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This research paper aims to study the effect of tram management on traffic fluidity and its impact on car drivers' behaviors at junctions crossed by trams. The methodology used in this research is based on a mathematical model and an investigation of car drivers. The first step is to analyze the data of annual travelers' attendance and assess the number of trams offered and needed in operation to respond adequately to the factual demand. The second step proceeds to show how the previous results of the trams' fleet influence traffic jams. That is, this step identifies how the number of trams used in operation blocks other motorists and reduces traffic flow capacity at junctions. Finally, the purpose of the questionnaire is to determine car drivers' opinions of the causes of traffic congestion at junctions and understand how this phenomenon affects their behaviors. The outcomes demonstrate that tram management is ineffective because there is a considerable gap between the annual offered tram fleet and the actual one needed according to the real statistical data. The high number of trams utilized is the leading cause of traffic congestion. Furthermore, this situation disturbs the control of traffic lights at common intersections. Unfortunately, this outcome is the main reason for drivers' poor behavior, as 75.20% of car drivers are always stressed. These issues have intensified traffic jams in several junctions along the tram line. The article recommends some solutions to improve tram management and traffic fluidity to avoid the substandard behavior of car drivers at junctions. [ABSTRACT FROM AUTHOR]

If we consider road traffic in urban areas, one of its major problems encountered nowadays is that of traffic congestion. The paper aims to provide an improvement, in terms of reducing traffic congestion, by analyzing different traffic light cycles. A traffic light cycle is determined by the red and green duration of the traffic lights. Traditionally these two have a fixed duration and controlling road traffic can be achieved by modifying it, resulting in a congestion reduction. We present a comparison between the fixed and an improved fixed situation, as well as a QL algorithm to further reduce the congestion at an intersection. Urban traffic can thus be optimized, in terms of reducing congestion, on an existing traffic light system. A study case validates the results, based on road traffic data obtained from the city of Bucharest. [ABSTRACT FROM AUTHOR]

Improving the efficiency of traffic signal control is an effective way to alleviate traffic congestion at signalized intersections. To achieve effective management of the system-wide traffic flows, current research tends to focus on applying reinforcement learning (RL) techniques for collaborative traffic signal control in a traffic road network. However, the existing collaboration-based methods often ignore the impact of transmission delay for exchanging traffic flow information on the system. Most of the studies assume that the signal controllers can collect all instantaneous vehicular features without delay. To fill the gap, we propose an RL-based cooperative traffic signal control scheme considering the data transmission delay issue in a traffic road network. In this paper, we (1) design our new RL agents to cooperatively control the traffic signals by improving the reward and state representation based on the state-of-the-art max-pressure control theory; (2) propose a traffic state prediction method to address the data transmission delay issue by decreasing the discrepancy between the real-time and delayed traffic conditions; (3) evaluated the performance of our proposed work on both synthetic and real-world scenarios with a different range of data transmission delays. The results demonstrate that our method surpassed the performance of the previous max-pressure-based traffic signal control methods and addressed the data transmission delay issue. [ABSTRACT FROM AUTHOR]

In the era of internet of vehicles, both the traffic signal control and the rerouting of connected and autonomous vehicles (CAVs) are essential techniques to alleviate traffic congestion and improve traffic efficiency. However, these two schemes have been treated separately without a unified framework allowing for joint optimization in the previous studies. In the case of traffic congestion at the bottleneck intersections, either of the two schemes alone is not enough. To this end, we propose to jointly control the traffic lights and the CAVs under the reinforcement learning (RL) framework. To lower the dimensionality of this learning problem, we propose to control the route choices of the CAVs through a few common scalar parameters instead of controlling the CAVs individually. In particular, the rerouting ratios are dynamically adjusted. A model-based method is further proposed to estimate the expected travel time with reduced estimation error. We then exploit the tools from deep RL and put forth an efficient algorithm that is able to control the green time allocation of traffic signals and the rerouting of CAVs jointly. Comprehensive numerical experiments demonstrate the validity of our proposed models and show the traffic efficiency can be significantly improved with our proposed algorithm. [ABSTRACT FROM AUTHOR]

Urban freeway traffic control is of great importance for traffic management and intelligent transportation systems. Various approaches have been proposed to relieve urban freeway traffic jam, among which, ALINEA, a ramp metering strategy, is commonly implemented with fixed triggering threshold and static controller parameter. However, such a strategy may not be able to effectively alleviate the traffic congestion while maintaining certain ramp throughput due to two reasons: i). the congestion threshold can be time-varying due to different factors, such as segment ID, weather condition, time, and etc. ii). The congestion evolution patterns are time-varying even for the same segment. In this paper, based on over 890 million records of vehicles collected on ramps in Hangzhou, China, we established dynamic congestion threshold for each road segment with external factors. Based on such dynamic congestion threshold, we further clustered the congestion evolution patterns, and designed adaptive ramp controller which could switch the controller parameter according to the predicted congestion evolution pattern. Finally, in order to show the performance among different strategies, we introduced three baseline groups, which are ‘without Controller’, ‘ALINEA controller’, and ‘Direct RBF(radial basis function)-neural network controller’, respectively.The evaluation of proposed controller design over real large-scale data indicated that our method achieves 8.4%(7.2%), 4.62%(9.48%) efficiency improvement in terms of average speed in km/h (average traffic flow in veh/h) than the performance with normal ALINEA controller and RBF-neural network controller respectively. [ABSTRACT FROM AUTHOR]

Two-stop-line signalized roundabouts (TSLSR) are widely utilized in China. To calculate the capacity and optimal cycle length accurately, a model considering internal space constraint of the roundabout is developed in this paper. Firstly, the operational principle of TSLSR is analyzed. TSLSR is modeled as being equivalent to a four-phase intersection with left-turn protection phases, and the left-turn lane is further simplified as the "short-lane model". Secondly, based on the above analysis, the capacity of TSLSR is modeled and additionally, the accuracy and sensitivity of the model are also analyzed. Finally, the optimal cycle length of TSLSR which maximizes the capacity is put forward. Results show that the accuracy of the capacity model is low without considering the internal space constraint of the roundabout. However, the model developed in this paper is shown to improve the accuracy (about 20%) and control the relative error to be within 10%. The parameter sensitivity analysis demonstrates that the cycle length and radius of the central island have a significant influence on the capacity of the left-turn lane and results in an optimal value of signal cycle length maximizing the capacity. [ABSTRACT FROM AUTHOR]

Traffic congestion in freeway network is a prevalent transportation problem and leads to a strong degradation of freeway network facilities. This paper proposes a new coordinated optimization approach for freeway network, aiming to relieve the traffic congestion in freeway network equipped with the coordinated traffic controllers. The challenge is how to solve the problem between the traffic control goals of each path and those of the network. In this paper, the bi-level programming theory is used to address this problem. More exactly, in the path level, the optimal control model is designed to relieve congestion in each path of the test network. Furthermore, in the network level, the optimal model considers the traffic equilibrium and cost optimization. The efficiency of the proposed approach is tested on a well-known traffic network calibrated by the field test data of freeway network. The results show that the proposed approach has positive effects in balancing traffic load and improving the service level of freeway networks. [ABSTRACT FROM AUTHOR]

Modelling and implementing adequate controllers for urban road traffic control constitute a huge challenge nowadays because of the complexity of systems, as well as possible scenarios and configurations, in each road in a city. A series of issues related to modelling these behaviours are common to arise when using formalisms, tools, and computation machines to perform complex calculations and limitations. This paper presents a formal, flexible, and adaptable approach, with no limitations, from the scientific point of view. For this purpose, modelling formalisms (cellular automata and timed automata) and analysis techniques (simulation and formal verification) are proposed to reach the main goals of modelling complex and adaptable behaviours in urban road traffic with multiple over time changeable configurations. A case study is presented, in order to illustrate the approach and demonstrate in detail the unlimited application of the presented approach. [ABSTRACT FROM AUTHOR]

The urban road networks undergo frequent traffic congestions during the peak hours and around the city center. Capturing the spatiotemporal evolution of the congestion scenario in real-time in an urban-scale can aid in developing smart traffic management systems, and guiding commuters in making informed decision about route choice. The congestion scenario is often represented by a set of distinguishable network partitions that have a homogeneous level of congestion inside them but are heterogeneous to others. Due to the dynamic nature of traffic, these partitions evolve with time in terms of their structure and location. In this paper, we propose a comprehensive framework to capture the evolution by incrementally updating the partitions in an efficient manner using a two-layer approach. The physical layer maintains a set of small-sized road network building blocks in a fine granularity, and performs low-level computations to incrementally update them, whereas the logical layer performs high-level computations in order to serve as an interface to query the physical layer about the congested partitions in a coarse granularity. We also propose an in-memory index called Bin that compactly stores the historical sets of building blocks in the main memory with no information loss, and facilitates their efficient retrieval. Our experimental results show that the proposed method is much efficient than the existing re-partitioning methods without significant sacrifice in accuracy. The proposed Bin consumes a minimum space with least redundancy at different time stamps. [ABSTRACT FROM AUTHOR]

Cities around the world are inundated by cars and suffer traffic congestion that results in excess delays, reduced safety and environmental pollution. The interplay between road infrastructure and travel choices defines the level and the spatio-temporal extent of congestion. Given the existing infrastructure, understanding how the route choice decisions are made and how travellers interact with each other is a crucial first step in mitigating traffic congestion. This is a problem with fundamental importance, as it has implications for other limited supply systems where agents compete for resources and reach an equilibrium. Here, we observe the route choice decisions and the traffic conditions through an extensive data set of GPS trajectories. We compare the actual paths followed by travellers to those implied by equilibrium conditions (i) at a microscopic scale, where we focus on individual path similarities, and (ii) at a macroscopic scale, where we perform network-level comparison of the traffic loads. We present that non-cooperative or selfish equilibrium replicates the actual traffic (to a certain extent) at the macroscopic scale, while the majority of individual decisions cannot be reproduced by neither selfish nor cooperative equilibrium models. [ABSTRACT FROM AUTHOR]

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions. [ABSTRACT FROM AUTHOR]

Urban transportation systems are multimodal, sociotechnical systems; however, while their multimodal aspect has received extensive attention in recent literature on multiplex networks, their sociotechnical aspect has been largely neglected. We present the first study of an urban transportation system using multiplex network analysis and validated Origin-Destination travel demand, with Riyadh’s planned metro as a case study. We develop methods for analyzing the impact of additional transportation layers on existing dynamics, and show that demand structure plays key quantitative and qualitative roles. There exist fundamental geometrical limits to the metro’s impact on traffic dynamics, and the bulk of environmental accrue at metro speeds only slightly faster than those planned. We develop a simple model for informing the use of additional, “feeder” layers to maximize reductions in global congestion. Our techniques are computationally practical, easily extensible to arbitrary transportation layers with complex transfer logic, and implementable in open-source software. [ABSTRACT FROM AUTHOR]

This paper proposes a self-adaptive interactive navigation tool (SAINT), which is tailored for cloud-based vehicular traffic optimization in road networks. The legacy navigation systems make vehicles navigate toward their destination less effectively with individually optimal navigation paths rather than network-wide optimal navigation paths, particularly during rush hours. To the best of our knowledge, SAINT is the first attempt to investigate a self-adaptive interactive navigation approach through the interaction between vehicles and vehicular cloud. The vehicles report their navigation experiences and travel paths to the vehicular cloud so that the vehicular cloud can know real-time road traffic conditions and vehicle trajectories for better navigation guidance for other vehicles. With these traffic conditions and vehicle trajectories, the vehicular cloud uses a mathematical model to calculate road segment congestion estimation for global traffic optimization. This model provides each vehicle with a navigation path that has minimum traffic congestion in the target road network. Using the simulation with a realistic road network, it is shown that our SAINT outperforms the legacy navigation scheme, which is based on Dijkstra's algorithm with a real-time road traffic snapshot. On a road map of Manhattan in New York City, our SAINT can significantly reduce the travel delay during rush hours by 19%. [ABSTRACT FROM PUBLISHER]

Adaptive control of traffic lights is a key component of any intelligent transportation system. Many real-time traffic light control (TLC) algorithms are based on graded thresholds, because precise information about the traffic congestion in the road network is hard to obtain in practice. For example, using thresholds L1 and L2, we could mark the congestion level on a particular lane as “low,” “medium,” or “high” based on whether the queue length on the lane is below L1, between L1 and L2, or above L2, respectively. However, the TLC algorithms that were proposed in the literature incorporate fixed values for the thresholds, which, in general, are not optimal for all traffic conditions. In this paper, we present an algorithm based on stochastic optimization to tune the thresholds that are associated with a TLC algorithm for optimal performance. We also propose the following three novel TLC algorithms: 1) a full-state Q-learning algorithm with state aggregation, 2) a Q-learning algorithm with function approximation that involves an enhanced feature selection scheme, and 3) a priority-based TLC scheme. All these algorithms are threshold based. Next, we combine the threshold-tuning algorithm with the three aforementioned algorithms. Such a combination results in several interesting consequences. For example, in the case of Q-learning with full-state representation, our threshold-tuning algorithm suggests an optimal way of clustering states to reduce the cardinality of the state space, and in the case of the Q-learning algorithm with function approximation, our (threshold-tuning) algorithm provides a novel feature adaptation scheme to obtain an “optimal” selection of features. Our tuning algorithm is an incremental-update online scheme with proven convergence to the optimal values of thresholds. Moreover, the additional computational effort that is required because of the integration of the tuning scheme in any of the graded-threshold-based TLC algorithms is minimal. Simulation results show a significant gain in performance when our threshold-tuning algorithm is used in conjunction with various TLC algorithms compared to the original TLC algorithms without tuning and with fixed thresholds. [ABSTRACT FROM PUBLISHER]

Vehicular ad hoc networks (VANETs) allow vehicles to generate and broadcast messages to inform nearby vehicles about road conditions, such as traffic congestion and accidents. Neighboring vehicles can utilize this information, which may improve road safety and traffic efficiency. However, messages generated by vehicles may not be reliable. We propose a novel announcement scheme for VANETs based on a reputation system that allows evaluation of message reliability. We present a secure and efficient scheme that is robust and fault tolerant against temporary unavailability of the central server. [ABSTRACT FROM PUBLISHER]

A transition from free flow to congested traffic on highways often spontaneously originates, despite the fact that the road could satisfy a higher traffic demand. The reasons for such a traffic breakdown are perturbations caused by human drivers in dense traffic. We present a strategy to reduce traffic congestion with the help of vehicle-to-vehicle communication. Periodically emitted beacons are used to analyze traffic flow and to warn other drivers of a possible traffic breakdown. Drivers who receive such a warning are told to keep a larger gap to their predecessor. By doing so, they are less likely to be the source of perturbations, which can cause a traffic breakdown. We analyze the proposed strategy via computer simulations and investigate which fraction of communicating vehicles is necessary until a beneficial influence on traffic flow is observable. We show that penetration rates of 10\% and less can have significant influence on traffic flow and travel times. In addition to applying a realistic mobility model, we further increase the degree of realism by the use of empirical traffic data from loop detectors on a German Autobahn. [ABSTRACT FROM PUBLISHER]

While vehicular congestion is very often defined in terms of aggregate parameters, such as traffic volume and lane occupancies, based on recent findings, the interpretation that receives most credit is that of a state of a road where traversing vehicles experience a delay exceeding the maximum value typically incurred under light or free-flow traffic conditions. We here propose a new definition according to which a road is in a congested state (be it high or low) only when the likelihood of finding it in the same congested state is high in the near future. Based on this new definition, we devised an algorithm that, exploiting probe vehicles, for any given road 1) identifies if it is congested or not and 2) provides the estimation that a current congested state will last for at least a given time interval. Unlike any other existing approach, an important advantage of ours is that it can generally be applied to any type of road, as it neither needs any a priori knowledge nor requires the estimation of any road parameter (e.g., number of lanes, traffic light cycle, etc.). Further, it allows performing short-term traffic congestion forecasting for any given road. We present several field trials gathered on different urban roads whose empirical results confirm the validity of our approach. [ABSTRACT FROM PUBLISHER]

Vehicle density is one of the main metrics used for assessing road traffic condition. High vehicle density indicates that traffic is congested. Currently, most vehicle density estimation approaches are designed for infrastructure-based traffic information systems. These approaches require detection devices such as inductive loop detectors or traffic surveillance cameras to be installed at various locations. Consequently, they are not appropriate for an emerging self-organizing vehicular traffic information system, where vehicles have to collect and process traffic information without relying on any fixed infrastructure. In this paper, we consider a few methods for estimating vehicle density based on the number of vehicles in the vicinity of the probe vehicle and the number of vehicles in a communication cluster. [ABSTRACT FROM PUBLISHER]

This study presents a multilane model for analyzing the dynamic traffic properties of a highway segment under a lane-closure operation that often incurs complex interactions between mandatory lane-changing vehicles and traffic at unblocked lanes. The proposed traffic flow formulations employ the hyperbolic model used in the non-Newtonian fluid dynamics, and assume the lane-changing intensity between neighboring lanes as a function of their difference in density. The results of extensive simulation experiments indicate that the proposed model is capable of realistically replicating the impacts of lane-changing maneuvers from the blocked lanes on the overall traffic conditions, including the interrelations between the approaching flow density, the resulting congestion level, and the exiting flow rate from the lane-closure zone. Our extensive experimental analyses also confirm that traffic conditions will deteriorate dramatically and evolve to the state of traffic jam if the density has exceeded its critical level that varies with the type of lane-closure operations. This study also provides a convenient way for computing such a critical density under various lane-closure conditions, and offers a theoretical basis for understanding the formation as well as dissipation of traffic jam. [ABSTRACT FROM AUTHOR]

The authors consider the problem of scheduling automated traffic in a city. Such automatic scheduling can improve efficiency of the system by decreasing delays, increasing capacity, and easing congestion. The algorithms described in this paper can be thought of as which belongs to a layer in the corresponding control-communication-computational system, which is responsible for generating timed trajectories for individual vehicles [S. Graham, G. Baliga, and P. Kumar, ‘Issues in the convergence of control with communication and computing: Proliferation, architecture, design, services, and middleware,’ in Proceedings of CDC '04, Nassau, Bahamas, December 2004]. Each vehicle has a specified route from its origin to its destination, and the task of the scheduler is to provide timed trajectories for all vehicles, which follow the respective vehicles' routes and further ensure that no collisions or deadlock will result. The authors' approach reduces the problem to a discrete-time graph-scheduling problem, by defining an appropriate graph to model the road network. Their main result is a sufficient condition on the graph of the road network and on the initial distribution of vehicles, under which there exists a scheduling algorithm that is guaranteed to clear the system in finite time. The nature of this result allows the design of provably correct scheduling algorithms that require only a small portion of future routes of all vehicles to be known, and are consequently able to work in real time. The authors also address the optimization of performance with respect to delay, by focusing on the ‘one-step move’ problem. Finding an optimal solution for the one-step problem would provide a greedy solution of the original network-wide scheduling problem, but is itself NP-hard. They present a polynomial time heuristic algorithm and evaluate its performance through simulations. [ABSTRACT FROM AUTHOR]

Increased peak period congestion causes the peak to spread. The travel time impact of congestion is mitigated for some travelers, but the inconvenience of traveling at a less preferred time also has a cost. Alternatively, increases in highway supply narrow the peak. This reduces the travel time savings, but it generates a benefit for those traveling closer to their preferred times. This benefit from roadway supply increases has been ignored in practical applications, largely due to the difficulty of quantifying the effect. This paper reports on an attempt to quantify the peak narrowing effect of increased highway supply. For the morning commute, the model estimates that each 1% increase in lane-miles per thousand population results in workers departing from home about 0.06-0.09% closer to the peak time.

This article explores the meaning of congestion on highway-type rural and suburban facilities, such as tollways and freeways, and develops congestion thresholds, above which it will be necessary to add lanes to specific sections of these facilities. The study originated from the need for the development of a tender for a major tollway facility in Israel. It was necessary to clearly identify the conditions at which the flow could be termed "congested," in the sense of lower levels of service, dense flows, and tense driving conditions. Several congestion measures for the defined purpose were explored. In the end, density of flow and the resulting flow rate were found to be the most appropriate measures. Based on the measures selected, specific thresholds were developed for four-and six-lane rural and suburban sections. The measures presented are dependent primarily on the percentage of heavy vehicles. It is assumed that the conditions of the facility are "ideal," meaning a high design speed, level terrain, and wide lanes and shoulders. An example is presented and evaluated of the estimation of a nonlinear growth curve for a rural section of a tollway facility and the approximate time at which the congestion threshold will be crossed. [ABSTRACT FROM AUTHOR]

This article addresses the "link-addition" or discrete network design problem in road travel. Given an existing urban road network, with projected increases in demands for road travel between various pairs of nodes, it is typically the case that severe congestion will exist if the capacity of the network is not expanded. Thus, the decision maker must consider building new links into the network, or increasing the capacity of some existing links. He must consider the various types of possible improvements to the network, as well as several locations in the network as candidates for improvement. When using a network to model a road transportation system, the travel time experienced by each driver along any link is given by the average travel time function for the link. Because of congestion, it is taken to be a nonlinear function of the total flow along the link. The sum of all drivers' travel times along a link is called the total travel time function. This is of considerable importance in solving large network design problems, since shortest route problems can be easily solved even for very large networks.

When a slow-moving vehicle occupies one of the lanes of a multilane highway, it often causes queueing behind it, not unlike one caused by an actual stoppage on that lane. This happens when the traffic flow rate upstream from the slow vehicle exceeds a certain critical value. We define this situation as the case of the Moving Bottleneck. There are other situations when people caught in a traffic jam slowly advance to the point of a suspected breakdown or other obstruction, only to find out that no such obstruction exists but suddenly the flow of traffic is free and rapid. We define this situation as that of the Phantom Bottleneck. We present a model describing these two situations on the basis of reasonable assumptions about the behavior of drivers whose movement is obstructed by the slow vehicle. Using a phenomenological relationship between flow and concentration, we derive formulas describing the behavior of the moving queue caused by the obstruction. Finally, we suggest procedures for validating the model and using it for a rational approach to the design of allowable minimum speeds on highways. [ABSTRACT FROM AUTHOR]

The discomfort and dangers experienced on Rome's crowded street led Julius Cesar to issue a traffic ban ordering unnecessary vehicles off the streets during daylight hours. It appears that congestion continued in Rome, as it did in many other European cities, as population grew and particularly after private horse-drawn vehicles came into fashion centuries later. A recent comparison of conditions in 50 urbanized areas in the United States found that congestion appears to be getting much worse in most of the regions studied. The greatest increases occurred in Salt Lake City, utah, wher congestion increased by 31%. In addition, congestion increased by 15% to 20% in Columbus, Ohio and in Cincinnati, Ohio.

Prediction of traffic congestion is one of the core issues in the realization of smart traffic. Accurate prediction depends on understanding of interactions and correlations between different city locations. While many methods merely consider the spatio-temporal correlation between two locations, here we propose a new approach of capturing the correlation network in a city based on realtime traffic data. We use the weighted degree and the impact distance as the two major measures to identify the most influential locations. A road segment with larger weighted degree or larger impact distance suggests that its traffic flow can strongly influence neighboring road sections driven by the congestion propagation. Using these indices, we find that the statistical properties of the identified correlation network is stable in different time periods during a day, including morning rush hours, evening rush hours, and the afternoon normal time respectively. Our work provides a new framework for assessing interactions between different local traffic flows. The captured correlation network between different locations might facilitate future studies on predicting and controlling the traffic flows. [ABSTRACT FROM AUTHOR]