Topic Review
Autonomous Driving Systems
Autonomous vehicles are increasingly becoming a necesAutonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc.
  • 236
  • 11 May 2021
Topic Review
Autonomous Vehicle
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving operations and functions without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors.  An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task.  Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030. 
  • 289
  • 17 Feb 2021
Topic Review
Autonomous Vehicles
An Autonomous Vehicle (AV), or a driverless car, or a self-driving vehicle is a car, bus, truck, or any other vehicle that is able to drive from point A to point B and perform all necessary driving functions, without any human intervention. An Autonomous Vehicle is normally equipped with different types of sensors to perceive the surrounding environment, including Normal Vision Cameras, Infrared Cameras, RADAR, LiDAR, and Ultrasonic Sensors.  An autonomous vehicle should be able to detect and recognise all type of road users including surrounding vehicles, pedestrians, cyclists, traffic signs, road markings, and can segment the free spaces, intersections, buildings, and trees to perform a safe driving task.  Currently, no realistic prediction expects we see fully autonomous vehicles earlier than 2030. 
  • 488
  • 11 Feb 2021
Topic Review
Available Travel Planning Applications Supporting Public Transport Users
Nowadays, there are many available applications supporting travelling around the city, but how does one choose the ones that will be the most useful and convenient for daily trips to destinations such as work, study or leisure. The applications will provide information on how to get from one place to another, what means of transport to use, how long the journey will take, what the travel cost will be, when the tram, bus, metro or train will arrive, the current location of the means of transport, etc. Some applications also offer the possibility of buying tickets, renting a car, a moped or an electric scooter. The most commonly used travel planning applications include: E-podróżnik, Google Maps, Jakdojade, Kiedyprzyjedzie.pl, Mobile MPK, Moovit, myBus online, Transportoid.
  • 154
  • 23 May 2022
Topic Review
Battery Management System Subsystems and Their Influence
As the battery provides the entire propulsion power in electric vehicles (EVs), the utmost importance should be ascribed to the battery management system (BMS) which controls all the activities associated with the battery. 
  • 187
  • 28 Jun 2022
Topic Review
Bus Scheduling with Evolutionary Optimization
In public transport operations, vehicles tend to bunch together due to the instability of passenger demand and traffic conditions. Fluctuation of the expected waiting times of passengers at bus stops due to bus bunching is perceived as service unreliability and degrades the overall quality of service. For assessing the performance of high-frequency bus services, transportation authorities monitor the daily operations via Transit Management Systems (TMS) that collect vehicle positioning information in near real-time. This work explores the potential of using Automated Vehicle Location (AVL) data from the running vehicles for generating bus schedules that improve the service reliability and conform to various regulatory constraints. The computer-aided generation of optimal bus schedules is a tedious task due to the nonlinear and multi-variable nature of the bus scheduling problem. For this reason, this work develops a two-level approach where (i) the regulatory constraints are satisfied and (ii) the waiting times of passengers are optimized with the introduction of an evolutionary algorithm. This work also discusses the experimental results from the implementation of such an approach in a bi-directional bus line operated by a major bus operator in northern Europe.
  • 953
  • 29 Oct 2020
Topic Review
Car-Free Day on a University Campus
Intensive car use is associated with serious damage to the environment, human health and the economy. It has a great impact on climate change as passenger cars account for nearly half of the worldwide carbon dioxide emissions from the transport sector. Locally, it is a major source of air pollution—mainly from nitrogen oxides, volatile organic compounds and particulate matter emissions, which causes hundreds of thousands premature deaths every year. Moreover, the growing number of cars in urban areas increases congestion and traffic accidents, decreases citizens’ quality of life and brings about considerable economic losses. Although recent research has indicated that car use has reached its peak and has begun a downward trajectory, there are still major concerns about other issues such as improvements in fuel consumption, the pace of electric vehicle adoption and the increasing demand for heavier and more polluting vehicles. More recently, with the onset of the COVID-19 pandemic, tight circulation restrictions significantly reduced the average distance traveled by car. However, post-pandemic trends in car use are uncertain as the combined result of widespread disruptions in public transit, increased substitution of traveling by teleactivities and the rise of active transport remains unclear.
  • 74
  • 06 Apr 2022
Topic Review
Charging Technology for Battery Electric Buses
The charging infrastructure has a key role in the implementation of battery electric buses (BEBs) in cities. BEBs only use off-board chargers, whereas the PEC to convert the three-phase AC power from the grid into DC power to charge the battery is located outside the BEB. These chargers allow higher charging power levels because they are not restricted in size and weight. Furthermore, since the driving range of a BEB is limited, a specific charging concept and interface is required to keep the BEB running during the day. This entry gives an overview of the existing charging interfaces and concepts and charger and PEC topologies to provide a reliable and efficient charging behavior.
  • 698
  • 18 Nov 2021
Topic Review
Classification Techniques
Eight classification techniques, including Multi-Layered Perceptron (MLP), Gaussian Naïve Bayes (NB), Logistic Regression (LR), Decision Tree classifier (DT), K-Nearest Neighbor classifier (KNN), Random Forest classifier (RF), Support Vector Machine classifier (SVM), and AdaBoost (AB) were applied to model and predict the CC-SoC. Moreover, these methods were employed to compare the performance of different classifiers and obtain the highest possible accuracy. The classifiers were briefly explained in this section.
  • 128
  • 24 Dec 2021
Topic Review
Classifying Travel Mode Choice through Adjustable SVM Model
The investigation of travel mode choice is an essential task in transport planning and policymaking for predicting travel demands. Typically, mode choice datasets are imbalanced and learning from such datasets is challenging. This study deals with imbalanced mode choice data by developing an algorithm (SVMAK) based on a support vector machine model and the theory of adjusting kernel scaling.
  • 153
  • 24 Dec 2021
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