Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Evaluation of Coordinated Ramp Metering (CRM) Implemented By Caltrans

Coordinated ramp metering (CRM) is a critical component of smart freeway corridors that rely on real-time traffic data from ramps and freeway mainline to improve decision-making by the motorists and Traffic Management Center (TMC) personnel. CRM uses an algorithm that considers real-time traffic volumes on freeway mainline and ramps and then adjusts the metering rates on the ramps accordingly for optimal flow along the entire corridor. Improving capacity through smart corridors is less costly and easier to deploy than freeway widening due to high costs associated with right-of-way acquisition and construction. Nevertheless, conversion to smart corridors still represents a sizable investment for public agencies. However, in the U.S. there have been limited evaluations of smart corridors in general, and CRM in particular, based on real operational data. This project examined the recent Smart Corridor implementation on Interstate 80 (I-80) in the Bay Area and State Route 99 (SR-99, SR99) in Sacramento based on travel time reliability measures, efficiency measures, and before-and-after safety evaluation using the Empirical Bayes (EB) approach. As such, this evaluation represents the most complete before-and-after evaluation of such systems. The reliability measures include buffer index, planning time, and measures from the literature that account for both the skew and width of the travel time distribution. For efficiency, the study estimates the ratio of vehicle miles traveled vs. vehicle hour traveled. The research contextualizes before-and-after comparisons for efficiency and reliability measures through similar measures from another corridor (i.e., the control corridor of I-280 in District 4 and I-5 in District 3) from the same region, which did not have CRM implemented. The results show there has been an improvement in freeway operation based on efficiency data. Post-CRM implementation, travel time reliability measures do not show a similar improvement. The report also provides a counterfactual estimate of expected crashes in the post-implementation period, which can be compared with the actual number of crashes in the “after” period to evaluate effectiveness

A Review of Traffic Signal Control.

The aim of this paper is to provide a starting point for the future research within the SERC sponsored project "Gating and Traffic Control: The Application of State Space Control Theory". It will provide an introduction to State Space Control Theory, State Space applications in transportation in general, an in-depth review of congestion control (specifically traffic signal control in congested situations), a review of theoretical works, a review of existing systems and will conclude with recommendations for the research to be undertaken within this project

Analysis of the Effects of Adaptive Ramp Metering on Measures of Efficiency with a Proposed Framework for Safety Evaluation

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new

A LIFE-CYCLE BENEFIT/COST ANALYSIS FRAMEWORK FOR ITS DEPLOYMENTS

The primary objective of Transportation Systems Management and Operation (TSM&O) strategies, or Intelligent Transportation Systems (ITS) is to optimize the capacity of existing transportation infrastructure by reducing congestion. Over past decades, agencies and researchers investigated the use of various strategies such as deployment of adaptive traffic control systems (ATCS), ramp metering systems (RMS), surveillance through closed circuit TV (CCTV) cameras, and information sharing systems to achieve this objective. Life Cycle Agency Cost Agency Analysis (LCCA) of various alternative intelligent transportation strategies has received particular attention to identify the strategy with the lowest cost. However, increasing concerns over the impacts of transportation systems on nearby communities as well as the environment are urging decision makers to consider the environmental impacts of various TSM&O strategies in addition to user costs. Sustainability refers to a long-term perspective of economic, social and environmental progress, which not only addresses the present conditions but also includes the needs of future generations. In United States, due to its vastness, transportation infrastructure can be considered as “major contributors of sustainability”. The triple bottom line of sustainability (TBL), if incorporated in TSM&O strategies decision-making, can address issues like climate change, environmental protection, funds optimization, and social equity. The work for this dissertation focuses on developing a comprehensive Life Cycle Benefit/Cost (LCB/C) analysis framework to evaluate existing and anticipated intelligent ITS strategies, particularly, adaptive traffic control systems (ATCS) and ramp metering systems (RMS), in terms of the triple bottom line (TBL) of sustainability. The B/C framework for each ITS category was divided into two main categories: Life Cycle Cost Analysis (LCCA) and Life Cycle Benefit Analysis (LCBA). The LCCA of ITS deployment includes initial infrastructure cost, periodical incremental cost, and O&M cost. A typical service life and interest rate are assumed for each ITS. For the benefits analysis, three main research areas are included. Conducted by the triple bottom line principal, the LCBA section is divided into analysis of benefits through travel time savings, reductions in energy consumption, and safety enhancements. ITS are known to have several advantages such as increasing link capacity, accelerating traffic flow, reducing delay and congestion, decreasing safety concerns, and in turn minimizing environmental and socio-economic impacts associated with affected traffic zones. However, it comes with its own share of disadvantages, like higher initial infrastructure cost and periodical incremental cost, design complexity, and challenges lie in operation and maintenance. Meanwhile, it is hard to evaluate the benefit/cost performance of ITS implementation over the service life span. The purpose of this study is to prepare such comprehensive benefit/cost framework, as well as the corresponding decision support tool featuring data obtained from national averages. The tool is spreadsheet based and it is easily customizable. The tool also generates graphical outputs as visual summaries. The framework and tool, will help decision makers to assess the overall performance of ITS from perspectives of long term costs and triple bottom line benefits, then opt for the most suitable alternatives from the life cycle point of view