Purpose: This research paper aims to investigate the effects of gradual deployment of market penetration rates (MPR) of connected vehicles (MPR of CVs) on delay time and fuel consumption. Design/methodology/approach: A real-world origin-destination demand matrix survey was conducted in Boston, MA to identify the number of peak hour passing vehicles in the case study. Findings: The results showed that as the number of CVs (MPR) in the network increases, the total delay time decreases by an average of 14% and the fuel consumption decreases by an average of 56%, respectively, from scenarios 3 to 15 compared to scenario 2. Research limitations/implications: The first limitation of this study was considering a small network. The considered network shows a small part of the case study. Originality/value: This study can be a milestone for future research regarding gradual deployment of CVs’ effects on transport networks. Efficient policy(s) may define based on the results of this network for Brockton transport network.

This paper examined the accuracy of six installed LiDAR sensors at six different signalized intersections in Trois-Rivières City, Quebec, Canada. At each intersection, the crucial leading and following movements that cause vehicle–vehicle (V2V) and vehicle–pedestrian (V2P) conflicts were identified, and the LiDAR results were compared to crash reports recorded by police, insurance companies, and other reliable resources. Furthermore, the intersection crash rates were calculated based on the daily entering vehicle traffic and the frequency of crashes at each intersection. Convolutional Neural Networks (CNNs) were utilized over 970,000 V2V and V2P conflicts based on the post encroachment time (PET) and time-to-collision (TTC) safety assessment measures. Bayesian models were used to assess the relationships between different intersection characteristics and the occurrence of conflicts, providing insights into the factors influencing V2V and V2P conflict occurrences. Additionally, a developed image-processing algorithm was utilized to examine the conflicts’ trajectories. The intersections’ crash rates indicated that safety considerations should be implemented at intersections #3, #6, #4, #1, #5, and #2, respectively. Additionally, intersections #6, #4, and #3 were the intersections with the highest rates of vehicle–pedestrian conflicts. Analysis revealed the intricate nature of vehicle and pedestrian interactions, demonstrating the potential of LiDAR sensors in discerning conflict-prone areas at intersections.

LIDAR is One of the recent technologies that highlight objects and makes them recognizable, and it detects movement and manages object recognition of both non-stationary and stationary objects. LIDAR sensor is capable of recording traffic data including the number of passing vehicles, pedestrians, and bicyclists, the speed of vehicles, and the number of conflicts among different road users. Since LIDAR collects high-density point clouds at high frequency, it offers huge potential to capture details that otherwise would not have been possible. Hereupon, as a part of intelligent mobility, multi-disciplinary research is presented to investigate an innovative methodology for car-pedestrian conflicts. The Velodyne VLP LIDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore city to collect real-time traffic data that would be helpful to current road users. The obtained data by the LIDAR sensor is analyzed during the time interval May 16th to June 7th, 2022, three weeks before closing the public schools in Baltimore city. The daily vehicle volumes including passenger cars, buses, trucks, and also vulnerable road users consisting of pedestrian and bicyclists are investigated. Furthermore, the average speed of vehicles passing from different approaches of the intersection is analyzed. One of efficient abilities of installed LIDAR sensor in Cold Spring Ln – Hillen Rd intersection is recording the Post Encroachment Time Threshold (PET) between two cars, car-pedestrian, and car-bicyclists. PET shows the time between the departure of the encroaching vehicle from the conflict point and the arrival of the vehicle with the right-of-way at the conflict point. The number of conflicts, and the rate of PET in each recorded dataset was analyzed and the results highlighted that 779 conflicts were recorded including 250 car-pedestrian, 10 car-bike, and 519 car-car in time interval May 16th to June 7th, 2022. After reviewing the literature, an innovative methodology for classifying PET values will be followed. In the proposed methodology, four groups including PET < 0.7 means a serious conflict, 0.7 ≤ PET <1.31 means a general conflict, 1.31 ≤ PET < 2.25 means a slight conflict, and 2.25 ≤ PET ≤ 5 means a potential conflict will be analyzed. This research concentrates on car-pedestrian conflicts especially those conflicts with 0.7 ≤ PET <1.31 and 1.31 ≤ PET <2.25.

Light Detection and Ranging (LiDAR) sensors are capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore City. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By using an innovative image-processing algorithm, a new safety Measure of Effectiveness (MOE) was proposed to recognize the critical zones for bicyclists entering each zone. Considering the trajectory of conflicts, the results of the analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

This study investigates the application of Light Detection and Ranging (LiDAR) sensor technology for data collection at signalized intersections characterized by a high rate of traffic crashes. The research aims to provide valuable insights into the potential of LiDAR-based data analysis to enhance road safety and traffic management at signalized intersections. The research methodology involved the deployment of LiDAR sensors at Marlboro Pike & Brooks Dr. signalized intersection in Coral Hills, Maryland. Two LiDAR sensors installed in this intersection to collect high-resolution, three-dimensional data of the intersection area from June, 1^st to July, 7^th 2023. The data included information on vehicle trajectories, speeds, and behaviors, as well as pedestrian and cyclist movement patterns. Concurrently, historical traffic crashes recorded and traffic flow data were obtained for the same intersection.The analysis of LiDAR data involved several key aspects including LiDAR data allowed for a precise evaluation of traffic flow patterns, including congestion points, traffic volume fluctuations, and peak-hour behavior. This information provided insights into potential factors contributing to crashes. By analyzing LiDAR data, the study identified near-miss incidents, which are often precursors to actual crashes. This proactive approach could assist in identifying crash-prone areas within the intersection. The LiDAR data analysis also focused on pedestrian and cyclist movements, including jaywalking and bike lane usage. The aim was to identify areas where infrastructure improvements could enhance safety for vulnerable road users. LiDAR data was compared with historical crash data to identify specific locations within the intersection that exhibited a high frequency of crashes. This information can guide targeted safety interventions. Last but not least, the study explored opportunities to optimize traffic signal timings based on real-time traffic data from LiDAR. Adaptive signal control could help mitigate congestion and reduce the risk of crashes.The results of this study demonstrated the potential of LiDAR sensor technology in collecting detailed data for traffic analysis in signalized intersections. By combining LiDAR data with historical crash records and traffic flow data, traffic engineers and urban planners can develop evidence-based strategies to reduce the frequency and severity of crashes in high-risk areas. Ultimately, this research contributes to a comprehensive understanding of how LiDAR technology can be employed to enhance road safety and traffic management, providing valuable insights for traffic engineers, urban planners, and policymakers seeking to improve the safety and efficiency of signalized intersections with a history of high traffic crashes.