in traveling speed on those links. As the impact of congestion varies both in space and in time, we then estimate a generalized linear mixed-effects model with spatiotemporal panel data to identify its determinants. Finally, we conduct a case study using real data in Beijing. We find that: (1) the level of congestion is mostly associated with the types of the traffic accidents, the types of vehicles involved, and the occurrence time; (2) for the three types of traffic accidents, namely, scrape among vehicles, collisions with fixed objects, and rear-end collisions, the level of congestion associated with the first two types are comparable, while that associated with the third type is 8.43% more intense; (3) for the types of vehicles involved, the level of congestion involving buses/trucks is 6.03% more intense than those involving only cars; (4) for the occurrence time, the level of congestion associated with morning peaks and afternoon peaks are 5.87% and 6.57% more intense than that associated with off-peak hours, respectively.

An Improved Critical-Edge Model for Finding Optimal Contraflow Links Considering the Influence of Intersections

A reversible roadway (contraflow) is one in which the direction of traffic flow in one or more lanes is reversed to the opposing direction for some period of time. Reversible roadways are most commonly used for accommodating directionally imbalanced traffic associated with daily commuter periods. Reversible lanes also have been widely used, in recent years, for evacuating major metropolitan regions threatened by hurricanes and other disasters. One important problem in the practice of evacuation traffic organization is the choice of road links for contraflow. Most research on the choice of contraflow links does not consider the influence of intersections, which leads to overestimation of evacuation capacity especially in congested urban road networks. We abstract an evacuation road network as a special network with directional node-weights by considering the capacity of intersection movements as directed weights of nodes. We define the critical edge for increasing the maximum flow value of such network as the one that can maximize the range of flow value increase by expanding its capacity. We obtain alternative links for contraflow by searching critical edges in such network. We presented a modified algorithm for finding such critical edges on the basis of the maximal capacity path algorithm for the classical maximum flow problem. We also provided a numerical example and tested the effects through traffic simulation. Our results show that the results considering the influence of intersections are more reasonable than those ignoring it and that taking the intersection effects into account enables us to reduce the total evacuation time.

Multiple-Factor Based Sparse Urban Travel Time Prediction

The prediction of travel time is challenging given the sparseness of real-time traffic data and the uncertainty of travel, because it is influenced by multiple factors on the congested urban road networks. In our paper, we propose a three-layer neural network from big probe vehicles data incorporating multi-factors to estimate travel time. The procedure includes the following three steps. First, we aggregate data according to the travel time of a single taxi traveling a target link on working days as traffic flows display similar traffic patterns over a weekly cycle. We then extract feature relationships between target and adjacent links at 30 min interval. About 224,830,178 records are extracted from probe vehicles. Second, we design a three-layer artificial neural network model. The number of neurons in input layer is eight, and the number of neurons in output layer is one. Finally, the trained neural network model is used for link travel time prediction. Different factors are included to examine their influence on the link travel time. Our model is verified using historical data from probe vehicles collected from May to July 2014 in Wuhan, China. The results show that we could obtain the link travel time prediction results using the designed artificial neural network model and detect the influence of different factors on link travel time.

Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both the mean and variance of travel times by using emerging low-frequency floating car data. Different from the existing studies, the path travel time distribution in this study is formulated as the sum of the deterministic link travel times and stochastic turning delays at intersections. Using this formulation, distinct travel time delays for different turning movements at the same intersection can be well captured. In this study, a speed estimation algorithm is developed to estimate the deterministic link travel times, and a distribution estimation algorithm is proposed to estimate the stochastic turning delays. Considering the low sampling rate of the floating car data, a weighted moving average algorithm is further developed for a robust estimation of the path travel time distribution. A real-world case study in Wuhan, China is carried out to validate the applicability of the proposed method. The results of the case study show that the proposed method can obtain a reliable and accurate estimation of path travel time distribution in congested urban road networks.

Node modeling for congested urban road networks

First-order network flow models are coupled systems of differential equations which describe the build-up and dissipation of congestion along network road segments, known as link models. Models describing flows across network junctions, referred to as node models, play the role of the coupling between the link models and are responsible for capturing the propagation of traffic dynamics through the network. Node models are typically stated as optimization problems, so that the coupling between the link dynamics is not known explicitly. This renders network flow models analytically intractable. This paper examines the properties of node models for urban networks. Solutions to node models that are free of traffic holding, referred to as holding-free solutions, are formally defined and it is shown that flow maximization is only a sufficient condition for holding-free solutions. A simple greedy algorithm is shown to produce holding-free solutions while also respecting the invariance principle. Staging movements through nodes in a manner that prevents conflicting flows from proceeding through the nodes simultaneously is shown to simplify the node models considerably and promote unique solutions. The staging also models intersection capacities in a more realistic way by preventing unrealistically large flows when there is ample supply in the downstream and preventing artificial blocking when some of the downstream supplies are restricted.

Traffic Congestion Evaluation using GIS Case Study: Jeddah City

The emergence of traffic and subsequently traffic congestion in urban road networks are increasing worldwide with the growing number of vehicles, which results in excess delays, and reduced safety. The aim of this paper is to use many GIS functions (network analyst, shortest path) in evaluating traffic congestions point during working day hours according to roads directions. The study area is a residential area of Jeddah city, Saudi Arabia. A geo-database is designed that includes the road network with their directions that located in Jeddah city. Several GIS functions are used in this paper including network analyst and overlay analysis using ArcGIS 10.2. The priority results are utilized in evaluating congestion points according to roads direction, which can help planner in reassigning roads directions to mitigate congestion points at all parts of Jeddah city.