New Framework and Decision Support Tool to Warrant Detour Operations During Freeway Corridor Incident Management

As reported in the literature, the mobility and reliability of the highway systems in the United States have been significantly undermined by traffic delays on freeway corridors due to non-recurrent traffic congestion. Many of those delays are caused by the reduced capacity and overwhelming demand on critical metropolitan corridors coupled with long incident durations. In most scenarios, if proper detour strategies could be implemented in time, motorists could circumvent the congested segments by detouring through parallel arterials, which will significantly improve the mobility of all vehicles in the corridor system. Nevertheless, prior to implementation of any detour strategy, traffic managers need a set of well-justified warrants, as implementing detour operations usually demand substantial amount of resources and manpower. To contend with the aforementioned issues, this study is focused on developing a new multi-criteria framework along with an advanced and computation-friendly tool for traffic managers to decide whether or not and when to implement corridor detour operations. The expected contributions of this study are: * Proposing a well-calibrated corridor simulation network and a comprehensive set of experimental scenarios to take into account many potential affecting factors on traffic manager\u27s decision making process and ensure the effectiveness of the proposed detour warrant tool; * Developing detour decision models, including a two-choice model and a multi-choice model, based on generated optima detour traffic flow rates for each scenario from a diversion control model to allow responsible traffic managers to make best detour decisions during real-time incident management; and * Estimating the resulting benefits for comparison with the operational costs using the output from the diversion control model to further validate the developed detour decision model from the overall societal perspective

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Integrated Corridor Management: Operational Strategies under Interstate Diversion Scenarios

This thesis looks at operational strategies to increase capacity within the context of Integrated Corridor Management (ICM) under a non-recurring Interstate incident scenario. This incident scenario creates lengthy queues and increased delay and travel times on the Interstate, forcing a portion of Interstate traffic to utilize alternate routes throughout the corridor, changing the network traffic patterns. Particular operational strategies are tested under this premise to qualify and mildly quantify the benefits of relaying incident and diversion routing information to corridor drivers, mimicking ITS information dissemination elements such as changeable message signs, highway advisory radio, in vehicle navigation systems and etc. This thesis assumes idealized institutional ICM aspects, data-sharing, and technology integration. The experimental analysis for the corridor network was conducted in VISSIM microsimulation, with its NEMA signal interface, also making use of VISUM macrosimulation, and Synchro 6 signal timing optimization. Based upon the results of this analysis, it was concluded that for the study area, implementing ICM strategies pertaining to advance driver warning and routing information pertaining to an incident can mildly reduce travel time and delay at the entire network-level, but travel time and delay do increase on the incident roadway corridor level when compared to a do nothing scenario during the off-peak period. This research also successfully validates the ability to convert a regional planning-level model into a working microsimulation, operations-level model

Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion.</p

Multi-Criteria Evaluation in Support of the Decision-Making Process in Highway Construction Projects

The decision-making process in highway construction projects identifies and selects the optimal alternative based on the user requirements and evaluation criteria. The current practice of the decision-making process does not consider all construction impacts in an integrated decision-making process. This dissertation developed a multi-criteria evaluation framework to support the decision-making process in highway construction projects. In addition to the construction cost and mobility impacts, reliability, safety, and emission impacts are assessed at different evaluation levels and used as inputs to the decision-making process. Two levels of analysis, referred to as the planning level and operation level, are proposed in this research to provide input to a Multi-Criteria Decision-Making (MCDM) process that considers user prioritization of the assessed criteria. The planning level analysis provides faster and less detailed assessments of the inputs to the MCDM utilizing analytical tools, mainly in a spreadsheet format. The second level of analysis produces more detailed inputs to the MCDM and utilizes a combination of mesoscopic simulation-based dynamic traffic assignment tool, and microscopic simulation tool, combined with other utilities. The outputs generated from the two levels of analysis are used as inputs to a decision-making process based on present worth analysis and the Fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Situation) MCDM method and the results are compared

A Corridor Level GIS-Based Decision Support Model to Evaluate Truck Diversion Strategies

Increased urbanization, population growth, and economic development within the U.S. have led to an increased demand for freight travel to meet the needs of individuals and businesses. Consequently, freight transportation has grown significantly over time and has expanded beyond the capacity of infrastructure, which has caused new challenges in many regions. To maintain quality of life and enhance public safety, more effort must be dedicated to investigating and planning in the area of traffic management and to assessing the impact of trucks on highway systems. Traffic diversion is an effective strategy to reduce the impact of incident-induced congestion, but alternative routes for truck traffic must be carefully selected based on a route\u27s restrictions on the size and weight of commercial vehicles, route\u27s operational characteristics, and safety considerations. This study presents a diversion decision methodology that integrates the network analyst tool package of the ArcGIS platform with regression analysis to determine optimal alternative routes for trucks under nonrecurrent delay conditions. When an incident occurs on a limited-access road, the diversion algorithm can be initiated. The algorithm is embedded with an incident clearance prediction model that estimates travel time on the current route based on a number of factors including incident severity; capacity reduction; number of lanes closed; type of incident; traffic characteristics; temporal characteristics; responders; and reporting, response, and clearance times. If travel time is expected to increase because of the event, a truck alternative route selection module is activated. This module evaluates available routes for diversion based on predefined criteria including roadway characteristics (number of lanes and lane width), heavy vehicle restrictions (vertical clearance, bridge efficiency ranking, bridge design load, and span limitations), traffic conditions (level of service and speed limit), and neighborhood impact (proximity to schools and hospitals and the intensity of commercial and residential development). If any available alternative routes reduce travel time, the trucks are provided with a diversion strategy. The proposed decision-making tool can assist transportation planners in making truck diversion decisions based on observed conditions. The results of a simulation and a feasibility analysis indicate that the tool can improve the safety and efficiency of the overall traffic network

Dynamic Message Sign and Diversion Traffic Optimization

This dissertation proposes a Dynamic Message Signs (DMS) diversion control system based on principles of existing Advanced Traveler Information Systems and Advanced Traffic Management Systems (ATMS). The objective of the proposed system is to alleviate total corridor traffic delay by choosing optimized diversion rate and alternative road signal-timing plan. The DMS displays adaptive messages at predefined time interval for guiding certain number of drivers to alternative roads. Messages to be displayed on the DMS are chosen by an on-line optimization model that minimizes corridor traffic delay. The expected diversion rate is assumed following a distribution. An optimization model that considers three traffic delay components: mainline travel delay, alternative road signal control delay, and the travel time difference between the mainline and alternative roads is constructed. Signal timing parameters of alternative road intersections and DMS message level are the decision variables; speeds, flow rates, and other corridor traffic data from detectors serve as inputs of the model. Traffic simulation software, CORSIM, served as a developmental environment and test bed for evaluating the proposed system. MATLAB optimization toolboxes have been applied to solve the proposed model. A CORSIM Run-Time-Extension (RTE) has been developed to exchange data between CORSIM and the adopted MATLAB optimization algorithms (Genetic Algorithm, Pattern Search in direct search toolbox, and Sequential Quadratic Programming). Among the three candidate algorithms, the Sequential Quadratic Programming showed the fastest execution speed and yielded the smallest total delays for numerical examples. TRANSYT-7F, the most credible traffic signal optimization software has been used as a benchmark to verify the proposed model. The total corridor delays obtained from CORSIM with the SQP solutions show average reductions of 8.97%, 14.09%, and 13.09% for heavy, moderate and light traffic congestion levels respectively when compared with TRANSYT-7F optimization results. The maximum model execution time at each MATLAB call is fewer than two minutes, which implies that the system is capable of real world implementation with a DMS message and signal update interval of two minutes

Incident Traffic Management Respone

The North Carolina State Highway Patrol (NCSHP) and the North Carolina Department of Transportation (NCDOT) are often called upon to assist in traffic incidents. Yet little systematic research has examined the extent to which these two agencies collaborate. This gap in understanding is problematic, as a lack of collaboration may result in significant delays in the clearing of traffic incidents. The purpose of this correlational study was to investigate circumstances when the two agencies collaborated in clearing major traffic incidents, and the efficiency of the clearance of the incidents, through the measurement of normal traffic flow. The theory of the convergence of resources from divergent organizations framed the study. The research questions addressed the extent of collaboration between the NCSHP and the NCDOT, the conditions under which this collaboration took place, and the efficiency of the clearance of these incidents. Data were obtained from the NCSHP and the NCDOT on characteristics of 1,580 traffic incidents that occurred on the North Carolina portion of Interstate 95 during the year 2014. The data were analyzed using chi-square tests, analyses of variance, and Z-tests for proportions. Collaboration between the two agencies occurred in 7.2% of all of the incidents and in 21.6% of incidents of major severity (p \u3c .001), which indicated a low level of interagency collaboration. The mean clearance time for incidents in which collaboration took place was 115.92 minutes compared to a national goal of 90 minutes. It is hoped that these results can contribute to policy dialogue relevant to the state\u27s Strategic Plan, leading to safer highways and less financial loss due to congestion caused by traffic incidents

Developing Emergency Preparedness Plans For Orlando International Airport (MCO) Using Microscopic Simulator WATSim

Emergency preparedness typically involves the preparation of detailed plans that can be implemented in response to a variety of possible emergencies or disruptions to the transportation system. One shortcoming of past response plans was that they were based on only rudimentary traffic analysis or in many cases none at all. With the advances in traffic simulation during the last decade, it is now possible to model many traffic problems, such as emergency management, signal control and testing of Intelligent Transportation System technologies. These problems are difficult to solve using the traditional tools, which are based on analytical methods. Therefore, emergency preparedness planning can greatly benefit from the use of micro-simulation models to evaluate the impacts of natural and man-made incidents and assess the effectiveness of various responses. This simulation based study assessed hypothetical emergency preparedness plans and what geometric and/or operational improvements need to be done in response to emergency incidents. A detailed framework outlining the model building, calibration and validation of the model using microscopic traffic simulation model WATSim (academic version) is provided. The Roadway network data consists of geometric layout of the network, number of lanes, intersection description which include the turning bays, signal timings, phasing sequence, turning movement information etc. The network in and around the OIA region is coded into WATSim with 3 main signalized intersections, 180 nodes and 235 links. The travel demand data includes the vehicle counts in each link of the network and was modeled as percentage turning count movements. After the OIA network was coded into WATSim, the road network was calibrated and validated for the peak hour mostly obtained from ADT with 8% K factor by comparing the simulated and actual link counts at 15 different key locations in the network and visual verification done. Ranges of scenarios were tested that includes security checkpoint, route diversion incase of incident in or near the airport and increasing demand on the network. Travel time, maximum queue length and delay were used as measures of effectiveness and the results tabulated. This research demonstrates the potential benefits of using microscopic simulation models when developing emergency preparedness strategies. In all 4 main Events were modeled and analyzed. In Event 1, occurrence of 15 minutes traffic incident on a section of South Access road was simulated and its impact on the network operations was studied. The averaged travel time under the incident duration to Side A was more than doubled (29 minutes, more than a 100% increase) compared to the base case and similarly that of Side B two and a half times more (23 minutes, also more than a 100% increase). The overall network performance in terms of delay was found to be 231.09 sec/veh. and baseline 198.9 sec/veh. In Event 2, two cases with and without traffic diversions were assumed and evaluated under 15 minutes traffic incident modeled at the same link and spot as in Event 1. It was assumed that information about the traffic incident was disseminated upstream of the incident 2 minutes after the incident had occurred. This scenario study demonstrated that on the average, 17% (4 minutes) to 41% (12 minutes) per vehicle of travel time savings are achieved when real-time traffic information was provided to 26% percent of the drivers diverted. The overall network performance in delay for this event was also found to improve significantly (166.92 sec/veh). These findings led to the conclusion that investment in ITS technologies that support dissemination of traffic information (such as Changeable Message Signs, Highway Advisory Radio, etc) would provide a great advantage in traffic management under emergency situations and road diversion strategies. Event 3 simulated a Security Check point. It was observed that on the average, travel times to Sides A and B was 3 and 5 minutes more respectively compared to its baseline. Averaged queue length of 650 feet and 890 feet worst case was observed. Event 4 determined when and where the network breaks down when loaded. Among 10 sets of demand created, the network appeared to be breaking down at 30% increase based on the network-wide delay and at 15% based on Level of Service (LOS). The 90% increase appeared to have the most effect on the network with a total network-wide delay close to 620 seconds per vehicle which is 3 and a half times compared to the baseline. Conclusions and future scope were provided to ensure continued safe and efficient traffic operations inside and outside the Orlando International Airport region and to support efficient and informed decision making in the face of emergency situations

On agent-based modeling: Multidimensional travel behavioral theory, procedural models and simulation-based applications

This dissertation proposes a theoretical framework to modeling multidimensional travel behavior based on artificially intelligent agents, search theory, procedural (dynamic) models, and bounded rationality. For decades, despite the number of heuristic explanations for different results, the fact that "almost no mathematical theory exists which explains the results of the simulations" remains as one of the large drawbacks of agent-based computational process approach. This is partly the side effect of its special feature that "no analytical functions are required". Among the rapidly growing literature devoted to the departure from rational behavior assumptions, this dissertation makes effort to embed a sound theoretical foundation for computational process approach and agent-based microsimulations for transportation system modeling and analyses. The theoretical contribution is three-fold: (1) It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modeled and integrated in a unified and coherent approach. (2) Procedural and dynamic agent-based decision-making is modeled. Within the model, agents make decisions. They also make decisions on how and when to make those decisions. (3) Replace conventional user equilibrium with a dynamic behavioral user equilibrium (BUE). Search start/stop criteria is defined in the way that the modeling process should eventually lead to a steady state that is structurally different to user equilibrium (UE) or dynamic user equilibrium (DUE). The theory is supported by empirical observations and the derived quantitative models are tested by agent-based simulation on a demonstration network. The model in its current form incorporates short-term behavioral dimensions: travel mode, departure time, pre-trip routing, and en-route diversion. Based on research needs and data availability, other dimensions can be added to the framework. The proposed model is successfully integrated with a dynamic traffic simulator (i.e. DTALite, a light-weight dynamic traffic assignment and simulation engine) and then applied to a mid-size study area in White Flint, Maryland. Results obtained from the integration corroborate the behavioral richness, computational efficiency, and convergence property of the proposed theoretical framework. The model is then applied to a number of applications in transportation planning, operations, and optimization, which highlights the capabilities of the proposed theory in estimating rich behavioral dynamics and the potential of large-scale implementation. Future research should experiment the integration with activity-based models, land-use development, energy consumption estimators, etc. to fully develop the potential of the agent-based model