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Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively. 

Managed lanes, such as a dedicated lane for connected and automated vehicles (CAVs), can provide not only technological accommodation but also desired market incentives for road users to adopt CAVs in the near future. In this paper, we investigate traffic flow characteristics with two configurations of the managed lane across different market penetration rates and quantify the benefits from the perspectives of lane-level headway distribution, fuel consumption, communication density, and overall network performance. The results highlight the benefits of implementing managed lane strategies for CAVs: (1) A dedicated CAV lane significantly extends the stable region of the speed-flow diagram and yields a greater road capacity. As the result shows, the highest flow rate is 3400 vehicles per hour per lane at 90% market penetration rate with one CAV lane. (2) The concentration of CAVs in one lane results in a narrower headway distribution (with smaller standard deviation) even with partial market penetration. (3) A dedicated CAV lane is also able to eliminate duel-bell-shape distribution that is caused by the heterogeneous traffic flow. (4) A dedicated CAV lane creates a more consistent CAV density, which facilitates communication activity and decreases the probability of packet dropping. 

This paper presents a systematic design of high-fidelity tele-operated scaled vehicles to be used as a research and development platform for intelligent transportation technologies. Compared to computer simulation and full-scale physical tests, the use of high-fidelity scaled setups provides advantages on testing time and financial effectiveness. The physical design of the vehicles features a 1:14 scale with realistic appearance licensed by car manufacturers. Customized steering system and propulsion control provide high-fidelity maneuver characteristics. Remote control is deployed using a target-host structure over WiFi and can provide seamless switching between human driving and autonomous/assisted driving on the host side. Several possible solutions for real-time panoramic vision feedback are explored, with a tri-camera design based on parallel acquisition interfaces adopted. An adaptive color compression technique is developed to shorten the video streaming latency. A customized miniature LIDAR system is introduced to provide an ultra-small package for on-board installation. As a solution balancing between test fidelity and costs, the proposed scaled vehicles are especially suitable for validation tests during the early stage of research and development. With a long-term goal of developing a multi-vehicle traffic network test platform, ongoing and future work on the construction of scaled buildings and road systems is also discussed. 

Accessible pedestrian signal was proposed as a mean to achieve the same level of service that is set forth by the Americans with Disabilities Act for the visually impaired. One of the major issues of existing accessible pedestrian signals is the failure to deliver adequate crossing information for the visually impaired. This article presents a mobile-based accessible pedestrian signal application, namely, Virtual Guide Dog. Integrating intersection information and onboard sensors (e.g. GPS, compass, accelerometer, and gyroscope sensor) of modern smartphones, the Virtual Guide Dog application can notify the visually impaired: (1) the close proximity of an intersection and (2) the street information for crossing. By employing a screen tapping interface, Virtual Guide Dog can remotely place a pedestrian crossing call to the controller, without the need of using a pushbutton. In addition, Virtual Guide Dog informs VIs the start of a crossing phase using text-to-speech technology. The proof-of-concept test shows that Virtual Guide Dog keeps the users informed about the remaining distance as they are approaching the intersection. It was also found that the GPS-only mode is accompanied by greater distance deviation compared to the mode jointly operating with both GPS and cellular positioning. 

This paper presents a cost-effective, non-intrusive, and easy-to-deploy traffic count data collection method using two-dimensional light-detection and ranging (LiDAR) technology. The proposed method integrates a LiDAR sensor, continuous wavelet transform (CWT), and support vector machine (SVM) into a single framework for traffic count. LiDAR is adopted since the technology is economical and easily accessible. Moreover, its 360° visibility and accurate distance information make it more reliable compared with radar, which uses electromagnetic waves instead of light rays. The obtained distance data are converted into the signals. CWT is employed to detect any deviation in distance profile, because of its efficiency in detecting modest changes over a period of time. SVM is one of the supervised machine learning tools for data classification and regression. In the methodology, the SVM is applied to classify the distance data points obtained from the sensor into detection and non-detection cases, which are highly complex. Proof-of-concept (POC) test is conducted in three different places in Newark, New Jersey, to examine the performance of the proposed method. The POC test results demonstrate that the proposed method achieves acceptable performances in vehicle count collection, resulting in 83–94% accuracy. It is discovered that the accuracy of the proposed method is affected by the color of the exterior surface of a vehicle.