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"transportation system"
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Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches
Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.
Journal Article
Neural Network-Augmented Actuation Control System Designed for Path Tracking of Autonomous Underwater-Transportation Systems Under Sensor and Process Noise
Underwater-transportation systems have significant potential for both military and commercial applications. Neural Network (NN)-based control offers enhanced robustness for actuators to manage the states of autonomous underwater-transportation systems which include Rigid-Connection Transportation Systems (RCTSs), Flexible-Connection Transportation Systems (FCTSs) and Leader–Follower-Formation Control Transportation Systems (LFFCTSs). In this study, NN-Augmented Control (NNAC) is applied to the aforementioned three transportation systems to enable accurate path tracking by the actuators installed onboard these systems under both ideal operating conditions and in the presence of sensor and process noise. The Extended Kalman Filter (EKF) is employed to estimate the system states under noisy conditions. The results demonstrate that NNAC provides robust and adaptive control of actuators, achieving efficient trajectory tracking via the transportation systems despite the influence of sensor and process noise disturbances. NNAC predominance was also observed in comparison with the conventional PID controller. Among the transportation configurations under the NNAC strategy, the RCTS exhibited the highest tracking accuracy with the lowest power consumption by the actuators. The power consumption of actuators installed on the LFFCTS was marginally higher than that of the RCTS. However, the translational motion accuracy of the follower vehicle in the LFFCTS was the lowest due to indirect actuation control through the formation controller. In contrast, actuators in the FCTS showed the highest power consumption while motion accuracy was comparatively lowest, attributed to the increased complexity of its dynamic positioning requirements.
Journal Article
A Review on IEEE 802.11p for Intelligent Transportation Systems
Road safety is an active area of research for the automotive industry, and certainly one of ongoing interest to governments around the world. The intelligent transportation system (ITS) is one of several viable solutions with which to improve road safety, where the communication medium (e.g., among vehicles and between vehicles and the other components in an ITS environment, such as roadside infrastructure) is typically wireless. A typical communication standard adopted by car manufacturers is IEEE 802.11p for communications. Thus, this paper presents an overview of IEEE 802.11p, with a particular focus on its adoption in an ITS setting. Specifically, we analyze both MAC and PHY layers in a dedicated short-range communication (DSRC) environment.
Journal Article
Sustainable transportation planning for a three-stage fixed charge multi-objective transportation problem
In the recent past, sustainability has become a major concern for transportation policies and planning in both developed and developing countries. This paper focuses on transportation sustainability for a three-stage fixed charge transportation problem. The major components of transportation sustainability considered include economical issues, social concerns, environmental concerns, and transportation system efficiency. Another important issue considered from a social point of view is the interrelationships between various customers of an end product, which has several benefits culminating in a healthier bottom line. The approach adopted in this paper consists of two phases, wherein the efficiency of vehicles is evaluated independently on all three parameters of sustainability using the data envelopment analysis technique in the first phase. The second phase consists of optimizing an integrated multi-objective optimization model that utilizes efficiency of the vehicles obtained from the first phase in a benefit criterion, considering the interrelationships among customers in terms of minimizing the independence values, and maximizing total profits along with many real-world constraints. Numerical illustration of a real-world case is included in order to demonstrate the utility of the proposed approach.
Journal Article
Shaping a Smart Transportation System for Sustainable Value Co-Creation
The smart transportation system (STS) leverages ubiquitous and networked computing to improve the efficiency of urban mobility. Whilst existing IS work has explored various factors influencing STS development, there is a lack of consideration of how value can be created for building a more sustainable STS. Drawing upon the value co-creation theory and stakeholder theory, we seek to understand the socio-technical shaping of the STS ecosystem and how government, firms and citizens collaboratively create sustainable value for designing and implementing STS initiatives. To reach this aim, we carry out a longitudinal case study over 2016–2018 in Shijiazhuang, China. We offer both theoretical and practical explanations on (i) key value facets with regard to sustainable STS design and implementation; and (ii) a holistic view of iterative value co-creation process pushed by key stakeholders. This study makes particular contributions to the IS, marketing and transportation literature by offering a critical understanding of the social dynamics for shaping a big data-driven STS ecosystem.
Journal Article
Long-term impacts of dust storms on transport systems in south-eastern Iran
Dust storms are frequent in the Sistan region of south-eastern Iran and cause a wide range of problems, but few studies have assessed their negative impacts. This paper makes an assessment of the impacts and estimates the damage costs associated with dust storms on transportation systems—aviation, road and rail—over the period 2013–2020. Daily visibility data were used to obtain the number of dust-storm days, and information on the damage to each transportation sector was collected from the relevant local authorities. A strong relationship was observed between dust storms and the number of flight cancellations and car accidents and the amount of accumulated sediment on the roads, by month and year. The impacts were greatest during summer months (June–August) and in 2018, the year of highest dust storm frequency. The total estimated cost to the three transport systems was US$46.5 million over the 8 years studied, with most of the costs (66%) associated with road transport. The results provide managers with a basis from which plans can be developed to control and mitigate the impacts of dust storms.
Journal Article
An efficient algorithm for optimal route node sensing in smart tourism Urban traffic based on priority constraints
The public transportation system is now dealing with a number of problems brought on by the sharp increase in automobile ownership in cities as well as the buildup of vehicles as a result of events and accidents. However, the city’s limited road network capacity cannot keep up with the increasing traffic demand, which further worsens travel conditions and results in a waste of time and money. Given that it is challenging to enhance the capacity of the road network in practice, efficient vehicle travel and evacuation using algorithms has emerged as a recent study focus. It is crucial to learn how to manage urban traffic issues during emergencies and maintain smooth and safe traffic flow. The existing studies only consider the optimized route selection for individual vehicles, signal cycle of traffic lights and deploy historical data to disperse the vehicles on alternative routes. However, such works do not consider the conflict of routes between vehicles, the customized traffic demand of each vehicle and uncertain traffic conditions. Therefore, this paper proposes a novel approach to facilitate the user to select the optimal route with real-time traffic scenario. Furthermore, the Nash equilibrium is established by mutual information swapping and self-adaptive learning method. Simulation results show that the proposed algorithm has better route selection capability in real-time personalized road traffic as compared with existing algorithms.
Journal Article
Exploring spatial heterogeneity in the impact of built environment on taxi ridership using multiscale geographically weighted regression
Due to its flexibility and door-to-door service, taxis are an integral part of the urban transportation system. They have become an essential solution to the first/last mile problem. Even though much research has been conducted on the effects of built environment variables on taxi passengers’ travel behaviors, few have accounted for the spatial heterogeneity embedded in multiscale spatial processes. This study applies multiscale geographically weighted regression (MGWR) to investigate the associations between taxi ridership and spatial contexts to address the gaps. The MGWR considerably improves modeling fit compared to the global OLS model by capturing the spatially varying processes at different scales. The results demonstrate the existence of strong spatial non-stationarity in the various built environment factors affecting the spatial distribution of taxi pick-ups and drop-offs. Specifically, increased residential density induces more taxi demand in areas with less access to public transportation than their surrounding units. Increasing bus coverage where bus coverage is relatively low may attract more commuters to adopt taxi plus bus mode for commuting. Road network density has a more substantial effect on taxi ridership in the south end of the city than in the north. The former is characterized by lower road density. This study reveals the complex relationships between the built environment and the distribution of taxi ridership at different spatial scales and provides valuable insights for transport planning, taxi resource allocation and urban governance.
Journal Article
Passenger flow prediction in bus transportation system using deep learning
The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.
Journal Article