Integrated Corridor Management Research Articles

Integrated corridor management by cooperative traffic signal and ramp metering control

AbstractThis paper formulates a cooperative traffic control methodology that integrates traffic signal timing and ramp metering decisions into an optimization model to improve traffic operations in a corridor network. A mixed integer linear model is formulated and is solved in real time within a model predictive controller framework, where the cell transmission model is used as the system state predictor. The methodology is benchmarked in a case study corridor in San Mateo, CA, in VISSIM with two optimization scenarios, namely, optimal metering and optimal signal control, and two simulation scenarios with a preset metering plan and no metering. The numerical results show that integrated traffic signal and ramp metering control reduces delays, stops, and travel times of the corridor by up to 33.1%, 36%, and 16.4%, respectively, compared to existing benchmark conditions. With appropriate weights prioritizing freeway or arterial street operations, the integrated control balances traffic congestion between the arterial street and the freeway.

Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling

There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

Evaluating the HERO Ramp-Metering Algorithm with San Diego’s Integrated Corridor Management System Model

The FHWA project “Alternative Designs to Alleviate Freeway Bottlenecks at Merge/Diverge and Weaving Areas” aims at evaluating six different methods to mitigate merge impacts. In this paper, the following ramp-metering algorithms were tested: HEuristic Ramp metering coOrdination (HERO), Asservissement Linéaire d’entrée sur Autoroute (ALINEA), and San Diego Ramp Meter System (SDRMS). These were compared with a “do-nothing” base scenario. The algorithms were tested during a 5 h morning peak period simulation using the integrated corridor management system (ICMS) Aimsun network, a complete and wide-ranging network covering San Diego’s I-15 mainline corridor, on-off ramps and arterial roads. Whereas ALINEA is already included in the base distribution of Aimsun, a new implementation of HERO was programmed from algorithms found in the literature and adapted for use on such a network. Different measures of performance (MOP) were used to assess the efficiency of each algorithm and HERO was found to outperform all the other algorithms: gains of 1.5% over ALINEA and 4% over do-nothing on the mainline average travel time, and gains of 0.5% over ALINEA and 1.5% over do-nothing for the average weighted harmonic speed on all mainline and ramp sections. However, although tests showed that better results can be obtained on the mainline, careful calibration was needed to attain overall positive MOP and not penalize vehicles entering on ramps. The paper concludes with proposed improvements to the original algorithm.

Analyzing Simulation-Based Active Traffic Management Impact on a Large-Scale Regional Network

A vast number of real-time corridor management strategies have been introduced because the dynamics of traffic patterns and increased congestion result in challenging problems on road systems. Although these strategies can offer positive impacts on regional traffic, their evaluation tools are often limited to the scope of one specific corridor. To fill this gap, this study integrates a mesoscopic dynamic traffic assignment simulation model with an existing traffic-responsive ramp metering strategy. This integrated model is suitable for network-wide analysis and large-scale simulation of integrated corridor management strategies. The integrated modeling platform is demonstrated as a practice-ready tool. We present a case study that explores the benefits of metering control under various traffic conditions in a real-world network in Maryland. Both local and network-wide impacts are illustrated in the case study. This is one of the first attempts to simultaneously analyze network-wide traffic impacts and capture minute-by-minute demand–supply interactions under managed corridor strategies. The results indicate that ramp metering is beneficial even under non-recurrent traffic conditions at multiple spatial resolutions.

A methodology for evaluating the performance of model-based traffic prediction systems

Model-based traffic prediction systems (mbTPS) are a central component of the decision support and ICM (integrated corridor management) systems currently used in several large urban traffic management centers. These models are intended to generate real-time predictions of the system’s response to candidate operational interventions. They must therefore be kept calibrated and trustworthy. The methodologies currently available for tracking the validity of a mbTPS have been adapted from approaches originally designed for off-line operational planning models. These approaches are insensitive to the complexity of the network and to the amount and quality of the data available. They also require significant human intervention and are therefore not suitable for real-time monitoring. This paper outlines a set of criteria for designing tests that are appropriate for the mbTPS task. It also proposes a test that meets the criteria. The test compares the predictions of the mbTPS in question to those of a model-less alternative. A t-test is used to determine whether the predictions of the mbTPS are superior to those of the model-less predictor. The approach is applied to two different systems using data from the I-210 freeway in Southern California.

Cross-Vendor and Cross-State Analysis of GPS Probe Data Latency

Crowd sourced GPS probe data have become a major source of real-time traffic information applications. In addition to traditional traveler advisory systems such as dynamic message signs (DMS) and 511 systems, probe data are being used for automatic incident detection, integrated corridor management (ICM), end of queue warning systems, and mobility-related smartphone applications. Several private sector vendors offer minute by minute network-wide travel time and speed probe data. The quality of such data in terms of deviation of the reported travel time and speeds from ground-truth has been extensively studied in recent years, and as a result concerns over the accuracy of probe data have mostly faded away. However, the latency of probe data—defined as the lag between the time at which disturbance in traffic speed is reported in the outsourced data feed, and the time at which the traffic is perturbed—has become a subject of interest. The extent of latency of probe data for real-time applications is critical, so it is important to have a good understanding of the amount of latency and its influencing factors. This paper uses high-quality independent Bluetooth/Wi-Fi re-identification data collected on multiple freeway segments in three different states, to measure the latency of the vehicle probe data provided by three major vendors. The statistical distribution of the latency and its sensitivity to speed slowdown and recovery periods are discussed.

Research on the Development of Intelligent Management System for Urban Underground Corridor Based on Cloud Computing

At present, there are many problems such as low reliability and poor interaction in the city comprehensive corridor monitoring system. In order to solve these problems, an integrated corridor management system based on cloud computing technology is put forward. The system hardware architecture is based on the Internet of Things technology, which consists of three layers: field equipment layer, network layer and monitoring center. The software architecture is divided into corridor integrated monitoring system, corridor pipeline management system, operation and maintenance management system and emergency management system, and each subsystem is integrated with service-oriented programming. In addition, the system establishes a unified access portal. Through the desktop computers and intelligent terminals, users can achieve the monitoring of the operation of the integrated corridor.

On node models for high-dimensional road networks

Macroscopic traffic models are necessary for simulation and study of traffic’s complex macro-scale dynamics, and are often used by practitioners for road network planning, integrated corridor management, and other applications. These models have two parts: a link model, which describes traffic flow behavior on individual roads, and a node model, which describes behavior at road junctions. As the road networks under study become larger and more complex — nowadays often including arterial networks — the node model becomes more important. Despite their great importance to macroscopic models, however, only recently have node models had similar levels of attention as link models in the literature. This paper focuses on the first order node model and has two main contributions. First, we formalize the multi-commodity flow distribution at a junction as an optimization problem with all the necessary constraints. Most interesting here is the formalization of input flow priorities. Then, we discuss a very common “conservation of turning fractions” or “first-in-first-out” (FIFO) constraint, and how it often produces unrealistic spillback. This spillback occurs when, at a diverge, a queue develops for a movement that only a few lanes service, but FIFO requires that all lanes experience spillback from this queue. As we show, avoiding this unrealistic spillback while retaining FIFO in the node model requires complicated network topologies. Our second contribution is a “partial FIFO” mechanism that avoids this unrealistic spillback, and a (first-order) node model and solution algorithm that incorporates this mechanism. The partial FIFO mechanism is parameterized through intervals that describe how individual movements influence each other, can be intuitively described from physical lane geometry and turning movement rules, and allows tuning to describe a link as having anything between full FIFO and no FIFO. Excepting the FIFO constraint, the present node model also fits within the well-established “general class of first-order node models” for multi-commodity flows. Several illustrative examples are presented.

Integrated Agent-Based Travel Behavior and Dynamic Traffic Microsimulation for Ramp-Metering Analysis

Ramp-metering control has long been an effective strategy in active traffic management and integrated corridor management and has received increased research attention in recent decades. Although most existing studies analyze ramp metering at the corridor level and in the context of traffic conditions and effects, how travelers respond to ramp-metering control often is neglected. This neglect is partially because of the lack of behavioral data. More important, a comprehensive modeling framework that models travel behavior and traffic dynamics for the evaluation of ramp-metering control is not readily available. This paper develops an integrated agent-based microsimulation to model travel behavior and traffic dynamics and applies the modeling approach to dynamic ramp-metering control. Minute-by-minute control of the ramp-metering rate not only encourages dynamic route changes but also can lead to day-to-day travel behavior adjustments because of the significant effect of ramp metering on traffic conditions in the affected freeway main line and the local area. A real-world application in Maryland demonstrates the model, showcases the behaviorally rich en route diversion and departure time responses to ramp-metering control, and assesses the corridor-level and regional traffic effects.

Real-time traffic network state estimation and prediction with decision support capabilities: Application to integrated corridor management

This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.

Traffic management: An outlook

Traffic congestion is caused by inefficient road operations and by excess demand. Inefficient traffic control is pervasive. Most urban streets and freeways do not have an adequate traffic sensing infrastructure, so one does not know how much congestion there is, its cause, or whether congestion mitigation projects have met the expected improvement. In the absence of adequate information, neither road operators nor travelers can gauge how poorly the road system is operated. Because the traffic changes randomly, the road system should be managed by effective feedback control of signals at intersections and at on-ramps. These control techniques are well known, and they have been successfully adopted in isolated road networks in different parts of the world. The investment in sensing needed to implement these control techniques is trivial compared to the benefits of an efficiently operated road system. But management is not able to quantify road system performance or how much improvement is possible and at what cost. Excess demand can be eliminated by appropriate incentives, including pricing. But empirical analysis of popular approaches such as HOV and HOT lanes suggests that they are ineffective unless the freeways are also efficiently managed. New ITS technologies, such as ‘integrated corridor management’ systems, while promising in theory, are likely to fail in the absence of a comprehensive traffic measurement system. More valuable might be initiatives that seek to shift modes away from private auto, adding bicycle and bus lanes, ridesharing, and telecommuting. Most of the data used in this analysis is from California.

Implementation of Real-Time Offset-Tuning Algorithm for Integrated Corridor Management

Integrated corridor management (ICM) has been proved to relieve recurrent and nonrecurrent congestion effectively in a transportation network. Traffic signal coordination is an effective approach for improving travel speed and decreasing delays. The incorporation of traffic signal coordination into ICM further improves the benefits of ICM strategies. However, traffic signal coordination is applied on the basis of commuter traffic, and detour traffic is not considered. A real-time and proactive offset-tuning algorithm is proposed to reduce the delays of detour traffic without disrupting existing traffic. The algorithm explicitly incorporates diverted traffic and diverted traffic patterns into the existing traffic. The offsets of each coordinated intersection are fine-tuned and updated every 10 cycles to provide smooth progression for upcoming traffic. A case study was conducted to prove the effectiveness of the proposed algorithm. The benchmark case was TRANSYT-7F coordinated traffic signal timing plans. The simulation statistics indicated that the proposed approach created a saving of 8.8% in travel time and 8.3% in control delay in the direction of the diversion. The proposed algorithm did not sacrifice the traffic conditions in the opposite direction of the detour. The statistical analysis proved that the average control delay for both the direction of the diversion and the opposite direction, for all intersections, decreased by 5.6%, with a lower control delay at the 95% confidence interval. The diversion contributed to a gain of 9.22 mph or 18.96% in average speed on the freeway.

Short-Term Traffic Flow Forecasting for Freeway Incident-Induced Delay Estimation

Freeway incidents not only threaten travelers’ safety but also cause severe congestion. Incident-induced delay (IID) refers to the extra travel delay resulting from incidents on top of the recurrent congestion. Quantifying IID would help people better understand the real cost of incidents, maximize the benefit-cost ratio of investment on incident remedy actions, and develop active traffic management and integrated corridor management strategies. By combining a modified queuing diagram and short-term traffic flow forecasting techniques, this study proposes an approach to estimate the temporal IID for a roadway section, given that the incidents occurs between two traffic flow detectors. The approach separates IID from the total travel delay, estimates IID for each individual incident, and only takes volume as input for IID quantification, avoiding using speed data that are widely involved in previous algorithms yet are less available or prone to poor data quality. Therefore, this approach can be easily deployed to broader ranges where only volume data are available. To verify its estimation accuracy, this study captures two incident videos and extracts ground-truth IID data, which is rarely done by previous studies. The verification shows that the IID estimation errors of the proposed approach are within 6% for both cases. The approach has been implemented in a Web-based system, which enables quick, convenient, and reliable freeway IID estimation in the Puget Sound region in the state of Washington.

Evaluation Framework for Rigorous Exploration of Potential Benefits of Integrated Corridor Management

Highway congestion continues to be a major problem in urban transportation, and the search for feasible mitigation measures continues to evolve with the advancement in technology and a better understanding of traveler behavior. Integrated corridor management (ICM) has recently emerged as a potential mitigation measure; however, for this potential to be fully harnessed, research into the ways to identify ICM strategies that best fit the particular circumstances of a transportation corridor is necessary. This study proposed an ICM evaluation framework that was based on which strategies critical to congestion mitigation in a corridor could be identified. The proposed evaluation framework was demonstrated by using the I-95–I-395 ICM corridor in northern Virginia, where the effectiveness of selected ICM strategies was tested under both incident and nonincident conditions through a microscopic simulation model. An analysis of the simulation results identified variable speed limits, an increase in transit and parking capacities, high-occupancy vehicle–high-occupancy vehicle bypass lanes, and high-occupancy toll lanes as the most beneficial ICM strategies for both incident and nonincident traffic conditions. However, the benefits of ICM determined through the strategies modeled were confirmed to be more significant under incident conditions than nonincident conditions. As a result of the ICM strategies implemented, an average flow increment in the corridor of 6,860 persons per hour (+ 37.8%) was experienced during incident conditions compared with 3,286 persons per hour (+ 14.4%) for nonincident conditions. Improvements were also observed in average travel times and vehicle emissions.

Traveler Use of and Response to Real-Time Traffic and Traveler Information

This paper presents baseline findings from a panel survey of regular peak hour travelers of the US-75 corridor in Dallas, Texas, and the I-15 corridor in San Diego, California. This survey was conducted as part of the evaluation of the Integrated Corridor Management (ICM) Initiative. The baseline survey, conducted before the deployment of the ICM Initiative, collected data on travelers’ typical travel in the corridor, their satisfaction with their peak hour trips, their awareness and use of real-time traffic information, and their satisfaction with the information. In addition, recruited respondents were asked to complete brief pulse surveys administered immediately following incidents in the corridor to measure the use of information and its impact on travel during incident conditions. The surveys provide insights on travelers’ use of real-time traffic and travel information and its impact on their travel decisions in two major metropolitan corridors. Through a comparison of the two sites, the findings illuminate how the use of traveler information is conditioned, at least in part, by regional and situational context. In Dallas, respondents were faced with greater traffic congestion and delay, and they were more likely to consult information for their peak hour trips in the corridor. Their use of radio and electronic message signs, two of the most common sources of information, was significantly higher compared with the San Diego sample. In addition, the availability of alternate routes may affect how travelers use real-time traffic information. In Dallas, where respondents reported greater use of alternate routes, both under typical circumstances as well as in the face of severe congestion, significantly more respondents used real-time information to make routing decisions.

Deriving Operational Origin-Destination Matrices From Large Scale Mobile Phone Data

A method is presented in this work that integrates both emerging and mature data sources to estimate the operational travel demand in fine spatial and temporal resolutions. By analyzing individuals' mobility patterns revealed from their mobile phones, researchers and practitioners are now equipped to derive the largest trip samples for a region. Because of its ubiquitous use, extensive coverage of telecommunication services and high penetration rates, travel demand can be studied continuously in fine spatial and temporal resolutions. The derived sample or seed trip matrices are coupled with surveyed commute flow data and prevalent travel demand modeling techniques to provide estimates of the total regional travel demand in the form of origin-destination (OD) matrices. The methodology is evaluated in a series of real world transportation planning studies and proved its potentials in application areas such as dynamic traffic assignment modeling, integrated corridor management and online traffic simulations.

Simulation-Based, Scenario-Driven Integrated Corridor Management Strategy Analysis

The U.S. Department of Transportation affirmed the integrated corridor management (ICM) initiative to mitigate traffic congestion on urban corridor networks by systematically leveraging and diverting traffic to better utilize the available capacities of parallel arterials. ICM strategies would be of utmost importance when traffic incidents that may cause significant delays occur on freeways. By effectively diverting upcoming traffic to the adjacent arterials via variable message signs and real-time traveler information systems (e.g., mobile devices), the impact of incidents can be alleviated. To understand fully the effectiveness of ICM strategies, a framework needs to be developed to model, simulate, and analyze ICM operations under different scenarios. This study developed and calibrated a VISSIM simulation model based on field-collected video and sensor data for the South of Downtown area of Seattle, Washington. The analysis aimed to quantify networkwide ICM performance by empirically diverting traffic to adjacent arterials in response to incident management for freeway operations. Multiple scenarios were built into the simulation model to account for different diversion rates as well as recurrent and nonrecurrent congestion situations. When more traffic is diverted into an arterial, the arterial's performance degrades while improvements in travel time and delay for the freeway become marginal. Through quantitative analysis of the delay, throughput, and travel time of freeway segments and the arterial network, the trade-off in the overall system performance was carefully studied and understood. The research findings set up a solid foundation for ICM strategy development and optimization of traffic system operations.

Integrated Corridor Management for Urban Transport

In developing cities, increases in travel resulting from exponential population, employment, income, and motorization growth can be accommodated only if the efficiency and effectiveness of all transport system resources are maximized. The developed cities of Europe and North America have already demonstrated that comprehensive packages of traffic demand and roadway supply management, public and non-motorized transport priority, and safety improvements integrated on a corridor basis are needed. The objective of this approach, known as integrated corridor management (ICM), is to use roadway infrastructure and public transport supply to move the most people safely, quickly, and reliably with minimum environmental effects. Examples of where ICM has worked effectively include the quality bus corridors of Dublin, Ireland, Manchester, United Kingdom, and London; Seoul's complete makeover of its bus system in South Korea; and New York City's selected bus services. The World Bank is involved in a substantial and growing number of cities in Asia where ICM is being introduced in places where resources are more constrained than in typical developed cities. After the basic approach and its applications in London and New York are described, the methodology being used by World Bank client cities to plan and implement ICM is discussed. Process details on corridor selection and problem diagnosis, identification and evaluation of improvement options, and implementation are described. Finally, a case study of the recent World Bank–funded urban transport project in Wuhan, the capital of Hubei Province, China, is provided to demonstrate the feasibility and ease of applying this methodology in China and elsewhere in the developing world.