Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems

Comparing Driver and Capacity Characteristics at Intersections With and Without Red Light Cameras

The primary purpose of installing Red Light Cameras (RLCs) is to improve intersection safety by discouraging motorists to cross the intersection when the signal for approaching vehicles turns red. Due to the fear of being fined when crossing an RLC equipped intersection at the onset of the red signal, many approaching vehicles may have a tendency of stopping during the yellow phase. This tendency may impact intersection capacity, which can be significant in congested transportation networks during rush hours, especially when several intersections are equipped with RLCs along a sequence of traffic signals, resulting in a disruption of traffic progression. In order to examine the driver and capacity characteristics at intersections with RLCs and compare them with those without RLCs we develop a binary probit choice model to understand driver's stop and go behavior at the onset of yellow intervals, also known as dilemma zone. Further, in order to capture the impact to intersection capacity at intersections with RLCs we develop a probabilistic computational procedure using data from ten intersection pairs (with and without RLCs) in the Baltimore area. The results indicate that, in general, RLCs reduce the intersection capacity since driver's travel behavior is influenced by the presence of the cameras. Other contributory factors for the so-called capacity reduction, such as driver population (e.g., familiar vs. unfamiliar drivers) and traffic-mix (e.g., trucks vs. passenger cars) characteristics have been left for future works

Dual-rate background subtraction approach for estimating traffic queue parameters in urban scenes

This study proposes traffic queue-parameter estimation based on background subtraction. An appropriate combination of two background models is used: a short-term model, very sensitive to moving vehicles, and a long-term model capable of retaining as foreground temporarily stopped vehicles at intersections or traffic lights. Experimental results in typical urban scenes demonstrate the suitability of the proposed approach. Its main advantage is the low computational cost, avoiding specific motion detection algorithms or post-processing operations after foreground vehicle detection.Ministerio de Educación y Ciencia DPI2010-19154Consejería de Innovación, Ciencia y Empresa P07-TIC-0262

Improvements to a queue and delay estimation algorithm utilized in video imaging vehicle detection systems

Video Imaging Vehicle Detection Systems (VIVDS) are steadily becoming the dominant method for the detection of vehicles at a signalized traffic approach. This research is intended to investigate the improvement of a queue and delay estimation algorithm (QDA), specifically the queue detection of vehicles during the red phase of a signal cycle. A previous version of the QDA used a weighted average technique that weighted previous estimates of queue length along with current measurements of queue length to produce a current estimate of queue length. The implementation of this method required some effort to calibrate, and produced a bias that inherently estimated queue lengths lower than baseline (actual) queue lengths. It was the researcherâÂÂs goal to produce a method of queue estimation during the red phase that minimized this bias, that required less calibration, yet produced an accurate estimate of queue length. This estimate of queue length was essential as many other calculations used by the QDA were dependent upon queue growth and length trends during red. The results of this research show that a linear regression method using previous queue measurements to establish a queue growth rate, plus the application of a Kalman Filter for minimizing error and controlling queue growth produced the most accurate queue estimates from the new methods attempted. This method was shown to outperform the weighted average technique used by the previous QDA during the calibration tests. During the validation tests, the linear regression technique was again shown to outperform the weighted average technique. This conclusion was supported by a statistical analysis of data and utilization of predicted vs. actual queue plots that produced desirable results supporting the accuracy of the linear regression method. A predicted vs. actual queue plot indicated that the linear regression method and Kalman Filter was capable of describing 85 percent of the variance in observed queue length data. The researcher would recommend the implementation of the linear regression method with a Kalman Filter, because this method requires little calibration, while also producing an adaptive queue estimation method that has proven to be accurate

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data

A Framework for Recommending Signal Timing Improvements Based on Automatic Vehicle Matching Technologies

Continuously monitoring and automatically identifying existing problems in traffic signal operation is a challenging and time-consuming task. Although data are becoming available due to the adoption of emerging detection technologies, efforts on utilizing the data to diagnose signal control are limited. The current practices of retiming signals are still periodic and based on several days of aggregated turning movement counts. This dissertation developed a framework of automatic signal operation diagnosis with the aim to support decision-making processes by assessing the signal control and identifying the signal retiming needs. The developed framework used a combination of relatively low-cost data from Wi-Fi sensors and historical signal timing records from existing signal controllers. The development involved applying multiple data matching and filtering algorithms to allow the estimation of travel times of vehicular traversals. The Travel Time Index (TTI) was then used as a measure to assess the traffic conditions of various movements. Historical signal timing records were also analyzed, and an additional signal-timing measure, referred to as the Max-out Ratio (MR), was proposed to evaluate the frequency in which the green time demand of a phase exceeded its preset value. Thresholds for the TTI and MR variables were used as a basis for the diagnosis. This diagnosis first identified the needs for assigning additional green times for individual signal phases. Further assessments were then made to determine whether or not the cycle length for the entire intersection or capacity was sufficient. The developed framework was implemented in a real-world signalized intersection and proved to be capable of identifying retiming needs, as well as providing support for the retiming process. Compared to field observations, the diagnosis results were able to reflect the signal operations of most of the movements during various time periods. Moreover, the flexibility of the developed framework allows users to select different thresholds for various movements and times of day, and thus customize the analysis to agency needs

Wireless Sensor Networks

The aim of this book is to present few important issues of WSNs, from the application, design and technology points of view. The book highlights power efficient design issues related to wireless sensor networks, the existing WSN applications, and discusses the research efforts being undertaken in this field which put the reader in good pace to be able to understand more advanced research and make a contribution in this field for themselves. It is believed that this book serves as a comprehensive reference for graduate and undergraduate senior students who seek to learn latest development in wireless sensor networks

Computer Vision and Internet of Things Application to Enhance Pedestrian Safety

With the increasing population, the issue of pedestrian safety is currently of major concern in most cities of the world. Pedestrian safety is concerned with ensuring the well-being of pedestrians and reducing the potential risk areas as well as implementing measures to reduce accidents. The aim of this study is to propose a computer vision and cloud-based solution that enhances pedestrian safety by collecting, visualizing and analyzing pedestrian and vehicular data across different intersections in the city of Montreal. In the past, the rate of accidents in the City of Montreal involving pedestrians has been quite high, therefore a method to solve this problem has led to this study. About 200,000 images were collected across 43 intersections in the city of Montreal from the Traffic cameras – Ville de Montreal website. The data was collected from March 8, 2020, up until March 22, 2020 and then from May 1st, 2020 to 11th May 2020. An object detection and classification model using Faster RCNN algorithm to identify pedestrian and vehicles at the intersection was implemented. Further, this model was used to obtain a dataset showing the number of pedestrians and vehicles at the intersections. The information obtained from this data set was used for visualization and in-depth analysis of the pedestrian and vehicle data in order to derive patterns of peak and non-peak hours and high-risk intersections. IV Furthermore, zero inflation poisson distribution model was implemented on our dataset to display the timings and intersections which had zero pedestrian counts for long hours of the day as compared to the vehicle count. A heat map was generated to visualize the dataset and to assist data viewers to identify which areas should get most attention. Finally, we created a prototype solution that mimicked the traffic control system by utilizing LEDs and microcontrollers (IoT device), cloud services, publish/subscribe model, and object detection. To implement this prototype, the data obtained through the object detection model was sent onto the cloud (Cloud MQTT), from where it was used to control the programmed microcontrollers (IoT devices) present at the different intersections based on the vehicle and pedestrian counts. The system managed to show excellent accuracy for detection of vehicles and pedestrians on the dataset, and the delay experienced in controlling the microcontroller was also negligible, thus making our system effective and reliable