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

Managing lane-changing of algorithm-assisted drivers

Theoretical models of vehicular traffic ascribe the fundamental cause of velocity oscillations and stop-and-go waves to suboptimal or unpredictable human driving behavior. In this paper we ask: if vehicles were controlled or assisted by algorithms, and hence driven “optimally,” would these phenomena simply go away? If they do not, how should a regulator manage algorithm-assisted vehicular traffic for a smooth flow? We study these questions in the context of a mandatory lane-changing scenario from the perspective of an algorithm-assisted driver on a curtailed lane that has to merge to an adjacent free lane with a relatively dense platoon. In a stylized model of algorithm-assisted driving, we liken the blocked-lane driver to a rational self-interested agent, whose objective is to minimize her expected travel time through the blockage, deciding (a) at what velocity to move, and (b) whether to merge to the free lane if an adequate gap arises. Moving at higher velocities reduces travel time, but also reduces the probability of finding a large enough gap to merge. We analyze the problem via dynamic programming, and we show that the optimal policy has a surprising structure: in the presence of uncertainty on adequate-sized gaps in the target lane, it may be optimal for the blocked-lane driver, in certain parameter regimes, to oscillate between high and low velocities while attempting to merge. Hence, traffic oscillations can arise not just due to suboptimal or unpredictable human driving behavior, but also endogenously, as the outcome of a driver’s rational, optimizing behavior. We provide theoretical support for this finding by drawing similarities to bang–bang control. As velocity oscillations are known to be detrimental to a smooth traffic flow, we provide sufficient conditions such that traffic oscillations, due to such optimizing behavior, do not arise. Finally, we investigate the fundamental flow-density and travel time-density diagrams through traffic micro-simulations performed in SUMO. We establish that the proposed approach exhibits consistently near-optimal performance, in a broad variety of traffic conditions

A Rule Based Control Algorithm for on-Ramp Merge With Connected and Automated Vehicles

One of the designs for future highways with the flow of Connected Automated Vehicles (CAVs) cars will be a dedicated lane for the CAVs to form platoons and travel with higher speeds and lower headways. The connectivity will enable the formation of platoons of CAVs traveling beside non-platoon lanes. The advent of connectivity between vehicles and the infrastructure will enable advanced control strategies ̶ improving the performance of the traffic ̶ to be incorporated in the traffic system. The merge area in a multilane highway with CAVs is one of the sections which can be enhanced by the operation of a control system. In this research, a model is developed for investigating the effects of a Rule Based control strategy yielding a more efficient and systematic method for the vehicles joining the highway mainlines comprised of platoon and non-platoon lanes. The actions tested for assisting the merge process included deceleration in the mainlines and lane change to join a platoon in the platoon lane. The model directs every CAV entering a multi-lane highway from an on-ramp, to the rightmost lane of the highway based on the appropriate action which is selected according to the traffic demand conditions and location of the on-ramp vehicle. To account for car following behavior, the vehicles in the platoon lanes are assumed to have a simplified CACC (cooperative adaptive cruise control) and those in the non-platoon lanes the IDM+ car-following model. The IDM+ car following model is modified with additional controls to incorporate the current technologies of Advanced Driver Assistant Systems (ADAS). The results of this study showed that the proposed car following model can increase the throughput of the non-platoon lane from approximately 2000 vehicle per hour (vph) to 3400 vph while the platoon lanes each had an average throughput of 3500 vph. The merge model enabled higher merging throughput for the merge area compared to current day conditions and displayed the potential for improved traffic performance in a connected environment comprised of platoon and non-platoon lanes. The results of this research will help in the design and development of advanced systems for controlling on-ramp merge sections in the future with CAVs

Dynamic Vehicular Trajectory Optimization for Bottleneck Mitigation and Safety Improvement

Traffic bottleneck is defined as a disruption of traffic flow through a freeway or an arterial, which can be divided as two categories: stationary bottleneck and moving bottleneck. The stationary bottleneck is mainly formed by the lane drops in the multi-lane roadways, while the moving bottleneck are due to the very slowing moving vehicles which disrupt the traffic flow. Traffic bottlenecks not only impact the mobility, but also potentially cause safety issues. Traditional strategies for eliminating bottlenecks mainly focus on expanding supply including road widening, green interval lengthening and optimization of intersection channelization. In addition, a few macroscopic methods are also made to optimize the traffic demand such as routing optimization, but these studies have some drawbacks due to the limitations of times and methodologies. Therefore, this research utilizes the Connected and Autonomous Vehicles (CAV) technology to develop several cooperative trajectory optimization models for mitigating mobility and safety impact caused by the urban bottlenecks. The multi-phases algorithms is developed to help solve the model, where a multi-stage-based nonlinear programming procedure is developed in the first phase to search trajectories that eliminate the conflicts in the bottleneck and minimize the travel time and the remaining ones refine the trajectories with a mixed integer linear programming to minimize idling time of vehicles, so that fuel consumption and emissions can be lowered down. Sensitivity analyses are also conducted towards those models and they imply that several indices may significantly impact the effectiveness and even cause the models lose efficacy under extreme values. Various illustrative examples and sensitivity analyses are provided to validate the proposed models. Results indicate that (a) the model is effective to mitigate the mobility and safety impact of bottleneck under the appropriate environment; (b) the model could simultaneously optimize the trajectories of vehicles to lower down fuel consumption and emissions; (c) Some environment indices may significantly impact the models, and even cause the model to lose efficacy under extreme values. Application of the developed models under a real-world case illustrates its capability of providing informative quantitative measures to support decisions in designing, maintaining, and operating the intelligent transportation management

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

Evaluation Of Lane Use Management Strategies

The limited funding available for roadway capacity expansion and the growing funding gap, in conjunction with the increasing congestion, creates a critical need for innovative lane use management options. Various cost-effective lane use management strategies have been implemented in the United States and worldwide to address these challenges. However, these strategies have their own costs, operational characteristics, and additional requirements for field deployment. Hence, there is a need for systematic methodologies to evaluate lane use management strategies. In this thesis, a systematic simulation-based methodology is proposed to evaluate lane use management strategies. It involves identifying traffic corridors that are suitable for lane use management strategies, and analyzing the strategies in terms of performance and financial feasibility. The state of Indiana is used as a case study for this purpose, and a set of traffic corridors is identified. From among them, a 10-mile stretch of the I-65 corridor south of downtown Indianapolis is selected as the study corridor using traffic analysis. The demand volumes for the study area are determined using subarea analysis. The performance of the traffic corridor is evaluated using a microsimulation-based analysis for alleviating congestion using three strategies: reversible lanes, high occupancy vehicle (HOV) lanes and ramp metering. Furthermore, an economic evaluation of these strategies is performed to determine the financial feasibility of their implementation. Results from the simulation based analysis indicate that the reversible lanes and ramp metering strategies improve traffic conditions on the freeway in the major flow direction. Implementation of the HOV lane strategy results in improved traffic flow conditions on the HOV lanes but aggravated congestion on the general purpose lanes. The HOV lane strategy is found to be economically infeasible due to low HOV volume on these lanes. The reversible lane and ramp metering strategies are found to be economically feasible with positive net present values (NPV), with the NPV for the reversible lane strategy being the highest. While reversible lanes, HOV lanes and ramp metering strategies are effective in mitigating congestion by optimizing lane usage, they do not generate additional revenue required to reduce the funding deficit. Inadequate funds and worsening congestion have prompted federal, state and local planning agencies to explore and implement various congestion pricing strategies. In this context, the high occupancy toll (HOT) lanes strategy is explored here. Equity concerns associated with pricing schemes in transportation systems have garnered increased attention in the recent past. Income inequity potentially exists under the HOT strategy whereby higher-income travelers may reap the benefits of HOT lane facilities. An income-based multi-toll pricing approach is proposed for a single HOT lane facility in a network to simultaneously maximize the toll revenue and address the income equity concern, while ensuring a minimum level-of-service on the HOT lanes and that the toll prices do not exceed thresholds specified by a regulatory entity. The problem is modeled as a bi-level optimization formulation. The upper level model seeks to maximize revenue for the tolling authority subject to pre-specified upper bounds on toll prices. The lower level model solves for the stochastic user equilibrium solution based on commuters\u27 objective of minimizing their generalized travel costs. Due to the computational intractability of the bi-level formulation, an approximate agent-based solution approach is used to determine the toll prices by considering the tolling authority and commuters as agents. Results from numerical experiments indicate that a multi-toll pricing scheme is more equitable and can yield higher revenues compared to a single toll price scheme across all travelers

AN INTEGRATED TRAFFIC CONTROL SYSTEM FOR FREEWAY CORRIDORS UNDER NON-RECURRENT CONGESTION

This research has focused on developing an advanced dynamic corridor traffic control system that can assist responsible traffic professionals in generating effective control strategies for contending with non-recurrent congestion that often concurrently plagues both the freeway and arterial systems. The developed system features its hierarchical operating structure that consists of an integrated-level control and a local-level module for bottleneck management. The primary function of the integrated-level control is to maximize the capacity utilization of the entire corridor under incident conditions with concurrently implemented strategies over dynamically computed windows, including diversion control at critical off-ramps, on-ramp metering, and optimal arterial signal timings. The system development process starts with design of a set of innovative network formulations that can accurately and efficiently capture the operational characteristics of traffic flows in the entire corridor optimization process. Grounded on the proposed formulations for network flows, the second part of the system development process is to construct two integrated control models, where the base model is designed for a single-segment detour operation and the extended model is designated for general network applications. To efficiently explore the control effectiveness under different policy priorities between the target freeway and available detour routes, this study has further proposed a multi-objective control process for best managing the complex traffic conditions during incident operations. Due to the nonlinear nature of the proposed formulations and the concerns of computing efficiency, this study has also developed a GA-based heuristic along with a successive optimization process that can yield sufficiently reliable solutions for operating the proposed system in a real-time traffic environment. To evaluate the effectiveness and efficiency of the developed system, this study has conducted extensive numerical experiments with real-world cases. The experimental results have demonstrated that with the information generated from the proposed models, the responsible agency can effectively implement control strategies in a timely manner at all control points to substantially improve the efficiency of the corridor control operations. In view of potential spillback blockage due to detour operations, this study has further developed a local-level bottleneck management module with enhanced arterial flow formulations that can fully capture the complex interrelations between the overflow in each lane group and its impact on the neighboring lanes. As a supplemental component for corridor control, this module has been integrated with the optimization model to fine-tune the arterial signal timings and to prevent the queue spillback or blockages at off-ramps and intersections. The results of extensive numerical experiments have shown that the supplemental module is quite effective in producing local control strategies that can prevent the formation of intersection bottlenecks in the local arterial

Calibrating the Highway Safety Manual Predictive Methods for Texas Highways: Technical Report

0-7083The Highway Safety Manual (HSM) contains safety performance functions (SPFs) that are used in project-level decision-making for estimating the average crash frequency by severity level for existing conditions, alternatives to existing conditions, or proposed new roadways. However, SPF calibration is needed because most of the existing HSM SPFs were developed for states other than Texas. In addition, the HSM does not contain predictive models for frontage roads. Texas has a large network of frontage road segments that are part of the freeway system. Also, the ramp models in the HSM are not applicable to Texas due to differences in ramp configurations. Ramps in Texas usually connect the freeway mainline to the adjacent frontage road rather than a ramp terminal that connects directly to the perpendicular road, as is typical in the states used for developing the SPFs in the HSM