Video-Based Vehicle Counting System for Urban Roads in Nigeria Using Yolo and DCF-CSR Algorithms

This study improves the traffic situation on, and condition of Nigerian roads by implementing a vehicle counting system that provides accurate data for traffic control agencies and systems. After comparing different detection and tracking algorithms, You Only Look Once and Discriminative Correlation Filter with Channel and Spatial Reliability were chosen as detection and tracking algorithms respectively for the system. The system was implemented using Python programming language and OpenCV. The significances of this system include estimating traffic flow on a given road per time, predicting future traffic conditions, understanding traffic patterns and the factors that affect them, and optimizing existing manual traffic management systems

Smart streetlights: a feasibility study

The world's cities are growing. The effects of population growth and urbanisation mean that more people are living in cities than ever before, a trend set to continue. This urbanisation poses problems for the future. With a growing population comes more strain on local resources, increased traffic and congestion, and environmental decline, including more pollution, loss of green spaces, and the formation of urban heat islands. Thankfully, many of these stressors can be alleviated with better management and procedures, particularly in the context of road infrastructure. For example, with better traffic data, signalling can be smoothed to reduce congestion, parking can be made easier, and streetlights can be dimmed in real time to match real-world road usage. However, obtaining this information on a citywide scale is prohibitively expensive due to the high costs of labour and materials associated with installing sensor hardware. This study investigated the viability of a streetlight-integrated sensor system to affordably obtain traffic and environmental information. This investigation was conducted in two stages: 1) the development of a hardware prototype, and 2) evaluation of an evolved prototype system. In Stage 1 of the study, the development of the prototype sensor system was conducted over three design iterations. These iterations involved, in iteration 1, the live deployment of the prototype system in an urban setting to select and evaluate sensors for environmental monitoring, and in iterations 2 and 3, deployments on roads with live and controlled traffic to develop and test sensors for remote traffic detection. In the final iteration, which involved controlled passes of over 600 vehicle, 600 pedestrian, and 400 cyclist passes, the developed system that comprised passive-infrared motion detectors, lidar, and thermal sensors, could detect and count traffic from a streetlight-integrated configuration with 99%, 84%, and 70% accuracy, respectively. With the finalised sensor system design, Stage 1 showed that traffic and environmental sensing from a streetlight-integrated configuration was feasible and effective using on-board processing with commercially available and inexpensive components. In Stage 2, financial and social assessments of the developed sensor system were conducted to evaluate its viability and value in a community. An evaluation tool for simulating streetlight installations was created to measure the effects of implementing the smart streetlight system. The evaluation showed that the on-demand traffic-adaptive dimming enabled by the smart streetlight system was able to reduce the electrical and maintenance costs of lighting installations. As a result, a 'smart' LED streetlight system was shown to outperform conventional always-on streetlight configurations in terms of financial value within a period of five to 12 years, depending on the installation's local traffic characteristics. A survey regarding the public acceptance of smart streetlight systems was also conducted and assessed the factors that influenced support of its applications. In particular, the Australia-wide survey investigated applications around road traffic improvement, streetlight dimming, and walkability, and quantified participants' support through willingness-to-pay assessments to enable each application. Community support of smart road applications was generally found to be positive and welcomed, especially in areas with a high dependence on personal road transport, and from participants adversely affected by spill light in their homes. Overall, the findings of this study indicate that our cities, and roads in particular, can and should be made smarter. The technology currently exists and is becoming more affordable to allow communities of all sizes to implement smart streetlight systems for the betterment of city services, resource management, and civilian health and wellbeing. The sooner that these technologies are embraced, the sooner they can be adapted to the specific needs of the community and environment for a more sustainable and innovative future

THE IMPACT OF CONTAINER CARRIER HEAVY GOODS VEHICLES ON ROAD TRANSPORTATION OPERATION, SAFETY, AND LOGISTICS

The purpose of this research is to investigate the impact of heavy goods vehicles that are carrying intermodal shipping containers on the traffic flow. The objective is to estimate the available capacity for HGVs on the road at every hour to accommodate the increasing demand due to the expansion of Liverpool container terminal. The author has developed a passenger car equivalent estimation method and a road traffic capacity estimation methods by considering the deceleration and acceleration performances of vehicles, and the methods consider the speed, reaction time, braking competency level of the driver, and road safety. The author has developed an average traffic speed prediction method to facilitate the rescheduling and planning of the traffic operation. The proposed prediction method provides higher accuracy than all other speed prediction methods and facilitates highly efficient rescheduling and planning operations. The author has proposed four level of service methods that consider the safety, prevention of accidents by available reaction time, stopping distance, and the risk of pedestrians sustaining severe injuries or death, and they unique method because they measure the level of service not from the prospective of the user but from the prospective the traffic management and local authority. The methods target the individual type of vehicle and drivers’ behaviour and competency level. The results showed that the required time gap to maintain a safe gap between the following vehicle and the leading vehicle ranges from 2.59 to 3.32s for passenger cars and from 2.94 to 4.89s for heavy goods vehicles. The results also showed that the passenger car equivalent for heavy goods vehicles with braking competency level of 100%-50% is 1.32-2.77 that depends on vehicle parameters. The results also showed that the passenger car equivalent for heavy goods vehicles at 64.4km/h with braking competency level of 100%-50% is 1.32-1.65, and 2.41- 2.77 for rigid heavy goods vehicle, and articulated heavy goods vehicles, respectively. The rescheduling results show how that it is possible to meet both of Mersey ports’ targets for Liverpool’s container port by either building an extra lane of an heavy goods vehicle access only two-lane road in parallel to the current road. However, to improve the traffic flow operation and safety, the second choice will be better because it will keep the average traffic speed above the optimum speed of the road at all times

Vehicle Detection and Classification Using Passive Infrared Sensing

We propose a new sensing device that can simultaneously monitor urban traffic congestion and another phenomenon of interest (flash floods on the present case). This sensing device is based on the combination of an ultrasonic rangefinder with one or multiple remote temperature sensors. We show an implementation of this device, and illustrate its performance in both traffic flow sensing. Field data shows that the sensor can detect vehicles with a 99% accuracy, in addition to estimating their speed and classifying them in function of their length. The same sensor can also monitor urban water levels with an accuracy of less than 2 cm