A fuzzy-logic-based system for freeway bottleneck severity diagnosis in a sensor network

It is essential for local traffic jurisdictions to systematically spot freeway bottlenecks and proactively deploy appropriate congestion mitigation strategies. However, diagnostic results may be influenced by unreliable measurements, analysts’ subjective knowledge and day-to-day traffic pattern variations. In order to suitably address these uncertainties and imprecise data, this study proposes a fuzzy-logic-based approach for bottleneck severity diagnosis in urban sensor networks. A dynamic bottleneck identification model is first proposed to identify bottleneck locations, and a fuzzy inference approach is then proposed to systematically diagnose the severities of the identified recurring and non-recurring bottlenecks by incorporating expert knowledge of local traffic conditions. Sample data over a 1-month period on an urban freeway in Northern Virginia was used as a case study for the analysis. The results reveal that the proposed approach can reasonably determine bottleneck severities and critical links, accounting for both spatial and temporal factors in a sensor network.     

Increasing the capacity of an isolated merge by metering its on-ramp

Measurements taken downstream of freeway/on-ramp merges have previously shown that discharge flow diminishes when a merge becomes an isolated bottleneck. By means of observation and experiment, we show here that metering an on-ramp can recover the higher discharge flow at a merge and thereby increase the merge capacity. Detailed observations were collected at a single merge using video. These data revealed that the reductions in discharge flow are triggered by a queue that forms near the merge in the freeway shoulder lane and then spreads laterally, as drivers change lanes to maneuver around slow traffic. Our experiments show that once restrictive metering mitigated this shoulder lane queue, high outflows often returned to the median lane. High merge outflows could be restored in all freeway lanes by then relaxing the metering rate so that inflows from the on-ramp increased. Although outflows recovered in this fashion were not sustained for periods greater than 13 min, the findings are the first real evidence that ramp metering can favorably affect the capacity of an isolated merge. Furthermore, these findings point to control strategies that might generate higher outflows for more prolonged periods and increase merge capacity even more. Finally, the findings uncover details of merge operation that are essential for developing realistic theories of merging traffic.     

Exploring the impact of connected and autonomous vehicles on freeway capacity using a revised Intelligent Driver Model

ABSTRACT

Connected and autonomous vehicle (CAV) technologies are expected to change driving/vehicle behavior on freeways. This study investigates the impact of CAVs on freeway capacity using a microsimulation tool. A four-lane basic freeway segment is selected as the case study through the Caltrans Performance Measurement System (PeMS). To obtain valid results, various driving behavior parameters are calibrated to the real traffic conditions for human-driven vehicles. In particular, the calibration is conducted using genetic algorithm. A revised Intelligent Driver Model (IDM) is developed and used as the car-following model for CAVs. The simulation is conducted on the basic freeway segment under different penetration rates of CAVs and different freeway speed limits. The results show that with an increase in the market penetration rate, freeway capacity increases, and will increase significantly as the speed limit increases.     

Autonomous cars and dynamic bottleneck congestion: The effects on capacity,value of time and preference heterogeneity

‘Autonomous cars’ are cars that can drive themselves without human control. Autonomous cars can safely drive closer together than cars driven by humans, thereby possibly increasing road capacity. By allowing drivers to perform other activities in the vehicle, they may reduce the value of travel time losses (VOT). We investigate the effects of autonomous cars using a dynamic equilibrium model of congestion that captures three main elements: the resulting increase in capacity, the decrease in the VOT for those who acquire one and the implications of the resulting changes in the heterogeneity of VOTs. We do so for three market organizations: private monopoly, perfect competition and public supply. Even though an increased share of autonomous cars raises average capacity, it may hurt existing autonomous car users as those who switch to an autonomous car will impose increased congestion externalities due to their altered departure time behaviour. Depending on which effect dominates, switching to an autonomous vehicle may impose a net negative or positive externality. Often public supply leads to 100% autonomous cars, but it may be optimal to have a mix of car types, especially when there is a net negative externality. With a positive (negative) externality, perfect competition leads to an undersupply (oversupply) of autonomous cars, and a public supplier needs to subsidise (tax) autonomous cars to maximise welfare. A monopolist supplier ignores the capacity effect and adds a mark-up to its price.     

A moving bottleneck

Suppose that a vehicle or convoy enters a two-lane unidirectional roadway and travels at a velocity v^* less than the prevailing traffic. This moving bottleneck may cause a queue to form as vehicles try to pass the obstruction. It is shown that by going to a moving coordinate system traveling at velocity v^* the analysis of this can be transformed into a corresponding analysis of flow past a stationary bottleneck. The theory is then extended to investigate possible consequences of trucks on grades.     

Towards universal freeway incident detection algorithms

This paper reports the intensive test of the new transport systems centre (TSC) algorithm applied to incident detection on freeways. The TSC algorithm is designed to fulfil the universality expectations of automated incident detection. The algorithm consists of two modules: data processing module and incident detection module. The data processing module is designed to handle specific features of different sites. The Bayesian network based incident detection module is used to store and manage general expert traffic knowledge, and to perform coherent reasoning to detect incidents. The TSC algorithm is tested using 100 field incident data sets obtained from Tullamarine Freeway and South Eastern Freeway in Melbourne, Australia. The performance of the algorithm demonstrates its competitiveness with the best performing neural network algorithm which was developed and tested using the same incident data sets in an early research. Most importantly, both the detection rate and false alarm rate of the TSC algorithm are not sensitive to the incident decision threshold, which greatly improves the stability of incident detection. In addition, a very consistent algorithm performance is achieved when the TSC algorithm is transferred from Southern Expressway of Adelaide to both Tullamarine Freeway and South Eastern Freeway of Melbourne. No substantial algorithm retraining is required. A significant step towards algorithm universality is possible from this research.     

On the macroscopic stability of freeway traffic

A simple model of traffic flow is used to analyze the spatio-temporal distribution of flow and density on closed-loop homogeneous freeways with many ramps, which produce inflows and allow outflows. As we would expect, if the on-ramp demand is space-independent then this distribution tends toward uniformity in space if the freeway is either: (i) uncongested; or (ii) congested with queues on its on-ramps and enough inflow to cause the average freeway density to increase with time. In all other cases, however, including any recovery phase of a rush hour where the freeway’s average density declines, the distribution of flow and density quickly becomes uneven. This happens even under conditions of perfect symmetry, where the percentage of vehicles exiting at every off ramp is the same. The flow-density deviations from the average are shown to grow exponentially in time and propagate backwards in space with a fixed wave speed. A consequence of this type of instability is that, during recovery, gaps of uncongested traffic will quickly appear in the unevenly congested stream, reducing average flow. This extends the duration of recovery and invariably creates clockwise hysteresis loops on scatter-plots of average system flow vs. density during any rush hour that oversaturates the freeway. All these effects are quantified with formulas and verified with simulations. Some have been observed in real networks. In a more practical vein, it is also shown that the negative effects of instability diminish (i.e., freeway flows increase) if (a) some drivers choose to exit the freeway prematurely when it is too congested and/or (b) freeway access is regulated in a certain traffic-responsive way. These two findings could be used to improve the algorithms behind VMS displays for driver guidance (finding a), and on-ramp metering rates (finding b).     

重力式锚杆挡土墙在高速公路中的运用

对于高度不超过5 m的路基挡土墙,通常选择施工简便、经济的重力式挡土墙。但该型式挡土墙受墙高限制,不适合处理较高路基边坡。通过借鉴锚杆挡土墙,将重力式挡土墙断面进行优化、组合,在充分利用当地施工条件的同时,也解决了高路基重力式挡土墙抗倾覆、抗滑移不满足设计规范要求的难题,拓展了其应用范围。     

A decentralized control strategy for freeway regulation

A multilevel decentralized control scheme, the cascading technique, with application to the regulation of traffic on an urban freeway is presented. Performance of the decentralized system is compared to the performance of a centralized and a fixed time control structure. It is shown that the decentralized structure performs better than the centralized structure when incidents (lane closures) occur on the freeway. The freeway is modeled in terms of the aggregate variables section density and section speed, and is considered as a system of interconnected subsystems.     

A hierarchical control system for freeway traffic

A hierarchical control system consisting of three control layers is developed for the freeway traffic control problem. A simplified optimization problem for the overall freeway system is solved on-line in an optimization layer. Optimization results are used as reference values for an inferior decentralized direct control layer. Prediction of slowly varying variables like on-ramp demands and origin-destination rates as well as of particular model parameters are provided by a supremal adaption layer. The overall control structure is shown to be robust even in the case of strong unexpected disturbances like incidents.     

互通立交匝道设计探讨

现代互通立交的设计应结合安全、环境、功能、用地和成本等多因素综合考虑,运用更为灵活的设计手法,从程式化或模板化设计中解脱出来,树立创作设计的思想.文章运用现代互通立交设计理论,对互通立交匝道设计中的一些基本问题进行了探讨.     

注浆加固法在滨德高速公路中的应用

通过滨德高速工程实例,介绍了注浆加固工艺在处理路基内部孔洞的应用。     

Ant Colony Routing algorithm for freeway networks

Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.     

A probabilistic optimization model for allocating freeway sensors

This paper proposes a sensor location model to identify a sensor configuration that minimizes overall freeway performance monitoring errors while considering the consequences of probabilistic sensor failures. To date, existing sensor location models for freeway monitoring inherently assume that either deployed sensors never fail or the consequences of sensor failure are trivial matters. However, history has revealed that neither assumption is realistic, suggesting that ignoring failures in sensor allocation models may actually produce a significantly suboptimal configuration in the real world. Our work addresses this dilemma by developing a probabilistic optimization model that will minimize the error expectation by examining all possible failure scenarios, each with an occurrence probability. To ensure the scenario completeness and uniqueness, a sensor failure scenario is represented by using a binary string with 1 indicating an operational sensor at a given site and 0 for sensor failure or no sensor deployed. When applied to a case study network, it is shown that an optimal configuration that considers sensor failure is significantly different from an optimal configuration that ignores sensor failure, revealing that sensor failures pose non-trivial consequences on performance monitoring accuracy.     

Observing freeway ramp merging phenomena in congested traffic

This work conducts a comprehensive investigation of traffic behavior and characteristics during freeway ramp merging under congested traffic conditions. On the Tokyo Metropolitan Expressway, traffic congestion frequently occurs at merging bottleneck sections, especially during heavy traffic demand. The Tokyo Metropolitan Expressway public corporation, generally applies different empirical strategies to increase the flow rate and decrease the accident rate at the merging sections. However, these strategies do not rely either on any behavioral characteristics of the merging traffic or on the geometric design of the merging segments. There have been only a few research publications concerned with traffic behavior and characteristics in these situations. Therefore, a three‐year study is undertaken to investigate traffic behavior and characteristics during the merging process under congested situations. Extensive traffic data capturing a wide range of traffic and geometric information were collected using detectors, videotaping, and surveys at eight interchanges in Tokyo Metropolitan Expressway. Maximum discharged flow rate from the head of the queue at merging sections in conjunction with traffic and geometric characteristics were analyzed. In addition, lane changing maneuver with respect to the freeway and ramp traffic behaviors were examined. It is believed that this study provides a thorough understanding of the freeway ramp merging dynamics. In addition, it forms a comprehensive database for the development and implementation of congestion management techniques at merging sections utilizing Intelligent Transportation System.     

Discrete dynamic models for freeway incident detection systems

This paper considers the problem of freeway incident detection within the general framework of computer‐based freeway surveillance and control. A new approach to the detection of freeway traffic incidents is presented based on a discrete‐time stochastic model of the form ARIMA (0, 1, 3) that describes the dynamics of traffic occupancy observations. This approach utilizes real‐time estimates of the variability in traffic occupancies as detection thresholds, thus eliminating the need for threshold calibration and lessening the problem of false‐alarms. Because the moving average parameters of the ARIMA (0, 1, 3) model change over time, these parameters can be updated occasionally. The performance of the developed detection algorithm has been evaluated in terms of detection rate, false‐alarm rate, and average time‐lag to detection, using a total of 1692 minutes of occupancy observations recorded during 50 representative traffic incidents.     

Speed estimation and length based vehicle classification from freeway single-loop detectors

Roadway usage, particularly by large vehicles, is one of the fundamental factors determining the lifespan of highway infrastructure. Operating agencies typically employ expensive classification stations to monitor large vehicle usage. Meanwhile, single-loop detectors are the most common vehicle detector and many new, out-of-pavement detectors seek to replace loop detectors by emulating the operation of single-loop detectors. In either case, collecting reliable length data from these detectors has been considered impossible due to the noisy speed estimates provided by conventional data aggregation at single-loop detectors. This research refines non-conventional techniques for estimating speed at single-loop detectors, yielding estimates that approach the accuracy of a dual-loop detector’s measurements. Employing these speed estimation advances, this research brings length based vehicle classification to single-loop detectors (and by extension, many of the emerging out-of-pavement detectors). The classification methodology is evaluated against concurrent measurements from video and dual-loop detectors. To capture higher truck volumes than empirically observed, a process of generating synthetic detector actuations is developed. By extending vehicle classification to single-loop detectors, this work leverages the existing investment deployed in single-loop detector count stations and real-time traffic management stations. The work also offers a viable treatment in the event that one of the loops in a dual-loop detector classification station fails and thus, also promises to improve the reliability of existing classification stations.     

Methods of dropping auxiliary lanes at freeway weaving segments

Auxiliary lanes connecting freeway entrance and exit ramps provide additional space for entering and exiting vehicles to change lanes. The method of dropping auxiliary lanes is critical in the design of freeway auxiliary lanes. This study investigates the performance of different methods of dropping auxiliary lanes. Case studies were conducted at two selected freeway segments with successive entrance or exit ramps in the City of Houston. Traffic simulation analysis results of these two case studies show that additional operational benefits can be achieved by extending an auxiliary lane beyond the freeway weaving segment. The study also found that if the weaving segment is followed by an entrance/exit ramp and this ramp has high traffic volume, it can be less operationally favorable to extend and terminate the auxiliary lane at this entrance/exit ramp location. Instead, dropping the auxiliary lane before this entrance/exit ramp represents a more operationally effective option.     

The smoothing effect of carpool lanes on freeway bottlenecks

Real data show that reserving a lane for carpools on congested freeways induces a smoothing effect that is characterized by significantly higher bottleneck discharge flows (capacities) in adjacent lanes. The effect is reproducible across days and freeway sites: it was observed, without exception, in all cases tested. Predicted by an earlier theory, the effect arises because disruptive vehicle lane changing diminishes in the presence of a carpool lane. We therefore conjecture that smoothing can also be induced by other means that would reduce lane changing.The benefits can be large. Queueing analysis shows that the smoothing effect greatly reduces the times spent by people and vehicles in queues. For example, by ignoring the smoothing effect at one of the sites we analyzed one would predict that its carpool lane increased both the people-hours and the vehicle-hours traveled by well over 300%. In reality, the carpool lane reduced both measures due to smoothing. The effect is so significant that even a severely underused carpool lane can in some instances increase a freeway bottleneck’s total discharge flow. This happens for the site we analyzed when carpool demand is as low as 1200 vph.     

A continuous-flow-intersection-lite design and traffic control for oversaturated bottleneck intersections

Oversaturation has become a severe problem for urban intersections, especially the bottleneck intersections that cause queue spillover and network gridlock. Further improvement of oversaturated arterial traffic using traditional mitigation strategies, which aim to improve intersection capacity by merely adjusting signal control parameters, becomes challenging since exiting strategies may (or already) have reached their “theoretical” limits of optimum. Under such circumstance, several novel unconventional intersection designs, including the well-recognized continuous flow intersection (CFI) design, are originated to improve the capacity at bottleneck intersections. However, the requirement of installing extra sub-intersections in a CFI design would increase vehicular stops and, more critically, is unacceptable in tight urban areas with closed spaced intersections. To address these issues, this research proposes a simplified continuous flow intersection (called CFI-Lite) design that is ideal for arterials with short links. It benefits from the CFI concept to enable simultaneous move of left-turn and through traffic at bottleneck intersections, but does not need installation of sub-intersections. Instead, the upstream intersection is utilized to allocate left-turn traffic to the displaced left-turn lane. It is found that the CFI-Lite design performs superiorly to the conventional design and regular CFI design in terms of bottleneck capacity. Pareto capacity improvement for every traffic stream in an arterial system can be achieved under effortless conditions. Case study using data collected at Foothill Blvd in Los Angeles, CA, shows that the new design is beneficial in more than 90% of the 408 studied cycles. The testing also shows that the average improvements of green bandwidths for the synchronized phases are significant.